/*************************************************************************
ALGLIB 3.9.0 (source code generated 2014-12-11)
Copyright (c) Sergey Bochkanov (ALGLIB project).

>>> SOURCE LICENSE >>>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation (www.fsf.org); either version 2 of the 
License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

A copy of the GNU General Public License is available at
http://www.fsf.org/licensing/licenses
>>> END OF LICENSE >>>
*************************************************************************/
#include "stdafx.h"
#include "interpolation.h"

// disable some irrelevant warnings
#if (AE_COMPILER==AE_MSVC)
#pragma warning(disable:4100)
#pragma warning(disable:4127)
#pragma warning(disable:4702)
#pragma warning(disable:4996)
#endif
using namespace std;

/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE
//
/////////////////////////////////////////////////////////////////////////
namespace alglib
{


/*************************************************************************
IDW interpolant.
*************************************************************************/
_idwinterpolant_owner::_idwinterpolant_owner()
{
    p_struct = (alglib_impl::idwinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::idwinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_idwinterpolant_init(p_struct, NULL);
}

_idwinterpolant_owner::_idwinterpolant_owner(const _idwinterpolant_owner &rhs)
{
    p_struct = (alglib_impl::idwinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::idwinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_idwinterpolant_init_copy(p_struct, const_cast<alglib_impl::idwinterpolant*>(rhs.p_struct), NULL);
}

_idwinterpolant_owner& _idwinterpolant_owner::operator=(const _idwinterpolant_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_idwinterpolant_clear(p_struct);
    alglib_impl::_idwinterpolant_init_copy(p_struct, const_cast<alglib_impl::idwinterpolant*>(rhs.p_struct), NULL);
    return *this;
}

_idwinterpolant_owner::~_idwinterpolant_owner()
{
    alglib_impl::_idwinterpolant_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::idwinterpolant* _idwinterpolant_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::idwinterpolant* _idwinterpolant_owner::c_ptr() const
{
    return const_cast<alglib_impl::idwinterpolant*>(p_struct);
}
idwinterpolant::idwinterpolant() : _idwinterpolant_owner() 
{
}

idwinterpolant::idwinterpolant(const idwinterpolant &rhs):_idwinterpolant_owner(rhs) 
{
}

idwinterpolant& idwinterpolant::operator=(const idwinterpolant &rhs)
{
    if( this==&rhs )
        return *this;
    _idwinterpolant_owner::operator=(rhs);
    return *this;
}

idwinterpolant::~idwinterpolant()
{
}

/*************************************************************************
IDW interpolation

INPUT PARAMETERS:
    Z   -   IDW interpolant built with one of model building
            subroutines.
    X   -   array[0..NX-1], interpolation point

Result:
    IDW interpolant Z(X)

  -- ALGLIB --
     Copyright 02.03.2010 by Bochkanov Sergey
*************************************************************************/
double idwcalc(const idwinterpolant &z, const real_1d_array &x)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::idwcalc(const_cast<alglib_impl::idwinterpolant*>(z.c_ptr()), const_cast<alglib_impl::ae_vector*>(x.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
IDW interpolant using modified Shepard method for uniform point
distributions.

INPUT PARAMETERS:
    XY  -   X and Y values, array[0..N-1,0..NX].
            First NX columns contain X-values, last column contain
            Y-values.
    N   -   number of nodes, N>0.
    NX  -   space dimension, NX>=1.
    D   -   nodal function type, either:
            * 0     constant  model.  Just  for  demonstration only, worst
                    model ever.
            * 1     linear model, least squares fitting. Simpe  model  for
                    datasets too small for quadratic models
            * 2     quadratic  model,  least  squares  fitting. Best model
                    available (if your dataset is large enough).
            * -1    "fast"  linear  model,  use  with  caution!!!   It  is
                    significantly  faster than linear/quadratic and better
                    than constant model. But it is less robust (especially
                    in the presence of noise).
    NQ  -   number of points used to calculate  nodal  functions  (ignored
            for constant models). NQ should be LARGER than:
            * max(1.5*(1+NX),2^NX+1) for linear model,
            * max(3/4*(NX+2)*(NX+1),2^NX+1) for quadratic model.
            Values less than this threshold will be silently increased.
    NW  -   number of points used to calculate weights and to interpolate.
            Required: >=2^NX+1, values less than this  threshold  will  be
            silently increased.
            Recommended value: about 2*NQ

OUTPUT PARAMETERS:
    Z   -   IDW interpolant.

NOTES:
  * best results are obtained with quadratic models, worst - with constant
    models
  * when N is large, NQ and NW must be significantly smaller than  N  both
    to obtain optimal performance and to obtain optimal accuracy. In 2  or
    3-dimensional tasks NQ=15 and NW=25 are good values to start with.
  * NQ  and  NW  may  be  greater  than  N.  In  such  cases  they will be
    automatically decreased.
  * this subroutine is always succeeds (as long as correct parameters  are
    passed).
  * see  'Multivariate  Interpolation  of Large Sets of Scattered Data' by
    Robert J. Renka for more information on this algorithm.
  * this subroutine assumes that point distribution is uniform at the small
    scales.  If  it  isn't  -  for  example,  points are concentrated along
    "lines", but "lines" distribution is uniform at the larger scale - then
    you should use IDWBuildModifiedShepardR()


  -- ALGLIB PROJECT --
     Copyright 02.03.2010 by Bochkanov Sergey
*************************************************************************/
void idwbuildmodifiedshepard(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t d, const ae_int_t nq, const ae_int_t nw, idwinterpolant &z)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::idwbuildmodifiedshepard(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, nx, d, nq, nw, const_cast<alglib_impl::idwinterpolant*>(z.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
IDW interpolant using modified Shepard method for non-uniform datasets.

This type of model uses  constant  nodal  functions and interpolates using
all nodes which are closer than user-specified radius R. It  may  be  used
when points distribution is non-uniform at the small scale, but it  is  at
the distances as large as R.

INPUT PARAMETERS:
    XY  -   X and Y values, array[0..N-1,0..NX].
            First NX columns contain X-values, last column contain
            Y-values.
    N   -   number of nodes, N>0.
    NX  -   space dimension, NX>=1.
    R   -   radius, R>0

OUTPUT PARAMETERS:
    Z   -   IDW interpolant.

NOTES:
* if there is less than IDWKMin points within  R-ball,  algorithm  selects
  IDWKMin closest ones, so that continuity properties of  interpolant  are
  preserved even far from points.

  -- ALGLIB PROJECT --
     Copyright 11.04.2010 by Bochkanov Sergey
*************************************************************************/
void idwbuildmodifiedshepardr(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const double r, idwinterpolant &z)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::idwbuildmodifiedshepardr(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, nx, r, const_cast<alglib_impl::idwinterpolant*>(z.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
IDW model for noisy data.

This subroutine may be used to handle noisy data, i.e. data with noise  in
OUTPUT values.  It differs from IDWBuildModifiedShepard() in the following
aspects:
* nodal functions are not constrained to pass through  nodes:  Qi(xi)<>yi,
  i.e. we have fitting  instead  of  interpolation.
* weights which are used during least  squares fitting stage are all equal
  to 1.0 (independently of distance)
* "fast"-linear or constant nodal functions are not supported (either  not
  robust enough or too rigid)

This problem require far more complex tuning than interpolation  problems.
Below you can find some recommendations regarding this problem:
* focus on tuning NQ; it controls noise reduction. As for NW, you can just
  make it equal to 2*NQ.
* you can use cross-validation to determine optimal NQ.
* optimal NQ is a result of complex tradeoff  between  noise  level  (more
  noise = larger NQ required) and underlying  function  complexity  (given
  fixed N, larger NQ means smoothing of compex features in the data).  For
  example, NQ=N will reduce noise to the minimum level possible,  but  you
  will end up with just constant/linear/quadratic (depending on  D)  least
  squares model for the whole dataset.

INPUT PARAMETERS:
    XY  -   X and Y values, array[0..N-1,0..NX].
            First NX columns contain X-values, last column contain
            Y-values.
    N   -   number of nodes, N>0.
    NX  -   space dimension, NX>=1.
    D   -   nodal function degree, either:
            * 1     linear model, least squares fitting. Simpe  model  for
                    datasets too small for quadratic models (or  for  very
                    noisy problems).
            * 2     quadratic  model,  least  squares  fitting. Best model
                    available (if your dataset is large enough).
    NQ  -   number of points used to calculate nodal functions.  NQ should
            be  significantly   larger   than  1.5  times  the  number  of
            coefficients in a nodal function to overcome effects of noise:
            * larger than 1.5*(1+NX) for linear model,
            * larger than 3/4*(NX+2)*(NX+1) for quadratic model.
            Values less than this threshold will be silently increased.
    NW  -   number of points used to calculate weights and to interpolate.
            Required: >=2^NX+1, values less than this  threshold  will  be
            silently increased.
            Recommended value: about 2*NQ or larger

OUTPUT PARAMETERS:
    Z   -   IDW interpolant.

NOTES:
  * best results are obtained with quadratic models, linear models are not
    recommended to use unless you are pretty sure that it is what you want
  * this subroutine is always succeeds (as long as correct parameters  are
    passed).
  * see  'Multivariate  Interpolation  of Large Sets of Scattered Data' by
    Robert J. Renka for more information on this algorithm.


  -- ALGLIB PROJECT --
     Copyright 02.03.2010 by Bochkanov Sergey
*************************************************************************/
void idwbuildnoisy(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t d, const ae_int_t nq, const ae_int_t nw, idwinterpolant &z)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::idwbuildnoisy(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, nx, d, nq, nw, const_cast<alglib_impl::idwinterpolant*>(z.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Barycentric interpolant.
*************************************************************************/
_barycentricinterpolant_owner::_barycentricinterpolant_owner()
{
    p_struct = (alglib_impl::barycentricinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::barycentricinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_barycentricinterpolant_init(p_struct, NULL);
}

_barycentricinterpolant_owner::_barycentricinterpolant_owner(const _barycentricinterpolant_owner &rhs)
{
    p_struct = (alglib_impl::barycentricinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::barycentricinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_barycentricinterpolant_init_copy(p_struct, const_cast<alglib_impl::barycentricinterpolant*>(rhs.p_struct), NULL);
}

_barycentricinterpolant_owner& _barycentricinterpolant_owner::operator=(const _barycentricinterpolant_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_barycentricinterpolant_clear(p_struct);
    alglib_impl::_barycentricinterpolant_init_copy(p_struct, const_cast<alglib_impl::barycentricinterpolant*>(rhs.p_struct), NULL);
    return *this;
}

_barycentricinterpolant_owner::~_barycentricinterpolant_owner()
{
    alglib_impl::_barycentricinterpolant_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::barycentricinterpolant* _barycentricinterpolant_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::barycentricinterpolant* _barycentricinterpolant_owner::c_ptr() const
{
    return const_cast<alglib_impl::barycentricinterpolant*>(p_struct);
}
barycentricinterpolant::barycentricinterpolant() : _barycentricinterpolant_owner() 
{
}

barycentricinterpolant::barycentricinterpolant(const barycentricinterpolant &rhs):_barycentricinterpolant_owner(rhs) 
{
}

barycentricinterpolant& barycentricinterpolant::operator=(const barycentricinterpolant &rhs)
{
    if( this==&rhs )
        return *this;
    _barycentricinterpolant_owner::operator=(rhs);
    return *this;
}

barycentricinterpolant::~barycentricinterpolant()
{
}

/*************************************************************************
Rational interpolation using barycentric formula

F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i]))

Input parameters:
    B   -   barycentric interpolant built with one of model building
            subroutines.
    T   -   interpolation point

Result:
    barycentric interpolant F(t)

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
double barycentriccalc(const barycentricinterpolant &b, const double t)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::barycentriccalc(const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), t, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Differentiation of barycentric interpolant: first derivative.

Algorithm used in this subroutine is very robust and should not fail until
provided with values too close to MaxRealNumber  (usually  MaxRealNumber/N
or greater will overflow).

INPUT PARAMETERS:
    B   -   barycentric interpolant built with one of model building
            subroutines.
    T   -   interpolation point

OUTPUT PARAMETERS:
    F   -   barycentric interpolant at T
    DF  -   first derivative

NOTE


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricdiff1(const barycentricinterpolant &b, const double t, double &f, double &df)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentricdiff1(const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), t, &f, &df, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Differentiation of barycentric interpolant: first/second derivatives.

INPUT PARAMETERS:
    B   -   barycentric interpolant built with one of model building
            subroutines.
    T   -   interpolation point

OUTPUT PARAMETERS:
    F   -   barycentric interpolant at T
    DF  -   first derivative
    D2F -   second derivative

NOTE: this algorithm may fail due to overflow/underflor if  used  on  data
whose values are close to MaxRealNumber or MinRealNumber.  Use more robust
BarycentricDiff1() subroutine in such cases.


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricdiff2(const barycentricinterpolant &b, const double t, double &f, double &df, double &d2f)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentricdiff2(const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), t, &f, &df, &d2f, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine performs linear transformation of the argument.

INPUT PARAMETERS:
    B       -   rational interpolant in barycentric form
    CA, CB  -   transformation coefficients: x = CA*t + CB

OUTPUT PARAMETERS:
    B       -   transformed interpolant with X replaced by T

  -- ALGLIB PROJECT --
     Copyright 19.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentriclintransx(const barycentricinterpolant &b, const double ca, const double cb)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentriclintransx(const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), ca, cb, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  subroutine   performs   linear  transformation  of  the  barycentric
interpolant.

INPUT PARAMETERS:
    B       -   rational interpolant in barycentric form
    CA, CB  -   transformation coefficients: B2(x) = CA*B(x) + CB

OUTPUT PARAMETERS:
    B       -   transformed interpolant

  -- ALGLIB PROJECT --
     Copyright 19.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentriclintransy(const barycentricinterpolant &b, const double ca, const double cb)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentriclintransy(const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), ca, cb, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Extracts X/Y/W arrays from rational interpolant

INPUT PARAMETERS:
    B   -   barycentric interpolant

OUTPUT PARAMETERS:
    N   -   nodes count, N>0
    X   -   interpolation nodes, array[0..N-1]
    F   -   function values, array[0..N-1]
    W   -   barycentric weights, array[0..N-1]

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricunpack(const barycentricinterpolant &b, ae_int_t &n, real_1d_array &x, real_1d_array &y, real_1d_array &w)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentricunpack(const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), &n, const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Rational interpolant from X/Y/W arrays

F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i]))

INPUT PARAMETERS:
    X   -   interpolation nodes, array[0..N-1]
    F   -   function values, array[0..N-1]
    W   -   barycentric weights, array[0..N-1]
    N   -   nodes count, N>0

OUTPUT PARAMETERS:
    B   -   barycentric interpolant built from (X, Y, W)

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricbuildxyw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, barycentricinterpolant &b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentricbuildxyw(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Rational interpolant without poles

The subroutine constructs the rational interpolating function without real
poles  (see  'Barycentric rational interpolation with no  poles  and  high
rates of approximation', Michael S. Floater. and  Kai  Hormann,  for  more
information on this subject).

Input parameters:
    X   -   interpolation nodes, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of nodes, N>0.
    D   -   order of the interpolation scheme, 0 <= D <= N-1.
            D<0 will cause an error.
            D>=N it will be replaced with D=N-1.
            if you don't know what D to choose, use small value about 3-5.

Output parameters:
    B   -   barycentric interpolant.

Note:
    this algorithm always succeeds and calculates the weights  with  close
    to machine precision.

  -- ALGLIB PROJECT --
     Copyright 17.06.2007 by Bochkanov Sergey
*************************************************************************/
void barycentricbuildfloaterhormann(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t d, barycentricinterpolant &b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentricbuildfloaterhormann(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, d, const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Conversion from barycentric representation to Chebyshev basis.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    P   -   polynomial in barycentric form
    A,B -   base interval for Chebyshev polynomials (see below)
            A<>B

OUTPUT PARAMETERS
    T   -   coefficients of Chebyshev representation;
            P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N-1 },
            where Ti - I-th Chebyshev polynomial.

NOTES:
    barycentric interpolant passed as P may be either polynomial  obtained
    from  polynomial  interpolation/ fitting or rational function which is
    NOT polynomial. We can't distinguish between these two cases, and this
    algorithm just tries to work assuming that P IS a polynomial.  If not,
    algorithm will return results, but they won't have any meaning.

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialbar2cheb(const barycentricinterpolant &p, const double a, const double b, real_1d_array &t)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbar2cheb(const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), a, b, const_cast<alglib_impl::ae_vector*>(t.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Conversion from Chebyshev basis to barycentric representation.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    T   -   coefficients of Chebyshev representation;
            P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N },
            where Ti - I-th Chebyshev polynomial.
    N   -   number of coefficients:
            * if given, only leading N elements of T are used
            * if not given, automatically determined from size of T
    A,B -   base interval for Chebyshev polynomials (see above)
            A<B

OUTPUT PARAMETERS
    P   -   polynomial in barycentric form

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialcheb2bar(const real_1d_array &t, const ae_int_t n, const double a, const double b, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialcheb2bar(const_cast<alglib_impl::ae_vector*>(t.c_ptr()), n, a, b, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Conversion from Chebyshev basis to barycentric representation.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    T   -   coefficients of Chebyshev representation;
            P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N },
            where Ti - I-th Chebyshev polynomial.
    N   -   number of coefficients:
            * if given, only leading N elements of T are used
            * if not given, automatically determined from size of T
    A,B -   base interval for Chebyshev polynomials (see above)
            A<B

OUTPUT PARAMETERS
    P   -   polynomial in barycentric form

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialcheb2bar(const real_1d_array &t, const double a, const double b, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;

    n = t.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialcheb2bar(const_cast<alglib_impl::ae_vector*>(t.c_ptr()), n, a, b, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Conversion from barycentric representation to power basis.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    P   -   polynomial in barycentric form
    C   -   offset (see below); 0.0 is used as default value.
    S   -   scale (see below);  1.0 is used as default value. S<>0.

OUTPUT PARAMETERS
    A   -   coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 }
    N   -   number of coefficients (polynomial degree plus 1)

NOTES:
1.  this function accepts offset and scale, which can be  set  to  improve
    numerical properties of polynomial. For example, if P was obtained  as
    result of interpolation on [-1,+1],  you  can  set  C=0  and  S=1  and
    represent  P  as sum of 1, x, x^2, x^3 and so on. In most cases you it
    is exactly what you need.

    However, if your interpolation model was built on [999,1001], you will
    see significant growth of numerical errors when using {1, x, x^2, x^3}
    as basis. Representing P as sum of 1, (x-1000), (x-1000)^2, (x-1000)^3
    will be better option. Such representation can be  obtained  by  using
    1000.0 as offset C and 1.0 as scale S.

2.  power basis is ill-conditioned and tricks described above can't  solve
    this problem completely. This function  will  return  coefficients  in
    any  case,  but  for  N>8  they  will  become unreliable. However, N's
    less than 5 are pretty safe.

3.  barycentric interpolant passed as P may be either polynomial  obtained
    from  polynomial  interpolation/ fitting or rational function which is
    NOT polynomial. We can't distinguish between these two cases, and this
    algorithm just tries to work assuming that P IS a polynomial.  If not,
    algorithm will return results, but they won't have any meaning.

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialbar2pow(const barycentricinterpolant &p, const double c, const double s, real_1d_array &a)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbar2pow(const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), c, s, const_cast<alglib_impl::ae_vector*>(a.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Conversion from barycentric representation to power basis.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    P   -   polynomial in barycentric form
    C   -   offset (see below); 0.0 is used as default value.
    S   -   scale (see below);  1.0 is used as default value. S<>0.

OUTPUT PARAMETERS
    A   -   coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 }
    N   -   number of coefficients (polynomial degree plus 1)

NOTES:
1.  this function accepts offset and scale, which can be  set  to  improve
    numerical properties of polynomial. For example, if P was obtained  as
    result of interpolation on [-1,+1],  you  can  set  C=0  and  S=1  and
    represent  P  as sum of 1, x, x^2, x^3 and so on. In most cases you it
    is exactly what you need.

    However, if your interpolation model was built on [999,1001], you will
    see significant growth of numerical errors when using {1, x, x^2, x^3}
    as basis. Representing P as sum of 1, (x-1000), (x-1000)^2, (x-1000)^3
    will be better option. Such representation can be  obtained  by  using
    1000.0 as offset C and 1.0 as scale S.

2.  power basis is ill-conditioned and tricks described above can't  solve
    this problem completely. This function  will  return  coefficients  in
    any  case,  but  for  N>8  they  will  become unreliable. However, N's
    less than 5 are pretty safe.

3.  barycentric interpolant passed as P may be either polynomial  obtained
    from  polynomial  interpolation/ fitting or rational function which is
    NOT polynomial. We can't distinguish between these two cases, and this
    algorithm just tries to work assuming that P IS a polynomial.  If not,
    algorithm will return results, but they won't have any meaning.

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialbar2pow(const barycentricinterpolant &p, real_1d_array &a)
{
    alglib_impl::ae_state _alglib_env_state;    
    double c;
    double s;

    c = 0;
    s = 1;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbar2pow(const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), c, s, const_cast<alglib_impl::ae_vector*>(a.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Conversion from power basis to barycentric representation.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    A   -   coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 }
    N   -   number of coefficients (polynomial degree plus 1)
            * if given, only leading N elements of A are used
            * if not given, automatically determined from size of A
    C   -   offset (see below); 0.0 is used as default value.
    S   -   scale (see below);  1.0 is used as default value. S<>0.

OUTPUT PARAMETERS
    P   -   polynomial in barycentric form


NOTES:
1.  this function accepts offset and scale, which can be  set  to  improve
    numerical properties of polynomial. For example, if you interpolate on
    [-1,+1],  you  can  set C=0 and S=1 and convert from sum of 1, x, x^2,
    x^3 and so on. In most cases you it is exactly what you need.

    However, if your interpolation model was built on [999,1001], you will
    see significant growth of numerical errors when using {1, x, x^2, x^3}
    as  input  basis.  Converting  from  sum  of  1, (x-1000), (x-1000)^2,
    (x-1000)^3 will be better option (you have to specify 1000.0 as offset
    C and 1.0 as scale S).

2.  power basis is ill-conditioned and tricks described above can't  solve
    this problem completely. This function  will  return barycentric model
    in any case, but for N>8 accuracy well degrade. However, N's less than
    5 are pretty safe.

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialpow2bar(const real_1d_array &a, const ae_int_t n, const double c, const double s, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialpow2bar(const_cast<alglib_impl::ae_vector*>(a.c_ptr()), n, c, s, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Conversion from power basis to barycentric representation.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    A   -   coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 }
    N   -   number of coefficients (polynomial degree plus 1)
            * if given, only leading N elements of A are used
            * if not given, automatically determined from size of A
    C   -   offset (see below); 0.0 is used as default value.
    S   -   scale (see below);  1.0 is used as default value. S<>0.

OUTPUT PARAMETERS
    P   -   polynomial in barycentric form


NOTES:
1.  this function accepts offset and scale, which can be  set  to  improve
    numerical properties of polynomial. For example, if you interpolate on
    [-1,+1],  you  can  set C=0 and S=1 and convert from sum of 1, x, x^2,
    x^3 and so on. In most cases you it is exactly what you need.

    However, if your interpolation model was built on [999,1001], you will
    see significant growth of numerical errors when using {1, x, x^2, x^3}
    as  input  basis.  Converting  from  sum  of  1, (x-1000), (x-1000)^2,
    (x-1000)^3 will be better option (you have to specify 1000.0 as offset
    C and 1.0 as scale S).

2.  power basis is ill-conditioned and tricks described above can't  solve
    this problem completely. This function  will  return barycentric model
    in any case, but for N>8 accuracy well degrade. However, N's less than
    5 are pretty safe.

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialpow2bar(const real_1d_array &a, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    double c;
    double s;

    n = a.length();
    c = 0;
    s = 1;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialpow2bar(const_cast<alglib_impl::ae_vector*>(a.c_ptr()), n, c, s, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Lagrange intepolant: generation of the model on the general grid.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    X   -   abscissas, array[0..N-1]
    Y   -   function values, array[0..N-1]
    N   -   number of points, N>=1

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuild(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbuild(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Lagrange intepolant: generation of the model on the general grid.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    X   -   abscissas, array[0..N-1]
    Y   -   function values, array[0..N-1]
    N   -   number of points, N>=1

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuild(const real_1d_array &x, const real_1d_array &y, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'polynomialbuild': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbuild(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Lagrange intepolant: generation of the model on equidistant grid.
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1]
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildeqdist(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbuildeqdist(a, b, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Lagrange intepolant: generation of the model on equidistant grid.
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1]
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildeqdist(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;

    n = y.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbuildeqdist(a, b, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Lagrange intepolant on Chebyshev grid (first kind).
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1],
            Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n)))
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildcheb1(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbuildcheb1(a, b, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Lagrange intepolant on Chebyshev grid (first kind).
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1],
            Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n)))
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildcheb1(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;

    n = y.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbuildcheb1(a, b, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Lagrange intepolant on Chebyshev grid (second kind).
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1],
            Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1)))
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildcheb2(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbuildcheb2(a, b, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Lagrange intepolant on Chebyshev grid (second kind).
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1],
            Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1)))
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildcheb2(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;

    n = y.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialbuildcheb2(a, b, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fast equidistant polynomial interpolation function with O(N) complexity

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on equidistant grid, N>=1
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T

IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use  PolynomialBuildEqDist()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalceqdist(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::polynomialcalceqdist(a, b, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), n, t, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fast equidistant polynomial interpolation function with O(N) complexity

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on equidistant grid, N>=1
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T

IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use  PolynomialBuildEqDist()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalceqdist(const double a, const double b, const real_1d_array &f, const double t)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;

    n = f.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::polynomialcalceqdist(a, b, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), n, t, &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fast polynomial interpolation function on Chebyshev points (first kind)
with O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on Chebyshev grid (first kind),
            X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n))
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T

IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use  PolIntBuildCheb1()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalccheb1(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::polynomialcalccheb1(a, b, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), n, t, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fast polynomial interpolation function on Chebyshev points (first kind)
with O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on Chebyshev grid (first kind),
            X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n))
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T

IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use  PolIntBuildCheb1()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalccheb1(const double a, const double b, const real_1d_array &f, const double t)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;

    n = f.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::polynomialcalccheb1(a, b, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), n, t, &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fast polynomial interpolation function on Chebyshev points (second kind)
with O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on Chebyshev grid (second kind),
            X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1))
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T

IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use PolIntBuildCheb2()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalccheb2(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::polynomialcalccheb2(a, b, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), n, t, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fast polynomial interpolation function on Chebyshev points (second kind)
with O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on Chebyshev grid (second kind),
            X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1))
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T

IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use PolIntBuildCheb2()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalccheb2(const double a, const double b, const real_1d_array &f, const double t)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;

    n = f.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::polynomialcalccheb2(a, b, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), n, t, &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
1-dimensional spline interpolant
*************************************************************************/
_spline1dinterpolant_owner::_spline1dinterpolant_owner()
{
    p_struct = (alglib_impl::spline1dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline1dinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_spline1dinterpolant_init(p_struct, NULL);
}

_spline1dinterpolant_owner::_spline1dinterpolant_owner(const _spline1dinterpolant_owner &rhs)
{
    p_struct = (alglib_impl::spline1dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline1dinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_spline1dinterpolant_init_copy(p_struct, const_cast<alglib_impl::spline1dinterpolant*>(rhs.p_struct), NULL);
}

_spline1dinterpolant_owner& _spline1dinterpolant_owner::operator=(const _spline1dinterpolant_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_spline1dinterpolant_clear(p_struct);
    alglib_impl::_spline1dinterpolant_init_copy(p_struct, const_cast<alglib_impl::spline1dinterpolant*>(rhs.p_struct), NULL);
    return *this;
}

_spline1dinterpolant_owner::~_spline1dinterpolant_owner()
{
    alglib_impl::_spline1dinterpolant_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::spline1dinterpolant* _spline1dinterpolant_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::spline1dinterpolant* _spline1dinterpolant_owner::c_ptr() const
{
    return const_cast<alglib_impl::spline1dinterpolant*>(p_struct);
}
spline1dinterpolant::spline1dinterpolant() : _spline1dinterpolant_owner() 
{
}

spline1dinterpolant::spline1dinterpolant(const spline1dinterpolant &rhs):_spline1dinterpolant_owner(rhs) 
{
}

spline1dinterpolant& spline1dinterpolant::operator=(const spline1dinterpolant &rhs)
{
    if( this==&rhs )
        return *this;
    _spline1dinterpolant_owner::operator=(rhs);
    return *this;
}

spline1dinterpolant::~spline1dinterpolant()
{
}

/*************************************************************************
This subroutine builds linear spline interpolant

INPUT PARAMETERS:
    X   -   spline nodes, array[0..N-1]
    Y   -   function values, array[0..N-1]
    N   -   points count (optional):
            * N>=2
            * if given, only first N points are used to build spline
            * if not given, automatically detected from X/Y sizes
              (len(X) must be equal to len(Y))

OUTPUT PARAMETERS:
    C   -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 24.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildlinear(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildlinear(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds linear spline interpolant

INPUT PARAMETERS:
    X   -   spline nodes, array[0..N-1]
    Y   -   function values, array[0..N-1]
    N   -   points count (optional):
            * N>=2
            * if given, only first N points are used to build spline
            * if not given, automatically detected from X/Y sizes
              (len(X) must be equal to len(Y))

OUTPUT PARAMETERS:
    C   -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 24.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildlinear(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dbuildlinear': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildlinear(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds cubic spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1].
    Y           -   function values, array[0..N-1].

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    C           -   spline interpolant

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds cubic spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1].
    Y           -   function values, array[0..N-1].

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    C           -   spline interpolant

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildcubic(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t boundltype;
    double boundl;
    ae_int_t boundrtype;
    double boundr;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dbuildcubic': looks like one of arguments has wrong size");
    n = x.length();
    boundltype = 0;
    boundl = 0;
    boundrtype = 0;
    boundr = 0;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at nodes x[], it calculates and returns table of function derivatives  d[]
(calculated at the same nodes x[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   spline nodes
    Y           -   function values

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    D           -   derivative values at X[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.
Derivative values are correctly reordered on return, so  D[I]  is  always
equal to S'(X[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dgriddiffcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, real_1d_array &d)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dgriddiffcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(d.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at nodes x[], it calculates and returns table of function derivatives  d[]
(calculated at the same nodes x[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   spline nodes
    Y           -   function values

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    D           -   derivative values at X[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.
Derivative values are correctly reordered on return, so  D[I]  is  always
equal to S'(X[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dgriddiffcubic(const real_1d_array &x, const real_1d_array &y, real_1d_array &d)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t boundltype;
    double boundl;
    ae_int_t boundrtype;
    double boundr;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dgriddiffcubic': looks like one of arguments has wrong size");
    n = x.length();
    boundltype = 0;
    boundl = 0;
    boundrtype = 0;
    boundr = 0;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dgriddiffcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(d.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at  nodes  x[],  it  calculates  and  returns  tables  of first and second
function derivatives d1[] and d2[] (calculated at the same nodes x[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   spline nodes
    Y           -   function values

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    D1          -   S' values at X[]
    D2          -   S'' values at X[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.
Derivative values are correctly reordered on return, so  D[I]  is  always
equal to S'(X[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dgriddiff2cubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, real_1d_array &d1, real_1d_array &d2)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dgriddiff2cubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(d1.c_ptr()), const_cast<alglib_impl::ae_vector*>(d2.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at  nodes  x[],  it  calculates  and  returns  tables  of first and second
function derivatives d1[] and d2[] (calculated at the same nodes x[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   spline nodes
    Y           -   function values

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    D1          -   S' values at X[]
    D2          -   S'' values at X[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.
Derivative values are correctly reordered on return, so  D[I]  is  always
equal to S'(X[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dgriddiff2cubic(const real_1d_array &x, const real_1d_array &y, real_1d_array &d1, real_1d_array &d2)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t boundltype;
    double boundl;
    ae_int_t boundrtype;
    double boundr;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dgriddiff2cubic': looks like one of arguments has wrong size");
    n = x.length();
    boundltype = 0;
    boundl = 0;
    boundrtype = 0;
    boundr = 0;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dgriddiff2cubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(d1.c_ptr()), const_cast<alglib_impl::ae_vector*>(d2.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function values y2[] (calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dconvcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(x2.c_ptr()), n2, const_cast<alglib_impl::ae_vector*>(y2.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function values y2[] (calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvcubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t boundltype;
    double boundl;
    ae_int_t boundrtype;
    double boundr;
    ae_int_t n2;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dconvcubic': looks like one of arguments has wrong size");
    n = x.length();
    boundltype = 0;
    boundl = 0;
    boundrtype = 0;
    boundr = 0;
    n2 = x2.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dconvcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(x2.c_ptr()), n2, const_cast<alglib_impl::ae_vector*>(y2.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function values y2[] and derivatives d2[] (calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]
    D2          -   first derivatives at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvdiffcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2, real_1d_array &d2)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dconvdiffcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(x2.c_ptr()), n2, const_cast<alglib_impl::ae_vector*>(y2.c_ptr()), const_cast<alglib_impl::ae_vector*>(d2.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function values y2[] and derivatives d2[] (calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]
    D2          -   first derivatives at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvdiffcubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2, real_1d_array &d2)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t boundltype;
    double boundl;
    ae_int_t boundrtype;
    double boundr;
    ae_int_t n2;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dconvdiffcubic': looks like one of arguments has wrong size");
    n = x.length();
    boundltype = 0;
    boundl = 0;
    boundrtype = 0;
    boundr = 0;
    n2 = x2.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dconvdiffcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(x2.c_ptr()), n2, const_cast<alglib_impl::ae_vector*>(y2.c_ptr()), const_cast<alglib_impl::ae_vector*>(d2.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function  values  y2[],  first  and  second  derivatives  d2[]  and  dd2[]
(calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]
    D2          -   first derivatives at X2[]
    DD2         -   second derivatives at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvdiff2cubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2, real_1d_array &d2, real_1d_array &dd2)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dconvdiff2cubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(x2.c_ptr()), n2, const_cast<alglib_impl::ae_vector*>(y2.c_ptr()), const_cast<alglib_impl::ae_vector*>(d2.c_ptr()), const_cast<alglib_impl::ae_vector*>(dd2.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function  values  y2[],  first  and  second  derivatives  d2[]  and  dd2[]
(calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]
    D2          -   first derivatives at X2[]
    DD2         -   second derivatives at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvdiff2cubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2, real_1d_array &d2, real_1d_array &dd2)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t boundltype;
    double boundl;
    ae_int_t boundrtype;
    double boundr;
    ae_int_t n2;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dconvdiff2cubic': looks like one of arguments has wrong size");
    n = x.length();
    boundltype = 0;
    boundl = 0;
    boundrtype = 0;
    boundr = 0;
    n2 = x2.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dconvdiff2cubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast<alglib_impl::ae_vector*>(x2.c_ptr()), n2, const_cast<alglib_impl::ae_vector*>(y2.c_ptr()), const_cast<alglib_impl::ae_vector*>(d2.c_ptr()), const_cast<alglib_impl::ae_vector*>(dd2.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds Catmull-Rom spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1].
    Y           -   function values, array[0..N-1].

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundType   -   boundary condition type:
                    * -1 for periodic boundary condition
                    *  0 for parabolically terminated spline (default)
    Tension     -   tension parameter:
                    * tension=0   corresponds to classic Catmull-Rom spline (default)
                    * 0<tension<1 corresponds to more general form - cardinal spline

OUTPUT PARAMETERS:
    C           -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildcatmullrom(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundtype, const double tension, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildcatmullrom(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundtype, tension, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds Catmull-Rom spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1].
    Y           -   function values, array[0..N-1].

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundType   -   boundary condition type:
                    * -1 for periodic boundary condition
                    *  0 for parabolically terminated spline (default)
    Tension     -   tension parameter:
                    * tension=0   corresponds to classic Catmull-Rom spline (default)
                    * 0<tension<1 corresponds to more general form - cardinal spline

OUTPUT PARAMETERS:
    C           -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildcatmullrom(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t boundtype;
    double tension;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dbuildcatmullrom': looks like one of arguments has wrong size");
    n = x.length();
    boundtype = 0;
    tension = 0;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildcatmullrom(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, boundtype, tension, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds Hermite spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]
    Y           -   function values, array[0..N-1]
    D           -   derivatives, array[0..N-1]
    N           -   points count (optional):
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))

OUTPUT PARAMETERS:
    C           -   spline interpolant.


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildhermite(const real_1d_array &x, const real_1d_array &y, const real_1d_array &d, const ae_int_t n, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildhermite(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(d.c_ptr()), n, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds Hermite spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]
    Y           -   function values, array[0..N-1]
    D           -   derivatives, array[0..N-1]
    N           -   points count (optional):
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))

OUTPUT PARAMETERS:
    C           -   spline interpolant.


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildhermite(const real_1d_array &x, const real_1d_array &y, const real_1d_array &d, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()) || (x.length()!=d.length()))
        throw ap_error("Error while calling 'spline1dbuildhermite': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildhermite(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(d.c_ptr()), n, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds Akima spline interpolant

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]
    Y           -   function values, array[0..N-1]
    N           -   points count (optional):
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))

OUTPUT PARAMETERS:
    C           -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 24.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildakima(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildakima(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds Akima spline interpolant

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]
    Y           -   function values, array[0..N-1]
    N           -   points count (optional):
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))

OUTPUT PARAMETERS:
    C           -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 24.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildakima(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dbuildakima': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildakima(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine calculates the value of the spline at the given point X.

INPUT PARAMETERS:
    C   -   spline interpolant
    X   -   point

Result:
    S(x)

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
double spline1dcalc(const spline1dinterpolant &c, const double x)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::spline1dcalc(const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), x, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine differentiates the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.
    X   -   point

Result:
    S   -   S(x)
    DS  -   S'(x)
    D2S -   S''(x)

  -- ALGLIB PROJECT --
     Copyright 24.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1ddiff(const spline1dinterpolant &c, const double x, double &s, double &ds, double &d2s)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1ddiff(const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), x, &s, &ds, &d2s, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine unpacks the spline into the coefficients table.

INPUT PARAMETERS:
    C   -   spline interpolant.
    X   -   point

OUTPUT PARAMETERS:
    Tbl -   coefficients table, unpacked format, array[0..N-2, 0..5].
            For I = 0...N-2:
                Tbl[I,0] = X[i]
                Tbl[I,1] = X[i+1]
                Tbl[I,2] = C0
                Tbl[I,3] = C1
                Tbl[I,4] = C2
                Tbl[I,5] = C3
            On [x[i], x[i+1]] spline is equals to:
                S(x) = C0 + C1*t + C2*t^2 + C3*t^3
                t = x-x[i]

NOTE:
    You  can rebuild spline with  Spline1DBuildHermite()  function,  which
    accepts as inputs function values and derivatives at nodes, which  are
    easy to calculate when you have coefficients.

  -- ALGLIB PROJECT --
     Copyright 29.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dunpack(const spline1dinterpolant &c, ae_int_t &n, real_2d_array &tbl)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dunpack(const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &n, const_cast<alglib_impl::ae_matrix*>(tbl.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine performs linear transformation of the spline argument.

INPUT PARAMETERS:
    C   -   spline interpolant.
    A, B-   transformation coefficients: x = A*t + B
Result:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 30.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dlintransx(const spline1dinterpolant &c, const double a, const double b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dlintransx(const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), a, b, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine performs linear transformation of the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.
    A, B-   transformation coefficients: S2(x) = A*S(x) + B
Result:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 30.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dlintransy(const spline1dinterpolant &c, const double a, const double b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dlintransy(const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), a, b, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine integrates the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.
    X   -   right bound of the integration interval [a, x],
            here 'a' denotes min(x[])
Result:
    integral(S(t)dt,a,x)

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
double spline1dintegrate(const spline1dinterpolant &c, const double x)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::spline1dintegrate(const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), x, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function builds monotone cubic Hermite interpolant. This interpolant
is monotonic in [x(0),x(n-1)] and is constant outside of this interval.

In  case  y[]  form  non-monotonic  sequence,  interpolant  is  piecewise
monotonic.  Say, for x=(0,1,2,3,4)  and  y=(0,1,2,1,0)  interpolant  will
monotonically grow at [0..2] and monotonically decrease at [2..4].

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]. Subroutine automatically
                    sorts points, so caller may pass unsorted array.
    Y           -   function values, array[0..N-1]
    N           -   the number of points(N>=2).

OUTPUT PARAMETERS:
    C           -   spline interpolant.

 -- ALGLIB PROJECT --
     Copyright 21.06.2012 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildmonotone(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildmonotone(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function builds monotone cubic Hermite interpolant. This interpolant
is monotonic in [x(0),x(n-1)] and is constant outside of this interval.

In  case  y[]  form  non-monotonic  sequence,  interpolant  is  piecewise
monotonic.  Say, for x=(0,1,2,3,4)  and  y=(0,1,2,1,0)  interpolant  will
monotonically grow at [0..2] and monotonically decrease at [2..4].

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]. Subroutine automatically
                    sorts points, so caller may pass unsorted array.
    Y           -   function values, array[0..N-1]
    N           -   the number of points(N>=2).

OUTPUT PARAMETERS:
    C           -   spline interpolant.

 -- ALGLIB PROJECT --
     Copyright 21.06.2012 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildmonotone(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dbuildmonotone': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dbuildmonotone(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, const_cast<alglib_impl::spline1dinterpolant*>(c.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Polynomial fitting report:
    TaskRCond       reciprocal of task's condition number
    RMSError        RMS error
    AvgError        average error
    AvgRelError     average relative error (for non-zero Y[I])
    MaxError        maximum error
*************************************************************************/
_polynomialfitreport_owner::_polynomialfitreport_owner()
{
    p_struct = (alglib_impl::polynomialfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::polynomialfitreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_polynomialfitreport_init(p_struct, NULL);
}

_polynomialfitreport_owner::_polynomialfitreport_owner(const _polynomialfitreport_owner &rhs)
{
    p_struct = (alglib_impl::polynomialfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::polynomialfitreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_polynomialfitreport_init_copy(p_struct, const_cast<alglib_impl::polynomialfitreport*>(rhs.p_struct), NULL);
}

_polynomialfitreport_owner& _polynomialfitreport_owner::operator=(const _polynomialfitreport_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_polynomialfitreport_clear(p_struct);
    alglib_impl::_polynomialfitreport_init_copy(p_struct, const_cast<alglib_impl::polynomialfitreport*>(rhs.p_struct), NULL);
    return *this;
}

_polynomialfitreport_owner::~_polynomialfitreport_owner()
{
    alglib_impl::_polynomialfitreport_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::polynomialfitreport* _polynomialfitreport_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::polynomialfitreport* _polynomialfitreport_owner::c_ptr() const
{
    return const_cast<alglib_impl::polynomialfitreport*>(p_struct);
}
polynomialfitreport::polynomialfitreport() : _polynomialfitreport_owner() ,taskrcond(p_struct->taskrcond),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror)
{
}

polynomialfitreport::polynomialfitreport(const polynomialfitreport &rhs):_polynomialfitreport_owner(rhs) ,taskrcond(p_struct->taskrcond),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror)
{
}

polynomialfitreport& polynomialfitreport::operator=(const polynomialfitreport &rhs)
{
    if( this==&rhs )
        return *this;
    _polynomialfitreport_owner::operator=(rhs);
    return *this;
}

polynomialfitreport::~polynomialfitreport()
{
}


/*************************************************************************
Barycentric fitting report:
    RMSError        RMS error
    AvgError        average error
    AvgRelError     average relative error (for non-zero Y[I])
    MaxError        maximum error
    TaskRCond       reciprocal of task's condition number
*************************************************************************/
_barycentricfitreport_owner::_barycentricfitreport_owner()
{
    p_struct = (alglib_impl::barycentricfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::barycentricfitreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_barycentricfitreport_init(p_struct, NULL);
}

_barycentricfitreport_owner::_barycentricfitreport_owner(const _barycentricfitreport_owner &rhs)
{
    p_struct = (alglib_impl::barycentricfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::barycentricfitreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_barycentricfitreport_init_copy(p_struct, const_cast<alglib_impl::barycentricfitreport*>(rhs.p_struct), NULL);
}

_barycentricfitreport_owner& _barycentricfitreport_owner::operator=(const _barycentricfitreport_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_barycentricfitreport_clear(p_struct);
    alglib_impl::_barycentricfitreport_init_copy(p_struct, const_cast<alglib_impl::barycentricfitreport*>(rhs.p_struct), NULL);
    return *this;
}

_barycentricfitreport_owner::~_barycentricfitreport_owner()
{
    alglib_impl::_barycentricfitreport_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::barycentricfitreport* _barycentricfitreport_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::barycentricfitreport* _barycentricfitreport_owner::c_ptr() const
{
    return const_cast<alglib_impl::barycentricfitreport*>(p_struct);
}
barycentricfitreport::barycentricfitreport() : _barycentricfitreport_owner() ,taskrcond(p_struct->taskrcond),dbest(p_struct->dbest),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror)
{
}

barycentricfitreport::barycentricfitreport(const barycentricfitreport &rhs):_barycentricfitreport_owner(rhs) ,taskrcond(p_struct->taskrcond),dbest(p_struct->dbest),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror)
{
}

barycentricfitreport& barycentricfitreport::operator=(const barycentricfitreport &rhs)
{
    if( this==&rhs )
        return *this;
    _barycentricfitreport_owner::operator=(rhs);
    return *this;
}

barycentricfitreport::~barycentricfitreport()
{
}


/*************************************************************************
Spline fitting report:
    RMSError        RMS error
    AvgError        average error
    AvgRelError     average relative error (for non-zero Y[I])
    MaxError        maximum error

Fields  below are  filled  by   obsolete    functions   (Spline1DFitCubic,
Spline1DFitHermite). Modern fitting functions do NOT fill these fields:
    TaskRCond       reciprocal of task's condition number
*************************************************************************/
_spline1dfitreport_owner::_spline1dfitreport_owner()
{
    p_struct = (alglib_impl::spline1dfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline1dfitreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_spline1dfitreport_init(p_struct, NULL);
}

_spline1dfitreport_owner::_spline1dfitreport_owner(const _spline1dfitreport_owner &rhs)
{
    p_struct = (alglib_impl::spline1dfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline1dfitreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_spline1dfitreport_init_copy(p_struct, const_cast<alglib_impl::spline1dfitreport*>(rhs.p_struct), NULL);
}

_spline1dfitreport_owner& _spline1dfitreport_owner::operator=(const _spline1dfitreport_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_spline1dfitreport_clear(p_struct);
    alglib_impl::_spline1dfitreport_init_copy(p_struct, const_cast<alglib_impl::spline1dfitreport*>(rhs.p_struct), NULL);
    return *this;
}

_spline1dfitreport_owner::~_spline1dfitreport_owner()
{
    alglib_impl::_spline1dfitreport_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::spline1dfitreport* _spline1dfitreport_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::spline1dfitreport* _spline1dfitreport_owner::c_ptr() const
{
    return const_cast<alglib_impl::spline1dfitreport*>(p_struct);
}
spline1dfitreport::spline1dfitreport() : _spline1dfitreport_owner() ,taskrcond(p_struct->taskrcond),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror)
{
}

spline1dfitreport::spline1dfitreport(const spline1dfitreport &rhs):_spline1dfitreport_owner(rhs) ,taskrcond(p_struct->taskrcond),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror)
{
}

spline1dfitreport& spline1dfitreport::operator=(const spline1dfitreport &rhs)
{
    if( this==&rhs )
        return *this;
    _spline1dfitreport_owner::operator=(rhs);
    return *this;
}

spline1dfitreport::~spline1dfitreport()
{
}


/*************************************************************************
Least squares fitting report. This structure contains informational fields
which are set by fitting functions provided by this unit.

Different functions initialize different sets of  fields,  so  you  should
read documentation on specific function you used in order  to  know  which
fields are initialized.

    TaskRCond       reciprocal of task's condition number
    IterationsCount number of internal iterations

    VarIdx          if user-supplied gradient contains errors  which  were
                    detected by nonlinear fitter, this  field  is  set  to
                    index  of  the  first  component  of gradient which is
                    suspected to be spoiled by bugs.

    RMSError        RMS error
    AvgError        average error
    AvgRelError     average relative error (for non-zero Y[I])
    MaxError        maximum error

    WRMSError       weighted RMS error

    CovPar          covariance matrix for parameters, filled by some solvers
    ErrPar          vector of errors in parameters, filled by some solvers
    ErrCurve        vector of fit errors -  variability  of  the  best-fit
                    curve, filled by some solvers.
    Noise           vector of per-point noise estimates, filled by
                    some solvers.
    R2              coefficient of determination (non-weighted, non-adjusted),
                    filled by some solvers.
*************************************************************************/
_lsfitreport_owner::_lsfitreport_owner()
{
    p_struct = (alglib_impl::lsfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::lsfitreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_lsfitreport_init(p_struct, NULL);
}

_lsfitreport_owner::_lsfitreport_owner(const _lsfitreport_owner &rhs)
{
    p_struct = (alglib_impl::lsfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::lsfitreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_lsfitreport_init_copy(p_struct, const_cast<alglib_impl::lsfitreport*>(rhs.p_struct), NULL);
}

_lsfitreport_owner& _lsfitreport_owner::operator=(const _lsfitreport_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_lsfitreport_clear(p_struct);
    alglib_impl::_lsfitreport_init_copy(p_struct, const_cast<alglib_impl::lsfitreport*>(rhs.p_struct), NULL);
    return *this;
}

_lsfitreport_owner::~_lsfitreport_owner()
{
    alglib_impl::_lsfitreport_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::lsfitreport* _lsfitreport_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::lsfitreport* _lsfitreport_owner::c_ptr() const
{
    return const_cast<alglib_impl::lsfitreport*>(p_struct);
}
lsfitreport::lsfitreport() : _lsfitreport_owner() ,taskrcond(p_struct->taskrcond),iterationscount(p_struct->iterationscount),varidx(p_struct->varidx),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror),wrmserror(p_struct->wrmserror),covpar(&p_struct->covpar),errpar(&p_struct->errpar),errcurve(&p_struct->errcurve),noise(&p_struct->noise),r2(p_struct->r2)
{
}

lsfitreport::lsfitreport(const lsfitreport &rhs):_lsfitreport_owner(rhs) ,taskrcond(p_struct->taskrcond),iterationscount(p_struct->iterationscount),varidx(p_struct->varidx),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror),wrmserror(p_struct->wrmserror),covpar(&p_struct->covpar),errpar(&p_struct->errpar),errcurve(&p_struct->errcurve),noise(&p_struct->noise),r2(p_struct->r2)
{
}

lsfitreport& lsfitreport::operator=(const lsfitreport &rhs)
{
    if( this==&rhs )
        return *this;
    _lsfitreport_owner::operator=(rhs);
    return *this;
}

lsfitreport::~lsfitreport()
{
}


/*************************************************************************
Nonlinear fitter.

You should use ALGLIB functions to work with fitter.
Never try to access its fields directly!
*************************************************************************/
_lsfitstate_owner::_lsfitstate_owner()
{
    p_struct = (alglib_impl::lsfitstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::lsfitstate), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_lsfitstate_init(p_struct, NULL);
}

_lsfitstate_owner::_lsfitstate_owner(const _lsfitstate_owner &rhs)
{
    p_struct = (alglib_impl::lsfitstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::lsfitstate), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_lsfitstate_init_copy(p_struct, const_cast<alglib_impl::lsfitstate*>(rhs.p_struct), NULL);
}

_lsfitstate_owner& _lsfitstate_owner::operator=(const _lsfitstate_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_lsfitstate_clear(p_struct);
    alglib_impl::_lsfitstate_init_copy(p_struct, const_cast<alglib_impl::lsfitstate*>(rhs.p_struct), NULL);
    return *this;
}

_lsfitstate_owner::~_lsfitstate_owner()
{
    alglib_impl::_lsfitstate_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::lsfitstate* _lsfitstate_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::lsfitstate* _lsfitstate_owner::c_ptr() const
{
    return const_cast<alglib_impl::lsfitstate*>(p_struct);
}
lsfitstate::lsfitstate() : _lsfitstate_owner() ,needf(p_struct->needf),needfg(p_struct->needfg),needfgh(p_struct->needfgh),xupdated(p_struct->xupdated),c(&p_struct->c),f(p_struct->f),g(&p_struct->g),h(&p_struct->h),x(&p_struct->x)
{
}

lsfitstate::lsfitstate(const lsfitstate &rhs):_lsfitstate_owner(rhs) ,needf(p_struct->needf),needfg(p_struct->needfg),needfgh(p_struct->needfgh),xupdated(p_struct->xupdated),c(&p_struct->c),f(p_struct->f),g(&p_struct->g),h(&p_struct->h),x(&p_struct->x)
{
}

lsfitstate& lsfitstate::operator=(const lsfitstate &rhs)
{
    if( this==&rhs )
        return *this;
    _lsfitstate_owner::operator=(rhs);
    return *this;
}

lsfitstate::~lsfitstate()
{
}

/*************************************************************************
This  subroutine fits piecewise linear curve to points with Ramer-Douglas-
Peucker algorithm, which stops after generating specified number of linear
sections.

IMPORTANT:
* it does NOT perform least-squares fitting; it  builds  curve,  but  this
  curve does not minimize some least squares metric.  See  description  of
  RDP algorithm (say, in Wikipedia) for more details on WHAT is performed.
* this function does NOT work with parametric curves  (i.e.  curves  which
  can be represented as {X(t),Y(t)}. It works with curves   which  can  be
  represented as Y(X). Thus,  it  is  impossible  to  model  figures  like
  circles  with  this  functions.
  If  you  want  to  work  with  parametric   curves,   you   should   use
  ParametricRDPFixed() function provided  by  "Parametric"  subpackage  of
  "Interpolation" package.

INPUT PARAMETERS:
    X       -   array of X-coordinates:
                * at least N elements
                * can be unordered (points are automatically sorted)
                * this function may accept non-distinct X (see below for
                  more information on handling of such inputs)
    Y       -   array of Y-coordinates:
                * at least N elements
    N       -   number of elements in X/Y
    M       -   desired number of sections:
                * at most M sections are generated by this function
                * less than M sections can be generated if we have N<M
                  (or some X are non-distinct).

OUTPUT PARAMETERS:
    X2      -   X-values of corner points for piecewise approximation,
                has length NSections+1 or zero (for NSections=0).
    Y2      -   Y-values of corner points,
                has length NSections+1 or zero (for NSections=0).
    NSections-  number of sections found by algorithm, NSections<=M,
                NSections can be zero for degenerate datasets
                (N<=1 or all X[] are non-distinct).

NOTE: X2/Y2 are ordered arrays, i.e. (X2[0],Y2[0]) is  a  first  point  of
      curve, (X2[NSection-1],Y2[NSection-1]) is the last point.

  -- ALGLIB --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
void lstfitpiecewiselinearrdpfixed(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, real_1d_array &x2, real_1d_array &y2, ae_int_t &nsections)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lstfitpiecewiselinearrdpfixed(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, const_cast<alglib_impl::ae_vector*>(x2.c_ptr()), const_cast<alglib_impl::ae_vector*>(y2.c_ptr()), &nsections, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  subroutine fits piecewise linear curve to points with Ramer-Douglas-
Peucker algorithm, which stops after achieving desired precision.

IMPORTANT:
* it performs non-least-squares fitting; it builds curve, but  this  curve
  does not minimize some least squares  metric.  See  description  of  RDP
  algorithm (say, in Wikipedia) for more details on WHAT is performed.
* this function does NOT work with parametric curves  (i.e.  curves  which
  can be represented as {X(t),Y(t)}. It works with curves   which  can  be
  represented as Y(X). Thus, it is impossible to model figures like circles
  with this functions.
  If  you  want  to  work  with  parametric   curves,   you   should   use
  ParametricRDPFixed() function provided  by  "Parametric"  subpackage  of
  "Interpolation" package.

INPUT PARAMETERS:
    X       -   array of X-coordinates:
                * at least N elements
                * can be unordered (points are automatically sorted)
                * this function may accept non-distinct X (see below for
                  more information on handling of such inputs)
    Y       -   array of Y-coordinates:
                * at least N elements
    N       -   number of elements in X/Y
    Eps     -   positive number, desired precision.


OUTPUT PARAMETERS:
    X2      -   X-values of corner points for piecewise approximation,
                has length NSections+1 or zero (for NSections=0).
    Y2      -   Y-values of corner points,
                has length NSections+1 or zero (for NSections=0).
    NSections-  number of sections found by algorithm,
                NSections can be zero for degenerate datasets
                (N<=1 or all X[] are non-distinct).

NOTE: X2/Y2 are ordered arrays, i.e. (X2[0],Y2[0]) is  a  first  point  of
      curve, (X2[NSection-1],Y2[NSection-1]) is the last point.

  -- ALGLIB --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
void lstfitpiecewiselinearrdp(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const double eps, real_1d_array &x2, real_1d_array &y2, ae_int_t &nsections)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lstfitpiecewiselinearrdp(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, eps, const_cast<alglib_impl::ae_vector*>(x2.c_ptr()), const_cast<alglib_impl::ae_vector*>(y2.c_ptr()), &nsections, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fitting by polynomials in barycentric form. This function provides  simple
unterface for unconstrained unweighted fitting. See  PolynomialFitWC()  if
you need constrained fitting.

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO:
    PolynomialFitWC()

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of points, N>0
            * if given, only leading N elements of X/Y are used
            * if not given, automatically determined from sizes of X/Y
    M   -   number of basis functions (= polynomial_degree + 1), M>=1

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
    P   -   interpolant in barycentric form.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

NOTES:
    you can convert P from barycentric form  to  the  power  or  Chebyshev
    basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions  from
    POLINT subpackage.

  -- ALGLIB PROJECT --
     Copyright 10.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialfit(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), const_cast<alglib_impl::polynomialfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_polynomialfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_polynomialfit(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), const_cast<alglib_impl::polynomialfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fitting by polynomials in barycentric form. This function provides  simple
unterface for unconstrained unweighted fitting. See  PolynomialFitWC()  if
you need constrained fitting.

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO:
    PolynomialFitWC()

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of points, N>0
            * if given, only leading N elements of X/Y are used
            * if not given, automatically determined from sizes of X/Y
    M   -   number of basis functions (= polynomial_degree + 1), M>=1

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
    P   -   interpolant in barycentric form.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

NOTES:
    you can convert P from barycentric form  to  the  power  or  Chebyshev
    basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions  from
    POLINT subpackage.

  -- ALGLIB PROJECT --
     Copyright 10.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'polynomialfit': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialfit(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), const_cast<alglib_impl::polynomialfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_polynomialfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'polynomialfit': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_polynomialfit(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), const_cast<alglib_impl::polynomialfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted  fitting by polynomials in barycentric form, with constraints  on
function values or first derivatives.

Small regularizing term is used when solving constrained tasks (to improve
stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO:
    PolynomialFit()

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points, N>0.
            * if given, only leading N elements of X/Y/W are used
            * if not given, automatically determined from sizes of X/Y/W
    XC  -   points where polynomial values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that P(XC[i])=YC[i]
            * DC[i]=1   means that P'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints, 0<=K<M.
            K=0 means no constraints (XC/YC/DC are not used in such cases)
    M   -   number of basis functions (= polynomial_degree + 1), M>=1

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    P   -   interpolant in barycentric form.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTES:
    you can convert P from barycentric form  to  the  power  or  Chebyshev
    basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions  from
    POLINT subpackage.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* even simple constraints can be inconsistent, see  Wikipedia  article  on
  this subject: http://en.wikipedia.org/wiki/Birkhoff_interpolation
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints is NOT GUARANTEED.
* in the one special cases, however, we can  guarantee  consistency.  This
  case  is:  M>1  and constraints on the function values (NOT DERIVATIVES)

Our final recommendation is to use constraints  WHEN  AND  ONLY  when  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.

  -- ALGLIB PROJECT --
     Copyright 10.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialfitwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialfitwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), const_cast<alglib_impl::polynomialfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_polynomialfitwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_polynomialfitwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), const_cast<alglib_impl::polynomialfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted  fitting by polynomials in barycentric form, with constraints  on
function values or first derivatives.

Small regularizing term is used when solving constrained tasks (to improve
stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO:
    PolynomialFit()

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points, N>0.
            * if given, only leading N elements of X/Y/W are used
            * if not given, automatically determined from sizes of X/Y/W
    XC  -   points where polynomial values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that P(XC[i])=YC[i]
            * DC[i]=1   means that P'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints, 0<=K<M.
            K=0 means no constraints (XC/YC/DC are not used in such cases)
    M   -   number of basis functions (= polynomial_degree + 1), M>=1

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    P   -   interpolant in barycentric form.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTES:
    you can convert P from barycentric form  to  the  power  or  Chebyshev
    basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions  from
    POLINT subpackage.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* even simple constraints can be inconsistent, see  Wikipedia  article  on
  this subject: http://en.wikipedia.org/wiki/Birkhoff_interpolation
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints is NOT GUARANTEED.
* in the one special cases, however, we can  guarantee  consistency.  This
  case  is:  M>1  and constraints on the function values (NOT DERIVATIVES)

Our final recommendation is to use constraints  WHEN  AND  ONLY  when  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.

  -- ALGLIB PROJECT --
     Copyright 10.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialfitwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t k;
    if( (x.length()!=y.length()) || (x.length()!=w.length()))
        throw ap_error("Error while calling 'polynomialfitwc': looks like one of arguments has wrong size");
    if( (xc.length()!=yc.length()) || (xc.length()!=dc.length()))
        throw ap_error("Error while calling 'polynomialfitwc': looks like one of arguments has wrong size");
    n = x.length();
    k = xc.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::polynomialfitwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), const_cast<alglib_impl::polynomialfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_polynomialfitwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t k;
    if( (x.length()!=y.length()) || (x.length()!=w.length()))
        throw ap_error("Error while calling 'polynomialfitwc': looks like one of arguments has wrong size");
    if( (xc.length()!=yc.length()) || (xc.length()!=dc.length()))
        throw ap_error("Error while calling 'polynomialfitwc': looks like one of arguments has wrong size");
    n = x.length();
    k = xc.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_polynomialfitwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(p.c_ptr()), const_cast<alglib_impl::polynomialfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates value of four-parameter logistic (4PL)  model  at
specified point X. 4PL model has following form:

    F(x|A,B,C,D) = D+(A-D)/(1+Power(x/C,B))

INPUT PARAMETERS:
    X       -   current point, X>=0:
                * zero X is correctly handled even for B<=0
                * negative X results in exception.
    A, B, C, D- parameters of 4PL model:
                * A is unconstrained
                * B is unconstrained; zero or negative values are handled
                  correctly.
                * C>0, non-positive value results in exception
                * D is unconstrained

RESULT:
    model value at X

NOTE: if B=0, denominator is assumed to be equal to 2.0 even  for  zero  X
      (strictly speaking, 0^0 is undefined).

NOTE: this function also throws exception  if  all  input  parameters  are
      correct, but overflow was detected during calculations.

NOTE: this function performs a lot of checks;  if  you  need  really  high
      performance, consider evaluating model  yourself,  without  checking
      for degenerate cases.


  -- ALGLIB PROJECT --
     Copyright 14.05.2014 by Bochkanov Sergey
*************************************************************************/
double logisticcalc4(const double x, const double a, const double b, const double c, const double d)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::logisticcalc4(x, a, b, c, d, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates value of five-parameter logistic (5PL)  model  at
specified point X. 5PL model has following form:

    F(x|A,B,C,D,G) = D+(A-D)/Power(1+Power(x/C,B),G)

INPUT PARAMETERS:
    X       -   current point, X>=0:
                * zero X is correctly handled even for B<=0
                * negative X results in exception.
    A, B, C, D, G- parameters of 5PL model:
                * A is unconstrained
                * B is unconstrained; zero or negative values are handled
                  correctly.
                * C>0, non-positive value results in exception
                * D is unconstrained
                * G>0, non-positive value results in exception

RESULT:
    model value at X

NOTE: if B=0, denominator is assumed to be equal to Power(2.0,G) even  for
      zero X (strictly speaking, 0^0 is undefined).

NOTE: this function also throws exception  if  all  input  parameters  are
      correct, but overflow was detected during calculations.

NOTE: this function performs a lot of checks;  if  you  need  really  high
      performance, consider evaluating model  yourself,  without  checking
      for degenerate cases.


  -- ALGLIB PROJECT --
     Copyright 14.05.2014 by Bochkanov Sergey
*************************************************************************/
double logisticcalc5(const double x, const double a, const double b, const double c, const double d, const double g)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::logisticcalc5(x, a, b, c, d, g, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function fits four-parameter logistic (4PL) model  to  data  provided
by user. 4PL model has following form:

    F(x|A,B,C,D) = D+(A-D)/(1+Power(x/C,B))

Here:
    * A, D - unconstrained (see LogisticFit4EC() for constrained 4PL)
    * B>=0
    * C>0

IMPORTANT: output of this function is constrained in  such  way that  B>0.
           Because 4PL model is symmetric with respect to B, there  is  no
           need to explore  B<0.  Constraining  B  makes  algorithm easier
           to stabilize and debug.
           Users  who  for  some  reason  prefer to work with negative B's
           should transform output themselves (swap A and D, replace B  by
           -B).

4PL fitting is implemented as follows:
* we perform small number of restarts from random locations which helps to
  solve problem of bad local extrema. Locations are only partially  random
  - we use input data to determine good  initial  guess,  but  we  include
  controlled amount of randomness.
* we perform Levenberg-Marquardt fitting with very  tight  constraints  on
  parameters B and C - it allows us to find good  initial  guess  for  the
  second stage without risk of running into "flat spot".
* second  Levenberg-Marquardt  round  is   performed   without   excessive
  constraints. Results from the previous round are used as initial guess.
* after fitting is done, we compare results with best values found so far,
  rewrite "best solution" if needed, and move to next random location.

Overall algorithm is very stable and is not prone to  bad  local  extrema.
Furthermore, it automatically scales when input data have  very  large  or
very small range.

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.

OUTPUT PARAMETERS:
    A, B, C, D- parameters of 4PL model
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).

NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc4() function.

NOTE: if you need better control over fitting process than provided by this
      function, you may use LogisticFit45X().

NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.


  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit4(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, double &a, double &b, double &c, double &d, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::logisticfit4(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, &a, &b, &c, &d, const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function fits four-parameter logistic (4PL) model  to  data  provided
by user, with optional constraints on parameters A and D.  4PL  model  has
following form:

    F(x|A,B,C,D) = D+(A-D)/(1+Power(x/C,B))

Here:
    * A, D - with optional equality constraints
    * B>=0
    * C>0

IMPORTANT: output of this function is constrained in  such  way that  B>0.
           Because 4PL model is symmetric with respect to B, there  is  no
           need to explore  B<0.  Constraining  B  makes  algorithm easier
           to stabilize and debug.
           Users  who  for  some  reason  prefer to work with negative B's
           should transform output themselves (swap A and D, replace B  by
           -B).

4PL fitting is implemented as follows:
* we perform small number of restarts from random locations which helps to
  solve problem of bad local extrema. Locations are only partially  random
  - we use input data to determine good  initial  guess,  but  we  include
  controlled amount of randomness.
* we perform Levenberg-Marquardt fitting with very  tight  constraints  on
  parameters B and C - it allows us to find good  initial  guess  for  the
  second stage without risk of running into "flat spot".
* second  Levenberg-Marquardt  round  is   performed   without   excessive
  constraints. Results from the previous round are used as initial guess.
* after fitting is done, we compare results with best values found so far,
  rewrite "best solution" if needed, and move to next random location.

Overall algorithm is very stable and is not prone to  bad  local  extrema.
Furthermore, it automatically scales when input data have  very  large  or
very small range.

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.
    CnstrLeft-  optional equality constraint for model value at the   left
                boundary (at X=0). Specify NAN (Not-a-Number)  if  you  do
                not need constraint on the model value at X=0 (in C++  you
                can pass alglib::fp_nan as parameter, in  C#  it  will  be
                Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
    CnstrRight- optional equality constraint for model value at X=infinity.
                Specify NAN (Not-a-Number) if you do not  need  constraint
                on the model value (in C++  you can pass alglib::fp_nan as
                parameter, in  C# it will  be Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.

OUTPUT PARAMETERS:
    A, B, C, D- parameters of 4PL model
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).

NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc4() function.

NOTE: if you need better control over fitting process than provided by this
      function, you may use LogisticFit45X().

NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.

EQUALITY CONSTRAINTS ON PARAMETERS

4PL/5PL solver supports equality constraints on model values at  the  left
boundary (X=0) and right  boundary  (X=infinity).  These  constraints  are
completely optional and you can specify both of them, only  one  -  or  no
constraints at all.

Parameter  CnstrLeft  contains  left  constraint (or NAN for unconstrained
fitting), and CnstrRight contains right  one.  For  4PL,  left  constraint
ALWAYS corresponds to parameter A, and right one is ALWAYS  constraint  on
D. That's because 4PL model is normalized in such way that B>=0.


  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit4ec(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const double cnstrleft, const double cnstrright, double &a, double &b, double &c, double &d, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::logisticfit4ec(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, cnstrleft, cnstrright, &a, &b, &c, &d, const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function fits five-parameter logistic (5PL) model  to  data  provided
by user. 5PL model has following form:

    F(x|A,B,C,D,G) = D+(A-D)/Power(1+Power(x/C,B),G)

Here:
    * A, D - unconstrained
    * B - unconstrained
    * C>0
    * G>0

IMPORTANT: unlike in  4PL  fitting,  output  of  this  function   is   NOT
           constrained in  such  way that B is guaranteed to be  positive.
           Furthermore,  unlike  4PL,  5PL  model  is  NOT  symmetric with
           respect to B, so you can NOT transform model to equivalent one,
           with B having desired sign (>0 or <0).

5PL fitting is implemented as follows:
* we perform small number of restarts from random locations which helps to
  solve problem of bad local extrema. Locations are only partially  random
  - we use input data to determine good  initial  guess,  but  we  include
  controlled amount of randomness.
* we perform Levenberg-Marquardt fitting with very  tight  constraints  on
  parameters B and C - it allows us to find good  initial  guess  for  the
  second stage without risk of running into "flat spot".  Parameter  G  is
  fixed at G=1.
* second  Levenberg-Marquardt  round  is   performed   without   excessive
  constraints on B and C, but with G still equal to 1.  Results  from  the
  previous round are used as initial guess.
* third Levenberg-Marquardt round relaxes constraints on G  and  tries  two
  different models - one with B>0 and one with B<0.
* after fitting is done, we compare results with best values found so far,
  rewrite "best solution" if needed, and move to next random location.

Overall algorithm is very stable and is not prone to  bad  local  extrema.
Furthermore, it automatically scales when input data have  very  large  or
very small range.

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.

OUTPUT PARAMETERS:
    A,B,C,D,G-  parameters of 5PL model
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).

NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc5() function.

NOTE: if you need better control over fitting process than provided by this
      function, you may use LogisticFit45X().

NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.


  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit5(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, double &a, double &b, double &c, double &d, double &g, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::logisticfit5(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, &a, &b, &c, &d, &g, const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function fits five-parameter logistic (5PL) model  to  data  provided
by user, subject to optional equality constraints on parameters A  and  D.
5PL model has following form:

    F(x|A,B,C,D,G) = D+(A-D)/Power(1+Power(x/C,B),G)

Here:
    * A, D - with optional equality constraints
    * B - unconstrained
    * C>0
    * G>0

IMPORTANT: unlike in  4PL  fitting,  output  of  this  function   is   NOT
           constrained in  such  way that B is guaranteed to be  positive.
           Furthermore,  unlike  4PL,  5PL  model  is  NOT  symmetric with
           respect to B, so you can NOT transform model to equivalent one,
           with B having desired sign (>0 or <0).

5PL fitting is implemented as follows:
* we perform small number of restarts from random locations which helps to
  solve problem of bad local extrema. Locations are only partially  random
  - we use input data to determine good  initial  guess,  but  we  include
  controlled amount of randomness.
* we perform Levenberg-Marquardt fitting with very  tight  constraints  on
  parameters B and C - it allows us to find good  initial  guess  for  the
  second stage without risk of running into "flat spot".  Parameter  G  is
  fixed at G=1.
* second  Levenberg-Marquardt  round  is   performed   without   excessive
  constraints on B and C, but with G still equal to 1.  Results  from  the
  previous round are used as initial guess.
* third Levenberg-Marquardt round relaxes constraints on G  and  tries  two
  different models - one with B>0 and one with B<0.
* after fitting is done, we compare results with best values found so far,
  rewrite "best solution" if needed, and move to next random location.

Overall algorithm is very stable and is not prone to  bad  local  extrema.
Furthermore, it automatically scales when input data have  very  large  or
very small range.

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.
    CnstrLeft-  optional equality constraint for model value at the   left
                boundary (at X=0). Specify NAN (Not-a-Number)  if  you  do
                not need constraint on the model value at X=0 (in C++  you
                can pass alglib::fp_nan as parameter, in  C#  it  will  be
                Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
    CnstrRight- optional equality constraint for model value at X=infinity.
                Specify NAN (Not-a-Number) if you do not  need  constraint
                on the model value (in C++  you can pass alglib::fp_nan as
                parameter, in  C# it will  be Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.

OUTPUT PARAMETERS:
    A,B,C,D,G-  parameters of 5PL model
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).

NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc5() function.

NOTE: if you need better control over fitting process than provided by this
      function, you may use LogisticFit45X().

NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.

EQUALITY CONSTRAINTS ON PARAMETERS

5PL solver supports equality constraints on model  values  at   the   left
boundary (X=0) and right  boundary  (X=infinity).  These  constraints  are
completely optional and you can specify both of them, only  one  -  or  no
constraints at all.

Parameter  CnstrLeft  contains  left  constraint (or NAN for unconstrained
fitting), and CnstrRight contains right  one.

Unlike 4PL one, 5PL model is NOT symmetric with respect to  change in sign
of B. Thus, negative B's are possible, and left constraint  may  constrain
parameter A (for positive B's)  -  or  parameter  D  (for  negative  B's).
Similarly changes meaning of right constraint.

You do not have to decide what parameter to  constrain  -  algorithm  will
automatically determine correct parameters as fitting progresses. However,
question highlighted above is important when you interpret fitting results.


  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit5ec(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const double cnstrleft, const double cnstrright, double &a, double &b, double &c, double &d, double &g, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::logisticfit5ec(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, cnstrleft, cnstrright, &a, &b, &c, &d, &g, const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This is "expert" 4PL/5PL fitting function, which can be used if  you  need
better control over fitting process than provided  by  LogisticFit4()  or
LogisticFit5().

This function fits model of the form

    F(x|A,B,C,D)   = D+(A-D)/(1+Power(x/C,B))           (4PL model)

or

    F(x|A,B,C,D,G) = D+(A-D)/Power(1+Power(x/C,B),G)    (5PL model)

Here:
    * A, D - unconstrained
    * B>=0 for 4PL, unconstrained for 5PL
    * C>0
    * G>0 (if present)

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.
    CnstrLeft-  optional equality constraint for model value at the   left
                boundary (at X=0). Specify NAN (Not-a-Number)  if  you  do
                not need constraint on the model value at X=0 (in C++  you
                can pass alglib::fp_nan as parameter, in  C#  it  will  be
                Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
    CnstrRight- optional equality constraint for model value at X=infinity.
                Specify NAN (Not-a-Number) if you do not  need  constraint
                on the model value (in C++  you can pass alglib::fp_nan as
                parameter, in  C# it will  be Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
    Is4PL   -   whether 4PL or 5PL models are fitted
    LambdaV -   regularization coefficient, LambdaV>=0.
                Set it to zero unless you know what you are doing.
    EpsX    -   stopping condition (step size), EpsX>=0.
                Zero value means that small step is automatically chosen.
                See notes below for more information.
    RsCnt   -   number of repeated restarts from  random  points.  4PL/5PL
                models are prone to problem of bad local extrema. Utilizing
                multiple random restarts allows  us  to  improve algorithm
                convergence.
                RsCnt>=0.
                Zero value means that function automatically choose  small
                amount of restarts (recommended).

OUTPUT PARAMETERS:
    A, B, C, D- parameters of 4PL model
    G       -   parameter of 5PL model; for Is4PL=True, G=1 is returned.
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).

NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc5() function.

NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.

EQUALITY CONSTRAINTS ON PARAMETERS

4PL/5PL solver supports equality constraints on model values at  the  left
boundary (X=0) and right  boundary  (X=infinity).  These  constraints  are
completely optional and you can specify both of them, only  one  -  or  no
constraints at all.

Parameter  CnstrLeft  contains  left  constraint (or NAN for unconstrained
fitting), and CnstrRight contains right  one.  For  4PL,  left  constraint
ALWAYS corresponds to parameter A, and right one is ALWAYS  constraint  on
D. That's because 4PL model is normalized in such way that B>=0.

For 5PL model things are different. Unlike  4PL  one,  5PL  model  is  NOT
symmetric with respect to  change  in  sign  of  B. Thus, negative B's are
possible, and left constraint may constrain parameter A (for positive B's)
- or parameter D (for negative B's). Similarly changes  meaning  of  right
constraint.

You do not have to decide what parameter to  constrain  -  algorithm  will
automatically determine correct parameters as fitting progresses. However,
question highlighted above is important when you interpret fitting results.


  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit45x(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const double cnstrleft, const double cnstrright, const bool is4pl, const double lambdav, const double epsx, const ae_int_t rscnt, double &a, double &b, double &c, double &d, double &g, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::logisticfit45x(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, cnstrleft, cnstrright, is4pl, lambdav, epsx, rscnt, &a, &b, &c, &d, &g, const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weghted rational least  squares  fitting  using  Floater-Hormann  rational
functions  with  optimal  D  chosen  from  [0,9],  with  constraints   and
individual weights.

Equidistant  grid  with M node on [min(x),max(x)]  is  used to build basis
functions. Different values of D are tried, optimal D (least WEIGHTED root
mean square error) is chosen.  Task  is  linear,  so  linear least squares
solver  is  used.  Complexity  of  this  computational  scheme is O(N*M^2)
(mostly dominated by the least squares solver).

SEE ALSO
* BarycentricFitFloaterHormann(), "lightweight" fitting without invididual
  weights and constraints.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points, N>0.
    XC  -   points where function values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that S(XC[i])=YC[i]
            * DC[i]=1   means that S'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints, 0<=K<M.
            K=0 means no constraints (XC/YC/DC are not used in such cases)
    M   -   number of basis functions ( = number_of_nodes), M>=2.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
                        -1 means another errors in parameters passed
                           (N<=0, for example)
    B   -   barycentric interpolant.
    Rep -   report, same format as in LSFitLinearWC() subroutine.
            Following fields are set:
            * DBest         best value of the D parameter
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroutine doesn't calculate task's condition number for K<>0.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained barycentric interpolants:
* excessive  constraints  can  be  inconsistent.   Floater-Hormann   basis
  functions aren't as flexible as splines (although they are very smooth).
* the more evenly constraints are spread across [min(x),max(x)],  the more
  chances that they will be consistent
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints IS NOT GUARANTEED.
* in the several special cases, however, we CAN guarantee consistency.
* one of this cases is constraints on the function  VALUES at the interval
  boundaries. Note that consustency of the  constraints  on  the  function
  DERIVATIVES is NOT guaranteed (you can use in such cases  cubic  splines
  which are more flexible).
* another  special  case  is ONE constraint on the function value (OR, but
  not AND, derivative) anywhere in the interval

Our final recommendation is to use constraints  WHEN  AND  ONLY  WHEN  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricfitfloaterhormannwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, barycentricinterpolant &b, barycentricfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentricfitfloaterhormannwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), const_cast<alglib_impl::barycentricfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_barycentricfitfloaterhormannwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, barycentricinterpolant &b, barycentricfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_barycentricfitfloaterhormannwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), const_cast<alglib_impl::barycentricfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Rational least squares fitting using  Floater-Hormann  rational  functions
with optimal D chosen from [0,9].

Equidistant  grid  with M node on [min(x),max(x)]  is  used to build basis
functions. Different values of D are tried, optimal  D  (least  root  mean
square error) is chosen.  Task  is  linear, so linear least squares solver
is used. Complexity  of  this  computational  scheme is  O(N*M^2)  (mostly
dominated by the least squares solver).

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of points, N>0.
    M   -   number of basis functions ( = number_of_nodes), M>=2.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    B   -   barycentric interpolant.
    Rep -   report, same format as in LSFitLinearWC() subroutine.
            Following fields are set:
            * DBest         best value of the D parameter
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricfitfloaterhormann(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, barycentricinterpolant &b, barycentricfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::barycentricfitfloaterhormann(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), const_cast<alglib_impl::barycentricfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_barycentricfitfloaterhormann(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, barycentricinterpolant &b, barycentricfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_barycentricfitfloaterhormann(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::barycentricinterpolant*>(b.c_ptr()), const_cast<alglib_impl::barycentricfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fitting by penalized cubic spline.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are cubic splines with  natural  boundary
conditions. Problem is regularized by  adding non-linearity penalty to the
usual least squares penalty function:

    S(x) = arg min { LS + P }, where
    LS   = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty
    P    = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty
    rho  - tunable constant given by user
    C    - automatically determined scale parameter,
           makes penalty invariant with respect to scaling of X, Y, W.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y are processed
            * if not given, automatically determined from X/Y sizes
    M   -   number of basis functions ( = number_of_nodes), M>=4.
    Rho -   regularization  constant  passed   by   user.   It   penalizes
            nonlinearity in the regression spline. It  is  logarithmically
            scaled,  i.e.  actual  value  of  regularization  constant  is
            calculated as 10^Rho. It is automatically scaled so that:
            * Rho=2.0 corresponds to moderate amount of nonlinearity
            * generally, it should be somewhere in the [-8.0,+8.0]
            If you do not want to penalize nonlineary,
            pass small Rho. Values as low as -15 should work.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD or
                           Cholesky decomposition; problem may be
                           too ill-conditioned (very rare)
    S   -   spline interpolant.
    Rep -   Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTE 1: additional nodes are added to the spline outside  of  the  fitting
interval to force linearity when x<min(x,xc) or x>max(x,xc).  It  is  done
for consistency - we penalize non-linearity  at [min(x,xc),max(x,xc)],  so
it is natural to force linearity outside of this interval.

NOTE 2: function automatically sorts points,  so  caller may pass unsorted
array.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitpenalized(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfitpenalized(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, rho, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfitpenalized(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfitpenalized(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, rho, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Fitting by penalized cubic spline.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are cubic splines with  natural  boundary
conditions. Problem is regularized by  adding non-linearity penalty to the
usual least squares penalty function:

    S(x) = arg min { LS + P }, where
    LS   = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty
    P    = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty
    rho  - tunable constant given by user
    C    - automatically determined scale parameter,
           makes penalty invariant with respect to scaling of X, Y, W.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y are processed
            * if not given, automatically determined from X/Y sizes
    M   -   number of basis functions ( = number_of_nodes), M>=4.
    Rho -   regularization  constant  passed   by   user.   It   penalizes
            nonlinearity in the regression spline. It  is  logarithmically
            scaled,  i.e.  actual  value  of  regularization  constant  is
            calculated as 10^Rho. It is automatically scaled so that:
            * Rho=2.0 corresponds to moderate amount of nonlinearity
            * generally, it should be somewhere in the [-8.0,+8.0]
            If you do not want to penalize nonlineary,
            pass small Rho. Values as low as -15 should work.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD or
                           Cholesky decomposition; problem may be
                           too ill-conditioned (very rare)
    S   -   spline interpolant.
    Rep -   Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTE 1: additional nodes are added to the spline outside  of  the  fitting
interval to force linearity when x<min(x,xc) or x>max(x,xc).  It  is  done
for consistency - we penalize non-linearity  at [min(x,xc),max(x,xc)],  so
it is natural to force linearity outside of this interval.

NOTE 2: function automatically sorts points,  so  caller may pass unsorted
array.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitpenalized(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dfitpenalized': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfitpenalized(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, rho, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfitpenalized(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dfitpenalized': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfitpenalized(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, rho, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted fitting by penalized cubic spline.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are cubic splines with  natural  boundary
conditions. Problem is regularized by  adding non-linearity penalty to the
usual least squares penalty function:

    S(x) = arg min { LS + P }, where
    LS   = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty
    P    = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty
    rho  - tunable constant given by user
    C    - automatically determined scale parameter,
           makes penalty invariant with respect to scaling of X, Y, W.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            problem.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    M   -   number of basis functions ( = number_of_nodes), M>=4.
    Rho -   regularization  constant  passed   by   user.   It   penalizes
            nonlinearity in the regression spline. It  is  logarithmically
            scaled,  i.e.  actual  value  of  regularization  constant  is
            calculated as 10^Rho. It is automatically scaled so that:
            * Rho=2.0 corresponds to moderate amount of nonlinearity
            * generally, it should be somewhere in the [-8.0,+8.0]
            If you do not want to penalize nonlineary,
            pass small Rho. Values as low as -15 should work.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD or
                           Cholesky decomposition; problem may be
                           too ill-conditioned (very rare)
    S   -   spline interpolant.
    Rep -   Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTE 1: additional nodes are added to the spline outside  of  the  fitting
interval to force linearity when x<min(x,xc) or x>max(x,xc).  It  is  done
for consistency - we penalize non-linearity  at [min(x,xc),max(x,xc)],  so
it is natural to force linearity outside of this interval.

NOTE 2: function automatically sorts points,  so  caller may pass unsorted
array.

  -- ALGLIB PROJECT --
     Copyright 19.10.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dfitpenalizedw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfitpenalizedw(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, m, rho, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfitpenalizedw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfitpenalizedw(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, m, rho, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted fitting by penalized cubic spline.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are cubic splines with  natural  boundary
conditions. Problem is regularized by  adding non-linearity penalty to the
usual least squares penalty function:

    S(x) = arg min { LS + P }, where
    LS   = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty
    P    = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty
    rho  - tunable constant given by user
    C    - automatically determined scale parameter,
           makes penalty invariant with respect to scaling of X, Y, W.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            problem.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    M   -   number of basis functions ( = number_of_nodes), M>=4.
    Rho -   regularization  constant  passed   by   user.   It   penalizes
            nonlinearity in the regression spline. It  is  logarithmically
            scaled,  i.e.  actual  value  of  regularization  constant  is
            calculated as 10^Rho. It is automatically scaled so that:
            * Rho=2.0 corresponds to moderate amount of nonlinearity
            * generally, it should be somewhere in the [-8.0,+8.0]
            If you do not want to penalize nonlineary,
            pass small Rho. Values as low as -15 should work.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD or
                           Cholesky decomposition; problem may be
                           too ill-conditioned (very rare)
    S   -   spline interpolant.
    Rep -   Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTE 1: additional nodes are added to the spline outside  of  the  fitting
interval to force linearity when x<min(x,xc) or x>max(x,xc).  It  is  done
for consistency - we penalize non-linearity  at [min(x,xc),max(x,xc)],  so
it is natural to force linearity outside of this interval.

NOTE 2: function automatically sorts points,  so  caller may pass unsorted
array.

  -- ALGLIB PROJECT --
     Copyright 19.10.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dfitpenalizedw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()) || (x.length()!=w.length()))
        throw ap_error("Error while calling 'spline1dfitpenalizedw': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfitpenalizedw(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, m, rho, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfitpenalizedw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()) || (x.length()!=w.length()))
        throw ap_error("Error while calling 'spline1dfitpenalizedw': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfitpenalizedw(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, m, rho, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted fitting by cubic  spline,  with constraints on function values or
derivatives.

Equidistant grid with M-2 nodes on [min(x,xc),max(x,xc)] is  used to build
basis functions. Basis functions are cubic splines with continuous  second
derivatives  and  non-fixed first  derivatives  at  interval  ends.  Small
regularizing term is used  when  solving  constrained  tasks  (to  improve
stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO
    Spline1DFitHermiteWC()  -   fitting by Hermite splines (more flexible,
                                less smooth)
    Spline1DFitCubic()      -   "lightweight" fitting  by  cubic  splines,
                                without invididual weights and constraints

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    XC  -   points where spline values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that S(XC[i])=YC[i]
            * DC[i]=1   means that S'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints (optional):
            * 0<=K<M.
            * K=0 means no constraints (XC/YC/DC are not used)
            * if given, only first K elements of XC/YC/DC are used
            * if not given, automatically determined from XC/YC/DC
    M   -   number of basis functions ( = number_of_nodes+2), M>=4.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    S   -   spline interpolant.
    Rep -   report, same format as in LSFitLinearWC() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* excessive constraints can be inconsistent. Splines are  piecewise  cubic
  functions, and it is easy to create an example, where  large  number  of
  constraints  concentrated  in  small  area will result in inconsistency.
  Just because spline is not flexible enough to satisfy all of  them.  And
  same constraints spread across the  [min(x),max(x)]  will  be  perfectly
  consistent.
* the more evenly constraints are spread across [min(x),max(x)],  the more
  chances that they will be consistent
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints IS NOT GUARANTEED.
* in the several special cases, however, we CAN guarantee consistency.
* one of this cases is constraints  on  the  function  values  AND/OR  its
  derivatives at the interval boundaries.
* another  special  case  is ONE constraint on the function value (OR, but
  not AND, derivative) anywhere in the interval

Our final recommendation is to use constraints  WHEN  AND  ONLY  WHEN  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.


  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitcubicwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfitcubicwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfitcubicwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfitcubicwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted fitting by cubic  spline,  with constraints on function values or
derivatives.

Equidistant grid with M-2 nodes on [min(x,xc),max(x,xc)] is  used to build
basis functions. Basis functions are cubic splines with continuous  second
derivatives  and  non-fixed first  derivatives  at  interval  ends.  Small
regularizing term is used  when  solving  constrained  tasks  (to  improve
stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO
    Spline1DFitHermiteWC()  -   fitting by Hermite splines (more flexible,
                                less smooth)
    Spline1DFitCubic()      -   "lightweight" fitting  by  cubic  splines,
                                without invididual weights and constraints

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    XC  -   points where spline values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that S(XC[i])=YC[i]
            * DC[i]=1   means that S'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints (optional):
            * 0<=K<M.
            * K=0 means no constraints (XC/YC/DC are not used)
            * if given, only first K elements of XC/YC/DC are used
            * if not given, automatically determined from XC/YC/DC
    M   -   number of basis functions ( = number_of_nodes+2), M>=4.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    S   -   spline interpolant.
    Rep -   report, same format as in LSFitLinearWC() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* excessive constraints can be inconsistent. Splines are  piecewise  cubic
  functions, and it is easy to create an example, where  large  number  of
  constraints  concentrated  in  small  area will result in inconsistency.
  Just because spline is not flexible enough to satisfy all of  them.  And
  same constraints spread across the  [min(x),max(x)]  will  be  perfectly
  consistent.
* the more evenly constraints are spread across [min(x),max(x)],  the more
  chances that they will be consistent
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints IS NOT GUARANTEED.
* in the several special cases, however, we CAN guarantee consistency.
* one of this cases is constraints  on  the  function  values  AND/OR  its
  derivatives at the interval boundaries.
* another  special  case  is ONE constraint on the function value (OR, but
  not AND, derivative) anywhere in the interval

Our final recommendation is to use constraints  WHEN  AND  ONLY  WHEN  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.


  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitcubicwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t k;
    if( (x.length()!=y.length()) || (x.length()!=w.length()))
        throw ap_error("Error while calling 'spline1dfitcubicwc': looks like one of arguments has wrong size");
    if( (xc.length()!=yc.length()) || (xc.length()!=dc.length()))
        throw ap_error("Error while calling 'spline1dfitcubicwc': looks like one of arguments has wrong size");
    n = x.length();
    k = xc.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfitcubicwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfitcubicwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t k;
    if( (x.length()!=y.length()) || (x.length()!=w.length()))
        throw ap_error("Error while calling 'spline1dfitcubicwc': looks like one of arguments has wrong size");
    if( (xc.length()!=yc.length()) || (xc.length()!=dc.length()))
        throw ap_error("Error while calling 'spline1dfitcubicwc': looks like one of arguments has wrong size");
    n = x.length();
    k = xc.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfitcubicwc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted  fitting  by Hermite spline,  with constraints on function values
or first derivatives.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are Hermite splines.  Small  regularizing
term is used when solving constrained tasks (to improve stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO
    Spline1DFitCubicWC()    -   fitting by Cubic splines (less flexible,
                                more smooth)
    Spline1DFitHermite()    -   "lightweight" Hermite fitting, without
                                invididual weights and constraints

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    XC  -   points where spline values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that S(XC[i])=YC[i]
            * DC[i]=1   means that S'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints (optional):
            * 0<=K<M.
            * K=0 means no constraints (XC/YC/DC are not used)
            * if given, only first K elements of XC/YC/DC are used
            * if not given, automatically determined from XC/YC/DC
    M   -   number of basis functions (= 2 * number of nodes),
            M>=4,
            M IS EVEN!

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
                        -2 means odd M was passed (which is not supported)
                        -1 means another errors in parameters passed
                           (N<=0, for example)
    S   -   spline interpolant.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

IMPORTANT:
    this subroitine supports only even M's


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* excessive constraints can be inconsistent. Splines are  piecewise  cubic
  functions, and it is easy to create an example, where  large  number  of
  constraints  concentrated  in  small  area will result in inconsistency.
  Just because spline is not flexible enough to satisfy all of  them.  And
  same constraints spread across the  [min(x),max(x)]  will  be  perfectly
  consistent.
* the more evenly constraints are spread across [min(x),max(x)],  the more
  chances that they will be consistent
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints is NOT GUARANTEED.
* in the several special cases, however, we can guarantee consistency.
* one of this cases is  M>=4  and   constraints  on   the  function  value
  (AND/OR its derivative) at the interval boundaries.
* another special case is M>=4  and  ONE  constraint on the function value
  (OR, BUT NOT AND, derivative) anywhere in [min(x),max(x)]

Our final recommendation is to use constraints  WHEN  AND  ONLY  when  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfithermitewc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfithermitewc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfithermitewc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfithermitewc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted  fitting  by Hermite spline,  with constraints on function values
or first derivatives.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are Hermite splines.  Small  regularizing
term is used when solving constrained tasks (to improve stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO
    Spline1DFitCubicWC()    -   fitting by Cubic splines (less flexible,
                                more smooth)
    Spline1DFitHermite()    -   "lightweight" Hermite fitting, without
                                invididual weights and constraints

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    XC  -   points where spline values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that S(XC[i])=YC[i]
            * DC[i]=1   means that S'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints (optional):
            * 0<=K<M.
            * K=0 means no constraints (XC/YC/DC are not used)
            * if given, only first K elements of XC/YC/DC are used
            * if not given, automatically determined from XC/YC/DC
    M   -   number of basis functions (= 2 * number of nodes),
            M>=4,
            M IS EVEN!

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
                        -2 means odd M was passed (which is not supported)
                        -1 means another errors in parameters passed
                           (N<=0, for example)
    S   -   spline interpolant.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

IMPORTANT:
    this subroitine supports only even M's


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* excessive constraints can be inconsistent. Splines are  piecewise  cubic
  functions, and it is easy to create an example, where  large  number  of
  constraints  concentrated  in  small  area will result in inconsistency.
  Just because spline is not flexible enough to satisfy all of  them.  And
  same constraints spread across the  [min(x),max(x)]  will  be  perfectly
  consistent.
* the more evenly constraints are spread across [min(x),max(x)],  the more
  chances that they will be consistent
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints is NOT GUARANTEED.
* in the several special cases, however, we can guarantee consistency.
* one of this cases is  M>=4  and   constraints  on   the  function  value
  (AND/OR its derivative) at the interval boundaries.
* another special case is M>=4  and  ONE  constraint on the function value
  (OR, BUT NOT AND, derivative) anywhere in [min(x),max(x)]

Our final recommendation is to use constraints  WHEN  AND  ONLY  when  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfithermitewc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t k;
    if( (x.length()!=y.length()) || (x.length()!=w.length()))
        throw ap_error("Error while calling 'spline1dfithermitewc': looks like one of arguments has wrong size");
    if( (xc.length()!=yc.length()) || (xc.length()!=dc.length()))
        throw ap_error("Error while calling 'spline1dfithermitewc': looks like one of arguments has wrong size");
    n = x.length();
    k = xc.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfithermitewc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfithermitewc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t k;
    if( (x.length()!=y.length()) || (x.length()!=w.length()))
        throw ap_error("Error while calling 'spline1dfithermitewc': looks like one of arguments has wrong size");
    if( (xc.length()!=yc.length()) || (xc.length()!=dc.length()))
        throw ap_error("Error while calling 'spline1dfithermitewc': looks like one of arguments has wrong size");
    n = x.length();
    k = xc.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfithermitewc(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(xc.c_ptr()), const_cast<alglib_impl::ae_vector*>(yc.c_ptr()), const_cast<alglib_impl::ae_vector*>(dc.c_ptr()), k, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Least squares fitting by cubic spline.

This subroutine is "lightweight" alternative for more complex and feature-
rich Spline1DFitCubicWC().  See  Spline1DFitCubicWC() for more information
about subroutine parameters (we don't duplicate it here because of length)

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfitcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfitcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfitcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Least squares fitting by cubic spline.

This subroutine is "lightweight" alternative for more complex and feature-
rich Spline1DFitCubicWC().  See  Spline1DFitCubicWC() for more information
about subroutine parameters (we don't duplicate it here because of length)

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dfitcubic': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfitcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfitcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dfitcubic': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfitcubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Least squares fitting by Hermite spline.

This subroutine is "lightweight" alternative for more complex and feature-
rich Spline1DFitHermiteWC().  See Spline1DFitHermiteWC()  description  for
more information about subroutine parameters (we don't duplicate  it  here
because of length).

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfithermite(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfithermite(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfithermite(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfithermite(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Least squares fitting by Hermite spline.

This subroutine is "lightweight" alternative for more complex and feature-
rich Spline1DFitHermiteWC().  See Spline1DFitHermiteWC()  description  for
more information about subroutine parameters (we don't duplicate  it  here
because of length).

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfithermite(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dfithermite': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline1dfithermite(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_spline1dfithermite(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    if( (x.length()!=y.length()))
        throw ap_error("Error while calling 'spline1dfithermite': looks like one of arguments has wrong size");
    n = x.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_spline1dfithermite(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), n, m, &info, const_cast<alglib_impl::spline1dinterpolant*>(s.c_ptr()), const_cast<alglib_impl::spline1dfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted linear least squares fitting.

QR decomposition is used to reduce task to MxM, then triangular solver  or
SVD-based solver is used depending on condition number of the  system.  It
allows to maximize speed and retain decent accuracy.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    W       -   array[0..N-1]  Weights  corresponding to function  values.
                Each summand in square  sum  of  approximation  deviations
                from  given  values  is  multiplied  by  the   square   of
                corresponding weight.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I, J] - value of J-th basis function in I-th point.
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -1    incorrect N/M were specified
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * Rep.TaskRCond     reciprocal of condition number
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearw(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const ae_int_t n, const ae_int_t m, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitlinearw(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), n, m, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_lsfitlinearw(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const ae_int_t n, const ae_int_t m, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_lsfitlinearw(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), n, m, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted linear least squares fitting.

QR decomposition is used to reduce task to MxM, then triangular solver  or
SVD-based solver is used depending on condition number of the  system.  It
allows to maximize speed and retain decent accuracy.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    W       -   array[0..N-1]  Weights  corresponding to function  values.
                Each summand in square  sum  of  approximation  deviations
                from  given  values  is  multiplied  by  the   square   of
                corresponding weight.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I, J] - value of J-th basis function in I-th point.
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -1    incorrect N/M were specified
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * Rep.TaskRCond     reciprocal of condition number
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearw(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    if( (y.length()!=w.length()) || (y.length()!=fmatrix.rows()))
        throw ap_error("Error while calling 'lsfitlinearw': looks like one of arguments has wrong size");
    n = y.length();
    m = fmatrix.cols();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitlinearw(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), n, m, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_lsfitlinearw(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    if( (y.length()!=w.length()) || (y.length()!=fmatrix.rows()))
        throw ap_error("Error while calling 'lsfitlinearw': looks like one of arguments has wrong size");
    n = y.length();
    m = fmatrix.cols();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_lsfitlinearw(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), n, m, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted constained linear least squares fitting.

This  is  variation  of LSFitLinearW(), which searchs for min|A*x=b| given
that  K  additional  constaints  C*x=bc are satisfied. It reduces original
task to modified one: min|B*y-d| WITHOUT constraints,  then LSFitLinearW()
is called.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    W       -   array[0..N-1]  Weights  corresponding to function  values.
                Each summand in square  sum  of  approximation  deviations
                from  given  values  is  multiplied  by  the   square   of
                corresponding weight.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I,J] - value of J-th basis function in I-th point.
    CMatrix -   a table of constaints, array[0..K-1,0..M].
                I-th row of CMatrix corresponds to I-th linear constraint:
                CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M]
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.
    K       -   number of constraints, 0 <= K < M
                K=0 corresponds to absence of constraints.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -3    either   too   many  constraints  (M   or   more),
                        degenerate  constraints   (some   constraints  are
                        repetead twice) or inconsistent  constraints  were
                        specified.
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 07.09.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearwc(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const real_2d_array &cmatrix, const ae_int_t n, const ae_int_t m, const ae_int_t k, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitlinearwc(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), const_cast<alglib_impl::ae_matrix*>(cmatrix.c_ptr()), n, m, k, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_lsfitlinearwc(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const real_2d_array &cmatrix, const ae_int_t n, const ae_int_t m, const ae_int_t k, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_lsfitlinearwc(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), const_cast<alglib_impl::ae_matrix*>(cmatrix.c_ptr()), n, m, k, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted constained linear least squares fitting.

This  is  variation  of LSFitLinearW(), which searchs for min|A*x=b| given
that  K  additional  constaints  C*x=bc are satisfied. It reduces original
task to modified one: min|B*y-d| WITHOUT constraints,  then LSFitLinearW()
is called.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    W       -   array[0..N-1]  Weights  corresponding to function  values.
                Each summand in square  sum  of  approximation  deviations
                from  given  values  is  multiplied  by  the   square   of
                corresponding weight.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I,J] - value of J-th basis function in I-th point.
    CMatrix -   a table of constaints, array[0..K-1,0..M].
                I-th row of CMatrix corresponds to I-th linear constraint:
                CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M]
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.
    K       -   number of constraints, 0 <= K < M
                K=0 corresponds to absence of constraints.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -3    either   too   many  constraints  (M   or   more),
                        degenerate  constraints   (some   constraints  are
                        repetead twice) or inconsistent  constraints  were
                        specified.
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 07.09.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearwc(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const real_2d_array &cmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (y.length()!=w.length()) || (y.length()!=fmatrix.rows()))
        throw ap_error("Error while calling 'lsfitlinearwc': looks like one of arguments has wrong size");
    if( (fmatrix.cols()!=cmatrix.cols()-1))
        throw ap_error("Error while calling 'lsfitlinearwc': looks like one of arguments has wrong size");
    n = y.length();
    m = fmatrix.cols();
    k = cmatrix.rows();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitlinearwc(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), const_cast<alglib_impl::ae_matrix*>(cmatrix.c_ptr()), n, m, k, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_lsfitlinearwc(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const real_2d_array &cmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (y.length()!=w.length()) || (y.length()!=fmatrix.rows()))
        throw ap_error("Error while calling 'lsfitlinearwc': looks like one of arguments has wrong size");
    if( (fmatrix.cols()!=cmatrix.cols()-1))
        throw ap_error("Error while calling 'lsfitlinearwc': looks like one of arguments has wrong size");
    n = y.length();
    m = fmatrix.cols();
    k = cmatrix.rows();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_lsfitlinearwc(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), const_cast<alglib_impl::ae_matrix*>(cmatrix.c_ptr()), n, m, k, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Linear least squares fitting.

QR decomposition is used to reduce task to MxM, then triangular solver  or
SVD-based solver is used depending on condition number of the  system.  It
allows to maximize speed and retain decent accuracy.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I, J] - value of J-th basis function in I-th point.
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * Rep.TaskRCond     reciprocal of condition number
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, const ae_int_t n, const ae_int_t m, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitlinear(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), n, m, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, const ae_int_t n, const ae_int_t m, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_lsfitlinear(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), n, m, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Linear least squares fitting.

QR decomposition is used to reduce task to MxM, then triangular solver  or
SVD-based solver is used depending on condition number of the  system.  It
allows to maximize speed and retain decent accuracy.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I, J] - value of J-th basis function in I-th point.
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * Rep.TaskRCond     reciprocal of condition number
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    if( (y.length()!=fmatrix.rows()))
        throw ap_error("Error while calling 'lsfitlinear': looks like one of arguments has wrong size");
    n = y.length();
    m = fmatrix.cols();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitlinear(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), n, m, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    if( (y.length()!=fmatrix.rows()))
        throw ap_error("Error while calling 'lsfitlinear': looks like one of arguments has wrong size");
    n = y.length();
    m = fmatrix.cols();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_lsfitlinear(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), n, m, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Constained linear least squares fitting.

This  is  variation  of LSFitLinear(),  which searchs for min|A*x=b| given
that  K  additional  constaints  C*x=bc are satisfied. It reduces original
task to modified one: min|B*y-d| WITHOUT constraints,  then  LSFitLinear()
is called.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I,J] - value of J-th basis function in I-th point.
    CMatrix -   a table of constaints, array[0..K-1,0..M].
                I-th row of CMatrix corresponds to I-th linear constraint:
                CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M]
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.
    K       -   number of constraints, 0 <= K < M
                K=0 corresponds to absence of constraints.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -3    either   too   many  constraints  (M   or   more),
                        degenerate  constraints   (some   constraints  are
                        repetead twice) or inconsistent  constraints  were
                        specified.
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 07.09.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearc(const real_1d_array &y, const real_2d_array &fmatrix, const real_2d_array &cmatrix, const ae_int_t n, const ae_int_t m, const ae_int_t k, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitlinearc(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), const_cast<alglib_impl::ae_matrix*>(cmatrix.c_ptr()), n, m, k, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_lsfitlinearc(const real_1d_array &y, const real_2d_array &fmatrix, const real_2d_array &cmatrix, const ae_int_t n, const ae_int_t m, const ae_int_t k, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_lsfitlinearc(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), const_cast<alglib_impl::ae_matrix*>(cmatrix.c_ptr()), n, m, k, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Constained linear least squares fitting.

This  is  variation  of LSFitLinear(),  which searchs for min|A*x=b| given
that  K  additional  constaints  C*x=bc are satisfied. It reduces original
task to modified one: min|B*y-d| WITHOUT constraints,  then  LSFitLinear()
is called.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I,J] - value of J-th basis function in I-th point.
    CMatrix -   a table of constaints, array[0..K-1,0..M].
                I-th row of CMatrix corresponds to I-th linear constraint:
                CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M]
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.
    K       -   number of constraints, 0 <= K < M
                K=0 corresponds to absence of constraints.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -3    either   too   many  constraints  (M   or   more),
                        degenerate  constraints   (some   constraints  are
                        repetead twice) or inconsistent  constraints  were
                        specified.
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 07.09.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearc(const real_1d_array &y, const real_2d_array &fmatrix, const real_2d_array &cmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (y.length()!=fmatrix.rows()))
        throw ap_error("Error while calling 'lsfitlinearc': looks like one of arguments has wrong size");
    if( (fmatrix.cols()!=cmatrix.cols()-1))
        throw ap_error("Error while calling 'lsfitlinearc': looks like one of arguments has wrong size");
    n = y.length();
    m = fmatrix.cols();
    k = cmatrix.rows();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitlinearc(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), const_cast<alglib_impl::ae_matrix*>(cmatrix.c_ptr()), n, m, k, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void smp_lsfitlinearc(const real_1d_array &y, const real_2d_array &fmatrix, const real_2d_array &cmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (y.length()!=fmatrix.rows()))
        throw ap_error("Error while calling 'lsfitlinearc': looks like one of arguments has wrong size");
    if( (fmatrix.cols()!=cmatrix.cols()-1))
        throw ap_error("Error while calling 'lsfitlinearc': looks like one of arguments has wrong size");
    n = y.length();
    m = fmatrix.cols();
    k = cmatrix.rows();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::_pexec_lsfitlinearc(const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(fmatrix.c_ptr()), const_cast<alglib_impl::ae_matrix*>(cmatrix.c_ptr()), n, m, k, &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted nonlinear least squares fitting using function values only.

Combination of numerical differentiation and secant updates is used to
obtain function Jacobian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]).

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    DiffStep-   numerical differentiation step;
                should not be very small or large;
                large = loss of accuracy
                small = growth of round-off errors

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 18.10.2008 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewf(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const double diffstep, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatewf(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, diffstep, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted nonlinear least squares fitting using function values only.

Combination of numerical differentiation and secant updates is used to
obtain function Jacobian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]).

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    DiffStep-   numerical differentiation step;
                should not be very small or large;
                large = loss of accuracy
                small = growth of round-off errors

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 18.10.2008 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewf(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const double diffstep, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (x.rows()!=y.length()) || (x.rows()!=w.length()))
        throw ap_error("Error while calling 'lsfitcreatewf': looks like one of arguments has wrong size");
    n = x.rows();
    m = x.cols();
    k = c.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatewf(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, diffstep, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Nonlinear least squares fitting using function values only.

Combination of numerical differentiation and secant updates is used to
obtain function Jacobian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (f(c,x[0])-y[0])^2 + ... + (f(c,x[n-1])-y[n-1])^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]).

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    DiffStep-   numerical differentiation step;
                should not be very small or large;
                large = loss of accuracy
                small = growth of round-off errors

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 18.10.2008 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatef(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const double diffstep, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatef(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, diffstep, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Nonlinear least squares fitting using function values only.

Combination of numerical differentiation and secant updates is used to
obtain function Jacobian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (f(c,x[0])-y[0])^2 + ... + (f(c,x[n-1])-y[n-1])^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]).

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    DiffStep-   numerical differentiation step;
                should not be very small or large;
                large = loss of accuracy
                small = growth of round-off errors

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 18.10.2008 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatef(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const double diffstep, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (x.rows()!=y.length()))
        throw ap_error("Error while calling 'lsfitcreatef': looks like one of arguments has wrong size");
    n = x.rows();
    m = x.cols();
    k = c.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatef(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, diffstep, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted nonlinear least squares fitting using gradient only.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]) and its gradient.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    CheapFG -   boolean flag, which is:
                * True  if both function and gradient calculation complexity
                        are less than O(M^2).  An improved  algorithm  can
                        be  used  which corresponds  to  FGJ  scheme  from
                        MINLM unit.
                * False otherwise.
                        Standard Jacibian-bases  Levenberg-Marquardt  algo
                        will be used (FJ scheme).

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

See also:
    LSFitResults
    LSFitCreateFG (fitting without weights)
    LSFitCreateWFGH (fitting using Hessian)
    LSFitCreateFGH (fitting using Hessian, without weights)

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewfg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const bool cheapfg, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatewfg(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, cheapfg, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted nonlinear least squares fitting using gradient only.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]) and its gradient.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    CheapFG -   boolean flag, which is:
                * True  if both function and gradient calculation complexity
                        are less than O(M^2).  An improved  algorithm  can
                        be  used  which corresponds  to  FGJ  scheme  from
                        MINLM unit.
                * False otherwise.
                        Standard Jacibian-bases  Levenberg-Marquardt  algo
                        will be used (FJ scheme).

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

See also:
    LSFitResults
    LSFitCreateFG (fitting without weights)
    LSFitCreateWFGH (fitting using Hessian)
    LSFitCreateFGH (fitting using Hessian, without weights)

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewfg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const bool cheapfg, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (x.rows()!=y.length()) || (x.rows()!=w.length()))
        throw ap_error("Error while calling 'lsfitcreatewfg': looks like one of arguments has wrong size");
    n = x.rows();
    m = x.cols();
    k = c.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatewfg(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, cheapfg, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Nonlinear least squares fitting using gradient only, without individual
weights.

Nonlinear task min(F(c)) is solved, where

    F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]) and its gradient.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    CheapFG -   boolean flag, which is:
                * True  if both function and gradient calculation complexity
                        are less than O(M^2).  An improved  algorithm  can
                        be  used  which corresponds  to  FGJ  scheme  from
                        MINLM unit.
                * False otherwise.
                        Standard Jacibian-bases  Levenberg-Marquardt  algo
                        will be used (FJ scheme).

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatefg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const bool cheapfg, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatefg(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, cheapfg, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Nonlinear least squares fitting using gradient only, without individual
weights.

Nonlinear task min(F(c)) is solved, where

    F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]) and its gradient.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    CheapFG -   boolean flag, which is:
                * True  if both function and gradient calculation complexity
                        are less than O(M^2).  An improved  algorithm  can
                        be  used  which corresponds  to  FGJ  scheme  from
                        MINLM unit.
                * False otherwise.
                        Standard Jacibian-bases  Levenberg-Marquardt  algo
                        will be used (FJ scheme).

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatefg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const bool cheapfg, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (x.rows()!=y.length()))
        throw ap_error("Error while calling 'lsfitcreatefg': looks like one of arguments has wrong size");
    n = x.rows();
    m = x.cols();
    k = c.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatefg(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, cheapfg, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted nonlinear least squares fitting using gradient/Hessian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses f(c,x[i]), its gradient and its Hessian.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewfgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatewfgh(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Weighted nonlinear least squares fitting using gradient/Hessian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses f(c,x[i]), its gradient and its Hessian.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewfgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (x.rows()!=y.length()) || (x.rows()!=w.length()))
        throw ap_error("Error while calling 'lsfitcreatewfgh': looks like one of arguments has wrong size");
    n = x.rows();
    m = x.cols();
    k = c.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatewfgh(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(w.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Nonlinear least squares fitting using gradient/Hessian, without individial
weights.

Nonlinear task min(F(c)) is solved, where

    F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses f(c,x[i]), its gradient and its Hessian.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatefgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatefgh(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Nonlinear least squares fitting using gradient/Hessian, without individial
weights.

Nonlinear task min(F(c)) is solved, where

    F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses f(c,x[i]), its gradient and its Hessian.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatefgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    if( (x.rows()!=y.length()))
        throw ap_error("Error while calling 'lsfitcreatefgh': looks like one of arguments has wrong size");
    n = x.rows();
    m = x.cols();
    k = c.length();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitcreatefgh(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_vector*>(c.c_ptr()), n, m, k, const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Stopping conditions for nonlinear least squares fitting.

INPUT PARAMETERS:
    State   -   structure which stores algorithm state
    EpsF    -   stopping criterion. Algorithm stops if
                |F(k+1)-F(k)| <= EpsF*max{|F(k)|, |F(k+1)|, 1}
    EpsX    -   >=0
                The subroutine finishes its work if  on  k+1-th  iteration
                the condition |v|<=EpsX is fulfilled, where:
                * |.| means Euclidian norm
                * v - scaled step vector, v[i]=dx[i]/s[i]
                * dx - ste pvector, dx=X(k+1)-X(k)
                * s - scaling coefficients set by LSFitSetScale()
    MaxIts  -   maximum number of iterations. If MaxIts=0, the  number  of
                iterations   is    unlimited.   Only   Levenberg-Marquardt
                iterations  are  counted  (L-BFGS/CG  iterations  are  NOT
                counted because their cost is very low compared to that of
                LM).

NOTE

Passing EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to automatic
stopping criterion selection (according to the scheme used by MINLM unit).


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitsetcond(const lsfitstate &state, const double epsf, const double epsx, const ae_int_t maxits)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitsetcond(const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), epsf, epsx, maxits, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function sets maximum step length

INPUT PARAMETERS:
    State   -   structure which stores algorithm state
    StpMax  -   maximum step length, >=0. Set StpMax to 0.0,  if you don't
                want to limit step length.

Use this subroutine when you optimize target function which contains exp()
or  other  fast  growing  functions,  and optimization algorithm makes too
large  steps  which  leads  to overflow. This function allows us to reject
steps  that  are  too  large  (and  therefore  expose  us  to the possible
overflow) without actually calculating function value at the x+stp*d.

NOTE: non-zero StpMax leads to moderate  performance  degradation  because
intermediate  step  of  preconditioned L-BFGS optimization is incompatible
with limits on step size.

  -- ALGLIB --
     Copyright 02.04.2010 by Bochkanov Sergey
*************************************************************************/
void lsfitsetstpmax(const lsfitstate &state, const double stpmax)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitsetstpmax(const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), stpmax, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function turns on/off reporting.

INPUT PARAMETERS:
    State   -   structure which stores algorithm state
    NeedXRep-   whether iteration reports are needed or not

When reports are needed, State.C (current parameters) and State.F (current
value of fitting function) are reported.


  -- ALGLIB --
     Copyright 15.08.2010 by Bochkanov Sergey
*************************************************************************/
void lsfitsetxrep(const lsfitstate &state, const bool needxrep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitsetxrep(const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), needxrep, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function sets scaling coefficients for underlying optimizer.

ALGLIB optimizers use scaling matrices to test stopping  conditions  (step
size and gradient are scaled before comparison with tolerances).  Scale of
the I-th variable is a translation invariant measure of:
a) "how large" the variable is
b) how large the step should be to make significant changes in the function

Generally, scale is NOT considered to be a form of preconditioner.  But LM
optimizer is unique in that it uses scaling matrix both  in  the  stopping
condition tests and as Marquardt damping factor.

Proper scaling is very important for the algorithm performance. It is less
important for the quality of results, but still has some influence (it  is
easier  to  converge  when  variables  are  properly  scaled, so premature
stopping is possible when very badly scalled variables are  combined  with
relaxed stopping conditions).

INPUT PARAMETERS:
    State   -   structure stores algorithm state
    S       -   array[N], non-zero scaling coefficients
                S[i] may be negative, sign doesn't matter.

  -- ALGLIB --
     Copyright 14.01.2011 by Bochkanov Sergey
*************************************************************************/
void lsfitsetscale(const lsfitstate &state, const real_1d_array &s)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitsetscale(const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), const_cast<alglib_impl::ae_vector*>(s.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function sets boundary constraints for underlying optimizer

Boundary constraints are inactive by default (after initial creation).
They are preserved until explicitly turned off with another SetBC() call.

INPUT PARAMETERS:
    State   -   structure stores algorithm state
    BndL    -   lower bounds, array[K].
                If some (all) variables are unbounded, you may specify
                very small number or -INF (latter is recommended because
                it will allow solver to use better algorithm).
    BndU    -   upper bounds, array[K].
                If some (all) variables are unbounded, you may specify
                very large number or +INF (latter is recommended because
                it will allow solver to use better algorithm).

NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th
variable will be "frozen" at X[i]=BndL[i]=BndU[i].

NOTE 2: unlike other constrained optimization algorithms, this solver  has
following useful properties:
* bound constraints are always satisfied exactly
* function is evaluated only INSIDE area specified by bound constraints

  -- ALGLIB --
     Copyright 14.01.2011 by Bochkanov Sergey
*************************************************************************/
void lsfitsetbc(const lsfitstate &state, const real_1d_array &bndl, const real_1d_array &bndu)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitsetbc(const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), const_cast<alglib_impl::ae_vector*>(bndl.c_ptr()), const_cast<alglib_impl::ae_vector*>(bndu.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function provides reverse communication interface
Reverse communication interface is not documented or recommended to use.
See below for functions which provide better documented API
*************************************************************************/
bool lsfititeration(const lsfitstate &state)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        ae_bool result = alglib_impl::lsfititeration(const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<bool*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void lsfitfit(lsfitstate &state,
    void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr),
    void  (*rep)(const real_1d_array &c, double func, void *ptr), 
    void *ptr)
{
    alglib_impl::ae_state _alglib_env_state;
    if( func==NULL )
        throw ap_error("ALGLIB: error in 'lsfitfit()' (func is NULL)");
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        while( alglib_impl::lsfititeration(state.c_ptr(), &_alglib_env_state) )
        {
            if( state.needf )
            {
                func(state.c, state.x, state.f, ptr);
                continue;
            }
            if( state.xupdated )
            {
                if( rep!=NULL )
                    rep(state.c, state.f, ptr);
                continue;
            }
            throw ap_error("ALGLIB: error in 'lsfitfit' (some derivatives were not provided?)");
        }
        alglib_impl::ae_state_clear(&_alglib_env_state);
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void lsfitfit(lsfitstate &state,
    void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr),
    void (*grad)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, void *ptr),
    void  (*rep)(const real_1d_array &c, double func, void *ptr), 
    void *ptr)
{
    alglib_impl::ae_state _alglib_env_state;
    if( func==NULL )
        throw ap_error("ALGLIB: error in 'lsfitfit()' (func is NULL)");
    if( grad==NULL )
        throw ap_error("ALGLIB: error in 'lsfitfit()' (grad is NULL)");
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        while( alglib_impl::lsfititeration(state.c_ptr(), &_alglib_env_state) )
        {
            if( state.needf )
            {
                func(state.c, state.x, state.f, ptr);
                continue;
            }
            if( state.needfg )
            {
                grad(state.c, state.x, state.f, state.g, ptr);
                continue;
            }
            if( state.xupdated )
            {
                if( rep!=NULL )
                    rep(state.c, state.f, ptr);
                continue;
            }
            throw ap_error("ALGLIB: error in 'lsfitfit' (some derivatives were not provided?)");
        }
        alglib_impl::ae_state_clear(&_alglib_env_state);
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}


void lsfitfit(lsfitstate &state,
    void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr),
    void (*grad)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, void *ptr),
    void (*hess)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, real_2d_array &hess, void *ptr),
    void  (*rep)(const real_1d_array &c, double func, void *ptr), 
    void *ptr)
{
    alglib_impl::ae_state _alglib_env_state;
    if( func==NULL )
        throw ap_error("ALGLIB: error in 'lsfitfit()' (func is NULL)");
    if( grad==NULL )
        throw ap_error("ALGLIB: error in 'lsfitfit()' (grad is NULL)");
    if( hess==NULL )
        throw ap_error("ALGLIB: error in 'lsfitfit()' (hess is NULL)");
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        while( alglib_impl::lsfititeration(state.c_ptr(), &_alglib_env_state) )
        {
            if( state.needf )
            {
                func(state.c, state.x, state.f, ptr);
                continue;
            }
            if( state.needfg )
            {
                grad(state.c, state.x, state.f, state.g, ptr);
                continue;
            }
            if( state.needfgh )
            {
                hess(state.c, state.x, state.f, state.g, state.h, ptr);
                continue;
            }
            if( state.xupdated )
            {
                if( rep!=NULL )
                    rep(state.c, state.f, ptr);
                continue;
            }
            throw ap_error("ALGLIB: error in 'lsfitfit' (some derivatives were not provided?)");
        }
        alglib_impl::ae_state_clear(&_alglib_env_state);
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}



/*************************************************************************
Nonlinear least squares fitting results.

Called after return from LSFitFit().

INPUT PARAMETERS:
    State   -   algorithm state

OUTPUT PARAMETERS:
    Info    -   completion code:
                    * -7    gradient verification failed.
                            See LSFitSetGradientCheck() for more information.
                    *  1    relative function improvement is no more than
                            EpsF.
                    *  2    relative step is no more than EpsX.
                    *  4    gradient norm is no more than EpsG
                    *  5    MaxIts steps was taken
                    *  7    stopping conditions are too stringent,
                            further improvement is impossible
    C       -   array[0..K-1], solution
    Rep     -   optimization report. On success following fields are set:
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED
                * WRMSError         weighted rms error on the (X,Y).

ERRORS IN PARAMETERS

This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(J*CovPar*J')),
                    where J is Jacobian matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.

NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.

NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).

            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitresults(const lsfitstate &state, ae_int_t &info, real_1d_array &c, lsfitreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitresults(const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), &info, const_cast<alglib_impl::ae_vector*>(c.c_ptr()), const_cast<alglib_impl::lsfitreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  subroutine  turns  on  verification  of  the  user-supplied analytic
gradient:
* user calls this subroutine before fitting begins
* LSFitFit() is called
* prior to actual fitting, for  each  point  in  data  set  X_i  and  each
  component  of  parameters  being  fited C_j algorithm performs following
  steps:
  * two trial steps are made to C_j-TestStep*S[j] and C_j+TestStep*S[j],
    where C_j is j-th parameter and S[j] is a scale of j-th parameter
  * if needed, steps are bounded with respect to constraints on C[]
  * F(X_i|C) is evaluated at these trial points
  * we perform one more evaluation in the middle point of the interval
  * we  build  cubic  model using function values and derivatives at trial
    points and we compare its prediction with actual value in  the  middle
    point
  * in case difference between prediction and actual value is higher  than
    some predetermined threshold, algorithm stops with completion code -7;
    Rep.VarIdx is set to index of the parameter with incorrect derivative.
* after verification is over, algorithm proceeds to the actual optimization.

NOTE 1: verification needs N*K (points count * parameters count)  gradient
        evaluations. It is very costly and you should use it only for  low
        dimensional  problems,  when  you  want  to  be  sure  that you've
        correctly calculated analytic derivatives. You should not  use  it
        in the production code  (unless  you  want  to  check  derivatives
        provided by some third party).

NOTE 2: you  should  carefully  choose  TestStep. Value which is too large
        (so large that function behaviour is significantly non-cubic) will
        lead to false alarms. You may use  different  step  for  different
        parameters by means of setting scale with LSFitSetScale().

NOTE 3: this function may lead to false positives. In case it reports that
        I-th  derivative was calculated incorrectly, you may decrease test
        step  and  try  one  more  time  - maybe your function changes too
        sharply  and  your  step  is  too  large for such rapidly chanding
        function.

NOTE 4: this function works only for optimizers created with LSFitCreateWFG()
        or LSFitCreateFG() constructors.

INPUT PARAMETERS:
    State       -   structure used to store algorithm state
    TestStep    -   verification step:
                    * TestStep=0 turns verification off
                    * TestStep>0 activates verification

  -- ALGLIB --
     Copyright 15.06.2012 by Bochkanov Sergey
*************************************************************************/
void lsfitsetgradientcheck(const lsfitstate &state, const double teststep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::lsfitsetgradientcheck(const_cast<alglib_impl::lsfitstate*>(state.c_ptr()), teststep, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Parametric spline inteprolant: 2-dimensional curve.

You should not try to access its members directly - use PSpline2XXXXXXXX()
functions instead.
*************************************************************************/
_pspline2interpolant_owner::_pspline2interpolant_owner()
{
    p_struct = (alglib_impl::pspline2interpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::pspline2interpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_pspline2interpolant_init(p_struct, NULL);
}

_pspline2interpolant_owner::_pspline2interpolant_owner(const _pspline2interpolant_owner &rhs)
{
    p_struct = (alglib_impl::pspline2interpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::pspline2interpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_pspline2interpolant_init_copy(p_struct, const_cast<alglib_impl::pspline2interpolant*>(rhs.p_struct), NULL);
}

_pspline2interpolant_owner& _pspline2interpolant_owner::operator=(const _pspline2interpolant_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_pspline2interpolant_clear(p_struct);
    alglib_impl::_pspline2interpolant_init_copy(p_struct, const_cast<alglib_impl::pspline2interpolant*>(rhs.p_struct), NULL);
    return *this;
}

_pspline2interpolant_owner::~_pspline2interpolant_owner()
{
    alglib_impl::_pspline2interpolant_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::pspline2interpolant* _pspline2interpolant_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::pspline2interpolant* _pspline2interpolant_owner::c_ptr() const
{
    return const_cast<alglib_impl::pspline2interpolant*>(p_struct);
}
pspline2interpolant::pspline2interpolant() : _pspline2interpolant_owner() 
{
}

pspline2interpolant::pspline2interpolant(const pspline2interpolant &rhs):_pspline2interpolant_owner(rhs) 
{
}

pspline2interpolant& pspline2interpolant::operator=(const pspline2interpolant &rhs)
{
    if( this==&rhs )
        return *this;
    _pspline2interpolant_owner::operator=(rhs);
    return *this;
}

pspline2interpolant::~pspline2interpolant()
{
}


/*************************************************************************
Parametric spline inteprolant: 3-dimensional curve.

You should not try to access its members directly - use PSpline3XXXXXXXX()
functions instead.
*************************************************************************/
_pspline3interpolant_owner::_pspline3interpolant_owner()
{
    p_struct = (alglib_impl::pspline3interpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::pspline3interpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_pspline3interpolant_init(p_struct, NULL);
}

_pspline3interpolant_owner::_pspline3interpolant_owner(const _pspline3interpolant_owner &rhs)
{
    p_struct = (alglib_impl::pspline3interpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::pspline3interpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_pspline3interpolant_init_copy(p_struct, const_cast<alglib_impl::pspline3interpolant*>(rhs.p_struct), NULL);
}

_pspline3interpolant_owner& _pspline3interpolant_owner::operator=(const _pspline3interpolant_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_pspline3interpolant_clear(p_struct);
    alglib_impl::_pspline3interpolant_init_copy(p_struct, const_cast<alglib_impl::pspline3interpolant*>(rhs.p_struct), NULL);
    return *this;
}

_pspline3interpolant_owner::~_pspline3interpolant_owner()
{
    alglib_impl::_pspline3interpolant_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::pspline3interpolant* _pspline3interpolant_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::pspline3interpolant* _pspline3interpolant_owner::c_ptr() const
{
    return const_cast<alglib_impl::pspline3interpolant*>(p_struct);
}
pspline3interpolant::pspline3interpolant() : _pspline3interpolant_owner() 
{
}

pspline3interpolant::pspline3interpolant(const pspline3interpolant &rhs):_pspline3interpolant_owner(rhs) 
{
}

pspline3interpolant& pspline3interpolant::operator=(const pspline3interpolant &rhs)
{
    if( this==&rhs )
        return *this;
    _pspline3interpolant_owner::operator=(rhs);
    return *this;
}

pspline3interpolant::~pspline3interpolant()
{
}

/*************************************************************************
This function  builds  non-periodic 2-dimensional parametric spline  which
starts at (X[0],Y[0]) and ends at (X[N-1],Y[N-1]).

INPUT PARAMETERS:
    XY  -   points, array[0..N-1,0..1].
            XY[I,0:1] corresponds to the Ith point.
            Order of points is important!
    N   -   points count, N>=5 for Akima splines, N>=2 for other types  of
            splines.
    ST  -   spline type:
            * 0     Akima spline
            * 1     parabolically terminated Catmull-Rom spline (Tension=0)
            * 2     parabolically terminated cubic spline
    PT  -   parameterization type:
            * 0     uniform
            * 1     chord length
            * 2     centripetal

OUTPUT PARAMETERS:
    P   -   parametric spline interpolant


NOTES:
* this function  assumes  that  there all consequent points  are distinct.
  I.e. (x0,y0)<>(x1,y1),  (x1,y1)<>(x2,y2),  (x2,y2)<>(x3,y3)  and  so on.
  However, non-consequent points may coincide, i.e. we can  have  (x0,y0)=
  =(x2,y2).

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2build(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline2interpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline2build(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, st, pt, const_cast<alglib_impl::pspline2interpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function  builds  non-periodic 3-dimensional parametric spline  which
starts at (X[0],Y[0],Z[0]) and ends at (X[N-1],Y[N-1],Z[N-1]).

Same as PSpline2Build() function, but for 3D, so we  won't  duplicate  its
description here.

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3build(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline3interpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline3build(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, st, pt, const_cast<alglib_impl::pspline3interpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  function  builds  periodic  2-dimensional  parametric  spline  which
starts at (X[0],Y[0]), goes through all points to (X[N-1],Y[N-1]) and then
back to (X[0],Y[0]).

INPUT PARAMETERS:
    XY  -   points, array[0..N-1,0..1].
            XY[I,0:1] corresponds to the Ith point.
            XY[N-1,0:1] must be different from XY[0,0:1].
            Order of points is important!
    N   -   points count, N>=3 for other types of splines.
    ST  -   spline type:
            * 1     Catmull-Rom spline (Tension=0) with cyclic boundary conditions
            * 2     cubic spline with cyclic boundary conditions
    PT  -   parameterization type:
            * 0     uniform
            * 1     chord length
            * 2     centripetal

OUTPUT PARAMETERS:
    P   -   parametric spline interpolant


NOTES:
* this function  assumes  that there all consequent points  are  distinct.
  I.e. (x0,y0)<>(x1,y1), (x1,y1)<>(x2,y2),  (x2,y2)<>(x3,y3)  and  so  on.
  However, non-consequent points may coincide, i.e. we can  have  (x0,y0)=
  =(x2,y2).
* last point of sequence is NOT equal to the first  point.  You  shouldn't
  make curve "explicitly periodic" by making them equal.

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2buildperiodic(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline2interpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline2buildperiodic(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, st, pt, const_cast<alglib_impl::pspline2interpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  function  builds  periodic  3-dimensional  parametric  spline  which
starts at (X[0],Y[0],Z[0]), goes through all points to (X[N-1],Y[N-1],Z[N-1])
and then back to (X[0],Y[0],Z[0]).

Same as PSpline2Build() function, but for 3D, so we  won't  duplicate  its
description here.

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3buildperiodic(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline3interpolant &p)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline3buildperiodic(const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, st, pt, const_cast<alglib_impl::pspline3interpolant*>(p.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function returns vector of parameter values correspoding to points.

I.e. for P created from (X[0],Y[0])...(X[N-1],Y[N-1]) and U=TValues(P)  we
have
    (X[0],Y[0]) = PSpline2Calc(P,U[0]),
    (X[1],Y[1]) = PSpline2Calc(P,U[1]),
    (X[2],Y[2]) = PSpline2Calc(P,U[2]),
    ...

INPUT PARAMETERS:
    P   -   parametric spline interpolant

OUTPUT PARAMETERS:
    N   -   array size
    T   -   array[0..N-1]


NOTES:
* for non-periodic splines U[0]=0, U[0]<U[1]<...<U[N-1], U[N-1]=1
* for periodic splines     U[0]=0, U[0]<U[1]<...<U[N-1], U[N-1]<1

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2parametervalues(const pspline2interpolant &p, ae_int_t &n, real_1d_array &t)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline2parametervalues(const_cast<alglib_impl::pspline2interpolant*>(p.c_ptr()), &n, const_cast<alglib_impl::ae_vector*>(t.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function returns vector of parameter values correspoding to points.

Same as PSpline2ParameterValues(), but for 3D.

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3parametervalues(const pspline3interpolant &p, ae_int_t &n, real_1d_array &t)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline3parametervalues(const_cast<alglib_impl::pspline3interpolant*>(p.c_ptr()), &n, const_cast<alglib_impl::ae_vector*>(t.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function  calculates  the value of the parametric spline for a  given
value of parameter T

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-position
    Y   -   Y-position


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2calc(const pspline2interpolant &p, const double t, double &x, double &y)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline2calc(const_cast<alglib_impl::pspline2interpolant*>(p.c_ptr()), t, &x, &y, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function  calculates  the value of the parametric spline for a  given
value of parameter T.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-position
    Y   -   Y-position
    Z   -   Z-position


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3calc(const pspline3interpolant &p, const double t, double &x, double &y, double &z)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline3calc(const_cast<alglib_impl::pspline3interpolant*>(p.c_ptr()), t, &x, &y, &z, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function  calculates  tangent vector for a given value of parameter T

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X    -   X-component of tangent vector (normalized)
    Y    -   Y-component of tangent vector (normalized)

NOTE:
    X^2+Y^2 is either 1 (for non-zero tangent vector) or 0.


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2tangent(const pspline2interpolant &p, const double t, double &x, double &y)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline2tangent(const_cast<alglib_impl::pspline2interpolant*>(p.c_ptr()), t, &x, &y, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function  calculates  tangent vector for a given value of parameter T

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X    -   X-component of tangent vector (normalized)
    Y    -   Y-component of tangent vector (normalized)
    Z    -   Z-component of tangent vector (normalized)

NOTE:
    X^2+Y^2+Z^2 is either 1 (for non-zero tangent vector) or 0.


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3tangent(const pspline3interpolant &p, const double t, double &x, double &y, double &z)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline3tangent(const_cast<alglib_impl::pspline3interpolant*>(p.c_ptr()), t, &x, &y, &z, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates derivative, i.e. it returns (dX/dT,dY/dT).

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-value
    DX  -   X-derivative
    Y   -   Y-value
    DY  -   Y-derivative


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2diff(const pspline2interpolant &p, const double t, double &x, double &dx, double &y, double &dy)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline2diff(const_cast<alglib_impl::pspline2interpolant*>(p.c_ptr()), t, &x, &dx, &y, &dy, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates derivative, i.e. it returns (dX/dT,dY/dT,dZ/dT).

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-value
    DX  -   X-derivative
    Y   -   Y-value
    DY  -   Y-derivative
    Z   -   Z-value
    DZ  -   Z-derivative


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3diff(const pspline3interpolant &p, const double t, double &x, double &dx, double &y, double &dy, double &z, double &dz)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline3diff(const_cast<alglib_impl::pspline3interpolant*>(p.c_ptr()), t, &x, &dx, &y, &dy, &z, &dz, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates first and second derivative with respect to T.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-value
    DX  -   derivative
    D2X -   second derivative
    Y   -   Y-value
    DY  -   derivative
    D2Y -   second derivative


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2diff2(const pspline2interpolant &p, const double t, double &x, double &dx, double &d2x, double &y, double &dy, double &d2y)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline2diff2(const_cast<alglib_impl::pspline2interpolant*>(p.c_ptr()), t, &x, &dx, &d2x, &y, &dy, &d2y, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates first and second derivative with respect to T.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-value
    DX  -   derivative
    D2X -   second derivative
    Y   -   Y-value
    DY  -   derivative
    D2Y -   second derivative
    Z   -   Z-value
    DZ  -   derivative
    D2Z -   second derivative


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3diff2(const pspline3interpolant &p, const double t, double &x, double &dx, double &d2x, double &y, double &dy, double &d2y, double &z, double &dz, double &d2z)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::pspline3diff2(const_cast<alglib_impl::pspline3interpolant*>(p.c_ptr()), t, &x, &dx, &d2x, &y, &dy, &d2y, &z, &dz, &d2z, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function  calculates  arc length, i.e. length of  curve  between  t=a
and t=b.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    A,B -   parameter values corresponding to arc ends:
            * B>A will result in positive length returned
            * B<A will result in negative length returned

RESULT:
    length of arc starting at T=A and ending at T=B.


  -- ALGLIB PROJECT --
     Copyright 30.05.2010 by Bochkanov Sergey
*************************************************************************/
double pspline2arclength(const pspline2interpolant &p, const double a, const double b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::pspline2arclength(const_cast<alglib_impl::pspline2interpolant*>(p.c_ptr()), a, b, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function  calculates  arc length, i.e. length of  curve  between  t=a
and t=b.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    A,B -   parameter values corresponding to arc ends:
            * B>A will result in positive length returned
            * B<A will result in negative length returned

RESULT:
    length of arc starting at T=A and ending at T=B.


  -- ALGLIB PROJECT --
     Copyright 30.05.2010 by Bochkanov Sergey
*************************************************************************/
double pspline3arclength(const pspline3interpolant &p, const double a, const double b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::pspline3arclength(const_cast<alglib_impl::pspline3interpolant*>(p.c_ptr()), a, b, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  subroutine fits piecewise linear curve to points with Ramer-Douglas-
Peucker algorithm. This  function  performs PARAMETRIC fit, i.e. it can be
used to fit curves like circles.

On  input  it  accepts dataset which describes parametric multidimensional
curve X(t), with X being vector, and t taking values in [0,N), where N  is
a number of points in dataset. As result, it returns reduced  dataset  X2,
which can be used to build  parametric  curve  X2(t),  which  approximates
X(t) with desired precision (or has specified number of sections).


INPUT PARAMETERS:
    X       -   array of multidimensional points:
                * at least N elements, leading N elements are used if more
                  than N elements were specified
                * order of points is IMPORTANT because  it  is  parametric
                  fit
                * each row of array is one point which has D coordinates
    N       -   number of elements in X
    D       -   number of dimensions (elements per row of X)
    StopM   -   stopping condition - desired number of sections:
                * at most M sections are generated by this function
                * less than M sections can be generated if we have N<M
                  (or some X are non-distinct).
                * zero StopM means that algorithm does not stop after
                  achieving some pre-specified section count
    StopEps -   stopping condition - desired precision:
                * algorithm stops after error in each section is at most Eps
                * zero Eps means that algorithm does not stop after
                  achieving some pre-specified precision

OUTPUT PARAMETERS:
    X2      -   array of corner points for piecewise approximation,
                has length NSections+1 or zero (for NSections=0).
    Idx2    -   array of indexes (parameter values):
                * has length NSections+1 or zero (for NSections=0).
                * each element of Idx2 corresponds to same-numbered
                  element of X2
                * each element of Idx2 is index of  corresponding  element
                  of X2 at original array X, i.e. I-th  row  of  X2  is
                  Idx2[I]-th row of X.
                * elements of Idx2 can be treated as parameter values
                  which should be used when building new parametric curve
                * Idx2[0]=0, Idx2[NSections]=N-1
    NSections-  number of sections found by algorithm, NSections<=M,
                NSections can be zero for degenerate datasets
                (N<=1 or all X[] are non-distinct).

NOTE: algorithm stops after:
      a) dividing curve into StopM sections
      b) achieving required precision StopEps
      c) dividing curve into N-1 sections
      If both StopM and StopEps are non-zero, algorithm is stopped by  the
      FIRST criterion which is satisfied. In case both StopM  and  StopEps
      are zero, algorithm stops because of (c).

  -- ALGLIB --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
void parametricrdpfixed(const real_2d_array &x, const ae_int_t n, const ae_int_t d, const ae_int_t stopm, const double stopeps, real_2d_array &x2, integer_1d_array &idx2, ae_int_t &nsections)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::parametricrdpfixed(const_cast<alglib_impl::ae_matrix*>(x.c_ptr()), n, d, stopm, stopeps, const_cast<alglib_impl::ae_matrix*>(x2.c_ptr()), const_cast<alglib_impl::ae_vector*>(idx2.c_ptr()), &nsections, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
RBF model.

Never try to directly work with fields of this object - always use  ALGLIB
functions to use this object.
*************************************************************************/
_rbfmodel_owner::_rbfmodel_owner()
{
    p_struct = (alglib_impl::rbfmodel*)alglib_impl::ae_malloc(sizeof(alglib_impl::rbfmodel), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_rbfmodel_init(p_struct, NULL);
}

_rbfmodel_owner::_rbfmodel_owner(const _rbfmodel_owner &rhs)
{
    p_struct = (alglib_impl::rbfmodel*)alglib_impl::ae_malloc(sizeof(alglib_impl::rbfmodel), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_rbfmodel_init_copy(p_struct, const_cast<alglib_impl::rbfmodel*>(rhs.p_struct), NULL);
}

_rbfmodel_owner& _rbfmodel_owner::operator=(const _rbfmodel_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_rbfmodel_clear(p_struct);
    alglib_impl::_rbfmodel_init_copy(p_struct, const_cast<alglib_impl::rbfmodel*>(rhs.p_struct), NULL);
    return *this;
}

_rbfmodel_owner::~_rbfmodel_owner()
{
    alglib_impl::_rbfmodel_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::rbfmodel* _rbfmodel_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::rbfmodel* _rbfmodel_owner::c_ptr() const
{
    return const_cast<alglib_impl::rbfmodel*>(p_struct);
}
rbfmodel::rbfmodel() : _rbfmodel_owner() 
{
}

rbfmodel::rbfmodel(const rbfmodel &rhs):_rbfmodel_owner(rhs) 
{
}

rbfmodel& rbfmodel::operator=(const rbfmodel &rhs)
{
    if( this==&rhs )
        return *this;
    _rbfmodel_owner::operator=(rhs);
    return *this;
}

rbfmodel::~rbfmodel()
{
}


/*************************************************************************
RBF solution report:
* TerminationType   -   termination type, positive values - success,
                        non-positive - failure.
*************************************************************************/
_rbfreport_owner::_rbfreport_owner()
{
    p_struct = (alglib_impl::rbfreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::rbfreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_rbfreport_init(p_struct, NULL);
}

_rbfreport_owner::_rbfreport_owner(const _rbfreport_owner &rhs)
{
    p_struct = (alglib_impl::rbfreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::rbfreport), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_rbfreport_init_copy(p_struct, const_cast<alglib_impl::rbfreport*>(rhs.p_struct), NULL);
}

_rbfreport_owner& _rbfreport_owner::operator=(const _rbfreport_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_rbfreport_clear(p_struct);
    alglib_impl::_rbfreport_init_copy(p_struct, const_cast<alglib_impl::rbfreport*>(rhs.p_struct), NULL);
    return *this;
}

_rbfreport_owner::~_rbfreport_owner()
{
    alglib_impl::_rbfreport_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::rbfreport* _rbfreport_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::rbfreport* _rbfreport_owner::c_ptr() const
{
    return const_cast<alglib_impl::rbfreport*>(p_struct);
}
rbfreport::rbfreport() : _rbfreport_owner() ,arows(p_struct->arows),acols(p_struct->acols),annz(p_struct->annz),iterationscount(p_struct->iterationscount),nmv(p_struct->nmv),terminationtype(p_struct->terminationtype)
{
}

rbfreport::rbfreport(const rbfreport &rhs):_rbfreport_owner(rhs) ,arows(p_struct->arows),acols(p_struct->acols),annz(p_struct->annz),iterationscount(p_struct->iterationscount),nmv(p_struct->nmv),terminationtype(p_struct->terminationtype)
{
}

rbfreport& rbfreport::operator=(const rbfreport &rhs)
{
    if( this==&rhs )
        return *this;
    _rbfreport_owner::operator=(rhs);
    return *this;
}

rbfreport::~rbfreport()
{
}


/*************************************************************************
This function serializes data structure to string.

Important properties of s_out:
* it contains alphanumeric characters, dots, underscores, minus signs
* these symbols are grouped into words, which are separated by spaces
  and Windows-style (CR+LF) newlines
* although  serializer  uses  spaces and CR+LF as separators, you can 
  replace any separator character by arbitrary combination of spaces,
  tabs, Windows or Unix newlines. It allows flexible reformatting  of
  the  string  in  case you want to include it into text or XML file. 
  But you should not insert separators into the middle of the "words"
  nor you should change case of letters.
* s_out can be freely moved between 32-bit and 64-bit systems, little
  and big endian machines, and so on. You can serialize structure  on
  32-bit machine and unserialize it on 64-bit one (or vice versa), or
  serialize  it  on  SPARC  and  unserialize  on  x86.  You  can also 
  serialize  it  in  C++ version of ALGLIB and unserialize in C# one, 
  and vice versa.
*************************************************************************/
void rbfserialize(rbfmodel &obj, std::string &s_out)
{
    alglib_impl::ae_state state;
    alglib_impl::ae_serializer serializer;
    alglib_impl::ae_int_t ssize;

    alglib_impl::ae_state_init(&state);
    try
    {
        alglib_impl::ae_serializer_init(&serializer);
        alglib_impl::ae_serializer_alloc_start(&serializer);
        alglib_impl::rbfalloc(&serializer, obj.c_ptr(), &state);
        ssize = alglib_impl::ae_serializer_get_alloc_size(&serializer);
        s_out.clear();
        s_out.reserve((size_t)(ssize+1));
        alglib_impl::ae_serializer_sstart_str(&serializer, &s_out);
        alglib_impl::rbfserialize(&serializer, obj.c_ptr(), &state);
        alglib_impl::ae_serializer_stop(&serializer);
        if( s_out.length()>(size_t)ssize )
            throw ap_error("ALGLIB: serialization integrity error");
        alglib_impl::ae_serializer_clear(&serializer);
        alglib_impl::ae_state_clear(&state);
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(state.error_msg);
    }
}
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void rbfunserialize(std::string &s_in, rbfmodel &obj)
{
    alglib_impl::ae_state state;
    alglib_impl::ae_serializer serializer;

    alglib_impl::ae_state_init(&state);
    try
    {
        alglib_impl::ae_serializer_init(&serializer);
        alglib_impl::ae_serializer_ustart_str(&serializer, &s_in);
        alglib_impl::rbfunserialize(&serializer, obj.c_ptr(), &state);
        alglib_impl::ae_serializer_stop(&serializer);
        alglib_impl::ae_serializer_clear(&serializer);
        alglib_impl::ae_state_clear(&state);
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(state.error_msg);
    }
}

/*************************************************************************
This function creates RBF  model  for  a  scalar (NY=1)  or  vector (NY>1)
function in a NX-dimensional space (NX=2 or NX=3).

Newly created model is empty. It can be used for interpolation right after
creation, but it just returns zeros. You have to add points to the  model,
tune interpolation settings, and then  call  model  construction  function
RBFBuildModel() which will update model according to your specification.

USAGE:
1. User creates model with RBFCreate()
2. User adds dataset with RBFSetPoints() (points do NOT have to  be  on  a
   regular grid)
3. (OPTIONAL) User chooses polynomial term by calling:
   * RBFLinTerm() to set linear term
   * RBFConstTerm() to set constant term
   * RBFZeroTerm() to set zero term
   By default, linear term is used.
4. User chooses specific RBF algorithm to use: either QNN (RBFSetAlgoQNN)
   or ML (RBFSetAlgoMultiLayer).
5. User calls RBFBuildModel() function which rebuilds model  according  to
   the specification
6. User may call RBFCalc() to calculate model value at the specified point,
   RBFGridCalc() to  calculate   model  values at the points of the regular
   grid. User may extract model coefficients with RBFUnpack() call.

INPUT PARAMETERS:
    NX      -   dimension of the space, NX=2 or NX=3
    NY      -   function dimension, NY>=1

OUTPUT PARAMETERS:
    S       -   RBF model (initially equals to zero)

NOTE 1: memory requirements. RBF models require amount of memory  which is
        proportional  to  the  number  of data points. Memory is allocated
        during model construction, but most of this memory is freed  after
        model coefficients are calculated.

        Some approximate estimates for N centers with default settings are
        given below:
        * about 250*N*(sizeof(double)+2*sizeof(int)) bytes  of  memory  is
          needed during model construction stage.
        * about 15*N*sizeof(double) bytes is needed after model is built.
        For example, for N=100000 we may need 0.6 GB of memory  to  build
        model, but just about 0.012 GB to store it.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfcreate(const ae_int_t nx, const ae_int_t ny, rbfmodel &s)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfcreate(nx, ny, const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function adds dataset.

This function overrides results of the previous calls, i.e. multiple calls
of this function will result in only the last set being added.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call.
    XY      -   points, array[N,NX+NY]. One row corresponds to  one  point
                in the dataset. First NX elements  are  coordinates,  next
                NY elements are function values. Array may  be larger than
                specific,  in  this  case  only leading [N,NX+NY] elements
                will be used.
    N       -   number of points in the dataset

After you've added dataset and (optionally) tuned algorithm  settings  you
should call RBFBuildModel() in order to build a model for you.

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.


  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetpoints(const rbfmodel &s, const real_2d_array &xy, const ae_int_t n)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetpoints(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function adds dataset.

This function overrides results of the previous calls, i.e. multiple calls
of this function will result in only the last set being added.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call.
    XY      -   points, array[N,NX+NY]. One row corresponds to  one  point
                in the dataset. First NX elements  are  coordinates,  next
                NY elements are function values. Array may  be larger than
                specific,  in  this  case  only leading [N,NX+NY] elements
                will be used.
    N       -   number of points in the dataset

After you've added dataset and (optionally) tuned algorithm  settings  you
should call RBFBuildModel() in order to build a model for you.

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.


  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetpoints(const rbfmodel &s, const real_2d_array &xy)
{
    alglib_impl::ae_state _alglib_env_state;    
    ae_int_t n;

    n = xy.rows();
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetpoints(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), const_cast<alglib_impl::ae_matrix*>(xy.c_ptr()), n, &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  function  sets  RBF interpolation algorithm. ALGLIB supports several
RBF algorithms with different properties.

This algorithm is called RBF-QNN and  it  is  good  for  point  sets  with
following properties:
a) all points are distinct
b) all points are well separated.
c) points  distribution  is  approximately  uniform.  There is no "contour
   lines", clusters of points, or other small-scale structures.

Algorithm description:
1) interpolation centers are allocated to data points
2) interpolation radii are calculated as distances to the  nearest centers
   times Q coefficient (where Q is a value from [0.75,1.50]).
3) after  performing (2) radii are transformed in order to avoid situation
   when single outlier has very large radius and  influences  many  points
   across all dataset. Transformation has following form:
       new_r[i] = min(r[i],Z*median(r[]))
   where r[i] is I-th radius, median()  is a median  radius across  entire
   dataset, Z is user-specified value which controls amount  of  deviation
   from median radius.

When (a) is violated,  we  will  be unable to build RBF model. When (b) or
(c) are violated, model will be built, but interpolation quality  will  be
low. See http://www.alglib.net/interpolation/ for more information on this
subject.

This algorithm is used by default.

Additional Q parameter controls smoothness properties of the RBF basis:
* Q<0.75 will give perfectly conditioned basis,  but  terrible  smoothness
  properties (RBF interpolant will have sharp peaks around function values)
* Q around 1.0 gives good balance between smoothness and condition number
* Q>1.5 will lead to badly conditioned systems and slow convergence of the
  underlying linear solver (although smoothness will be very good)
* Q>2.0 will effectively make optimizer useless because it won't  converge
  within reasonable amount of iterations. It is possible to set such large
  Q, but it is advised not to do so.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    Q       -   Q parameter, Q>0, recommended value - 1.0
    Z       -   Z parameter, Z>0, recommended value - 5.0

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.


  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetalgoqnn(const rbfmodel &s, const double q, const double z)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetalgoqnn(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), q, z, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  function  sets  RBF interpolation algorithm. ALGLIB supports several
RBF algorithms with different properties.

This algorithm is called RBF-QNN and  it  is  good  for  point  sets  with
following properties:
a) all points are distinct
b) all points are well separated.
c) points  distribution  is  approximately  uniform.  There is no "contour
   lines", clusters of points, or other small-scale structures.

Algorithm description:
1) interpolation centers are allocated to data points
2) interpolation radii are calculated as distances to the  nearest centers
   times Q coefficient (where Q is a value from [0.75,1.50]).
3) after  performing (2) radii are transformed in order to avoid situation
   when single outlier has very large radius and  influences  many  points
   across all dataset. Transformation has following form:
       new_r[i] = min(r[i],Z*median(r[]))
   where r[i] is I-th radius, median()  is a median  radius across  entire
   dataset, Z is user-specified value which controls amount  of  deviation
   from median radius.

When (a) is violated,  we  will  be unable to build RBF model. When (b) or
(c) are violated, model will be built, but interpolation quality  will  be
low. See http://www.alglib.net/interpolation/ for more information on this
subject.

This algorithm is used by default.

Additional Q parameter controls smoothness properties of the RBF basis:
* Q<0.75 will give perfectly conditioned basis,  but  terrible  smoothness
  properties (RBF interpolant will have sharp peaks around function values)
* Q around 1.0 gives good balance between smoothness and condition number
* Q>1.5 will lead to badly conditioned systems and slow convergence of the
  underlying linear solver (although smoothness will be very good)
* Q>2.0 will effectively make optimizer useless because it won't  converge
  within reasonable amount of iterations. It is possible to set such large
  Q, but it is advised not to do so.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    Q       -   Q parameter, Q>0, recommended value - 1.0
    Z       -   Z parameter, Z>0, recommended value - 5.0

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.


  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetalgoqnn(const rbfmodel &s)
{
    alglib_impl::ae_state _alglib_env_state;    
    double q;
    double z;

    q = 1.0;
    z = 5.0;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetalgoqnn(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), q, z, &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  function  sets  RBF interpolation algorithm. ALGLIB supports several
RBF algorithms with different properties.

This  algorithm is called RBF-ML. It builds  multilayer  RBF  model,  i.e.
model with subsequently decreasing  radii,  which  allows  us  to  combine
smoothness (due to  large radii of  the first layers) with  exactness (due
to small radii of the last layers) and fast convergence.

Internally RBF-ML uses many different  means  of acceleration, from sparse
matrices  to  KD-trees,  which  results in algorithm whose working time is
roughly proportional to N*log(N)*Density*RBase^2*NLayers,  where  N  is  a
number of points, Density is an average density if points per unit of  the
interpolation space, RBase is an initial radius, NLayers is  a  number  of
layers.

RBF-ML is good for following kinds of interpolation problems:
1. "exact" problems (perfect fit) with well separated points
2. least squares problems with arbitrary distribution of points (algorithm
   gives  perfect  fit  where it is possible, and resorts to least squares
   fit in the hard areas).
3. noisy problems where  we  want  to  apply  some  controlled  amount  of
   smoothing.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    RBase   -   RBase parameter, RBase>0
    NLayers -   NLayers parameter, NLayers>0, recommended value  to  start
                with - about 5.
    LambdaV -   regularization value, can be useful when  solving  problem
                in the least squares sense.  Optimal  lambda  is  problem-
                dependent and require trial and error. In our  experience,
                good lambda can be as large as 0.1, and you can use  0.001
                as initial guess.
                Default  value  - 0.01, which is used when LambdaV is  not
                given.  You  can  specify  zero  value,  but  it  is   not
                recommended to do so.

TUNING ALGORITHM

In order to use this algorithm you have to choose three parameters:
* initial radius RBase
* number of layers in the model NLayers
* regularization coefficient LambdaV

Initial radius is easy to choose - you can pick any number  several  times
larger  than  the  average  distance between points. Algorithm won't break
down if you choose radius which is too large (model construction time will
increase, but model will be built correctly).

Choose such number of layers that RLast=RBase/2^(NLayers-1)  (radius  used
by  the  last  layer)  will  be  smaller than the typical distance between
points.  In  case  model  error  is  too large, you can increase number of
layers.  Having  more  layers  will make model construction and evaluation
proportionally slower, but it will allow you to have model which precisely
fits your data. From the other side, if you want to  suppress  noise,  you
can DECREASE number of layers to make your model less flexible.

Regularization coefficient LambdaV controls smoothness of  the  individual
models built for each layer. We recommend you to use default value in case
you don't want to tune this parameter,  because  having  non-zero  LambdaV
accelerates and stabilizes internal iterative algorithm. In case you  want
to suppress noise you can use  LambdaV  as  additional  parameter  (larger
value = more smoothness) to tune.

TYPICAL ERRORS

1. Using  initial  radius  which is too large. Memory requirements  of the
   RBF-ML are roughly proportional to N*Density*RBase^2 (where Density  is
   an average density of points per unit of the interpolation  space).  In
   the extreme case of the very large RBase we will need O(N^2)  units  of
   memory - and many layers in order to decrease radius to some reasonably
   small value.

2. Using too small number of layers - RBF models with large radius are not
   flexible enough to reproduce small variations in the  target  function.
   You  need  many  layers  with  different radii, from large to small, in
   order to have good model.

3. Using  initial  radius  which  is  too  small.  You will get model with
   "holes" in the areas which are too far away from interpolation centers.
   However, algorithm will work correctly (and quickly) in this case.

4. Using too many layers - you will get too large and too slow model. This
   model  will  perfectly  reproduce  your function, but maybe you will be
   able to achieve similar results with less layers (and less memory).

  -- ALGLIB --
     Copyright 02.03.2012 by Bochkanov Sergey
*************************************************************************/
void rbfsetalgomultilayer(const rbfmodel &s, const double rbase, const ae_int_t nlayers, const double lambdav)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetalgomultilayer(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), rbase, nlayers, lambdav, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  function  sets  RBF interpolation algorithm. ALGLIB supports several
RBF algorithms with different properties.

This  algorithm is called RBF-ML. It builds  multilayer  RBF  model,  i.e.
model with subsequently decreasing  radii,  which  allows  us  to  combine
smoothness (due to  large radii of  the first layers) with  exactness (due
to small radii of the last layers) and fast convergence.

Internally RBF-ML uses many different  means  of acceleration, from sparse
matrices  to  KD-trees,  which  results in algorithm whose working time is
roughly proportional to N*log(N)*Density*RBase^2*NLayers,  where  N  is  a
number of points, Density is an average density if points per unit of  the
interpolation space, RBase is an initial radius, NLayers is  a  number  of
layers.

RBF-ML is good for following kinds of interpolation problems:
1. "exact" problems (perfect fit) with well separated points
2. least squares problems with arbitrary distribution of points (algorithm
   gives  perfect  fit  where it is possible, and resorts to least squares
   fit in the hard areas).
3. noisy problems where  we  want  to  apply  some  controlled  amount  of
   smoothing.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    RBase   -   RBase parameter, RBase>0
    NLayers -   NLayers parameter, NLayers>0, recommended value  to  start
                with - about 5.
    LambdaV -   regularization value, can be useful when  solving  problem
                in the least squares sense.  Optimal  lambda  is  problem-
                dependent and require trial and error. In our  experience,
                good lambda can be as large as 0.1, and you can use  0.001
                as initial guess.
                Default  value  - 0.01, which is used when LambdaV is  not
                given.  You  can  specify  zero  value,  but  it  is   not
                recommended to do so.

TUNING ALGORITHM

In order to use this algorithm you have to choose three parameters:
* initial radius RBase
* number of layers in the model NLayers
* regularization coefficient LambdaV

Initial radius is easy to choose - you can pick any number  several  times
larger  than  the  average  distance between points. Algorithm won't break
down if you choose radius which is too large (model construction time will
increase, but model will be built correctly).

Choose such number of layers that RLast=RBase/2^(NLayers-1)  (radius  used
by  the  last  layer)  will  be  smaller than the typical distance between
points.  In  case  model  error  is  too large, you can increase number of
layers.  Having  more  layers  will make model construction and evaluation
proportionally slower, but it will allow you to have model which precisely
fits your data. From the other side, if you want to  suppress  noise,  you
can DECREASE number of layers to make your model less flexible.

Regularization coefficient LambdaV controls smoothness of  the  individual
models built for each layer. We recommend you to use default value in case
you don't want to tune this parameter,  because  having  non-zero  LambdaV
accelerates and stabilizes internal iterative algorithm. In case you  want
to suppress noise you can use  LambdaV  as  additional  parameter  (larger
value = more smoothness) to tune.

TYPICAL ERRORS

1. Using  initial  radius  which is too large. Memory requirements  of the
   RBF-ML are roughly proportional to N*Density*RBase^2 (where Density  is
   an average density of points per unit of the interpolation  space).  In
   the extreme case of the very large RBase we will need O(N^2)  units  of
   memory - and many layers in order to decrease radius to some reasonably
   small value.

2. Using too small number of layers - RBF models with large radius are not
   flexible enough to reproduce small variations in the  target  function.
   You  need  many  layers  with  different radii, from large to small, in
   order to have good model.

3. Using  initial  radius  which  is  too  small.  You will get model with
   "holes" in the areas which are too far away from interpolation centers.
   However, algorithm will work correctly (and quickly) in this case.

4. Using too many layers - you will get too large and too slow model. This
   model  will  perfectly  reproduce  your function, but maybe you will be
   able to achieve similar results with less layers (and less memory).

  -- ALGLIB --
     Copyright 02.03.2012 by Bochkanov Sergey
*************************************************************************/
void rbfsetalgomultilayer(const rbfmodel &s, const double rbase, const ae_int_t nlayers)
{
    alglib_impl::ae_state _alglib_env_state;    
    double lambdav;

    lambdav = 0.01;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetalgomultilayer(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), rbase, nlayers, lambdav, &_alglib_env_state);

        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function sets linear term (model is a sum of radial  basis  functions
plus linear polynomial). This function won't have effect until  next  call
to RBFBuildModel().

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetlinterm(const rbfmodel &s)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetlinterm(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function sets constant term (model is a sum of radial basis functions
plus constant).  This  function  won't  have  effect  until  next  call to
RBFBuildModel().

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetconstterm(const rbfmodel &s)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetconstterm(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This  function  sets  zero  term (model is a sum of radial basis functions
without polynomial term). This function won't have effect until next  call
to RBFBuildModel().

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetzeroterm(const rbfmodel &s)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfsetzeroterm(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This   function  builds  RBF  model  and  returns  report  (contains  some
information which can be used for evaluation of the algorithm properties).

Call to this function modifies RBF model by calculating its centers/radii/
weights  and  saving  them  into  RBFModel  structure.  Initially RBFModel
contain zero coefficients, but after call to this function  we  will  have
coefficients which were calculated in order to fit our dataset.

After you called this function you can call RBFCalc(),  RBFGridCalc()  and
other model calculation functions.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    Rep     -   report:
                * Rep.TerminationType:
                  * -5 - non-distinct basis function centers were detected,
                         interpolation aborted
                  * -4 - nonconvergence of the internal SVD solver
                  *  1 - successful termination
                Fields are used for debugging purposes:
                * Rep.IterationsCount - iterations count of the LSQR solver
                * Rep.NMV - number of matrix-vector products
                * Rep.ARows - rows count for the system matrix
                * Rep.ACols - columns count for the system matrix
                * Rep.ANNZ - number of significantly non-zero elements
                  (elements above some algorithm-determined threshold)

NOTE:  failure  to  build  model will leave current state of the structure
unchanged.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfbuildmodel(const rbfmodel &s, rbfreport &rep)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfbuildmodel(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), const_cast<alglib_impl::rbfreport*>(rep.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates values of the RBF model in the given point.

This function should be used when we have NY=1 (scalar function) and  NX=2
(2-dimensional space). If you have 3-dimensional space, use RBFCalc3(). If
you have general situation (NX-dimensional space, NY-dimensional function)
you should use general, less efficient implementation RBFCalc().

If  you  want  to  calculate  function  values  many times, consider using
RBFGridCalc2(), which is far more efficient than many subsequent calls  to
RBFCalc2().

This function returns 0.0 when:
* model is not initialized
* NX<>2
 *NY<>1

INPUT PARAMETERS:
    S       -   RBF model
    X0      -   first coordinate, finite number
    X1      -   second coordinate, finite number

RESULT:
    value of the model or 0.0 (as defined above)

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
double rbfcalc2(const rbfmodel &s, const double x0, const double x1)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::rbfcalc2(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), x0, x1, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates values of the RBF model in the given point.

This function should be used when we have NY=1 (scalar function) and  NX=3
(3-dimensional space). If you have 2-dimensional space, use RBFCalc2(). If
you have general situation (NX-dimensional space, NY-dimensional function)
you should use general, less efficient implementation RBFCalc().

This function returns 0.0 when:
* model is not initialized
* NX<>3
 *NY<>1

INPUT PARAMETERS:
    S       -   RBF model
    X0      -   first coordinate, finite number
    X1      -   second coordinate, finite number
    X2      -   third coordinate, finite number

RESULT:
    value of the model or 0.0 (as defined above)

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
double rbfcalc3(const rbfmodel &s, const double x0, const double x1, const double x2)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::rbfcalc3(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), x0, x1, x2, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates values of the RBF model at the given point.

This is general function which can be used for arbitrary NX (dimension  of
the space of arguments) and NY (dimension of the function itself). However
when  you  have  NY=1  you  may  find more convenient to use RBFCalc2() or
RBFCalc3().

This function returns 0.0 when model is not initialized.

INPUT PARAMETERS:
    S       -   RBF model
    X       -   coordinates, array[NX].
                X may have more than NX elements, in this case only
                leading NX will be used.

OUTPUT PARAMETERS:
    Y       -   function value, array[NY]. Y is out-parameter and
                reallocated after call to this function. In case you  want
                to reuse previously allocated Y, you may use RBFCalcBuf(),
                which reallocates Y only when it is too small.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfcalc(const rbfmodel &s, const real_1d_array &x, real_1d_array &y)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfcalc(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates values of the RBF model at the given point.

Same as RBFCalc(), but does not reallocate Y when in is large enough to
store function values.

INPUT PARAMETERS:
    S       -   RBF model
    X       -   coordinates, array[NX].
                X may have more than NX elements, in this case only
                leading NX will be used.
    Y       -   possibly preallocated array

OUTPUT PARAMETERS:
    Y       -   function value, array[NY]. Y is not reallocated when it
                is larger than NY.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfcalcbuf(const rbfmodel &s, const real_1d_array &x, real_1d_array &y)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfcalcbuf(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function calculates values of the RBF model at the regular grid.

Grid have N0*N1 points, with Point[I,J] = (X0[I], X1[J])

This function returns 0.0 when:
* model is not initialized
* NX<>2
 *NY<>1

INPUT PARAMETERS:
    S       -   RBF model
    X0      -   array of grid nodes, first coordinates, array[N0]
    N0      -   grid size (number of nodes) in the first dimension
    X1      -   array of grid nodes, second coordinates, array[N1]
    N1      -   grid size (number of nodes) in the second dimension

OUTPUT PARAMETERS:
    Y       -   function values, array[N0,N1]. Y is out-variable and
                is reallocated by this function.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfgridcalc2(const rbfmodel &s, const real_1d_array &x0, const ae_int_t n0, const real_1d_array &x1, const ae_int_t n1, real_2d_array &y)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfgridcalc2(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), const_cast<alglib_impl::ae_vector*>(x0.c_ptr()), n0, const_cast<alglib_impl::ae_vector*>(x1.c_ptr()), n1, const_cast<alglib_impl::ae_matrix*>(y.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This function "unpacks" RBF model by extracting its coefficients.

INPUT PARAMETERS:
    S       -   RBF model

OUTPUT PARAMETERS:
    NX      -   dimensionality of argument
    NY      -   dimensionality of the target function
    XWR     -   model information, array[NC,NX+NY+1].
                One row of the array corresponds to one basis function:
                * first NX columns  - coordinates of the center
                * next NY columns   - weights, one per dimension of the
                                      function being modelled
                * last column       - radius, same for all dimensions of
                                      the function being modelled
    NC      -   number of the centers
    V       -   polynomial  term , array[NY,NX+1]. One row per one
                dimension of the function being modelled. First NX
                elements are linear coefficients, V[NX] is equal to the
                constant part.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfunpack(const rbfmodel &s, ae_int_t &nx, ae_int_t &ny, real_2d_array &xwr, ae_int_t &nc, real_2d_array &v)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::rbfunpack(const_cast<alglib_impl::rbfmodel*>(s.c_ptr()), &nx, &ny, const_cast<alglib_impl::ae_matrix*>(xwr.c_ptr()), &nc, const_cast<alglib_impl::ae_matrix*>(v.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
2-dimensional spline inteprolant
*************************************************************************/
_spline2dinterpolant_owner::_spline2dinterpolant_owner()
{
    p_struct = (alglib_impl::spline2dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline2dinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_spline2dinterpolant_init(p_struct, NULL);
}

_spline2dinterpolant_owner::_spline2dinterpolant_owner(const _spline2dinterpolant_owner &rhs)
{
    p_struct = (alglib_impl::spline2dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline2dinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_spline2dinterpolant_init_copy(p_struct, const_cast<alglib_impl::spline2dinterpolant*>(rhs.p_struct), NULL);
}

_spline2dinterpolant_owner& _spline2dinterpolant_owner::operator=(const _spline2dinterpolant_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_spline2dinterpolant_clear(p_struct);
    alglib_impl::_spline2dinterpolant_init_copy(p_struct, const_cast<alglib_impl::spline2dinterpolant*>(rhs.p_struct), NULL);
    return *this;
}

_spline2dinterpolant_owner::~_spline2dinterpolant_owner()
{
    alglib_impl::_spline2dinterpolant_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::spline2dinterpolant* _spline2dinterpolant_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::spline2dinterpolant* _spline2dinterpolant_owner::c_ptr() const
{
    return const_cast<alglib_impl::spline2dinterpolant*>(p_struct);
}
spline2dinterpolant::spline2dinterpolant() : _spline2dinterpolant_owner() 
{
}

spline2dinterpolant::spline2dinterpolant(const spline2dinterpolant &rhs):_spline2dinterpolant_owner(rhs) 
{
}

spline2dinterpolant& spline2dinterpolant::operator=(const spline2dinterpolant &rhs)
{
    if( this==&rhs )
        return *this;
    _spline2dinterpolant_owner::operator=(rhs);
    return *this;
}

spline2dinterpolant::~spline2dinterpolant()
{
}

/*************************************************************************
This subroutine calculates the value of the bilinear or bicubic spline  at
the given point X.

Input parameters:
    C   -   coefficients table.
            Built by BuildBilinearSpline or BuildBicubicSpline.
    X, Y-   point

Result:
    S(x,y)

  -- ALGLIB PROJECT --
     Copyright 05.07.2007 by Bochkanov Sergey
*************************************************************************/
double spline2dcalc(const spline2dinterpolant &c, const double x, const double y)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::spline2dcalc(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), x, y, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine calculates the value of the bilinear or bicubic spline  at
the given point X and its derivatives.

Input parameters:
    C   -   spline interpolant.
    X, Y-   point

Output parameters:
    F   -   S(x,y)
    FX  -   dS(x,y)/dX
    FY  -   dS(x,y)/dY
    FXY -   d2S(x,y)/dXdY

  -- ALGLIB PROJECT --
     Copyright 05.07.2007 by Bochkanov Sergey
*************************************************************************/
void spline2ddiff(const spline2dinterpolant &c, const double x, const double y, double &f, double &fx, double &fy, double &fxy)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2ddiff(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), x, y, &f, &fx, &fy, &fxy, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine performs linear transformation of the spline argument.

Input parameters:
    C       -   spline interpolant
    AX, BX  -   transformation coefficients: x = A*t + B
    AY, BY  -   transformation coefficients: y = A*u + B
Result:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 30.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dlintransxy(const spline2dinterpolant &c, const double ax, const double bx, const double ay, const double by)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dlintransxy(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), ax, bx, ay, by, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine performs linear transformation of the spline.

Input parameters:
    C   -   spline interpolant.
    A, B-   transformation coefficients: S2(x,y) = A*S(x,y) + B

Output parameters:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 30.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dlintransf(const spline2dinterpolant &c, const double a, const double b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dlintransf(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), a, b, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine makes the copy of the spline model.

Input parameters:
    C   -   spline interpolant

Output parameters:
    CC  -   spline copy

  -- ALGLIB PROJECT --
     Copyright 29.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dcopy(const spline2dinterpolant &c, spline2dinterpolant &cc)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dcopy(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), const_cast<alglib_impl::spline2dinterpolant*>(cc.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Bicubic spline resampling

Input parameters:
    A           -   function values at the old grid,
                    array[0..OldHeight-1, 0..OldWidth-1]
    OldHeight   -   old grid height, OldHeight>1
    OldWidth    -   old grid width, OldWidth>1
    NewHeight   -   new grid height, NewHeight>1
    NewWidth    -   new grid width, NewWidth>1

Output parameters:
    B           -   function values at the new grid,
                    array[0..NewHeight-1, 0..NewWidth-1]

  -- ALGLIB routine --
     15 May, 2007
     Copyright by Bochkanov Sergey
*************************************************************************/
void spline2dresamplebicubic(const real_2d_array &a, const ae_int_t oldheight, const ae_int_t oldwidth, real_2d_array &b, const ae_int_t newheight, const ae_int_t newwidth)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dresamplebicubic(const_cast<alglib_impl::ae_matrix*>(a.c_ptr()), oldheight, oldwidth, const_cast<alglib_impl::ae_matrix*>(b.c_ptr()), newheight, newwidth, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Bilinear spline resampling

Input parameters:
    A           -   function values at the old grid,
                    array[0..OldHeight-1, 0..OldWidth-1]
    OldHeight   -   old grid height, OldHeight>1
    OldWidth    -   old grid width, OldWidth>1
    NewHeight   -   new grid height, NewHeight>1
    NewWidth    -   new grid width, NewWidth>1

Output parameters:
    B           -   function values at the new grid,
                    array[0..NewHeight-1, 0..NewWidth-1]

  -- ALGLIB routine --
     09.07.2007
     Copyright by Bochkanov Sergey
*************************************************************************/
void spline2dresamplebilinear(const real_2d_array &a, const ae_int_t oldheight, const ae_int_t oldwidth, real_2d_array &b, const ae_int_t newheight, const ae_int_t newwidth)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dresamplebilinear(const_cast<alglib_impl::ae_matrix*>(a.c_ptr()), oldheight, oldwidth, const_cast<alglib_impl::ae_matrix*>(b.c_ptr()), newheight, newwidth, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds bilinear vector-valued spline.

Input parameters:
    X   -   spline abscissas, array[0..N-1]
    Y   -   spline ordinates, array[0..M-1]
    F   -   function values, array[0..M*N*D-1]:
            * first D elements store D values at (X[0],Y[0])
            * next D elements store D values at (X[1],Y[0])
            * general form - D function values at (X[i],Y[j]) are stored
              at F[D*(J*N+I)...D*(J*N+I)+D-1].
    M,N -   grid size, M>=2, N>=2
    D   -   vector dimension, D>=1

Output parameters:
    C   -   spline interpolant

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildbilinearv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &f, const ae_int_t d, spline2dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dbuildbilinearv(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), m, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), d, const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds bicubic vector-valued spline.

Input parameters:
    X   -   spline abscissas, array[0..N-1]
    Y   -   spline ordinates, array[0..M-1]
    F   -   function values, array[0..M*N*D-1]:
            * first D elements store D values at (X[0],Y[0])
            * next D elements store D values at (X[1],Y[0])
            * general form - D function values at (X[i],Y[j]) are stored
              at F[D*(J*N+I)...D*(J*N+I)+D-1].
    M,N -   grid size, M>=2, N>=2
    D   -   vector dimension, D>=1

Output parameters:
    C   -   spline interpolant

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildbicubicv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &f, const ae_int_t d, spline2dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dbuildbicubicv(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), m, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), d, const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine calculates bilinear or bicubic vector-valued spline at the
given point (X,Y).

INPUT PARAMETERS:
    C   -   spline interpolant.
    X, Y-   point
    F   -   output buffer, possibly preallocated array. In case array size
            is large enough to store result, it is not reallocated.  Array
            which is too short will be reallocated

OUTPUT PARAMETERS:
    F   -   array[D] (or larger) which stores function values

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dcalcvbuf(const spline2dinterpolant &c, const double x, const double y, real_1d_array &f)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dcalcvbuf(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), x, y, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine calculates bilinear or bicubic vector-valued spline at the
given point (X,Y).

INPUT PARAMETERS:
    C   -   spline interpolant.
    X, Y-   point

OUTPUT PARAMETERS:
    F   -   array[D] which stores function values.  F is out-parameter and
            it  is  reallocated  after  call to this function. In case you
            want  to    reuse  previously  allocated  F,   you   may   use
            Spline2DCalcVBuf(),  which  reallocates  F only when it is too
            small.

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dcalcv(const spline2dinterpolant &c, const double x, const double y, real_1d_array &f)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dcalcv(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), x, y, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine unpacks two-dimensional spline into the coefficients table

Input parameters:
    C   -   spline interpolant.

Result:
    M, N-   grid size (x-axis and y-axis)
    D   -   number of components
    Tbl -   coefficients table, unpacked format,
            D - components: [0..(N-1)*(M-1)*D-1, 0..19].
            For T=0..D-1 (component index), I = 0...N-2 (x index),
            J=0..M-2 (y index):
                K :=  T + I*D + J*D*(N-1)

                K-th row stores decomposition for T-th component of the
                vector-valued function

                Tbl[K,0] = X[i]
                Tbl[K,1] = X[i+1]
                Tbl[K,2] = Y[j]
                Tbl[K,3] = Y[j+1]
                Tbl[K,4] = C00
                Tbl[K,5] = C01
                Tbl[K,6] = C02
                Tbl[K,7] = C03
                Tbl[K,8] = C10
                Tbl[K,9] = C11
                ...
                Tbl[K,19] = C33
            On each grid square spline is equals to:
                S(x) = SUM(c[i,j]*(t^i)*(u^j), i=0..3, j=0..3)
                t = x-x[j]
                u = y-y[i]

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dunpackv(const spline2dinterpolant &c, ae_int_t &m, ae_int_t &n, ae_int_t &d, real_2d_array &tbl)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dunpackv(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), &m, &n, &d, const_cast<alglib_impl::ae_matrix*>(tbl.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine was deprecated in ALGLIB 3.6.0

We recommend you to switch  to  Spline2DBuildBilinearV(),  which  is  more
flexible and accepts its arguments in more convenient order.

  -- ALGLIB PROJECT --
     Copyright 05.07.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildbilinear(const real_1d_array &x, const real_1d_array &y, const real_2d_array &f, const ae_int_t m, const ae_int_t n, spline2dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dbuildbilinear(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(f.c_ptr()), m, n, const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine was deprecated in ALGLIB 3.6.0

We recommend you to switch  to  Spline2DBuildBicubicV(),  which  is  more
flexible and accepts its arguments in more convenient order.

  -- ALGLIB PROJECT --
     Copyright 05.07.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildbicubic(const real_1d_array &x, const real_1d_array &y, const real_2d_array &f, const ae_int_t m, const ae_int_t n, spline2dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dbuildbicubic(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), const_cast<alglib_impl::ae_vector*>(y.c_ptr()), const_cast<alglib_impl::ae_matrix*>(f.c_ptr()), m, n, const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine was deprecated in ALGLIB 3.6.0

We recommend you to switch  to  Spline2DUnpackV(),  which is more flexible
and accepts its arguments in more convenient order.

  -- ALGLIB PROJECT --
     Copyright 29.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dunpack(const spline2dinterpolant &c, ae_int_t &m, ae_int_t &n, real_2d_array &tbl)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline2dunpack(const_cast<alglib_impl::spline2dinterpolant*>(c.c_ptr()), &m, &n, const_cast<alglib_impl::ae_matrix*>(tbl.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
3-dimensional spline inteprolant
*************************************************************************/
_spline3dinterpolant_owner::_spline3dinterpolant_owner()
{
    p_struct = (alglib_impl::spline3dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline3dinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_spline3dinterpolant_init(p_struct, NULL);
}

_spline3dinterpolant_owner::_spline3dinterpolant_owner(const _spline3dinterpolant_owner &rhs)
{
    p_struct = (alglib_impl::spline3dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline3dinterpolant), NULL);
    if( p_struct==NULL )
        throw ap_error("ALGLIB: malloc error");
    alglib_impl::_spline3dinterpolant_init_copy(p_struct, const_cast<alglib_impl::spline3dinterpolant*>(rhs.p_struct), NULL);
}

_spline3dinterpolant_owner& _spline3dinterpolant_owner::operator=(const _spline3dinterpolant_owner &rhs)
{
    if( this==&rhs )
        return *this;
    alglib_impl::_spline3dinterpolant_clear(p_struct);
    alglib_impl::_spline3dinterpolant_init_copy(p_struct, const_cast<alglib_impl::spline3dinterpolant*>(rhs.p_struct), NULL);
    return *this;
}

_spline3dinterpolant_owner::~_spline3dinterpolant_owner()
{
    alglib_impl::_spline3dinterpolant_clear(p_struct);
    ae_free(p_struct);
}

alglib_impl::spline3dinterpolant* _spline3dinterpolant_owner::c_ptr()
{
    return p_struct;
}

alglib_impl::spline3dinterpolant* _spline3dinterpolant_owner::c_ptr() const
{
    return const_cast<alglib_impl::spline3dinterpolant*>(p_struct);
}
spline3dinterpolant::spline3dinterpolant() : _spline3dinterpolant_owner() 
{
}

spline3dinterpolant::spline3dinterpolant(const spline3dinterpolant &rhs):_spline3dinterpolant_owner(rhs) 
{
}

spline3dinterpolant& spline3dinterpolant::operator=(const spline3dinterpolant &rhs)
{
    if( this==&rhs )
        return *this;
    _spline3dinterpolant_owner::operator=(rhs);
    return *this;
}

spline3dinterpolant::~spline3dinterpolant()
{
}

/*************************************************************************
This subroutine calculates the value of the trilinear or tricubic spline at
the given point (X,Y,Z).

INPUT PARAMETERS:
    C   -   coefficients table.
            Built by BuildBilinearSpline or BuildBicubicSpline.
    X, Y,
    Z   -   point

Result:
    S(x,y,z)

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
double spline3dcalc(const spline3dinterpolant &c, const double x, const double y, const double z)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        double result = alglib_impl::spline3dcalc(const_cast<alglib_impl::spline3dinterpolant*>(c.c_ptr()), x, y, z, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return *(reinterpret_cast<double*>(&result));
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine performs linear transformation of the spline argument.

INPUT PARAMETERS:
    C       -   spline interpolant
    AX, BX  -   transformation coefficients: x = A*u + B
    AY, BY  -   transformation coefficients: y = A*v + B
    AZ, BZ  -   transformation coefficients: z = A*w + B

OUTPUT PARAMETERS:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dlintransxyz(const spline3dinterpolant &c, const double ax, const double bx, const double ay, const double by, const double az, const double bz)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline3dlintransxyz(const_cast<alglib_impl::spline3dinterpolant*>(c.c_ptr()), ax, bx, ay, by, az, bz, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine performs linear transformation of the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.
    A, B-   transformation coefficients: S2(x,y) = A*S(x,y,z) + B

OUTPUT PARAMETERS:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dlintransf(const spline3dinterpolant &c, const double a, const double b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline3dlintransf(const_cast<alglib_impl::spline3dinterpolant*>(c.c_ptr()), a, b, &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
Trilinear spline resampling

INPUT PARAMETERS:
    A           -   array[0..OldXCount*OldYCount*OldZCount-1], function
                    values at the old grid, :
                        A[0]        x=0,y=0,z=0
                        A[1]        x=1,y=0,z=0
                        A[..]       ...
                        A[..]       x=oldxcount-1,y=0,z=0
                        A[..]       x=0,y=1,z=0
                        A[..]       ...
                        ...
    OldZCount   -   old Z-count, OldZCount>1
    OldYCount   -   old Y-count, OldYCount>1
    OldXCount   -   old X-count, OldXCount>1
    NewZCount   -   new Z-count, NewZCount>1
    NewYCount   -   new Y-count, NewYCount>1
    NewXCount   -   new X-count, NewXCount>1

OUTPUT PARAMETERS:
    B           -   array[0..NewXCount*NewYCount*NewZCount-1], function
                    values at the new grid:
                        B[0]        x=0,y=0,z=0
                        B[1]        x=1,y=0,z=0
                        B[..]       ...
                        B[..]       x=newxcount-1,y=0,z=0
                        B[..]       x=0,y=1,z=0
                        B[..]       ...
                        ...

  -- ALGLIB routine --
     26.04.2012
     Copyright by Bochkanov Sergey
*************************************************************************/
void spline3dresampletrilinear(const real_1d_array &a, const ae_int_t oldzcount, const ae_int_t oldycount, const ae_int_t oldxcount, const ae_int_t newzcount, const ae_int_t newycount, const ae_int_t newxcount, real_1d_array &b)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline3dresampletrilinear(const_cast<alglib_impl::ae_vector*>(a.c_ptr()), oldzcount, oldycount, oldxcount, newzcount, newycount, newxcount, const_cast<alglib_impl::ae_vector*>(b.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine builds trilinear vector-valued spline.

INPUT PARAMETERS:
    X   -   spline abscissas,  array[0..N-1]
    Y   -   spline ordinates,  array[0..M-1]
    Z   -   spline applicates, array[0..L-1]
    F   -   function values, array[0..M*N*L*D-1]:
            * first D elements store D values at (X[0],Y[0],Z[0])
            * next D elements store D values at (X[1],Y[0],Z[0])
            * next D elements store D values at (X[2],Y[0],Z[0])
            * ...
            * next D elements store D values at (X[0],Y[1],Z[0])
            * next D elements store D values at (X[1],Y[1],Z[0])
            * next D elements store D values at (X[2],Y[1],Z[0])
            * ...
            * next D elements store D values at (X[0],Y[0],Z[1])
            * next D elements store D values at (X[1],Y[0],Z[1])
            * next D elements store D values at (X[2],Y[0],Z[1])
            * ...
            * general form - D function values at (X[i],Y[j]) are stored
              at F[D*(N*(M*K+J)+I)...D*(N*(M*K+J)+I)+D-1].
    M,N,
    L   -   grid size, M>=2, N>=2, L>=2
    D   -   vector dimension, D>=1

OUTPUT PARAMETERS:
    C   -   spline interpolant

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dbuildtrilinearv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &z, const ae_int_t l, const real_1d_array &f, const ae_int_t d, spline3dinterpolant &c)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline3dbuildtrilinearv(const_cast<alglib_impl::ae_vector*>(x.c_ptr()), n, const_cast<alglib_impl::ae_vector*>(y.c_ptr()), m, const_cast<alglib_impl::ae_vector*>(z.c_ptr()), l, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), d, const_cast<alglib_impl::spline3dinterpolant*>(c.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine calculates bilinear or bicubic vector-valued spline at the
given point (X,Y,Z).

INPUT PARAMETERS:
    C   -   spline interpolant.
    X, Y,
    Z   -   point
    F   -   output buffer, possibly preallocated array. In case array size
            is large enough to store result, it is not reallocated.  Array
            which is too short will be reallocated

OUTPUT PARAMETERS:
    F   -   array[D] (or larger) which stores function values

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dcalcvbuf(const spline3dinterpolant &c, const double x, const double y, const double z, real_1d_array &f)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline3dcalcvbuf(const_cast<alglib_impl::spline3dinterpolant*>(c.c_ptr()), x, y, z, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine calculates trilinear or tricubic vector-valued spline at the
given point (X,Y,Z).

INPUT PARAMETERS:
    C   -   spline interpolant.
    X, Y,
    Z   -   point

OUTPUT PARAMETERS:
    F   -   array[D] which stores function values.  F is out-parameter and
            it  is  reallocated  after  call to this function. In case you
            want  to    reuse  previously  allocated  F,   you   may   use
            Spline2DCalcVBuf(),  which  reallocates  F only when it is too
            small.

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dcalcv(const spline3dinterpolant &c, const double x, const double y, const double z, real_1d_array &f)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline3dcalcv(const_cast<alglib_impl::spline3dinterpolant*>(c.c_ptr()), x, y, z, const_cast<alglib_impl::ae_vector*>(f.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}

/*************************************************************************
This subroutine unpacks tri-dimensional spline into the coefficients table

INPUT PARAMETERS:
    C   -   spline interpolant.

Result:
    N   -   grid size (X)
    M   -   grid size (Y)
    L   -   grid size (Z)
    D   -   number of components
    SType-  spline type. Currently, only one spline type is supported:
            trilinear spline, as indicated by SType=1.
    Tbl -   spline coefficients: [0..(N-1)*(M-1)*(L-1)*D-1, 0..13].
            For T=0..D-1 (component index), I = 0...N-2 (x index),
            J=0..M-2 (y index), K=0..L-2 (z index):
                Q := T + I*D + J*D*(N-1) + K*D*(N-1)*(M-1),

                Q-th row stores decomposition for T-th component of the
                vector-valued function

                Tbl[Q,0] = X[i]
                Tbl[Q,1] = X[i+1]
                Tbl[Q,2] = Y[j]
                Tbl[Q,3] = Y[j+1]
                Tbl[Q,4] = Z[k]
                Tbl[Q,5] = Z[k+1]

                Tbl[Q,6] = C000
                Tbl[Q,7] = C100
                Tbl[Q,8] = C010
                Tbl[Q,9] = C110
                Tbl[Q,10]= C001
                Tbl[Q,11]= C101
                Tbl[Q,12]= C011
                Tbl[Q,13]= C111
            On each grid square spline is equals to:
                S(x) = SUM(c[i,j,k]*(x^i)*(y^j)*(z^k), i=0..1, j=0..1, k=0..1)
                t = x-x[j]
                u = y-y[i]
                v = z-z[k]

            NOTE: format of Tbl is given for SType=1. Future versions of
                  ALGLIB can use different formats for different values of
                  SType.

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dunpackv(const spline3dinterpolant &c, ae_int_t &n, ae_int_t &m, ae_int_t &l, ae_int_t &d, ae_int_t &stype, real_2d_array &tbl)
{
    alglib_impl::ae_state _alglib_env_state;
    alglib_impl::ae_state_init(&_alglib_env_state);
    try
    {
        alglib_impl::spline3dunpackv(const_cast<alglib_impl::spline3dinterpolant*>(c.c_ptr()), &n, &m, &l, &d, &stype, const_cast<alglib_impl::ae_matrix*>(tbl.c_ptr()), &_alglib_env_state);
        alglib_impl::ae_state_clear(&_alglib_env_state);
        return;
    }
    catch(alglib_impl::ae_error_type)
    {
        throw ap_error(_alglib_env_state.error_msg);
    }
}
}

/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
static double idwint_idwqfactor = 1.5;
static ae_int_t idwint_idwkmin = 5;
static double idwint_idwcalcq(idwinterpolant* z,
     /* Real    */ ae_vector* x,
     ae_int_t k,
     ae_state *_state);
static void idwint_idwinit1(ae_int_t n,
     ae_int_t nx,
     ae_int_t d,
     ae_int_t nq,
     ae_int_t nw,
     idwinterpolant* z,
     ae_state *_state);
static void idwint_idwinternalsolver(/* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_matrix* fmatrix,
     /* Real    */ ae_vector* temp,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     /* Real    */ ae_vector* x,
     double* taskrcond,
     ae_state *_state);


static void ratint_barycentricnormalize(barycentricinterpolant* b,
     ae_state *_state);




static void spline1d_spline1dgriddiffcubicinternal(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundltype,
     double boundl,
     ae_int_t boundrtype,
     double boundr,
     /* Real    */ ae_vector* d,
     /* Real    */ ae_vector* a1,
     /* Real    */ ae_vector* a2,
     /* Real    */ ae_vector* a3,
     /* Real    */ ae_vector* b,
     /* Real    */ ae_vector* dt,
     ae_state *_state);
static void spline1d_heapsortpoints(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_state *_state);
static void spline1d_heapsortppoints(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Integer */ ae_vector* p,
     ae_int_t n,
     ae_state *_state);
static void spline1d_solvetridiagonal(/* Real    */ ae_vector* a,
     /* Real    */ ae_vector* b,
     /* Real    */ ae_vector* c,
     /* Real    */ ae_vector* d,
     ae_int_t n,
     /* Real    */ ae_vector* x,
     ae_state *_state);
static void spline1d_solvecyclictridiagonal(/* Real    */ ae_vector* a,
     /* Real    */ ae_vector* b,
     /* Real    */ ae_vector* c,
     /* Real    */ ae_vector* d,
     ae_int_t n,
     /* Real    */ ae_vector* x,
     ae_state *_state);
static double spline1d_diffthreepoint(double t,
     double x0,
     double f0,
     double x1,
     double f1,
     double x2,
     double f2,
     ae_state *_state);
static void spline1d_hermitecalc(double p0,
     double m0,
     double p1,
     double m1,
     double t,
     double* s,
     double* ds,
     ae_state *_state);
static double spline1d_rescaleval(double a0,
     double b0,
     double a1,
     double b1,
     double t,
     ae_state *_state);


static void lsfit_rdpanalyzesection(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t i0,
     ae_int_t i1,
     ae_int_t* worstidx,
     double* worsterror,
     ae_state *_state);
static void lsfit_rdprecursive(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t i0,
     ae_int_t i1,
     double eps,
     /* Real    */ ae_vector* xout,
     /* Real    */ ae_vector* yout,
     ae_int_t* nout,
     ae_state *_state);
static void lsfit_logisticfitinternal(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_bool is4pl,
     double lambdav,
     minlmstate* state,
     minlmreport* replm,
     /* Real    */ ae_vector* p1,
     double* flast,
     ae_state *_state);
static void lsfit_spline1dfitinternal(ae_int_t st,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t* info,
     spline1dinterpolant* s,
     spline1dfitreport* rep,
     ae_state *_state);
static void lsfit_lsfitlinearinternal(/* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_matrix* fmatrix,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state);
static void lsfit_lsfitclearrequestfields(lsfitstate* state,
     ae_state *_state);
static void lsfit_barycentriccalcbasis(barycentricinterpolant* b,
     double t,
     /* Real    */ ae_vector* y,
     ae_state *_state);
static void lsfit_internalchebyshevfit(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state);
static void lsfit_barycentricfitwcfixedd(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t d,
     ae_int_t* info,
     barycentricinterpolant* b,
     barycentricfitreport* rep,
     ae_state *_state);
static void lsfit_clearreport(lsfitreport* rep, ae_state *_state);
static void lsfit_estimateerrors(/* Real    */ ae_matrix* f1,
     /* Real    */ ae_vector* f0,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* s,
     ae_int_t n,
     ae_int_t k,
     lsfitreport* rep,
     /* Real    */ ae_matrix* z,
     ae_int_t zkind,
     ae_state *_state);


static void parametric_pspline2par(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t pt,
     /* Real    */ ae_vector* p,
     ae_state *_state);
static void parametric_pspline3par(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t pt,
     /* Real    */ ae_vector* p,
     ae_state *_state);
static void parametric_rdpanalyzesectionpar(/* Real    */ ae_matrix* xy,
     ae_int_t i0,
     ae_int_t i1,
     ae_int_t d,
     ae_int_t* worstidx,
     double* worsterror,
     ae_state *_state);


static double rbf_eps = 1.0E-6;
static ae_int_t rbf_mxnx = 3;
static double rbf_rbffarradius = 6;
static double rbf_rbfnearradius = 2.1;
static double rbf_rbfmlradius = 3;
static ae_int_t rbf_rbffirstversion = 0;
static void rbf_rbfgridpoints(rbfmodel* s, ae_state *_state);
static void rbf_rbfradnn(rbfmodel* s,
     double q,
     double z,
     ae_state *_state);
static ae_bool rbf_buildlinearmodel(/* Real    */ ae_matrix* x,
     /* Real    */ ae_matrix* y,
     ae_int_t n,
     ae_int_t ny,
     ae_int_t modeltype,
     /* Real    */ ae_matrix* v,
     ae_state *_state);
static void rbf_buildrbfmodellsqr(/* Real    */ ae_matrix* x,
     /* Real    */ ae_matrix* y,
     /* Real    */ ae_matrix* xc,
     /* Real    */ ae_vector* r,
     ae_int_t n,
     ae_int_t nc,
     ae_int_t ny,
     kdtree* pointstree,
     kdtree* centerstree,
     double epsort,
     double epserr,
     ae_int_t maxits,
     ae_int_t* gnnz,
     ae_int_t* snnz,
     /* Real    */ ae_matrix* w,
     ae_int_t* info,
     ae_int_t* iterationscount,
     ae_int_t* nmv,
     ae_state *_state);
static void rbf_buildrbfmlayersmodellsqr(/* Real    */ ae_matrix* x,
     /* Real    */ ae_matrix* y,
     /* Real    */ ae_matrix* xc,
     double rval,
     /* Real    */ ae_vector* r,
     ae_int_t n,
     ae_int_t* nc,
     ae_int_t ny,
     ae_int_t nlayers,
     kdtree* centerstree,
     double epsort,
     double epserr,
     ae_int_t maxits,
     double lambdav,
     ae_int_t* annz,
     /* Real    */ ae_matrix* w,
     ae_int_t* info,
     ae_int_t* iterationscount,
     ae_int_t* nmv,
     ae_state *_state);


static void spline2d_bicubiccalcderivatives(/* Real    */ ae_matrix* a,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t m,
     ae_int_t n,
     /* Real    */ ae_matrix* dx,
     /* Real    */ ae_matrix* dy,
     /* Real    */ ae_matrix* dxy,
     ae_state *_state);


static void spline3d_spline3ddiff(spline3dinterpolant* c,
     double x,
     double y,
     double z,
     double* f,
     double* fx,
     double* fy,
     double* fxy,
     ae_state *_state);





/*************************************************************************
IDW interpolation

INPUT PARAMETERS:
    Z   -   IDW interpolant built with one of model building
            subroutines.
    X   -   array[0..NX-1], interpolation point

Result:
    IDW interpolant Z(X)

  -- ALGLIB --
     Copyright 02.03.2010 by Bochkanov Sergey
*************************************************************************/
double idwcalc(idwinterpolant* z,
     /* Real    */ ae_vector* x,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t k;
    double r;
    double s;
    double w;
    double v1;
    double v2;
    double d0;
    double di;
    double result;


    
    /*
     * these initializers are not really necessary,
     * but without them compiler complains about uninitialized locals
     */
    k = 0;
    
    /*
     * Query
     */
    if( z->modeltype==0 )
    {
        
        /*
         * NQ/NW-based model
         */
        k = kdtreequeryknn(&z->tree, x, z->nw, ae_true, _state);
        kdtreequeryresultsdistances(&z->tree, &z->rbuf, _state);
        kdtreequeryresultstags(&z->tree, &z->tbuf, _state);
    }
    if( z->modeltype==1 )
    {
        
        /*
         * R-based model
         */
        k = kdtreequeryrnn(&z->tree, x, z->r, ae_true, _state);
        kdtreequeryresultsdistances(&z->tree, &z->rbuf, _state);
        kdtreequeryresultstags(&z->tree, &z->tbuf, _state);
        if( k<idwint_idwkmin )
        {
            
            /*
             * we need at least IDWKMin points
             */
            k = kdtreequeryknn(&z->tree, x, idwint_idwkmin, ae_true, _state);
            kdtreequeryresultsdistances(&z->tree, &z->rbuf, _state);
            kdtreequeryresultstags(&z->tree, &z->tbuf, _state);
        }
    }
    
    /*
     * initialize weights for linear/quadratic members calculation.
     *
     * NOTE 1: weights are calculated using NORMALIZED modified
     * Shepard's formula. Original formula gives w(i) = sqr((R-di)/(R*di)),
     * where di is i-th distance, R is max(di). Modified formula have
     * following form:
     *     w_mod(i) = 1, if di=d0
     *     w_mod(i) = w(i)/w(0), if di<>d0
     *
     * NOTE 2: self-match is USED for this query
     *
     * NOTE 3: last point almost always gain zero weight, but it MUST
     * be used for fitting because sometimes it will gain NON-ZERO
     * weight - for example, when all distances are equal.
     */
    r = z->rbuf.ptr.p_double[k-1];
    d0 = z->rbuf.ptr.p_double[0];
    result = (double)(0);
    s = (double)(0);
    for(i=0; i<=k-1; i++)
    {
        di = z->rbuf.ptr.p_double[i];
        if( ae_fp_eq(di,d0) )
        {
            
            /*
             * distance is equal to shortest, set it 1.0
             * without explicitly calculating (which would give
             * us same result, but 'll expose us to the risk of
             * division by zero).
             */
            w = (double)(1);
        }
        else
        {
            
            /*
             * use normalized formula
             */
            v1 = (r-di)/(r-d0);
            v2 = d0/di;
            w = ae_sqr(v1*v2, _state);
        }
        result = result+w*idwint_idwcalcq(z, x, z->tbuf.ptr.p_int[i], _state);
        s = s+w;
    }
    result = result/s;
    return result;
}


/*************************************************************************
IDW interpolant using modified Shepard method for uniform point
distributions.

INPUT PARAMETERS:
    XY  -   X and Y values, array[0..N-1,0..NX].
            First NX columns contain X-values, last column contain
            Y-values.
    N   -   number of nodes, N>0.
    NX  -   space dimension, NX>=1.
    D   -   nodal function type, either:
            * 0     constant  model.  Just  for  demonstration only, worst
                    model ever.
            * 1     linear model, least squares fitting. Simpe  model  for
                    datasets too small for quadratic models
            * 2     quadratic  model,  least  squares  fitting. Best model
                    available (if your dataset is large enough).
            * -1    "fast"  linear  model,  use  with  caution!!!   It  is
                    significantly  faster than linear/quadratic and better
                    than constant model. But it is less robust (especially
                    in the presence of noise).
    NQ  -   number of points used to calculate  nodal  functions  (ignored
            for constant models). NQ should be LARGER than:
            * max(1.5*(1+NX),2^NX+1) for linear model,
            * max(3/4*(NX+2)*(NX+1),2^NX+1) for quadratic model.
            Values less than this threshold will be silently increased.
    NW  -   number of points used to calculate weights and to interpolate.
            Required: >=2^NX+1, values less than this  threshold  will  be
            silently increased.
            Recommended value: about 2*NQ

OUTPUT PARAMETERS:
    Z   -   IDW interpolant.
    
NOTES:
  * best results are obtained with quadratic models, worst - with constant
    models
  * when N is large, NQ and NW must be significantly smaller than  N  both
    to obtain optimal performance and to obtain optimal accuracy. In 2  or
    3-dimensional tasks NQ=15 and NW=25 are good values to start with.
  * NQ  and  NW  may  be  greater  than  N.  In  such  cases  they will be
    automatically decreased.
  * this subroutine is always succeeds (as long as correct parameters  are
    passed).
  * see  'Multivariate  Interpolation  of Large Sets of Scattered Data' by
    Robert J. Renka for more information on this algorithm.
  * this subroutine assumes that point distribution is uniform at the small
    scales.  If  it  isn't  -  for  example,  points are concentrated along
    "lines", but "lines" distribution is uniform at the larger scale - then
    you should use IDWBuildModifiedShepardR()


  -- ALGLIB PROJECT --
     Copyright 02.03.2010 by Bochkanov Sergey
*************************************************************************/
void idwbuildmodifiedshepard(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t nx,
     ae_int_t d,
     ae_int_t nq,
     ae_int_t nw,
     idwinterpolant* z,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t j2;
    ae_int_t j3;
    double v;
    double r;
    double s;
    double d0;
    double di;
    double v1;
    double v2;
    ae_int_t nc;
    ae_int_t offs;
    ae_vector x;
    ae_vector qrbuf;
    ae_matrix qxybuf;
    ae_vector y;
    ae_matrix fmatrix;
    ae_vector w;
    ae_vector qsol;
    ae_vector temp;
    ae_vector tags;
    ae_int_t info;
    double taskrcond;

    ae_frame_make(_state, &_frame_block);
    _idwinterpolant_clear(z);
    ae_vector_init(&x, 0, DT_REAL, _state);
    ae_vector_init(&qrbuf, 0, DT_REAL, _state);
    ae_matrix_init(&qxybuf, 0, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&qsol, 0, DT_REAL, _state);
    ae_vector_init(&temp, 0, DT_REAL, _state);
    ae_vector_init(&tags, 0, DT_INT, _state);

    
    /*
     * these initializers are not really necessary,
     * but without them compiler complains about uninitialized locals
     */
    nc = 0;
    
    /*
     * assertions
     */
    ae_assert(n>0, "IDWBuildModifiedShepard: N<=0!", _state);
    ae_assert(nx>=1, "IDWBuildModifiedShepard: NX<1!", _state);
    ae_assert(d>=-1&&d<=2, "IDWBuildModifiedShepard: D<>-1 and D<>0 and D<>1 and D<>2!", _state);
    
    /*
     * Correct parameters if needed
     */
    if( d==1 )
    {
        nq = ae_maxint(nq, ae_iceil(idwint_idwqfactor*(1+nx), _state)+1, _state);
        nq = ae_maxint(nq, ae_round(ae_pow((double)(2), (double)(nx), _state), _state)+1, _state);
    }
    if( d==2 )
    {
        nq = ae_maxint(nq, ae_iceil(idwint_idwqfactor*(nx+2)*(nx+1)/2, _state)+1, _state);
        nq = ae_maxint(nq, ae_round(ae_pow((double)(2), (double)(nx), _state), _state)+1, _state);
    }
    nw = ae_maxint(nw, ae_round(ae_pow((double)(2), (double)(nx), _state), _state)+1, _state);
    nq = ae_minint(nq, n, _state);
    nw = ae_minint(nw, n, _state);
    
    /*
     * primary initialization of Z
     */
    idwint_idwinit1(n, nx, d, nq, nw, z, _state);
    z->modeltype = 0;
    
    /*
     * Create KD-tree
     */
    ae_vector_set_length(&tags, n, _state);
    for(i=0; i<=n-1; i++)
    {
        tags.ptr.p_int[i] = i;
    }
    kdtreebuildtagged(xy, &tags, n, nx, 1, 2, &z->tree, _state);
    
    /*
     * build nodal functions
     */
    ae_vector_set_length(&temp, nq+1, _state);
    ae_vector_set_length(&x, nx, _state);
    ae_vector_set_length(&qrbuf, nq, _state);
    ae_matrix_set_length(&qxybuf, nq, nx+1, _state);
    if( d==-1 )
    {
        ae_vector_set_length(&w, nq, _state);
    }
    if( d==1 )
    {
        ae_vector_set_length(&y, nq, _state);
        ae_vector_set_length(&w, nq, _state);
        ae_vector_set_length(&qsol, nx, _state);
        
        /*
         * NX for linear members,
         * 1 for temporary storage
         */
        ae_matrix_set_length(&fmatrix, nq, nx+1, _state);
    }
    if( d==2 )
    {
        ae_vector_set_length(&y, nq, _state);
        ae_vector_set_length(&w, nq, _state);
        ae_vector_set_length(&qsol, nx+ae_round(nx*(nx+1)*0.5, _state), _state);
        
        /*
         * NX for linear members,
         * Round(NX*(NX+1)*0.5) for quadratic model,
         * 1 for temporary storage
         */
        ae_matrix_set_length(&fmatrix, nq, nx+ae_round(nx*(nx+1)*0.5, _state)+1, _state);
    }
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * Initialize center and function value.
         * If D=0 it is all what we need
         */
        ae_v_move(&z->q.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx));
        if( d==0 )
        {
            continue;
        }
        
        /*
         * calculate weights for linear/quadratic members calculation.
         *
         * NOTE 1: weights are calculated using NORMALIZED modified
         * Shepard's formula. Original formula is w(i) = sqr((R-di)/(R*di)),
         * where di is i-th distance, R is max(di). Modified formula have
         * following form:
         *     w_mod(i) = 1, if di=d0
         *     w_mod(i) = w(i)/w(0), if di<>d0
         *
         * NOTE 2: self-match is NOT used for this query
         *
         * NOTE 3: last point almost always gain zero weight, but it MUST
         * be used for fitting because sometimes it will gain NON-ZERO
         * weight - for example, when all distances are equal.
         */
        ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx-1));
        k = kdtreequeryknn(&z->tree, &x, nq, ae_false, _state);
        kdtreequeryresultsxy(&z->tree, &qxybuf, _state);
        kdtreequeryresultsdistances(&z->tree, &qrbuf, _state);
        r = qrbuf.ptr.p_double[k-1];
        d0 = qrbuf.ptr.p_double[0];
        for(j=0; j<=k-1; j++)
        {
            di = qrbuf.ptr.p_double[j];
            if( ae_fp_eq(di,d0) )
            {
                
                /*
                 * distance is equal to shortest, set it 1.0
                 * without explicitly calculating (which would give
                 * us same result, but 'll expose us to the risk of
                 * division by zero).
                 */
                w.ptr.p_double[j] = (double)(1);
            }
            else
            {
                
                /*
                 * use normalized formula
                 */
                v1 = (r-di)/(r-d0);
                v2 = d0/di;
                w.ptr.p_double[j] = ae_sqr(v1*v2, _state);
            }
        }
        
        /*
         * calculate linear/quadratic members
         */
        if( d==-1 )
        {
            
            /*
             * "Fast" linear nodal function calculated using
             * inverse distance weighting
             */
            for(j=0; j<=nx-1; j++)
            {
                x.ptr.p_double[j] = (double)(0);
            }
            s = (double)(0);
            for(j=0; j<=k-1; j++)
            {
                
                /*
                 * calculate J-th inverse distance weighted gradient:
                 *     grad_k = (y_j-y_k)*(x_j-x_k)/sqr(norm(x_j-x_k))
                 *     grad   = sum(wk*grad_k)/sum(w_k)
                 */
                v = (double)(0);
                for(j2=0; j2<=nx-1; j2++)
                {
                    v = v+ae_sqr(qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2], _state);
                }
                
                /*
                 * Although x_j<>x_k, sqr(norm(x_j-x_k)) may be zero due to
                 * underflow. If it is, we assume than J-th gradient is zero
                 * (i.e. don't add anything)
                 */
                if( ae_fp_neq(v,(double)(0)) )
                {
                    for(j2=0; j2<=nx-1; j2++)
                    {
                        x.ptr.p_double[j2] = x.ptr.p_double[j2]+w.ptr.p_double[j]*(qxybuf.ptr.pp_double[j][nx]-xy->ptr.pp_double[i][nx])*(qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2])/v;
                    }
                }
                s = s+w.ptr.p_double[j];
            }
            for(j=0; j<=nx-1; j++)
            {
                z->q.ptr.pp_double[i][nx+1+j] = x.ptr.p_double[j]/s;
            }
        }
        else
        {
            
            /*
             * Least squares models: build
             */
            if( d==1 )
            {
                
                /*
                 * Linear nodal function calculated using
                 * least squares fitting to its neighbors
                 */
                for(j=0; j<=k-1; j++)
                {
                    for(j2=0; j2<=nx-1; j2++)
                    {
                        fmatrix.ptr.pp_double[j][j2] = qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2];
                    }
                    y.ptr.p_double[j] = qxybuf.ptr.pp_double[j][nx]-xy->ptr.pp_double[i][nx];
                }
                nc = nx;
            }
            if( d==2 )
            {
                
                /*
                 * Quadratic nodal function calculated using
                 * least squares fitting to its neighbors
                 */
                for(j=0; j<=k-1; j++)
                {
                    offs = 0;
                    for(j2=0; j2<=nx-1; j2++)
                    {
                        fmatrix.ptr.pp_double[j][offs] = qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2];
                        offs = offs+1;
                    }
                    for(j2=0; j2<=nx-1; j2++)
                    {
                        for(j3=j2; j3<=nx-1; j3++)
                        {
                            fmatrix.ptr.pp_double[j][offs] = (qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2])*(qxybuf.ptr.pp_double[j][j3]-xy->ptr.pp_double[i][j3]);
                            offs = offs+1;
                        }
                    }
                    y.ptr.p_double[j] = qxybuf.ptr.pp_double[j][nx]-xy->ptr.pp_double[i][nx];
                }
                nc = nx+ae_round(nx*(nx+1)*0.5, _state);
            }
            idwint_idwinternalsolver(&y, &w, &fmatrix, &temp, k, nc, &info, &qsol, &taskrcond, _state);
            
            /*
             * Least squares models: copy results
             */
            if( info>0 )
            {
                
                /*
                 * LLS task is solved, copy results
                 */
                z->debugworstrcond = ae_minreal(z->debugworstrcond, taskrcond, _state);
                z->debugbestrcond = ae_maxreal(z->debugbestrcond, taskrcond, _state);
                for(j=0; j<=nc-1; j++)
                {
                    z->q.ptr.pp_double[i][nx+1+j] = qsol.ptr.p_double[j];
                }
            }
            else
            {
                
                /*
                 * Solver failure, very strange, but we will use
                 * zero values to handle it.
                 */
                z->debugsolverfailures = z->debugsolverfailures+1;
                for(j=0; j<=nc-1; j++)
                {
                    z->q.ptr.pp_double[i][nx+1+j] = (double)(0);
                }
            }
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
IDW interpolant using modified Shepard method for non-uniform datasets.

This type of model uses  constant  nodal  functions and interpolates using
all nodes which are closer than user-specified radius R. It  may  be  used
when points distribution is non-uniform at the small scale, but it  is  at
the distances as large as R.

INPUT PARAMETERS:
    XY  -   X and Y values, array[0..N-1,0..NX].
            First NX columns contain X-values, last column contain
            Y-values.
    N   -   number of nodes, N>0.
    NX  -   space dimension, NX>=1.
    R   -   radius, R>0

OUTPUT PARAMETERS:
    Z   -   IDW interpolant.

NOTES:
* if there is less than IDWKMin points within  R-ball,  algorithm  selects
  IDWKMin closest ones, so that continuity properties of  interpolant  are
  preserved even far from points.

  -- ALGLIB PROJECT --
     Copyright 11.04.2010 by Bochkanov Sergey
*************************************************************************/
void idwbuildmodifiedshepardr(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t nx,
     double r,
     idwinterpolant* z,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_vector tags;

    ae_frame_make(_state, &_frame_block);
    _idwinterpolant_clear(z);
    ae_vector_init(&tags, 0, DT_INT, _state);

    
    /*
     * assertions
     */
    ae_assert(n>0, "IDWBuildModifiedShepardR: N<=0!", _state);
    ae_assert(nx>=1, "IDWBuildModifiedShepardR: NX<1!", _state);
    ae_assert(ae_fp_greater(r,(double)(0)), "IDWBuildModifiedShepardR: R<=0!", _state);
    
    /*
     * primary initialization of Z
     */
    idwint_idwinit1(n, nx, 0, 0, n, z, _state);
    z->modeltype = 1;
    z->r = r;
    
    /*
     * Create KD-tree
     */
    ae_vector_set_length(&tags, n, _state);
    for(i=0; i<=n-1; i++)
    {
        tags.ptr.p_int[i] = i;
    }
    kdtreebuildtagged(xy, &tags, n, nx, 1, 2, &z->tree, _state);
    
    /*
     * build nodal functions
     */
    for(i=0; i<=n-1; i++)
    {
        ae_v_move(&z->q.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx));
    }
    ae_frame_leave(_state);
}


/*************************************************************************
IDW model for noisy data.

This subroutine may be used to handle noisy data, i.e. data with noise  in
OUTPUT values.  It differs from IDWBuildModifiedShepard() in the following
aspects:
* nodal functions are not constrained to pass through  nodes:  Qi(xi)<>yi,
  i.e. we have fitting  instead  of  interpolation.
* weights which are used during least  squares fitting stage are all equal
  to 1.0 (independently of distance)
* "fast"-linear or constant nodal functions are not supported (either  not
  robust enough or too rigid)

This problem require far more complex tuning than interpolation  problems.
Below you can find some recommendations regarding this problem:
* focus on tuning NQ; it controls noise reduction. As for NW, you can just
  make it equal to 2*NQ.
* you can use cross-validation to determine optimal NQ.
* optimal NQ is a result of complex tradeoff  between  noise  level  (more
  noise = larger NQ required) and underlying  function  complexity  (given
  fixed N, larger NQ means smoothing of compex features in the data).  For
  example, NQ=N will reduce noise to the minimum level possible,  but  you
  will end up with just constant/linear/quadratic (depending on  D)  least
  squares model for the whole dataset.

INPUT PARAMETERS:
    XY  -   X and Y values, array[0..N-1,0..NX].
            First NX columns contain X-values, last column contain
            Y-values.
    N   -   number of nodes, N>0.
    NX  -   space dimension, NX>=1.
    D   -   nodal function degree, either:
            * 1     linear model, least squares fitting. Simpe  model  for
                    datasets too small for quadratic models (or  for  very
                    noisy problems).
            * 2     quadratic  model,  least  squares  fitting. Best model
                    available (if your dataset is large enough).
    NQ  -   number of points used to calculate nodal functions.  NQ should
            be  significantly   larger   than  1.5  times  the  number  of
            coefficients in a nodal function to overcome effects of noise:
            * larger than 1.5*(1+NX) for linear model,
            * larger than 3/4*(NX+2)*(NX+1) for quadratic model.
            Values less than this threshold will be silently increased.
    NW  -   number of points used to calculate weights and to interpolate.
            Required: >=2^NX+1, values less than this  threshold  will  be
            silently increased.
            Recommended value: about 2*NQ or larger

OUTPUT PARAMETERS:
    Z   -   IDW interpolant.

NOTES:
  * best results are obtained with quadratic models, linear models are not
    recommended to use unless you are pretty sure that it is what you want
  * this subroutine is always succeeds (as long as correct parameters  are
    passed).
  * see  'Multivariate  Interpolation  of Large Sets of Scattered Data' by
    Robert J. Renka for more information on this algorithm.


  -- ALGLIB PROJECT --
     Copyright 02.03.2010 by Bochkanov Sergey
*************************************************************************/
void idwbuildnoisy(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t nx,
     ae_int_t d,
     ae_int_t nq,
     ae_int_t nw,
     idwinterpolant* z,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t j2;
    ae_int_t j3;
    double v;
    ae_int_t nc;
    ae_int_t offs;
    double taskrcond;
    ae_vector x;
    ae_vector qrbuf;
    ae_matrix qxybuf;
    ae_vector y;
    ae_vector w;
    ae_matrix fmatrix;
    ae_vector qsol;
    ae_vector tags;
    ae_vector temp;
    ae_int_t info;

    ae_frame_make(_state, &_frame_block);
    _idwinterpolant_clear(z);
    ae_vector_init(&x, 0, DT_REAL, _state);
    ae_vector_init(&qrbuf, 0, DT_REAL, _state);
    ae_matrix_init(&qxybuf, 0, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state);
    ae_vector_init(&qsol, 0, DT_REAL, _state);
    ae_vector_init(&tags, 0, DT_INT, _state);
    ae_vector_init(&temp, 0, DT_REAL, _state);

    
    /*
     * these initializers are not really necessary,
     * but without them compiler complains about uninitialized locals
     */
    nc = 0;
    
    /*
     * assertions
     */
    ae_assert(n>0, "IDWBuildNoisy: N<=0!", _state);
    ae_assert(nx>=1, "IDWBuildNoisy: NX<1!", _state);
    ae_assert(d>=1&&d<=2, "IDWBuildNoisy: D<>1 and D<>2!", _state);
    
    /*
     * Correct parameters if needed
     */
    if( d==1 )
    {
        nq = ae_maxint(nq, ae_iceil(idwint_idwqfactor*(1+nx), _state)+1, _state);
    }
    if( d==2 )
    {
        nq = ae_maxint(nq, ae_iceil(idwint_idwqfactor*(nx+2)*(nx+1)/2, _state)+1, _state);
    }
    nw = ae_maxint(nw, ae_round(ae_pow((double)(2), (double)(nx), _state), _state)+1, _state);
    nq = ae_minint(nq, n, _state);
    nw = ae_minint(nw, n, _state);
    
    /*
     * primary initialization of Z
     */
    idwint_idwinit1(n, nx, d, nq, nw, z, _state);
    z->modeltype = 0;
    
    /*
     * Create KD-tree
     */
    ae_vector_set_length(&tags, n, _state);
    for(i=0; i<=n-1; i++)
    {
        tags.ptr.p_int[i] = i;
    }
    kdtreebuildtagged(xy, &tags, n, nx, 1, 2, &z->tree, _state);
    
    /*
     * build nodal functions
     * (special algorithm for noisy data is used)
     */
    ae_vector_set_length(&temp, nq+1, _state);
    ae_vector_set_length(&x, nx, _state);
    ae_vector_set_length(&qrbuf, nq, _state);
    ae_matrix_set_length(&qxybuf, nq, nx+1, _state);
    if( d==1 )
    {
        ae_vector_set_length(&y, nq, _state);
        ae_vector_set_length(&w, nq, _state);
        ae_vector_set_length(&qsol, 1+nx, _state);
        
        /*
         * 1 for constant member,
         * NX for linear members,
         * 1 for temporary storage
         */
        ae_matrix_set_length(&fmatrix, nq, 1+nx+1, _state);
    }
    if( d==2 )
    {
        ae_vector_set_length(&y, nq, _state);
        ae_vector_set_length(&w, nq, _state);
        ae_vector_set_length(&qsol, 1+nx+ae_round(nx*(nx+1)*0.5, _state), _state);
        
        /*
         * 1 for constant member,
         * NX for linear members,
         * Round(NX*(NX+1)*0.5) for quadratic model,
         * 1 for temporary storage
         */
        ae_matrix_set_length(&fmatrix, nq, 1+nx+ae_round(nx*(nx+1)*0.5, _state)+1, _state);
    }
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * Initialize center.
         */
        ae_v_move(&z->q.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx-1));
        
        /*
         * Calculate linear/quadratic members
         * using least squares fit
         * NOTE 1: all weight are equal to 1.0
         * NOTE 2: self-match is USED for this query
         */
        ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx-1));
        k = kdtreequeryknn(&z->tree, &x, nq, ae_true, _state);
        kdtreequeryresultsxy(&z->tree, &qxybuf, _state);
        kdtreequeryresultsdistances(&z->tree, &qrbuf, _state);
        if( d==1 )
        {
            
            /*
             * Linear nodal function calculated using
             * least squares fitting to its neighbors
             */
            for(j=0; j<=k-1; j++)
            {
                fmatrix.ptr.pp_double[j][0] = 1.0;
                for(j2=0; j2<=nx-1; j2++)
                {
                    fmatrix.ptr.pp_double[j][1+j2] = qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2];
                }
                y.ptr.p_double[j] = qxybuf.ptr.pp_double[j][nx];
                w.ptr.p_double[j] = (double)(1);
            }
            nc = 1+nx;
        }
        if( d==2 )
        {
            
            /*
             * Quadratic nodal function calculated using
             * least squares fitting to its neighbors
             */
            for(j=0; j<=k-1; j++)
            {
                fmatrix.ptr.pp_double[j][0] = (double)(1);
                offs = 1;
                for(j2=0; j2<=nx-1; j2++)
                {
                    fmatrix.ptr.pp_double[j][offs] = qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2];
                    offs = offs+1;
                }
                for(j2=0; j2<=nx-1; j2++)
                {
                    for(j3=j2; j3<=nx-1; j3++)
                    {
                        fmatrix.ptr.pp_double[j][offs] = (qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2])*(qxybuf.ptr.pp_double[j][j3]-xy->ptr.pp_double[i][j3]);
                        offs = offs+1;
                    }
                }
                y.ptr.p_double[j] = qxybuf.ptr.pp_double[j][nx];
                w.ptr.p_double[j] = (double)(1);
            }
            nc = 1+nx+ae_round(nx*(nx+1)*0.5, _state);
        }
        idwint_idwinternalsolver(&y, &w, &fmatrix, &temp, k, nc, &info, &qsol, &taskrcond, _state);
        
        /*
         * Least squares models: copy results
         */
        if( info>0 )
        {
            
            /*
             * LLS task is solved, copy results
             */
            z->debugworstrcond = ae_minreal(z->debugworstrcond, taskrcond, _state);
            z->debugbestrcond = ae_maxreal(z->debugbestrcond, taskrcond, _state);
            for(j=0; j<=nc-1; j++)
            {
                z->q.ptr.pp_double[i][nx+j] = qsol.ptr.p_double[j];
            }
        }
        else
        {
            
            /*
             * Solver failure, very strange, but we will use
             * zero values to handle it.
             */
            z->debugsolverfailures = z->debugsolverfailures+1;
            v = (double)(0);
            for(j=0; j<=k-1; j++)
            {
                v = v+qxybuf.ptr.pp_double[j][nx];
            }
            z->q.ptr.pp_double[i][nx] = v/k;
            for(j=0; j<=nc-2; j++)
            {
                z->q.ptr.pp_double[i][nx+1+j] = (double)(0);
            }
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Internal subroutine: K-th nodal function calculation

  -- ALGLIB --
     Copyright 02.03.2010 by Bochkanov Sergey
*************************************************************************/
static double idwint_idwcalcq(idwinterpolant* z,
     /* Real    */ ae_vector* x,
     ae_int_t k,
     ae_state *_state)
{
    ae_int_t nx;
    ae_int_t i;
    ae_int_t j;
    ae_int_t offs;
    double result;


    nx = z->nx;
    
    /*
     * constant member
     */
    result = z->q.ptr.pp_double[k][nx];
    
    /*
     * linear members
     */
    if( z->d>=1 )
    {
        for(i=0; i<=nx-1; i++)
        {
            result = result+z->q.ptr.pp_double[k][nx+1+i]*(x->ptr.p_double[i]-z->q.ptr.pp_double[k][i]);
        }
    }
    
    /*
     * quadratic members
     */
    if( z->d>=2 )
    {
        offs = nx+1+nx;
        for(i=0; i<=nx-1; i++)
        {
            for(j=i; j<=nx-1; j++)
            {
                result = result+z->q.ptr.pp_double[k][offs]*(x->ptr.p_double[i]-z->q.ptr.pp_double[k][i])*(x->ptr.p_double[j]-z->q.ptr.pp_double[k][j]);
                offs = offs+1;
            }
        }
    }
    return result;
}


/*************************************************************************
Initialization of internal structures.

It assumes correctness of all parameters.

  -- ALGLIB --
     Copyright 02.03.2010 by Bochkanov Sergey
*************************************************************************/
static void idwint_idwinit1(ae_int_t n,
     ae_int_t nx,
     ae_int_t d,
     ae_int_t nq,
     ae_int_t nw,
     idwinterpolant* z,
     ae_state *_state)
{


    z->debugsolverfailures = 0;
    z->debugworstrcond = 1.0;
    z->debugbestrcond = (double)(0);
    z->n = n;
    z->nx = nx;
    z->d = 0;
    if( d==1 )
    {
        z->d = 1;
    }
    if( d==2 )
    {
        z->d = 2;
    }
    if( d==-1 )
    {
        z->d = 1;
    }
    z->nw = nw;
    if( d==-1 )
    {
        ae_matrix_set_length(&z->q, n, nx+1+nx, _state);
    }
    if( d==0 )
    {
        ae_matrix_set_length(&z->q, n, nx+1, _state);
    }
    if( d==1 )
    {
        ae_matrix_set_length(&z->q, n, nx+1+nx, _state);
    }
    if( d==2 )
    {
        ae_matrix_set_length(&z->q, n, nx+1+nx+ae_round(nx*(nx+1)*0.5, _state), _state);
    }
    ae_vector_set_length(&z->tbuf, nw, _state);
    ae_vector_set_length(&z->rbuf, nw, _state);
    ae_matrix_set_length(&z->xybuf, nw, nx+1, _state);
    ae_vector_set_length(&z->xbuf, nx, _state);
}


/*************************************************************************
Linear least squares solver for small tasks.

Works faster than standard ALGLIB solver in non-degenerate cases  (due  to
absense of internal allocations and optimized row/colums).  In  degenerate
cases it calls standard solver, which results in small performance penalty
associated with preliminary steps.

INPUT PARAMETERS:
    Y           array[0..N-1]
    W           array[0..N-1]
    FMatrix     array[0..N-1,0..M], have additional column for temporary
                values
    Temp        array[0..N]
*************************************************************************/
static void idwint_idwinternalsolver(/* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_matrix* fmatrix,
     /* Real    */ ae_vector* temp,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     /* Real    */ ae_vector* x,
     double* taskrcond,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t j;
    double v;
    double tau;
    ae_vector b;
    densesolverlsreport srep;

    ae_frame_make(_state, &_frame_block);
    *info = 0;
    ae_vector_init(&b, 0, DT_REAL, _state);
    _densesolverlsreport_init(&srep, _state);

    
    /*
     * set up info
     */
    *info = 1;
    
    /*
     * prepare matrix
     */
    for(i=0; i<=n-1; i++)
    {
        fmatrix->ptr.pp_double[i][m] = y->ptr.p_double[i];
        v = w->ptr.p_double[i];
        ae_v_muld(&fmatrix->ptr.pp_double[i][0], 1, ae_v_len(0,m), v);
    }
    
    /*
     * use either fast algorithm or general algorithm
     */
    if( m<=n )
    {
        
        /*
         * QR decomposition
         * We assume that M<=N (we would have called LSFit() otherwise)
         */
        for(i=0; i<=m-1; i++)
        {
            if( i<n-1 )
            {
                ae_v_move(&temp->ptr.p_double[1], 1, &fmatrix->ptr.pp_double[i][i], fmatrix->stride, ae_v_len(1,n-i));
                generatereflection(temp, n-i, &tau, _state);
                fmatrix->ptr.pp_double[i][i] = temp->ptr.p_double[1];
                temp->ptr.p_double[1] = (double)(1);
                for(j=i+1; j<=m; j++)
                {
                    v = ae_v_dotproduct(&fmatrix->ptr.pp_double[i][j], fmatrix->stride, &temp->ptr.p_double[1], 1, ae_v_len(i,n-1));
                    v = tau*v;
                    ae_v_subd(&fmatrix->ptr.pp_double[i][j], fmatrix->stride, &temp->ptr.p_double[1], 1, ae_v_len(i,n-1), v);
                }
            }
        }
        
        /*
         * Check condition number
         */
        *taskrcond = rmatrixtrrcondinf(fmatrix, m, ae_true, ae_false, _state);
        
        /*
         * use either fast algorithm for non-degenerate cases
         * or slow algorithm for degenerate cases
         */
        if( ae_fp_greater(*taskrcond,10000*n*ae_machineepsilon) )
        {
            
            /*
             * solve triangular system R*x = FMatrix[0:M-1,M]
             * using fast algorithm, then exit
             */
            x->ptr.p_double[m-1] = fmatrix->ptr.pp_double[m-1][m]/fmatrix->ptr.pp_double[m-1][m-1];
            for(i=m-2; i>=0; i--)
            {
                v = ae_v_dotproduct(&fmatrix->ptr.pp_double[i][i+1], 1, &x->ptr.p_double[i+1], 1, ae_v_len(i+1,m-1));
                x->ptr.p_double[i] = (fmatrix->ptr.pp_double[i][m]-v)/fmatrix->ptr.pp_double[i][i];
            }
        }
        else
        {
            
            /*
             * use more general algorithm
             */
            ae_vector_set_length(&b, m, _state);
            for(i=0; i<=m-1; i++)
            {
                for(j=0; j<=i-1; j++)
                {
                    fmatrix->ptr.pp_double[i][j] = 0.0;
                }
                b.ptr.p_double[i] = fmatrix->ptr.pp_double[i][m];
            }
            rmatrixsolvels(fmatrix, m, m, &b, 10000*ae_machineepsilon, info, &srep, x, _state);
        }
    }
    else
    {
        
        /*
         * use more general algorithm
         */
        ae_vector_set_length(&b, n, _state);
        for(i=0; i<=n-1; i++)
        {
            b.ptr.p_double[i] = fmatrix->ptr.pp_double[i][m];
        }
        rmatrixsolvels(fmatrix, n, m, &b, 10000*ae_machineepsilon, info, &srep, x, _state);
        *taskrcond = srep.r2;
    }
    ae_frame_leave(_state);
}


void _idwinterpolant_init(void* _p, ae_state *_state)
{
    idwinterpolant *p = (idwinterpolant*)_p;
    ae_touch_ptr((void*)p);
    _kdtree_init(&p->tree, _state);
    ae_matrix_init(&p->q, 0, 0, DT_REAL, _state);
    ae_vector_init(&p->xbuf, 0, DT_REAL, _state);
    ae_vector_init(&p->tbuf, 0, DT_INT, _state);
    ae_vector_init(&p->rbuf, 0, DT_REAL, _state);
    ae_matrix_init(&p->xybuf, 0, 0, DT_REAL, _state);
}


void _idwinterpolant_init_copy(void* _dst, void* _src, ae_state *_state)
{
    idwinterpolant *dst = (idwinterpolant*)_dst;
    idwinterpolant *src = (idwinterpolant*)_src;
    dst->n = src->n;
    dst->nx = src->nx;
    dst->d = src->d;
    dst->r = src->r;
    dst->nw = src->nw;
    _kdtree_init_copy(&dst->tree, &src->tree, _state);
    dst->modeltype = src->modeltype;
    ae_matrix_init_copy(&dst->q, &src->q, _state);
    ae_vector_init_copy(&dst->xbuf, &src->xbuf, _state);
    ae_vector_init_copy(&dst->tbuf, &src->tbuf, _state);
    ae_vector_init_copy(&dst->rbuf, &src->rbuf, _state);
    ae_matrix_init_copy(&dst->xybuf, &src->xybuf, _state);
    dst->debugsolverfailures = src->debugsolverfailures;
    dst->debugworstrcond = src->debugworstrcond;
    dst->debugbestrcond = src->debugbestrcond;
}


void _idwinterpolant_clear(void* _p)
{
    idwinterpolant *p = (idwinterpolant*)_p;
    ae_touch_ptr((void*)p);
    _kdtree_clear(&p->tree);
    ae_matrix_clear(&p->q);
    ae_vector_clear(&p->xbuf);
    ae_vector_clear(&p->tbuf);
    ae_vector_clear(&p->rbuf);
    ae_matrix_clear(&p->xybuf);
}


void _idwinterpolant_destroy(void* _p)
{
    idwinterpolant *p = (idwinterpolant*)_p;
    ae_touch_ptr((void*)p);
    _kdtree_destroy(&p->tree);
    ae_matrix_destroy(&p->q);
    ae_vector_destroy(&p->xbuf);
    ae_vector_destroy(&p->tbuf);
    ae_vector_destroy(&p->rbuf);
    ae_matrix_destroy(&p->xybuf);
}




/*************************************************************************
Rational interpolation using barycentric formula

F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i]))

Input parameters:
    B   -   barycentric interpolant built with one of model building
            subroutines.
    T   -   interpolation point

Result:
    barycentric interpolant F(t)

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
double barycentriccalc(barycentricinterpolant* b,
     double t,
     ae_state *_state)
{
    double s1;
    double s2;
    double s;
    double v;
    ae_int_t i;
    double result;


    ae_assert(!ae_isinf(t, _state), "BarycentricCalc: infinite T!", _state);
    
    /*
     * special case: NaN
     */
    if( ae_isnan(t, _state) )
    {
        result = _state->v_nan;
        return result;
    }
    
    /*
     * special case: N=1
     */
    if( b->n==1 )
    {
        result = b->sy*b->y.ptr.p_double[0];
        return result;
    }
    
    /*
     * Here we assume that task is normalized, i.e.:
     * 1. abs(Y[i])<=1
     * 2. abs(W[i])<=1
     * 3. X[] is ordered
     */
    s = ae_fabs(t-b->x.ptr.p_double[0], _state);
    for(i=0; i<=b->n-1; i++)
    {
        v = b->x.ptr.p_double[i];
        if( ae_fp_eq(v,t) )
        {
            result = b->sy*b->y.ptr.p_double[i];
            return result;
        }
        v = ae_fabs(t-v, _state);
        if( ae_fp_less(v,s) )
        {
            s = v;
        }
    }
    s1 = (double)(0);
    s2 = (double)(0);
    for(i=0; i<=b->n-1; i++)
    {
        v = s/(t-b->x.ptr.p_double[i]);
        v = v*b->w.ptr.p_double[i];
        s1 = s1+v*b->y.ptr.p_double[i];
        s2 = s2+v;
    }
    result = b->sy*s1/s2;
    return result;
}


/*************************************************************************
Differentiation of barycentric interpolant: first derivative.

Algorithm used in this subroutine is very robust and should not fail until
provided with values too close to MaxRealNumber  (usually  MaxRealNumber/N
or greater will overflow).

INPUT PARAMETERS:
    B   -   barycentric interpolant built with one of model building
            subroutines.
    T   -   interpolation point

OUTPUT PARAMETERS:
    F   -   barycentric interpolant at T
    DF  -   first derivative
    
NOTE


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricdiff1(barycentricinterpolant* b,
     double t,
     double* f,
     double* df,
     ae_state *_state)
{
    double v;
    double vv;
    ae_int_t i;
    ae_int_t k;
    double n0;
    double n1;
    double d0;
    double d1;
    double s0;
    double s1;
    double xk;
    double xi;
    double xmin;
    double xmax;
    double xscale1;
    double xoffs1;
    double xscale2;
    double xoffs2;
    double xprev;

    *f = 0;
    *df = 0;

    ae_assert(!ae_isinf(t, _state), "BarycentricDiff1: infinite T!", _state);
    
    /*
     * special case: NaN
     */
    if( ae_isnan(t, _state) )
    {
        *f = _state->v_nan;
        *df = _state->v_nan;
        return;
    }
    
    /*
     * special case: N=1
     */
    if( b->n==1 )
    {
        *f = b->sy*b->y.ptr.p_double[0];
        *df = (double)(0);
        return;
    }
    if( ae_fp_eq(b->sy,(double)(0)) )
    {
        *f = (double)(0);
        *df = (double)(0);
        return;
    }
    ae_assert(ae_fp_greater(b->sy,(double)(0)), "BarycentricDiff1: internal error", _state);
    
    /*
     * We assume than N>1 and B.SY>0. Find:
     * 1. pivot point (X[i] closest to T)
     * 2. width of interval containing X[i]
     */
    v = ae_fabs(b->x.ptr.p_double[0]-t, _state);
    k = 0;
    xmin = b->x.ptr.p_double[0];
    xmax = b->x.ptr.p_double[0];
    for(i=1; i<=b->n-1; i++)
    {
        vv = b->x.ptr.p_double[i];
        if( ae_fp_less(ae_fabs(vv-t, _state),v) )
        {
            v = ae_fabs(vv-t, _state);
            k = i;
        }
        xmin = ae_minreal(xmin, vv, _state);
        xmax = ae_maxreal(xmax, vv, _state);
    }
    
    /*
     * pivot point found, calculate dNumerator and dDenominator
     */
    xscale1 = 1/(xmax-xmin);
    xoffs1 = -xmin/(xmax-xmin)+1;
    xscale2 = (double)(2);
    xoffs2 = (double)(-3);
    t = t*xscale1+xoffs1;
    t = t*xscale2+xoffs2;
    xk = b->x.ptr.p_double[k];
    xk = xk*xscale1+xoffs1;
    xk = xk*xscale2+xoffs2;
    v = t-xk;
    n0 = (double)(0);
    n1 = (double)(0);
    d0 = (double)(0);
    d1 = (double)(0);
    xprev = (double)(-2);
    for(i=0; i<=b->n-1; i++)
    {
        xi = b->x.ptr.p_double[i];
        xi = xi*xscale1+xoffs1;
        xi = xi*xscale2+xoffs2;
        ae_assert(ae_fp_greater(xi,xprev), "BarycentricDiff1: points are too close!", _state);
        xprev = xi;
        if( i!=k )
        {
            vv = ae_sqr(t-xi, _state);
            s0 = (t-xk)/(t-xi);
            s1 = (xk-xi)/vv;
        }
        else
        {
            s0 = (double)(1);
            s1 = (double)(0);
        }
        vv = b->w.ptr.p_double[i]*b->y.ptr.p_double[i];
        n0 = n0+s0*vv;
        n1 = n1+s1*vv;
        vv = b->w.ptr.p_double[i];
        d0 = d0+s0*vv;
        d1 = d1+s1*vv;
    }
    *f = b->sy*n0/d0;
    *df = (n1*d0-n0*d1)/ae_sqr(d0, _state);
    if( ae_fp_neq(*df,(double)(0)) )
    {
        *df = ae_sign(*df, _state)*ae_exp(ae_log(ae_fabs(*df, _state), _state)+ae_log(b->sy, _state)+ae_log(xscale1, _state)+ae_log(xscale2, _state), _state);
    }
}


/*************************************************************************
Differentiation of barycentric interpolant: first/second derivatives.

INPUT PARAMETERS:
    B   -   barycentric interpolant built with one of model building
            subroutines.
    T   -   interpolation point

OUTPUT PARAMETERS:
    F   -   barycentric interpolant at T
    DF  -   first derivative
    D2F -   second derivative

NOTE: this algorithm may fail due to overflow/underflor if  used  on  data
whose values are close to MaxRealNumber or MinRealNumber.  Use more robust
BarycentricDiff1() subroutine in such cases.


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricdiff2(barycentricinterpolant* b,
     double t,
     double* f,
     double* df,
     double* d2f,
     ae_state *_state)
{
    double v;
    double vv;
    ae_int_t i;
    ae_int_t k;
    double n0;
    double n1;
    double n2;
    double d0;
    double d1;
    double d2;
    double s0;
    double s1;
    double s2;
    double xk;
    double xi;

    *f = 0;
    *df = 0;
    *d2f = 0;

    ae_assert(!ae_isinf(t, _state), "BarycentricDiff1: infinite T!", _state);
    
    /*
     * special case: NaN
     */
    if( ae_isnan(t, _state) )
    {
        *f = _state->v_nan;
        *df = _state->v_nan;
        *d2f = _state->v_nan;
        return;
    }
    
    /*
     * special case: N=1
     */
    if( b->n==1 )
    {
        *f = b->sy*b->y.ptr.p_double[0];
        *df = (double)(0);
        *d2f = (double)(0);
        return;
    }
    if( ae_fp_eq(b->sy,(double)(0)) )
    {
        *f = (double)(0);
        *df = (double)(0);
        *d2f = (double)(0);
        return;
    }
    
    /*
     * We assume than N>1 and B.SY>0. Find:
     * 1. pivot point (X[i] closest to T)
     * 2. width of interval containing X[i]
     */
    ae_assert(ae_fp_greater(b->sy,(double)(0)), "BarycentricDiff: internal error", _state);
    *f = (double)(0);
    *df = (double)(0);
    *d2f = (double)(0);
    v = ae_fabs(b->x.ptr.p_double[0]-t, _state);
    k = 0;
    for(i=1; i<=b->n-1; i++)
    {
        vv = b->x.ptr.p_double[i];
        if( ae_fp_less(ae_fabs(vv-t, _state),v) )
        {
            v = ae_fabs(vv-t, _state);
            k = i;
        }
    }
    
    /*
     * pivot point found, calculate dNumerator and dDenominator
     */
    xk = b->x.ptr.p_double[k];
    v = t-xk;
    n0 = (double)(0);
    n1 = (double)(0);
    n2 = (double)(0);
    d0 = (double)(0);
    d1 = (double)(0);
    d2 = (double)(0);
    for(i=0; i<=b->n-1; i++)
    {
        if( i!=k )
        {
            xi = b->x.ptr.p_double[i];
            vv = ae_sqr(t-xi, _state);
            s0 = (t-xk)/(t-xi);
            s1 = (xk-xi)/vv;
            s2 = -2*(xk-xi)/(vv*(t-xi));
        }
        else
        {
            s0 = (double)(1);
            s1 = (double)(0);
            s2 = (double)(0);
        }
        vv = b->w.ptr.p_double[i]*b->y.ptr.p_double[i];
        n0 = n0+s0*vv;
        n1 = n1+s1*vv;
        n2 = n2+s2*vv;
        vv = b->w.ptr.p_double[i];
        d0 = d0+s0*vv;
        d1 = d1+s1*vv;
        d2 = d2+s2*vv;
    }
    *f = b->sy*n0/d0;
    *df = b->sy*(n1*d0-n0*d1)/ae_sqr(d0, _state);
    *d2f = b->sy*((n2*d0-n0*d2)*ae_sqr(d0, _state)-(n1*d0-n0*d1)*2*d0*d1)/ae_sqr(ae_sqr(d0, _state), _state);
}


/*************************************************************************
This subroutine performs linear transformation of the argument.

INPUT PARAMETERS:
    B       -   rational interpolant in barycentric form
    CA, CB  -   transformation coefficients: x = CA*t + CB

OUTPUT PARAMETERS:
    B       -   transformed interpolant with X replaced by T

  -- ALGLIB PROJECT --
     Copyright 19.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentriclintransx(barycentricinterpolant* b,
     double ca,
     double cb,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;
    double v;


    
    /*
     * special case, replace by constant F(CB)
     */
    if( ae_fp_eq(ca,(double)(0)) )
    {
        b->sy = barycentriccalc(b, cb, _state);
        v = (double)(1);
        for(i=0; i<=b->n-1; i++)
        {
            b->y.ptr.p_double[i] = (double)(1);
            b->w.ptr.p_double[i] = v;
            v = -v;
        }
        return;
    }
    
    /*
     * general case: CA<>0
     */
    for(i=0; i<=b->n-1; i++)
    {
        b->x.ptr.p_double[i] = (b->x.ptr.p_double[i]-cb)/ca;
    }
    if( ae_fp_less(ca,(double)(0)) )
    {
        for(i=0; i<=b->n-1; i++)
        {
            if( i<b->n-1-i )
            {
                j = b->n-1-i;
                v = b->x.ptr.p_double[i];
                b->x.ptr.p_double[i] = b->x.ptr.p_double[j];
                b->x.ptr.p_double[j] = v;
                v = b->y.ptr.p_double[i];
                b->y.ptr.p_double[i] = b->y.ptr.p_double[j];
                b->y.ptr.p_double[j] = v;
                v = b->w.ptr.p_double[i];
                b->w.ptr.p_double[i] = b->w.ptr.p_double[j];
                b->w.ptr.p_double[j] = v;
            }
            else
            {
                break;
            }
        }
    }
}


/*************************************************************************
This  subroutine   performs   linear  transformation  of  the  barycentric
interpolant.

INPUT PARAMETERS:
    B       -   rational interpolant in barycentric form
    CA, CB  -   transformation coefficients: B2(x) = CA*B(x) + CB

OUTPUT PARAMETERS:
    B       -   transformed interpolant

  -- ALGLIB PROJECT --
     Copyright 19.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentriclintransy(barycentricinterpolant* b,
     double ca,
     double cb,
     ae_state *_state)
{
    ae_int_t i;
    double v;


    for(i=0; i<=b->n-1; i++)
    {
        b->y.ptr.p_double[i] = ca*b->sy*b->y.ptr.p_double[i]+cb;
    }
    b->sy = (double)(0);
    for(i=0; i<=b->n-1; i++)
    {
        b->sy = ae_maxreal(b->sy, ae_fabs(b->y.ptr.p_double[i], _state), _state);
    }
    if( ae_fp_greater(b->sy,(double)(0)) )
    {
        v = 1/b->sy;
        ae_v_muld(&b->y.ptr.p_double[0], 1, ae_v_len(0,b->n-1), v);
    }
}


/*************************************************************************
Extracts X/Y/W arrays from rational interpolant

INPUT PARAMETERS:
    B   -   barycentric interpolant

OUTPUT PARAMETERS:
    N   -   nodes count, N>0
    X   -   interpolation nodes, array[0..N-1]
    F   -   function values, array[0..N-1]
    W   -   barycentric weights, array[0..N-1]

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricunpack(barycentricinterpolant* b,
     ae_int_t* n,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_state *_state)
{
    double v;

    *n = 0;
    ae_vector_clear(x);
    ae_vector_clear(y);
    ae_vector_clear(w);

    *n = b->n;
    ae_vector_set_length(x, *n, _state);
    ae_vector_set_length(y, *n, _state);
    ae_vector_set_length(w, *n, _state);
    v = b->sy;
    ae_v_move(&x->ptr.p_double[0], 1, &b->x.ptr.p_double[0], 1, ae_v_len(0,*n-1));
    ae_v_moved(&y->ptr.p_double[0], 1, &b->y.ptr.p_double[0], 1, ae_v_len(0,*n-1), v);
    ae_v_move(&w->ptr.p_double[0], 1, &b->w.ptr.p_double[0], 1, ae_v_len(0,*n-1));
}


/*************************************************************************
Rational interpolant from X/Y/W arrays

F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i]))

INPUT PARAMETERS:
    X   -   interpolation nodes, array[0..N-1]
    F   -   function values, array[0..N-1]
    W   -   barycentric weights, array[0..N-1]
    N   -   nodes count, N>0

OUTPUT PARAMETERS:
    B   -   barycentric interpolant built from (X, Y, W)

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricbuildxyw(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     barycentricinterpolant* b,
     ae_state *_state)
{

    _barycentricinterpolant_clear(b);

    ae_assert(n>0, "BarycentricBuildXYW: incorrect N!", _state);
    
    /*
     * fill X/Y/W
     */
    ae_vector_set_length(&b->x, n, _state);
    ae_vector_set_length(&b->y, n, _state);
    ae_vector_set_length(&b->w, n, _state);
    ae_v_move(&b->x.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_v_move(&b->y.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_v_move(&b->w.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1));
    b->n = n;
    
    /*
     * Normalize
     */
    ratint_barycentricnormalize(b, _state);
}


/*************************************************************************
Rational interpolant without poles

The subroutine constructs the rational interpolating function without real
poles  (see  'Barycentric rational interpolation with no  poles  and  high
rates of approximation', Michael S. Floater. and  Kai  Hormann,  for  more
information on this subject).

Input parameters:
    X   -   interpolation nodes, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of nodes, N>0.
    D   -   order of the interpolation scheme, 0 <= D <= N-1.
            D<0 will cause an error.
            D>=N it will be replaced with D=N-1.
            if you don't know what D to choose, use small value about 3-5.

Output parameters:
    B   -   barycentric interpolant.

Note:
    this algorithm always succeeds and calculates the weights  with  close
    to machine precision.

  -- ALGLIB PROJECT --
     Copyright 17.06.2007 by Bochkanov Sergey
*************************************************************************/
void barycentricbuildfloaterhormann(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t d,
     barycentricinterpolant* b,
     ae_state *_state)
{
    ae_frame _frame_block;
    double s0;
    double s;
    double v;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_vector perm;
    ae_vector wtemp;
    ae_vector sortrbuf;
    ae_vector sortrbuf2;

    ae_frame_make(_state, &_frame_block);
    _barycentricinterpolant_clear(b);
    ae_vector_init(&perm, 0, DT_INT, _state);
    ae_vector_init(&wtemp, 0, DT_REAL, _state);
    ae_vector_init(&sortrbuf, 0, DT_REAL, _state);
    ae_vector_init(&sortrbuf2, 0, DT_REAL, _state);

    ae_assert(n>0, "BarycentricFloaterHormann: N<=0!", _state);
    ae_assert(d>=0, "BarycentricFloaterHormann: incorrect D!", _state);
    
    /*
     * Prepare
     */
    if( d>n-1 )
    {
        d = n-1;
    }
    b->n = n;
    
    /*
     * special case: N=1
     */
    if( n==1 )
    {
        ae_vector_set_length(&b->x, n, _state);
        ae_vector_set_length(&b->y, n, _state);
        ae_vector_set_length(&b->w, n, _state);
        b->x.ptr.p_double[0] = x->ptr.p_double[0];
        b->y.ptr.p_double[0] = y->ptr.p_double[0];
        b->w.ptr.p_double[0] = (double)(1);
        ratint_barycentricnormalize(b, _state);
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Fill X/Y
     */
    ae_vector_set_length(&b->x, n, _state);
    ae_vector_set_length(&b->y, n, _state);
    ae_v_move(&b->x.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_v_move(&b->y.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1));
    tagsortfastr(&b->x, &b->y, &sortrbuf, &sortrbuf2, n, _state);
    
    /*
     * Calculate Wk
     */
    ae_vector_set_length(&b->w, n, _state);
    s0 = (double)(1);
    for(k=1; k<=d; k++)
    {
        s0 = -s0;
    }
    for(k=0; k<=n-1; k++)
    {
        
        /*
         * Wk
         */
        s = (double)(0);
        for(i=ae_maxint(k-d, 0, _state); i<=ae_minint(k, n-1-d, _state); i++)
        {
            v = (double)(1);
            for(j=i; j<=i+d; j++)
            {
                if( j!=k )
                {
                    v = v/ae_fabs(b->x.ptr.p_double[k]-b->x.ptr.p_double[j], _state);
                }
            }
            s = s+v;
        }
        b->w.ptr.p_double[k] = s0*s;
        
        /*
         * Next S0
         */
        s0 = -s0;
    }
    
    /*
     * Normalize
     */
    ratint_barycentricnormalize(b, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Copying of the barycentric interpolant (for internal use only)

INPUT PARAMETERS:
    B   -   barycentric interpolant

OUTPUT PARAMETERS:
    B2  -   copy(B1)

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentriccopy(barycentricinterpolant* b,
     barycentricinterpolant* b2,
     ae_state *_state)
{

    _barycentricinterpolant_clear(b2);

    b2->n = b->n;
    b2->sy = b->sy;
    ae_vector_set_length(&b2->x, b2->n, _state);
    ae_vector_set_length(&b2->y, b2->n, _state);
    ae_vector_set_length(&b2->w, b2->n, _state);
    ae_v_move(&b2->x.ptr.p_double[0], 1, &b->x.ptr.p_double[0], 1, ae_v_len(0,b2->n-1));
    ae_v_move(&b2->y.ptr.p_double[0], 1, &b->y.ptr.p_double[0], 1, ae_v_len(0,b2->n-1));
    ae_v_move(&b2->w.ptr.p_double[0], 1, &b->w.ptr.p_double[0], 1, ae_v_len(0,b2->n-1));
}


/*************************************************************************
Normalization of barycentric interpolant:
* B.N, B.X, B.Y and B.W are initialized
* B.SY is NOT initialized
* Y[] is normalized, scaling coefficient is stored in B.SY
* W[] is normalized, no scaling coefficient is stored
* X[] is sorted

Internal subroutine.
*************************************************************************/
static void ratint_barycentricnormalize(barycentricinterpolant* b,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector p1;
    ae_vector p2;
    ae_int_t i;
    ae_int_t j;
    ae_int_t j2;
    double v;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&p1, 0, DT_INT, _state);
    ae_vector_init(&p2, 0, DT_INT, _state);

    
    /*
     * Normalize task: |Y|<=1, |W|<=1, sort X[]
     */
    b->sy = (double)(0);
    for(i=0; i<=b->n-1; i++)
    {
        b->sy = ae_maxreal(b->sy, ae_fabs(b->y.ptr.p_double[i], _state), _state);
    }
    if( ae_fp_greater(b->sy,(double)(0))&&ae_fp_greater(ae_fabs(b->sy-1, _state),10*ae_machineepsilon) )
    {
        v = 1/b->sy;
        ae_v_muld(&b->y.ptr.p_double[0], 1, ae_v_len(0,b->n-1), v);
    }
    v = (double)(0);
    for(i=0; i<=b->n-1; i++)
    {
        v = ae_maxreal(v, ae_fabs(b->w.ptr.p_double[i], _state), _state);
    }
    if( ae_fp_greater(v,(double)(0))&&ae_fp_greater(ae_fabs(v-1, _state),10*ae_machineepsilon) )
    {
        v = 1/v;
        ae_v_muld(&b->w.ptr.p_double[0], 1, ae_v_len(0,b->n-1), v);
    }
    for(i=0; i<=b->n-2; i++)
    {
        if( ae_fp_less(b->x.ptr.p_double[i+1],b->x.ptr.p_double[i]) )
        {
            tagsort(&b->x, b->n, &p1, &p2, _state);
            for(j=0; j<=b->n-1; j++)
            {
                j2 = p2.ptr.p_int[j];
                v = b->y.ptr.p_double[j];
                b->y.ptr.p_double[j] = b->y.ptr.p_double[j2];
                b->y.ptr.p_double[j2] = v;
                v = b->w.ptr.p_double[j];
                b->w.ptr.p_double[j] = b->w.ptr.p_double[j2];
                b->w.ptr.p_double[j2] = v;
            }
            break;
        }
    }
    ae_frame_leave(_state);
}


void _barycentricinterpolant_init(void* _p, ae_state *_state)
{
    barycentricinterpolant *p = (barycentricinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_init(&p->x, 0, DT_REAL, _state);
    ae_vector_init(&p->y, 0, DT_REAL, _state);
    ae_vector_init(&p->w, 0, DT_REAL, _state);
}


void _barycentricinterpolant_init_copy(void* _dst, void* _src, ae_state *_state)
{
    barycentricinterpolant *dst = (barycentricinterpolant*)_dst;
    barycentricinterpolant *src = (barycentricinterpolant*)_src;
    dst->n = src->n;
    dst->sy = src->sy;
    ae_vector_init_copy(&dst->x, &src->x, _state);
    ae_vector_init_copy(&dst->y, &src->y, _state);
    ae_vector_init_copy(&dst->w, &src->w, _state);
}


void _barycentricinterpolant_clear(void* _p)
{
    barycentricinterpolant *p = (barycentricinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_clear(&p->x);
    ae_vector_clear(&p->y);
    ae_vector_clear(&p->w);
}


void _barycentricinterpolant_destroy(void* _p)
{
    barycentricinterpolant *p = (barycentricinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_destroy(&p->x);
    ae_vector_destroy(&p->y);
    ae_vector_destroy(&p->w);
}




/*************************************************************************
Conversion from barycentric representation to Chebyshev basis.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    P   -   polynomial in barycentric form
    A,B -   base interval for Chebyshev polynomials (see below)
            A<>B

OUTPUT PARAMETERS
    T   -   coefficients of Chebyshev representation;
            P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N-1 },
            where Ti - I-th Chebyshev polynomial.

NOTES:
    barycentric interpolant passed as P may be either polynomial  obtained
    from  polynomial  interpolation/ fitting or rational function which is
    NOT polynomial. We can't distinguish between these two cases, and this
    algorithm just tries to work assuming that P IS a polynomial.  If not,
    algorithm will return results, but they won't have any meaning.

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialbar2cheb(barycentricinterpolant* p,
     double a,
     double b,
     /* Real    */ ae_vector* t,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t k;
    ae_vector vp;
    ae_vector vx;
    ae_vector tk;
    ae_vector tk1;
    double v;

    ae_frame_make(_state, &_frame_block);
    ae_vector_clear(t);
    ae_vector_init(&vp, 0, DT_REAL, _state);
    ae_vector_init(&vx, 0, DT_REAL, _state);
    ae_vector_init(&tk, 0, DT_REAL, _state);
    ae_vector_init(&tk1, 0, DT_REAL, _state);

    ae_assert(ae_isfinite(a, _state), "PolynomialBar2Cheb: A is not finite!", _state);
    ae_assert(ae_isfinite(b, _state), "PolynomialBar2Cheb: B is not finite!", _state);
    ae_assert(ae_fp_neq(a,b), "PolynomialBar2Cheb: A=B!", _state);
    ae_assert(p->n>0, "PolynomialBar2Cheb: P is not correctly initialized barycentric interpolant!", _state);
    
    /*
     * Calculate function values on a Chebyshev grid
     */
    ae_vector_set_length(&vp, p->n, _state);
    ae_vector_set_length(&vx, p->n, _state);
    for(i=0; i<=p->n-1; i++)
    {
        vx.ptr.p_double[i] = ae_cos(ae_pi*(i+0.5)/p->n, _state);
        vp.ptr.p_double[i] = barycentriccalc(p, 0.5*(vx.ptr.p_double[i]+1)*(b-a)+a, _state);
    }
    
    /*
     * T[0]
     */
    ae_vector_set_length(t, p->n, _state);
    v = (double)(0);
    for(i=0; i<=p->n-1; i++)
    {
        v = v+vp.ptr.p_double[i];
    }
    t->ptr.p_double[0] = v/p->n;
    
    /*
     * other T's.
     *
     * NOTES:
     * 1. TK stores T{k} on VX, TK1 stores T{k-1} on VX
     * 2. we can do same calculations with fast DCT, but it
     *    * adds dependencies
     *    * still leaves us with O(N^2) algorithm because
     *      preparation of function values is O(N^2) process
     */
    if( p->n>1 )
    {
        ae_vector_set_length(&tk, p->n, _state);
        ae_vector_set_length(&tk1, p->n, _state);
        for(i=0; i<=p->n-1; i++)
        {
            tk.ptr.p_double[i] = vx.ptr.p_double[i];
            tk1.ptr.p_double[i] = (double)(1);
        }
        for(k=1; k<=p->n-1; k++)
        {
            
            /*
             * calculate discrete product of function vector and TK
             */
            v = ae_v_dotproduct(&tk.ptr.p_double[0], 1, &vp.ptr.p_double[0], 1, ae_v_len(0,p->n-1));
            t->ptr.p_double[k] = v/(0.5*p->n);
            
            /*
             * Update TK and TK1
             */
            for(i=0; i<=p->n-1; i++)
            {
                v = 2*vx.ptr.p_double[i]*tk.ptr.p_double[i]-tk1.ptr.p_double[i];
                tk1.ptr.p_double[i] = tk.ptr.p_double[i];
                tk.ptr.p_double[i] = v;
            }
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Conversion from Chebyshev basis to barycentric representation.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    T   -   coefficients of Chebyshev representation;
            P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N },
            where Ti - I-th Chebyshev polynomial.
    N   -   number of coefficients:
            * if given, only leading N elements of T are used
            * if not given, automatically determined from size of T
    A,B -   base interval for Chebyshev polynomials (see above)
            A<B

OUTPUT PARAMETERS
    P   -   polynomial in barycentric form

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialcheb2bar(/* Real    */ ae_vector* t,
     ae_int_t n,
     double a,
     double b,
     barycentricinterpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t k;
    ae_vector y;
    double tk;
    double tk1;
    double vx;
    double vy;
    double v;

    ae_frame_make(_state, &_frame_block);
    _barycentricinterpolant_clear(p);
    ae_vector_init(&y, 0, DT_REAL, _state);

    ae_assert(ae_isfinite(a, _state), "PolynomialBar2Cheb: A is not finite!", _state);
    ae_assert(ae_isfinite(b, _state), "PolynomialBar2Cheb: B is not finite!", _state);
    ae_assert(ae_fp_neq(a,b), "PolynomialBar2Cheb: A=B!", _state);
    ae_assert(n>=1, "PolynomialBar2Cheb: N<1", _state);
    ae_assert(t->cnt>=n, "PolynomialBar2Cheb: Length(T)<N", _state);
    ae_assert(isfinitevector(t, n, _state), "PolynomialBar2Cheb: T[] contains INF or NAN", _state);
    
    /*
     * Calculate function values on a Chebyshev grid spanning [-1,+1]
     */
    ae_vector_set_length(&y, n, _state);
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * Calculate value on a grid spanning [-1,+1]
         */
        vx = ae_cos(ae_pi*(i+0.5)/n, _state);
        vy = t->ptr.p_double[0];
        tk1 = (double)(1);
        tk = vx;
        for(k=1; k<=n-1; k++)
        {
            vy = vy+t->ptr.p_double[k]*tk;
            v = 2*vx*tk-tk1;
            tk1 = tk;
            tk = v;
        }
        y.ptr.p_double[i] = vy;
    }
    
    /*
     * Build barycentric interpolant, map grid from [-1,+1] to [A,B]
     */
    polynomialbuildcheb1(a, b, &y, n, p, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Conversion from barycentric representation to power basis.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    P   -   polynomial in barycentric form
    C   -   offset (see below); 0.0 is used as default value.
    S   -   scale (see below);  1.0 is used as default value. S<>0.

OUTPUT PARAMETERS
    A   -   coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 }
    N   -   number of coefficients (polynomial degree plus 1)

NOTES:
1.  this function accepts offset and scale, which can be  set  to  improve
    numerical properties of polynomial. For example, if P was obtained  as
    result of interpolation on [-1,+1],  you  can  set  C=0  and  S=1  and
    represent  P  as sum of 1, x, x^2, x^3 and so on. In most cases you it
    is exactly what you need.

    However, if your interpolation model was built on [999,1001], you will
    see significant growth of numerical errors when using {1, x, x^2, x^3}
    as basis. Representing P as sum of 1, (x-1000), (x-1000)^2, (x-1000)^3
    will be better option. Such representation can be  obtained  by  using
    1000.0 as offset C and 1.0 as scale S.

2.  power basis is ill-conditioned and tricks described above can't  solve
    this problem completely. This function  will  return  coefficients  in
    any  case,  but  for  N>8  they  will  become unreliable. However, N's
    less than 5 are pretty safe.
    
3.  barycentric interpolant passed as P may be either polynomial  obtained
    from  polynomial  interpolation/ fitting or rational function which is
    NOT polynomial. We can't distinguish between these two cases, and this
    algorithm just tries to work assuming that P IS a polynomial.  If not,
    algorithm will return results, but they won't have any meaning.

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialbar2pow(barycentricinterpolant* p,
     double c,
     double s,
     /* Real    */ ae_vector* a,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t k;
    double e;
    double d;
    ae_vector vp;
    ae_vector vx;
    ae_vector tk;
    ae_vector tk1;
    ae_vector t;
    double v;
    double c0;
    double s0;
    double va;
    double vb;
    ae_vector vai;
    ae_vector vbi;
    double minx;
    double maxx;

    ae_frame_make(_state, &_frame_block);
    ae_vector_clear(a);
    ae_vector_init(&vp, 0, DT_REAL, _state);
    ae_vector_init(&vx, 0, DT_REAL, _state);
    ae_vector_init(&tk, 0, DT_REAL, _state);
    ae_vector_init(&tk1, 0, DT_REAL, _state);
    ae_vector_init(&t, 0, DT_REAL, _state);
    ae_vector_init(&vai, 0, DT_REAL, _state);
    ae_vector_init(&vbi, 0, DT_REAL, _state);

    
    /*
     * We have barycentric model built using set of points X[], and we
     * want to convert it to power basis centered about point  C  with
     * scale S: I-th basis function is ((X-C)/S)^i.
     *
     * We use following three-stage algorithm:
     *
     * 1. we build Chebyshev representation of polynomial using
     *    intermediate center C0 and scale S0, which are derived from X[]:
     *    C0 = 0.5*(min(X)+max(X)), S0 = 0.5*(max(X)-min(X)). Chebyshev
     *    representation is built by sampling points around center C0,
     *    with typical distance between them proportional to S0.
     * 2. then we transform form Chebyshev basis to intermediate power
     *    basis, using same center/scale C0/S0.
     * 3. after that, we apply linear transformation to intermediate
     *    power basis which moves it to final center/scale C/S.
     *
     * The idea of such multi-stage algorithm is that it is much easier to
     * transform barycentric model to Chebyshev basis, and only later to
     * power basis, than transforming it directly to power basis. It is
     * also more numerically stable to sample points using intermediate C0/S0,
     * which are derived from user-supplied model, than using "final" C/S,
     * which may be unsuitable for sampling (say, if S=1, we may have stability
     * problems when working with models built from dataset with non-unit
     * scale of abscissas).
     */
    ae_assert(ae_isfinite(c, _state), "PolynomialBar2Pow: C is not finite!", _state);
    ae_assert(ae_isfinite(s, _state), "PolynomialBar2Pow: S is not finite!", _state);
    ae_assert(ae_fp_neq(s,(double)(0)), "PolynomialBar2Pow: S=0!", _state);
    ae_assert(p->n>0, "PolynomialBar2Pow: P is not correctly initialized barycentric interpolant!", _state);
    
    /*
     * Select intermediate center/scale
     */
    minx = p->x.ptr.p_double[0];
    maxx = p->x.ptr.p_double[0];
    for(i=1; i<=p->n-1; i++)
    {
        minx = ae_minreal(minx, p->x.ptr.p_double[i], _state);
        maxx = ae_maxreal(maxx, p->x.ptr.p_double[i], _state);
    }
    if( ae_fp_eq(minx,maxx) )
    {
        c0 = minx;
        s0 = 1.0;
    }
    else
    {
        c0 = 0.5*(maxx+minx);
        s0 = 0.5*(maxx-minx);
    }
    
    /*
     * Calculate function values on a Chebyshev grid using intermediate C0/S0
     */
    ae_vector_set_length(&vp, p->n+1, _state);
    ae_vector_set_length(&vx, p->n, _state);
    for(i=0; i<=p->n-1; i++)
    {
        vx.ptr.p_double[i] = ae_cos(ae_pi*(i+0.5)/p->n, _state);
        vp.ptr.p_double[i] = barycentriccalc(p, s0*vx.ptr.p_double[i]+c0, _state);
    }
    
    /*
     * T[0]
     */
    ae_vector_set_length(&t, p->n, _state);
    v = (double)(0);
    for(i=0; i<=p->n-1; i++)
    {
        v = v+vp.ptr.p_double[i];
    }
    t.ptr.p_double[0] = v/p->n;
    
    /*
     * other T's.
     *
     * NOTES:
     * 1. TK stores T{k} on VX, TK1 stores T{k-1} on VX
     * 2. we can do same calculations with fast DCT, but it
     *    * adds dependencies
     *    * still leaves us with O(N^2) algorithm because
     *      preparation of function values is O(N^2) process
     */
    if( p->n>1 )
    {
        ae_vector_set_length(&tk, p->n, _state);
        ae_vector_set_length(&tk1, p->n, _state);
        for(i=0; i<=p->n-1; i++)
        {
            tk.ptr.p_double[i] = vx.ptr.p_double[i];
            tk1.ptr.p_double[i] = (double)(1);
        }
        for(k=1; k<=p->n-1; k++)
        {
            
            /*
             * calculate discrete product of function vector and TK
             */
            v = ae_v_dotproduct(&tk.ptr.p_double[0], 1, &vp.ptr.p_double[0], 1, ae_v_len(0,p->n-1));
            t.ptr.p_double[k] = v/(0.5*p->n);
            
            /*
             * Update TK and TK1
             */
            for(i=0; i<=p->n-1; i++)
            {
                v = 2*vx.ptr.p_double[i]*tk.ptr.p_double[i]-tk1.ptr.p_double[i];
                tk1.ptr.p_double[i] = tk.ptr.p_double[i];
                tk.ptr.p_double[i] = v;
            }
        }
    }
    
    /*
     * Convert from Chebyshev basis to power basis
     */
    ae_vector_set_length(a, p->n, _state);
    for(i=0; i<=p->n-1; i++)
    {
        a->ptr.p_double[i] = (double)(0);
    }
    d = (double)(0);
    for(i=0; i<=p->n-1; i++)
    {
        for(k=i; k<=p->n-1; k++)
        {
            e = a->ptr.p_double[k];
            a->ptr.p_double[k] = (double)(0);
            if( i<=1&&k==i )
            {
                a->ptr.p_double[k] = (double)(1);
            }
            else
            {
                if( i!=0 )
                {
                    a->ptr.p_double[k] = 2*d;
                }
                if( k>i+1 )
                {
                    a->ptr.p_double[k] = a->ptr.p_double[k]-a->ptr.p_double[k-2];
                }
            }
            d = e;
        }
        d = a->ptr.p_double[i];
        e = (double)(0);
        k = i;
        while(k<=p->n-1)
        {
            e = e+a->ptr.p_double[k]*t.ptr.p_double[k];
            k = k+2;
        }
        a->ptr.p_double[i] = e;
    }
    
    /*
     * Apply linear transformation which converts basis from intermediate
     * one Fi=((x-C0)/S0)^i to final one Fi=((x-C)/S)^i.
     *
     * We have y=(x-C0)/S0, z=(x-C)/S, and coefficients A[] for basis Fi(y).
     * Because we have y=A*z+B, for A=s/s0 and B=c/s0-c0/s0, we can perform
     * substitution and get coefficients A_new[] in basis Fi(z).
     */
    ae_assert(vp.cnt>=p->n+1, "PolynomialBar2Pow: internal error", _state);
    ae_assert(t.cnt>=p->n, "PolynomialBar2Pow: internal error", _state);
    for(i=0; i<=p->n-1; i++)
    {
        t.ptr.p_double[i] = 0.0;
    }
    va = s/s0;
    vb = c/s0-c0/s0;
    ae_vector_set_length(&vai, p->n, _state);
    ae_vector_set_length(&vbi, p->n, _state);
    vai.ptr.p_double[0] = (double)(1);
    vbi.ptr.p_double[0] = (double)(1);
    for(k=1; k<=p->n-1; k++)
    {
        vai.ptr.p_double[k] = vai.ptr.p_double[k-1]*va;
        vbi.ptr.p_double[k] = vbi.ptr.p_double[k-1]*vb;
    }
    for(k=0; k<=p->n-1; k++)
    {
        
        /*
         * Generate set of binomial coefficients in VP[]
         */
        if( k>0 )
        {
            vp.ptr.p_double[k] = (double)(1);
            for(i=k-1; i>=1; i--)
            {
                vp.ptr.p_double[i] = vp.ptr.p_double[i]+vp.ptr.p_double[i-1];
            }
            vp.ptr.p_double[0] = (double)(1);
        }
        else
        {
            vp.ptr.p_double[0] = (double)(1);
        }
        
        /*
         * Update T[] with expansion of K-th basis function
         */
        for(i=0; i<=k; i++)
        {
            t.ptr.p_double[i] = t.ptr.p_double[i]+a->ptr.p_double[k]*vai.ptr.p_double[i]*vbi.ptr.p_double[k-i]*vp.ptr.p_double[i];
        }
    }
    for(k=0; k<=p->n-1; k++)
    {
        a->ptr.p_double[k] = t.ptr.p_double[k];
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Conversion from power basis to barycentric representation.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    A   -   coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 }
    N   -   number of coefficients (polynomial degree plus 1)
            * if given, only leading N elements of A are used
            * if not given, automatically determined from size of A
    C   -   offset (see below); 0.0 is used as default value.
    S   -   scale (see below);  1.0 is used as default value. S<>0.

OUTPUT PARAMETERS
    P   -   polynomial in barycentric form


NOTES:
1.  this function accepts offset and scale, which can be  set  to  improve
    numerical properties of polynomial. For example, if you interpolate on
    [-1,+1],  you  can  set C=0 and S=1 and convert from sum of 1, x, x^2,
    x^3 and so on. In most cases you it is exactly what you need.

    However, if your interpolation model was built on [999,1001], you will
    see significant growth of numerical errors when using {1, x, x^2, x^3}
    as  input  basis.  Converting  from  sum  of  1, (x-1000), (x-1000)^2,
    (x-1000)^3 will be better option (you have to specify 1000.0 as offset
    C and 1.0 as scale S).

2.  power basis is ill-conditioned and tricks described above can't  solve
    this problem completely. This function  will  return barycentric model
    in any case, but for N>8 accuracy well degrade. However, N's less than
    5 are pretty safe.

  -- ALGLIB --
     Copyright 30.09.2010 by Bochkanov Sergey
*************************************************************************/
void polynomialpow2bar(/* Real    */ ae_vector* a,
     ae_int_t n,
     double c,
     double s,
     barycentricinterpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t k;
    ae_vector y;
    double vx;
    double vy;
    double px;

    ae_frame_make(_state, &_frame_block);
    _barycentricinterpolant_clear(p);
    ae_vector_init(&y, 0, DT_REAL, _state);

    ae_assert(ae_isfinite(c, _state), "PolynomialPow2Bar: C is not finite!", _state);
    ae_assert(ae_isfinite(s, _state), "PolynomialPow2Bar: S is not finite!", _state);
    ae_assert(ae_fp_neq(s,(double)(0)), "PolynomialPow2Bar: S is zero!", _state);
    ae_assert(n>=1, "PolynomialPow2Bar: N<1", _state);
    ae_assert(a->cnt>=n, "PolynomialPow2Bar: Length(A)<N", _state);
    ae_assert(isfinitevector(a, n, _state), "PolynomialPow2Bar: A[] contains INF or NAN", _state);
    
    /*
     * Calculate function values on a Chebyshev grid spanning [-1,+1]
     */
    ae_vector_set_length(&y, n, _state);
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * Calculate value on a grid spanning [-1,+1]
         */
        vx = ae_cos(ae_pi*(i+0.5)/n, _state);
        vy = a->ptr.p_double[0];
        px = vx;
        for(k=1; k<=n-1; k++)
        {
            vy = vy+px*a->ptr.p_double[k];
            px = px*vx;
        }
        y.ptr.p_double[i] = vy;
    }
    
    /*
     * Build barycentric interpolant, map grid from [-1,+1] to [A,B]
     */
    polynomialbuildcheb1(c-s, c+s, &y, n, p, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Lagrange intepolant: generation of the model on the general grid.
This function has O(N^2) complexity.

INPUT PARAMETERS:
    X   -   abscissas, array[0..N-1]
    Y   -   function values, array[0..N-1]
    N   -   number of points, N>=1

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuild(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     barycentricinterpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_int_t j;
    ae_int_t k;
    ae_vector w;
    double b;
    double a;
    double v;
    double mx;
    ae_vector sortrbuf;
    ae_vector sortrbuf2;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    _barycentricinterpolant_clear(p);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&sortrbuf, 0, DT_REAL, _state);
    ae_vector_init(&sortrbuf2, 0, DT_REAL, _state);

    ae_assert(n>0, "PolynomialBuild: N<=0!", _state);
    ae_assert(x->cnt>=n, "PolynomialBuild: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "PolynomialBuild: Length(Y)<N!", _state);
    ae_assert(isfinitevector(x, n, _state), "PolynomialBuild: X contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "PolynomialBuild: Y contains infinite or NaN values!", _state);
    tagsortfastr(x, y, &sortrbuf, &sortrbuf2, n, _state);
    ae_assert(aredistinct(x, n, _state), "PolynomialBuild: at least two consequent points are too close!", _state);
    
    /*
     * calculate W[j]
     * multi-pass algorithm is used to avoid overflow
     */
    ae_vector_set_length(&w, n, _state);
    a = x->ptr.p_double[0];
    b = x->ptr.p_double[0];
    for(j=0; j<=n-1; j++)
    {
        w.ptr.p_double[j] = (double)(1);
        a = ae_minreal(a, x->ptr.p_double[j], _state);
        b = ae_maxreal(b, x->ptr.p_double[j], _state);
    }
    for(k=0; k<=n-1; k++)
    {
        
        /*
         * W[K] is used instead of 0.0 because
         * cycle on J does not touch K-th element
         * and we MUST get maximum from ALL elements
         */
        mx = ae_fabs(w.ptr.p_double[k], _state);
        for(j=0; j<=n-1; j++)
        {
            if( j!=k )
            {
                v = (b-a)/(x->ptr.p_double[j]-x->ptr.p_double[k]);
                w.ptr.p_double[j] = w.ptr.p_double[j]*v;
                mx = ae_maxreal(mx, ae_fabs(w.ptr.p_double[j], _state), _state);
            }
        }
        if( k%5==0 )
        {
            
            /*
             * every 5-th run we renormalize W[]
             */
            v = 1/mx;
            ae_v_muld(&w.ptr.p_double[0], 1, ae_v_len(0,n-1), v);
        }
    }
    barycentricbuildxyw(x, y, &w, n, p, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Lagrange intepolant: generation of the model on equidistant grid.
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1]
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildeqdist(double a,
     double b,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     barycentricinterpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_vector w;
    ae_vector x;
    double v;

    ae_frame_make(_state, &_frame_block);
    _barycentricinterpolant_clear(p);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&x, 0, DT_REAL, _state);

    ae_assert(n>0, "PolynomialBuildEqDist: N<=0!", _state);
    ae_assert(y->cnt>=n, "PolynomialBuildEqDist: Length(Y)<N!", _state);
    ae_assert(ae_isfinite(a, _state), "PolynomialBuildEqDist: A is infinite or NaN!", _state);
    ae_assert(ae_isfinite(b, _state), "PolynomialBuildEqDist: B is infinite or NaN!", _state);
    ae_assert(isfinitevector(y, n, _state), "PolynomialBuildEqDist: Y contains infinite or NaN values!", _state);
    ae_assert(ae_fp_neq(b,a), "PolynomialBuildEqDist: B=A!", _state);
    ae_assert(ae_fp_neq(a+(b-a)/n,a), "PolynomialBuildEqDist: B is too close to A!", _state);
    
    /*
     * Special case: N=1
     */
    if( n==1 )
    {
        ae_vector_set_length(&x, 1, _state);
        ae_vector_set_length(&w, 1, _state);
        x.ptr.p_double[0] = 0.5*(b+a);
        w.ptr.p_double[0] = (double)(1);
        barycentricbuildxyw(&x, y, &w, 1, p, _state);
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * general case
     */
    ae_vector_set_length(&x, n, _state);
    ae_vector_set_length(&w, n, _state);
    v = (double)(1);
    for(i=0; i<=n-1; i++)
    {
        w.ptr.p_double[i] = v;
        x.ptr.p_double[i] = a+(b-a)*i/(n-1);
        v = -v*(n-1-i);
        v = v/(i+1);
    }
    barycentricbuildxyw(&x, y, &w, n, p, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Lagrange intepolant on Chebyshev grid (first kind).
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1],
            Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n)))
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildcheb1(double a,
     double b,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     barycentricinterpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_vector w;
    ae_vector x;
    double v;
    double t;

    ae_frame_make(_state, &_frame_block);
    _barycentricinterpolant_clear(p);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&x, 0, DT_REAL, _state);

    ae_assert(n>0, "PolynomialBuildCheb1: N<=0!", _state);
    ae_assert(y->cnt>=n, "PolynomialBuildCheb1: Length(Y)<N!", _state);
    ae_assert(ae_isfinite(a, _state), "PolynomialBuildCheb1: A is infinite or NaN!", _state);
    ae_assert(ae_isfinite(b, _state), "PolynomialBuildCheb1: B is infinite or NaN!", _state);
    ae_assert(isfinitevector(y, n, _state), "PolynomialBuildCheb1: Y contains infinite or NaN values!", _state);
    ae_assert(ae_fp_neq(b,a), "PolynomialBuildCheb1: B=A!", _state);
    
    /*
     * Special case: N=1
     */
    if( n==1 )
    {
        ae_vector_set_length(&x, 1, _state);
        ae_vector_set_length(&w, 1, _state);
        x.ptr.p_double[0] = 0.5*(b+a);
        w.ptr.p_double[0] = (double)(1);
        barycentricbuildxyw(&x, y, &w, 1, p, _state);
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * general case
     */
    ae_vector_set_length(&x, n, _state);
    ae_vector_set_length(&w, n, _state);
    v = (double)(1);
    for(i=0; i<=n-1; i++)
    {
        t = ae_tan(0.5*ae_pi*(2*i+1)/(2*n), _state);
        w.ptr.p_double[i] = 2*v*t/(1+ae_sqr(t, _state));
        x.ptr.p_double[i] = 0.5*(b+a)+0.5*(b-a)*(1-ae_sqr(t, _state))/(1+ae_sqr(t, _state));
        v = -v;
    }
    barycentricbuildxyw(&x, y, &w, n, p, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Lagrange intepolant on Chebyshev grid (second kind).
This function has O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    Y   -   function values at the nodes, array[0..N-1],
            Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1)))
    N   -   number of points, N>=1
            for N=1 a constant model is constructed.

OUTPUT PARAMETERS
    P   -   barycentric model which represents Lagrange interpolant
            (see ratint unit info and BarycentricCalc() description for
            more information).

  -- ALGLIB --
     Copyright 03.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialbuildcheb2(double a,
     double b,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     barycentricinterpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_vector w;
    ae_vector x;
    double v;

    ae_frame_make(_state, &_frame_block);
    _barycentricinterpolant_clear(p);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&x, 0, DT_REAL, _state);

    ae_assert(n>0, "PolynomialBuildCheb2: N<=0!", _state);
    ae_assert(y->cnt>=n, "PolynomialBuildCheb2: Length(Y)<N!", _state);
    ae_assert(ae_isfinite(a, _state), "PolynomialBuildCheb2: A is infinite or NaN!", _state);
    ae_assert(ae_isfinite(b, _state), "PolynomialBuildCheb2: B is infinite or NaN!", _state);
    ae_assert(ae_fp_neq(b,a), "PolynomialBuildCheb2: B=A!", _state);
    ae_assert(isfinitevector(y, n, _state), "PolynomialBuildCheb2: Y contains infinite or NaN values!", _state);
    
    /*
     * Special case: N=1
     */
    if( n==1 )
    {
        ae_vector_set_length(&x, 1, _state);
        ae_vector_set_length(&w, 1, _state);
        x.ptr.p_double[0] = 0.5*(b+a);
        w.ptr.p_double[0] = (double)(1);
        barycentricbuildxyw(&x, y, &w, 1, p, _state);
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * general case
     */
    ae_vector_set_length(&x, n, _state);
    ae_vector_set_length(&w, n, _state);
    v = (double)(1);
    for(i=0; i<=n-1; i++)
    {
        if( i==0||i==n-1 )
        {
            w.ptr.p_double[i] = v*0.5;
        }
        else
        {
            w.ptr.p_double[i] = v;
        }
        x.ptr.p_double[i] = 0.5*(b+a)+0.5*(b-a)*ae_cos(ae_pi*i/(n-1), _state);
        v = -v;
    }
    barycentricbuildxyw(&x, y, &w, n, p, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Fast equidistant polynomial interpolation function with O(N) complexity

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on equidistant grid, N>=1
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T
    
IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use  PolynomialBuildEqDist()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalceqdist(double a,
     double b,
     /* Real    */ ae_vector* f,
     ae_int_t n,
     double t,
     ae_state *_state)
{
    double s1;
    double s2;
    double v;
    double threshold;
    double s;
    double h;
    ae_int_t i;
    ae_int_t j;
    double w;
    double x;
    double result;


    ae_assert(n>0, "PolynomialCalcEqDist: N<=0!", _state);
    ae_assert(f->cnt>=n, "PolynomialCalcEqDist: Length(F)<N!", _state);
    ae_assert(ae_isfinite(a, _state), "PolynomialCalcEqDist: A is infinite or NaN!", _state);
    ae_assert(ae_isfinite(b, _state), "PolynomialCalcEqDist: B is infinite or NaN!", _state);
    ae_assert(isfinitevector(f, n, _state), "PolynomialCalcEqDist: F contains infinite or NaN values!", _state);
    ae_assert(ae_fp_neq(b,a), "PolynomialCalcEqDist: B=A!", _state);
    ae_assert(!ae_isinf(t, _state), "PolynomialCalcEqDist: T is infinite!", _state);
    
    /*
     * Special case: T is NAN
     */
    if( ae_isnan(t, _state) )
    {
        result = _state->v_nan;
        return result;
    }
    
    /*
     * Special case: N=1
     */
    if( n==1 )
    {
        result = f->ptr.p_double[0];
        return result;
    }
    
    /*
     * First, decide: should we use "safe" formula (guarded
     * against overflow) or fast one?
     */
    threshold = ae_sqrt(ae_minrealnumber, _state);
    j = 0;
    s = t-a;
    for(i=1; i<=n-1; i++)
    {
        x = a+(double)i/(double)(n-1)*(b-a);
        if( ae_fp_less(ae_fabs(t-x, _state),ae_fabs(s, _state)) )
        {
            s = t-x;
            j = i;
        }
    }
    if( ae_fp_eq(s,(double)(0)) )
    {
        result = f->ptr.p_double[j];
        return result;
    }
    if( ae_fp_greater(ae_fabs(s, _state),threshold) )
    {
        
        /*
         * use fast formula
         */
        j = -1;
        s = 1.0;
    }
    
    /*
     * Calculate using safe or fast barycentric formula
     */
    s1 = (double)(0);
    s2 = (double)(0);
    w = 1.0;
    h = (b-a)/(n-1);
    for(i=0; i<=n-1; i++)
    {
        if( i!=j )
        {
            v = s*w/(t-(a+i*h));
            s1 = s1+v*f->ptr.p_double[i];
            s2 = s2+v;
        }
        else
        {
            v = w;
            s1 = s1+v*f->ptr.p_double[i];
            s2 = s2+v;
        }
        w = -w*(n-1-i);
        w = w/(i+1);
    }
    result = s1/s2;
    return result;
}


/*************************************************************************
Fast polynomial interpolation function on Chebyshev points (first kind)
with O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on Chebyshev grid (first kind),
            X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n))
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T

IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use  PolIntBuildCheb1()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalccheb1(double a,
     double b,
     /* Real    */ ae_vector* f,
     ae_int_t n,
     double t,
     ae_state *_state)
{
    double s1;
    double s2;
    double v;
    double threshold;
    double s;
    ae_int_t i;
    ae_int_t j;
    double a0;
    double delta;
    double alpha;
    double beta;
    double ca;
    double sa;
    double tempc;
    double temps;
    double x;
    double w;
    double p1;
    double result;


    ae_assert(n>0, "PolynomialCalcCheb1: N<=0!", _state);
    ae_assert(f->cnt>=n, "PolynomialCalcCheb1: Length(F)<N!", _state);
    ae_assert(ae_isfinite(a, _state), "PolynomialCalcCheb1: A is infinite or NaN!", _state);
    ae_assert(ae_isfinite(b, _state), "PolynomialCalcCheb1: B is infinite or NaN!", _state);
    ae_assert(isfinitevector(f, n, _state), "PolynomialCalcCheb1: F contains infinite or NaN values!", _state);
    ae_assert(ae_fp_neq(b,a), "PolynomialCalcCheb1: B=A!", _state);
    ae_assert(!ae_isinf(t, _state), "PolynomialCalcCheb1: T is infinite!", _state);
    
    /*
     * Special case: T is NAN
     */
    if( ae_isnan(t, _state) )
    {
        result = _state->v_nan;
        return result;
    }
    
    /*
     * Special case: N=1
     */
    if( n==1 )
    {
        result = f->ptr.p_double[0];
        return result;
    }
    
    /*
     * Prepare information for the recurrence formula
     * used to calculate sin(pi*(2j+1)/(2n+2)) and
     * cos(pi*(2j+1)/(2n+2)):
     *
     * A0    = pi/(2n+2)
     * Delta = pi/(n+1)
     * Alpha = 2 sin^2 (Delta/2)
     * Beta  = sin(Delta)
     *
     * so that sin(..) = sin(A0+j*delta) and cos(..) = cos(A0+j*delta).
     * Then we use
     *
     * sin(x+delta) = sin(x) - (alpha*sin(x) - beta*cos(x))
     * cos(x+delta) = cos(x) - (alpha*cos(x) - beta*sin(x))
     *
     * to repeatedly calculate sin(..) and cos(..).
     */
    threshold = ae_sqrt(ae_minrealnumber, _state);
    t = (t-0.5*(a+b))/(0.5*(b-a));
    a0 = ae_pi/(2*(n-1)+2);
    delta = 2*ae_pi/(2*(n-1)+2);
    alpha = 2*ae_sqr(ae_sin(delta/2, _state), _state);
    beta = ae_sin(delta, _state);
    
    /*
     * First, decide: should we use "safe" formula (guarded
     * against overflow) or fast one?
     */
    ca = ae_cos(a0, _state);
    sa = ae_sin(a0, _state);
    j = 0;
    x = ca;
    s = t-x;
    for(i=1; i<=n-1; i++)
    {
        
        /*
         * Next X[i]
         */
        temps = sa-(alpha*sa-beta*ca);
        tempc = ca-(alpha*ca+beta*sa);
        sa = temps;
        ca = tempc;
        x = ca;
        
        /*
         * Use X[i]
         */
        if( ae_fp_less(ae_fabs(t-x, _state),ae_fabs(s, _state)) )
        {
            s = t-x;
            j = i;
        }
    }
    if( ae_fp_eq(s,(double)(0)) )
    {
        result = f->ptr.p_double[j];
        return result;
    }
    if( ae_fp_greater(ae_fabs(s, _state),threshold) )
    {
        
        /*
         * use fast formula
         */
        j = -1;
        s = 1.0;
    }
    
    /*
     * Calculate using safe or fast barycentric formula
     */
    s1 = (double)(0);
    s2 = (double)(0);
    ca = ae_cos(a0, _state);
    sa = ae_sin(a0, _state);
    p1 = 1.0;
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * Calculate X[i], W[i]
         */
        x = ca;
        w = p1*sa;
        
        /*
         * Proceed
         */
        if( i!=j )
        {
            v = s*w/(t-x);
            s1 = s1+v*f->ptr.p_double[i];
            s2 = s2+v;
        }
        else
        {
            v = w;
            s1 = s1+v*f->ptr.p_double[i];
            s2 = s2+v;
        }
        
        /*
         * Next CA, SA, P1
         */
        temps = sa-(alpha*sa-beta*ca);
        tempc = ca-(alpha*ca+beta*sa);
        sa = temps;
        ca = tempc;
        p1 = -p1;
    }
    result = s1/s2;
    return result;
}


/*************************************************************************
Fast polynomial interpolation function on Chebyshev points (second kind)
with O(N) complexity.

INPUT PARAMETERS:
    A   -   left boundary of [A,B]
    B   -   right boundary of [A,B]
    F   -   function values, array[0..N-1]
    N   -   number of points on Chebyshev grid (second kind),
            X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1))
            for N=1 a constant model is constructed.
    T   -   position where P(x) is calculated

RESULT
    value of the Lagrange interpolant at T

IMPORTANT
    this function provides fast interface which is not overflow-safe
    nor it is very precise.
    the best option is to use PolIntBuildCheb2()/BarycentricCalc()
    subroutines unless you are pretty sure that your data will not result
    in overflow.

  -- ALGLIB --
     Copyright 02.12.2009 by Bochkanov Sergey
*************************************************************************/
double polynomialcalccheb2(double a,
     double b,
     /* Real    */ ae_vector* f,
     ae_int_t n,
     double t,
     ae_state *_state)
{
    double s1;
    double s2;
    double v;
    double threshold;
    double s;
    ae_int_t i;
    ae_int_t j;
    double a0;
    double delta;
    double alpha;
    double beta;
    double ca;
    double sa;
    double tempc;
    double temps;
    double x;
    double w;
    double p1;
    double result;


    ae_assert(n>0, "PolynomialCalcCheb2: N<=0!", _state);
    ae_assert(f->cnt>=n, "PolynomialCalcCheb2: Length(F)<N!", _state);
    ae_assert(ae_isfinite(a, _state), "PolynomialCalcCheb2: A is infinite or NaN!", _state);
    ae_assert(ae_isfinite(b, _state), "PolynomialCalcCheb2: B is infinite or NaN!", _state);
    ae_assert(ae_fp_neq(b,a), "PolynomialCalcCheb2: B=A!", _state);
    ae_assert(isfinitevector(f, n, _state), "PolynomialCalcCheb2: F contains infinite or NaN values!", _state);
    ae_assert(!ae_isinf(t, _state), "PolynomialCalcEqDist: T is infinite!", _state);
    
    /*
     * Special case: T is NAN
     */
    if( ae_isnan(t, _state) )
    {
        result = _state->v_nan;
        return result;
    }
    
    /*
     * Special case: N=1
     */
    if( n==1 )
    {
        result = f->ptr.p_double[0];
        return result;
    }
    
    /*
     * Prepare information for the recurrence formula
     * used to calculate sin(pi*i/n) and
     * cos(pi*i/n):
     *
     * A0    = 0
     * Delta = pi/n
     * Alpha = 2 sin^2 (Delta/2)
     * Beta  = sin(Delta)
     *
     * so that sin(..) = sin(A0+j*delta) and cos(..) = cos(A0+j*delta).
     * Then we use
     *
     * sin(x+delta) = sin(x) - (alpha*sin(x) - beta*cos(x))
     * cos(x+delta) = cos(x) - (alpha*cos(x) - beta*sin(x))
     *
     * to repeatedly calculate sin(..) and cos(..).
     */
    threshold = ae_sqrt(ae_minrealnumber, _state);
    t = (t-0.5*(a+b))/(0.5*(b-a));
    a0 = 0.0;
    delta = ae_pi/(n-1);
    alpha = 2*ae_sqr(ae_sin(delta/2, _state), _state);
    beta = ae_sin(delta, _state);
    
    /*
     * First, decide: should we use "safe" formula (guarded
     * against overflow) or fast one?
     */
    ca = ae_cos(a0, _state);
    sa = ae_sin(a0, _state);
    j = 0;
    x = ca;
    s = t-x;
    for(i=1; i<=n-1; i++)
    {
        
        /*
         * Next X[i]
         */
        temps = sa-(alpha*sa-beta*ca);
        tempc = ca-(alpha*ca+beta*sa);
        sa = temps;
        ca = tempc;
        x = ca;
        
        /*
         * Use X[i]
         */
        if( ae_fp_less(ae_fabs(t-x, _state),ae_fabs(s, _state)) )
        {
            s = t-x;
            j = i;
        }
    }
    if( ae_fp_eq(s,(double)(0)) )
    {
        result = f->ptr.p_double[j];
        return result;
    }
    if( ae_fp_greater(ae_fabs(s, _state),threshold) )
    {
        
        /*
         * use fast formula
         */
        j = -1;
        s = 1.0;
    }
    
    /*
     * Calculate using safe or fast barycentric formula
     */
    s1 = (double)(0);
    s2 = (double)(0);
    ca = ae_cos(a0, _state);
    sa = ae_sin(a0, _state);
    p1 = 1.0;
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * Calculate X[i], W[i]
         */
        x = ca;
        if( i==0||i==n-1 )
        {
            w = 0.5*p1;
        }
        else
        {
            w = 1.0*p1;
        }
        
        /*
         * Proceed
         */
        if( i!=j )
        {
            v = s*w/(t-x);
            s1 = s1+v*f->ptr.p_double[i];
            s2 = s2+v;
        }
        else
        {
            v = w;
            s1 = s1+v*f->ptr.p_double[i];
            s2 = s2+v;
        }
        
        /*
         * Next CA, SA, P1
         */
        temps = sa-(alpha*sa-beta*ca);
        tempc = ca-(alpha*ca+beta*sa);
        sa = temps;
        ca = tempc;
        p1 = -p1;
    }
    result = s1/s2;
    return result;
}




/*************************************************************************
This subroutine builds linear spline interpolant

INPUT PARAMETERS:
    X   -   spline nodes, array[0..N-1]
    Y   -   function values, array[0..N-1]
    N   -   points count (optional):
            * N>=2
            * if given, only first N points are used to build spline
            * if not given, automatically detected from X/Y sizes
              (len(X) must be equal to len(Y))
    
OUTPUT PARAMETERS:
    C   -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 24.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildlinear(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     spline1dinterpolant* c,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_int_t i;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    _spline1dinterpolant_clear(c);

    ae_assert(n>1, "Spline1DBuildLinear: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DBuildLinear: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DBuildLinear: Length(Y)<N!", _state);
    
    /*
     * check and sort points
     */
    ae_assert(isfinitevector(x, n, _state), "Spline1DBuildLinear: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DBuildLinear: Y contains infinite or NAN values!", _state);
    spline1d_heapsortpoints(x, y, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DBuildLinear: at least two consequent points are too close!", _state);
    
    /*
     * Build
     */
    c->periodic = ae_false;
    c->n = n;
    c->k = 3;
    c->continuity = 0;
    ae_vector_set_length(&c->x, n, _state);
    ae_vector_set_length(&c->c, 4*(n-1)+2, _state);
    for(i=0; i<=n-1; i++)
    {
        c->x.ptr.p_double[i] = x->ptr.p_double[i];
    }
    for(i=0; i<=n-2; i++)
    {
        c->c.ptr.p_double[4*i+0] = y->ptr.p_double[i];
        c->c.ptr.p_double[4*i+1] = (y->ptr.p_double[i+1]-y->ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i]);
        c->c.ptr.p_double[4*i+2] = (double)(0);
        c->c.ptr.p_double[4*i+3] = (double)(0);
    }
    c->c.ptr.p_double[4*(n-1)+0] = y->ptr.p_double[n-1];
    c->c.ptr.p_double[4*(n-1)+1] = c->c.ptr.p_double[4*(n-2)+1];
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine builds cubic spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1].
    Y           -   function values, array[0..N-1].
    
OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    C           -   spline interpolant

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildcubic(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundltype,
     double boundl,
     ae_int_t boundrtype,
     double boundr,
     spline1dinterpolant* c,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector a1;
    ae_vector a2;
    ae_vector a3;
    ae_vector b;
    ae_vector dt;
    ae_vector d;
    ae_vector p;
    ae_int_t ylen;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    _spline1dinterpolant_clear(c);
    ae_vector_init(&a1, 0, DT_REAL, _state);
    ae_vector_init(&a2, 0, DT_REAL, _state);
    ae_vector_init(&a3, 0, DT_REAL, _state);
    ae_vector_init(&b, 0, DT_REAL, _state);
    ae_vector_init(&dt, 0, DT_REAL, _state);
    ae_vector_init(&d, 0, DT_REAL, _state);
    ae_vector_init(&p, 0, DT_INT, _state);

    
    /*
     * check correctness of boundary conditions
     */
    ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DBuildCubic: incorrect BoundLType!", _state);
    ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DBuildCubic: incorrect BoundRType!", _state);
    ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DBuildCubic: incorrect BoundLType/BoundRType!", _state);
    if( boundltype==1||boundltype==2 )
    {
        ae_assert(ae_isfinite(boundl, _state), "Spline1DBuildCubic: BoundL is infinite or NAN!", _state);
    }
    if( boundrtype==1||boundrtype==2 )
    {
        ae_assert(ae_isfinite(boundr, _state), "Spline1DBuildCubic: BoundR is infinite or NAN!", _state);
    }
    
    /*
     * check lengths of arguments
     */
    ae_assert(n>=2, "Spline1DBuildCubic: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DBuildCubic: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DBuildCubic: Length(Y)<N!", _state);
    
    /*
     * check and sort points
     */
    ylen = n;
    if( boundltype==-1 )
    {
        ylen = n-1;
    }
    ae_assert(isfinitevector(x, n, _state), "Spline1DBuildCubic: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, ylen, _state), "Spline1DBuildCubic: Y contains infinite or NAN values!", _state);
    spline1d_heapsortppoints(x, y, &p, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DBuildCubic: at least two consequent points are too close!", _state);
    
    /*
     * Now we've checked and preordered everything,
     * so we can call internal function to calculate derivatives,
     * and then build Hermite spline using these derivatives
     */
    if( boundltype==-1||boundrtype==-1 )
    {
        y->ptr.p_double[n-1] = y->ptr.p_double[0];
    }
    spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, &d, &a1, &a2, &a3, &b, &dt, _state);
    spline1dbuildhermite(x, y, &d, n, c, _state);
    c->periodic = boundltype==-1||boundrtype==-1;
    c->continuity = 2;
    ae_frame_leave(_state);
}


/*************************************************************************
This function solves following problem: given table y[] of function values
at nodes x[], it calculates and returns table of function derivatives  d[]
(calculated at the same nodes x[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   spline nodes
    Y           -   function values

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    D           -   derivative values at X[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.
Derivative values are correctly reordered on return, so  D[I]  is  always
equal to S'(X[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dgriddiffcubic(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundltype,
     double boundl,
     ae_int_t boundrtype,
     double boundr,
     /* Real    */ ae_vector* d,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector a1;
    ae_vector a2;
    ae_vector a3;
    ae_vector b;
    ae_vector dt;
    ae_vector p;
    ae_int_t i;
    ae_int_t ylen;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_clear(d);
    ae_vector_init(&a1, 0, DT_REAL, _state);
    ae_vector_init(&a2, 0, DT_REAL, _state);
    ae_vector_init(&a3, 0, DT_REAL, _state);
    ae_vector_init(&b, 0, DT_REAL, _state);
    ae_vector_init(&dt, 0, DT_REAL, _state);
    ae_vector_init(&p, 0, DT_INT, _state);

    
    /*
     * check correctness of boundary conditions
     */
    ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DGridDiffCubic: incorrect BoundLType!", _state);
    ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DGridDiffCubic: incorrect BoundRType!", _state);
    ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DGridDiffCubic: incorrect BoundLType/BoundRType!", _state);
    if( boundltype==1||boundltype==2 )
    {
        ae_assert(ae_isfinite(boundl, _state), "Spline1DGridDiffCubic: BoundL is infinite or NAN!", _state);
    }
    if( boundrtype==1||boundrtype==2 )
    {
        ae_assert(ae_isfinite(boundr, _state), "Spline1DGridDiffCubic: BoundR is infinite or NAN!", _state);
    }
    
    /*
     * check lengths of arguments
     */
    ae_assert(n>=2, "Spline1DGridDiffCubic: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DGridDiffCubic: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DGridDiffCubic: Length(Y)<N!", _state);
    
    /*
     * check and sort points
     */
    ylen = n;
    if( boundltype==-1 )
    {
        ylen = n-1;
    }
    ae_assert(isfinitevector(x, n, _state), "Spline1DGridDiffCubic: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, ylen, _state), "Spline1DGridDiffCubic: Y contains infinite or NAN values!", _state);
    spline1d_heapsortppoints(x, y, &p, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DGridDiffCubic: at least two consequent points are too close!", _state);
    
    /*
     * Now we've checked and preordered everything,
     * so we can call internal function.
     */
    spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, d, &a1, &a2, &a3, &b, &dt, _state);
    
    /*
     * Remember that HeapSortPPoints() call?
     * Now we have to reorder them back.
     */
    if( dt.cnt<n )
    {
        ae_vector_set_length(&dt, n, _state);
    }
    for(i=0; i<=n-1; i++)
    {
        dt.ptr.p_double[p.ptr.p_int[i]] = d->ptr.p_double[i];
    }
    ae_v_move(&d->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_frame_leave(_state);
}


/*************************************************************************
This function solves following problem: given table y[] of function values
at  nodes  x[],  it  calculates  and  returns  tables  of first and second
function derivatives d1[] and d2[] (calculated at the same nodes x[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   spline nodes
    Y           -   function values

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)

OUTPUT PARAMETERS:
    D1          -   S' values at X[]
    D2          -   S'' values at X[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.
Derivative values are correctly reordered on return, so  D[I]  is  always
equal to S'(X[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dgriddiff2cubic(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundltype,
     double boundl,
     ae_int_t boundrtype,
     double boundr,
     /* Real    */ ae_vector* d1,
     /* Real    */ ae_vector* d2,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector a1;
    ae_vector a2;
    ae_vector a3;
    ae_vector b;
    ae_vector dt;
    ae_vector p;
    ae_int_t i;
    ae_int_t ylen;
    double delta;
    double delta2;
    double delta3;
    double s2;
    double s3;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_clear(d1);
    ae_vector_clear(d2);
    ae_vector_init(&a1, 0, DT_REAL, _state);
    ae_vector_init(&a2, 0, DT_REAL, _state);
    ae_vector_init(&a3, 0, DT_REAL, _state);
    ae_vector_init(&b, 0, DT_REAL, _state);
    ae_vector_init(&dt, 0, DT_REAL, _state);
    ae_vector_init(&p, 0, DT_INT, _state);

    
    /*
     * check correctness of boundary conditions
     */
    ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DGridDiff2Cubic: incorrect BoundLType!", _state);
    ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DGridDiff2Cubic: incorrect BoundRType!", _state);
    ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DGridDiff2Cubic: incorrect BoundLType/BoundRType!", _state);
    if( boundltype==1||boundltype==2 )
    {
        ae_assert(ae_isfinite(boundl, _state), "Spline1DGridDiff2Cubic: BoundL is infinite or NAN!", _state);
    }
    if( boundrtype==1||boundrtype==2 )
    {
        ae_assert(ae_isfinite(boundr, _state), "Spline1DGridDiff2Cubic: BoundR is infinite or NAN!", _state);
    }
    
    /*
     * check lengths of arguments
     */
    ae_assert(n>=2, "Spline1DGridDiff2Cubic: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DGridDiff2Cubic: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DGridDiff2Cubic: Length(Y)<N!", _state);
    
    /*
     * check and sort points
     */
    ylen = n;
    if( boundltype==-1 )
    {
        ylen = n-1;
    }
    ae_assert(isfinitevector(x, n, _state), "Spline1DGridDiff2Cubic: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, ylen, _state), "Spline1DGridDiff2Cubic: Y contains infinite or NAN values!", _state);
    spline1d_heapsortppoints(x, y, &p, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DGridDiff2Cubic: at least two consequent points are too close!", _state);
    
    /*
     * Now we've checked and preordered everything,
     * so we can call internal function.
     *
     * After this call we will calculate second derivatives
     * (manually, by converting to the power basis)
     */
    spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, d1, &a1, &a2, &a3, &b, &dt, _state);
    ae_vector_set_length(d2, n, _state);
    delta = (double)(0);
    s2 = (double)(0);
    s3 = (double)(0);
    for(i=0; i<=n-2; i++)
    {
        
        /*
         * We convert from Hermite basis to the power basis.
         * Si is coefficient before x^i.
         *
         * Inside this cycle we need just S2,
         * because we calculate S'' exactly at spline node,
         * (only x^2 matters at x=0), but after iterations
         * will be over, we will need other coefficients
         * to calculate spline value at the last node.
         */
        delta = x->ptr.p_double[i+1]-x->ptr.p_double[i];
        delta2 = ae_sqr(delta, _state);
        delta3 = delta*delta2;
        s2 = (3*(y->ptr.p_double[i+1]-y->ptr.p_double[i])-2*d1->ptr.p_double[i]*delta-d1->ptr.p_double[i+1]*delta)/delta2;
        s3 = (2*(y->ptr.p_double[i]-y->ptr.p_double[i+1])+d1->ptr.p_double[i]*delta+d1->ptr.p_double[i+1]*delta)/delta3;
        d2->ptr.p_double[i] = 2*s2;
    }
    d2->ptr.p_double[n-1] = 2*s2+6*s3*delta;
    
    /*
     * Remember that HeapSortPPoints() call?
     * Now we have to reorder them back.
     */
    if( dt.cnt<n )
    {
        ae_vector_set_length(&dt, n, _state);
    }
    for(i=0; i<=n-1; i++)
    {
        dt.ptr.p_double[p.ptr.p_int[i]] = d1->ptr.p_double[i];
    }
    ae_v_move(&d1->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n-1));
    for(i=0; i<=n-1; i++)
    {
        dt.ptr.p_double[p.ptr.p_int[i]] = d2->ptr.p_double[i];
    }
    ae_v_move(&d2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_frame_leave(_state);
}


/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function values y2[] (calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvcubic(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundltype,
     double boundl,
     ae_int_t boundrtype,
     double boundr,
     /* Real    */ ae_vector* x2,
     ae_int_t n2,
     /* Real    */ ae_vector* y2,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector _x2;
    ae_vector a1;
    ae_vector a2;
    ae_vector a3;
    ae_vector b;
    ae_vector d;
    ae_vector dt;
    ae_vector d1;
    ae_vector d2;
    ae_vector p;
    ae_vector p2;
    ae_int_t i;
    ae_int_t ylen;
    double t;
    double t2;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_init_copy(&_x2, x2, _state);
    x2 = &_x2;
    ae_vector_clear(y2);
    ae_vector_init(&a1, 0, DT_REAL, _state);
    ae_vector_init(&a2, 0, DT_REAL, _state);
    ae_vector_init(&a3, 0, DT_REAL, _state);
    ae_vector_init(&b, 0, DT_REAL, _state);
    ae_vector_init(&d, 0, DT_REAL, _state);
    ae_vector_init(&dt, 0, DT_REAL, _state);
    ae_vector_init(&d1, 0, DT_REAL, _state);
    ae_vector_init(&d2, 0, DT_REAL, _state);
    ae_vector_init(&p, 0, DT_INT, _state);
    ae_vector_init(&p2, 0, DT_INT, _state);

    
    /*
     * check correctness of boundary conditions
     */
    ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DConvCubic: incorrect BoundLType!", _state);
    ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DConvCubic: incorrect BoundRType!", _state);
    ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DConvCubic: incorrect BoundLType/BoundRType!", _state);
    if( boundltype==1||boundltype==2 )
    {
        ae_assert(ae_isfinite(boundl, _state), "Spline1DConvCubic: BoundL is infinite or NAN!", _state);
    }
    if( boundrtype==1||boundrtype==2 )
    {
        ae_assert(ae_isfinite(boundr, _state), "Spline1DConvCubic: BoundR is infinite or NAN!", _state);
    }
    
    /*
     * check lengths of arguments
     */
    ae_assert(n>=2, "Spline1DConvCubic: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DConvCubic: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DConvCubic: Length(Y)<N!", _state);
    ae_assert(n2>=2, "Spline1DConvCubic: N2<2!", _state);
    ae_assert(x2->cnt>=n2, "Spline1DConvCubic: Length(X2)<N2!", _state);
    
    /*
     * check and sort X/Y
     */
    ylen = n;
    if( boundltype==-1 )
    {
        ylen = n-1;
    }
    ae_assert(isfinitevector(x, n, _state), "Spline1DConvCubic: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, ylen, _state), "Spline1DConvCubic: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(x2, n2, _state), "Spline1DConvCubic: X2 contains infinite or NAN values!", _state);
    spline1d_heapsortppoints(x, y, &p, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DConvCubic: at least two consequent points are too close!", _state);
    
    /*
     * set up DT (we will need it below)
     */
    ae_vector_set_length(&dt, ae_maxint(n, n2, _state), _state);
    
    /*
     * sort X2:
     * * use fake array DT because HeapSortPPoints() needs both integer AND real arrays
     * * if we have periodic problem, wrap points
     * * sort them, store permutation at P2
     */
    if( boundrtype==-1&&boundltype==-1 )
    {
        for(i=0; i<=n2-1; i++)
        {
            t = x2->ptr.p_double[i];
            apperiodicmap(&t, x->ptr.p_double[0], x->ptr.p_double[n-1], &t2, _state);
            x2->ptr.p_double[i] = t;
        }
    }
    spline1d_heapsortppoints(x2, &dt, &p2, n2, _state);
    
    /*
     * Now we've checked and preordered everything, so we:
     * * call internal GridDiff() function to get Hermite form of spline
     * * convert using internal Conv() function
     * * convert Y2 back to original order
     */
    spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, &d, &a1, &a2, &a3, &b, &dt, _state);
    spline1dconvdiffinternal(x, y, &d, n, x2, n2, y2, ae_true, &d1, ae_false, &d2, ae_false, _state);
    ae_assert(dt.cnt>=n2, "Spline1DConvCubic: internal error!", _state);
    for(i=0; i<=n2-1; i++)
    {
        dt.ptr.p_double[p2.ptr.p_int[i]] = y2->ptr.p_double[i];
    }
    ae_v_move(&y2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1));
    ae_frame_leave(_state);
}


/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function values y2[] and derivatives d2[] (calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]
    D2          -   first derivatives at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvdiffcubic(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundltype,
     double boundl,
     ae_int_t boundrtype,
     double boundr,
     /* Real    */ ae_vector* x2,
     ae_int_t n2,
     /* Real    */ ae_vector* y2,
     /* Real    */ ae_vector* d2,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector _x2;
    ae_vector a1;
    ae_vector a2;
    ae_vector a3;
    ae_vector b;
    ae_vector d;
    ae_vector dt;
    ae_vector rt1;
    ae_vector p;
    ae_vector p2;
    ae_int_t i;
    ae_int_t ylen;
    double t;
    double t2;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_init_copy(&_x2, x2, _state);
    x2 = &_x2;
    ae_vector_clear(y2);
    ae_vector_clear(d2);
    ae_vector_init(&a1, 0, DT_REAL, _state);
    ae_vector_init(&a2, 0, DT_REAL, _state);
    ae_vector_init(&a3, 0, DT_REAL, _state);
    ae_vector_init(&b, 0, DT_REAL, _state);
    ae_vector_init(&d, 0, DT_REAL, _state);
    ae_vector_init(&dt, 0, DT_REAL, _state);
    ae_vector_init(&rt1, 0, DT_REAL, _state);
    ae_vector_init(&p, 0, DT_INT, _state);
    ae_vector_init(&p2, 0, DT_INT, _state);

    
    /*
     * check correctness of boundary conditions
     */
    ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DConvDiffCubic: incorrect BoundLType!", _state);
    ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DConvDiffCubic: incorrect BoundRType!", _state);
    ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DConvDiffCubic: incorrect BoundLType/BoundRType!", _state);
    if( boundltype==1||boundltype==2 )
    {
        ae_assert(ae_isfinite(boundl, _state), "Spline1DConvDiffCubic: BoundL is infinite or NAN!", _state);
    }
    if( boundrtype==1||boundrtype==2 )
    {
        ae_assert(ae_isfinite(boundr, _state), "Spline1DConvDiffCubic: BoundR is infinite or NAN!", _state);
    }
    
    /*
     * check lengths of arguments
     */
    ae_assert(n>=2, "Spline1DConvDiffCubic: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DConvDiffCubic: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DConvDiffCubic: Length(Y)<N!", _state);
    ae_assert(n2>=2, "Spline1DConvDiffCubic: N2<2!", _state);
    ae_assert(x2->cnt>=n2, "Spline1DConvDiffCubic: Length(X2)<N2!", _state);
    
    /*
     * check and sort X/Y
     */
    ylen = n;
    if( boundltype==-1 )
    {
        ylen = n-1;
    }
    ae_assert(isfinitevector(x, n, _state), "Spline1DConvDiffCubic: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, ylen, _state), "Spline1DConvDiffCubic: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(x2, n2, _state), "Spline1DConvDiffCubic: X2 contains infinite or NAN values!", _state);
    spline1d_heapsortppoints(x, y, &p, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DConvDiffCubic: at least two consequent points are too close!", _state);
    
    /*
     * set up DT (we will need it below)
     */
    ae_vector_set_length(&dt, ae_maxint(n, n2, _state), _state);
    
    /*
     * sort X2:
     * * use fake array DT because HeapSortPPoints() needs both integer AND real arrays
     * * if we have periodic problem, wrap points
     * * sort them, store permutation at P2
     */
    if( boundrtype==-1&&boundltype==-1 )
    {
        for(i=0; i<=n2-1; i++)
        {
            t = x2->ptr.p_double[i];
            apperiodicmap(&t, x->ptr.p_double[0], x->ptr.p_double[n-1], &t2, _state);
            x2->ptr.p_double[i] = t;
        }
    }
    spline1d_heapsortppoints(x2, &dt, &p2, n2, _state);
    
    /*
     * Now we've checked and preordered everything, so we:
     * * call internal GridDiff() function to get Hermite form of spline
     * * convert using internal Conv() function
     * * convert Y2 back to original order
     */
    spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, &d, &a1, &a2, &a3, &b, &dt, _state);
    spline1dconvdiffinternal(x, y, &d, n, x2, n2, y2, ae_true, d2, ae_true, &rt1, ae_false, _state);
    ae_assert(dt.cnt>=n2, "Spline1DConvDiffCubic: internal error!", _state);
    for(i=0; i<=n2-1; i++)
    {
        dt.ptr.p_double[p2.ptr.p_int[i]] = y2->ptr.p_double[i];
    }
    ae_v_move(&y2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1));
    for(i=0; i<=n2-1; i++)
    {
        dt.ptr.p_double[p2.ptr.p_int[i]] = d2->ptr.p_double[i];
    }
    ae_v_move(&d2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1));
    ae_frame_leave(_state);
}


/*************************************************************************
This function solves following problem: given table y[] of function values
at old nodes x[]  and new nodes  x2[],  it calculates and returns table of
function  values  y2[],  first  and  second  derivatives  d2[]  and  dd2[]
(calculated at x2[]).

This function yields same result as Spline1DBuildCubic() call followed  by
sequence of Spline1DDiff() calls, but it can be several times faster  when
called for ordered X[] and X2[].

INPUT PARAMETERS:
    X           -   old spline nodes
    Y           -   function values
    X2           -  new spline nodes

OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points from X/Y are used
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundLType  -   boundary condition type for the left boundary
    BoundL      -   left boundary condition (first or second derivative,
                    depending on the BoundLType)
    BoundRType  -   boundary condition type for the right boundary
    BoundR      -   right boundary condition (first or second derivative,
                    depending on the BoundRType)
    N2          -   new points count:
                    * N2>=2
                    * if given, only first N2 points from X2 are used
                    * if not given, automatically detected from X2 size

OUTPUT PARAMETERS:
    F2          -   function values at X2[]
    D2          -   first derivatives at X2[]
    DD2         -   second derivatives at X2[]

ORDER OF POINTS

Subroutine automatically sorts points, so caller  may pass unsorted array.
Function  values  are correctly reordered on  return, so F2[I]  is  always
equal to S(X2[I]) independently of points order.

SETTING BOUNDARY VALUES:

The BoundLType/BoundRType parameters can have the following values:
    * -1, which corresonds to the periodic (cyclic) boundary conditions.
          In this case:
          * both BoundLType and BoundRType must be equal to -1.
          * BoundL/BoundR are ignored
          * Y[last] is ignored (it is assumed to be equal to Y[first]).
    *  0, which  corresponds  to  the  parabolically   terminated  spline
          (BoundL and/or BoundR are ignored).
    *  1, which corresponds to the first derivative boundary condition
    *  2, which corresponds to the second derivative boundary condition
    *  by default, BoundType=0 is used

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvdiff2cubic(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundltype,
     double boundl,
     ae_int_t boundrtype,
     double boundr,
     /* Real    */ ae_vector* x2,
     ae_int_t n2,
     /* Real    */ ae_vector* y2,
     /* Real    */ ae_vector* d2,
     /* Real    */ ae_vector* dd2,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector _x2;
    ae_vector a1;
    ae_vector a2;
    ae_vector a3;
    ae_vector b;
    ae_vector d;
    ae_vector dt;
    ae_vector p;
    ae_vector p2;
    ae_int_t i;
    ae_int_t ylen;
    double t;
    double t2;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_init_copy(&_x2, x2, _state);
    x2 = &_x2;
    ae_vector_clear(y2);
    ae_vector_clear(d2);
    ae_vector_clear(dd2);
    ae_vector_init(&a1, 0, DT_REAL, _state);
    ae_vector_init(&a2, 0, DT_REAL, _state);
    ae_vector_init(&a3, 0, DT_REAL, _state);
    ae_vector_init(&b, 0, DT_REAL, _state);
    ae_vector_init(&d, 0, DT_REAL, _state);
    ae_vector_init(&dt, 0, DT_REAL, _state);
    ae_vector_init(&p, 0, DT_INT, _state);
    ae_vector_init(&p2, 0, DT_INT, _state);

    
    /*
     * check correctness of boundary conditions
     */
    ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DConvDiff2Cubic: incorrect BoundLType!", _state);
    ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DConvDiff2Cubic: incorrect BoundRType!", _state);
    ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DConvDiff2Cubic: incorrect BoundLType/BoundRType!", _state);
    if( boundltype==1||boundltype==2 )
    {
        ae_assert(ae_isfinite(boundl, _state), "Spline1DConvDiff2Cubic: BoundL is infinite or NAN!", _state);
    }
    if( boundrtype==1||boundrtype==2 )
    {
        ae_assert(ae_isfinite(boundr, _state), "Spline1DConvDiff2Cubic: BoundR is infinite or NAN!", _state);
    }
    
    /*
     * check lengths of arguments
     */
    ae_assert(n>=2, "Spline1DConvDiff2Cubic: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DConvDiff2Cubic: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DConvDiff2Cubic: Length(Y)<N!", _state);
    ae_assert(n2>=2, "Spline1DConvDiff2Cubic: N2<2!", _state);
    ae_assert(x2->cnt>=n2, "Spline1DConvDiff2Cubic: Length(X2)<N2!", _state);
    
    /*
     * check and sort X/Y
     */
    ylen = n;
    if( boundltype==-1 )
    {
        ylen = n-1;
    }
    ae_assert(isfinitevector(x, n, _state), "Spline1DConvDiff2Cubic: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, ylen, _state), "Spline1DConvDiff2Cubic: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(x2, n2, _state), "Spline1DConvDiff2Cubic: X2 contains infinite or NAN values!", _state);
    spline1d_heapsortppoints(x, y, &p, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DConvDiff2Cubic: at least two consequent points are too close!", _state);
    
    /*
     * set up DT (we will need it below)
     */
    ae_vector_set_length(&dt, ae_maxint(n, n2, _state), _state);
    
    /*
     * sort X2:
     * * use fake array DT because HeapSortPPoints() needs both integer AND real arrays
     * * if we have periodic problem, wrap points
     * * sort them, store permutation at P2
     */
    if( boundrtype==-1&&boundltype==-1 )
    {
        for(i=0; i<=n2-1; i++)
        {
            t = x2->ptr.p_double[i];
            apperiodicmap(&t, x->ptr.p_double[0], x->ptr.p_double[n-1], &t2, _state);
            x2->ptr.p_double[i] = t;
        }
    }
    spline1d_heapsortppoints(x2, &dt, &p2, n2, _state);
    
    /*
     * Now we've checked and preordered everything, so we:
     * * call internal GridDiff() function to get Hermite form of spline
     * * convert using internal Conv() function
     * * convert Y2 back to original order
     */
    spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, &d, &a1, &a2, &a3, &b, &dt, _state);
    spline1dconvdiffinternal(x, y, &d, n, x2, n2, y2, ae_true, d2, ae_true, dd2, ae_true, _state);
    ae_assert(dt.cnt>=n2, "Spline1DConvDiff2Cubic: internal error!", _state);
    for(i=0; i<=n2-1; i++)
    {
        dt.ptr.p_double[p2.ptr.p_int[i]] = y2->ptr.p_double[i];
    }
    ae_v_move(&y2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1));
    for(i=0; i<=n2-1; i++)
    {
        dt.ptr.p_double[p2.ptr.p_int[i]] = d2->ptr.p_double[i];
    }
    ae_v_move(&d2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1));
    for(i=0; i<=n2-1; i++)
    {
        dt.ptr.p_double[p2.ptr.p_int[i]] = dd2->ptr.p_double[i];
    }
    ae_v_move(&dd2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1));
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine builds Catmull-Rom spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1].
    Y           -   function values, array[0..N-1].
    
OPTIONAL PARAMETERS:
    N           -   points count:
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))
    BoundType   -   boundary condition type:
                    * -1 for periodic boundary condition
                    *  0 for parabolically terminated spline (default)
    Tension     -   tension parameter:
                    * tension=0   corresponds to classic Catmull-Rom spline (default)
                    * 0<tension<1 corresponds to more general form - cardinal spline

OUTPUT PARAMETERS:
    C           -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS:

Problems with periodic boundary conditions have Y[first_point]=Y[last_point].
However, this subroutine doesn't require you to specify equal  values  for
the first and last points - it automatically forces them  to  be  equal by
copying  Y[first_point]  (corresponds  to the leftmost,  minimal  X[])  to
Y[last_point]. However it is recommended to pass consistent values of Y[],
i.e. to make Y[first_point]=Y[last_point].

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildcatmullrom(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundtype,
     double tension,
     spline1dinterpolant* c,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector d;
    ae_int_t i;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    _spline1dinterpolant_clear(c);
    ae_vector_init(&d, 0, DT_REAL, _state);

    ae_assert(n>=2, "Spline1DBuildCatmullRom: N<2!", _state);
    ae_assert(boundtype==-1||boundtype==0, "Spline1DBuildCatmullRom: incorrect BoundType!", _state);
    ae_assert(ae_fp_greater_eq(tension,(double)(0)), "Spline1DBuildCatmullRom: Tension<0!", _state);
    ae_assert(ae_fp_less_eq(tension,(double)(1)), "Spline1DBuildCatmullRom: Tension>1!", _state);
    ae_assert(x->cnt>=n, "Spline1DBuildCatmullRom: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DBuildCatmullRom: Length(Y)<N!", _state);
    
    /*
     * check and sort points
     */
    ae_assert(isfinitevector(x, n, _state), "Spline1DBuildCatmullRom: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DBuildCatmullRom: Y contains infinite or NAN values!", _state);
    spline1d_heapsortpoints(x, y, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DBuildCatmullRom: at least two consequent points are too close!", _state);
    
    /*
     * Special cases:
     * * N=2, parabolic terminated boundary condition on both ends
     * * N=2, periodic boundary condition
     */
    if( n==2&&boundtype==0 )
    {
        
        /*
         * Just linear spline
         */
        spline1dbuildlinear(x, y, n, c, _state);
        ae_frame_leave(_state);
        return;
    }
    if( n==2&&boundtype==-1 )
    {
        
        /*
         * Same as cubic spline with periodic conditions
         */
        spline1dbuildcubic(x, y, n, -1, 0.0, -1, 0.0, c, _state);
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Periodic or non-periodic boundary conditions
     */
    if( boundtype==-1 )
    {
        
        /*
         * Periodic boundary conditions
         */
        y->ptr.p_double[n-1] = y->ptr.p_double[0];
        ae_vector_set_length(&d, n, _state);
        d.ptr.p_double[0] = (y->ptr.p_double[1]-y->ptr.p_double[n-2])/(2*(x->ptr.p_double[1]-x->ptr.p_double[0]+x->ptr.p_double[n-1]-x->ptr.p_double[n-2]));
        for(i=1; i<=n-2; i++)
        {
            d.ptr.p_double[i] = (1-tension)*(y->ptr.p_double[i+1]-y->ptr.p_double[i-1])/(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]);
        }
        d.ptr.p_double[n-1] = d.ptr.p_double[0];
        
        /*
         * Now problem is reduced to the cubic Hermite spline
         */
        spline1dbuildhermite(x, y, &d, n, c, _state);
        c->periodic = ae_true;
    }
    else
    {
        
        /*
         * Non-periodic boundary conditions
         */
        ae_vector_set_length(&d, n, _state);
        for(i=1; i<=n-2; i++)
        {
            d.ptr.p_double[i] = (1-tension)*(y->ptr.p_double[i+1]-y->ptr.p_double[i-1])/(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]);
        }
        d.ptr.p_double[0] = 2*(y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0])-d.ptr.p_double[1];
        d.ptr.p_double[n-1] = 2*(y->ptr.p_double[n-1]-y->ptr.p_double[n-2])/(x->ptr.p_double[n-1]-x->ptr.p_double[n-2])-d.ptr.p_double[n-2];
        
        /*
         * Now problem is reduced to the cubic Hermite spline
         */
        spline1dbuildhermite(x, y, &d, n, c, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine builds Hermite spline interpolant.

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]
    Y           -   function values, array[0..N-1]
    D           -   derivatives, array[0..N-1]
    N           -   points count (optional):
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))

OUTPUT PARAMETERS:
    C           -   spline interpolant.


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildhermite(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* d,
     ae_int_t n,
     spline1dinterpolant* c,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector _d;
    ae_int_t i;
    double delta;
    double delta2;
    double delta3;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_init_copy(&_d, d, _state);
    d = &_d;
    _spline1dinterpolant_clear(c);

    ae_assert(n>=2, "Spline1DBuildHermite: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DBuildHermite: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DBuildHermite: Length(Y)<N!", _state);
    ae_assert(d->cnt>=n, "Spline1DBuildHermite: Length(D)<N!", _state);
    
    /*
     * check and sort points
     */
    ae_assert(isfinitevector(x, n, _state), "Spline1DBuildHermite: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DBuildHermite: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(d, n, _state), "Spline1DBuildHermite: D contains infinite or NAN values!", _state);
    heapsortdpoints(x, y, d, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DBuildHermite: at least two consequent points are too close!", _state);
    
    /*
     * Build
     */
    ae_vector_set_length(&c->x, n, _state);
    ae_vector_set_length(&c->c, 4*(n-1)+2, _state);
    c->periodic = ae_false;
    c->k = 3;
    c->n = n;
    c->continuity = 1;
    for(i=0; i<=n-1; i++)
    {
        c->x.ptr.p_double[i] = x->ptr.p_double[i];
    }
    for(i=0; i<=n-2; i++)
    {
        delta = x->ptr.p_double[i+1]-x->ptr.p_double[i];
        delta2 = ae_sqr(delta, _state);
        delta3 = delta*delta2;
        c->c.ptr.p_double[4*i+0] = y->ptr.p_double[i];
        c->c.ptr.p_double[4*i+1] = d->ptr.p_double[i];
        c->c.ptr.p_double[4*i+2] = (3*(y->ptr.p_double[i+1]-y->ptr.p_double[i])-2*d->ptr.p_double[i]*delta-d->ptr.p_double[i+1]*delta)/delta2;
        c->c.ptr.p_double[4*i+3] = (2*(y->ptr.p_double[i]-y->ptr.p_double[i+1])+d->ptr.p_double[i]*delta+d->ptr.p_double[i+1]*delta)/delta3;
    }
    c->c.ptr.p_double[4*(n-1)+0] = y->ptr.p_double[n-1];
    c->c.ptr.p_double[4*(n-1)+1] = d->ptr.p_double[n-1];
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine builds Akima spline interpolant

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]
    Y           -   function values, array[0..N-1]
    N           -   points count (optional):
                    * N>=2
                    * if given, only first N points are used to build spline
                    * if not given, automatically detected from X/Y sizes
                      (len(X) must be equal to len(Y))

OUTPUT PARAMETERS:
    C           -   spline interpolant


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

  -- ALGLIB PROJECT --
     Copyright 24.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildakima(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     spline1dinterpolant* c,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_int_t i;
    ae_vector d;
    ae_vector w;
    ae_vector diff;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    _spline1dinterpolant_clear(c);
    ae_vector_init(&d, 0, DT_REAL, _state);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&diff, 0, DT_REAL, _state);

    ae_assert(n>=2, "Spline1DBuildAkima: N<2!", _state);
    ae_assert(x->cnt>=n, "Spline1DBuildAkima: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DBuildAkima: Length(Y)<N!", _state);
    
    /*
     * check and sort points
     */
    ae_assert(isfinitevector(x, n, _state), "Spline1DBuildAkima: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DBuildAkima: Y contains infinite or NAN values!", _state);
    spline1d_heapsortpoints(x, y, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DBuildAkima: at least two consequent points are too close!", _state);
    
    /*
     * Handle special cases: N=2, N=3, N=4
     */
    if( n<=4 )
    {
        spline1dbuildcubic(x, y, n, 0, 0.0, 0, 0.0, c, _state);
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Prepare W (weights), Diff (divided differences)
     */
    ae_vector_set_length(&w, n-1, _state);
    ae_vector_set_length(&diff, n-1, _state);
    for(i=0; i<=n-2; i++)
    {
        diff.ptr.p_double[i] = (y->ptr.p_double[i+1]-y->ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i]);
    }
    for(i=1; i<=n-2; i++)
    {
        w.ptr.p_double[i] = ae_fabs(diff.ptr.p_double[i]-diff.ptr.p_double[i-1], _state);
    }
    
    /*
     * Prepare Hermite interpolation scheme
     */
    ae_vector_set_length(&d, n, _state);
    for(i=2; i<=n-3; i++)
    {
        if( ae_fp_neq(ae_fabs(w.ptr.p_double[i-1], _state)+ae_fabs(w.ptr.p_double[i+1], _state),(double)(0)) )
        {
            d.ptr.p_double[i] = (w.ptr.p_double[i+1]*diff.ptr.p_double[i-1]+w.ptr.p_double[i-1]*diff.ptr.p_double[i])/(w.ptr.p_double[i+1]+w.ptr.p_double[i-1]);
        }
        else
        {
            d.ptr.p_double[i] = ((x->ptr.p_double[i+1]-x->ptr.p_double[i])*diff.ptr.p_double[i-1]+(x->ptr.p_double[i]-x->ptr.p_double[i-1])*diff.ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]);
        }
    }
    d.ptr.p_double[0] = spline1d_diffthreepoint(x->ptr.p_double[0], x->ptr.p_double[0], y->ptr.p_double[0], x->ptr.p_double[1], y->ptr.p_double[1], x->ptr.p_double[2], y->ptr.p_double[2], _state);
    d.ptr.p_double[1] = spline1d_diffthreepoint(x->ptr.p_double[1], x->ptr.p_double[0], y->ptr.p_double[0], x->ptr.p_double[1], y->ptr.p_double[1], x->ptr.p_double[2], y->ptr.p_double[2], _state);
    d.ptr.p_double[n-2] = spline1d_diffthreepoint(x->ptr.p_double[n-2], x->ptr.p_double[n-3], y->ptr.p_double[n-3], x->ptr.p_double[n-2], y->ptr.p_double[n-2], x->ptr.p_double[n-1], y->ptr.p_double[n-1], _state);
    d.ptr.p_double[n-1] = spline1d_diffthreepoint(x->ptr.p_double[n-1], x->ptr.p_double[n-3], y->ptr.p_double[n-3], x->ptr.p_double[n-2], y->ptr.p_double[n-2], x->ptr.p_double[n-1], y->ptr.p_double[n-1], _state);
    
    /*
     * Build Akima spline using Hermite interpolation scheme
     */
    spline1dbuildhermite(x, y, &d, n, c, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine calculates the value of the spline at the given point X.

INPUT PARAMETERS:
    C   -   spline interpolant
    X   -   point

Result:
    S(x)

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
double spline1dcalc(spline1dinterpolant* c, double x, ae_state *_state)
{
    ae_int_t l;
    ae_int_t r;
    ae_int_t m;
    double t;
    double result;


    ae_assert(c->k==3, "Spline1DCalc: internal error", _state);
    ae_assert(!ae_isinf(x, _state), "Spline1DCalc: infinite X!", _state);
    
    /*
     * special case: NaN
     */
    if( ae_isnan(x, _state) )
    {
        result = _state->v_nan;
        return result;
    }
    
    /*
     * correct if periodic
     */
    if( c->periodic )
    {
        apperiodicmap(&x, c->x.ptr.p_double[0], c->x.ptr.p_double[c->n-1], &t, _state);
    }
    
    /*
     * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included)
     */
    l = 0;
    r = c->n-2+1;
    while(l!=r-1)
    {
        m = (l+r)/2;
        if( c->x.ptr.p_double[m]>=x )
        {
            r = m;
        }
        else
        {
            l = m;
        }
    }
    
    /*
     * Interpolation
     */
    x = x-c->x.ptr.p_double[l];
    m = 4*l;
    result = c->c.ptr.p_double[m]+x*(c->c.ptr.p_double[m+1]+x*(c->c.ptr.p_double[m+2]+x*c->c.ptr.p_double[m+3]));
    return result;
}


/*************************************************************************
This subroutine differentiates the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.
    X   -   point

Result:
    S   -   S(x)
    DS  -   S'(x)
    D2S -   S''(x)

  -- ALGLIB PROJECT --
     Copyright 24.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1ddiff(spline1dinterpolant* c,
     double x,
     double* s,
     double* ds,
     double* d2s,
     ae_state *_state)
{
    ae_int_t l;
    ae_int_t r;
    ae_int_t m;
    double t;

    *s = 0;
    *ds = 0;
    *d2s = 0;

    ae_assert(c->k==3, "Spline1DDiff: internal error", _state);
    ae_assert(!ae_isinf(x, _state), "Spline1DDiff: infinite X!", _state);
    
    /*
     * special case: NaN
     */
    if( ae_isnan(x, _state) )
    {
        *s = _state->v_nan;
        *ds = _state->v_nan;
        *d2s = _state->v_nan;
        return;
    }
    
    /*
     * correct if periodic
     */
    if( c->periodic )
    {
        apperiodicmap(&x, c->x.ptr.p_double[0], c->x.ptr.p_double[c->n-1], &t, _state);
    }
    
    /*
     * Binary search
     */
    l = 0;
    r = c->n-2+1;
    while(l!=r-1)
    {
        m = (l+r)/2;
        if( c->x.ptr.p_double[m]>=x )
        {
            r = m;
        }
        else
        {
            l = m;
        }
    }
    
    /*
     * Differentiation
     */
    x = x-c->x.ptr.p_double[l];
    m = 4*l;
    *s = c->c.ptr.p_double[m]+x*(c->c.ptr.p_double[m+1]+x*(c->c.ptr.p_double[m+2]+x*c->c.ptr.p_double[m+3]));
    *ds = c->c.ptr.p_double[m+1]+2*x*c->c.ptr.p_double[m+2]+3*ae_sqr(x, _state)*c->c.ptr.p_double[m+3];
    *d2s = 2*c->c.ptr.p_double[m+2]+6*x*c->c.ptr.p_double[m+3];
}


/*************************************************************************
This subroutine makes the copy of the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.

Result:
    CC  -   spline copy

  -- ALGLIB PROJECT --
     Copyright 29.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dcopy(spline1dinterpolant* c,
     spline1dinterpolant* cc,
     ae_state *_state)
{
    ae_int_t s;

    _spline1dinterpolant_clear(cc);

    cc->periodic = c->periodic;
    cc->n = c->n;
    cc->k = c->k;
    cc->continuity = c->continuity;
    ae_vector_set_length(&cc->x, cc->n, _state);
    ae_v_move(&cc->x.ptr.p_double[0], 1, &c->x.ptr.p_double[0], 1, ae_v_len(0,cc->n-1));
    s = c->c.cnt;
    ae_vector_set_length(&cc->c, s, _state);
    ae_v_move(&cc->c.ptr.p_double[0], 1, &c->c.ptr.p_double[0], 1, ae_v_len(0,s-1));
}


/*************************************************************************
This subroutine unpacks the spline into the coefficients table.

INPUT PARAMETERS:
    C   -   spline interpolant.
    X   -   point

OUTPUT PARAMETERS:
    Tbl -   coefficients table, unpacked format, array[0..N-2, 0..5].
            For I = 0...N-2:
                Tbl[I,0] = X[i]
                Tbl[I,1] = X[i+1]
                Tbl[I,2] = C0
                Tbl[I,3] = C1
                Tbl[I,4] = C2
                Tbl[I,5] = C3
            On [x[i], x[i+1]] spline is equals to:
                S(x) = C0 + C1*t + C2*t^2 + C3*t^3
                t = x-x[i]
                
NOTE:
    You  can rebuild spline with  Spline1DBuildHermite()  function,  which
    accepts as inputs function values and derivatives at nodes, which  are
    easy to calculate when you have coefficients.

  -- ALGLIB PROJECT --
     Copyright 29.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dunpack(spline1dinterpolant* c,
     ae_int_t* n,
     /* Real    */ ae_matrix* tbl,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;

    *n = 0;
    ae_matrix_clear(tbl);

    ae_matrix_set_length(tbl, c->n-2+1, 2+c->k+1, _state);
    *n = c->n;
    
    /*
     * Fill
     */
    for(i=0; i<=*n-2; i++)
    {
        tbl->ptr.pp_double[i][0] = c->x.ptr.p_double[i];
        tbl->ptr.pp_double[i][1] = c->x.ptr.p_double[i+1];
        for(j=0; j<=c->k; j++)
        {
            tbl->ptr.pp_double[i][2+j] = c->c.ptr.p_double[(c->k+1)*i+j];
        }
    }
}


/*************************************************************************
This subroutine performs linear transformation of the spline argument.

INPUT PARAMETERS:
    C   -   spline interpolant.
    A, B-   transformation coefficients: x = A*t + B
Result:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 30.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dlintransx(spline1dinterpolant* c,
     double a,
     double b,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t n;
    double v;
    double dv;
    double d2v;
    ae_vector x;
    ae_vector y;
    ae_vector d;
    ae_bool isperiodic;
    ae_int_t contval;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&x, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_vector_init(&d, 0, DT_REAL, _state);

    ae_assert(c->k==3, "Spline1DLinTransX: internal error", _state);
    n = c->n;
    ae_vector_set_length(&x, n, _state);
    ae_vector_set_length(&y, n, _state);
    ae_vector_set_length(&d, n, _state);
    
    /*
     * Unpack, X, Y, dY/dX.
     * Scale and pack with Spline1DBuildHermite again.
     */
    if( ae_fp_eq(a,(double)(0)) )
    {
        
        /*
         * Special case: A=0
         */
        v = spline1dcalc(c, b, _state);
        for(i=0; i<=n-1; i++)
        {
            x.ptr.p_double[i] = c->x.ptr.p_double[i];
            y.ptr.p_double[i] = v;
            d.ptr.p_double[i] = 0.0;
        }
    }
    else
    {
        
        /*
         * General case, A<>0
         */
        for(i=0; i<=n-1; i++)
        {
            x.ptr.p_double[i] = c->x.ptr.p_double[i];
            spline1ddiff(c, x.ptr.p_double[i], &v, &dv, &d2v, _state);
            x.ptr.p_double[i] = (x.ptr.p_double[i]-b)/a;
            y.ptr.p_double[i] = v;
            d.ptr.p_double[i] = a*dv;
        }
    }
    isperiodic = c->periodic;
    contval = c->continuity;
    if( contval>0 )
    {
        spline1dbuildhermite(&x, &y, &d, n, c, _state);
    }
    else
    {
        spline1dbuildlinear(&x, &y, n, c, _state);
    }
    c->periodic = isperiodic;
    c->continuity = contval;
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine performs linear transformation of the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.
    A, B-   transformation coefficients: S2(x) = A*S(x) + B
Result:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 30.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline1dlintransy(spline1dinterpolant* c,
     double a,
     double b,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;
    ae_int_t n;


    ae_assert(c->k==3, "Spline1DLinTransX: internal error", _state);
    n = c->n;
    for(i=0; i<=n-2; i++)
    {
        c->c.ptr.p_double[4*i] = a*c->c.ptr.p_double[4*i]+b;
        for(j=1; j<=3; j++)
        {
            c->c.ptr.p_double[4*i+j] = a*c->c.ptr.p_double[4*i+j];
        }
    }
    c->c.ptr.p_double[4*(n-1)+0] = a*c->c.ptr.p_double[4*(n-1)+0]+b;
    c->c.ptr.p_double[4*(n-1)+1] = a*c->c.ptr.p_double[4*(n-1)+1];
}


/*************************************************************************
This subroutine integrates the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.
    X   -   right bound of the integration interval [a, x],
            here 'a' denotes min(x[])
Result:
    integral(S(t)dt,a,x)

  -- ALGLIB PROJECT --
     Copyright 23.06.2007 by Bochkanov Sergey
*************************************************************************/
double spline1dintegrate(spline1dinterpolant* c,
     double x,
     ae_state *_state)
{
    ae_int_t n;
    ae_int_t i;
    ae_int_t j;
    ae_int_t l;
    ae_int_t r;
    ae_int_t m;
    double w;
    double v;
    double t;
    double intab;
    double additionalterm;
    double result;


    n = c->n;
    
    /*
     * Periodic splines require special treatment. We make
     * following transformation:
     *
     *     integral(S(t)dt,A,X) = integral(S(t)dt,A,Z)+AdditionalTerm
     *
     * here X may lie outside of [A,B], Z lies strictly in [A,B],
     * AdditionalTerm is equals to integral(S(t)dt,A,B) times some
     * integer number (may be zero).
     */
    if( c->periodic&&(ae_fp_less(x,c->x.ptr.p_double[0])||ae_fp_greater(x,c->x.ptr.p_double[c->n-1])) )
    {
        
        /*
         * compute integral(S(x)dx,A,B)
         */
        intab = (double)(0);
        for(i=0; i<=c->n-2; i++)
        {
            w = c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i];
            m = (c->k+1)*i;
            intab = intab+c->c.ptr.p_double[m]*w;
            v = w;
            for(j=1; j<=c->k; j++)
            {
                v = v*w;
                intab = intab+c->c.ptr.p_double[m+j]*v/(j+1);
            }
        }
        
        /*
         * map X into [A,B]
         */
        apperiodicmap(&x, c->x.ptr.p_double[0], c->x.ptr.p_double[c->n-1], &t, _state);
        additionalterm = t*intab;
    }
    else
    {
        additionalterm = (double)(0);
    }
    
    /*
     * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included)
     */
    l = 0;
    r = n-2+1;
    while(l!=r-1)
    {
        m = (l+r)/2;
        if( ae_fp_greater_eq(c->x.ptr.p_double[m],x) )
        {
            r = m;
        }
        else
        {
            l = m;
        }
    }
    
    /*
     * Integration
     */
    result = (double)(0);
    for(i=0; i<=l-1; i++)
    {
        w = c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i];
        m = (c->k+1)*i;
        result = result+c->c.ptr.p_double[m]*w;
        v = w;
        for(j=1; j<=c->k; j++)
        {
            v = v*w;
            result = result+c->c.ptr.p_double[m+j]*v/(j+1);
        }
    }
    w = x-c->x.ptr.p_double[l];
    m = (c->k+1)*l;
    v = w;
    result = result+c->c.ptr.p_double[m]*w;
    for(j=1; j<=c->k; j++)
    {
        v = v*w;
        result = result+c->c.ptr.p_double[m+j]*v/(j+1);
    }
    result = result+additionalterm;
    return result;
}


/*************************************************************************
Internal version of Spline1DConvDiff

Converts from Hermite spline given by grid XOld to new grid X2

INPUT PARAMETERS:
    XOld    -   old grid
    YOld    -   values at old grid
    DOld    -   first derivative at old grid
    N       -   grid size
    X2      -   new grid
    N2      -   new grid size
    Y       -   possibly preallocated output array
                (reallocate if too small)
    NeedY   -   do we need Y?
    D1      -   possibly preallocated output array
                (reallocate if too small)
    NeedD1  -   do we need D1?
    D2      -   possibly preallocated output array
                (reallocate if too small)
    NeedD2  -   do we need D1?

OUTPUT ARRAYS:
    Y       -   values, if needed
    D1      -   first derivative, if needed
    D2      -   second derivative, if needed

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dconvdiffinternal(/* Real    */ ae_vector* xold,
     /* Real    */ ae_vector* yold,
     /* Real    */ ae_vector* dold,
     ae_int_t n,
     /* Real    */ ae_vector* x2,
     ae_int_t n2,
     /* Real    */ ae_vector* y,
     ae_bool needy,
     /* Real    */ ae_vector* d1,
     ae_bool needd1,
     /* Real    */ ae_vector* d2,
     ae_bool needd2,
     ae_state *_state)
{
    ae_int_t intervalindex;
    ae_int_t pointindex;
    ae_bool havetoadvance;
    double c0;
    double c1;
    double c2;
    double c3;
    double a;
    double b;
    double w;
    double w2;
    double w3;
    double fa;
    double fb;
    double da;
    double db;
    double t;


    
    /*
     * Prepare space
     */
    if( needy&&y->cnt<n2 )
    {
        ae_vector_set_length(y, n2, _state);
    }
    if( needd1&&d1->cnt<n2 )
    {
        ae_vector_set_length(d1, n2, _state);
    }
    if( needd2&&d2->cnt<n2 )
    {
        ae_vector_set_length(d2, n2, _state);
    }
    
    /*
     * These assignments aren't actually needed
     * (variables are initialized in the loop below),
     * but without them compiler will complain about uninitialized locals
     */
    c0 = (double)(0);
    c1 = (double)(0);
    c2 = (double)(0);
    c3 = (double)(0);
    a = (double)(0);
    b = (double)(0);
    
    /*
     * Cycle
     */
    intervalindex = -1;
    pointindex = 0;
    for(;;)
    {
        
        /*
         * are we ready to exit?
         */
        if( pointindex>=n2 )
        {
            break;
        }
        t = x2->ptr.p_double[pointindex];
        
        /*
         * do we need to advance interval?
         */
        havetoadvance = ae_false;
        if( intervalindex==-1 )
        {
            havetoadvance = ae_true;
        }
        else
        {
            if( intervalindex<n-2 )
            {
                havetoadvance = ae_fp_greater_eq(t,b);
            }
        }
        if( havetoadvance )
        {
            intervalindex = intervalindex+1;
            a = xold->ptr.p_double[intervalindex];
            b = xold->ptr.p_double[intervalindex+1];
            w = b-a;
            w2 = w*w;
            w3 = w*w2;
            fa = yold->ptr.p_double[intervalindex];
            fb = yold->ptr.p_double[intervalindex+1];
            da = dold->ptr.p_double[intervalindex];
            db = dold->ptr.p_double[intervalindex+1];
            c0 = fa;
            c1 = da;
            c2 = (3*(fb-fa)-2*da*w-db*w)/w2;
            c3 = (2*(fa-fb)+da*w+db*w)/w3;
            continue;
        }
        
        /*
         * Calculate spline and its derivatives using power basis
         */
        t = t-a;
        if( needy )
        {
            y->ptr.p_double[pointindex] = c0+t*(c1+t*(c2+t*c3));
        }
        if( needd1 )
        {
            d1->ptr.p_double[pointindex] = c1+2*t*c2+3*t*t*c3;
        }
        if( needd2 )
        {
            d2->ptr.p_double[pointindex] = 2*c2+6*t*c3;
        }
        pointindex = pointindex+1;
    }
}


/*************************************************************************
This function finds all roots and extrema of the spline S(x)  defined  at
[A,B] (interval which contains spline nodes).

It  does not extrapolates function, so roots and extrema located  outside 
of [A,B] will not be found. It returns all isolated (including  multiple)
roots and extrema.

INPUT PARAMETERS
    C           -   spline interpolant
    
OUTPUT PARAMETERS
    R           -   array[NR], contains roots of the spline. 
                    In case there is no roots, this array has zero length.
    NR          -   number of roots, >=0
    DR          -   is set to True in case there is at least one interval
                    where spline is just a zero constant. Such degenerate
                    cases are not reported in the R/NR
    E           -   array[NE], contains  extrema  (maximums/minimums)  of 
                    the spline. In case there is no extrema,  this  array 
                    has zero length.
    ET          -   array[NE], extrema types:
                    * ET[i]>0 in case I-th extrema is a minimum
                    * ET[i]<0 in case I-th extrema is a maximum
    NE          -   number of extrema, >=0
    DE          -   is set to True in case there is at least one interval
                    where spline is a constant. Such degenerate cases are  
                    not reported in the E/NE.
                    
NOTES:

1. This function does NOT report following kinds of roots:
   * intervals where function is constantly zero
   * roots which are outside of [A,B] (note: it CAN return A or B)

2. This function does NOT report following kinds of extrema:
   * intervals where function is a constant
   * extrema which are outside of (A,B) (note: it WON'T return A or B)
   
 -- ALGLIB PROJECT --
     Copyright 26.09.2011 by Bochkanov Sergey   
*************************************************************************/
void spline1drootsandextrema(spline1dinterpolant* c,
     /* Real    */ ae_vector* r,
     ae_int_t* nr,
     ae_bool* dr,
     /* Real    */ ae_vector* e,
     /* Integer */ ae_vector* et,
     ae_int_t* ne,
     ae_bool* de,
     ae_state *_state)
{
    ae_frame _frame_block;
    double pl;
    double ml;
    double pll;
    double pr;
    double mr;
    ae_vector tr;
    ae_vector tmpr;
    ae_vector tmpe;
    ae_vector tmpet;
    ae_vector tmpc;
    double x0;
    double x1;
    double x2;
    double ex0;
    double ex1;
    ae_int_t tne;
    ae_int_t tnr;
    ae_int_t i;
    ae_int_t j;
    ae_bool nstep;

    ae_frame_make(_state, &_frame_block);
    ae_vector_clear(r);
    *nr = 0;
    *dr = ae_false;
    ae_vector_clear(e);
    ae_vector_clear(et);
    *ne = 0;
    *de = ae_false;
    ae_vector_init(&tr, 0, DT_REAL, _state);
    ae_vector_init(&tmpr, 0, DT_REAL, _state);
    ae_vector_init(&tmpe, 0, DT_REAL, _state);
    ae_vector_init(&tmpet, 0, DT_INT, _state);
    ae_vector_init(&tmpc, 0, DT_REAL, _state);

    
    /*
     *exception handling
     */
    ae_assert(c->k==3, "Spline1DRootsAndExtrema : incorrect parameter C.K!", _state);
    ae_assert(c->continuity>=0, "Spline1DRootsAndExtrema : parameter C.Continuity must not be less than 0!", _state);
    
    /*
     *initialization of variable
     */
    *nr = 0;
    *ne = 0;
    *dr = ae_false;
    *de = ae_false;
    nstep = ae_true;
    
    /*
     *consider case, when C.Continuty=0
     */
    if( c->continuity==0 )
    {
        
        /*
         *allocation for auxiliary arrays 
         *'TmpR ' - it stores a time value for roots
         *'TmpE ' - it stores a time value for extremums
         *'TmpET '- it stores a time value for extremums type
         */
        rvectorsetlengthatleast(&tmpr, 3*(c->n-1), _state);
        rvectorsetlengthatleast(&tmpe, 2*(c->n-1), _state);
        ivectorsetlengthatleast(&tmpet, 2*(c->n-1), _state);
        
        /*
         *start calculating
         */
        for(i=0; i<=c->n-2; i++)
        {
            
            /*
             *initialization pL, mL, pR, mR
             */
            pl = c->c.ptr.p_double[4*i];
            ml = c->c.ptr.p_double[4*i+1];
            pr = c->c.ptr.p_double[4*(i+1)];
            mr = c->c.ptr.p_double[4*i+1]+2*c->c.ptr.p_double[4*i+2]*(c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i])+3*c->c.ptr.p_double[4*i+3]*(c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i])*(c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i]);
            
            /*
             *pre-searching roots and extremums
             */
            solvecubicpolinom(pl, ml, pr, mr, c->x.ptr.p_double[i], c->x.ptr.p_double[i+1], &x0, &x1, &x2, &ex0, &ex1, &tnr, &tne, &tr, _state);
            *dr = *dr||tnr==-1;
            *de = *de||tne==-1;
            
            /*
             *searching of roots
             */
            if( tnr==1&&nstep )
            {
                
                /*
                 *is there roots?
                 */
                if( *nr>0 )
                {
                    
                    /*
                     *is a next root equal a previous root?
                     *if is't, then write new root
                     */
                    if( ae_fp_neq(x0,tmpr.ptr.p_double[*nr-1]) )
                    {
                        tmpr.ptr.p_double[*nr] = x0;
                        *nr = *nr+1;
                    }
                }
                else
                {
                    
                    /*
                     *write a first root
                     */
                    tmpr.ptr.p_double[*nr] = x0;
                    *nr = *nr+1;
                }
            }
            else
            {
                
                /*
                 *case when function at a segment identically to zero
                 *then we have to clear a root, if the one located on a 
                 *constant segment
                 */
                if( tnr==-1 )
                {
                    
                    /*
                     *safe state variable as constant
                     */
                    if( nstep )
                    {
                        nstep = ae_false;
                    }
                    
                    /*
                     *clear the root, if there is
                     */
                    if( *nr>0 )
                    {
                        if( ae_fp_eq(c->x.ptr.p_double[i],tmpr.ptr.p_double[*nr-1]) )
                        {
                            *nr = *nr-1;
                        }
                    }
                    
                    /*
                     *change state for 'DR'
                     */
                    if( !*dr )
                    {
                        *dr = ae_true;
                    }
                }
                else
                {
                    nstep = ae_true;
                }
            }
            
            /*
             *searching of extremums
             */
            if( i>0 )
            {
                pll = c->c.ptr.p_double[4*(i-1)];
                
                /*
                 *if pL=pLL or pL=pR then
                 */
                if( tne==-1 )
                {
                    if( !*de )
                    {
                        *de = ae_true;
                    }
                }
                else
                {
                    if( ae_fp_greater(pl,pll)&&ae_fp_greater(pl,pr) )
                    {
                        
                        /*
                         *maximum
                         */
                        tmpet.ptr.p_int[*ne] = -1;
                        tmpe.ptr.p_double[*ne] = c->x.ptr.p_double[i];
                        *ne = *ne+1;
                    }
                    else
                    {
                        if( ae_fp_less(pl,pll)&&ae_fp_less(pl,pr) )
                        {
                            
                            /*
                             *minimum
                             */
                            tmpet.ptr.p_int[*ne] = 1;
                            tmpe.ptr.p_double[*ne] = c->x.ptr.p_double[i];
                            *ne = *ne+1;
                        }
                    }
                }
            }
        }
        
        /*
         *write final result
         */
        rvectorsetlengthatleast(r, *nr, _state);
        rvectorsetlengthatleast(e, *ne, _state);
        ivectorsetlengthatleast(et, *ne, _state);
        
        /*
         *write roots
         */
        for(i=0; i<=*nr-1; i++)
        {
            r->ptr.p_double[i] = tmpr.ptr.p_double[i];
        }
        
        /*
         *write extremums and their types
         */
        for(i=0; i<=*ne-1; i++)
        {
            e->ptr.p_double[i] = tmpe.ptr.p_double[i];
            et->ptr.p_int[i] = tmpet.ptr.p_int[i];
        }
    }
    else
    {
        
        /*
         *case, when C.Continuity>=1 
         *'TmpR ' - it stores a time value for roots
         *'TmpC' - it stores a time value for extremums and 
         *their function value (TmpC={EX0,F(EX0), EX1,F(EX1), ..., EXn,F(EXn)};)
         *'TmpE' - it stores a time value for extremums only
         *'TmpET'- it stores a time value for extremums type
         */
        rvectorsetlengthatleast(&tmpr, 2*c->n-1, _state);
        rvectorsetlengthatleast(&tmpc, 4*c->n, _state);
        rvectorsetlengthatleast(&tmpe, 2*c->n, _state);
        ivectorsetlengthatleast(&tmpet, 2*c->n, _state);
        
        /*
         *start calculating
         */
        for(i=0; i<=c->n-2; i++)
        {
            
            /*
             *we calculate pL,mL, pR,mR as Fi+1(F'i+1) at left border
             */
            pl = c->c.ptr.p_double[4*i];
            ml = c->c.ptr.p_double[4*i+1];
            pr = c->c.ptr.p_double[4*(i+1)];
            mr = c->c.ptr.p_double[4*(i+1)+1];
            
            /*
             *calculating roots and extremums at [X[i],X[i+1]]
             */
            solvecubicpolinom(pl, ml, pr, mr, c->x.ptr.p_double[i], c->x.ptr.p_double[i+1], &x0, &x1, &x2, &ex0, &ex1, &tnr, &tne, &tr, _state);
            
            /*
             *searching roots
             */
            if( tnr>0 )
            {
                
                /*
                 *re-init tR
                 */
                if( tnr>=1 )
                {
                    tr.ptr.p_double[0] = x0;
                }
                if( tnr>=2 )
                {
                    tr.ptr.p_double[1] = x1;
                }
                if( tnr==3 )
                {
                    tr.ptr.p_double[2] = x2;
                }
                
                /*
                 *start root selection
                 */
                if( *nr>0 )
                {
                    if( ae_fp_neq(tmpr.ptr.p_double[*nr-1],x0) )
                    {
                        
                        /*
                         *previous segment was't constant identical zero
                         */
                        if( nstep )
                        {
                            for(j=0; j<=tnr-1; j++)
                            {
                                tmpr.ptr.p_double[*nr+j] = tr.ptr.p_double[j];
                            }
                            *nr = *nr+tnr;
                        }
                        else
                        {
                            
                            /*
                             *previous segment was constant identical zero
                             *and we must ignore [NR+j-1] root
                             */
                            for(j=1; j<=tnr-1; j++)
                            {
                                tmpr.ptr.p_double[*nr+j-1] = tr.ptr.p_double[j];
                            }
                            *nr = *nr+tnr-1;
                            nstep = ae_true;
                        }
                    }
                    else
                    {
                        for(j=1; j<=tnr-1; j++)
                        {
                            tmpr.ptr.p_double[*nr+j-1] = tr.ptr.p_double[j];
                        }
                        *nr = *nr+tnr-1;
                    }
                }
                else
                {
                    
                    /*
                     *write first root
                     */
                    for(j=0; j<=tnr-1; j++)
                    {
                        tmpr.ptr.p_double[*nr+j] = tr.ptr.p_double[j];
                    }
                    *nr = *nr+tnr;
                }
            }
            else
            {
                if( tnr==-1 )
                {
                    
                    /*
                     *decrement 'NR' if at previous step was writen a root
                     *(previous segment identical zero)
                     */
                    if( *nr>0&&nstep )
                    {
                        *nr = *nr-1;
                    }
                    
                    /*
                     *previous segment is't constant
                     */
                    if( nstep )
                    {
                        nstep = ae_false;
                    }
                    
                    /*
                     *rewrite 'DR'
                     */
                    if( !*dr )
                    {
                        *dr = ae_true;
                    }
                }
            }
            
            /*
             *searching extremums
             *write all term like extremums
             */
            if( tne==1 )
            {
                if( *ne>0 )
                {
                    
                    /*
                     *just ignore identical extremums
                     *because he must be one
                     */
                    if( ae_fp_neq(tmpc.ptr.p_double[*ne-2],ex0) )
                    {
                        tmpc.ptr.p_double[*ne] = ex0;
                        tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i]);
                        *ne = *ne+2;
                    }
                }
                else
                {
                    
                    /*
                     *write first extremum and it function value
                     */
                    tmpc.ptr.p_double[*ne] = ex0;
                    tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i]);
                    *ne = *ne+2;
                }
            }
            else
            {
                if( tne==2 )
                {
                    if( *ne>0 )
                    {
                        
                        /*
                         *ignore identical extremum
                         */
                        if( ae_fp_neq(tmpc.ptr.p_double[*ne-2],ex0) )
                        {
                            tmpc.ptr.p_double[*ne] = ex0;
                            tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i]);
                            *ne = *ne+2;
                        }
                    }
                    else
                    {
                        
                        /*
                         *write first extremum
                         */
                        tmpc.ptr.p_double[*ne] = ex0;
                        tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i]);
                        *ne = *ne+2;
                    }
                    
                    /*
                     *write second extremum
                     */
                    tmpc.ptr.p_double[*ne] = ex1;
                    tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex1-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex1-c->x.ptr.p_double[i])*(ex1-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex1-c->x.ptr.p_double[i])*(ex1-c->x.ptr.p_double[i])*(ex1-c->x.ptr.p_double[i]);
                    *ne = *ne+2;
                }
                else
                {
                    if( tne==-1 )
                    {
                        if( !*de )
                        {
                            *de = ae_true;
                        }
                    }
                }
            }
        }
        
        /*
         *checking of arrays
         *get number of extremums (tNe=NE/2)
         *initialize pL as value F0(X[0]) and
         *initialize pR as value Fn-1(X[N])
         */
        tne = *ne/2;
        *ne = 0;
        pl = c->c.ptr.p_double[0];
        pr = c->c.ptr.p_double[4*(c->n-1)];
        for(i=0; i<=tne-1; i++)
        {
            if( i>0&&i<tne-1 )
            {
                if( ae_fp_greater(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i-1)+1])&&ae_fp_greater(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i+1)+1]) )
                {
                    
                    /*
                     *maximum
                     */
                    tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i];
                    tmpet.ptr.p_int[*ne] = -1;
                    *ne = *ne+1;
                }
                else
                {
                    if( ae_fp_less(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i-1)+1])&&ae_fp_less(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i+1)+1]) )
                    {
                        
                        /*
                         *minimum
                         */
                        tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i];
                        tmpet.ptr.p_int[*ne] = 1;
                        *ne = *ne+1;
                    }
                }
            }
            else
            {
                if( i==0 )
                {
                    if( ae_fp_neq(tmpc.ptr.p_double[2*i],c->x.ptr.p_double[0]) )
                    {
                        if( ae_fp_greater(tmpc.ptr.p_double[2*i+1],pl)&&ae_fp_greater(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i+1)+1]) )
                        {
                            
                            /*
                             *maximum
                             */
                            tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i];
                            tmpet.ptr.p_int[*ne] = -1;
                            *ne = *ne+1;
                        }
                        else
                        {
                            if( ae_fp_less(tmpc.ptr.p_double[2*i+1],pl)&&ae_fp_less(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i+1)+1]) )
                            {
                                
                                /*
                                 *minimum
                                 */
                                tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i];
                                tmpet.ptr.p_int[*ne] = 1;
                                *ne = *ne+1;
                            }
                        }
                    }
                }
                else
                {
                    if( i==tne-1 )
                    {
                        if( ae_fp_neq(tmpc.ptr.p_double[2*i],c->x.ptr.p_double[c->n-1]) )
                        {
                            if( ae_fp_greater(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i-1)+1])&&ae_fp_greater(tmpc.ptr.p_double[2*i+1],pr) )
                            {
                                
                                /*
                                 *maximum
                                 */
                                tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i];
                                tmpet.ptr.p_int[*ne] = -1;
                                *ne = *ne+1;
                            }
                            else
                            {
                                if( ae_fp_less(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i-1)+1])&&ae_fp_less(tmpc.ptr.p_double[2*i+1],pr) )
                                {
                                    
                                    /*
                                     *minimum
                                     */
                                    tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i];
                                    tmpet.ptr.p_int[*ne] = 1;
                                    *ne = *ne+1;
                                }
                            }
                        }
                    }
                }
            }
        }
        
        /*
         *final results
         *allocate R, E, ET
         */
        rvectorsetlengthatleast(r, *nr, _state);
        rvectorsetlengthatleast(e, *ne, _state);
        ivectorsetlengthatleast(et, *ne, _state);
        
        /*
         *write result for extremus and their types
         */
        for(i=0; i<=*ne-1; i++)
        {
            e->ptr.p_double[i] = tmpe.ptr.p_double[i];
            et->ptr.p_int[i] = tmpet.ptr.p_int[i];
        }
        
        /*
         *write result for roots
         */
        for(i=0; i<=*nr-1; i++)
        {
            r->ptr.p_double[i] = tmpr.ptr.p_double[i];
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Internal subroutine. Heap sort.
*************************************************************************/
void heapsortdpoints(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* d,
     ae_int_t n,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector rbuf;
    ae_vector ibuf;
    ae_vector rbuf2;
    ae_vector ibuf2;
    ae_int_t i;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&rbuf, 0, DT_REAL, _state);
    ae_vector_init(&ibuf, 0, DT_INT, _state);
    ae_vector_init(&rbuf2, 0, DT_REAL, _state);
    ae_vector_init(&ibuf2, 0, DT_INT, _state);

    ae_vector_set_length(&ibuf, n, _state);
    ae_vector_set_length(&rbuf, n, _state);
    for(i=0; i<=n-1; i++)
    {
        ibuf.ptr.p_int[i] = i;
    }
    tagsortfasti(x, &ibuf, &rbuf2, &ibuf2, n, _state);
    for(i=0; i<=n-1; i++)
    {
        rbuf.ptr.p_double[i] = y->ptr.p_double[ibuf.ptr.p_int[i]];
    }
    ae_v_move(&y->ptr.p_double[0], 1, &rbuf.ptr.p_double[0], 1, ae_v_len(0,n-1));
    for(i=0; i<=n-1; i++)
    {
        rbuf.ptr.p_double[i] = d->ptr.p_double[ibuf.ptr.p_int[i]];
    }
    ae_v_move(&d->ptr.p_double[0], 1, &rbuf.ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_frame_leave(_state);
}


/*************************************************************************
This procedure search roots of an quadratic equation inside [0;1] and it number of roots.

INPUT PARAMETERS:
    P0   -   value of a function at 0
    M0   -   value of a derivative at 0
    P1   -   value of a function at 1
    M1   -   value of a derivative at 1

OUTPUT PARAMETERS:
    X0   -  first root of an equation
    X1   -  second root of an equation
    NR   -  number of roots
    
RESTRICTIONS OF PARAMETERS:

Parameters for this procedure has't to be zero simultaneously. Is expected, 
that input polinom is't degenerate or constant identicaly ZERO.


REMARK:

The procedure always fill value for X1 and X2, even if it is't belongs to [0;1].
But first true root(even if existing one) is in X1.
Number of roots is NR.

 -- ALGLIB PROJECT --
     Copyright 26.09.2011 by Bochkanov Sergey
*************************************************************************/
void solvepolinom2(double p0,
     double m0,
     double p1,
     double m1,
     double* x0,
     double* x1,
     ae_int_t* nr,
     ae_state *_state)
{
    double a;
    double b;
    double c;
    double dd;
    double tmp;
    double exf;
    double extr;

    *x0 = 0;
    *x1 = 0;
    *nr = 0;

    
    /*
     *calculate parameters for equation: A, B  and C
     */
    a = 6*p0+3*m0-6*p1+3*m1;
    b = -6*p0-4*m0+6*p1-2*m1;
    c = m0;
    
    /*
     *check case, when A=0
     *we are considering the linear equation
     */
    if( ae_fp_eq(a,(double)(0)) )
    {
        
        /*
         *B<>0 and root inside [0;1]
         *one root
         */
        if( (ae_fp_neq(b,(double)(0))&&ae_sign(c, _state)*ae_sign(b, _state)<=0)&&ae_fp_greater_eq(ae_fabs(b, _state),ae_fabs(c, _state)) )
        {
            *x0 = -c/b;
            *nr = 1;
            return;
        }
        else
        {
            *nr = 0;
            return;
        }
    }
    
    /*
     *consider case, when extremumu outside (0;1)
     *exist one root only
     */
    if( ae_fp_less_eq(ae_fabs(2*a, _state),ae_fabs(b, _state))||ae_sign(b, _state)*ae_sign(a, _state)>=0 )
    {
        if( ae_sign(m0, _state)*ae_sign(m1, _state)>0 )
        {
            *nr = 0;
            return;
        }
        
        /*
         *consider case, when the one exist
         *same sign of derivative
         */
        if( ae_sign(m0, _state)*ae_sign(m1, _state)<0 )
        {
            *nr = 1;
            extr = -b/(2*a);
            dd = b*b-4*a*c;
            if( ae_fp_less(dd,(double)(0)) )
            {
                return;
            }
            *x0 = (-b-ae_sqrt(dd, _state))/(2*a);
            *x1 = (-b+ae_sqrt(dd, _state))/(2*a);
            if( (ae_fp_greater_eq(extr,(double)(1))&&ae_fp_less_eq(*x1,extr))||(ae_fp_less_eq(extr,(double)(0))&&ae_fp_greater_eq(*x1,extr)) )
            {
                *x0 = *x1;
            }
            return;
        }
        
        /*
         *consider case, when the one is 0
         */
        if( ae_fp_eq(m0,(double)(0)) )
        {
            *x0 = (double)(0);
            *nr = 1;
            return;
        }
        if( ae_fp_eq(m1,(double)(0)) )
        {
            *x0 = (double)(1);
            *nr = 1;
            return;
        }
    }
    else
    {
        
        /*
         *consider case, when both of derivatives is 0
         */
        if( ae_fp_eq(m0,(double)(0))&&ae_fp_eq(m1,(double)(0)) )
        {
            *x0 = (double)(0);
            *x1 = (double)(1);
            *nr = 2;
            return;
        }
        
        /*
         *consider case, when derivative at 0 is 0, and derivative at 1 is't 0
         */
        if( ae_fp_eq(m0,(double)(0))&&ae_fp_neq(m1,(double)(0)) )
        {
            dd = b*b-4*a*c;
            if( ae_fp_less(dd,(double)(0)) )
            {
                *x0 = (double)(0);
                *nr = 1;
                return;
            }
            *x0 = (-b-ae_sqrt(dd, _state))/(2*a);
            *x1 = (-b+ae_sqrt(dd, _state))/(2*a);
            extr = -b/(2*a);
            exf = a*extr*extr+b*extr+c;
            if( ae_sign(exf, _state)*ae_sign(m1, _state)>0 )
            {
                *x0 = (double)(0);
                *nr = 1;
                return;
            }
            else
            {
                if( ae_fp_greater(extr,*x0) )
                {
                    *x0 = (double)(0);
                }
                else
                {
                    *x1 = (double)(0);
                }
                *nr = 2;
                
                /*
                 *roots must placed ascending
                 */
                if( ae_fp_greater(*x0,*x1) )
                {
                    tmp = *x0;
                    *x0 = *x1;
                    *x1 = tmp;
                }
                return;
            }
        }
        if( ae_fp_eq(m1,(double)(0))&&ae_fp_neq(m0,(double)(0)) )
        {
            dd = b*b-4*a*c;
            if( ae_fp_less(dd,(double)(0)) )
            {
                *x0 = (double)(1);
                *nr = 1;
                return;
            }
            *x0 = (-b-ae_sqrt(dd, _state))/(2*a);
            *x1 = (-b+ae_sqrt(dd, _state))/(2*a);
            extr = -b/(2*a);
            exf = a*extr*extr+b*extr+c;
            if( ae_sign(exf, _state)*ae_sign(m0, _state)>0 )
            {
                *x0 = (double)(1);
                *nr = 1;
                return;
            }
            else
            {
                if( ae_fp_less(extr,*x0) )
                {
                    *x0 = (double)(1);
                }
                else
                {
                    *x1 = (double)(1);
                }
                *nr = 2;
                
                /*
                 *roots must placed ascending
                 */
                if( ae_fp_greater(*x0,*x1) )
                {
                    tmp = *x0;
                    *x0 = *x1;
                    *x1 = tmp;
                }
                return;
            }
        }
        else
        {
            extr = -b/(2*a);
            exf = a*extr*extr+b*extr+c;
            if( ae_sign(exf, _state)*ae_sign(m0, _state)>0&&ae_sign(exf, _state)*ae_sign(m1, _state)>0 )
            {
                *nr = 0;
                return;
            }
            dd = b*b-4*a*c;
            if( ae_fp_less(dd,(double)(0)) )
            {
                *nr = 0;
                return;
            }
            *x0 = (-b-ae_sqrt(dd, _state))/(2*a);
            *x1 = (-b+ae_sqrt(dd, _state))/(2*a);
            
            /*
             *if EXF and m0, EXF and m1 has different signs, then equation has two roots              
             */
            if( ae_sign(exf, _state)*ae_sign(m0, _state)<0&&ae_sign(exf, _state)*ae_sign(m1, _state)<0 )
            {
                *nr = 2;
                
                /*
                 *roots must placed ascending
                 */
                if( ae_fp_greater(*x0,*x1) )
                {
                    tmp = *x0;
                    *x0 = *x1;
                    *x1 = tmp;
                }
                return;
            }
            else
            {
                *nr = 1;
                if( ae_sign(exf, _state)*ae_sign(m0, _state)<0 )
                {
                    if( ae_fp_less(*x1,extr) )
                    {
                        *x0 = *x1;
                    }
                    return;
                }
                if( ae_sign(exf, _state)*ae_sign(m1, _state)<0 )
                {
                    if( ae_fp_greater(*x1,extr) )
                    {
                        *x0 = *x1;
                    }
                    return;
                }
            }
        }
    }
}


/*************************************************************************
This procedure search roots of an cubic equation inside [A;B], it number of roots 
and number of extremums.

INPUT PARAMETERS:
    pA   -   value of a function at A
    mA   -   value of a derivative at A
    pB   -   value of a function at B
    mB   -   value of a derivative at B
    A0   -   left border [A0;B0]
    B0   -   right border [A0;B0]

OUTPUT PARAMETERS:
    X0   -  first root of an equation
    X1   -  second root of an equation
    X2   -  third root of an equation
    EX0  -  first extremum of a function
    EX0  -  second extremum of a function
    NR   -  number of roots
    NR   -  number of extrmums
    
RESTRICTIONS OF PARAMETERS:

Length of [A;B] must be positive and is't zero, i.e. A<>B and A<B.


REMARK:

If 'NR' is -1 it's mean, than polinom has infiniti roots.
If 'NE' is -1 it's mean, than polinom has infiniti extremums.

 -- ALGLIB PROJECT --
     Copyright 26.09.2011 by Bochkanov Sergey
*************************************************************************/
void solvecubicpolinom(double pa,
     double ma,
     double pb,
     double mb,
     double a,
     double b,
     double* x0,
     double* x1,
     double* x2,
     double* ex0,
     double* ex1,
     ae_int_t* nr,
     ae_int_t* ne,
     /* Real    */ ae_vector* tempdata,
     ae_state *_state)
{
    ae_int_t i;
    double tmpma;
    double tmpmb;
    double tex0;
    double tex1;

    *x0 = 0;
    *x1 = 0;
    *x2 = 0;
    *ex0 = 0;
    *ex1 = 0;
    *nr = 0;
    *ne = 0;

    rvectorsetlengthatleast(tempdata, 3, _state);
    
    /*
     *case, when A>B
     */
    ae_assert(ae_fp_less(a,b), "\nSolveCubicPolinom: incorrect borders for [A;B]!\n", _state);
    
    /*
     *case 1    
     *function can be identicaly to ZERO
     */
    if( ((ae_fp_eq(ma,(double)(0))&&ae_fp_eq(mb,(double)(0)))&&ae_fp_eq(pa,pb))&&ae_fp_eq(pa,(double)(0)) )
    {
        *nr = -1;
        *ne = -1;
        return;
    }
    if( (ae_fp_eq(ma,(double)(0))&&ae_fp_eq(mb,(double)(0)))&&ae_fp_eq(pa,pb) )
    {
        *nr = 0;
        *ne = -1;
        return;
    }
    tmpma = ma*(b-a);
    tmpmb = mb*(b-a);
    solvepolinom2(pa, tmpma, pb, tmpmb, ex0, ex1, ne, _state);
    *ex0 = spline1d_rescaleval((double)(0), (double)(1), a, b, *ex0, _state);
    *ex1 = spline1d_rescaleval((double)(0), (double)(1), a, b, *ex1, _state);
    
    /*
     *case 3.1
     *no extremums at [A;B]
     */
    if( *ne==0 )
    {
        *nr = bisectmethod(pa, tmpma, pb, tmpmb, (double)(0), (double)(1), x0, _state);
        if( *nr==1 )
        {
            *x0 = spline1d_rescaleval((double)(0), (double)(1), a, b, *x0, _state);
        }
        return;
    }
    
    /*
     *case 3.2
     *one extremum
     */
    if( *ne==1 )
    {
        if( ae_fp_eq(*ex0,a)||ae_fp_eq(*ex0,b) )
        {
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, (double)(0), (double)(1), x0, _state);
            if( *nr==1 )
            {
                *x0 = spline1d_rescaleval((double)(0), (double)(1), a, b, *x0, _state);
            }
            return;
        }
        else
        {
            *nr = 0;
            i = 0;
            tex0 = spline1d_rescaleval(a, b, (double)(0), (double)(1), *ex0, _state);
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, (double)(0), tex0, x0, _state)+(*nr);
            if( *nr>i )
            {
                tempdata->ptr.p_double[i] = spline1d_rescaleval((double)(0), tex0, a, *ex0, *x0, _state);
                i = i+1;
            }
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex0, (double)(1), x0, _state)+(*nr);
            if( *nr>i )
            {
                *x0 = spline1d_rescaleval(tex0, (double)(1), *ex0, b, *x0, _state);
                if( i>0 )
                {
                    if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) )
                    {
                        tempdata->ptr.p_double[i] = *x0;
                        i = i+1;
                    }
                    else
                    {
                        *nr = *nr-1;
                    }
                }
                else
                {
                    tempdata->ptr.p_double[i] = *x0;
                    i = i+1;
                }
            }
            if( *nr>0 )
            {
                *x0 = tempdata->ptr.p_double[0];
                if( *nr>1 )
                {
                    *x1 = tempdata->ptr.p_double[1];
                }
                return;
            }
        }
        return;
    }
    else
    {
        
        /*
         *case 3.3
         *two extremums(or more, but it's impossible)
         *
         *
         *case 3.3.0
         *both extremums at the border
         */
        if( ae_fp_eq(*ex0,a)&&ae_fp_eq(*ex1,b) )
        {
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, (double)(0), (double)(1), x0, _state);
            if( *nr==1 )
            {
                *x0 = spline1d_rescaleval((double)(0), (double)(1), a, b, *x0, _state);
            }
            return;
        }
        if( ae_fp_eq(*ex0,a)&&ae_fp_neq(*ex1,b) )
        {
            *nr = 0;
            i = 0;
            tex1 = spline1d_rescaleval(a, b, (double)(0), (double)(1), *ex1, _state);
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, (double)(0), tex1, x0, _state)+(*nr);
            if( *nr>i )
            {
                tempdata->ptr.p_double[i] = spline1d_rescaleval((double)(0), tex1, a, *ex1, *x0, _state);
                i = i+1;
            }
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex1, (double)(1), x0, _state)+(*nr);
            if( *nr>i )
            {
                *x0 = spline1d_rescaleval(tex1, (double)(1), *ex1, b, *x0, _state);
                if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) )
                {
                    tempdata->ptr.p_double[i] = *x0;
                    i = i+1;
                }
                else
                {
                    *nr = *nr-1;
                }
            }
            if( *nr>0 )
            {
                *x0 = tempdata->ptr.p_double[0];
                if( *nr>1 )
                {
                    *x1 = tempdata->ptr.p_double[1];
                }
                return;
            }
        }
        if( ae_fp_eq(*ex1,b)&&ae_fp_neq(*ex0,a) )
        {
            *nr = 0;
            i = 0;
            tex0 = spline1d_rescaleval(a, b, (double)(0), (double)(1), *ex0, _state);
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, (double)(0), tex0, x0, _state)+(*nr);
            if( *nr>i )
            {
                tempdata->ptr.p_double[i] = spline1d_rescaleval((double)(0), tex0, a, *ex0, *x0, _state);
                i = i+1;
            }
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex0, (double)(1), x0, _state)+(*nr);
            if( *nr>i )
            {
                *x0 = spline1d_rescaleval(tex0, (double)(1), *ex0, b, *x0, _state);
                if( i>0 )
                {
                    if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) )
                    {
                        tempdata->ptr.p_double[i] = *x0;
                        i = i+1;
                    }
                    else
                    {
                        *nr = *nr-1;
                    }
                }
                else
                {
                    tempdata->ptr.p_double[i] = *x0;
                    i = i+1;
                }
            }
            if( *nr>0 )
            {
                *x0 = tempdata->ptr.p_double[0];
                if( *nr>1 )
                {
                    *x1 = tempdata->ptr.p_double[1];
                }
                return;
            }
        }
        else
        {
            
            /*
             *case 3.3.2
             *both extremums inside (0;1)
             */
            *nr = 0;
            i = 0;
            tex0 = spline1d_rescaleval(a, b, (double)(0), (double)(1), *ex0, _state);
            tex1 = spline1d_rescaleval(a, b, (double)(0), (double)(1), *ex1, _state);
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, (double)(0), tex0, x0, _state)+(*nr);
            if( *nr>i )
            {
                tempdata->ptr.p_double[i] = spline1d_rescaleval((double)(0), tex0, a, *ex0, *x0, _state);
                i = i+1;
            }
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex0, tex1, x0, _state)+(*nr);
            if( *nr>i )
            {
                *x0 = spline1d_rescaleval(tex0, tex1, *ex0, *ex1, *x0, _state);
                if( i>0 )
                {
                    if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) )
                    {
                        tempdata->ptr.p_double[i] = *x0;
                        i = i+1;
                    }
                    else
                    {
                        *nr = *nr-1;
                    }
                }
                else
                {
                    tempdata->ptr.p_double[i] = *x0;
                    i = i+1;
                }
            }
            *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex1, (double)(1), x0, _state)+(*nr);
            if( *nr>i )
            {
                *x0 = spline1d_rescaleval(tex1, (double)(1), *ex1, b, *x0, _state);
                if( i>0 )
                {
                    if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) )
                    {
                        tempdata->ptr.p_double[i] = *x0;
                        i = i+1;
                    }
                    else
                    {
                        *nr = *nr-1;
                    }
                }
                else
                {
                    tempdata->ptr.p_double[i] = *x0;
                    i = i+1;
                }
            }
            
            /*
             *write are found roots
             */
            if( *nr>0 )
            {
                *x0 = tempdata->ptr.p_double[0];
                if( *nr>1 )
                {
                    *x1 = tempdata->ptr.p_double[1];
                }
                if( *nr>2 )
                {
                    *x2 = tempdata->ptr.p_double[2];
                }
                return;
            }
        }
    }
}


/*************************************************************************
Function for searching a root at [A;B] by bisection method and return number of roots
(0 or 1)

INPUT PARAMETERS:
    pA   -   value of a function at A
    mA   -   value of a derivative at A
    pB   -   value of a function at B
    mB   -   value of a derivative at B
    A0   -   left border [A0;B0] 
    B0   -   right border [A0;B0]
    
RESTRICTIONS OF PARAMETERS:

We assume, that B0>A0.


REMARK:

Assume, that exist one root only at [A;B], else 
function may be work incorrectly.
The function dont check value A0,B0!

 -- ALGLIB PROJECT --
     Copyright 26.09.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t bisectmethod(double pa,
     double ma,
     double pb,
     double mb,
     double a,
     double b,
     double* x,
     ae_state *_state)
{
    double vacuum;
    double eps;
    double a0;
    double b0;
    double m;
    double lf;
    double rf;
    double mf;
    ae_int_t result;

    *x = 0;

    
    /*
     *accuracy
     */
    eps = 1000*(b-a)*ae_machineepsilon;
    
    /*
     *initialization left and right borders
     */
    a0 = a;
    b0 = b;
    
    /*
     *initialize function value at 'A' and 'B'
     */
    spline1d_hermitecalc(pa, ma, pb, mb, a, &lf, &vacuum, _state);
    spline1d_hermitecalc(pa, ma, pb, mb, b, &rf, &vacuum, _state);
    
    /*
     *check, that 'A' and 'B' are't roots,
     *and that root exist
     */
    if( ae_sign(lf, _state)*ae_sign(rf, _state)>0 )
    {
        result = 0;
        return result;
    }
    else
    {
        if( ae_fp_eq(lf,(double)(0)) )
        {
            *x = a;
            result = 1;
            return result;
        }
        else
        {
            if( ae_fp_eq(rf,(double)(0)) )
            {
                *x = b;
                result = 1;
                return result;
            }
        }
    }
    
    /*
     *searching a root
     */
    do
    {
        m = (b0+a0)/2;
        spline1d_hermitecalc(pa, ma, pb, mb, a0, &lf, &vacuum, _state);
        spline1d_hermitecalc(pa, ma, pb, mb, b0, &rf, &vacuum, _state);
        spline1d_hermitecalc(pa, ma, pb, mb, m, &mf, &vacuum, _state);
        if( ae_sign(mf, _state)*ae_sign(lf, _state)<0 )
        {
            b0 = m;
        }
        else
        {
            if( ae_sign(mf, _state)*ae_sign(rf, _state)<0 )
            {
                a0 = m;
            }
            else
            {
                if( ae_fp_eq(lf,(double)(0)) )
                {
                    *x = a0;
                    result = 1;
                    return result;
                }
                if( ae_fp_eq(rf,(double)(0)) )
                {
                    *x = b0;
                    result = 1;
                    return result;
                }
                if( ae_fp_eq(mf,(double)(0)) )
                {
                    *x = m;
                    result = 1;
                    return result;
                }
            }
        }
    }
    while(ae_fp_greater_eq(ae_fabs(b0-a0, _state),eps));
    *x = m;
    result = 1;
    return result;
}


/*************************************************************************
This function builds monotone cubic Hermite interpolant. This interpolant
is monotonic in [x(0),x(n-1)] and is constant outside of this interval.

In  case  y[]  form  non-monotonic  sequence,  interpolant  is  piecewise
monotonic.  Say, for x=(0,1,2,3,4)  and  y=(0,1,2,1,0)  interpolant  will
monotonically grow at [0..2] and monotonically decrease at [2..4].

INPUT PARAMETERS:
    X           -   spline nodes, array[0..N-1]. Subroutine automatically
                    sorts points, so caller may pass unsorted array.
    Y           -   function values, array[0..N-1]
    N           -   the number of points(N>=2).

OUTPUT PARAMETERS:
    C           -   spline interpolant.

 -- ALGLIB PROJECT --
     Copyright 21.06.2012 by Bochkanov Sergey
*************************************************************************/
void spline1dbuildmonotone(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     spline1dinterpolant* c,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector d;
    ae_vector ex;
    ae_vector ey;
    ae_vector p;
    double delta;
    double alpha;
    double beta;
    ae_int_t tmpn;
    ae_int_t sn;
    double ca;
    double cb;
    double epsilon;
    ae_int_t i;
    ae_int_t j;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    _spline1dinterpolant_clear(c);
    ae_vector_init(&d, 0, DT_REAL, _state);
    ae_vector_init(&ex, 0, DT_REAL, _state);
    ae_vector_init(&ey, 0, DT_REAL, _state);
    ae_vector_init(&p, 0, DT_INT, _state);

    
    /*
     * Check lengths of arguments
     */
    ae_assert(n>=2, "Spline1DBuildMonotone: N<2", _state);
    ae_assert(x->cnt>=n, "Spline1DBuildMonotone: Length(X)<N", _state);
    ae_assert(y->cnt>=n, "Spline1DBuildMonotone: Length(Y)<N", _state);
    
    /*
     * Check and sort points
     */
    ae_assert(isfinitevector(x, n, _state), "Spline1DBuildMonotone: X contains infinite or NAN values", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DBuildMonotone: Y contains infinite or NAN values", _state);
    spline1d_heapsortppoints(x, y, &p, n, _state);
    ae_assert(aredistinct(x, n, _state), "Spline1DBuildMonotone: at least two consequent points are too close", _state);
    epsilon = ae_machineepsilon;
    n = n+2;
    ae_vector_set_length(&d, n, _state);
    ae_vector_set_length(&ex, n, _state);
    ae_vector_set_length(&ey, n, _state);
    ex.ptr.p_double[0] = x->ptr.p_double[0]-ae_fabs(x->ptr.p_double[1]-x->ptr.p_double[0], _state);
    ex.ptr.p_double[n-1] = x->ptr.p_double[n-3]+ae_fabs(x->ptr.p_double[n-3]-x->ptr.p_double[n-4], _state);
    ey.ptr.p_double[0] = y->ptr.p_double[0];
    ey.ptr.p_double[n-1] = y->ptr.p_double[n-3];
    for(i=1; i<=n-2; i++)
    {
        ex.ptr.p_double[i] = x->ptr.p_double[i-1];
        ey.ptr.p_double[i] = y->ptr.p_double[i-1];
    }
    
    /*
     * Init sign of the function for first segment
     */
    i = 0;
    ca = (double)(0);
    do
    {
        ca = ey.ptr.p_double[i+1]-ey.ptr.p_double[i];
        i = i+1;
    }
    while(!(ae_fp_neq(ca,(double)(0))||i>n-2));
    if( ae_fp_neq(ca,(double)(0)) )
    {
        ca = ca/ae_fabs(ca, _state);
    }
    i = 0;
    while(i<n-1)
    {
        
        /*
         * Partition of the segment [X0;Xn]
         */
        tmpn = 1;
        for(j=i; j<=n-2; j++)
        {
            cb = ey.ptr.p_double[j+1]-ey.ptr.p_double[j];
            if( ae_fp_greater_eq(ca*cb,(double)(0)) )
            {
                tmpn = tmpn+1;
            }
            else
            {
                ca = cb/ae_fabs(cb, _state);
                break;
            }
        }
        sn = i+tmpn;
        ae_assert(tmpn>=2, "Spline1DBuildMonotone: internal error", _state);
        
        /*
         * Calculate derivatives for current segment
         */
        d.ptr.p_double[i] = (double)(0);
        d.ptr.p_double[sn-1] = (double)(0);
        for(j=i+1; j<=sn-2; j++)
        {
            d.ptr.p_double[j] = ((ey.ptr.p_double[j]-ey.ptr.p_double[j-1])/(ex.ptr.p_double[j]-ex.ptr.p_double[j-1])+(ey.ptr.p_double[j+1]-ey.ptr.p_double[j])/(ex.ptr.p_double[j+1]-ex.ptr.p_double[j]))/2;
        }
        for(j=i; j<=sn-2; j++)
        {
            delta = (ey.ptr.p_double[j+1]-ey.ptr.p_double[j])/(ex.ptr.p_double[j+1]-ex.ptr.p_double[j]);
            if( ae_fp_less_eq(ae_fabs(delta, _state),epsilon) )
            {
                d.ptr.p_double[j] = (double)(0);
                d.ptr.p_double[j+1] = (double)(0);
            }
            else
            {
                alpha = d.ptr.p_double[j]/delta;
                beta = d.ptr.p_double[j+1]/delta;
                if( ae_fp_neq(alpha,(double)(0)) )
                {
                    cb = alpha*ae_sqrt(1+ae_sqr(beta/alpha, _state), _state);
                }
                else
                {
                    if( ae_fp_neq(beta,(double)(0)) )
                    {
                        cb = beta;
                    }
                    else
                    {
                        continue;
                    }
                }
                if( ae_fp_greater(cb,(double)(3)) )
                {
                    d.ptr.p_double[j] = 3*alpha*delta/cb;
                    d.ptr.p_double[j+1] = 3*beta*delta/cb;
                }
            }
        }
        
        /*
         * Transition to next segment
         */
        i = sn-1;
    }
    spline1dbuildhermite(&ex, &ey, &d, n, c, _state);
    c->continuity = 2;
    ae_frame_leave(_state);
}


/*************************************************************************
Internal version of Spline1DGridDiffCubic.

Accepts pre-ordered X/Y, temporary arrays (which may be  preallocated,  if
you want to save time, or not) and output array (which may be preallocated
too).

Y is passed as var-parameter because we may need to force last element  to
be equal to the first one (if periodic boundary conditions are specified).

  -- ALGLIB PROJECT --
     Copyright 03.09.2010 by Bochkanov Sergey
*************************************************************************/
static void spline1d_spline1dgriddiffcubicinternal(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t boundltype,
     double boundl,
     ae_int_t boundrtype,
     double boundr,
     /* Real    */ ae_vector* d,
     /* Real    */ ae_vector* a1,
     /* Real    */ ae_vector* a2,
     /* Real    */ ae_vector* a3,
     /* Real    */ ae_vector* b,
     /* Real    */ ae_vector* dt,
     ae_state *_state)
{
    ae_int_t i;


    
    /*
     * allocate arrays
     */
    if( d->cnt<n )
    {
        ae_vector_set_length(d, n, _state);
    }
    if( a1->cnt<n )
    {
        ae_vector_set_length(a1, n, _state);
    }
    if( a2->cnt<n )
    {
        ae_vector_set_length(a2, n, _state);
    }
    if( a3->cnt<n )
    {
        ae_vector_set_length(a3, n, _state);
    }
    if( b->cnt<n )
    {
        ae_vector_set_length(b, n, _state);
    }
    if( dt->cnt<n )
    {
        ae_vector_set_length(dt, n, _state);
    }
    
    /*
     * Special cases:
     * * N=2, parabolic terminated boundary condition on both ends
     * * N=2, periodic boundary condition
     */
    if( (n==2&&boundltype==0)&&boundrtype==0 )
    {
        d->ptr.p_double[0] = (y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0]);
        d->ptr.p_double[1] = d->ptr.p_double[0];
        return;
    }
    if( (n==2&&boundltype==-1)&&boundrtype==-1 )
    {
        d->ptr.p_double[0] = (double)(0);
        d->ptr.p_double[1] = (double)(0);
        return;
    }
    
    /*
     * Periodic and non-periodic boundary conditions are
     * two separate classes
     */
    if( boundrtype==-1&&boundltype==-1 )
    {
        
        /*
         * Periodic boundary conditions
         */
        y->ptr.p_double[n-1] = y->ptr.p_double[0];
        
        /*
         * Boundary conditions at N-1 points
         * (one point less because last point is the same as first point).
         */
        a1->ptr.p_double[0] = x->ptr.p_double[1]-x->ptr.p_double[0];
        a2->ptr.p_double[0] = 2*(x->ptr.p_double[1]-x->ptr.p_double[0]+x->ptr.p_double[n-1]-x->ptr.p_double[n-2]);
        a3->ptr.p_double[0] = x->ptr.p_double[n-1]-x->ptr.p_double[n-2];
        b->ptr.p_double[0] = 3*(y->ptr.p_double[n-1]-y->ptr.p_double[n-2])/(x->ptr.p_double[n-1]-x->ptr.p_double[n-2])*(x->ptr.p_double[1]-x->ptr.p_double[0])+3*(y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0])*(x->ptr.p_double[n-1]-x->ptr.p_double[n-2]);
        for(i=1; i<=n-2; i++)
        {
            
            /*
             * Altough last point is [N-2], we use X[N-1] and Y[N-1]
             * (because of periodicity)
             */
            a1->ptr.p_double[i] = x->ptr.p_double[i+1]-x->ptr.p_double[i];
            a2->ptr.p_double[i] = 2*(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]);
            a3->ptr.p_double[i] = x->ptr.p_double[i]-x->ptr.p_double[i-1];
            b->ptr.p_double[i] = 3*(y->ptr.p_double[i]-y->ptr.p_double[i-1])/(x->ptr.p_double[i]-x->ptr.p_double[i-1])*(x->ptr.p_double[i+1]-x->ptr.p_double[i])+3*(y->ptr.p_double[i+1]-y->ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i])*(x->ptr.p_double[i]-x->ptr.p_double[i-1]);
        }
        
        /*
         * Solve, add last point (with index N-1)
         */
        spline1d_solvecyclictridiagonal(a1, a2, a3, b, n-1, dt, _state);
        ae_v_move(&d->ptr.p_double[0], 1, &dt->ptr.p_double[0], 1, ae_v_len(0,n-2));
        d->ptr.p_double[n-1] = d->ptr.p_double[0];
    }
    else
    {
        
        /*
         * Non-periodic boundary condition.
         * Left boundary conditions.
         */
        if( boundltype==0 )
        {
            a1->ptr.p_double[0] = (double)(0);
            a2->ptr.p_double[0] = (double)(1);
            a3->ptr.p_double[0] = (double)(1);
            b->ptr.p_double[0] = 2*(y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0]);
        }
        if( boundltype==1 )
        {
            a1->ptr.p_double[0] = (double)(0);
            a2->ptr.p_double[0] = (double)(1);
            a3->ptr.p_double[0] = (double)(0);
            b->ptr.p_double[0] = boundl;
        }
        if( boundltype==2 )
        {
            a1->ptr.p_double[0] = (double)(0);
            a2->ptr.p_double[0] = (double)(2);
            a3->ptr.p_double[0] = (double)(1);
            b->ptr.p_double[0] = 3*(y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0])-0.5*boundl*(x->ptr.p_double[1]-x->ptr.p_double[0]);
        }
        
        /*
         * Central conditions
         */
        for(i=1; i<=n-2; i++)
        {
            a1->ptr.p_double[i] = x->ptr.p_double[i+1]-x->ptr.p_double[i];
            a2->ptr.p_double[i] = 2*(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]);
            a3->ptr.p_double[i] = x->ptr.p_double[i]-x->ptr.p_double[i-1];
            b->ptr.p_double[i] = 3*(y->ptr.p_double[i]-y->ptr.p_double[i-1])/(x->ptr.p_double[i]-x->ptr.p_double[i-1])*(x->ptr.p_double[i+1]-x->ptr.p_double[i])+3*(y->ptr.p_double[i+1]-y->ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i])*(x->ptr.p_double[i]-x->ptr.p_double[i-1]);
        }
        
        /*
         * Right boundary conditions
         */
        if( boundrtype==0 )
        {
            a1->ptr.p_double[n-1] = (double)(1);
            a2->ptr.p_double[n-1] = (double)(1);
            a3->ptr.p_double[n-1] = (double)(0);
            b->ptr.p_double[n-1] = 2*(y->ptr.p_double[n-1]-y->ptr.p_double[n-2])/(x->ptr.p_double[n-1]-x->ptr.p_double[n-2]);
        }
        if( boundrtype==1 )
        {
            a1->ptr.p_double[n-1] = (double)(0);
            a2->ptr.p_double[n-1] = (double)(1);
            a3->ptr.p_double[n-1] = (double)(0);
            b->ptr.p_double[n-1] = boundr;
        }
        if( boundrtype==2 )
        {
            a1->ptr.p_double[n-1] = (double)(1);
            a2->ptr.p_double[n-1] = (double)(2);
            a3->ptr.p_double[n-1] = (double)(0);
            b->ptr.p_double[n-1] = 3*(y->ptr.p_double[n-1]-y->ptr.p_double[n-2])/(x->ptr.p_double[n-1]-x->ptr.p_double[n-2])+0.5*boundr*(x->ptr.p_double[n-1]-x->ptr.p_double[n-2]);
        }
        
        /*
         * Solve
         */
        spline1d_solvetridiagonal(a1, a2, a3, b, n, d, _state);
    }
}


/*************************************************************************
Internal subroutine. Heap sort.
*************************************************************************/
static void spline1d_heapsortpoints(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector bufx;
    ae_vector bufy;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&bufx, 0, DT_REAL, _state);
    ae_vector_init(&bufy, 0, DT_REAL, _state);

    tagsortfastr(x, y, &bufx, &bufy, n, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Internal subroutine. Heap sort.

Accepts:
    X, Y    -   points
    P       -   empty or preallocated array
    
Returns:
    X, Y    -   sorted by X
    P       -   array of permutations; I-th position of output
                arrays X/Y contains (X[P[I]],Y[P[I]])
*************************************************************************/
static void spline1d_heapsortppoints(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Integer */ ae_vector* p,
     ae_int_t n,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector rbuf;
    ae_vector ibuf;
    ae_int_t i;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&rbuf, 0, DT_REAL, _state);
    ae_vector_init(&ibuf, 0, DT_INT, _state);

    if( p->cnt<n )
    {
        ae_vector_set_length(p, n, _state);
    }
    ae_vector_set_length(&rbuf, n, _state);
    for(i=0; i<=n-1; i++)
    {
        p->ptr.p_int[i] = i;
    }
    tagsortfasti(x, p, &rbuf, &ibuf, n, _state);
    for(i=0; i<=n-1; i++)
    {
        rbuf.ptr.p_double[i] = y->ptr.p_double[p->ptr.p_int[i]];
    }
    ae_v_move(&y->ptr.p_double[0], 1, &rbuf.ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_frame_leave(_state);
}


/*************************************************************************
Internal subroutine. Tridiagonal solver. Solves

( B[0] C[0]                      
( A[1] B[1] C[1]                 )
(      A[2] B[2] C[2]            )
(            ..........          ) * X = D
(            ..........          )
(           A[N-2] B[N-2] C[N-2] )
(                  A[N-1] B[N-1] )

*************************************************************************/
static void spline1d_solvetridiagonal(/* Real    */ ae_vector* a,
     /* Real    */ ae_vector* b,
     /* Real    */ ae_vector* c,
     /* Real    */ ae_vector* d,
     ae_int_t n,
     /* Real    */ ae_vector* x,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _b;
    ae_vector _d;
    ae_int_t k;
    double t;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_b, b, _state);
    b = &_b;
    ae_vector_init_copy(&_d, d, _state);
    d = &_d;

    if( x->cnt<n )
    {
        ae_vector_set_length(x, n, _state);
    }
    for(k=1; k<=n-1; k++)
    {
        t = a->ptr.p_double[k]/b->ptr.p_double[k-1];
        b->ptr.p_double[k] = b->ptr.p_double[k]-t*c->ptr.p_double[k-1];
        d->ptr.p_double[k] = d->ptr.p_double[k]-t*d->ptr.p_double[k-1];
    }
    x->ptr.p_double[n-1] = d->ptr.p_double[n-1]/b->ptr.p_double[n-1];
    for(k=n-2; k>=0; k--)
    {
        x->ptr.p_double[k] = (d->ptr.p_double[k]-c->ptr.p_double[k]*x->ptr.p_double[k+1])/b->ptr.p_double[k];
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Internal subroutine. Cyclic tridiagonal solver. Solves

( B[0] C[0]                 A[0] )
( A[1] B[1] C[1]                 )
(      A[2] B[2] C[2]            )
(            ..........          ) * X = D
(            ..........          )
(           A[N-2] B[N-2] C[N-2] )
( C[N-1]           A[N-1] B[N-1] )
*************************************************************************/
static void spline1d_solvecyclictridiagonal(/* Real    */ ae_vector* a,
     /* Real    */ ae_vector* b,
     /* Real    */ ae_vector* c,
     /* Real    */ ae_vector* d,
     ae_int_t n,
     /* Real    */ ae_vector* x,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _b;
    ae_int_t k;
    double alpha;
    double beta;
    double gamma;
    ae_vector y;
    ae_vector z;
    ae_vector u;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_b, b, _state);
    b = &_b;
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_vector_init(&z, 0, DT_REAL, _state);
    ae_vector_init(&u, 0, DT_REAL, _state);

    if( x->cnt<n )
    {
        ae_vector_set_length(x, n, _state);
    }
    beta = a->ptr.p_double[0];
    alpha = c->ptr.p_double[n-1];
    gamma = -b->ptr.p_double[0];
    b->ptr.p_double[0] = 2*b->ptr.p_double[0];
    b->ptr.p_double[n-1] = b->ptr.p_double[n-1]-alpha*beta/gamma;
    ae_vector_set_length(&u, n, _state);
    for(k=0; k<=n-1; k++)
    {
        u.ptr.p_double[k] = (double)(0);
    }
    u.ptr.p_double[0] = gamma;
    u.ptr.p_double[n-1] = alpha;
    spline1d_solvetridiagonal(a, b, c, d, n, &y, _state);
    spline1d_solvetridiagonal(a, b, c, &u, n, &z, _state);
    for(k=0; k<=n-1; k++)
    {
        x->ptr.p_double[k] = y.ptr.p_double[k]-(y.ptr.p_double[0]+beta/gamma*y.ptr.p_double[n-1])/(1+z.ptr.p_double[0]+beta/gamma*z.ptr.p_double[n-1])*z.ptr.p_double[k];
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Internal subroutine. Three-point differentiation
*************************************************************************/
static double spline1d_diffthreepoint(double t,
     double x0,
     double f0,
     double x1,
     double f1,
     double x2,
     double f2,
     ae_state *_state)
{
    double a;
    double b;
    double result;


    t = t-x0;
    x1 = x1-x0;
    x2 = x2-x0;
    a = (f2-f0-x2/x1*(f1-f0))/(ae_sqr(x2, _state)-x1*x2);
    b = (f1-f0-a*ae_sqr(x1, _state))/x1;
    result = 2*a*t+b;
    return result;
}


/*************************************************************************
Procedure for calculating value of a function is providet in the form of
Hermite polinom  

INPUT PARAMETERS:
    P0   -   value of a function at 0
    M0   -   value of a derivative at 0
    P1   -   value of a function at 1
    M1   -   value of a derivative at 1
    T    -   point inside [0;1]
    
OUTPUT PARAMETERS:
    S    -   value of a function at T
    B0   -   value of a derivative function at T
    
 -- ALGLIB PROJECT --
     Copyright 26.09.2011 by Bochkanov Sergey
*************************************************************************/
static void spline1d_hermitecalc(double p0,
     double m0,
     double p1,
     double m1,
     double t,
     double* s,
     double* ds,
     ae_state *_state)
{

    *s = 0;
    *ds = 0;

    *s = p0*(1+2*t)*(1-t)*(1-t)+m0*t*(1-t)*(1-t)+p1*(3-2*t)*t*t+m1*t*t*(t-1);
    *ds = -p0*6*t*(1-t)+m0*(1-t)*(1-3*t)+p1*6*t*(1-t)+m1*t*(3*t-2);
}


/*************************************************************************
Function for mapping from [A0;B0] to [A1;B1]

INPUT PARAMETERS:
    A0   -   left border [A0;B0]
    B0   -   right border [A0;B0]
    A1   -   left border [A1;B1]
    B1   -   right border [A1;B1]
    T    -   value inside [A0;B0]  
    
RESTRICTIONS OF PARAMETERS:

We assume, that B0>A0 and B1>A1. But we chech, that T is inside [A0;B0], 
and if T<A0 then T become A1, if T>B0 then T - B1. 

INPUT PARAMETERS:
        A0   -   left border for segment [A0;B0] from 'T' is converted to [A1;B1] 
        B0   -   right border for segment [A0;B0] from 'T' is converted to [A1;B1] 
        A1   -   left border for segment [A1;B1] to 'T' is converted from [A0;B0] 
        B1   -   right border for segment [A1;B1] to 'T' is converted from [A0;B0] 
        T    -   the parameter is mapped from [A0;B0] to [A1;B1] 

Result:
    is converted value for 'T' from [A0;B0] to [A1;B1]
         
REMARK:

The function dont check value A0,B0 and A1,B1!

 -- ALGLIB PROJECT --
     Copyright 26.09.2011 by Bochkanov Sergey
*************************************************************************/
static double spline1d_rescaleval(double a0,
     double b0,
     double a1,
     double b1,
     double t,
     ae_state *_state)
{
    double result;


    
    /*
     *return left border
     */
    if( ae_fp_less_eq(t,a0) )
    {
        result = a1;
        return result;
    }
    
    /*
     *return right border
     */
    if( ae_fp_greater_eq(t,b0) )
    {
        result = b1;
        return result;
    }
    
    /*
     *return value between left and right borders
     */
    result = (b1-a1)*(t-a0)/(b0-a0)+a1;
    return result;
}


void _spline1dinterpolant_init(void* _p, ae_state *_state)
{
    spline1dinterpolant *p = (spline1dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_init(&p->x, 0, DT_REAL, _state);
    ae_vector_init(&p->c, 0, DT_REAL, _state);
}


void _spline1dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state)
{
    spline1dinterpolant *dst = (spline1dinterpolant*)_dst;
    spline1dinterpolant *src = (spline1dinterpolant*)_src;
    dst->periodic = src->periodic;
    dst->n = src->n;
    dst->k = src->k;
    dst->continuity = src->continuity;
    ae_vector_init_copy(&dst->x, &src->x, _state);
    ae_vector_init_copy(&dst->c, &src->c, _state);
}


void _spline1dinterpolant_clear(void* _p)
{
    spline1dinterpolant *p = (spline1dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_clear(&p->x);
    ae_vector_clear(&p->c);
}


void _spline1dinterpolant_destroy(void* _p)
{
    spline1dinterpolant *p = (spline1dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_destroy(&p->x);
    ae_vector_destroy(&p->c);
}




/*************************************************************************
This  subroutine fits piecewise linear curve to points with Ramer-Douglas-
Peucker algorithm, which stops after generating specified number of linear
sections.

IMPORTANT:
* it does NOT perform least-squares fitting; it  builds  curve,  but  this
  curve does not minimize some least squares metric.  See  description  of
  RDP algorithm (say, in Wikipedia) for more details on WHAT is performed.
* this function does NOT work with parametric curves  (i.e.  curves  which
  can be represented as {X(t),Y(t)}. It works with curves   which  can  be
  represented as Y(X). Thus,  it  is  impossible  to  model  figures  like
  circles  with  this  functions.
  If  you  want  to  work  with  parametric   curves,   you   should   use
  ParametricRDPFixed() function provided  by  "Parametric"  subpackage  of
  "Interpolation" package.

INPUT PARAMETERS:
    X       -   array of X-coordinates:
                * at least N elements
                * can be unordered (points are automatically sorted)
                * this function may accept non-distinct X (see below for
                  more information on handling of such inputs)
    Y       -   array of Y-coordinates:
                * at least N elements
    N       -   number of elements in X/Y
    M       -   desired number of sections:
                * at most M sections are generated by this function
                * less than M sections can be generated if we have N<M
                  (or some X are non-distinct).

OUTPUT PARAMETERS:
    X2      -   X-values of corner points for piecewise approximation,
                has length NSections+1 or zero (for NSections=0).
    Y2      -   Y-values of corner points,
                has length NSections+1 or zero (for NSections=0).
    NSections-  number of sections found by algorithm, NSections<=M,
                NSections can be zero for degenerate datasets
                (N<=1 or all X[] are non-distinct).

NOTE: X2/Y2 are ordered arrays, i.e. (X2[0],Y2[0]) is  a  first  point  of
      curve, (X2[NSection-1],Y2[NSection-1]) is the last point.

  -- ALGLIB --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
void lstfitpiecewiselinearrdpfixed(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t m,
     /* Real    */ ae_vector* x2,
     /* Real    */ ae_vector* y2,
     ae_int_t* nsections,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t k0;
    ae_int_t k1;
    ae_int_t k2;
    ae_vector buf0;
    ae_vector buf1;
    ae_matrix sections;
    ae_vector points;
    double v;
    ae_int_t worstidx;
    double worsterror;
    ae_int_t idx0;
    ae_int_t idx1;
    double e0;
    double e1;
    ae_vector heaperrors;
    ae_vector heaptags;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_clear(x2);
    ae_vector_clear(y2);
    *nsections = 0;
    ae_vector_init(&buf0, 0, DT_REAL, _state);
    ae_vector_init(&buf1, 0, DT_REAL, _state);
    ae_matrix_init(&sections, 0, 0, DT_REAL, _state);
    ae_vector_init(&points, 0, DT_REAL, _state);
    ae_vector_init(&heaperrors, 0, DT_REAL, _state);
    ae_vector_init(&heaptags, 0, DT_INT, _state);

    ae_assert(n>=0, "LSTFitPiecewiseLinearRDPFixed: N<0", _state);
    ae_assert(m>=1, "LSTFitPiecewiseLinearRDPFixed: M<1", _state);
    ae_assert(x->cnt>=n, "LSTFitPiecewiseLinearRDPFixed: Length(X)<N", _state);
    ae_assert(y->cnt>=n, "LSTFitPiecewiseLinearRDPFixed: Length(Y)<N", _state);
    if( n<=1 )
    {
        *nsections = 0;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Sort points.
     * Handle possible ties (tied values are replaced by their mean)
     */
    tagsortfastr(x, y, &buf0, &buf1, n, _state);
    i = 0;
    while(i<=n-1)
    {
        j = i+1;
        v = y->ptr.p_double[i];
        while(j<=n-1&&ae_fp_eq(x->ptr.p_double[j],x->ptr.p_double[i]))
        {
            v = v+y->ptr.p_double[j];
            j = j+1;
        }
        v = v/(j-i);
        for(k=i; k<=j-1; k++)
        {
            y->ptr.p_double[k] = v;
        }
        i = j;
    }
    
    /*
     * Handle degenerate case x[0]=x[N-1]
     */
    if( ae_fp_eq(x->ptr.p_double[n-1],x->ptr.p_double[0]) )
    {
        *nsections = 0;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Prepare first section
     */
    lsfit_rdpanalyzesection(x, y, 0, n-1, &worstidx, &worsterror, _state);
    ae_matrix_set_length(&sections, m, 4, _state);
    ae_vector_set_length(&heaperrors, m, _state);
    ae_vector_set_length(&heaptags, m, _state);
    *nsections = 1;
    sections.ptr.pp_double[0][0] = (double)(0);
    sections.ptr.pp_double[0][1] = (double)(n-1);
    sections.ptr.pp_double[0][2] = (double)(worstidx);
    sections.ptr.pp_double[0][3] = worsterror;
    heaperrors.ptr.p_double[0] = worsterror;
    heaptags.ptr.p_int[0] = 0;
    ae_assert(ae_fp_eq(sections.ptr.pp_double[0][1],(double)(n-1)), "RDP algorithm: integrity check failed", _state);
    
    /*
     * Main loop.
     * Repeatedly find section with worst error and divide it.
     * Terminate after M-th section, or because of other reasons (see loop internals).
     */
    while(*nsections<m)
    {
        
        /*
         * Break if worst section has zero error.
         * Store index of worst section to K.
         */
        if( ae_fp_eq(heaperrors.ptr.p_double[0],(double)(0)) )
        {
            break;
        }
        k = heaptags.ptr.p_int[0];
        
        /*
         * K-th section is divided in two:
         * * first  one spans interval from X[Sections[K,0]] to X[Sections[K,2]]
         * * second one spans interval from X[Sections[K,2]] to X[Sections[K,1]]
         *
         * First section is stored at K-th position, second one is appended to the table.
         * Then we update heap which stores pairs of (error,section_index)
         */
        k0 = ae_round(sections.ptr.pp_double[k][0], _state);
        k1 = ae_round(sections.ptr.pp_double[k][1], _state);
        k2 = ae_round(sections.ptr.pp_double[k][2], _state);
        lsfit_rdpanalyzesection(x, y, k0, k2, &idx0, &e0, _state);
        lsfit_rdpanalyzesection(x, y, k2, k1, &idx1, &e1, _state);
        sections.ptr.pp_double[k][0] = (double)(k0);
        sections.ptr.pp_double[k][1] = (double)(k2);
        sections.ptr.pp_double[k][2] = (double)(idx0);
        sections.ptr.pp_double[k][3] = e0;
        tagheapreplacetopi(&heaperrors, &heaptags, *nsections, e0, k, _state);
        sections.ptr.pp_double[*nsections][0] = (double)(k2);
        sections.ptr.pp_double[*nsections][1] = (double)(k1);
        sections.ptr.pp_double[*nsections][2] = (double)(idx1);
        sections.ptr.pp_double[*nsections][3] = e1;
        tagheappushi(&heaperrors, &heaptags, nsections, e1, *nsections, _state);
    }
    
    /*
     * Convert from sections to points
     */
    ae_vector_set_length(&points, *nsections+1, _state);
    k = ae_round(sections.ptr.pp_double[0][1], _state);
    for(i=0; i<=*nsections-1; i++)
    {
        points.ptr.p_double[i] = (double)(ae_round(sections.ptr.pp_double[i][0], _state));
        if( ae_fp_greater(x->ptr.p_double[ae_round(sections.ptr.pp_double[i][1], _state)],x->ptr.p_double[k]) )
        {
            k = ae_round(sections.ptr.pp_double[i][1], _state);
        }
    }
    points.ptr.p_double[*nsections] = (double)(k);
    tagsortfast(&points, &buf0, *nsections+1, _state);
    
    /*
     * Output sections:
     * * first NSection elements of X2/Y2 are filled by x/y at left boundaries of sections
     * * last element of X2/Y2 is filled by right boundary of rightmost section
     * * X2/Y2 is sorted by ascending of X2
     */
    ae_vector_set_length(x2, *nsections+1, _state);
    ae_vector_set_length(y2, *nsections+1, _state);
    for(i=0; i<=*nsections; i++)
    {
        x2->ptr.p_double[i] = x->ptr.p_double[ae_round(points.ptr.p_double[i], _state)];
        y2->ptr.p_double[i] = y->ptr.p_double[ae_round(points.ptr.p_double[i], _state)];
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This  subroutine fits piecewise linear curve to points with Ramer-Douglas-
Peucker algorithm, which stops after achieving desired precision.

IMPORTANT:
* it performs non-least-squares fitting; it builds curve, but  this  curve
  does not minimize some least squares  metric.  See  description  of  RDP
  algorithm (say, in Wikipedia) for more details on WHAT is performed.
* this function does NOT work with parametric curves  (i.e.  curves  which
  can be represented as {X(t),Y(t)}. It works with curves   which  can  be
  represented as Y(X). Thus, it is impossible to model figures like circles
  with this functions.
  If  you  want  to  work  with  parametric   curves,   you   should   use
  ParametricRDPFixed() function provided  by  "Parametric"  subpackage  of
  "Interpolation" package.

INPUT PARAMETERS:
    X       -   array of X-coordinates:
                * at least N elements
                * can be unordered (points are automatically sorted)
                * this function may accept non-distinct X (see below for
                  more information on handling of such inputs)
    Y       -   array of Y-coordinates:
                * at least N elements
    N       -   number of elements in X/Y
    Eps     -   positive number, desired precision.
    

OUTPUT PARAMETERS:
    X2      -   X-values of corner points for piecewise approximation,
                has length NSections+1 or zero (for NSections=0).
    Y2      -   Y-values of corner points,
                has length NSections+1 or zero (for NSections=0).
    NSections-  number of sections found by algorithm,
                NSections can be zero for degenerate datasets
                (N<=1 or all X[] are non-distinct).

NOTE: X2/Y2 are ordered arrays, i.e. (X2[0],Y2[0]) is  a  first  point  of
      curve, (X2[NSection-1],Y2[NSection-1]) is the last point.

  -- ALGLIB --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
void lstfitpiecewiselinearrdp(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     double eps,
     /* Real    */ ae_vector* x2,
     /* Real    */ ae_vector* y2,
     ae_int_t* nsections,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_vector buf0;
    ae_vector buf1;
    ae_vector xtmp;
    ae_vector ytmp;
    double v;
    ae_int_t npts;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_clear(x2);
    ae_vector_clear(y2);
    *nsections = 0;
    ae_vector_init(&buf0, 0, DT_REAL, _state);
    ae_vector_init(&buf1, 0, DT_REAL, _state);
    ae_vector_init(&xtmp, 0, DT_REAL, _state);
    ae_vector_init(&ytmp, 0, DT_REAL, _state);

    ae_assert(n>=0, "LSTFitPiecewiseLinearRDP: N<0", _state);
    ae_assert(ae_fp_greater(eps,(double)(0)), "LSTFitPiecewiseLinearRDP: Eps<=0", _state);
    ae_assert(x->cnt>=n, "LSTFitPiecewiseLinearRDP: Length(X)<N", _state);
    ae_assert(y->cnt>=n, "LSTFitPiecewiseLinearRDP: Length(Y)<N", _state);
    if( n<=1 )
    {
        *nsections = 0;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Sort points.
     * Handle possible ties (tied values are replaced by their mean)
     */
    tagsortfastr(x, y, &buf0, &buf1, n, _state);
    i = 0;
    while(i<=n-1)
    {
        j = i+1;
        v = y->ptr.p_double[i];
        while(j<=n-1&&ae_fp_eq(x->ptr.p_double[j],x->ptr.p_double[i]))
        {
            v = v+y->ptr.p_double[j];
            j = j+1;
        }
        v = v/(j-i);
        for(k=i; k<=j-1; k++)
        {
            y->ptr.p_double[k] = v;
        }
        i = j;
    }
    
    /*
     * Handle degenerate case x[0]=x[N-1]
     */
    if( ae_fp_eq(x->ptr.p_double[n-1],x->ptr.p_double[0]) )
    {
        *nsections = 0;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Prepare data for recursive algorithm
     */
    ae_vector_set_length(&xtmp, n, _state);
    ae_vector_set_length(&ytmp, n, _state);
    npts = 2;
    xtmp.ptr.p_double[0] = x->ptr.p_double[0];
    ytmp.ptr.p_double[0] = y->ptr.p_double[0];
    xtmp.ptr.p_double[1] = x->ptr.p_double[n-1];
    ytmp.ptr.p_double[1] = y->ptr.p_double[n-1];
    lsfit_rdprecursive(x, y, 0, n-1, eps, &xtmp, &ytmp, &npts, _state);
    
    /*
     * Output sections:
     * * first NSection elements of X2/Y2 are filled by x/y at left boundaries of sections
     * * last element of X2/Y2 is filled by right boundary of rightmost section
     * * X2/Y2 is sorted by ascending of X2
     */
    *nsections = npts-1;
    ae_vector_set_length(x2, npts, _state);
    ae_vector_set_length(y2, npts, _state);
    for(i=0; i<=*nsections; i++)
    {
        x2->ptr.p_double[i] = xtmp.ptr.p_double[i];
        y2->ptr.p_double[i] = ytmp.ptr.p_double[i];
    }
    tagsortfastr(x2, y2, &buf0, &buf1, npts, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Fitting by polynomials in barycentric form. This function provides  simple
unterface for unconstrained unweighted fitting. See  PolynomialFitWC()  if
you need constrained fitting.

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO:
    PolynomialFitWC()
    
COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of points, N>0
            * if given, only leading N elements of X/Y are used
            * if not given, automatically determined from sizes of X/Y
    M   -   number of basis functions (= polynomial_degree + 1), M>=1

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
    P   -   interpolant in barycentric form.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

NOTES:
    you can convert P from barycentric form  to  the  power  or  Chebyshev
    basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions  from
    POLINT subpackage.

  -- ALGLIB PROJECT --
     Copyright 10.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialfit(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     barycentricinterpolant* p,
     polynomialfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_vector w;
    ae_vector xc;
    ae_vector yc;
    ae_vector dc;

    ae_frame_make(_state, &_frame_block);
    *info = 0;
    _barycentricinterpolant_clear(p);
    _polynomialfitreport_clear(rep);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&xc, 0, DT_REAL, _state);
    ae_vector_init(&yc, 0, DT_REAL, _state);
    ae_vector_init(&dc, 0, DT_INT, _state);

    ae_assert(n>0, "PolynomialFit: N<=0!", _state);
    ae_assert(m>0, "PolynomialFit: M<=0!", _state);
    ae_assert(x->cnt>=n, "PolynomialFit: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "PolynomialFit: Length(Y)<N!", _state);
    ae_assert(isfinitevector(x, n, _state), "PolynomialFit: X contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "PolynomialFit: Y contains infinite or NaN values!", _state);
    ae_vector_set_length(&w, n, _state);
    for(i=0; i<=n-1; i++)
    {
        w.ptr.p_double[i] = (double)(1);
    }
    polynomialfitwc(x, y, &w, n, &xc, &yc, &dc, 0, m, info, p, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_polynomialfit(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    ae_int_t n,
    ae_int_t m,
    ae_int_t* info,
    barycentricinterpolant* p,
    polynomialfitreport* rep, ae_state *_state)
{
    polynomialfit(x,y,n,m,info,p,rep, _state);
}


/*************************************************************************
Weighted  fitting by polynomials in barycentric form, with constraints  on
function values or first derivatives.

Small regularizing term is used when solving constrained tasks (to improve
stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO:
    PolynomialFit()
    
COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points, N>0.
            * if given, only leading N elements of X/Y/W are used
            * if not given, automatically determined from sizes of X/Y/W
    XC  -   points where polynomial values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that P(XC[i])=YC[i]
            * DC[i]=1   means that P'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints, 0<=K<M.
            K=0 means no constraints (XC/YC/DC are not used in such cases)
    M   -   number of basis functions (= polynomial_degree + 1), M>=1

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    P   -   interpolant in barycentric form.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTES:
    you can convert P from barycentric form  to  the  power  or  Chebyshev
    basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions  from
    POLINT subpackage.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* even simple constraints can be inconsistent, see  Wikipedia  article  on
  this subject: http://en.wikipedia.org/wiki/Birkhoff_interpolation
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints is NOT GUARANTEED.
* in the one special cases, however, we can  guarantee  consistency.  This
  case  is:  M>1  and constraints on the function values (NOT DERIVATIVES)

Our final recommendation is to use constraints  WHEN  AND  ONLY  when  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.

  -- ALGLIB PROJECT --
     Copyright 10.12.2009 by Bochkanov Sergey
*************************************************************************/
void polynomialfitwc(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t* info,
     barycentricinterpolant* p,
     polynomialfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector _w;
    ae_vector _xc;
    ae_vector _yc;
    double xa;
    double xb;
    double sa;
    double sb;
    ae_vector xoriginal;
    ae_vector yoriginal;
    ae_vector y2;
    ae_vector w2;
    ae_vector tmp;
    ae_vector tmp2;
    ae_vector bx;
    ae_vector by;
    ae_vector bw;
    ae_int_t i;
    ae_int_t j;
    double u;
    double v;
    double s;
    ae_int_t relcnt;
    lsfitreport lrep;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_init_copy(&_w, w, _state);
    w = &_w;
    ae_vector_init_copy(&_xc, xc, _state);
    xc = &_xc;
    ae_vector_init_copy(&_yc, yc, _state);
    yc = &_yc;
    *info = 0;
    _barycentricinterpolant_clear(p);
    _polynomialfitreport_clear(rep);
    ae_vector_init(&xoriginal, 0, DT_REAL, _state);
    ae_vector_init(&yoriginal, 0, DT_REAL, _state);
    ae_vector_init(&y2, 0, DT_REAL, _state);
    ae_vector_init(&w2, 0, DT_REAL, _state);
    ae_vector_init(&tmp, 0, DT_REAL, _state);
    ae_vector_init(&tmp2, 0, DT_REAL, _state);
    ae_vector_init(&bx, 0, DT_REAL, _state);
    ae_vector_init(&by, 0, DT_REAL, _state);
    ae_vector_init(&bw, 0, DT_REAL, _state);
    _lsfitreport_init(&lrep, _state);

    ae_assert(n>0, "PolynomialFitWC: N<=0!", _state);
    ae_assert(m>0, "PolynomialFitWC: M<=0!", _state);
    ae_assert(k>=0, "PolynomialFitWC: K<0!", _state);
    ae_assert(k<m, "PolynomialFitWC: K>=M!", _state);
    ae_assert(x->cnt>=n, "PolynomialFitWC: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "PolynomialFitWC: Length(Y)<N!", _state);
    ae_assert(w->cnt>=n, "PolynomialFitWC: Length(W)<N!", _state);
    ae_assert(xc->cnt>=k, "PolynomialFitWC: Length(XC)<K!", _state);
    ae_assert(yc->cnt>=k, "PolynomialFitWC: Length(YC)<K!", _state);
    ae_assert(dc->cnt>=k, "PolynomialFitWC: Length(DC)<K!", _state);
    ae_assert(isfinitevector(x, n, _state), "PolynomialFitWC: X contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "PolynomialFitWC: Y contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(w, n, _state), "PolynomialFitWC: X contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(xc, k, _state), "PolynomialFitWC: XC contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(yc, k, _state), "PolynomialFitWC: YC contains infinite or NaN values!", _state);
    for(i=0; i<=k-1; i++)
    {
        ae_assert(dc->ptr.p_int[i]==0||dc->ptr.p_int[i]==1, "PolynomialFitWC: one of DC[] is not 0 or 1!", _state);
    }
    
    /*
     * Scale X, Y, XC, YC.
     * Solve scaled problem using internal Chebyshev fitting function.
     */
    lsfitscalexy(x, y, w, n, xc, yc, dc, k, &xa, &xb, &sa, &sb, &xoriginal, &yoriginal, _state);
    lsfit_internalchebyshevfit(x, y, w, n, xc, yc, dc, k, m, info, &tmp, &lrep, _state);
    if( *info<0 )
    {
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Generate barycentric model and scale it
     * * BX, BY store barycentric model nodes
     * * FMatrix is reused (remember - it is at least MxM, what we need)
     *
     * Model intialization is done in O(M^2). In principle, it can be
     * done in O(M*log(M)), but before it we solved task with O(N*M^2)
     * complexity, so it is only a small amount of total time spent.
     */
    ae_vector_set_length(&bx, m, _state);
    ae_vector_set_length(&by, m, _state);
    ae_vector_set_length(&bw, m, _state);
    ae_vector_set_length(&tmp2, m, _state);
    s = (double)(1);
    for(i=0; i<=m-1; i++)
    {
        if( m!=1 )
        {
            u = ae_cos(ae_pi*i/(m-1), _state);
        }
        else
        {
            u = (double)(0);
        }
        v = (double)(0);
        for(j=0; j<=m-1; j++)
        {
            if( j==0 )
            {
                tmp2.ptr.p_double[j] = (double)(1);
            }
            else
            {
                if( j==1 )
                {
                    tmp2.ptr.p_double[j] = u;
                }
                else
                {
                    tmp2.ptr.p_double[j] = 2*u*tmp2.ptr.p_double[j-1]-tmp2.ptr.p_double[j-2];
                }
            }
            v = v+tmp.ptr.p_double[j]*tmp2.ptr.p_double[j];
        }
        bx.ptr.p_double[i] = u;
        by.ptr.p_double[i] = v;
        bw.ptr.p_double[i] = s;
        if( i==0||i==m-1 )
        {
            bw.ptr.p_double[i] = 0.5*bw.ptr.p_double[i];
        }
        s = -s;
    }
    barycentricbuildxyw(&bx, &by, &bw, m, p, _state);
    barycentriclintransx(p, 2/(xb-xa), -(xa+xb)/(xb-xa), _state);
    barycentriclintransy(p, sb-sa, sa, _state);
    
    /*
     * Scale absolute errors obtained from LSFitLinearW.
     * Relative error should be calculated separately
     * (because of shifting/scaling of the task)
     */
    rep->taskrcond = lrep.taskrcond;
    rep->rmserror = lrep.rmserror*(sb-sa);
    rep->avgerror = lrep.avgerror*(sb-sa);
    rep->maxerror = lrep.maxerror*(sb-sa);
    rep->avgrelerror = (double)(0);
    relcnt = 0;
    for(i=0; i<=n-1; i++)
    {
        if( ae_fp_neq(yoriginal.ptr.p_double[i],(double)(0)) )
        {
            rep->avgrelerror = rep->avgrelerror+ae_fabs(barycentriccalc(p, xoriginal.ptr.p_double[i], _state)-yoriginal.ptr.p_double[i], _state)/ae_fabs(yoriginal.ptr.p_double[i], _state);
            relcnt = relcnt+1;
        }
    }
    if( relcnt!=0 )
    {
        rep->avgrelerror = rep->avgrelerror/relcnt;
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_polynomialfitwc(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    /* Real    */ ae_vector* w,
    ae_int_t n,
    /* Real    */ ae_vector* xc,
    /* Real    */ ae_vector* yc,
    /* Integer */ ae_vector* dc,
    ae_int_t k,
    ae_int_t m,
    ae_int_t* info,
    barycentricinterpolant* p,
    polynomialfitreport* rep, ae_state *_state)
{
    polynomialfitwc(x,y,w,n,xc,yc,dc,k,m,info,p,rep, _state);
}


/*************************************************************************
This function calculates value of four-parameter logistic (4PL)  model  at
specified point X. 4PL model has following form:

    F(x|A,B,C,D) = D+(A-D)/(1+Power(x/C,B))

INPUT PARAMETERS:
    X       -   current point, X>=0:
                * zero X is correctly handled even for B<=0
                * negative X results in exception.
    A, B, C, D- parameters of 4PL model:
                * A is unconstrained
                * B is unconstrained; zero or negative values are handled
                  correctly.
                * C>0, non-positive value results in exception
                * D is unconstrained
                
RESULT:
    model value at X

NOTE: if B=0, denominator is assumed to be equal to 2.0 even  for  zero  X
      (strictly speaking, 0^0 is undefined).

NOTE: this function also throws exception  if  all  input  parameters  are
      correct, but overflow was detected during calculations.
      
NOTE: this function performs a lot of checks;  if  you  need  really  high
      performance, consider evaluating model  yourself,  without  checking
      for degenerate cases.
      
    
  -- ALGLIB PROJECT --
     Copyright 14.05.2014 by Bochkanov Sergey
*************************************************************************/
double logisticcalc4(double x,
     double a,
     double b,
     double c,
     double d,
     ae_state *_state)
{
    double result;


    ae_assert(ae_isfinite(x, _state), "LogisticCalc4: X is not finite", _state);
    ae_assert(ae_isfinite(a, _state), "LogisticCalc4: A is not finite", _state);
    ae_assert(ae_isfinite(b, _state), "LogisticCalc4: B is not finite", _state);
    ae_assert(ae_isfinite(c, _state), "LogisticCalc4: C is not finite", _state);
    ae_assert(ae_isfinite(d, _state), "LogisticCalc4: D is not finite", _state);
    ae_assert(ae_fp_greater_eq(x,(double)(0)), "LogisticCalc4: X is negative", _state);
    ae_assert(ae_fp_greater(c,(double)(0)), "LogisticCalc4: C is non-positive", _state);
    
    /*
     * Check for degenerate cases
     */
    if( ae_fp_eq(b,(double)(0)) )
    {
        result = 0.5*(a+d);
        return result;
    }
    if( ae_fp_eq(x,(double)(0)) )
    {
        if( ae_fp_greater(b,(double)(0)) )
        {
            result = a;
        }
        else
        {
            result = d;
        }
        return result;
    }
    
    /*
     * General case
     */
    result = d+(a-d)/(1.0+ae_pow(x/c, b, _state));
    ae_assert(ae_isfinite(result, _state), "LogisticCalc4: overflow during calculations", _state);
    return result;
}


/*************************************************************************
This function calculates value of five-parameter logistic (5PL)  model  at
specified point X. 5PL model has following form:

    F(x|A,B,C,D,G) = D+(A-D)/Power(1+Power(x/C,B),G)

INPUT PARAMETERS:
    X       -   current point, X>=0:
                * zero X is correctly handled even for B<=0
                * negative X results in exception.
    A, B, C, D, G- parameters of 5PL model:
                * A is unconstrained
                * B is unconstrained; zero or negative values are handled
                  correctly.
                * C>0, non-positive value results in exception
                * D is unconstrained
                * G>0, non-positive value results in exception
                
RESULT:
    model value at X

NOTE: if B=0, denominator is assumed to be equal to Power(2.0,G) even  for
      zero X (strictly speaking, 0^0 is undefined).

NOTE: this function also throws exception  if  all  input  parameters  are
      correct, but overflow was detected during calculations.
      
NOTE: this function performs a lot of checks;  if  you  need  really  high
      performance, consider evaluating model  yourself,  without  checking
      for degenerate cases.
      
    
  -- ALGLIB PROJECT --
     Copyright 14.05.2014 by Bochkanov Sergey
*************************************************************************/
double logisticcalc5(double x,
     double a,
     double b,
     double c,
     double d,
     double g,
     ae_state *_state)
{
    double result;


    ae_assert(ae_isfinite(x, _state), "LogisticCalc5: X is not finite", _state);
    ae_assert(ae_isfinite(a, _state), "LogisticCalc5: A is not finite", _state);
    ae_assert(ae_isfinite(b, _state), "LogisticCalc5: B is not finite", _state);
    ae_assert(ae_isfinite(c, _state), "LogisticCalc5: C is not finite", _state);
    ae_assert(ae_isfinite(d, _state), "LogisticCalc5: D is not finite", _state);
    ae_assert(ae_isfinite(g, _state), "LogisticCalc5: G is not finite", _state);
    ae_assert(ae_fp_greater_eq(x,(double)(0)), "LogisticCalc5: X is negative", _state);
    ae_assert(ae_fp_greater(c,(double)(0)), "LogisticCalc5: C is non-positive", _state);
    ae_assert(ae_fp_greater(g,(double)(0)), "LogisticCalc5: G is non-positive", _state);
    
    /*
     * Check for degenerate cases
     */
    if( ae_fp_eq(b,(double)(0)) )
    {
        result = d+(a-d)/ae_pow(2.0, g, _state);
        return result;
    }
    if( ae_fp_eq(x,(double)(0)) )
    {
        if( ae_fp_greater(b,(double)(0)) )
        {
            result = a;
        }
        else
        {
            result = d;
        }
        return result;
    }
    
    /*
     * General case
     */
    result = d+(a-d)/ae_pow(1.0+ae_pow(x/c, b, _state), g, _state);
    ae_assert(ae_isfinite(result, _state), "LogisticCalc5: overflow during calculations", _state);
    return result;
}


/*************************************************************************
This function fits four-parameter logistic (4PL) model  to  data  provided
by user. 4PL model has following form:

    F(x|A,B,C,D) = D+(A-D)/(1+Power(x/C,B))

Here:
    * A, D - unconstrained (see LogisticFit4EC() for constrained 4PL)
    * B>=0
    * C>0
    
IMPORTANT: output of this function is constrained in  such  way that  B>0.
           Because 4PL model is symmetric with respect to B, there  is  no
           need to explore  B<0.  Constraining  B  makes  algorithm easier
           to stabilize and debug.
           Users  who  for  some  reason  prefer to work with negative B's
           should transform output themselves (swap A and D, replace B  by
           -B).
           
4PL fitting is implemented as follows:
* we perform small number of restarts from random locations which helps to
  solve problem of bad local extrema. Locations are only partially  random
  - we use input data to determine good  initial  guess,  but  we  include
  controlled amount of randomness.
* we perform Levenberg-Marquardt fitting with very  tight  constraints  on
  parameters B and C - it allows us to find good  initial  guess  for  the
  second stage without risk of running into "flat spot".
* second  Levenberg-Marquardt  round  is   performed   without   excessive
  constraints. Results from the previous round are used as initial guess.
* after fitting is done, we compare results with best values found so far,
  rewrite "best solution" if needed, and move to next random location.
  
Overall algorithm is very stable and is not prone to  bad  local  extrema.
Furthermore, it automatically scales when input data have  very  large  or
very small range.

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.
                
OUTPUT PARAMETERS:
    A, B, C, D- parameters of 4PL model
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).
                  
NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc4() function.

NOTE: if you need better control over fitting process than provided by this
      function, you may use LogisticFit45X().
                
NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.
    

  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit4(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     double* a,
     double* b,
     double* c,
     double* d,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    double g;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    *a = 0;
    *b = 0;
    *c = 0;
    *d = 0;
    _lsfitreport_clear(rep);

    logisticfit45x(x, y, n, _state->v_nan, _state->v_nan, ae_true, 0.0, 0.0, 0, a, b, c, d, &g, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
This function fits four-parameter logistic (4PL) model  to  data  provided
by user, with optional constraints on parameters A and D.  4PL  model  has
following form:

    F(x|A,B,C,D) = D+(A-D)/(1+Power(x/C,B))

Here:
    * A, D - with optional equality constraints
    * B>=0
    * C>0
    
IMPORTANT: output of this function is constrained in  such  way that  B>0.
           Because 4PL model is symmetric with respect to B, there  is  no
           need to explore  B<0.  Constraining  B  makes  algorithm easier
           to stabilize and debug.
           Users  who  for  some  reason  prefer to work with negative B's
           should transform output themselves (swap A and D, replace B  by
           -B).
           
4PL fitting is implemented as follows:
* we perform small number of restarts from random locations which helps to
  solve problem of bad local extrema. Locations are only partially  random
  - we use input data to determine good  initial  guess,  but  we  include
  controlled amount of randomness.
* we perform Levenberg-Marquardt fitting with very  tight  constraints  on
  parameters B and C - it allows us to find good  initial  guess  for  the
  second stage without risk of running into "flat spot".
* second  Levenberg-Marquardt  round  is   performed   without   excessive
  constraints. Results from the previous round are used as initial guess.
* after fitting is done, we compare results with best values found so far,
  rewrite "best solution" if needed, and move to next random location.
  
Overall algorithm is very stable and is not prone to  bad  local  extrema.
Furthermore, it automatically scales when input data have  very  large  or
very small range.

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.
    CnstrLeft-  optional equality constraint for model value at the   left
                boundary (at X=0). Specify NAN (Not-a-Number)  if  you  do
                not need constraint on the model value at X=0 (in C++  you
                can pass alglib::fp_nan as parameter, in  C#  it  will  be
                Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
    CnstrRight- optional equality constraint for model value at X=infinity.
                Specify NAN (Not-a-Number) if you do not  need  constraint
                on the model value (in C++  you can pass alglib::fp_nan as
                parameter, in  C# it will  be Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
                
OUTPUT PARAMETERS:
    A, B, C, D- parameters of 4PL model
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).

NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc4() function.

NOTE: if you need better control over fitting process than provided by this
      function, you may use LogisticFit45X().
                
NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.

EQUALITY CONSTRAINTS ON PARAMETERS

4PL/5PL solver supports equality constraints on model values at  the  left
boundary (X=0) and right  boundary  (X=infinity).  These  constraints  are
completely optional and you can specify both of them, only  one  -  or  no
constraints at all.

Parameter  CnstrLeft  contains  left  constraint (or NAN for unconstrained
fitting), and CnstrRight contains right  one.  For  4PL,  left  constraint
ALWAYS corresponds to parameter A, and right one is ALWAYS  constraint  on
D. That's because 4PL model is normalized in such way that B>=0.
    

  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit4ec(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     double cnstrleft,
     double cnstrright,
     double* a,
     double* b,
     double* c,
     double* d,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    double g;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    *a = 0;
    *b = 0;
    *c = 0;
    *d = 0;
    _lsfitreport_clear(rep);

    logisticfit45x(x, y, n, cnstrleft, cnstrright, ae_true, 0.0, 0.0, 0, a, b, c, d, &g, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
This function fits five-parameter logistic (5PL) model  to  data  provided
by user. 5PL model has following form:

    F(x|A,B,C,D,G) = D+(A-D)/Power(1+Power(x/C,B),G)

Here:
    * A, D - unconstrained
    * B - unconstrained
    * C>0
    * G>0
    
IMPORTANT: unlike in  4PL  fitting,  output  of  this  function   is   NOT
           constrained in  such  way that B is guaranteed to be  positive.
           Furthermore,  unlike  4PL,  5PL  model  is  NOT  symmetric with
           respect to B, so you can NOT transform model to equivalent one,
           with B having desired sign (>0 or <0).
    
5PL fitting is implemented as follows:
* we perform small number of restarts from random locations which helps to
  solve problem of bad local extrema. Locations are only partially  random
  - we use input data to determine good  initial  guess,  but  we  include
  controlled amount of randomness.
* we perform Levenberg-Marquardt fitting with very  tight  constraints  on
  parameters B and C - it allows us to find good  initial  guess  for  the
  second stage without risk of running into "flat spot".  Parameter  G  is
  fixed at G=1.
* second  Levenberg-Marquardt  round  is   performed   without   excessive
  constraints on B and C, but with G still equal to 1.  Results  from  the
  previous round are used as initial guess.
* third Levenberg-Marquardt round relaxes constraints on G  and  tries  two
  different models - one with B>0 and one with B<0.
* after fitting is done, we compare results with best values found so far,
  rewrite "best solution" if needed, and move to next random location.
  
Overall algorithm is very stable and is not prone to  bad  local  extrema.
Furthermore, it automatically scales when input data have  very  large  or
very small range.

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.
                
OUTPUT PARAMETERS:
    A,B,C,D,G-  parameters of 5PL model
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).

NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc5() function.

NOTE: if you need better control over fitting process than provided by this
      function, you may use LogisticFit45X().
                
NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.
    

  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit5(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     double* a,
     double* b,
     double* c,
     double* d,
     double* g,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    *a = 0;
    *b = 0;
    *c = 0;
    *d = 0;
    *g = 0;
    _lsfitreport_clear(rep);

    logisticfit45x(x, y, n, _state->v_nan, _state->v_nan, ae_false, 0.0, 0.0, 0, a, b, c, d, g, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
This function fits five-parameter logistic (5PL) model  to  data  provided
by user, subject to optional equality constraints on parameters A  and  D.
5PL model has following form:

    F(x|A,B,C,D,G) = D+(A-D)/Power(1+Power(x/C,B),G)

Here:
    * A, D - with optional equality constraints
    * B - unconstrained
    * C>0
    * G>0
    
IMPORTANT: unlike in  4PL  fitting,  output  of  this  function   is   NOT
           constrained in  such  way that B is guaranteed to be  positive.
           Furthermore,  unlike  4PL,  5PL  model  is  NOT  symmetric with
           respect to B, so you can NOT transform model to equivalent one,
           with B having desired sign (>0 or <0).
    
5PL fitting is implemented as follows:
* we perform small number of restarts from random locations which helps to
  solve problem of bad local extrema. Locations are only partially  random
  - we use input data to determine good  initial  guess,  but  we  include
  controlled amount of randomness.
* we perform Levenberg-Marquardt fitting with very  tight  constraints  on
  parameters B and C - it allows us to find good  initial  guess  for  the
  second stage without risk of running into "flat spot".  Parameter  G  is
  fixed at G=1.
* second  Levenberg-Marquardt  round  is   performed   without   excessive
  constraints on B and C, but with G still equal to 1.  Results  from  the
  previous round are used as initial guess.
* third Levenberg-Marquardt round relaxes constraints on G  and  tries  two
  different models - one with B>0 and one with B<0.
* after fitting is done, we compare results with best values found so far,
  rewrite "best solution" if needed, and move to next random location.
  
Overall algorithm is very stable and is not prone to  bad  local  extrema.
Furthermore, it automatically scales when input data have  very  large  or
very small range.

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.
    CnstrLeft-  optional equality constraint for model value at the   left
                boundary (at X=0). Specify NAN (Not-a-Number)  if  you  do
                not need constraint on the model value at X=0 (in C++  you
                can pass alglib::fp_nan as parameter, in  C#  it  will  be
                Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
    CnstrRight- optional equality constraint for model value at X=infinity.
                Specify NAN (Not-a-Number) if you do not  need  constraint
                on the model value (in C++  you can pass alglib::fp_nan as
                parameter, in  C# it will  be Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
                
OUTPUT PARAMETERS:
    A,B,C,D,G-  parameters of 5PL model
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).

NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc5() function.

NOTE: if you need better control over fitting process than provided by this
      function, you may use LogisticFit45X().
                
NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.

EQUALITY CONSTRAINTS ON PARAMETERS

5PL solver supports equality constraints on model  values  at   the   left
boundary (X=0) and right  boundary  (X=infinity).  These  constraints  are
completely optional and you can specify both of them, only  one  -  or  no
constraints at all.

Parameter  CnstrLeft  contains  left  constraint (or NAN for unconstrained
fitting), and CnstrRight contains right  one.

Unlike 4PL one, 5PL model is NOT symmetric with respect to  change in sign
of B. Thus, negative B's are possible, and left constraint  may  constrain
parameter A (for positive B's)  -  or  parameter  D  (for  negative  B's).
Similarly changes meaning of right constraint.

You do not have to decide what parameter to  constrain  -  algorithm  will
automatically determine correct parameters as fitting progresses. However,
question highlighted above is important when you interpret fitting results.
    

  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit5ec(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     double cnstrleft,
     double cnstrright,
     double* a,
     double* b,
     double* c,
     double* d,
     double* g,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    *a = 0;
    *b = 0;
    *c = 0;
    *d = 0;
    *g = 0;
    _lsfitreport_clear(rep);

    logisticfit45x(x, y, n, cnstrleft, cnstrright, ae_false, 0.0, 0.0, 0, a, b, c, d, g, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
This is "expert" 4PL/5PL fitting function, which can be used if  you  need
better control over fitting process than provided  by  LogisticFit4()  or
LogisticFit5().

This function fits model of the form

    F(x|A,B,C,D)   = D+(A-D)/(1+Power(x/C,B))           (4PL model)

or

    F(x|A,B,C,D,G) = D+(A-D)/Power(1+Power(x/C,B),G)    (5PL model)
    
Here:
    * A, D - unconstrained
    * B>=0 for 4PL, unconstrained for 5PL
    * C>0
    * G>0 (if present)

INPUT PARAMETERS:
    X       -   array[N], stores X-values.
                MUST include only non-negative numbers  (but  may  include
                zero values). Can be unsorted.
    Y       -   array[N], values to fit.
    N       -   number of points. If N is less than  length  of  X/Y, only
                leading N elements are used.
    CnstrLeft-  optional equality constraint for model value at the   left
                boundary (at X=0). Specify NAN (Not-a-Number)  if  you  do
                not need constraint on the model value at X=0 (in C++  you
                can pass alglib::fp_nan as parameter, in  C#  it  will  be
                Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
    CnstrRight- optional equality constraint for model value at X=infinity.
                Specify NAN (Not-a-Number) if you do not  need  constraint
                on the model value (in C++  you can pass alglib::fp_nan as
                parameter, in  C# it will  be Double.NaN).
                See  below,  section  "EQUALITY  CONSTRAINTS"   for   more
                information about constraints.
    Is4PL   -   whether 4PL or 5PL models are fitted
    LambdaV -   regularization coefficient, LambdaV>=0.
                Set it to zero unless you know what you are doing.
    EpsX    -   stopping condition (step size), EpsX>=0.
                Zero value means that small step is automatically chosen.
                See notes below for more information.
    RsCnt   -   number of repeated restarts from  random  points.  4PL/5PL
                models are prone to problem of bad local extrema. Utilizing
                multiple random restarts allows  us  to  improve algorithm
                convergence.
                RsCnt>=0.
                Zero value means that function automatically choose  small
                amount of restarts (recommended).
                
OUTPUT PARAMETERS:
    A, B, C, D- parameters of 4PL model
    G       -   parameter of 5PL model; for Is4PL=True, G=1 is returned.
    Rep     -   fitting report. This structure has many fields,  but  ONLY
                ONES LISTED BELOW ARE SET:
                * Rep.IterationsCount - number of iterations performed
                * Rep.RMSError - root-mean-square error
                * Rep.AvgError - average absolute error
                * Rep.AvgRelError - average relative error (calculated for
                  non-zero Y-values)
                * Rep.MaxError - maximum absolute error
                * Rep.R2 - coefficient of determination,  R-squared.  This
                  coefficient   is  calculated  as  R2=1-RSS/TSS  (in case
                  of nonlinear  regression  there  are  multiple  ways  to
                  define R2, each of them giving different results).
                
NOTE: after  you  obtained  coefficients,  you  can  evaluate  model  with
      LogisticCalc5() function.

NOTE: step is automatically scaled according to scale of parameters  being
      fitted before we compare its length with EpsX. Thus,  this  function
      can be used to fit data with very small or very large values without
      changing EpsX.

EQUALITY CONSTRAINTS ON PARAMETERS

4PL/5PL solver supports equality constraints on model values at  the  left
boundary (X=0) and right  boundary  (X=infinity).  These  constraints  are
completely optional and you can specify both of them, only  one  -  or  no
constraints at all.

Parameter  CnstrLeft  contains  left  constraint (or NAN for unconstrained
fitting), and CnstrRight contains right  one.  For  4PL,  left  constraint
ALWAYS corresponds to parameter A, and right one is ALWAYS  constraint  on
D. That's because 4PL model is normalized in such way that B>=0.

For 5PL model things are different. Unlike  4PL  one,  5PL  model  is  NOT
symmetric with respect to  change  in  sign  of  B. Thus, negative B's are
possible, and left constraint may constrain parameter A (for positive B's)
- or parameter D (for negative B's). Similarly changes  meaning  of  right
constraint.

You do not have to decide what parameter to  constrain  -  algorithm  will
automatically determine correct parameters as fitting progresses. However,
question highlighted above is important when you interpret fitting results.
    

  -- ALGLIB PROJECT --
     Copyright 14.02.2014 by Bochkanov Sergey
*************************************************************************/
void logisticfit45x(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     double cnstrleft,
     double cnstrright,
     ae_bool is4pl,
     double lambdav,
     double epsx,
     ae_int_t rscnt,
     double* a,
     double* b,
     double* c,
     double* d,
     double* g,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_int_t i;
    ae_int_t k;
    ae_int_t innerit;
    ae_int_t outerit;
    ae_int_t nz;
    double v;
    double b00;
    double b01;
    double b10;
    double b11;
    double b30;
    double b31;
    ae_vector p0;
    ae_vector p1;
    ae_vector p2;
    ae_vector bndl;
    ae_vector bndu;
    ae_vector s;
    ae_matrix z;
    hqrndstate rs;
    minlmstate state;
    minlmreport replm;
    ae_int_t maxits;
    double fbest;
    double flast;
    double flast2;
    double scalex;
    double scaley;
    ae_vector bufx;
    ae_vector bufy;
    double rss;
    double tss;
    double meany;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    *a = 0;
    *b = 0;
    *c = 0;
    *d = 0;
    *g = 0;
    _lsfitreport_clear(rep);
    ae_vector_init(&p0, 0, DT_REAL, _state);
    ae_vector_init(&p1, 0, DT_REAL, _state);
    ae_vector_init(&p2, 0, DT_REAL, _state);
    ae_vector_init(&bndl, 0, DT_REAL, _state);
    ae_vector_init(&bndu, 0, DT_REAL, _state);
    ae_vector_init(&s, 0, DT_REAL, _state);
    ae_matrix_init(&z, 0, 0, DT_REAL, _state);
    _hqrndstate_init(&rs, _state);
    _minlmstate_init(&state, _state);
    _minlmreport_init(&replm, _state);
    ae_vector_init(&bufx, 0, DT_REAL, _state);
    ae_vector_init(&bufy, 0, DT_REAL, _state);

    ae_assert(ae_isfinite(epsx, _state), "LogisticFitX: EpsX is infinite/NAN", _state);
    ae_assert(ae_isfinite(lambdav, _state), "LogisticFitX: LambdaV is infinite/NAN", _state);
    ae_assert(ae_isfinite(cnstrleft, _state)||ae_isnan(cnstrleft, _state), "LogisticFitX: CnstrLeft is NOT finite or NAN", _state);
    ae_assert(ae_isfinite(cnstrright, _state)||ae_isnan(cnstrright, _state), "LogisticFitX: CnstrRight is NOT finite or NAN", _state);
    ae_assert(ae_fp_greater_eq(lambdav,(double)(0)), "LogisticFitX: negative LambdaV", _state);
    ae_assert(n>0, "LogisticFitX: N<=0", _state);
    ae_assert(rscnt>=0, "LogisticFitX: RsCnt<0", _state);
    ae_assert(ae_fp_greater_eq(epsx,(double)(0)), "LogisticFitX: EpsX<0", _state);
    ae_assert(x->cnt>=n, "LogisticFitX: Length(X)<N", _state);
    ae_assert(y->cnt>=n, "LogisticFitX: Length(Y)<N", _state);
    ae_assert(isfinitevector(x, n, _state), "LogisticFitX: X contains infinite/NAN values", _state);
    ae_assert(isfinitevector(y, n, _state), "LogisticFitX: X contains infinite/NAN values", _state);
    hqrndseed(2211, 1033044, &rs, _state);
    lsfit_clearreport(rep, _state);
    if( ae_fp_eq(epsx,(double)(0)) )
    {
        epsx = 1.0E-10;
    }
    if( rscnt==0 )
    {
        rscnt = 4;
    }
    maxits = 1000;
    
    /*
     * Sort points by X.
     * Determine number of zero and non-zero values.
     */
    tagsortfastr(x, y, &bufx, &bufy, n, _state);
    ae_assert(ae_fp_greater_eq(x->ptr.p_double[0],(double)(0)), "LogisticFitX: some X[] are negative", _state);
    nz = n;
    for(i=0; i<=n-1; i++)
    {
        if( ae_fp_greater(x->ptr.p_double[i],(double)(0)) )
        {
            nz = i;
            break;
        }
    }
    
    /*
     * For NZ=N (all X[] are zero) special code is used.
     * For NZ<N we use general-purpose code.
     */
    rep->iterationscount = 0;
    if( nz==n )
    {
        
        /*
         * NZ=N, degenerate problem.
         * No need to run optimizer.
         */
        v = 0.0;
        for(i=0; i<=n-1; i++)
        {
            v = v+y->ptr.p_double[i];
        }
        v = v/n;
        if( ae_isfinite(cnstrleft, _state) )
        {
            *a = cnstrleft;
        }
        else
        {
            *a = v;
        }
        *b = (double)(1);
        *c = (double)(1);
        if( ae_isfinite(cnstrright, _state) )
        {
            *d = cnstrright;
        }
        else
        {
            *d = *a;
        }
        *g = (double)(1);
    }
    else
    {
        
        /*
         * Non-degenerate problem.
         * Determine scale of data.
         */
        scalex = x->ptr.p_double[nz+(n-nz)/2];
        ae_assert(ae_fp_greater(scalex,(double)(0)), "LogisticFitX: internal error", _state);
        v = 0.0;
        for(i=0; i<=n-1; i++)
        {
            v = v+y->ptr.p_double[i];
        }
        v = v/n;
        scaley = 0.0;
        for(i=0; i<=n-1; i++)
        {
            scaley = scaley+ae_sqr(y->ptr.p_double[i]-v, _state);
        }
        scaley = ae_sqrt(scaley/n, _state);
        if( ae_fp_eq(scaley,(double)(0)) )
        {
            scaley = 1.0;
        }
        ae_vector_set_length(&s, 5, _state);
        s.ptr.p_double[0] = scaley;
        s.ptr.p_double[1] = 0.1;
        s.ptr.p_double[2] = scalex;
        s.ptr.p_double[3] = scaley;
        s.ptr.p_double[4] = 0.1;
        ae_vector_set_length(&p0, 5, _state);
        p0.ptr.p_double[0] = (double)(0);
        p0.ptr.p_double[1] = (double)(0);
        p0.ptr.p_double[2] = (double)(0);
        p0.ptr.p_double[3] = (double)(0);
        p0.ptr.p_double[4] = (double)(0);
        ae_vector_set_length(&bndl, 5, _state);
        ae_vector_set_length(&bndu, 5, _state);
        minlmcreatevj(5, n+5, &p0, &state, _state);
        minlmsetscale(&state, &s, _state);
        minlmsetcond(&state, 0.0, 0.0, epsx, maxits, _state);
        minlmsetxrep(&state, ae_true, _state);
        
        /*
         * Main loop - includes THREE (!) nested iterations:
         *
         * 1. Inner iteration is minimization of target function from
         *    the current initial point P1 subject to boundary constraints
         *    given by arrays BndL and BndU.
         *
         * 2. Middle iteration changes boundary constraints from tight to
         *    relaxed ones:
         *    * at the first middle iteration we optimize with "tight"
         *      constraints on parameters B and C (P[1] and P[2]). It
         *      allows us to find good initial point for the next middle
         *      iteration without risk of running into "hard" points (B=0, C=0).
         *      Initial point is initialized by outer iteration.
         *      Solution is placed to P1.
         *    * at the second middle iteration we relax boundary constraints
         *      on B and C. Solution P1 from the first middle iteration is
         *      used as initial point for the second one.
         *    * both first and second iterations are 4PL models, even when
         *      we fit 5PL.
         *    * additionally, for 5PL models, we use results from the second
         *      middle iteration is initial guess for 5PL fit.
         *    * after middle iteration is over we compare quality of the
         *      solution stored in P1 and offload it to A/B/C/D/G, if it
         *      is better.
         *
         * 3. Outer iteration (starts below) changes following parameters:
         *    * initial point
         *    * "tight" constraints BndL/BndU
         *    * "relaxed" constraints BndL/BndU
         *
         * Below we prepare combined matrix Z of optimization settings for
         * outer/middle iterations:
         *
         *     [ P00 BndL00 BndU00 BndL01 BndU01 ]
         *     [                                 ]
         *     [ P10 BndL10 BndU10 BndL11 BndU11 ]
         *
         * Here:
         * * Pi0 is initial point for I-th outer iteration
         * * BndLij is lower boundary for I-th outer iteration, J-th inner iteration
         * * BndUij - same as BndLij
         */
        ae_matrix_set_length(&z, rscnt, 5+4*5, _state);
        for(i=0; i<=rscnt-1; i++)
        {
            if( ae_isfinite(cnstrleft, _state) )
            {
                z.ptr.pp_double[i][0] = cnstrleft;
            }
            else
            {
                z.ptr.pp_double[i][0] = y->ptr.p_double[0]+0.25*scaley*(hqrnduniformr(&rs, _state)-0.5);
            }
            z.ptr.pp_double[i][1] = 0.5+hqrnduniformr(&rs, _state);
            z.ptr.pp_double[i][2] = x->ptr.p_double[nz+hqrnduniformi(&rs, n-nz, _state)];
            if( ae_isfinite(cnstrright, _state) )
            {
                z.ptr.pp_double[i][3] = cnstrright;
            }
            else
            {
                z.ptr.pp_double[i][3] = y->ptr.p_double[n-1]+0.25*scaley*(hqrnduniformr(&rs, _state)-0.5);
            }
            z.ptr.pp_double[i][4] = 1.0;
            if( ae_isfinite(cnstrleft, _state) )
            {
                z.ptr.pp_double[i][5+0] = cnstrleft;
                z.ptr.pp_double[i][10+0] = cnstrleft;
            }
            else
            {
                z.ptr.pp_double[i][5+0] = _state->v_neginf;
                z.ptr.pp_double[i][10+0] = _state->v_posinf;
            }
            z.ptr.pp_double[i][5+1] = 0.5;
            z.ptr.pp_double[i][10+1] = 2.0;
            z.ptr.pp_double[i][5+2] = 0.5*scalex;
            z.ptr.pp_double[i][10+2] = 2.0*scalex;
            if( ae_isfinite(cnstrright, _state) )
            {
                z.ptr.pp_double[i][5+3] = cnstrright;
                z.ptr.pp_double[i][10+3] = cnstrright;
            }
            else
            {
                z.ptr.pp_double[i][5+3] = _state->v_neginf;
                z.ptr.pp_double[i][10+3] = _state->v_posinf;
            }
            z.ptr.pp_double[i][5+4] = 1.0;
            z.ptr.pp_double[i][10+4] = 1.0;
            if( ae_isfinite(cnstrleft, _state) )
            {
                z.ptr.pp_double[i][15+0] = cnstrleft;
                z.ptr.pp_double[i][20+0] = cnstrleft;
            }
            else
            {
                z.ptr.pp_double[i][15+0] = _state->v_neginf;
                z.ptr.pp_double[i][20+0] = _state->v_posinf;
            }
            z.ptr.pp_double[i][15+1] = 0.01;
            z.ptr.pp_double[i][20+1] = _state->v_posinf;
            z.ptr.pp_double[i][15+2] = ae_machineepsilon*scalex;
            z.ptr.pp_double[i][20+2] = _state->v_posinf;
            if( ae_isfinite(cnstrright, _state) )
            {
                z.ptr.pp_double[i][15+3] = cnstrright;
                z.ptr.pp_double[i][20+3] = cnstrright;
            }
            else
            {
                z.ptr.pp_double[i][15+3] = _state->v_neginf;
                z.ptr.pp_double[i][20+3] = _state->v_posinf;
            }
            z.ptr.pp_double[i][15+4] = 1.0;
            z.ptr.pp_double[i][20+4] = 1.0;
        }
        
        /*
         * Run outer iterations
         */
        *a = (double)(0);
        *b = (double)(1);
        *c = (double)(1);
        *d = (double)(1);
        *g = (double)(1);
        fbest = ae_maxrealnumber;
        ae_vector_set_length(&p1, 5, _state);
        ae_vector_set_length(&p2, 5, _state);
        for(outerit=0; outerit<=z.rows-1; outerit++)
        {
            
            /*
             * Beginning of the middle iterations.
             * Prepare initial point P1.
             */
            for(i=0; i<=4; i++)
            {
                p1.ptr.p_double[i] = z.ptr.pp_double[outerit][i];
            }
            flast = ae_maxrealnumber;
            for(innerit=0; innerit<=1; innerit++)
            {
                
                /*
                 * Set current boundary constraints.
                 * Run inner iteration.
                 */
                for(i=0; i<=4; i++)
                {
                    bndl.ptr.p_double[i] = z.ptr.pp_double[outerit][5+innerit*10+0+i];
                    bndu.ptr.p_double[i] = z.ptr.pp_double[outerit][5+innerit*10+5+i];
                }
                minlmsetbc(&state, &bndl, &bndu, _state);
                lsfit_logisticfitinternal(x, y, n, ae_true, lambdav, &state, &replm, &p1, &flast, _state);
                rep->iterationscount = rep->iterationscount+replm.iterationscount;
            }
            
            /*
             * Middle iteration: try to fit with 5-parameter logistic model (if needed).
             *
             * We perform two attempts to fit: one with B>0, another one with B<0.
             * For PL4, these are equivalent up to transposition of A/D, but for 5PL
             * sign of B is very important.
             *
             * NOTE: results of 4PL fit are used as initial point for 5PL.
             */
            if( !is4pl )
            {
                
                /*
                 * Loosen constraints on G,
                 * save constraints on A/B/D to B0/B1
                 */
                bndl.ptr.p_double[4] = 0.1;
                bndu.ptr.p_double[4] = 10.0;
                b00 = bndl.ptr.p_double[0];
                b01 = bndu.ptr.p_double[0];
                b10 = bndl.ptr.p_double[1];
                b11 = bndu.ptr.p_double[1];
                b30 = bndl.ptr.p_double[3];
                b31 = bndu.ptr.p_double[3];
                
                /*
                 * First attempt: fitting with positive B
                 */
                p2.ptr.p_double[0] = p1.ptr.p_double[0];
                p2.ptr.p_double[1] = p1.ptr.p_double[1];
                p2.ptr.p_double[2] = p1.ptr.p_double[2];
                p2.ptr.p_double[3] = p1.ptr.p_double[3];
                p2.ptr.p_double[4] = p1.ptr.p_double[4];
                bndl.ptr.p_double[0] = b00;
                bndu.ptr.p_double[0] = b01;
                bndl.ptr.p_double[1] = b10;
                bndu.ptr.p_double[1] = b11;
                bndl.ptr.p_double[3] = b30;
                bndu.ptr.p_double[3] = b31;
                minlmsetbc(&state, &bndl, &bndu, _state);
                lsfit_logisticfitinternal(x, y, n, ae_false, lambdav, &state, &replm, &p2, &flast2, _state);
                rep->iterationscount = rep->iterationscount+replm.iterationscount;
                if( ae_fp_less(flast2,flast) )
                {
                    for(i=0; i<=4; i++)
                    {
                        p1.ptr.p_double[i] = p2.ptr.p_double[i];
                    }
                    flast = flast2;
                }
                
                /*
                 * First attempt: fitting with negative B
                 */
                p2.ptr.p_double[0] = p1.ptr.p_double[3];
                p2.ptr.p_double[1] = -p1.ptr.p_double[1];
                p2.ptr.p_double[2] = p1.ptr.p_double[2];
                p2.ptr.p_double[3] = p1.ptr.p_double[0];
                p2.ptr.p_double[4] = p1.ptr.p_double[4];
                bndl.ptr.p_double[0] = b30;
                bndu.ptr.p_double[0] = b31;
                bndl.ptr.p_double[1] = -b11;
                bndu.ptr.p_double[1] = -b10;
                bndl.ptr.p_double[3] = b00;
                bndu.ptr.p_double[3] = b01;
                minlmsetbc(&state, &bndl, &bndu, _state);
                lsfit_logisticfitinternal(x, y, n, ae_false, lambdav, &state, &replm, &p2, &flast2, _state);
                rep->iterationscount = rep->iterationscount+replm.iterationscount;
                if( ae_fp_less(flast2,flast) )
                {
                    for(i=0; i<=4; i++)
                    {
                        p1.ptr.p_double[i] = p2.ptr.p_double[i];
                    }
                    flast = flast2;
                }
            }
            
            /*
             * Middle iteration is done, compare its results with best value
             * found so far.
             */
            if( ae_fp_less(flast,fbest) )
            {
                *a = p1.ptr.p_double[0];
                *b = p1.ptr.p_double[1];
                *c = p1.ptr.p_double[2];
                *d = p1.ptr.p_double[3];
                *g = p1.ptr.p_double[4];
                fbest = flast;
            }
        }
    }
    
    /*
     * Calculate errors
     */
    rep->rmserror = (double)(0);
    rep->avgerror = (double)(0);
    rep->avgrelerror = (double)(0);
    rep->maxerror = (double)(0);
    k = 0;
    rss = 0.0;
    tss = 0.0;
    meany = 0.0;
    for(i=0; i<=n-1; i++)
    {
        meany = meany+y->ptr.p_double[i];
    }
    meany = meany/n;
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * Calculate residual from regression
         */
        if( ae_fp_greater(x->ptr.p_double[i],(double)(0)) )
        {
            v = *d+(*a-(*d))/ae_pow(1.0+ae_pow(x->ptr.p_double[i]/(*c), *b, _state), *g, _state)-y->ptr.p_double[i];
        }
        else
        {
            if( ae_fp_greater_eq(*b,(double)(0)) )
            {
                v = *a-y->ptr.p_double[i];
            }
            else
            {
                v = *d-y->ptr.p_double[i];
            }
        }
        
        /*
         * Update RSS (residual sum of squares) and TSS (total sum of squares)
         * which are used to calculate coefficient of determination.
         *
         * NOTE: we use formula R2 = 1-RSS/TSS because it has nice property of
         *       being equal to 0.0 if and only if model perfectly fits data.
         *
         *       When we fit nonlinear models, there are exist multiple ways of
         *       determining R2, each of them giving different results. Formula
         *       above is the most intuitive one.
         */
        rss = rss+v*v;
        tss = tss+ae_sqr(y->ptr.p_double[i]-meany, _state);
        
        /*
         * Update errors
         */
        rep->rmserror = rep->rmserror+ae_sqr(v, _state);
        rep->avgerror = rep->avgerror+ae_fabs(v, _state);
        if( ae_fp_neq(y->ptr.p_double[i],(double)(0)) )
        {
            rep->avgrelerror = rep->avgrelerror+ae_fabs(v/y->ptr.p_double[i], _state);
            k = k+1;
        }
        rep->maxerror = ae_maxreal(rep->maxerror, ae_fabs(v, _state), _state);
    }
    rep->rmserror = ae_sqrt(rep->rmserror/n, _state);
    rep->avgerror = rep->avgerror/n;
    if( k>0 )
    {
        rep->avgrelerror = rep->avgrelerror/k;
    }
    rep->r2 = 1.0-rss/tss;
    ae_frame_leave(_state);
}


/*************************************************************************
Weghted rational least  squares  fitting  using  Floater-Hormann  rational
functions  with  optimal  D  chosen  from  [0,9],  with  constraints   and
individual weights.

Equidistant  grid  with M node on [min(x),max(x)]  is  used to build basis
functions. Different values of D are tried, optimal D (least WEIGHTED root
mean square error) is chosen.  Task  is  linear,  so  linear least squares
solver  is  used.  Complexity  of  this  computational  scheme is O(N*M^2)
(mostly dominated by the least squares solver).

SEE ALSO
* BarycentricFitFloaterHormann(), "lightweight" fitting without invididual
  weights and constraints.
  
COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points, N>0.
    XC  -   points where function values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that S(XC[i])=YC[i]
            * DC[i]=1   means that S'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints, 0<=K<M.
            K=0 means no constraints (XC/YC/DC are not used in such cases)
    M   -   number of basis functions ( = number_of_nodes), M>=2.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
                        -1 means another errors in parameters passed
                           (N<=0, for example)
    B   -   barycentric interpolant.
    Rep -   report, same format as in LSFitLinearWC() subroutine.
            Following fields are set:
            * DBest         best value of the D parameter
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroutine doesn't calculate task's condition number for K<>0.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained barycentric interpolants:
* excessive  constraints  can  be  inconsistent.   Floater-Hormann   basis
  functions aren't as flexible as splines (although they are very smooth).
* the more evenly constraints are spread across [min(x),max(x)],  the more
  chances that they will be consistent
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints IS NOT GUARANTEED.
* in the several special cases, however, we CAN guarantee consistency.
* one of this cases is constraints on the function  VALUES at the interval
  boundaries. Note that consustency of the  constraints  on  the  function
  DERIVATIVES is NOT guaranteed (you can use in such cases  cubic  splines
  which are more flexible).
* another  special  case  is ONE constraint on the function value (OR, but
  not AND, derivative) anywhere in the interval

Our final recommendation is to use constraints  WHEN  AND  ONLY  WHEN  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricfitfloaterhormannwc(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t* info,
     barycentricinterpolant* b,
     barycentricfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t d;
    ae_int_t i;
    double wrmscur;
    double wrmsbest;
    barycentricinterpolant locb;
    barycentricfitreport locrep;
    ae_int_t locinfo;

    ae_frame_make(_state, &_frame_block);
    *info = 0;
    _barycentricinterpolant_clear(b);
    _barycentricfitreport_clear(rep);
    _barycentricinterpolant_init(&locb, _state);
    _barycentricfitreport_init(&locrep, _state);

    ae_assert(n>0, "BarycentricFitFloaterHormannWC: N<=0!", _state);
    ae_assert(m>0, "BarycentricFitFloaterHormannWC: M<=0!", _state);
    ae_assert(k>=0, "BarycentricFitFloaterHormannWC: K<0!", _state);
    ae_assert(k<m, "BarycentricFitFloaterHormannWC: K>=M!", _state);
    ae_assert(x->cnt>=n, "BarycentricFitFloaterHormannWC: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "BarycentricFitFloaterHormannWC: Length(Y)<N!", _state);
    ae_assert(w->cnt>=n, "BarycentricFitFloaterHormannWC: Length(W)<N!", _state);
    ae_assert(xc->cnt>=k, "BarycentricFitFloaterHormannWC: Length(XC)<K!", _state);
    ae_assert(yc->cnt>=k, "BarycentricFitFloaterHormannWC: Length(YC)<K!", _state);
    ae_assert(dc->cnt>=k, "BarycentricFitFloaterHormannWC: Length(DC)<K!", _state);
    ae_assert(isfinitevector(x, n, _state), "BarycentricFitFloaterHormannWC: X contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "BarycentricFitFloaterHormannWC: Y contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(w, n, _state), "BarycentricFitFloaterHormannWC: X contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(xc, k, _state), "BarycentricFitFloaterHormannWC: XC contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(yc, k, _state), "BarycentricFitFloaterHormannWC: YC contains infinite or NaN values!", _state);
    for(i=0; i<=k-1; i++)
    {
        ae_assert(dc->ptr.p_int[i]==0||dc->ptr.p_int[i]==1, "BarycentricFitFloaterHormannWC: one of DC[] is not 0 or 1!", _state);
    }
    
    /*
     * Find optimal D
     *
     * Info is -3 by default (degenerate constraints).
     * If LocInfo will always be equal to -3, Info will remain equal to -3.
     * If at least once LocInfo will be -4, Info will be -4.
     */
    wrmsbest = ae_maxrealnumber;
    rep->dbest = -1;
    *info = -3;
    for(d=0; d<=ae_minint(9, n-1, _state); d++)
    {
        lsfit_barycentricfitwcfixedd(x, y, w, n, xc, yc, dc, k, m, d, &locinfo, &locb, &locrep, _state);
        ae_assert((locinfo==-4||locinfo==-3)||locinfo>0, "BarycentricFitFloaterHormannWC: unexpected result from BarycentricFitWCFixedD!", _state);
        if( locinfo>0 )
        {
            
            /*
             * Calculate weghted RMS
             */
            wrmscur = (double)(0);
            for(i=0; i<=n-1; i++)
            {
                wrmscur = wrmscur+ae_sqr(w->ptr.p_double[i]*(y->ptr.p_double[i]-barycentriccalc(&locb, x->ptr.p_double[i], _state)), _state);
            }
            wrmscur = ae_sqrt(wrmscur/n, _state);
            if( ae_fp_less(wrmscur,wrmsbest)||rep->dbest<0 )
            {
                barycentriccopy(&locb, b, _state);
                rep->dbest = d;
                *info = 1;
                rep->rmserror = locrep.rmserror;
                rep->avgerror = locrep.avgerror;
                rep->avgrelerror = locrep.avgrelerror;
                rep->maxerror = locrep.maxerror;
                rep->taskrcond = locrep.taskrcond;
                wrmsbest = wrmscur;
            }
        }
        else
        {
            if( locinfo!=-3&&*info<0 )
            {
                *info = locinfo;
            }
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_barycentricfitfloaterhormannwc(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    /* Real    */ ae_vector* w,
    ae_int_t n,
    /* Real    */ ae_vector* xc,
    /* Real    */ ae_vector* yc,
    /* Integer */ ae_vector* dc,
    ae_int_t k,
    ae_int_t m,
    ae_int_t* info,
    barycentricinterpolant* b,
    barycentricfitreport* rep, ae_state *_state)
{
    barycentricfitfloaterhormannwc(x,y,w,n,xc,yc,dc,k,m,info,b,rep, _state);
}


/*************************************************************************
Rational least squares fitting using  Floater-Hormann  rational  functions
with optimal D chosen from [0,9].

Equidistant  grid  with M node on [min(x),max(x)]  is  used to build basis
functions. Different values of D are tried, optimal  D  (least  root  mean
square error) is chosen.  Task  is  linear, so linear least squares solver
is used. Complexity  of  this  computational  scheme is  O(N*M^2)  (mostly
dominated by the least squares solver).

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of points, N>0.
    M   -   number of basis functions ( = number_of_nodes), M>=2.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    B   -   barycentric interpolant.
    Rep -   report, same format as in LSFitLinearWC() subroutine.
            Following fields are set:
            * DBest         best value of the D parameter
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void barycentricfitfloaterhormann(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     barycentricinterpolant* b,
     barycentricfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector w;
    ae_vector xc;
    ae_vector yc;
    ae_vector dc;
    ae_int_t i;

    ae_frame_make(_state, &_frame_block);
    *info = 0;
    _barycentricinterpolant_clear(b);
    _barycentricfitreport_clear(rep);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&xc, 0, DT_REAL, _state);
    ae_vector_init(&yc, 0, DT_REAL, _state);
    ae_vector_init(&dc, 0, DT_INT, _state);

    ae_assert(n>0, "BarycentricFitFloaterHormann: N<=0!", _state);
    ae_assert(m>0, "BarycentricFitFloaterHormann: M<=0!", _state);
    ae_assert(x->cnt>=n, "BarycentricFitFloaterHormann: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "BarycentricFitFloaterHormann: Length(Y)<N!", _state);
    ae_assert(isfinitevector(x, n, _state), "BarycentricFitFloaterHormann: X contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "BarycentricFitFloaterHormann: Y contains infinite or NaN values!", _state);
    ae_vector_set_length(&w, n, _state);
    for(i=0; i<=n-1; i++)
    {
        w.ptr.p_double[i] = (double)(1);
    }
    barycentricfitfloaterhormannwc(x, y, &w, n, &xc, &yc, &dc, 0, m, info, b, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_barycentricfitfloaterhormann(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    ae_int_t n,
    ae_int_t m,
    ae_int_t* info,
    barycentricinterpolant* b,
    barycentricfitreport* rep, ae_state *_state)
{
    barycentricfitfloaterhormann(x,y,n,m,info,b,rep, _state);
}


/*************************************************************************
Fitting by penalized cubic spline.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are cubic splines with  natural  boundary
conditions. Problem is regularized by  adding non-linearity penalty to the
usual least squares penalty function:

    S(x) = arg min { LS + P }, where
    LS   = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty
    P    = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty
    rho  - tunable constant given by user
    C    - automatically determined scale parameter,
           makes penalty invariant with respect to scaling of X, Y, W.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.
           
INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y are processed
            * if not given, automatically determined from X/Y sizes
    M   -   number of basis functions ( = number_of_nodes), M>=4.
    Rho -   regularization  constant  passed   by   user.   It   penalizes
            nonlinearity in the regression spline. It  is  logarithmically
            scaled,  i.e.  actual  value  of  regularization  constant  is
            calculated as 10^Rho. It is automatically scaled so that:
            * Rho=2.0 corresponds to moderate amount of nonlinearity
            * generally, it should be somewhere in the [-8.0,+8.0]
            If you do not want to penalize nonlineary,
            pass small Rho. Values as low as -15 should work.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD or
                           Cholesky decomposition; problem may be
                           too ill-conditioned (very rare)
    S   -   spline interpolant.
    Rep -   Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTE 1: additional nodes are added to the spline outside  of  the  fitting
interval to force linearity when x<min(x,xc) or x>max(x,xc).  It  is  done
for consistency - we penalize non-linearity  at [min(x,xc),max(x,xc)],  so
it is natural to force linearity outside of this interval.

NOTE 2: function automatically sorts points,  so  caller may pass unsorted
array.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitpenalized(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t m,
     double rho,
     ae_int_t* info,
     spline1dinterpolant* s,
     spline1dfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector w;
    ae_int_t i;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    *info = 0;
    _spline1dinterpolant_clear(s);
    _spline1dfitreport_clear(rep);
    ae_vector_init(&w, 0, DT_REAL, _state);

    ae_assert(n>=1, "Spline1DFitPenalized: N<1!", _state);
    ae_assert(m>=4, "Spline1DFitPenalized: M<4!", _state);
    ae_assert(x->cnt>=n, "Spline1DFitPenalized: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DFitPenalized: Length(Y)<N!", _state);
    ae_assert(isfinitevector(x, n, _state), "Spline1DFitPenalized: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DFitPenalized: Y contains infinite or NAN values!", _state);
    ae_assert(ae_isfinite(rho, _state), "Spline1DFitPenalized: Rho is infinite!", _state);
    ae_vector_set_length(&w, n, _state);
    for(i=0; i<=n-1; i++)
    {
        w.ptr.p_double[i] = (double)(1);
    }
    spline1dfitpenalizedw(x, y, &w, n, m, rho, info, s, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_spline1dfitpenalized(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    ae_int_t n,
    ae_int_t m,
    double rho,
    ae_int_t* info,
    spline1dinterpolant* s,
    spline1dfitreport* rep, ae_state *_state)
{
    spline1dfitpenalized(x,y,n,m,rho,info,s,rep, _state);
}


/*************************************************************************
Weighted fitting by penalized cubic spline.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are cubic splines with  natural  boundary
conditions. Problem is regularized by  adding non-linearity penalty to the
usual least squares penalty function:

    S(x) = arg min { LS + P }, where
    LS   = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty
    P    = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty
    rho  - tunable constant given by user
    C    - automatically determined scale parameter,
           makes penalty invariant with respect to scaling of X, Y, W.

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.
           
INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            problem.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    M   -   number of basis functions ( = number_of_nodes), M>=4.
    Rho -   regularization  constant  passed   by   user.   It   penalizes
            nonlinearity in the regression spline. It  is  logarithmically
            scaled,  i.e.  actual  value  of  regularization  constant  is
            calculated as 10^Rho. It is automatically scaled so that:
            * Rho=2.0 corresponds to moderate amount of nonlinearity
            * generally, it should be somewhere in the [-8.0,+8.0]
            If you do not want to penalize nonlineary,
            pass small Rho. Values as low as -15 should work.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD or
                           Cholesky decomposition; problem may be
                           too ill-conditioned (very rare)
    S   -   spline interpolant.
    Rep -   Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

NOTE 1: additional nodes are added to the spline outside  of  the  fitting
interval to force linearity when x<min(x,xc) or x>max(x,xc).  It  is  done
for consistency - we penalize non-linearity  at [min(x,xc),max(x,xc)],  so
it is natural to force linearity outside of this interval.

NOTE 2: function automatically sorts points,  so  caller may pass unsorted
array.

  -- ALGLIB PROJECT --
     Copyright 19.10.2010 by Bochkanov Sergey
*************************************************************************/
void spline1dfitpenalizedw(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     ae_int_t m,
     double rho,
     ae_int_t* info,
     spline1dinterpolant* s,
     spline1dfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector _w;
    ae_int_t i;
    ae_int_t j;
    ae_int_t b;
    double v;
    double relcnt;
    double xa;
    double xb;
    double sa;
    double sb;
    ae_vector xoriginal;
    ae_vector yoriginal;
    double pdecay;
    double tdecay;
    ae_matrix fmatrix;
    ae_vector fcolumn;
    ae_vector y2;
    ae_vector w2;
    ae_vector xc;
    ae_vector yc;
    ae_vector dc;
    double fdmax;
    double admax;
    ae_matrix amatrix;
    ae_matrix d2matrix;
    double fa;
    double ga;
    double fb;
    double gb;
    double lambdav;
    ae_vector bx;
    ae_vector by;
    ae_vector bd1;
    ae_vector bd2;
    ae_vector tx;
    ae_vector ty;
    ae_vector td;
    spline1dinterpolant bs;
    ae_matrix nmatrix;
    ae_vector rightpart;
    fblslincgstate cgstate;
    ae_vector c;
    ae_vector tmp0;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_init_copy(&_w, w, _state);
    w = &_w;
    *info = 0;
    _spline1dinterpolant_clear(s);
    _spline1dfitreport_clear(rep);
    ae_vector_init(&xoriginal, 0, DT_REAL, _state);
    ae_vector_init(&yoriginal, 0, DT_REAL, _state);
    ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state);
    ae_vector_init(&fcolumn, 0, DT_REAL, _state);
    ae_vector_init(&y2, 0, DT_REAL, _state);
    ae_vector_init(&w2, 0, DT_REAL, _state);
    ae_vector_init(&xc, 0, DT_REAL, _state);
    ae_vector_init(&yc, 0, DT_REAL, _state);
    ae_vector_init(&dc, 0, DT_INT, _state);
    ae_matrix_init(&amatrix, 0, 0, DT_REAL, _state);
    ae_matrix_init(&d2matrix, 0, 0, DT_REAL, _state);
    ae_vector_init(&bx, 0, DT_REAL, _state);
    ae_vector_init(&by, 0, DT_REAL, _state);
    ae_vector_init(&bd1, 0, DT_REAL, _state);
    ae_vector_init(&bd2, 0, DT_REAL, _state);
    ae_vector_init(&tx, 0, DT_REAL, _state);
    ae_vector_init(&ty, 0, DT_REAL, _state);
    ae_vector_init(&td, 0, DT_REAL, _state);
    _spline1dinterpolant_init(&bs, _state);
    ae_matrix_init(&nmatrix, 0, 0, DT_REAL, _state);
    ae_vector_init(&rightpart, 0, DT_REAL, _state);
    _fblslincgstate_init(&cgstate, _state);
    ae_vector_init(&c, 0, DT_REAL, _state);
    ae_vector_init(&tmp0, 0, DT_REAL, _state);

    ae_assert(n>=1, "Spline1DFitPenalizedW: N<1!", _state);
    ae_assert(m>=4, "Spline1DFitPenalizedW: M<4!", _state);
    ae_assert(x->cnt>=n, "Spline1DFitPenalizedW: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DFitPenalizedW: Length(Y)<N!", _state);
    ae_assert(w->cnt>=n, "Spline1DFitPenalizedW: Length(W)<N!", _state);
    ae_assert(isfinitevector(x, n, _state), "Spline1DFitPenalizedW: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DFitPenalizedW: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(w, n, _state), "Spline1DFitPenalizedW: Y contains infinite or NAN values!", _state);
    ae_assert(ae_isfinite(rho, _state), "Spline1DFitPenalizedW: Rho is infinite!", _state);
    
    /*
     * Prepare LambdaV
     */
    v = -ae_log(ae_machineepsilon, _state)/ae_log((double)(10), _state);
    if( ae_fp_less(rho,-v) )
    {
        rho = -v;
    }
    if( ae_fp_greater(rho,v) )
    {
        rho = v;
    }
    lambdav = ae_pow((double)(10), rho, _state);
    
    /*
     * Sort X, Y, W
     */
    heapsortdpoints(x, y, w, n, _state);
    
    /*
     * Scale X, Y, XC, YC
     */
    lsfitscalexy(x, y, w, n, &xc, &yc, &dc, 0, &xa, &xb, &sa, &sb, &xoriginal, &yoriginal, _state);
    
    /*
     * Allocate space
     */
    ae_matrix_set_length(&fmatrix, n, m, _state);
    ae_matrix_set_length(&amatrix, m, m, _state);
    ae_matrix_set_length(&d2matrix, m, m, _state);
    ae_vector_set_length(&bx, m, _state);
    ae_vector_set_length(&by, m, _state);
    ae_vector_set_length(&fcolumn, n, _state);
    ae_matrix_set_length(&nmatrix, m, m, _state);
    ae_vector_set_length(&rightpart, m, _state);
    ae_vector_set_length(&tmp0, ae_maxint(m, n, _state), _state);
    ae_vector_set_length(&c, m, _state);
    
    /*
     * Fill:
     * * FMatrix by values of basis functions
     * * TmpAMatrix by second derivatives of I-th function at J-th point
     * * CMatrix by constraints
     */
    fdmax = (double)(0);
    for(b=0; b<=m-1; b++)
    {
        
        /*
         * Prepare I-th basis function
         */
        for(j=0; j<=m-1; j++)
        {
            bx.ptr.p_double[j] = (double)(2*j)/(double)(m-1)-1;
            by.ptr.p_double[j] = (double)(0);
        }
        by.ptr.p_double[b] = (double)(1);
        spline1dgriddiff2cubic(&bx, &by, m, 2, 0.0, 2, 0.0, &bd1, &bd2, _state);
        spline1dbuildcubic(&bx, &by, m, 2, 0.0, 2, 0.0, &bs, _state);
        
        /*
         * Calculate B-th column of FMatrix
         * Update FDMax (maximum column norm)
         */
        spline1dconvcubic(&bx, &by, m, 2, 0.0, 2, 0.0, x, n, &fcolumn, _state);
        ae_v_move(&fmatrix.ptr.pp_double[0][b], fmatrix.stride, &fcolumn.ptr.p_double[0], 1, ae_v_len(0,n-1));
        v = (double)(0);
        for(i=0; i<=n-1; i++)
        {
            v = v+ae_sqr(w->ptr.p_double[i]*fcolumn.ptr.p_double[i], _state);
        }
        fdmax = ae_maxreal(fdmax, v, _state);
        
        /*
         * Fill temporary with second derivatives of basis function
         */
        ae_v_move(&d2matrix.ptr.pp_double[b][0], 1, &bd2.ptr.p_double[0], 1, ae_v_len(0,m-1));
    }
    
    /*
     * * calculate penalty matrix A
     * * calculate max of diagonal elements of A
     * * calculate PDecay - coefficient before penalty matrix
     */
    for(i=0; i<=m-1; i++)
    {
        for(j=i; j<=m-1; j++)
        {
            
            /*
             * calculate integral(B_i''*B_j'') where B_i and B_j are
             * i-th and j-th basis splines.
             * B_i and B_j are piecewise linear functions.
             */
            v = (double)(0);
            for(b=0; b<=m-2; b++)
            {
                fa = d2matrix.ptr.pp_double[i][b];
                fb = d2matrix.ptr.pp_double[i][b+1];
                ga = d2matrix.ptr.pp_double[j][b];
                gb = d2matrix.ptr.pp_double[j][b+1];
                v = v+(bx.ptr.p_double[b+1]-bx.ptr.p_double[b])*(fa*ga+(fa*(gb-ga)+ga*(fb-fa))/2+(fb-fa)*(gb-ga)/3);
            }
            amatrix.ptr.pp_double[i][j] = v;
            amatrix.ptr.pp_double[j][i] = v;
        }
    }
    admax = (double)(0);
    for(i=0; i<=m-1; i++)
    {
        admax = ae_maxreal(admax, ae_fabs(amatrix.ptr.pp_double[i][i], _state), _state);
    }
    pdecay = lambdav*fdmax/admax;
    
    /*
     * Calculate TDecay for Tikhonov regularization
     */
    tdecay = fdmax*(1+pdecay)*10*ae_machineepsilon;
    
    /*
     * Prepare system
     *
     * NOTE: FMatrix is spoiled during this process
     */
    for(i=0; i<=n-1; i++)
    {
        v = w->ptr.p_double[i];
        ae_v_muld(&fmatrix.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v);
    }
    rmatrixgemm(m, m, n, 1.0, &fmatrix, 0, 0, 1, &fmatrix, 0, 0, 0, 0.0, &nmatrix, 0, 0, _state);
    for(i=0; i<=m-1; i++)
    {
        for(j=0; j<=m-1; j++)
        {
            nmatrix.ptr.pp_double[i][j] = nmatrix.ptr.pp_double[i][j]+pdecay*amatrix.ptr.pp_double[i][j];
        }
    }
    for(i=0; i<=m-1; i++)
    {
        nmatrix.ptr.pp_double[i][i] = nmatrix.ptr.pp_double[i][i]+tdecay;
    }
    for(i=0; i<=m-1; i++)
    {
        rightpart.ptr.p_double[i] = (double)(0);
    }
    for(i=0; i<=n-1; i++)
    {
        v = y->ptr.p_double[i]*w->ptr.p_double[i];
        ae_v_addd(&rightpart.ptr.p_double[0], 1, &fmatrix.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v);
    }
    
    /*
     * Solve system
     */
    if( !spdmatrixcholesky(&nmatrix, m, ae_true, _state) )
    {
        *info = -4;
        ae_frame_leave(_state);
        return;
    }
    fblscholeskysolve(&nmatrix, 1.0, m, ae_true, &rightpart, &tmp0, _state);
    ae_v_move(&c.ptr.p_double[0], 1, &rightpart.ptr.p_double[0], 1, ae_v_len(0,m-1));
    
    /*
     * add nodes to force linearity outside of the fitting interval
     */
    spline1dgriddiffcubic(&bx, &c, m, 2, 0.0, 2, 0.0, &bd1, _state);
    ae_vector_set_length(&tx, m+2, _state);
    ae_vector_set_length(&ty, m+2, _state);
    ae_vector_set_length(&td, m+2, _state);
    ae_v_move(&tx.ptr.p_double[1], 1, &bx.ptr.p_double[0], 1, ae_v_len(1,m));
    ae_v_move(&ty.ptr.p_double[1], 1, &rightpart.ptr.p_double[0], 1, ae_v_len(1,m));
    ae_v_move(&td.ptr.p_double[1], 1, &bd1.ptr.p_double[0], 1, ae_v_len(1,m));
    tx.ptr.p_double[0] = tx.ptr.p_double[1]-(tx.ptr.p_double[2]-tx.ptr.p_double[1]);
    ty.ptr.p_double[0] = ty.ptr.p_double[1]-td.ptr.p_double[1]*(tx.ptr.p_double[2]-tx.ptr.p_double[1]);
    td.ptr.p_double[0] = td.ptr.p_double[1];
    tx.ptr.p_double[m+1] = tx.ptr.p_double[m]+(tx.ptr.p_double[m]-tx.ptr.p_double[m-1]);
    ty.ptr.p_double[m+1] = ty.ptr.p_double[m]+td.ptr.p_double[m]*(tx.ptr.p_double[m]-tx.ptr.p_double[m-1]);
    td.ptr.p_double[m+1] = td.ptr.p_double[m];
    spline1dbuildhermite(&tx, &ty, &td, m+2, s, _state);
    spline1dlintransx(s, 2/(xb-xa), -(xa+xb)/(xb-xa), _state);
    spline1dlintransy(s, sb-sa, sa, _state);
    *info = 1;
    
    /*
     * Fill report
     */
    rep->rmserror = (double)(0);
    rep->avgerror = (double)(0);
    rep->avgrelerror = (double)(0);
    rep->maxerror = (double)(0);
    relcnt = (double)(0);
    spline1dconvcubic(&bx, &rightpart, m, 2, 0.0, 2, 0.0, x, n, &fcolumn, _state);
    for(i=0; i<=n-1; i++)
    {
        v = (sb-sa)*fcolumn.ptr.p_double[i]+sa;
        rep->rmserror = rep->rmserror+ae_sqr(v-yoriginal.ptr.p_double[i], _state);
        rep->avgerror = rep->avgerror+ae_fabs(v-yoriginal.ptr.p_double[i], _state);
        if( ae_fp_neq(yoriginal.ptr.p_double[i],(double)(0)) )
        {
            rep->avgrelerror = rep->avgrelerror+ae_fabs(v-yoriginal.ptr.p_double[i], _state)/ae_fabs(yoriginal.ptr.p_double[i], _state);
            relcnt = relcnt+1;
        }
        rep->maxerror = ae_maxreal(rep->maxerror, ae_fabs(v-yoriginal.ptr.p_double[i], _state), _state);
    }
    rep->rmserror = ae_sqrt(rep->rmserror/n, _state);
    rep->avgerror = rep->avgerror/n;
    if( ae_fp_neq(relcnt,(double)(0)) )
    {
        rep->avgrelerror = rep->avgrelerror/relcnt;
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_spline1dfitpenalizedw(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    /* Real    */ ae_vector* w,
    ae_int_t n,
    ae_int_t m,
    double rho,
    ae_int_t* info,
    spline1dinterpolant* s,
    spline1dfitreport* rep, ae_state *_state)
{
    spline1dfitpenalizedw(x,y,w,n,m,rho,info,s,rep, _state);
}


/*************************************************************************
Weighted fitting by cubic  spline,  with constraints on function values or
derivatives.

Equidistant grid with M-2 nodes on [min(x,xc),max(x,xc)] is  used to build
basis functions. Basis functions are cubic splines with continuous  second
derivatives  and  non-fixed first  derivatives  at  interval  ends.  Small
regularizing term is used  when  solving  constrained  tasks  (to  improve
stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO
    Spline1DFitHermiteWC()  -   fitting by Hermite splines (more flexible,
                                less smooth)
    Spline1DFitCubic()      -   "lightweight" fitting  by  cubic  splines,
                                without invididual weights and constraints

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.
                                
INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    XC  -   points where spline values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that S(XC[i])=YC[i]
            * DC[i]=1   means that S'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints (optional):
            * 0<=K<M.
            * K=0 means no constraints (XC/YC/DC are not used)
            * if given, only first K elements of XC/YC/DC are used
            * if not given, automatically determined from XC/YC/DC
    M   -   number of basis functions ( = number_of_nodes+2), M>=4.

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearWC() subroutine.
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    S   -   spline interpolant.
    Rep -   report, same format as in LSFitLinearWC() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* excessive constraints can be inconsistent. Splines are  piecewise  cubic
  functions, and it is easy to create an example, where  large  number  of
  constraints  concentrated  in  small  area will result in inconsistency.
  Just because spline is not flexible enough to satisfy all of  them.  And
  same constraints spread across the  [min(x),max(x)]  will  be  perfectly
  consistent.
* the more evenly constraints are spread across [min(x),max(x)],  the more
  chances that they will be consistent
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints IS NOT GUARANTEED.
* in the several special cases, however, we CAN guarantee consistency.
* one of this cases is constraints  on  the  function  values  AND/OR  its
  derivatives at the interval boundaries.
* another  special  case  is ONE constraint on the function value (OR, but
  not AND, derivative) anywhere in the interval

Our final recommendation is to use constraints  WHEN  AND  ONLY  WHEN  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.


  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitcubicwc(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t* info,
     spline1dinterpolant* s,
     spline1dfitreport* rep,
     ae_state *_state)
{
    ae_int_t i;

    *info = 0;
    _spline1dinterpolant_clear(s);
    _spline1dfitreport_clear(rep);

    ae_assert(n>=1, "Spline1DFitCubicWC: N<1!", _state);
    ae_assert(m>=4, "Spline1DFitCubicWC: M<4!", _state);
    ae_assert(k>=0, "Spline1DFitCubicWC: K<0!", _state);
    ae_assert(k<m, "Spline1DFitCubicWC: K>=M!", _state);
    ae_assert(x->cnt>=n, "Spline1DFitCubicWC: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DFitCubicWC: Length(Y)<N!", _state);
    ae_assert(w->cnt>=n, "Spline1DFitCubicWC: Length(W)<N!", _state);
    ae_assert(xc->cnt>=k, "Spline1DFitCubicWC: Length(XC)<K!", _state);
    ae_assert(yc->cnt>=k, "Spline1DFitCubicWC: Length(YC)<K!", _state);
    ae_assert(dc->cnt>=k, "Spline1DFitCubicWC: Length(DC)<K!", _state);
    ae_assert(isfinitevector(x, n, _state), "Spline1DFitCubicWC: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DFitCubicWC: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(w, n, _state), "Spline1DFitCubicWC: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(xc, k, _state), "Spline1DFitCubicWC: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(yc, k, _state), "Spline1DFitCubicWC: Y contains infinite or NAN values!", _state);
    for(i=0; i<=k-1; i++)
    {
        ae_assert(dc->ptr.p_int[i]==0||dc->ptr.p_int[i]==1, "Spline1DFitCubicWC: DC[i] is neither 0 or 1!", _state);
    }
    lsfit_spline1dfitinternal(0, x, y, w, n, xc, yc, dc, k, m, info, s, rep, _state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_spline1dfitcubicwc(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    /* Real    */ ae_vector* w,
    ae_int_t n,
    /* Real    */ ae_vector* xc,
    /* Real    */ ae_vector* yc,
    /* Integer */ ae_vector* dc,
    ae_int_t k,
    ae_int_t m,
    ae_int_t* info,
    spline1dinterpolant* s,
    spline1dfitreport* rep, ae_state *_state)
{
    spline1dfitcubicwc(x,y,w,n,xc,yc,dc,k,m,info,s,rep, _state);
}


/*************************************************************************
Weighted  fitting  by Hermite spline,  with constraints on function values
or first derivatives.

Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is  used  to  build
basis functions. Basis functions are Hermite splines.  Small  regularizing
term is used when solving constrained tasks (to improve stability).

Task is linear, so linear least squares solver is used. Complexity of this
computational scheme is O(N*M^2), mostly dominated by least squares solver

SEE ALSO
    Spline1DFitCubicWC()    -   fitting by Cubic splines (less flexible,
                                more smooth)
    Spline1DFitHermite()    -   "lightweight" Hermite fitting, without
                                invididual weights and constraints

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.
                                
INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
            Each summand in square  sum  of  approximation deviations from
            given  values  is  multiplied  by  the square of corresponding
            weight. Fill it by 1's if you don't  want  to  solve  weighted
            task.
    N   -   number of points (optional):
            * N>0
            * if given, only first N elements of X/Y/W are processed
            * if not given, automatically determined from X/Y/W sizes
    XC  -   points where spline values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that S(XC[i])=YC[i]
            * DC[i]=1   means that S'(XC[i])=YC[i]
            SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS
    K   -   number of constraints (optional):
            * 0<=K<M.
            * K=0 means no constraints (XC/YC/DC are not used)
            * if given, only first K elements of XC/YC/DC are used
            * if not given, automatically determined from XC/YC/DC
    M   -   number of basis functions (= 2 * number of nodes),
            M>=4,
            M IS EVEN!

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
                        -2 means odd M was passed (which is not supported)
                        -1 means another errors in parameters passed
                           (N<=0, for example)
    S   -   spline interpolant.
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

IMPORTANT:
    this subroitine supports only even M's


ORDER OF POINTS

Subroutine automatically sorts points, so caller may pass unsorted array.

SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES:

Setting constraints can lead  to undesired  results,  like ill-conditioned
behavior, or inconsistency being detected. From the other side,  it allows
us to improve quality of the fit. Here we summarize  our  experience  with
constrained regression splines:
* excessive constraints can be inconsistent. Splines are  piecewise  cubic
  functions, and it is easy to create an example, where  large  number  of
  constraints  concentrated  in  small  area will result in inconsistency.
  Just because spline is not flexible enough to satisfy all of  them.  And
  same constraints spread across the  [min(x),max(x)]  will  be  perfectly
  consistent.
* the more evenly constraints are spread across [min(x),max(x)],  the more
  chances that they will be consistent
* the  greater  is  M (given  fixed  constraints),  the  more chances that
  constraints will be consistent
* in the general case, consistency of constraints is NOT GUARANTEED.
* in the several special cases, however, we can guarantee consistency.
* one of this cases is  M>=4  and   constraints  on   the  function  value
  (AND/OR its derivative) at the interval boundaries.
* another special case is M>=4  and  ONE  constraint on the function value
  (OR, BUT NOT AND, derivative) anywhere in [min(x),max(x)]

Our final recommendation is to use constraints  WHEN  AND  ONLY  when  you
can't solve your task without them. Anything beyond  special  cases  given
above is not guaranteed and may result in inconsistency.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfithermitewc(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t* info,
     spline1dinterpolant* s,
     spline1dfitreport* rep,
     ae_state *_state)
{
    ae_int_t i;

    *info = 0;
    _spline1dinterpolant_clear(s);
    _spline1dfitreport_clear(rep);

    ae_assert(n>=1, "Spline1DFitHermiteWC: N<1!", _state);
    ae_assert(m>=4, "Spline1DFitHermiteWC: M<4!", _state);
    ae_assert(m%2==0, "Spline1DFitHermiteWC: M is odd!", _state);
    ae_assert(k>=0, "Spline1DFitHermiteWC: K<0!", _state);
    ae_assert(k<m, "Spline1DFitHermiteWC: K>=M!", _state);
    ae_assert(x->cnt>=n, "Spline1DFitHermiteWC: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DFitHermiteWC: Length(Y)<N!", _state);
    ae_assert(w->cnt>=n, "Spline1DFitHermiteWC: Length(W)<N!", _state);
    ae_assert(xc->cnt>=k, "Spline1DFitHermiteWC: Length(XC)<K!", _state);
    ae_assert(yc->cnt>=k, "Spline1DFitHermiteWC: Length(YC)<K!", _state);
    ae_assert(dc->cnt>=k, "Spline1DFitHermiteWC: Length(DC)<K!", _state);
    ae_assert(isfinitevector(x, n, _state), "Spline1DFitHermiteWC: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DFitHermiteWC: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(w, n, _state), "Spline1DFitHermiteWC: Y contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(xc, k, _state), "Spline1DFitHermiteWC: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(yc, k, _state), "Spline1DFitHermiteWC: Y contains infinite or NAN values!", _state);
    for(i=0; i<=k-1; i++)
    {
        ae_assert(dc->ptr.p_int[i]==0||dc->ptr.p_int[i]==1, "Spline1DFitHermiteWC: DC[i] is neither 0 or 1!", _state);
    }
    lsfit_spline1dfitinternal(1, x, y, w, n, xc, yc, dc, k, m, info, s, rep, _state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_spline1dfithermitewc(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    /* Real    */ ae_vector* w,
    ae_int_t n,
    /* Real    */ ae_vector* xc,
    /* Real    */ ae_vector* yc,
    /* Integer */ ae_vector* dc,
    ae_int_t k,
    ae_int_t m,
    ae_int_t* info,
    spline1dinterpolant* s,
    spline1dfitreport* rep, ae_state *_state)
{
    spline1dfithermitewc(x,y,w,n,xc,yc,dc,k,m,info,s,rep, _state);
}


/*************************************************************************
Least squares fitting by cubic spline.

This subroutine is "lightweight" alternative for more complex and feature-
rich Spline1DFitCubicWC().  See  Spline1DFitCubicWC() for more information
about subroutine parameters (we don't duplicate it here because of length)

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfitcubic(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     spline1dinterpolant* s,
     spline1dfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_vector w;
    ae_vector xc;
    ae_vector yc;
    ae_vector dc;

    ae_frame_make(_state, &_frame_block);
    *info = 0;
    _spline1dinterpolant_clear(s);
    _spline1dfitreport_clear(rep);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&xc, 0, DT_REAL, _state);
    ae_vector_init(&yc, 0, DT_REAL, _state);
    ae_vector_init(&dc, 0, DT_INT, _state);

    ae_assert(n>=1, "Spline1DFitCubic: N<1!", _state);
    ae_assert(m>=4, "Spline1DFitCubic: M<4!", _state);
    ae_assert(x->cnt>=n, "Spline1DFitCubic: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DFitCubic: Length(Y)<N!", _state);
    ae_assert(isfinitevector(x, n, _state), "Spline1DFitCubic: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DFitCubic: Y contains infinite or NAN values!", _state);
    ae_vector_set_length(&w, n, _state);
    for(i=0; i<=n-1; i++)
    {
        w.ptr.p_double[i] = (double)(1);
    }
    spline1dfitcubicwc(x, y, &w, n, &xc, &yc, &dc, 0, m, info, s, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_spline1dfitcubic(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    ae_int_t n,
    ae_int_t m,
    ae_int_t* info,
    spline1dinterpolant* s,
    spline1dfitreport* rep, ae_state *_state)
{
    spline1dfitcubic(x,y,n,m,info,s,rep, _state);
}


/*************************************************************************
Least squares fitting by Hermite spline.

This subroutine is "lightweight" alternative for more complex and feature-
rich Spline1DFitHermiteWC().  See Spline1DFitHermiteWC()  description  for
more information about subroutine parameters (we don't duplicate  it  here
because of length).

COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

  -- ALGLIB PROJECT --
     Copyright 18.08.2009 by Bochkanov Sergey
*************************************************************************/
void spline1dfithermite(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     spline1dinterpolant* s,
     spline1dfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_vector w;
    ae_vector xc;
    ae_vector yc;
    ae_vector dc;

    ae_frame_make(_state, &_frame_block);
    *info = 0;
    _spline1dinterpolant_clear(s);
    _spline1dfitreport_clear(rep);
    ae_vector_init(&w, 0, DT_REAL, _state);
    ae_vector_init(&xc, 0, DT_REAL, _state);
    ae_vector_init(&yc, 0, DT_REAL, _state);
    ae_vector_init(&dc, 0, DT_INT, _state);

    ae_assert(n>=1, "Spline1DFitHermite: N<1!", _state);
    ae_assert(m>=4, "Spline1DFitHermite: M<4!", _state);
    ae_assert(m%2==0, "Spline1DFitHermite: M is odd!", _state);
    ae_assert(x->cnt>=n, "Spline1DFitHermite: Length(X)<N!", _state);
    ae_assert(y->cnt>=n, "Spline1DFitHermite: Length(Y)<N!", _state);
    ae_assert(isfinitevector(x, n, _state), "Spline1DFitHermite: X contains infinite or NAN values!", _state);
    ae_assert(isfinitevector(y, n, _state), "Spline1DFitHermite: Y contains infinite or NAN values!", _state);
    ae_vector_set_length(&w, n, _state);
    for(i=0; i<=n-1; i++)
    {
        w.ptr.p_double[i] = (double)(1);
    }
    spline1dfithermitewc(x, y, &w, n, &xc, &yc, &dc, 0, m, info, s, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_spline1dfithermite(/* Real    */ ae_vector* x,
    /* Real    */ ae_vector* y,
    ae_int_t n,
    ae_int_t m,
    ae_int_t* info,
    spline1dinterpolant* s,
    spline1dfitreport* rep, ae_state *_state)
{
    spline1dfithermite(x,y,n,m,info,s,rep, _state);
}


/*************************************************************************
Weighted linear least squares fitting.

QR decomposition is used to reduce task to MxM, then triangular solver  or
SVD-based solver is used depending on condition number of the  system.  It
allows to maximize speed and retain decent accuracy.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.
           
COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    W       -   array[0..N-1]  Weights  corresponding to function  values.
                Each summand in square  sum  of  approximation  deviations
                from  given  values  is  multiplied  by  the   square   of
                corresponding weight.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I, J] - value of J-th basis function in I-th point.
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -1    incorrect N/M were specified
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * Rep.TaskRCond     reciprocal of condition number
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED
                
ERRORS IN PARAMETERS                
                
This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]
            
NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.
            
NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).
            
            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.
            
NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).
                                    
  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearw(/* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_matrix* fmatrix,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state)
{

    *info = 0;
    ae_vector_clear(c);
    _lsfitreport_clear(rep);

    ae_assert(n>=1, "LSFitLinearW: N<1!", _state);
    ae_assert(m>=1, "LSFitLinearW: M<1!", _state);
    ae_assert(y->cnt>=n, "LSFitLinearW: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitLinearW: Y contains infinite or NaN values!", _state);
    ae_assert(w->cnt>=n, "LSFitLinearW: length(W)<N!", _state);
    ae_assert(isfinitevector(w, n, _state), "LSFitLinearW: W contains infinite or NaN values!", _state);
    ae_assert(fmatrix->rows>=n, "LSFitLinearW: rows(FMatrix)<N!", _state);
    ae_assert(fmatrix->cols>=m, "LSFitLinearW: cols(FMatrix)<M!", _state);
    ae_assert(apservisfinitematrix(fmatrix, n, m, _state), "LSFitLinearW: FMatrix contains infinite or NaN values!", _state);
    lsfit_lsfitlinearinternal(y, w, fmatrix, n, m, info, c, rep, _state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_lsfitlinearw(/* Real    */ ae_vector* y,
    /* Real    */ ae_vector* w,
    /* Real    */ ae_matrix* fmatrix,
    ae_int_t n,
    ae_int_t m,
    ae_int_t* info,
    /* Real    */ ae_vector* c,
    lsfitreport* rep, ae_state *_state)
{
    lsfitlinearw(y,w,fmatrix,n,m,info,c,rep, _state);
}


/*************************************************************************
Weighted constained linear least squares fitting.

This  is  variation  of LSFitLinearW(), which searchs for min|A*x=b| given
that  K  additional  constaints  C*x=bc are satisfied. It reduces original
task to modified one: min|B*y-d| WITHOUT constraints,  then LSFitLinearW()
is called.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.
           
COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    W       -   array[0..N-1]  Weights  corresponding to function  values.
                Each summand in square  sum  of  approximation  deviations
                from  given  values  is  multiplied  by  the   square   of
                corresponding weight.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I,J] - value of J-th basis function in I-th point.
    CMatrix -   a table of constaints, array[0..K-1,0..M].
                I-th row of CMatrix corresponds to I-th linear constraint:
                CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M]
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.
    K       -   number of constraints, 0 <= K < M
                K=0 corresponds to absence of constraints.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -3    either   too   many  constraints  (M   or   more),
                        degenerate  constraints   (some   constraints  are
                        repetead twice) or inconsistent  constraints  were
                        specified.
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.
                
ERRORS IN PARAMETERS                
                
This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.
            
NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.
            
NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).
            
            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.
            
NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 07.09.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearwc(/* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_matrix* fmatrix,
     /* Real    */ ae_matrix* cmatrix,
     ae_int_t n,
     ae_int_t m,
     ae_int_t k,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _y;
    ae_matrix _cmatrix;
    ae_int_t i;
    ae_int_t j;
    ae_vector tau;
    ae_matrix q;
    ae_matrix f2;
    ae_vector tmp;
    ae_vector c0;
    double v;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_matrix_init_copy(&_cmatrix, cmatrix, _state);
    cmatrix = &_cmatrix;
    *info = 0;
    ae_vector_clear(c);
    _lsfitreport_clear(rep);
    ae_vector_init(&tau, 0, DT_REAL, _state);
    ae_matrix_init(&q, 0, 0, DT_REAL, _state);
    ae_matrix_init(&f2, 0, 0, DT_REAL, _state);
    ae_vector_init(&tmp, 0, DT_REAL, _state);
    ae_vector_init(&c0, 0, DT_REAL, _state);

    ae_assert(n>=1, "LSFitLinearWC: N<1!", _state);
    ae_assert(m>=1, "LSFitLinearWC: M<1!", _state);
    ae_assert(k>=0, "LSFitLinearWC: K<0!", _state);
    ae_assert(y->cnt>=n, "LSFitLinearWC: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitLinearWC: Y contains infinite or NaN values!", _state);
    ae_assert(w->cnt>=n, "LSFitLinearWC: length(W)<N!", _state);
    ae_assert(isfinitevector(w, n, _state), "LSFitLinearWC: W contains infinite or NaN values!", _state);
    ae_assert(fmatrix->rows>=n, "LSFitLinearWC: rows(FMatrix)<N!", _state);
    ae_assert(fmatrix->cols>=m, "LSFitLinearWC: cols(FMatrix)<M!", _state);
    ae_assert(apservisfinitematrix(fmatrix, n, m, _state), "LSFitLinearWC: FMatrix contains infinite or NaN values!", _state);
    ae_assert(cmatrix->rows>=k, "LSFitLinearWC: rows(CMatrix)<K!", _state);
    ae_assert(cmatrix->cols>=m+1||k==0, "LSFitLinearWC: cols(CMatrix)<M+1!", _state);
    ae_assert(apservisfinitematrix(cmatrix, k, m+1, _state), "LSFitLinearWC: CMatrix contains infinite or NaN values!", _state);
    if( k>=m )
    {
        *info = -3;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Solve
     */
    if( k==0 )
    {
        
        /*
         * no constraints
         */
        lsfit_lsfitlinearinternal(y, w, fmatrix, n, m, info, c, rep, _state);
    }
    else
    {
        
        /*
         * First, find general form solution of constraints system:
         * * factorize C = L*Q
         * * unpack Q
         * * fill upper part of C with zeros (for RCond)
         *
         * We got C=C0+Q2'*y where Q2 is lower M-K rows of Q.
         */
        rmatrixlq(cmatrix, k, m, &tau, _state);
        rmatrixlqunpackq(cmatrix, k, m, &tau, m, &q, _state);
        for(i=0; i<=k-1; i++)
        {
            for(j=i+1; j<=m-1; j++)
            {
                cmatrix->ptr.pp_double[i][j] = 0.0;
            }
        }
        if( ae_fp_less(rmatrixlurcondinf(cmatrix, k, _state),1000*ae_machineepsilon) )
        {
            *info = -3;
            ae_frame_leave(_state);
            return;
        }
        ae_vector_set_length(&tmp, k, _state);
        for(i=0; i<=k-1; i++)
        {
            if( i>0 )
            {
                v = ae_v_dotproduct(&cmatrix->ptr.pp_double[i][0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,i-1));
            }
            else
            {
                v = (double)(0);
            }
            tmp.ptr.p_double[i] = (cmatrix->ptr.pp_double[i][m]-v)/cmatrix->ptr.pp_double[i][i];
        }
        ae_vector_set_length(&c0, m, _state);
        for(i=0; i<=m-1; i++)
        {
            c0.ptr.p_double[i] = (double)(0);
        }
        for(i=0; i<=k-1; i++)
        {
            v = tmp.ptr.p_double[i];
            ae_v_addd(&c0.ptr.p_double[0], 1, &q.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v);
        }
        
        /*
         * Second, prepare modified matrix F2 = F*Q2' and solve modified task
         */
        ae_vector_set_length(&tmp, ae_maxint(n, m, _state)+1, _state);
        ae_matrix_set_length(&f2, n, m-k, _state);
        matrixvectormultiply(fmatrix, 0, n-1, 0, m-1, ae_false, &c0, 0, m-1, -1.0, y, 0, n-1, 1.0, _state);
        rmatrixgemm(n, m-k, m, 1.0, fmatrix, 0, 0, 0, &q, k, 0, 1, 0.0, &f2, 0, 0, _state);
        lsfit_lsfitlinearinternal(y, w, &f2, n, m-k, info, &tmp, rep, _state);
        rep->taskrcond = (double)(-1);
        if( *info<=0 )
        {
            ae_frame_leave(_state);
            return;
        }
        
        /*
         * then, convert back to original answer: C = C0 + Q2'*Y0
         */
        ae_vector_set_length(c, m, _state);
        ae_v_move(&c->ptr.p_double[0], 1, &c0.ptr.p_double[0], 1, ae_v_len(0,m-1));
        matrixvectormultiply(&q, k, m-1, 0, m-1, ae_true, &tmp, 0, m-k-1, 1.0, c, 0, m-1, 1.0, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_lsfitlinearwc(/* Real    */ ae_vector* y,
    /* Real    */ ae_vector* w,
    /* Real    */ ae_matrix* fmatrix,
    /* Real    */ ae_matrix* cmatrix,
    ae_int_t n,
    ae_int_t m,
    ae_int_t k,
    ae_int_t* info,
    /* Real    */ ae_vector* c,
    lsfitreport* rep, ae_state *_state)
{
    lsfitlinearwc(y,w,fmatrix,cmatrix,n,m,k,info,c,rep, _state);
}


/*************************************************************************
Linear least squares fitting.

QR decomposition is used to reduce task to MxM, then triangular solver  or
SVD-based solver is used depending on condition number of the  system.  It
allows to maximize speed and retain decent accuracy.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.
           
COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I, J] - value of J-th basis function in I-th point.
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * Rep.TaskRCond     reciprocal of condition number
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED
                
ERRORS IN PARAMETERS                
                
This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]
            
NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.
            
NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).
            
            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.
            
NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinear(/* Real    */ ae_vector* y,
     /* Real    */ ae_matrix* fmatrix,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector w;
    ae_int_t i;

    ae_frame_make(_state, &_frame_block);
    *info = 0;
    ae_vector_clear(c);
    _lsfitreport_clear(rep);
    ae_vector_init(&w, 0, DT_REAL, _state);

    ae_assert(n>=1, "LSFitLinear: N<1!", _state);
    ae_assert(m>=1, "LSFitLinear: M<1!", _state);
    ae_assert(y->cnt>=n, "LSFitLinear: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitLinear: Y contains infinite or NaN values!", _state);
    ae_assert(fmatrix->rows>=n, "LSFitLinear: rows(FMatrix)<N!", _state);
    ae_assert(fmatrix->cols>=m, "LSFitLinear: cols(FMatrix)<M!", _state);
    ae_assert(apservisfinitematrix(fmatrix, n, m, _state), "LSFitLinear: FMatrix contains infinite or NaN values!", _state);
    ae_vector_set_length(&w, n, _state);
    for(i=0; i<=n-1; i++)
    {
        w.ptr.p_double[i] = (double)(1);
    }
    lsfit_lsfitlinearinternal(y, &w, fmatrix, n, m, info, c, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_lsfitlinear(/* Real    */ ae_vector* y,
    /* Real    */ ae_matrix* fmatrix,
    ae_int_t n,
    ae_int_t m,
    ae_int_t* info,
    /* Real    */ ae_vector* c,
    lsfitreport* rep, ae_state *_state)
{
    lsfitlinear(y,fmatrix,n,m,info,c,rep, _state);
}


/*************************************************************************
Constained linear least squares fitting.

This  is  variation  of LSFitLinear(),  which searchs for min|A*x=b| given
that  K  additional  constaints  C*x=bc are satisfied. It reduces original
task to modified one: min|B*y-d| WITHOUT constraints,  then  LSFitLinear()
is called.

IMPORTANT: if you want to perform  polynomial  fitting,  it  may  be  more
           convenient to use PolynomialFit() function. This function gives
           best  results  on  polynomial  problems  and  solves  numerical
           stability  issues  which  arise  when   you   fit   high-degree
           polynomials to your data.
           
COMMERCIAL EDITION OF ALGLIB:

  ! Commercial version of ALGLIB includes two  important  improvements  of
  ! this function, which can be used from C++ and C#:
  ! * Intel MKL support (lightweight Intel MKL is shipped with ALGLIB)
  ! * multithreading support
  !
  ! Intel MKL gives approximately constant  (with  respect  to  number  of
  ! worker threads) acceleration factor which depends on CPU  being  used,
  ! problem  size  and  "baseline"  ALGLIB  edition  which  is  used   for
  ! comparison.
  !
  ! Speed-up provided by multithreading greatly depends  on  problem  size
  ! - only large problems (number of coefficients is more than 500) can be
  ! efficiently multithreaded.
  !
  ! Generally, commercial ALGLIB is several times faster than  open-source
  ! generic C edition, and many times faster than open-source C# edition.
  !
  ! We recommend you to read 'Working with commercial version' section  of
  ! ALGLIB Reference Manual in order to find out how to  use  performance-
  ! related features provided by commercial edition of ALGLIB.

INPUT PARAMETERS:
    Y       -   array[0..N-1] Function values in  N  points.
    FMatrix -   a table of basis functions values, array[0..N-1, 0..M-1].
                FMatrix[I,J] - value of J-th basis function in I-th point.
    CMatrix -   a table of constaints, array[0..K-1,0..M].
                I-th row of CMatrix corresponds to I-th linear constraint:
                CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M]
    N       -   number of points used. N>=1.
    M       -   number of basis functions, M>=1.
    K       -   number of constraints, 0 <= K < M
                K=0 corresponds to absence of constraints.

OUTPUT PARAMETERS:
    Info    -   error code:
                * -4    internal SVD decomposition subroutine failed (very
                        rare and for degenerate systems only)
                * -3    either   too   many  constraints  (M   or   more),
                        degenerate  constraints   (some   constraints  are
                        repetead twice) or inconsistent  constraints  were
                        specified.
                *  1    task is solved
    C       -   decomposition coefficients, array[0..M-1]
    Rep     -   fitting report. Following fields are set:
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.
                
ERRORS IN PARAMETERS                
                
This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(F*CovPar*F')),
                    where F is functions matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.
            
NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.
            
NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).
            
            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.
            
NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 07.09.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitlinearc(/* Real    */ ae_vector* y,
     /* Real    */ ae_matrix* fmatrix,
     /* Real    */ ae_matrix* cmatrix,
     ae_int_t n,
     ae_int_t m,
     ae_int_t k,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _y;
    ae_vector w;
    ae_int_t i;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    *info = 0;
    ae_vector_clear(c);
    _lsfitreport_clear(rep);
    ae_vector_init(&w, 0, DT_REAL, _state);

    ae_assert(n>=1, "LSFitLinearC: N<1!", _state);
    ae_assert(m>=1, "LSFitLinearC: M<1!", _state);
    ae_assert(k>=0, "LSFitLinearC: K<0!", _state);
    ae_assert(y->cnt>=n, "LSFitLinearC: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitLinearC: Y contains infinite or NaN values!", _state);
    ae_assert(fmatrix->rows>=n, "LSFitLinearC: rows(FMatrix)<N!", _state);
    ae_assert(fmatrix->cols>=m, "LSFitLinearC: cols(FMatrix)<M!", _state);
    ae_assert(apservisfinitematrix(fmatrix, n, m, _state), "LSFitLinearC: FMatrix contains infinite or NaN values!", _state);
    ae_assert(cmatrix->rows>=k, "LSFitLinearC: rows(CMatrix)<K!", _state);
    ae_assert(cmatrix->cols>=m+1||k==0, "LSFitLinearC: cols(CMatrix)<M+1!", _state);
    ae_assert(apservisfinitematrix(cmatrix, k, m+1, _state), "LSFitLinearC: CMatrix contains infinite or NaN values!", _state);
    ae_vector_set_length(&w, n, _state);
    for(i=0; i<=n-1; i++)
    {
        w.ptr.p_double[i] = (double)(1);
    }
    lsfitlinearwc(y, &w, fmatrix, cmatrix, n, m, k, info, c, rep, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Single-threaded stub. HPC ALGLIB replaces it by multithreaded code.
*************************************************************************/
void _pexec_lsfitlinearc(/* Real    */ ae_vector* y,
    /* Real    */ ae_matrix* fmatrix,
    /* Real    */ ae_matrix* cmatrix,
    ae_int_t n,
    ae_int_t m,
    ae_int_t k,
    ae_int_t* info,
    /* Real    */ ae_vector* c,
    lsfitreport* rep, ae_state *_state)
{
    lsfitlinearc(y,fmatrix,cmatrix,n,m,k,info,c,rep, _state);
}


/*************************************************************************
Weighted nonlinear least squares fitting using function values only.

Combination of numerical differentiation and secant updates is used to
obtain function Jacobian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]).

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    DiffStep-   numerical differentiation step;
                should not be very small or large;
                large = loss of accuracy
                small = growth of round-off errors

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 18.10.2008 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewf(/* Real    */ ae_matrix* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_vector* c,
     ae_int_t n,
     ae_int_t m,
     ae_int_t k,
     double diffstep,
     lsfitstate* state,
     ae_state *_state)
{
    ae_int_t i;

    _lsfitstate_clear(state);

    ae_assert(n>=1, "LSFitCreateWF: N<1!", _state);
    ae_assert(m>=1, "LSFitCreateWF: M<1!", _state);
    ae_assert(k>=1, "LSFitCreateWF: K<1!", _state);
    ae_assert(c->cnt>=k, "LSFitCreateWF: length(C)<K!", _state);
    ae_assert(isfinitevector(c, k, _state), "LSFitCreateWF: C contains infinite or NaN values!", _state);
    ae_assert(y->cnt>=n, "LSFitCreateWF: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitCreateWF: Y contains infinite or NaN values!", _state);
    ae_assert(w->cnt>=n, "LSFitCreateWF: length(W)<N!", _state);
    ae_assert(isfinitevector(w, n, _state), "LSFitCreateWF: W contains infinite or NaN values!", _state);
    ae_assert(x->rows>=n, "LSFitCreateWF: rows(X)<N!", _state);
    ae_assert(x->cols>=m, "LSFitCreateWF: cols(X)<M!", _state);
    ae_assert(apservisfinitematrix(x, n, m, _state), "LSFitCreateWF: X contains infinite or NaN values!", _state);
    ae_assert(ae_isfinite(diffstep, _state), "LSFitCreateWF: DiffStep is not finite!", _state);
    ae_assert(ae_fp_greater(diffstep,(double)(0)), "LSFitCreateWF: DiffStep<=0!", _state);
    state->teststep = (double)(0);
    state->diffstep = diffstep;
    state->npoints = n;
    state->nweights = n;
    state->wkind = 1;
    state->m = m;
    state->k = k;
    lsfitsetcond(state, 0.0, 0.0, 0, _state);
    lsfitsetstpmax(state, 0.0, _state);
    lsfitsetxrep(state, ae_false, _state);
    ae_matrix_set_length(&state->taskx, n, m, _state);
    ae_vector_set_length(&state->tasky, n, _state);
    ae_vector_set_length(&state->taskw, n, _state);
    ae_vector_set_length(&state->c, k, _state);
    ae_vector_set_length(&state->x, m, _state);
    ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->taskw.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1));
    for(i=0; i<=n-1; i++)
    {
        ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
        state->tasky.ptr.p_double[i] = y->ptr.p_double[i];
    }
    ae_vector_set_length(&state->s, k, _state);
    ae_vector_set_length(&state->bndl, k, _state);
    ae_vector_set_length(&state->bndu, k, _state);
    for(i=0; i<=k-1; i++)
    {
        state->s.ptr.p_double[i] = 1.0;
        state->bndl.ptr.p_double[i] = _state->v_neginf;
        state->bndu.ptr.p_double[i] = _state->v_posinf;
    }
    state->optalgo = 0;
    state->prevnpt = -1;
    state->prevalgo = -1;
    minlmcreatev(k, n, &state->c, diffstep, &state->optstate, _state);
    lsfit_lsfitclearrequestfields(state, _state);
    ae_vector_set_length(&state->rstate.ia, 6+1, _state);
    ae_vector_set_length(&state->rstate.ra, 8+1, _state);
    state->rstate.stage = -1;
}


/*************************************************************************
Nonlinear least squares fitting using function values only.

Combination of numerical differentiation and secant updates is used to
obtain function Jacobian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (f(c,x[0])-y[0])^2 + ... + (f(c,x[n-1])-y[n-1])^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]).

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    DiffStep-   numerical differentiation step;
                should not be very small or large;
                large = loss of accuracy
                small = growth of round-off errors

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 18.10.2008 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatef(/* Real    */ ae_matrix* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* c,
     ae_int_t n,
     ae_int_t m,
     ae_int_t k,
     double diffstep,
     lsfitstate* state,
     ae_state *_state)
{
    ae_int_t i;

    _lsfitstate_clear(state);

    ae_assert(n>=1, "LSFitCreateF: N<1!", _state);
    ae_assert(m>=1, "LSFitCreateF: M<1!", _state);
    ae_assert(k>=1, "LSFitCreateF: K<1!", _state);
    ae_assert(c->cnt>=k, "LSFitCreateF: length(C)<K!", _state);
    ae_assert(isfinitevector(c, k, _state), "LSFitCreateF: C contains infinite or NaN values!", _state);
    ae_assert(y->cnt>=n, "LSFitCreateF: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitCreateF: Y contains infinite or NaN values!", _state);
    ae_assert(x->rows>=n, "LSFitCreateF: rows(X)<N!", _state);
    ae_assert(x->cols>=m, "LSFitCreateF: cols(X)<M!", _state);
    ae_assert(apservisfinitematrix(x, n, m, _state), "LSFitCreateF: X contains infinite or NaN values!", _state);
    ae_assert(x->rows>=n, "LSFitCreateF: rows(X)<N!", _state);
    ae_assert(x->cols>=m, "LSFitCreateF: cols(X)<M!", _state);
    ae_assert(apservisfinitematrix(x, n, m, _state), "LSFitCreateF: X contains infinite or NaN values!", _state);
    ae_assert(ae_isfinite(diffstep, _state), "LSFitCreateF: DiffStep is not finite!", _state);
    ae_assert(ae_fp_greater(diffstep,(double)(0)), "LSFitCreateF: DiffStep<=0!", _state);
    state->teststep = (double)(0);
    state->diffstep = diffstep;
    state->npoints = n;
    state->wkind = 0;
    state->m = m;
    state->k = k;
    lsfitsetcond(state, 0.0, 0.0, 0, _state);
    lsfitsetstpmax(state, 0.0, _state);
    lsfitsetxrep(state, ae_false, _state);
    ae_matrix_set_length(&state->taskx, n, m, _state);
    ae_vector_set_length(&state->tasky, n, _state);
    ae_vector_set_length(&state->c, k, _state);
    ae_vector_set_length(&state->x, m, _state);
    ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1));
    for(i=0; i<=n-1; i++)
    {
        ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
        state->tasky.ptr.p_double[i] = y->ptr.p_double[i];
    }
    ae_vector_set_length(&state->s, k, _state);
    ae_vector_set_length(&state->bndl, k, _state);
    ae_vector_set_length(&state->bndu, k, _state);
    for(i=0; i<=k-1; i++)
    {
        state->s.ptr.p_double[i] = 1.0;
        state->bndl.ptr.p_double[i] = _state->v_neginf;
        state->bndu.ptr.p_double[i] = _state->v_posinf;
    }
    state->optalgo = 0;
    state->prevnpt = -1;
    state->prevalgo = -1;
    minlmcreatev(k, n, &state->c, diffstep, &state->optstate, _state);
    lsfit_lsfitclearrequestfields(state, _state);
    ae_vector_set_length(&state->rstate.ia, 6+1, _state);
    ae_vector_set_length(&state->rstate.ra, 8+1, _state);
    state->rstate.stage = -1;
}


/*************************************************************************
Weighted nonlinear least squares fitting using gradient only.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,
    
    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted
    
This subroutine uses only f(c,x[i]) and its gradient.
    
INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    CheapFG -   boolean flag, which is:
                * True  if both function and gradient calculation complexity
                        are less than O(M^2).  An improved  algorithm  can
                        be  used  which corresponds  to  FGJ  scheme  from
                        MINLM unit.
                * False otherwise.
                        Standard Jacibian-bases  Levenberg-Marquardt  algo
                        will be used (FJ scheme).

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

See also:
    LSFitResults
    LSFitCreateFG (fitting without weights)
    LSFitCreateWFGH (fitting using Hessian)
    LSFitCreateFGH (fitting using Hessian, without weights)

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewfg(/* Real    */ ae_matrix* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_vector* c,
     ae_int_t n,
     ae_int_t m,
     ae_int_t k,
     ae_bool cheapfg,
     lsfitstate* state,
     ae_state *_state)
{
    ae_int_t i;

    _lsfitstate_clear(state);

    ae_assert(n>=1, "LSFitCreateWFG: N<1!", _state);
    ae_assert(m>=1, "LSFitCreateWFG: M<1!", _state);
    ae_assert(k>=1, "LSFitCreateWFG: K<1!", _state);
    ae_assert(c->cnt>=k, "LSFitCreateWFG: length(C)<K!", _state);
    ae_assert(isfinitevector(c, k, _state), "LSFitCreateWFG: C contains infinite or NaN values!", _state);
    ae_assert(y->cnt>=n, "LSFitCreateWFG: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitCreateWFG: Y contains infinite or NaN values!", _state);
    ae_assert(w->cnt>=n, "LSFitCreateWFG: length(W)<N!", _state);
    ae_assert(isfinitevector(w, n, _state), "LSFitCreateWFG: W contains infinite or NaN values!", _state);
    ae_assert(x->rows>=n, "LSFitCreateWFG: rows(X)<N!", _state);
    ae_assert(x->cols>=m, "LSFitCreateWFG: cols(X)<M!", _state);
    ae_assert(apservisfinitematrix(x, n, m, _state), "LSFitCreateWFG: X contains infinite or NaN values!", _state);
    state->teststep = (double)(0);
    state->diffstep = (double)(0);
    state->npoints = n;
    state->nweights = n;
    state->wkind = 1;
    state->m = m;
    state->k = k;
    lsfitsetcond(state, 0.0, 0.0, 0, _state);
    lsfitsetstpmax(state, 0.0, _state);
    lsfitsetxrep(state, ae_false, _state);
    ae_matrix_set_length(&state->taskx, n, m, _state);
    ae_vector_set_length(&state->tasky, n, _state);
    ae_vector_set_length(&state->taskw, n, _state);
    ae_vector_set_length(&state->c, k, _state);
    ae_vector_set_length(&state->x, m, _state);
    ae_vector_set_length(&state->g, k, _state);
    ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->taskw.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1));
    for(i=0; i<=n-1; i++)
    {
        ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
        state->tasky.ptr.p_double[i] = y->ptr.p_double[i];
    }
    ae_vector_set_length(&state->s, k, _state);
    ae_vector_set_length(&state->bndl, k, _state);
    ae_vector_set_length(&state->bndu, k, _state);
    for(i=0; i<=k-1; i++)
    {
        state->s.ptr.p_double[i] = 1.0;
        state->bndl.ptr.p_double[i] = _state->v_neginf;
        state->bndu.ptr.p_double[i] = _state->v_posinf;
    }
    state->optalgo = 1;
    state->prevnpt = -1;
    state->prevalgo = -1;
    if( cheapfg )
    {
        minlmcreatevgj(k, n, &state->c, &state->optstate, _state);
    }
    else
    {
        minlmcreatevj(k, n, &state->c, &state->optstate, _state);
    }
    lsfit_lsfitclearrequestfields(state, _state);
    ae_vector_set_length(&state->rstate.ia, 6+1, _state);
    ae_vector_set_length(&state->rstate.ra, 8+1, _state);
    state->rstate.stage = -1;
}


/*************************************************************************
Nonlinear least squares fitting using gradient only, without individual
weights.

Nonlinear task min(F(c)) is solved, where

    F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses only f(c,x[i]) and its gradient.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted
    CheapFG -   boolean flag, which is:
                * True  if both function and gradient calculation complexity
                        are less than O(M^2).  An improved  algorithm  can
                        be  used  which corresponds  to  FGJ  scheme  from
                        MINLM unit.
                * False otherwise.
                        Standard Jacibian-bases  Levenberg-Marquardt  algo
                        will be used (FJ scheme).

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatefg(/* Real    */ ae_matrix* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* c,
     ae_int_t n,
     ae_int_t m,
     ae_int_t k,
     ae_bool cheapfg,
     lsfitstate* state,
     ae_state *_state)
{
    ae_int_t i;

    _lsfitstate_clear(state);

    ae_assert(n>=1, "LSFitCreateFG: N<1!", _state);
    ae_assert(m>=1, "LSFitCreateFG: M<1!", _state);
    ae_assert(k>=1, "LSFitCreateFG: K<1!", _state);
    ae_assert(c->cnt>=k, "LSFitCreateFG: length(C)<K!", _state);
    ae_assert(isfinitevector(c, k, _state), "LSFitCreateFG: C contains infinite or NaN values!", _state);
    ae_assert(y->cnt>=n, "LSFitCreateFG: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitCreateFG: Y contains infinite or NaN values!", _state);
    ae_assert(x->rows>=n, "LSFitCreateFG: rows(X)<N!", _state);
    ae_assert(x->cols>=m, "LSFitCreateFG: cols(X)<M!", _state);
    ae_assert(apservisfinitematrix(x, n, m, _state), "LSFitCreateFG: X contains infinite or NaN values!", _state);
    ae_assert(x->rows>=n, "LSFitCreateFG: rows(X)<N!", _state);
    ae_assert(x->cols>=m, "LSFitCreateFG: cols(X)<M!", _state);
    ae_assert(apservisfinitematrix(x, n, m, _state), "LSFitCreateFG: X contains infinite or NaN values!", _state);
    state->teststep = (double)(0);
    state->diffstep = (double)(0);
    state->npoints = n;
    state->wkind = 0;
    state->m = m;
    state->k = k;
    lsfitsetcond(state, 0.0, 0.0, 0, _state);
    lsfitsetstpmax(state, 0.0, _state);
    lsfitsetxrep(state, ae_false, _state);
    ae_matrix_set_length(&state->taskx, n, m, _state);
    ae_vector_set_length(&state->tasky, n, _state);
    ae_vector_set_length(&state->c, k, _state);
    ae_vector_set_length(&state->x, m, _state);
    ae_vector_set_length(&state->g, k, _state);
    ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1));
    for(i=0; i<=n-1; i++)
    {
        ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
        state->tasky.ptr.p_double[i] = y->ptr.p_double[i];
    }
    ae_vector_set_length(&state->s, k, _state);
    ae_vector_set_length(&state->bndl, k, _state);
    ae_vector_set_length(&state->bndu, k, _state);
    for(i=0; i<=k-1; i++)
    {
        state->s.ptr.p_double[i] = 1.0;
        state->bndl.ptr.p_double[i] = _state->v_neginf;
        state->bndu.ptr.p_double[i] = _state->v_posinf;
    }
    state->optalgo = 1;
    state->prevnpt = -1;
    state->prevalgo = -1;
    if( cheapfg )
    {
        minlmcreatevgj(k, n, &state->c, &state->optstate, _state);
    }
    else
    {
        minlmcreatevj(k, n, &state->c, &state->optstate, _state);
    }
    lsfit_lsfitclearrequestfields(state, _state);
    ae_vector_set_length(&state->rstate.ia, 6+1, _state);
    ae_vector_set_length(&state->rstate.ra, 8+1, _state);
    state->rstate.stage = -1;
}


/*************************************************************************
Weighted nonlinear least squares fitting using gradient/Hessian.

Nonlinear task min(F(c)) is solved, where

    F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * w is an N-dimensional vector of weight coefficients,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses f(c,x[i]), its gradient and its Hessian.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    W       -   weights, array[0..N-1]
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatewfgh(/* Real    */ ae_matrix* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_vector* c,
     ae_int_t n,
     ae_int_t m,
     ae_int_t k,
     lsfitstate* state,
     ae_state *_state)
{
    ae_int_t i;

    _lsfitstate_clear(state);

    ae_assert(n>=1, "LSFitCreateWFGH: N<1!", _state);
    ae_assert(m>=1, "LSFitCreateWFGH: M<1!", _state);
    ae_assert(k>=1, "LSFitCreateWFGH: K<1!", _state);
    ae_assert(c->cnt>=k, "LSFitCreateWFGH: length(C)<K!", _state);
    ae_assert(isfinitevector(c, k, _state), "LSFitCreateWFGH: C contains infinite or NaN values!", _state);
    ae_assert(y->cnt>=n, "LSFitCreateWFGH: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitCreateWFGH: Y contains infinite or NaN values!", _state);
    ae_assert(w->cnt>=n, "LSFitCreateWFGH: length(W)<N!", _state);
    ae_assert(isfinitevector(w, n, _state), "LSFitCreateWFGH: W contains infinite or NaN values!", _state);
    ae_assert(x->rows>=n, "LSFitCreateWFGH: rows(X)<N!", _state);
    ae_assert(x->cols>=m, "LSFitCreateWFGH: cols(X)<M!", _state);
    ae_assert(apservisfinitematrix(x, n, m, _state), "LSFitCreateWFGH: X contains infinite or NaN values!", _state);
    state->teststep = (double)(0);
    state->diffstep = (double)(0);
    state->npoints = n;
    state->nweights = n;
    state->wkind = 1;
    state->m = m;
    state->k = k;
    lsfitsetcond(state, 0.0, 0.0, 0, _state);
    lsfitsetstpmax(state, 0.0, _state);
    lsfitsetxrep(state, ae_false, _state);
    ae_matrix_set_length(&state->taskx, n, m, _state);
    ae_vector_set_length(&state->tasky, n, _state);
    ae_vector_set_length(&state->taskw, n, _state);
    ae_vector_set_length(&state->c, k, _state);
    ae_matrix_set_length(&state->h, k, k, _state);
    ae_vector_set_length(&state->x, m, _state);
    ae_vector_set_length(&state->g, k, _state);
    ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->taskw.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1));
    for(i=0; i<=n-1; i++)
    {
        ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
        state->tasky.ptr.p_double[i] = y->ptr.p_double[i];
    }
    ae_vector_set_length(&state->s, k, _state);
    ae_vector_set_length(&state->bndl, k, _state);
    ae_vector_set_length(&state->bndu, k, _state);
    for(i=0; i<=k-1; i++)
    {
        state->s.ptr.p_double[i] = 1.0;
        state->bndl.ptr.p_double[i] = _state->v_neginf;
        state->bndu.ptr.p_double[i] = _state->v_posinf;
    }
    state->optalgo = 2;
    state->prevnpt = -1;
    state->prevalgo = -1;
    minlmcreatefgh(k, &state->c, &state->optstate, _state);
    lsfit_lsfitclearrequestfields(state, _state);
    ae_vector_set_length(&state->rstate.ia, 6+1, _state);
    ae_vector_set_length(&state->rstate.ra, 8+1, _state);
    state->rstate.stage = -1;
}


/*************************************************************************
Nonlinear least squares fitting using gradient/Hessian, without individial
weights.

Nonlinear task min(F(c)) is solved, where

    F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2,

    * N is a number of points,
    * M is a dimension of a space points belong to,
    * K is a dimension of a space of parameters being fitted,
    * x is a set of N points, each of them is an M-dimensional vector,
    * c is a K-dimensional vector of parameters being fitted

This subroutine uses f(c,x[i]), its gradient and its Hessian.

INPUT PARAMETERS:
    X       -   array[0..N-1,0..M-1], points (one row = one point)
    Y       -   array[0..N-1], function values.
    C       -   array[0..K-1], initial approximation to the solution,
    N       -   number of points, N>1
    M       -   dimension of space
    K       -   number of parameters being fitted

OUTPUT PARAMETERS:
    State   -   structure which stores algorithm state


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitcreatefgh(/* Real    */ ae_matrix* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* c,
     ae_int_t n,
     ae_int_t m,
     ae_int_t k,
     lsfitstate* state,
     ae_state *_state)
{
    ae_int_t i;

    _lsfitstate_clear(state);

    ae_assert(n>=1, "LSFitCreateFGH: N<1!", _state);
    ae_assert(m>=1, "LSFitCreateFGH: M<1!", _state);
    ae_assert(k>=1, "LSFitCreateFGH: K<1!", _state);
    ae_assert(c->cnt>=k, "LSFitCreateFGH: length(C)<K!", _state);
    ae_assert(isfinitevector(c, k, _state), "LSFitCreateFGH: C contains infinite or NaN values!", _state);
    ae_assert(y->cnt>=n, "LSFitCreateFGH: length(Y)<N!", _state);
    ae_assert(isfinitevector(y, n, _state), "LSFitCreateFGH: Y contains infinite or NaN values!", _state);
    ae_assert(x->rows>=n, "LSFitCreateFGH: rows(X)<N!", _state);
    ae_assert(x->cols>=m, "LSFitCreateFGH: cols(X)<M!", _state);
    ae_assert(apservisfinitematrix(x, n, m, _state), "LSFitCreateFGH: X contains infinite or NaN values!", _state);
    state->teststep = (double)(0);
    state->diffstep = (double)(0);
    state->npoints = n;
    state->wkind = 0;
    state->m = m;
    state->k = k;
    lsfitsetcond(state, 0.0, 0.0, 0, _state);
    lsfitsetstpmax(state, 0.0, _state);
    lsfitsetxrep(state, ae_false, _state);
    ae_matrix_set_length(&state->taskx, n, m, _state);
    ae_vector_set_length(&state->tasky, n, _state);
    ae_vector_set_length(&state->c, k, _state);
    ae_matrix_set_length(&state->h, k, k, _state);
    ae_vector_set_length(&state->x, m, _state);
    ae_vector_set_length(&state->g, k, _state);
    ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1));
    for(i=0; i<=n-1; i++)
    {
        ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
        state->tasky.ptr.p_double[i] = y->ptr.p_double[i];
    }
    ae_vector_set_length(&state->s, k, _state);
    ae_vector_set_length(&state->bndl, k, _state);
    ae_vector_set_length(&state->bndu, k, _state);
    for(i=0; i<=k-1; i++)
    {
        state->s.ptr.p_double[i] = 1.0;
        state->bndl.ptr.p_double[i] = _state->v_neginf;
        state->bndu.ptr.p_double[i] = _state->v_posinf;
    }
    state->optalgo = 2;
    state->prevnpt = -1;
    state->prevalgo = -1;
    minlmcreatefgh(k, &state->c, &state->optstate, _state);
    lsfit_lsfitclearrequestfields(state, _state);
    ae_vector_set_length(&state->rstate.ia, 6+1, _state);
    ae_vector_set_length(&state->rstate.ra, 8+1, _state);
    state->rstate.stage = -1;
}


/*************************************************************************
Stopping conditions for nonlinear least squares fitting.

INPUT PARAMETERS:
    State   -   structure which stores algorithm state
    EpsF    -   stopping criterion. Algorithm stops if
                |F(k+1)-F(k)| <= EpsF*max{|F(k)|, |F(k+1)|, 1}
    EpsX    -   >=0
                The subroutine finishes its work if  on  k+1-th  iteration
                the condition |v|<=EpsX is fulfilled, where:
                * |.| means Euclidian norm
                * v - scaled step vector, v[i]=dx[i]/s[i]
                * dx - ste pvector, dx=X(k+1)-X(k)
                * s - scaling coefficients set by LSFitSetScale()
    MaxIts  -   maximum number of iterations. If MaxIts=0, the  number  of
                iterations   is    unlimited.   Only   Levenberg-Marquardt
                iterations  are  counted  (L-BFGS/CG  iterations  are  NOT
                counted because their cost is very low compared to that of
                LM).

NOTE

Passing EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to automatic
stopping criterion selection (according to the scheme used by MINLM unit).


  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitsetcond(lsfitstate* state,
     double epsf,
     double epsx,
     ae_int_t maxits,
     ae_state *_state)
{


    ae_assert(ae_isfinite(epsf, _state), "LSFitSetCond: EpsF is not finite!", _state);
    ae_assert(ae_fp_greater_eq(epsf,(double)(0)), "LSFitSetCond: negative EpsF!", _state);
    ae_assert(ae_isfinite(epsx, _state), "LSFitSetCond: EpsX is not finite!", _state);
    ae_assert(ae_fp_greater_eq(epsx,(double)(0)), "LSFitSetCond: negative EpsX!", _state);
    ae_assert(maxits>=0, "LSFitSetCond: negative MaxIts!", _state);
    state->epsf = epsf;
    state->epsx = epsx;
    state->maxits = maxits;
}


/*************************************************************************
This function sets maximum step length

INPUT PARAMETERS:
    State   -   structure which stores algorithm state
    StpMax  -   maximum step length, >=0. Set StpMax to 0.0,  if you don't
                want to limit step length.

Use this subroutine when you optimize target function which contains exp()
or  other  fast  growing  functions,  and optimization algorithm makes too
large  steps  which  leads  to overflow. This function allows us to reject
steps  that  are  too  large  (and  therefore  expose  us  to the possible
overflow) without actually calculating function value at the x+stp*d.

NOTE: non-zero StpMax leads to moderate  performance  degradation  because
intermediate  step  of  preconditioned L-BFGS optimization is incompatible
with limits on step size.

  -- ALGLIB --
     Copyright 02.04.2010 by Bochkanov Sergey
*************************************************************************/
void lsfitsetstpmax(lsfitstate* state, double stpmax, ae_state *_state)
{


    ae_assert(ae_fp_greater_eq(stpmax,(double)(0)), "LSFitSetStpMax: StpMax<0!", _state);
    state->stpmax = stpmax;
}


/*************************************************************************
This function turns on/off reporting.

INPUT PARAMETERS:
    State   -   structure which stores algorithm state
    NeedXRep-   whether iteration reports are needed or not
    
When reports are needed, State.C (current parameters) and State.F (current
value of fitting function) are reported.


  -- ALGLIB --
     Copyright 15.08.2010 by Bochkanov Sergey
*************************************************************************/
void lsfitsetxrep(lsfitstate* state, ae_bool needxrep, ae_state *_state)
{


    state->xrep = needxrep;
}


/*************************************************************************
This function sets scaling coefficients for underlying optimizer.

ALGLIB optimizers use scaling matrices to test stopping  conditions  (step
size and gradient are scaled before comparison with tolerances).  Scale of
the I-th variable is a translation invariant measure of:
a) "how large" the variable is
b) how large the step should be to make significant changes in the function

Generally, scale is NOT considered to be a form of preconditioner.  But LM
optimizer is unique in that it uses scaling matrix both  in  the  stopping
condition tests and as Marquardt damping factor.

Proper scaling is very important for the algorithm performance. It is less
important for the quality of results, but still has some influence (it  is
easier  to  converge  when  variables  are  properly  scaled, so premature
stopping is possible when very badly scalled variables are  combined  with
relaxed stopping conditions).

INPUT PARAMETERS:
    State   -   structure stores algorithm state
    S       -   array[N], non-zero scaling coefficients
                S[i] may be negative, sign doesn't matter.

  -- ALGLIB --
     Copyright 14.01.2011 by Bochkanov Sergey
*************************************************************************/
void lsfitsetscale(lsfitstate* state,
     /* Real    */ ae_vector* s,
     ae_state *_state)
{
    ae_int_t i;


    ae_assert(s->cnt>=state->k, "LSFitSetScale: Length(S)<K", _state);
    for(i=0; i<=state->k-1; i++)
    {
        ae_assert(ae_isfinite(s->ptr.p_double[i], _state), "LSFitSetScale: S contains infinite or NAN elements", _state);
        ae_assert(ae_fp_neq(s->ptr.p_double[i],(double)(0)), "LSFitSetScale: S contains infinite or NAN elements", _state);
        state->s.ptr.p_double[i] = ae_fabs(s->ptr.p_double[i], _state);
    }
}


/*************************************************************************
This function sets boundary constraints for underlying optimizer

Boundary constraints are inactive by default (after initial creation).
They are preserved until explicitly turned off with another SetBC() call.

INPUT PARAMETERS:
    State   -   structure stores algorithm state
    BndL    -   lower bounds, array[K].
                If some (all) variables are unbounded, you may specify
                very small number or -INF (latter is recommended because
                it will allow solver to use better algorithm).
    BndU    -   upper bounds, array[K].
                If some (all) variables are unbounded, you may specify
                very large number or +INF (latter is recommended because
                it will allow solver to use better algorithm).

NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th
variable will be "frozen" at X[i]=BndL[i]=BndU[i].

NOTE 2: unlike other constrained optimization algorithms, this solver  has
following useful properties:
* bound constraints are always satisfied exactly
* function is evaluated only INSIDE area specified by bound constraints

  -- ALGLIB --
     Copyright 14.01.2011 by Bochkanov Sergey
*************************************************************************/
void lsfitsetbc(lsfitstate* state,
     /* Real    */ ae_vector* bndl,
     /* Real    */ ae_vector* bndu,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t k;


    k = state->k;
    ae_assert(bndl->cnt>=k, "LSFitSetBC: Length(BndL)<K", _state);
    ae_assert(bndu->cnt>=k, "LSFitSetBC: Length(BndU)<K", _state);
    for(i=0; i<=k-1; i++)
    {
        ae_assert(ae_isfinite(bndl->ptr.p_double[i], _state)||ae_isneginf(bndl->ptr.p_double[i], _state), "LSFitSetBC: BndL contains NAN or +INF", _state);
        ae_assert(ae_isfinite(bndu->ptr.p_double[i], _state)||ae_isposinf(bndu->ptr.p_double[i], _state), "LSFitSetBC: BndU contains NAN or -INF", _state);
        if( ae_isfinite(bndl->ptr.p_double[i], _state)&&ae_isfinite(bndu->ptr.p_double[i], _state) )
        {
            ae_assert(ae_fp_less_eq(bndl->ptr.p_double[i],bndu->ptr.p_double[i]), "LSFitSetBC: BndL[i]>BndU[i]", _state);
        }
        state->bndl.ptr.p_double[i] = bndl->ptr.p_double[i];
        state->bndu.ptr.p_double[i] = bndu->ptr.p_double[i];
    }
}


/*************************************************************************
NOTES:

1. this algorithm is somewhat unusual because it works with  parameterized
   function f(C,X), where X is a function argument (we  have  many  points
   which are characterized by different  argument  values),  and  C  is  a
   parameter to fit.

   For example, if we want to do linear fit by f(c0,c1,x) = c0*x+c1,  then
   x will be argument, and {c0,c1} will be parameters.
   
   It is important to understand that this algorithm finds minimum in  the
   space of function PARAMETERS (not arguments), so it  needs  derivatives
   of f() with respect to C, not X.
   
   In the example above it will need f=c0*x+c1 and {df/dc0,df/dc1} = {x,1}
   instead of {df/dx} = {c0}.

2. Callback functions accept C as the first parameter, and X as the second

3. If  state  was  created  with  LSFitCreateFG(),  algorithm  needs  just
   function   and   its   gradient,   but   if   state   was  created with
   LSFitCreateFGH(), algorithm will need function, gradient and Hessian.
   
   According  to  the  said  above,  there  ase  several  versions of this
   function, which accept different sets of callbacks.
   
   This flexibility opens way to subtle errors - you may create state with
   LSFitCreateFGH() (optimization using Hessian), but call function  which
   does not accept Hessian. So when algorithm will request Hessian,  there
   will be no callback to call. In this case exception will be thrown.
   
   Be careful to avoid such errors because there is no way to find them at
   compile time - you can see them at runtime only.

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
ae_bool lsfititeration(lsfitstate* state, ae_state *_state)
{
    double lx;
    double lf;
    double ld;
    double rx;
    double rf;
    double rd;
    ae_int_t n;
    ae_int_t m;
    ae_int_t k;
    double v;
    double vv;
    double relcnt;
    ae_int_t i;
    ae_int_t j;
    ae_int_t j1;
    ae_int_t info;
    ae_bool result;


    
    /*
     * Reverse communication preparations
     * I know it looks ugly, but it works the same way
     * anywhere from C++ to Python.
     *
     * This code initializes locals by:
     * * random values determined during code
     *   generation - on first subroutine call
     * * values from previous call - on subsequent calls
     */
    if( state->rstate.stage>=0 )
    {
        n = state->rstate.ia.ptr.p_int[0];
        m = state->rstate.ia.ptr.p_int[1];
        k = state->rstate.ia.ptr.p_int[2];
        i = state->rstate.ia.ptr.p_int[3];
        j = state->rstate.ia.ptr.p_int[4];
        j1 = state->rstate.ia.ptr.p_int[5];
        info = state->rstate.ia.ptr.p_int[6];
        lx = state->rstate.ra.ptr.p_double[0];
        lf = state->rstate.ra.ptr.p_double[1];
        ld = state->rstate.ra.ptr.p_double[2];
        rx = state->rstate.ra.ptr.p_double[3];
        rf = state->rstate.ra.ptr.p_double[4];
        rd = state->rstate.ra.ptr.p_double[5];
        v = state->rstate.ra.ptr.p_double[6];
        vv = state->rstate.ra.ptr.p_double[7];
        relcnt = state->rstate.ra.ptr.p_double[8];
    }
    else
    {
        n = -983;
        m = -989;
        k = -834;
        i = 900;
        j = -287;
        j1 = 364;
        info = 214;
        lx = -338;
        lf = -686;
        ld = 912;
        rx = 585;
        rf = 497;
        rd = -271;
        v = -581;
        vv = 745;
        relcnt = -533;
    }
    if( state->rstate.stage==0 )
    {
        goto lbl_0;
    }
    if( state->rstate.stage==1 )
    {
        goto lbl_1;
    }
    if( state->rstate.stage==2 )
    {
        goto lbl_2;
    }
    if( state->rstate.stage==3 )
    {
        goto lbl_3;
    }
    if( state->rstate.stage==4 )
    {
        goto lbl_4;
    }
    if( state->rstate.stage==5 )
    {
        goto lbl_5;
    }
    if( state->rstate.stage==6 )
    {
        goto lbl_6;
    }
    if( state->rstate.stage==7 )
    {
        goto lbl_7;
    }
    if( state->rstate.stage==8 )
    {
        goto lbl_8;
    }
    if( state->rstate.stage==9 )
    {
        goto lbl_9;
    }
    if( state->rstate.stage==10 )
    {
        goto lbl_10;
    }
    if( state->rstate.stage==11 )
    {
        goto lbl_11;
    }
    if( state->rstate.stage==12 )
    {
        goto lbl_12;
    }
    if( state->rstate.stage==13 )
    {
        goto lbl_13;
    }
    
    /*
     * Routine body
     */
    
    /*
     * Init
     */
    if( state->wkind==1 )
    {
        ae_assert(state->npoints==state->nweights, "LSFitFit: number of points is not equal to the number of weights", _state);
    }
    state->repvaridx = -1;
    n = state->npoints;
    m = state->m;
    k = state->k;
    minlmsetcond(&state->optstate, 0.0, state->epsf, state->epsx, state->maxits, _state);
    minlmsetstpmax(&state->optstate, state->stpmax, _state);
    minlmsetxrep(&state->optstate, state->xrep, _state);
    minlmsetscale(&state->optstate, &state->s, _state);
    minlmsetbc(&state->optstate, &state->bndl, &state->bndu, _state);
    
    /*
     *  Check that user-supplied gradient is correct
     */
    lsfit_lsfitclearrequestfields(state, _state);
    if( !(ae_fp_greater(state->teststep,(double)(0))&&state->optalgo==1) )
    {
        goto lbl_14;
    }
    for(i=0; i<=k-1; i++)
    {
        if( ae_isfinite(state->bndl.ptr.p_double[i], _state) )
        {
            state->c.ptr.p_double[i] = ae_maxreal(state->c.ptr.p_double[i], state->bndl.ptr.p_double[i], _state);
        }
        if( ae_isfinite(state->bndu.ptr.p_double[i], _state) )
        {
            state->c.ptr.p_double[i] = ae_minreal(state->c.ptr.p_double[i], state->bndu.ptr.p_double[i], _state);
        }
    }
    state->needfg = ae_true;
    i = 0;
lbl_16:
    if( i>k-1 )
    {
        goto lbl_18;
    }
    ae_assert(ae_fp_less_eq(state->bndl.ptr.p_double[i],state->c.ptr.p_double[i])&&ae_fp_less_eq(state->c.ptr.p_double[i],state->bndu.ptr.p_double[i]), "LSFitIteration: internal error(State.C is out of bounds)", _state);
    v = state->c.ptr.p_double[i];
    j = 0;
lbl_19:
    if( j>n-1 )
    {
        goto lbl_21;
    }
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[j][0], 1, ae_v_len(0,m-1));
    state->c.ptr.p_double[i] = v-state->teststep*state->s.ptr.p_double[i];
    if( ae_isfinite(state->bndl.ptr.p_double[i], _state) )
    {
        state->c.ptr.p_double[i] = ae_maxreal(state->c.ptr.p_double[i], state->bndl.ptr.p_double[i], _state);
    }
    lx = state->c.ptr.p_double[i];
    state->rstate.stage = 0;
    goto lbl_rcomm;
lbl_0:
    lf = state->f;
    ld = state->g.ptr.p_double[i];
    state->c.ptr.p_double[i] = v+state->teststep*state->s.ptr.p_double[i];
    if( ae_isfinite(state->bndu.ptr.p_double[i], _state) )
    {
        state->c.ptr.p_double[i] = ae_minreal(state->c.ptr.p_double[i], state->bndu.ptr.p_double[i], _state);
    }
    rx = state->c.ptr.p_double[i];
    state->rstate.stage = 1;
    goto lbl_rcomm;
lbl_1:
    rf = state->f;
    rd = state->g.ptr.p_double[i];
    state->c.ptr.p_double[i] = (lx+rx)/2;
    if( ae_isfinite(state->bndl.ptr.p_double[i], _state) )
    {
        state->c.ptr.p_double[i] = ae_maxreal(state->c.ptr.p_double[i], state->bndl.ptr.p_double[i], _state);
    }
    if( ae_isfinite(state->bndu.ptr.p_double[i], _state) )
    {
        state->c.ptr.p_double[i] = ae_minreal(state->c.ptr.p_double[i], state->bndu.ptr.p_double[i], _state);
    }
    state->rstate.stage = 2;
    goto lbl_rcomm;
lbl_2:
    state->c.ptr.p_double[i] = v;
    if( !derivativecheck(lf, ld, rf, rd, state->f, state->g.ptr.p_double[i], rx-lx, _state) )
    {
        state->repvaridx = i;
        state->repterminationtype = -7;
        result = ae_false;
        return result;
    }
    j = j+1;
    goto lbl_19;
lbl_21:
    i = i+1;
    goto lbl_16;
lbl_18:
    state->needfg = ae_false;
lbl_14:
    
    /*
     * Fill WCur by weights:
     * * for WKind=0 unit weights are chosen
     * * for WKind=1 we use user-supplied weights stored in State.TaskW
     */
    rvectorsetlengthatleast(&state->wcur, n, _state);
    for(i=0; i<=n-1; i++)
    {
        state->wcur.ptr.p_double[i] = 1.0;
        if( state->wkind==1 )
        {
            state->wcur.ptr.p_double[i] = state->taskw.ptr.p_double[i];
        }
    }
    
    /*
     * Optimize
     */
lbl_22:
    if( !minlmiteration(&state->optstate, _state) )
    {
        goto lbl_23;
    }
    if( !state->optstate.needfi )
    {
        goto lbl_24;
    }
    
    /*
     * calculate f[] = wi*(f(xi,c)-yi)
     */
    i = 0;
lbl_26:
    if( i>n-1 )
    {
        goto lbl_28;
    }
    ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
    state->pointindex = i;
    lsfit_lsfitclearrequestfields(state, _state);
    state->needf = ae_true;
    state->rstate.stage = 3;
    goto lbl_rcomm;
lbl_3:
    state->needf = ae_false;
    vv = state->wcur.ptr.p_double[i];
    state->optstate.fi.ptr.p_double[i] = vv*(state->f-state->tasky.ptr.p_double[i]);
    i = i+1;
    goto lbl_26;
lbl_28:
    goto lbl_22;
lbl_24:
    if( !state->optstate.needf )
    {
        goto lbl_29;
    }
    
    /*
     * calculate F = sum (wi*(f(xi,c)-yi))^2
     */
    state->optstate.f = (double)(0);
    i = 0;
lbl_31:
    if( i>n-1 )
    {
        goto lbl_33;
    }
    ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
    state->pointindex = i;
    lsfit_lsfitclearrequestfields(state, _state);
    state->needf = ae_true;
    state->rstate.stage = 4;
    goto lbl_rcomm;
lbl_4:
    state->needf = ae_false;
    vv = state->wcur.ptr.p_double[i];
    state->optstate.f = state->optstate.f+ae_sqr(vv*(state->f-state->tasky.ptr.p_double[i]), _state);
    i = i+1;
    goto lbl_31;
lbl_33:
    goto lbl_22;
lbl_29:
    if( !state->optstate.needfg )
    {
        goto lbl_34;
    }
    
    /*
     * calculate F/gradF
     */
    state->optstate.f = (double)(0);
    for(i=0; i<=k-1; i++)
    {
        state->optstate.g.ptr.p_double[i] = (double)(0);
    }
    i = 0;
lbl_36:
    if( i>n-1 )
    {
        goto lbl_38;
    }
    ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
    state->pointindex = i;
    lsfit_lsfitclearrequestfields(state, _state);
    state->needfg = ae_true;
    state->rstate.stage = 5;
    goto lbl_rcomm;
lbl_5:
    state->needfg = ae_false;
    vv = state->wcur.ptr.p_double[i];
    state->optstate.f = state->optstate.f+ae_sqr(vv*(state->f-state->tasky.ptr.p_double[i]), _state);
    v = ae_sqr(vv, _state)*2*(state->f-state->tasky.ptr.p_double[i]);
    ae_v_addd(&state->optstate.g.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,k-1), v);
    i = i+1;
    goto lbl_36;
lbl_38:
    goto lbl_22;
lbl_34:
    if( !state->optstate.needfij )
    {
        goto lbl_39;
    }
    
    /*
     * calculate Fi/jac(Fi)
     */
    i = 0;
lbl_41:
    if( i>n-1 )
    {
        goto lbl_43;
    }
    ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
    state->pointindex = i;
    lsfit_lsfitclearrequestfields(state, _state);
    state->needfg = ae_true;
    state->rstate.stage = 6;
    goto lbl_rcomm;
lbl_6:
    state->needfg = ae_false;
    vv = state->wcur.ptr.p_double[i];
    state->optstate.fi.ptr.p_double[i] = vv*(state->f-state->tasky.ptr.p_double[i]);
    ae_v_moved(&state->optstate.j.ptr.pp_double[i][0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,k-1), vv);
    i = i+1;
    goto lbl_41;
lbl_43:
    goto lbl_22;
lbl_39:
    if( !state->optstate.needfgh )
    {
        goto lbl_44;
    }
    
    /*
     * calculate F/grad(F)/hess(F)
     */
    state->optstate.f = (double)(0);
    for(i=0; i<=k-1; i++)
    {
        state->optstate.g.ptr.p_double[i] = (double)(0);
    }
    for(i=0; i<=k-1; i++)
    {
        for(j=0; j<=k-1; j++)
        {
            state->optstate.h.ptr.pp_double[i][j] = (double)(0);
        }
    }
    i = 0;
lbl_46:
    if( i>n-1 )
    {
        goto lbl_48;
    }
    ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
    state->pointindex = i;
    lsfit_lsfitclearrequestfields(state, _state);
    state->needfgh = ae_true;
    state->rstate.stage = 7;
    goto lbl_rcomm;
lbl_7:
    state->needfgh = ae_false;
    vv = state->wcur.ptr.p_double[i];
    state->optstate.f = state->optstate.f+ae_sqr(vv*(state->f-state->tasky.ptr.p_double[i]), _state);
    v = ae_sqr(vv, _state)*2*(state->f-state->tasky.ptr.p_double[i]);
    ae_v_addd(&state->optstate.g.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,k-1), v);
    for(j=0; j<=k-1; j++)
    {
        v = 2*ae_sqr(vv, _state)*state->g.ptr.p_double[j];
        ae_v_addd(&state->optstate.h.ptr.pp_double[j][0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,k-1), v);
        v = 2*ae_sqr(vv, _state)*(state->f-state->tasky.ptr.p_double[i]);
        ae_v_addd(&state->optstate.h.ptr.pp_double[j][0], 1, &state->h.ptr.pp_double[j][0], 1, ae_v_len(0,k-1), v);
    }
    i = i+1;
    goto lbl_46;
lbl_48:
    goto lbl_22;
lbl_44:
    if( !state->optstate.xupdated )
    {
        goto lbl_49;
    }
    
    /*
     * Report new iteration
     */
    ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1));
    state->f = state->optstate.f;
    lsfit_lsfitclearrequestfields(state, _state);
    state->xupdated = ae_true;
    state->rstate.stage = 8;
    goto lbl_rcomm;
lbl_8:
    state->xupdated = ae_false;
    goto lbl_22;
lbl_49:
    goto lbl_22;
lbl_23:
    minlmresults(&state->optstate, &state->c, &state->optrep, _state);
    state->repterminationtype = state->optrep.terminationtype;
    state->repiterationscount = state->optrep.iterationscount;
    
    /*
     * calculate errors
     */
    if( state->repterminationtype<=0 )
    {
        goto lbl_51;
    }
    
    /*
     * Calculate RMS/Avg/Max/... errors
     */
    state->reprmserror = (double)(0);
    state->repwrmserror = (double)(0);
    state->repavgerror = (double)(0);
    state->repavgrelerror = (double)(0);
    state->repmaxerror = (double)(0);
    relcnt = (double)(0);
    i = 0;
lbl_53:
    if( i>n-1 )
    {
        goto lbl_55;
    }
    ae_v_move(&state->c.ptr.p_double[0], 1, &state->c.ptr.p_double[0], 1, ae_v_len(0,k-1));
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
    state->pointindex = i;
    lsfit_lsfitclearrequestfields(state, _state);
    state->needf = ae_true;
    state->rstate.stage = 9;
    goto lbl_rcomm;
lbl_9:
    state->needf = ae_false;
    v = state->f;
    vv = state->wcur.ptr.p_double[i];
    state->reprmserror = state->reprmserror+ae_sqr(v-state->tasky.ptr.p_double[i], _state);
    state->repwrmserror = state->repwrmserror+ae_sqr(vv*(v-state->tasky.ptr.p_double[i]), _state);
    state->repavgerror = state->repavgerror+ae_fabs(v-state->tasky.ptr.p_double[i], _state);
    if( ae_fp_neq(state->tasky.ptr.p_double[i],(double)(0)) )
    {
        state->repavgrelerror = state->repavgrelerror+ae_fabs(v-state->tasky.ptr.p_double[i], _state)/ae_fabs(state->tasky.ptr.p_double[i], _state);
        relcnt = relcnt+1;
    }
    state->repmaxerror = ae_maxreal(state->repmaxerror, ae_fabs(v-state->tasky.ptr.p_double[i], _state), _state);
    i = i+1;
    goto lbl_53;
lbl_55:
    state->reprmserror = ae_sqrt(state->reprmserror/n, _state);
    state->repwrmserror = ae_sqrt(state->repwrmserror/n, _state);
    state->repavgerror = state->repavgerror/n;
    if( ae_fp_neq(relcnt,(double)(0)) )
    {
        state->repavgrelerror = state->repavgrelerror/relcnt;
    }
    
    /*
     * Calculate covariance matrix
     */
    rmatrixsetlengthatleast(&state->tmpjac, n, k, _state);
    rvectorsetlengthatleast(&state->tmpf, n, _state);
    rvectorsetlengthatleast(&state->tmp, k, _state);
    if( ae_fp_less_eq(state->diffstep,(double)(0)) )
    {
        goto lbl_56;
    }
    
    /*
     * Compute Jacobian by means of numerical differentiation
     */
    lsfit_lsfitclearrequestfields(state, _state);
    state->needf = ae_true;
    i = 0;
lbl_58:
    if( i>n-1 )
    {
        goto lbl_60;
    }
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
    state->pointindex = i;
    state->rstate.stage = 10;
    goto lbl_rcomm;
lbl_10:
    state->tmpf.ptr.p_double[i] = state->f;
    j = 0;
lbl_61:
    if( j>k-1 )
    {
        goto lbl_63;
    }
    v = state->c.ptr.p_double[j];
    lx = v-state->diffstep*state->s.ptr.p_double[j];
    state->c.ptr.p_double[j] = lx;
    if( ae_isfinite(state->bndl.ptr.p_double[j], _state) )
    {
        state->c.ptr.p_double[j] = ae_maxreal(state->c.ptr.p_double[j], state->bndl.ptr.p_double[j], _state);
    }
    state->rstate.stage = 11;
    goto lbl_rcomm;
lbl_11:
    lf = state->f;
    rx = v+state->diffstep*state->s.ptr.p_double[j];
    state->c.ptr.p_double[j] = rx;
    if( ae_isfinite(state->bndu.ptr.p_double[j], _state) )
    {
        state->c.ptr.p_double[j] = ae_minreal(state->c.ptr.p_double[j], state->bndu.ptr.p_double[j], _state);
    }
    state->rstate.stage = 12;
    goto lbl_rcomm;
lbl_12:
    rf = state->f;
    state->c.ptr.p_double[j] = v;
    if( ae_fp_neq(rx,lx) )
    {
        state->tmpjac.ptr.pp_double[i][j] = (rf-lf)/(rx-lx);
    }
    else
    {
        state->tmpjac.ptr.pp_double[i][j] = (double)(0);
    }
    j = j+1;
    goto lbl_61;
lbl_63:
    i = i+1;
    goto lbl_58;
lbl_60:
    state->needf = ae_false;
    goto lbl_57;
lbl_56:
    
    /*
     * Jacobian is calculated with user-provided analytic gradient
     */
    lsfit_lsfitclearrequestfields(state, _state);
    state->needfg = ae_true;
    i = 0;
lbl_64:
    if( i>n-1 )
    {
        goto lbl_66;
    }
    ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1));
    state->pointindex = i;
    state->rstate.stage = 13;
    goto lbl_rcomm;
lbl_13:
    state->tmpf.ptr.p_double[i] = state->f;
    for(j=0; j<=k-1; j++)
    {
        state->tmpjac.ptr.pp_double[i][j] = state->g.ptr.p_double[j];
    }
    i = i+1;
    goto lbl_64;
lbl_66:
    state->needfg = ae_false;
lbl_57:
    for(i=0; i<=k-1; i++)
    {
        state->tmp.ptr.p_double[i] = 0.0;
    }
    lsfit_estimateerrors(&state->tmpjac, &state->tmpf, &state->tasky, &state->wcur, &state->tmp, &state->s, n, k, &state->rep, &state->tmpjacw, 0, _state);
lbl_51:
    result = ae_false;
    return result;
    
    /*
     * Saving state
     */
lbl_rcomm:
    result = ae_true;
    state->rstate.ia.ptr.p_int[0] = n;
    state->rstate.ia.ptr.p_int[1] = m;
    state->rstate.ia.ptr.p_int[2] = k;
    state->rstate.ia.ptr.p_int[3] = i;
    state->rstate.ia.ptr.p_int[4] = j;
    state->rstate.ia.ptr.p_int[5] = j1;
    state->rstate.ia.ptr.p_int[6] = info;
    state->rstate.ra.ptr.p_double[0] = lx;
    state->rstate.ra.ptr.p_double[1] = lf;
    state->rstate.ra.ptr.p_double[2] = ld;
    state->rstate.ra.ptr.p_double[3] = rx;
    state->rstate.ra.ptr.p_double[4] = rf;
    state->rstate.ra.ptr.p_double[5] = rd;
    state->rstate.ra.ptr.p_double[6] = v;
    state->rstate.ra.ptr.p_double[7] = vv;
    state->rstate.ra.ptr.p_double[8] = relcnt;
    return result;
}


/*************************************************************************
Nonlinear least squares fitting results.

Called after return from LSFitFit().

INPUT PARAMETERS:
    State   -   algorithm state

OUTPUT PARAMETERS:
    Info    -   completion code:
                    * -7    gradient verification failed.
                            See LSFitSetGradientCheck() for more information.
                    *  1    relative function improvement is no more than
                            EpsF.
                    *  2    relative step is no more than EpsX.
                    *  4    gradient norm is no more than EpsG
                    *  5    MaxIts steps was taken
                    *  7    stopping conditions are too stringent,
                            further improvement is impossible
    C       -   array[0..K-1], solution
    Rep     -   optimization report. On success following fields are set:
                * R2                non-adjusted coefficient of determination
                                    (non-weighted)
                * RMSError          rms error on the (X,Y).
                * AvgError          average error on the (X,Y).
                * AvgRelError       average relative error on the non-zero Y
                * MaxError          maximum error
                                    NON-WEIGHTED ERRORS ARE CALCULATED
                * WRMSError         weighted rms error on the (X,Y).
                
ERRORS IN PARAMETERS                
                
This  solver  also  calculates different kinds of errors in parameters and
fills corresponding fields of report:
* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(J*CovPar*J')),
                    where J is Jacobian matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.
            
NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.
            
NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).
            
            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.
            
NOTE:       covariance matrix is estimated using  correction  for  degrees
            of freedom (covariances are divided by N-M instead of dividing
            by N).

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitresults(lsfitstate* state,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;

    *info = 0;
    ae_vector_clear(c);
    _lsfitreport_clear(rep);

    lsfit_clearreport(rep, _state);
    *info = state->repterminationtype;
    rep->varidx = state->repvaridx;
    if( *info>0 )
    {
        ae_vector_set_length(c, state->k, _state);
        ae_v_move(&c->ptr.p_double[0], 1, &state->c.ptr.p_double[0], 1, ae_v_len(0,state->k-1));
        rep->rmserror = state->reprmserror;
        rep->wrmserror = state->repwrmserror;
        rep->avgerror = state->repavgerror;
        rep->avgrelerror = state->repavgrelerror;
        rep->maxerror = state->repmaxerror;
        rep->iterationscount = state->repiterationscount;
        ae_matrix_set_length(&rep->covpar, state->k, state->k, _state);
        ae_vector_set_length(&rep->errpar, state->k, _state);
        ae_vector_set_length(&rep->errcurve, state->npoints, _state);
        ae_vector_set_length(&rep->noise, state->npoints, _state);
        rep->r2 = state->rep.r2;
        for(i=0; i<=state->k-1; i++)
        {
            for(j=0; j<=state->k-1; j++)
            {
                rep->covpar.ptr.pp_double[i][j] = state->rep.covpar.ptr.pp_double[i][j];
            }
            rep->errpar.ptr.p_double[i] = state->rep.errpar.ptr.p_double[i];
        }
        for(i=0; i<=state->npoints-1; i++)
        {
            rep->errcurve.ptr.p_double[i] = state->rep.errcurve.ptr.p_double[i];
            rep->noise.ptr.p_double[i] = state->rep.noise.ptr.p_double[i];
        }
    }
}


/*************************************************************************
This  subroutine  turns  on  verification  of  the  user-supplied analytic
gradient:
* user calls this subroutine before fitting begins
* LSFitFit() is called
* prior to actual fitting, for  each  point  in  data  set  X_i  and  each
  component  of  parameters  being  fited C_j algorithm performs following
  steps:
  * two trial steps are made to C_j-TestStep*S[j] and C_j+TestStep*S[j],
    where C_j is j-th parameter and S[j] is a scale of j-th parameter
  * if needed, steps are bounded with respect to constraints on C[]
  * F(X_i|C) is evaluated at these trial points
  * we perform one more evaluation in the middle point of the interval
  * we  build  cubic  model using function values and derivatives at trial
    points and we compare its prediction with actual value in  the  middle
    point
  * in case difference between prediction and actual value is higher  than
    some predetermined threshold, algorithm stops with completion code -7;
    Rep.VarIdx is set to index of the parameter with incorrect derivative.
* after verification is over, algorithm proceeds to the actual optimization.

NOTE 1: verification needs N*K (points count * parameters count)  gradient
        evaluations. It is very costly and you should use it only for  low
        dimensional  problems,  when  you  want  to  be  sure  that you've
        correctly calculated analytic derivatives. You should not  use  it
        in the production code  (unless  you  want  to  check  derivatives
        provided by some third party).

NOTE 2: you  should  carefully  choose  TestStep. Value which is too large
        (so large that function behaviour is significantly non-cubic) will
        lead to false alarms. You may use  different  step  for  different
        parameters by means of setting scale with LSFitSetScale().

NOTE 3: this function may lead to false positives. In case it reports that
        I-th  derivative was calculated incorrectly, you may decrease test
        step  and  try  one  more  time  - maybe your function changes too
        sharply  and  your  step  is  too  large for such rapidly chanding
        function.

NOTE 4: this function works only for optimizers created with LSFitCreateWFG()
        or LSFitCreateFG() constructors.
        
INPUT PARAMETERS:
    State       -   structure used to store algorithm state
    TestStep    -   verification step:
                    * TestStep=0 turns verification off
                    * TestStep>0 activates verification

  -- ALGLIB --
     Copyright 15.06.2012 by Bochkanov Sergey
*************************************************************************/
void lsfitsetgradientcheck(lsfitstate* state,
     double teststep,
     ae_state *_state)
{


    ae_assert(ae_isfinite(teststep, _state), "LSFitSetGradientCheck: TestStep contains NaN or Infinite", _state);
    ae_assert(ae_fp_greater_eq(teststep,(double)(0)), "LSFitSetGradientCheck: invalid argument TestStep(TestStep<0)", _state);
    state->teststep = teststep;
}


/*************************************************************************
Internal subroutine: automatic scaling for LLS tasks.
NEVER CALL IT DIRECTLY!

Maps abscissas to [-1,1], standartizes ordinates and correspondingly scales
constraints. It also scales weights so that max(W[i])=1

Transformations performed:
* X, XC         [XA,XB] => [-1,+1]
                transformation makes min(X)=-1, max(X)=+1

* Y             [SA,SB] => [0,1]
                transformation makes mean(Y)=0, stddev(Y)=1
                
* YC            transformed accordingly to SA, SB, DC[I]

  -- ALGLIB PROJECT --
     Copyright 08.09.2009 by Bochkanov Sergey
*************************************************************************/
void lsfitscalexy(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     double* xa,
     double* xb,
     double* sa,
     double* sb,
     /* Real    */ ae_vector* xoriginal,
     /* Real    */ ae_vector* yoriginal,
     ae_state *_state)
{
    double xmin;
    double xmax;
    ae_int_t i;
    double mx;

    *xa = 0;
    *xb = 0;
    *sa = 0;
    *sb = 0;
    ae_vector_clear(xoriginal);
    ae_vector_clear(yoriginal);

    ae_assert(n>=1, "LSFitScaleXY: incorrect N", _state);
    ae_assert(k>=0, "LSFitScaleXY: incorrect K", _state);
    
    /*
     * Calculate xmin/xmax.
     * Force xmin<>xmax.
     */
    xmin = x->ptr.p_double[0];
    xmax = x->ptr.p_double[0];
    for(i=1; i<=n-1; i++)
    {
        xmin = ae_minreal(xmin, x->ptr.p_double[i], _state);
        xmax = ae_maxreal(xmax, x->ptr.p_double[i], _state);
    }
    for(i=0; i<=k-1; i++)
    {
        xmin = ae_minreal(xmin, xc->ptr.p_double[i], _state);
        xmax = ae_maxreal(xmax, xc->ptr.p_double[i], _state);
    }
    if( ae_fp_eq(xmin,xmax) )
    {
        if( ae_fp_eq(xmin,(double)(0)) )
        {
            xmin = (double)(-1);
            xmax = (double)(1);
        }
        else
        {
            if( ae_fp_greater(xmin,(double)(0)) )
            {
                xmin = 0.5*xmin;
            }
            else
            {
                xmax = 0.5*xmax;
            }
        }
    }
    
    /*
     * Transform abscissas: map [XA,XB] to [0,1]
     *
     * Store old X[] in XOriginal[] (it will be used
     * to calculate relative error).
     */
    ae_vector_set_length(xoriginal, n, _state);
    ae_v_move(&xoriginal->ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1));
    *xa = xmin;
    *xb = xmax;
    for(i=0; i<=n-1; i++)
    {
        x->ptr.p_double[i] = 2*(x->ptr.p_double[i]-0.5*(*xa+(*xb)))/(*xb-(*xa));
    }
    for(i=0; i<=k-1; i++)
    {
        ae_assert(dc->ptr.p_int[i]>=0, "LSFitScaleXY: internal error!", _state);
        xc->ptr.p_double[i] = 2*(xc->ptr.p_double[i]-0.5*(*xa+(*xb)))/(*xb-(*xa));
        yc->ptr.p_double[i] = yc->ptr.p_double[i]*ae_pow(0.5*(*xb-(*xa)), (double)(dc->ptr.p_int[i]), _state);
    }
    
    /*
     * Transform function values: map [SA,SB] to [0,1]
     * SA = mean(Y),
     * SB = SA+stddev(Y).
     *
     * Store old Y[] in YOriginal[] (it will be used
     * to calculate relative error).
     */
    ae_vector_set_length(yoriginal, n, _state);
    ae_v_move(&yoriginal->ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1));
    *sa = (double)(0);
    for(i=0; i<=n-1; i++)
    {
        *sa = *sa+y->ptr.p_double[i];
    }
    *sa = *sa/n;
    *sb = (double)(0);
    for(i=0; i<=n-1; i++)
    {
        *sb = *sb+ae_sqr(y->ptr.p_double[i]-(*sa), _state);
    }
    *sb = ae_sqrt(*sb/n, _state)+(*sa);
    if( ae_fp_eq(*sb,*sa) )
    {
        *sb = 2*(*sa);
    }
    if( ae_fp_eq(*sb,*sa) )
    {
        *sb = *sa+1;
    }
    for(i=0; i<=n-1; i++)
    {
        y->ptr.p_double[i] = (y->ptr.p_double[i]-(*sa))/(*sb-(*sa));
    }
    for(i=0; i<=k-1; i++)
    {
        if( dc->ptr.p_int[i]==0 )
        {
            yc->ptr.p_double[i] = (yc->ptr.p_double[i]-(*sa))/(*sb-(*sa));
        }
        else
        {
            yc->ptr.p_double[i] = yc->ptr.p_double[i]/(*sb-(*sa));
        }
    }
    
    /*
     * Scale weights
     */
    mx = (double)(0);
    for(i=0; i<=n-1; i++)
    {
        mx = ae_maxreal(mx, ae_fabs(w->ptr.p_double[i], _state), _state);
    }
    if( ae_fp_neq(mx,(double)(0)) )
    {
        for(i=0; i<=n-1; i++)
        {
            w->ptr.p_double[i] = w->ptr.p_double[i]/mx;
        }
    }
}


/*************************************************************************
This function analyzes section of curve for processing by RDP algorithm:
given set of points X,Y with indexes [I0,I1] it returns point with
worst deviation from linear model (non-parametric version which sees curve
as Y(x)).

Input parameters:
    X, Y        -   SORTED arrays.
    I0,I1       -   interval (boundaries included) to process
    Eps         -   desired precision
    
OUTPUT PARAMETERS:
    WorstIdx    -   index of worst point
    WorstError  -   error at worst point
    
NOTE: this function guarantees that it returns exactly zero for a section
      with less than 3 points.

  -- ALGLIB PROJECT --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
static void lsfit_rdpanalyzesection(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t i0,
     ae_int_t i1,
     ae_int_t* worstidx,
     double* worsterror,
     ae_state *_state)
{
    ae_int_t i;
    double xleft;
    double xright;
    double vx;
    double ve;
    double a;
    double b;

    *worstidx = 0;
    *worsterror = 0;

    xleft = x->ptr.p_double[i0];
    xright = x->ptr.p_double[i1];
    if( i1-i0+1<3||ae_fp_eq(xright,xleft) )
    {
        *worstidx = i0;
        *worsterror = 0.0;
        return;
    }
    a = (y->ptr.p_double[i1]-y->ptr.p_double[i0])/(xright-xleft);
    b = (y->ptr.p_double[i0]*xright-y->ptr.p_double[i1]*xleft)/(xright-xleft);
    *worstidx = -1;
    *worsterror = (double)(0);
    for(i=i0+1; i<=i1-1; i++)
    {
        vx = x->ptr.p_double[i];
        ve = ae_fabs(a*vx+b-y->ptr.p_double[i], _state);
        if( (ae_fp_greater(vx,xleft)&&ae_fp_less(vx,xright))&&ae_fp_greater(ve,*worsterror) )
        {
            *worsterror = ve;
            *worstidx = i;
        }
    }
}


/*************************************************************************
Recursive splitting of interval [I0,I1] (right boundary included) with RDP
algorithm (non-parametric version which sees curve as Y(x)).

Input parameters:
    X, Y        -   SORTED arrays.
    I0,I1       -   interval (boundaries included) to process
    Eps         -   desired precision
    XOut,YOut   -   preallocated output arrays large enough to store result;
                    XOut[0..1], YOut[0..1] contain first and last points of
                    curve
    NOut        -   must contain 2 on input
    
OUTPUT PARAMETERS:
    XOut, YOut  -   curve generated by RDP algorithm, UNSORTED
    NOut        -   number of points in curve

  -- ALGLIB PROJECT --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
static void lsfit_rdprecursive(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t i0,
     ae_int_t i1,
     double eps,
     /* Real    */ ae_vector* xout,
     /* Real    */ ae_vector* yout,
     ae_int_t* nout,
     ae_state *_state)
{
    ae_int_t worstidx;
    double worsterror;


    ae_assert(ae_fp_greater(eps,(double)(0)), "RDPRecursive: internal error, Eps<0", _state);
    lsfit_rdpanalyzesection(x, y, i0, i1, &worstidx, &worsterror, _state);
    if( ae_fp_less_eq(worsterror,eps) )
    {
        return;
    }
    xout->ptr.p_double[*nout] = x->ptr.p_double[worstidx];
    yout->ptr.p_double[*nout] = y->ptr.p_double[worstidx];
    *nout = *nout+1;
    if( worstidx-i0<i1-worstidx )
    {
        lsfit_rdprecursive(x, y, i0, worstidx, eps, xout, yout, nout, _state);
        lsfit_rdprecursive(x, y, worstidx, i1, eps, xout, yout, nout, _state);
    }
    else
    {
        lsfit_rdprecursive(x, y, worstidx, i1, eps, xout, yout, nout, _state);
        lsfit_rdprecursive(x, y, i0, worstidx, eps, xout, yout, nout, _state);
    }
}


/*************************************************************************
Internal 4PL/5PL fitting function.

Accepts X, Y and already initialized and prepared MinLMState structure.
On input P1 contains initial guess, on output it contains solution.  FLast
stores function value at P1.
*************************************************************************/
static void lsfit_logisticfitinternal(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t n,
     ae_bool is4pl,
     double lambdav,
     minlmstate* state,
     minlmreport* replm,
     /* Real    */ ae_vector* p1,
     double* flast,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;
    double ta;
    double tb;
    double tc;
    double td;
    double tg;
    double vp;
    double vp0;
    double vp1;

    *flast = 0;

    minlmrestartfrom(state, p1, _state);
    while(minlmiteration(state, _state))
    {
        ta = state->x.ptr.p_double[0];
        tb = state->x.ptr.p_double[1];
        tc = state->x.ptr.p_double[2];
        td = state->x.ptr.p_double[3];
        tg = state->x.ptr.p_double[4];
        if( state->xupdated )
        {
            
            /*
             * Save best function value obtained so far.
             */
            *flast = state->f;
            continue;
        }
        if( state->needfi )
        {
            
            /*
             * Function vector
             */
            for(i=0; i<=n-1; i++)
            {
                if( ae_fp_greater(x->ptr.p_double[i],(double)(0)) )
                {
                    state->fi.ptr.p_double[i] = td+(ta-td)/ae_pow(1.0+ae_pow(x->ptr.p_double[i]/tc, tb, _state), tg, _state)-y->ptr.p_double[i];
                }
                else
                {
                    if( ae_fp_greater_eq(tb,(double)(0)) )
                    {
                        state->fi.ptr.p_double[i] = ta-y->ptr.p_double[i];
                    }
                    else
                    {
                        state->fi.ptr.p_double[i] = td-y->ptr.p_double[i];
                    }
                }
            }
            for(i=0; i<=4; i++)
            {
                state->fi.ptr.p_double[n+i] = lambdav*state->x.ptr.p_double[i];
            }
            continue;
        }
        if( state->needfij )
        {
            
            /*
             * Function vector and Jacobian
             */
            for(i=0; i<=n-1; i++)
            {
                if( ae_fp_greater(x->ptr.p_double[i],(double)(0)) )
                {
                    if( is4pl )
                    {
                        vp = ae_pow(x->ptr.p_double[i]/tc, tb, _state);
                        state->fi.ptr.p_double[i] = td+(ta-td)/(1+vp)-y->ptr.p_double[i];
                        state->j.ptr.pp_double[i][0] = 1/(1+vp);
                        state->j.ptr.pp_double[i][1] = -(ta-td)*vp*ae_log(x->ptr.p_double[i]/tc, _state)/ae_sqr(1+vp, _state);
                        state->j.ptr.pp_double[i][2] = (ta-td)*(tb/tc)*vp/ae_sqr(1+vp, _state);
                        state->j.ptr.pp_double[i][3] = 1-1/(1+vp);
                        state->j.ptr.pp_double[i][4] = (double)(0);
                    }
                    else
                    {
                        vp0 = ae_pow(x->ptr.p_double[i]/tc, tb, _state);
                        vp1 = ae_pow(1+vp0, tg, _state);
                        state->fi.ptr.p_double[i] = td+(ta-td)/vp1-y->ptr.p_double[i];
                        state->j.ptr.pp_double[i][0] = 1/vp1;
                        state->j.ptr.pp_double[i][1] = (ta-td)*(-tg)*ae_pow(1+vp0, -tg-1, _state)*vp0*ae_log(x->ptr.p_double[i]/tc, _state);
                        state->j.ptr.pp_double[i][2] = (ta-td)*(-tg)*ae_pow(1+vp0, -tg-1, _state)*vp0*(-tb/tc);
                        state->j.ptr.pp_double[i][3] = 1-1/vp1;
                        state->j.ptr.pp_double[i][4] = -(ta-td)/vp1*ae_log(1+vp0, _state);
                    }
                }
                else
                {
                    if( ae_fp_greater_eq(tb,(double)(0)) )
                    {
                        state->fi.ptr.p_double[i] = ta-y->ptr.p_double[i];
                        state->j.ptr.pp_double[i][0] = (double)(1);
                        state->j.ptr.pp_double[i][1] = (double)(0);
                        state->j.ptr.pp_double[i][2] = (double)(0);
                        state->j.ptr.pp_double[i][3] = (double)(0);
                        state->j.ptr.pp_double[i][4] = (double)(0);
                    }
                    else
                    {
                        state->fi.ptr.p_double[i] = td-y->ptr.p_double[i];
                        state->j.ptr.pp_double[i][0] = (double)(0);
                        state->j.ptr.pp_double[i][1] = (double)(0);
                        state->j.ptr.pp_double[i][2] = (double)(0);
                        state->j.ptr.pp_double[i][3] = (double)(1);
                        state->j.ptr.pp_double[i][4] = (double)(0);
                    }
                }
            }
            for(i=0; i<=4; i++)
            {
                for(j=0; j<=4; j++)
                {
                    state->j.ptr.pp_double[n+i][j] = 0.0;
                }
            }
            for(i=0; i<=4; i++)
            {
                state->fi.ptr.p_double[n+i] = lambdav*state->x.ptr.p_double[i];
                state->j.ptr.pp_double[n+i][i] = lambdav;
            }
            continue;
        }
        ae_assert(ae_false, "LogisticFitX: internal error", _state);
    }
    minlmresultsbuf(state, p1, replm, _state);
    ae_assert(replm->terminationtype>0, "LogisticFitX: internal error", _state);
}


/*************************************************************************
Internal spline fitting subroutine

  -- ALGLIB PROJECT --
     Copyright 08.09.2009 by Bochkanov Sergey
*************************************************************************/
static void lsfit_spline1dfitinternal(ae_int_t st,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t* info,
     spline1dinterpolant* s,
     spline1dfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector _w;
    ae_vector _xc;
    ae_vector _yc;
    ae_matrix fmatrix;
    ae_matrix cmatrix;
    ae_vector y2;
    ae_vector w2;
    ae_vector sx;
    ae_vector sy;
    ae_vector sd;
    ae_vector tmp;
    ae_vector xoriginal;
    ae_vector yoriginal;
    lsfitreport lrep;
    double v0;
    double v1;
    double v2;
    double mx;
    spline1dinterpolant s2;
    ae_int_t i;
    ae_int_t j;
    ae_int_t relcnt;
    double xa;
    double xb;
    double sa;
    double sb;
    double bl;
    double br;
    double decay;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_init_copy(&_w, w, _state);
    w = &_w;
    ae_vector_init_copy(&_xc, xc, _state);
    xc = &_xc;
    ae_vector_init_copy(&_yc, yc, _state);
    yc = &_yc;
    *info = 0;
    _spline1dinterpolant_clear(s);
    _spline1dfitreport_clear(rep);
    ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state);
    ae_matrix_init(&cmatrix, 0, 0, DT_REAL, _state);
    ae_vector_init(&y2, 0, DT_REAL, _state);
    ae_vector_init(&w2, 0, DT_REAL, _state);
    ae_vector_init(&sx, 0, DT_REAL, _state);
    ae_vector_init(&sy, 0, DT_REAL, _state);
    ae_vector_init(&sd, 0, DT_REAL, _state);
    ae_vector_init(&tmp, 0, DT_REAL, _state);
    ae_vector_init(&xoriginal, 0, DT_REAL, _state);
    ae_vector_init(&yoriginal, 0, DT_REAL, _state);
    _lsfitreport_init(&lrep, _state);
    _spline1dinterpolant_init(&s2, _state);

    ae_assert(st==0||st==1, "Spline1DFit: internal error!", _state);
    if( st==0&&m<4 )
    {
        *info = -1;
        ae_frame_leave(_state);
        return;
    }
    if( st==1&&m<4 )
    {
        *info = -1;
        ae_frame_leave(_state);
        return;
    }
    if( (n<1||k<0)||k>=m )
    {
        *info = -1;
        ae_frame_leave(_state);
        return;
    }
    for(i=0; i<=k-1; i++)
    {
        *info = 0;
        if( dc->ptr.p_int[i]<0 )
        {
            *info = -1;
        }
        if( dc->ptr.p_int[i]>1 )
        {
            *info = -1;
        }
        if( *info<0 )
        {
            ae_frame_leave(_state);
            return;
        }
    }
    if( st==1&&m%2!=0 )
    {
        
        /*
         * Hermite fitter must have even number of basis functions
         */
        *info = -2;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * weight decay for correct handling of task which becomes
     * degenerate after constraints are applied
     */
    decay = 10000*ae_machineepsilon;
    
    /*
     * Scale X, Y, XC, YC
     */
    lsfitscalexy(x, y, w, n, xc, yc, dc, k, &xa, &xb, &sa, &sb, &xoriginal, &yoriginal, _state);
    
    /*
     * allocate space, initialize:
     * * SX     -   grid for basis functions
     * * SY     -   values of basis functions at grid points
     * * FMatrix-   values of basis functions at X[]
     * * CMatrix-   values (derivatives) of basis functions at XC[]
     */
    ae_vector_set_length(&y2, n+m, _state);
    ae_vector_set_length(&w2, n+m, _state);
    ae_matrix_set_length(&fmatrix, n+m, m, _state);
    if( k>0 )
    {
        ae_matrix_set_length(&cmatrix, k, m+1, _state);
    }
    if( st==0 )
    {
        
        /*
         * allocate space for cubic spline
         */
        ae_vector_set_length(&sx, m-2, _state);
        ae_vector_set_length(&sy, m-2, _state);
        for(j=0; j<=m-2-1; j++)
        {
            sx.ptr.p_double[j] = (double)(2*j)/(double)(m-2-1)-1;
        }
    }
    if( st==1 )
    {
        
        /*
         * allocate space for Hermite spline
         */
        ae_vector_set_length(&sx, m/2, _state);
        ae_vector_set_length(&sy, m/2, _state);
        ae_vector_set_length(&sd, m/2, _state);
        for(j=0; j<=m/2-1; j++)
        {
            sx.ptr.p_double[j] = (double)(2*j)/(double)(m/2-1)-1;
        }
    }
    
    /*
     * Prepare design and constraints matrices:
     * * fill constraints matrix
     * * fill first N rows of design matrix with values
     * * fill next M rows of design matrix with regularizing term
     * * append M zeros to Y
     * * append M elements, mean(abs(W)) each, to W
     */
    for(j=0; j<=m-1; j++)
    {
        
        /*
         * prepare Jth basis function
         */
        if( st==0 )
        {
            
            /*
             * cubic spline basis
             */
            for(i=0; i<=m-2-1; i++)
            {
                sy.ptr.p_double[i] = (double)(0);
            }
            bl = (double)(0);
            br = (double)(0);
            if( j<m-2 )
            {
                sy.ptr.p_double[j] = (double)(1);
            }
            if( j==m-2 )
            {
                bl = (double)(1);
            }
            if( j==m-1 )
            {
                br = (double)(1);
            }
            spline1dbuildcubic(&sx, &sy, m-2, 1, bl, 1, br, &s2, _state);
        }
        if( st==1 )
        {
            
            /*
             * Hermite basis
             */
            for(i=0; i<=m/2-1; i++)
            {
                sy.ptr.p_double[i] = (double)(0);
                sd.ptr.p_double[i] = (double)(0);
            }
            if( j%2==0 )
            {
                sy.ptr.p_double[j/2] = (double)(1);
            }
            else
            {
                sd.ptr.p_double[j/2] = (double)(1);
            }
            spline1dbuildhermite(&sx, &sy, &sd, m/2, &s2, _state);
        }
        
        /*
         * values at X[], XC[]
         */
        for(i=0; i<=n-1; i++)
        {
            fmatrix.ptr.pp_double[i][j] = spline1dcalc(&s2, x->ptr.p_double[i], _state);
        }
        for(i=0; i<=k-1; i++)
        {
            ae_assert(dc->ptr.p_int[i]>=0&&dc->ptr.p_int[i]<=2, "Spline1DFit: internal error!", _state);
            spline1ddiff(&s2, xc->ptr.p_double[i], &v0, &v1, &v2, _state);
            if( dc->ptr.p_int[i]==0 )
            {
                cmatrix.ptr.pp_double[i][j] = v0;
            }
            if( dc->ptr.p_int[i]==1 )
            {
                cmatrix.ptr.pp_double[i][j] = v1;
            }
            if( dc->ptr.p_int[i]==2 )
            {
                cmatrix.ptr.pp_double[i][j] = v2;
            }
        }
    }
    for(i=0; i<=k-1; i++)
    {
        cmatrix.ptr.pp_double[i][m] = yc->ptr.p_double[i];
    }
    for(i=0; i<=m-1; i++)
    {
        for(j=0; j<=m-1; j++)
        {
            if( i==j )
            {
                fmatrix.ptr.pp_double[n+i][j] = decay;
            }
            else
            {
                fmatrix.ptr.pp_double[n+i][j] = (double)(0);
            }
        }
    }
    ae_vector_set_length(&y2, n+m, _state);
    ae_vector_set_length(&w2, n+m, _state);
    ae_v_move(&y2.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_v_move(&w2.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1));
    mx = (double)(0);
    for(i=0; i<=n-1; i++)
    {
        mx = mx+ae_fabs(w->ptr.p_double[i], _state);
    }
    mx = mx/n;
    for(i=0; i<=m-1; i++)
    {
        y2.ptr.p_double[n+i] = (double)(0);
        w2.ptr.p_double[n+i] = mx;
    }
    
    /*
     * Solve constrained task
     */
    if( k>0 )
    {
        
        /*
         * solve using regularization
         */
        lsfitlinearwc(&y2, &w2, &fmatrix, &cmatrix, n+m, m, k, info, &tmp, &lrep, _state);
    }
    else
    {
        
        /*
         * no constraints, no regularization needed
         */
        lsfitlinearwc(y, w, &fmatrix, &cmatrix, n, m, k, info, &tmp, &lrep, _state);
    }
    if( *info<0 )
    {
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Generate spline and scale it
     */
    if( st==0 )
    {
        
        /*
         * cubic spline basis
         */
        ae_v_move(&sy.ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-2-1));
        spline1dbuildcubic(&sx, &sy, m-2, 1, tmp.ptr.p_double[m-2], 1, tmp.ptr.p_double[m-1], s, _state);
    }
    if( st==1 )
    {
        
        /*
         * Hermite basis
         */
        for(i=0; i<=m/2-1; i++)
        {
            sy.ptr.p_double[i] = tmp.ptr.p_double[2*i];
            sd.ptr.p_double[i] = tmp.ptr.p_double[2*i+1];
        }
        spline1dbuildhermite(&sx, &sy, &sd, m/2, s, _state);
    }
    spline1dlintransx(s, 2/(xb-xa), -(xa+xb)/(xb-xa), _state);
    spline1dlintransy(s, sb-sa, sa, _state);
    
    /*
     * Scale absolute errors obtained from LSFitLinearW.
     * Relative error should be calculated separately
     * (because of shifting/scaling of the task)
     */
    rep->taskrcond = lrep.taskrcond;
    rep->rmserror = lrep.rmserror*(sb-sa);
    rep->avgerror = lrep.avgerror*(sb-sa);
    rep->maxerror = lrep.maxerror*(sb-sa);
    rep->avgrelerror = (double)(0);
    relcnt = 0;
    for(i=0; i<=n-1; i++)
    {
        if( ae_fp_neq(yoriginal.ptr.p_double[i],(double)(0)) )
        {
            rep->avgrelerror = rep->avgrelerror+ae_fabs(spline1dcalc(s, xoriginal.ptr.p_double[i], _state)-yoriginal.ptr.p_double[i], _state)/ae_fabs(yoriginal.ptr.p_double[i], _state);
            relcnt = relcnt+1;
        }
    }
    if( relcnt!=0 )
    {
        rep->avgrelerror = rep->avgrelerror/relcnt;
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Internal fitting subroutine
*************************************************************************/
static void lsfit_lsfitlinearinternal(/* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_matrix* fmatrix,
     ae_int_t n,
     ae_int_t m,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    double threshold;
    ae_matrix ft;
    ae_matrix q;
    ae_matrix l;
    ae_matrix r;
    ae_vector b;
    ae_vector wmod;
    ae_vector tau;
    ae_vector nzeros;
    ae_vector s;
    ae_int_t i;
    ae_int_t j;
    double v;
    ae_vector sv;
    ae_matrix u;
    ae_matrix vt;
    ae_vector tmp;
    ae_vector utb;
    ae_vector sutb;
    ae_int_t relcnt;

    ae_frame_make(_state, &_frame_block);
    *info = 0;
    ae_vector_clear(c);
    _lsfitreport_clear(rep);
    ae_matrix_init(&ft, 0, 0, DT_REAL, _state);
    ae_matrix_init(&q, 0, 0, DT_REAL, _state);
    ae_matrix_init(&l, 0, 0, DT_REAL, _state);
    ae_matrix_init(&r, 0, 0, DT_REAL, _state);
    ae_vector_init(&b, 0, DT_REAL, _state);
    ae_vector_init(&wmod, 0, DT_REAL, _state);
    ae_vector_init(&tau, 0, DT_REAL, _state);
    ae_vector_init(&nzeros, 0, DT_REAL, _state);
    ae_vector_init(&s, 0, DT_REAL, _state);
    ae_vector_init(&sv, 0, DT_REAL, _state);
    ae_matrix_init(&u, 0, 0, DT_REAL, _state);
    ae_matrix_init(&vt, 0, 0, DT_REAL, _state);
    ae_vector_init(&tmp, 0, DT_REAL, _state);
    ae_vector_init(&utb, 0, DT_REAL, _state);
    ae_vector_init(&sutb, 0, DT_REAL, _state);

    lsfit_clearreport(rep, _state);
    if( n<1||m<1 )
    {
        *info = -1;
        ae_frame_leave(_state);
        return;
    }
    *info = 1;
    threshold = ae_sqrt(ae_machineepsilon, _state);
    
    /*
     * Degenerate case, needs special handling
     */
    if( n<m )
    {
        
        /*
         * Create design matrix.
         */
        ae_matrix_set_length(&ft, n, m, _state);
        ae_vector_set_length(&b, n, _state);
        ae_vector_set_length(&wmod, n, _state);
        for(j=0; j<=n-1; j++)
        {
            v = w->ptr.p_double[j];
            ae_v_moved(&ft.ptr.pp_double[j][0], 1, &fmatrix->ptr.pp_double[j][0], 1, ae_v_len(0,m-1), v);
            b.ptr.p_double[j] = w->ptr.p_double[j]*y->ptr.p_double[j];
            wmod.ptr.p_double[j] = (double)(1);
        }
        
        /*
         * LQ decomposition and reduction to M=N
         */
        ae_vector_set_length(c, m, _state);
        for(i=0; i<=m-1; i++)
        {
            c->ptr.p_double[i] = (double)(0);
        }
        rep->taskrcond = (double)(0);
        rmatrixlq(&ft, n, m, &tau, _state);
        rmatrixlqunpackq(&ft, n, m, &tau, n, &q, _state);
        rmatrixlqunpackl(&ft, n, m, &l, _state);
        lsfit_lsfitlinearinternal(&b, &wmod, &l, n, n, info, &tmp, rep, _state);
        if( *info<=0 )
        {
            ae_frame_leave(_state);
            return;
        }
        for(i=0; i<=n-1; i++)
        {
            v = tmp.ptr.p_double[i];
            ae_v_addd(&c->ptr.p_double[0], 1, &q.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v);
        }
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * N>=M. Generate design matrix and reduce to N=M using
     * QR decomposition.
     */
    ae_matrix_set_length(&ft, n, m, _state);
    ae_vector_set_length(&b, n, _state);
    for(j=0; j<=n-1; j++)
    {
        v = w->ptr.p_double[j];
        ae_v_moved(&ft.ptr.pp_double[j][0], 1, &fmatrix->ptr.pp_double[j][0], 1, ae_v_len(0,m-1), v);
        b.ptr.p_double[j] = w->ptr.p_double[j]*y->ptr.p_double[j];
    }
    rmatrixqr(&ft, n, m, &tau, _state);
    rmatrixqrunpackq(&ft, n, m, &tau, m, &q, _state);
    rmatrixqrunpackr(&ft, n, m, &r, _state);
    ae_vector_set_length(&tmp, m, _state);
    for(i=0; i<=m-1; i++)
    {
        tmp.ptr.p_double[i] = (double)(0);
    }
    for(i=0; i<=n-1; i++)
    {
        v = b.ptr.p_double[i];
        ae_v_addd(&tmp.ptr.p_double[0], 1, &q.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v);
    }
    ae_vector_set_length(&b, m, _state);
    ae_v_move(&b.ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-1));
    
    /*
     * R contains reduced MxM design upper triangular matrix,
     * B contains reduced Mx1 right part.
     *
     * Determine system condition number and decide
     * should we use triangular solver (faster) or
     * SVD-based solver (more stable).
     *
     * We can use LU-based RCond estimator for this task.
     */
    rep->taskrcond = rmatrixlurcondinf(&r, m, _state);
    if( ae_fp_greater(rep->taskrcond,threshold) )
    {
        
        /*
         * use QR-based solver
         */
        ae_vector_set_length(c, m, _state);
        c->ptr.p_double[m-1] = b.ptr.p_double[m-1]/r.ptr.pp_double[m-1][m-1];
        for(i=m-2; i>=0; i--)
        {
            v = ae_v_dotproduct(&r.ptr.pp_double[i][i+1], 1, &c->ptr.p_double[i+1], 1, ae_v_len(i+1,m-1));
            c->ptr.p_double[i] = (b.ptr.p_double[i]-v)/r.ptr.pp_double[i][i];
        }
    }
    else
    {
        
        /*
         * use SVD-based solver
         */
        if( !rmatrixsvd(&r, m, m, 1, 1, 2, &sv, &u, &vt, _state) )
        {
            *info = -4;
            ae_frame_leave(_state);
            return;
        }
        ae_vector_set_length(&utb, m, _state);
        ae_vector_set_length(&sutb, m, _state);
        for(i=0; i<=m-1; i++)
        {
            utb.ptr.p_double[i] = (double)(0);
        }
        for(i=0; i<=m-1; i++)
        {
            v = b.ptr.p_double[i];
            ae_v_addd(&utb.ptr.p_double[0], 1, &u.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v);
        }
        if( ae_fp_greater(sv.ptr.p_double[0],(double)(0)) )
        {
            rep->taskrcond = sv.ptr.p_double[m-1]/sv.ptr.p_double[0];
            for(i=0; i<=m-1; i++)
            {
                if( ae_fp_greater(sv.ptr.p_double[i],threshold*sv.ptr.p_double[0]) )
                {
                    sutb.ptr.p_double[i] = utb.ptr.p_double[i]/sv.ptr.p_double[i];
                }
                else
                {
                    sutb.ptr.p_double[i] = (double)(0);
                }
            }
        }
        else
        {
            rep->taskrcond = (double)(0);
            for(i=0; i<=m-1; i++)
            {
                sutb.ptr.p_double[i] = (double)(0);
            }
        }
        ae_vector_set_length(c, m, _state);
        for(i=0; i<=m-1; i++)
        {
            c->ptr.p_double[i] = (double)(0);
        }
        for(i=0; i<=m-1; i++)
        {
            v = sutb.ptr.p_double[i];
            ae_v_addd(&c->ptr.p_double[0], 1, &vt.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v);
        }
    }
    
    /*
     * calculate errors
     */
    rep->rmserror = (double)(0);
    rep->avgerror = (double)(0);
    rep->avgrelerror = (double)(0);
    rep->maxerror = (double)(0);
    relcnt = 0;
    for(i=0; i<=n-1; i++)
    {
        v = ae_v_dotproduct(&fmatrix->ptr.pp_double[i][0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,m-1));
        rep->rmserror = rep->rmserror+ae_sqr(v-y->ptr.p_double[i], _state);
        rep->avgerror = rep->avgerror+ae_fabs(v-y->ptr.p_double[i], _state);
        if( ae_fp_neq(y->ptr.p_double[i],(double)(0)) )
        {
            rep->avgrelerror = rep->avgrelerror+ae_fabs(v-y->ptr.p_double[i], _state)/ae_fabs(y->ptr.p_double[i], _state);
            relcnt = relcnt+1;
        }
        rep->maxerror = ae_maxreal(rep->maxerror, ae_fabs(v-y->ptr.p_double[i], _state), _state);
    }
    rep->rmserror = ae_sqrt(rep->rmserror/n, _state);
    rep->avgerror = rep->avgerror/n;
    if( relcnt!=0 )
    {
        rep->avgrelerror = rep->avgrelerror/relcnt;
    }
    ae_vector_set_length(&nzeros, n, _state);
    ae_vector_set_length(&s, m, _state);
    for(i=0; i<=m-1; i++)
    {
        s.ptr.p_double[i] = (double)(0);
    }
    for(i=0; i<=n-1; i++)
    {
        for(j=0; j<=m-1; j++)
        {
            s.ptr.p_double[j] = s.ptr.p_double[j]+ae_sqr(fmatrix->ptr.pp_double[i][j], _state);
        }
        nzeros.ptr.p_double[i] = (double)(0);
    }
    for(i=0; i<=m-1; i++)
    {
        if( ae_fp_neq(s.ptr.p_double[i],(double)(0)) )
        {
            s.ptr.p_double[i] = ae_sqrt(1/s.ptr.p_double[i], _state);
        }
        else
        {
            s.ptr.p_double[i] = (double)(1);
        }
    }
    lsfit_estimateerrors(fmatrix, &nzeros, y, w, c, &s, n, m, rep, &r, 1, _state);
    ae_frame_leave(_state);
}


/*************************************************************************
Internal subroutine
*************************************************************************/
static void lsfit_lsfitclearrequestfields(lsfitstate* state,
     ae_state *_state)
{


    state->needf = ae_false;
    state->needfg = ae_false;
    state->needfgh = ae_false;
    state->xupdated = ae_false;
}


/*************************************************************************
Internal subroutine, calculates barycentric basis functions.
Used for efficient simultaneous calculation of N basis functions.

  -- ALGLIB --
     Copyright 17.08.2009 by Bochkanov Sergey
*************************************************************************/
static void lsfit_barycentriccalcbasis(barycentricinterpolant* b,
     double t,
     /* Real    */ ae_vector* y,
     ae_state *_state)
{
    double s2;
    double s;
    double v;
    ae_int_t i;
    ae_int_t j;


    
    /*
     * special case: N=1
     */
    if( b->n==1 )
    {
        y->ptr.p_double[0] = (double)(1);
        return;
    }
    
    /*
     * Here we assume that task is normalized, i.e.:
     * 1. abs(Y[i])<=1
     * 2. abs(W[i])<=1
     * 3. X[] is ordered
     *
     * First, we decide: should we use "safe" formula (guarded
     * against overflow) or fast one?
     */
    s = ae_fabs(t-b->x.ptr.p_double[0], _state);
    for(i=0; i<=b->n-1; i++)
    {
        v = b->x.ptr.p_double[i];
        if( ae_fp_eq(v,t) )
        {
            for(j=0; j<=b->n-1; j++)
            {
                y->ptr.p_double[j] = (double)(0);
            }
            y->ptr.p_double[i] = (double)(1);
            return;
        }
        v = ae_fabs(t-v, _state);
        if( ae_fp_less(v,s) )
        {
            s = v;
        }
    }
    s2 = (double)(0);
    for(i=0; i<=b->n-1; i++)
    {
        v = s/(t-b->x.ptr.p_double[i]);
        v = v*b->w.ptr.p_double[i];
        y->ptr.p_double[i] = v;
        s2 = s2+v;
    }
    v = 1/s2;
    ae_v_muld(&y->ptr.p_double[0], 1, ae_v_len(0,b->n-1), v);
}


/*************************************************************************
This is internal function for Chebyshev fitting.

It assumes that input data are normalized:
* X/XC belong to [-1,+1],
* mean(Y)=0, stddev(Y)=1.

It does not checks inputs for errors.

This function is used to fit general (shifted) Chebyshev models, power
basis models or barycentric models.

INPUT PARAMETERS:
    X   -   points, array[0..N-1].
    Y   -   function values, array[0..N-1].
    W   -   weights, array[0..N-1]
    N   -   number of points, N>0.
    XC  -   points where polynomial values/derivatives are constrained,
            array[0..K-1].
    YC  -   values of constraints, array[0..K-1]
    DC  -   array[0..K-1], types of constraints:
            * DC[i]=0   means that P(XC[i])=YC[i]
            * DC[i]=1   means that P'(XC[i])=YC[i]
    K   -   number of constraints, 0<=K<M.
            K=0 means no constraints (XC/YC/DC are not used in such cases)
    M   -   number of basis functions (= polynomial_degree + 1), M>=1

OUTPUT PARAMETERS:
    Info-   same format as in LSFitLinearW() subroutine:
            * Info>0    task is solved
            * Info<=0   an error occured:
                        -4 means inconvergence of internal SVD
                        -3 means inconsistent constraints
    C   -   interpolant in Chebyshev form; [-1,+1] is used as base interval
    Rep -   report, same format as in LSFitLinearW() subroutine.
            Following fields are set:
            * RMSError      rms error on the (X,Y).
            * AvgError      average error on the (X,Y).
            * AvgRelError   average relative error on the non-zero Y
            * MaxError      maximum error
                            NON-WEIGHTED ERRORS ARE CALCULATED

IMPORTANT:
    this subroitine doesn't calculate task's condition number for K<>0.

  -- ALGLIB PROJECT --
     Copyright 10.12.2009 by Bochkanov Sergey
*************************************************************************/
static void lsfit_internalchebyshevfit(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t* info,
     /* Real    */ ae_vector* c,
     lsfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _xc;
    ae_vector _yc;
    ae_vector y2;
    ae_vector w2;
    ae_vector tmp;
    ae_vector tmp2;
    ae_vector tmpdiff;
    ae_vector bx;
    ae_vector by;
    ae_vector bw;
    ae_matrix fmatrix;
    ae_matrix cmatrix;
    ae_int_t i;
    ae_int_t j;
    double mx;
    double decay;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_xc, xc, _state);
    xc = &_xc;
    ae_vector_init_copy(&_yc, yc, _state);
    yc = &_yc;
    *info = 0;
    ae_vector_clear(c);
    _lsfitreport_clear(rep);
    ae_vector_init(&y2, 0, DT_REAL, _state);
    ae_vector_init(&w2, 0, DT_REAL, _state);
    ae_vector_init(&tmp, 0, DT_REAL, _state);
    ae_vector_init(&tmp2, 0, DT_REAL, _state);
    ae_vector_init(&tmpdiff, 0, DT_REAL, _state);
    ae_vector_init(&bx, 0, DT_REAL, _state);
    ae_vector_init(&by, 0, DT_REAL, _state);
    ae_vector_init(&bw, 0, DT_REAL, _state);
    ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state);
    ae_matrix_init(&cmatrix, 0, 0, DT_REAL, _state);

    lsfit_clearreport(rep, _state);
    
    /*
     * weight decay for correct handling of task which becomes
     * degenerate after constraints are applied
     */
    decay = 10000*ae_machineepsilon;
    
    /*
     * allocate space, initialize/fill:
     * * FMatrix-   values of basis functions at X[]
     * * CMatrix-   values (derivatives) of basis functions at XC[]
     * * fill constraints matrix
     * * fill first N rows of design matrix with values
     * * fill next M rows of design matrix with regularizing term
     * * append M zeros to Y
     * * append M elements, mean(abs(W)) each, to W
     */
    ae_vector_set_length(&y2, n+m, _state);
    ae_vector_set_length(&w2, n+m, _state);
    ae_vector_set_length(&tmp, m, _state);
    ae_vector_set_length(&tmpdiff, m, _state);
    ae_matrix_set_length(&fmatrix, n+m, m, _state);
    if( k>0 )
    {
        ae_matrix_set_length(&cmatrix, k, m+1, _state);
    }
    
    /*
     * Fill design matrix, Y2, W2:
     * * first N rows with basis functions for original points
     * * next M rows with decay terms
     */
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * prepare Ith row
         * use Tmp for calculations to avoid multidimensional arrays overhead
         */
        for(j=0; j<=m-1; j++)
        {
            if( j==0 )
            {
                tmp.ptr.p_double[j] = (double)(1);
            }
            else
            {
                if( j==1 )
                {
                    tmp.ptr.p_double[j] = x->ptr.p_double[i];
                }
                else
                {
                    tmp.ptr.p_double[j] = 2*x->ptr.p_double[i]*tmp.ptr.p_double[j-1]-tmp.ptr.p_double[j-2];
                }
            }
        }
        ae_v_move(&fmatrix.ptr.pp_double[i][0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-1));
    }
    for(i=0; i<=m-1; i++)
    {
        for(j=0; j<=m-1; j++)
        {
            if( i==j )
            {
                fmatrix.ptr.pp_double[n+i][j] = decay;
            }
            else
            {
                fmatrix.ptr.pp_double[n+i][j] = (double)(0);
            }
        }
    }
    ae_v_move(&y2.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1));
    ae_v_move(&w2.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1));
    mx = (double)(0);
    for(i=0; i<=n-1; i++)
    {
        mx = mx+ae_fabs(w->ptr.p_double[i], _state);
    }
    mx = mx/n;
    for(i=0; i<=m-1; i++)
    {
        y2.ptr.p_double[n+i] = (double)(0);
        w2.ptr.p_double[n+i] = mx;
    }
    
    /*
     * fill constraints matrix
     */
    for(i=0; i<=k-1; i++)
    {
        
        /*
         * prepare Ith row
         * use Tmp for basis function values,
         * TmpDiff for basos function derivatives
         */
        for(j=0; j<=m-1; j++)
        {
            if( j==0 )
            {
                tmp.ptr.p_double[j] = (double)(1);
                tmpdiff.ptr.p_double[j] = (double)(0);
            }
            else
            {
                if( j==1 )
                {
                    tmp.ptr.p_double[j] = xc->ptr.p_double[i];
                    tmpdiff.ptr.p_double[j] = (double)(1);
                }
                else
                {
                    tmp.ptr.p_double[j] = 2*xc->ptr.p_double[i]*tmp.ptr.p_double[j-1]-tmp.ptr.p_double[j-2];
                    tmpdiff.ptr.p_double[j] = 2*(tmp.ptr.p_double[j-1]+xc->ptr.p_double[i]*tmpdiff.ptr.p_double[j-1])-tmpdiff.ptr.p_double[j-2];
                }
            }
        }
        if( dc->ptr.p_int[i]==0 )
        {
            ae_v_move(&cmatrix.ptr.pp_double[i][0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-1));
        }
        if( dc->ptr.p_int[i]==1 )
        {
            ae_v_move(&cmatrix.ptr.pp_double[i][0], 1, &tmpdiff.ptr.p_double[0], 1, ae_v_len(0,m-1));
        }
        cmatrix.ptr.pp_double[i][m] = yc->ptr.p_double[i];
    }
    
    /*
     * Solve constrained task
     */
    if( k>0 )
    {
        
        /*
         * solve using regularization
         */
        lsfitlinearwc(&y2, &w2, &fmatrix, &cmatrix, n+m, m, k, info, c, rep, _state);
    }
    else
    {
        
        /*
         * no constraints, no regularization needed
         */
        lsfitlinearwc(y, w, &fmatrix, &cmatrix, n, m, 0, info, c, rep, _state);
    }
    if( *info<0 )
    {
        ae_frame_leave(_state);
        return;
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Internal Floater-Hormann fitting subroutine for fixed D
*************************************************************************/
static void lsfit_barycentricfitwcfixedd(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     ae_int_t n,
     /* Real    */ ae_vector* xc,
     /* Real    */ ae_vector* yc,
     /* Integer */ ae_vector* dc,
     ae_int_t k,
     ae_int_t m,
     ae_int_t d,
     ae_int_t* info,
     barycentricinterpolant* b,
     barycentricfitreport* rep,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _x;
    ae_vector _y;
    ae_vector _w;
    ae_vector _xc;
    ae_vector _yc;
    ae_matrix fmatrix;
    ae_matrix cmatrix;
    ae_vector y2;
    ae_vector w2;
    ae_vector sx;
    ae_vector sy;
    ae_vector sbf;
    ae_vector xoriginal;
    ae_vector yoriginal;
    ae_vector tmp;
    lsfitreport lrep;
    double v0;
    double v1;
    double mx;
    barycentricinterpolant b2;
    ae_int_t i;
    ae_int_t j;
    ae_int_t relcnt;
    double xa;
    double xb;
    double sa;
    double sb;
    double decay;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_x, x, _state);
    x = &_x;
    ae_vector_init_copy(&_y, y, _state);
    y = &_y;
    ae_vector_init_copy(&_w, w, _state);
    w = &_w;
    ae_vector_init_copy(&_xc, xc, _state);
    xc = &_xc;
    ae_vector_init_copy(&_yc, yc, _state);
    yc = &_yc;
    *info = 0;
    _barycentricinterpolant_clear(b);
    _barycentricfitreport_clear(rep);
    ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state);
    ae_matrix_init(&cmatrix, 0, 0, DT_REAL, _state);
    ae_vector_init(&y2, 0, DT_REAL, _state);
    ae_vector_init(&w2, 0, DT_REAL, _state);
    ae_vector_init(&sx, 0, DT_REAL, _state);
    ae_vector_init(&sy, 0, DT_REAL, _state);
    ae_vector_init(&sbf, 0, DT_REAL, _state);
    ae_vector_init(&xoriginal, 0, DT_REAL, _state);
    ae_vector_init(&yoriginal, 0, DT_REAL, _state);
    ae_vector_init(&tmp, 0, DT_REAL, _state);
    _lsfitreport_init(&lrep, _state);
    _barycentricinterpolant_init(&b2, _state);

    if( ((n<1||m<2)||k<0)||k>=m )
    {
        *info = -1;
        ae_frame_leave(_state);
        return;
    }
    for(i=0; i<=k-1; i++)
    {
        *info = 0;
        if( dc->ptr.p_int[i]<0 )
        {
            *info = -1;
        }
        if( dc->ptr.p_int[i]>1 )
        {
            *info = -1;
        }
        if( *info<0 )
        {
            ae_frame_leave(_state);
            return;
        }
    }
    
    /*
     * weight decay for correct handling of task which becomes
     * degenerate after constraints are applied
     */
    decay = 10000*ae_machineepsilon;
    
    /*
     * Scale X, Y, XC, YC
     */
    lsfitscalexy(x, y, w, n, xc, yc, dc, k, &xa, &xb, &sa, &sb, &xoriginal, &yoriginal, _state);
    
    /*
     * allocate space, initialize:
     * * FMatrix-   values of basis functions at X[]
     * * CMatrix-   values (derivatives) of basis functions at XC[]
     */
    ae_vector_set_length(&y2, n+m, _state);
    ae_vector_set_length(&w2, n+m, _state);
    ae_matrix_set_length(&fmatrix, n+m, m, _state);
    if( k>0 )
    {
        ae_matrix_set_length(&cmatrix, k, m+1, _state);
    }
    ae_vector_set_length(&y2, n+m, _state);
    ae_vector_set_length(&w2, n+m, _state);
    
    /*
     * Prepare design and constraints matrices:
     * * fill constraints matrix
     * * fill first N rows of design matrix with values
     * * fill next M rows of design matrix with regularizing term
     * * append M zeros to Y
     * * append M elements, mean(abs(W)) each, to W
     */
    ae_vector_set_length(&sx, m, _state);
    ae_vector_set_length(&sy, m, _state);
    ae_vector_set_length(&sbf, m, _state);
    for(j=0; j<=m-1; j++)
    {
        sx.ptr.p_double[j] = (double)(2*j)/(double)(m-1)-1;
    }
    for(i=0; i<=m-1; i++)
    {
        sy.ptr.p_double[i] = (double)(1);
    }
    barycentricbuildfloaterhormann(&sx, &sy, m, d, &b2, _state);
    mx = (double)(0);
    for(i=0; i<=n-1; i++)
    {
        lsfit_barycentriccalcbasis(&b2, x->ptr.p_double[i], &sbf, _state);
        ae_v_move(&fmatrix.ptr.pp_double[i][0], 1, &sbf.ptr.p_double[0], 1, ae_v_len(0,m-1));
        y2.ptr.p_double[i] = y->ptr.p_double[i];
        w2.ptr.p_double[i] = w->ptr.p_double[i];
        mx = mx+ae_fabs(w->ptr.p_double[i], _state)/n;
    }
    for(i=0; i<=m-1; i++)
    {
        for(j=0; j<=m-1; j++)
        {
            if( i==j )
            {
                fmatrix.ptr.pp_double[n+i][j] = decay;
            }
            else
            {
                fmatrix.ptr.pp_double[n+i][j] = (double)(0);
            }
        }
        y2.ptr.p_double[n+i] = (double)(0);
        w2.ptr.p_double[n+i] = mx;
    }
    if( k>0 )
    {
        for(j=0; j<=m-1; j++)
        {
            for(i=0; i<=m-1; i++)
            {
                sy.ptr.p_double[i] = (double)(0);
            }
            sy.ptr.p_double[j] = (double)(1);
            barycentricbuildfloaterhormann(&sx, &sy, m, d, &b2, _state);
            for(i=0; i<=k-1; i++)
            {
                ae_assert(dc->ptr.p_int[i]>=0&&dc->ptr.p_int[i]<=1, "BarycentricFit: internal error!", _state);
                barycentricdiff1(&b2, xc->ptr.p_double[i], &v0, &v1, _state);
                if( dc->ptr.p_int[i]==0 )
                {
                    cmatrix.ptr.pp_double[i][j] = v0;
                }
                if( dc->ptr.p_int[i]==1 )
                {
                    cmatrix.ptr.pp_double[i][j] = v1;
                }
            }
        }
        for(i=0; i<=k-1; i++)
        {
            cmatrix.ptr.pp_double[i][m] = yc->ptr.p_double[i];
        }
    }
    
    /*
     * Solve constrained task
     */
    if( k>0 )
    {
        
        /*
         * solve using regularization
         */
        lsfitlinearwc(&y2, &w2, &fmatrix, &cmatrix, n+m, m, k, info, &tmp, &lrep, _state);
    }
    else
    {
        
        /*
         * no constraints, no regularization needed
         */
        lsfitlinearwc(y, w, &fmatrix, &cmatrix, n, m, k, info, &tmp, &lrep, _state);
    }
    if( *info<0 )
    {
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Generate interpolant and scale it
     */
    ae_v_move(&sy.ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-1));
    barycentricbuildfloaterhormann(&sx, &sy, m, d, b, _state);
    barycentriclintransx(b, 2/(xb-xa), -(xa+xb)/(xb-xa), _state);
    barycentriclintransy(b, sb-sa, sa, _state);
    
    /*
     * Scale absolute errors obtained from LSFitLinearW.
     * Relative error should be calculated separately
     * (because of shifting/scaling of the task)
     */
    rep->taskrcond = lrep.taskrcond;
    rep->rmserror = lrep.rmserror*(sb-sa);
    rep->avgerror = lrep.avgerror*(sb-sa);
    rep->maxerror = lrep.maxerror*(sb-sa);
    rep->avgrelerror = (double)(0);
    relcnt = 0;
    for(i=0; i<=n-1; i++)
    {
        if( ae_fp_neq(yoriginal.ptr.p_double[i],(double)(0)) )
        {
            rep->avgrelerror = rep->avgrelerror+ae_fabs(barycentriccalc(b, xoriginal.ptr.p_double[i], _state)-yoriginal.ptr.p_double[i], _state)/ae_fabs(yoriginal.ptr.p_double[i], _state);
            relcnt = relcnt+1;
        }
    }
    if( relcnt!=0 )
    {
        rep->avgrelerror = rep->avgrelerror/relcnt;
    }
    ae_frame_leave(_state);
}


static void lsfit_clearreport(lsfitreport* rep, ae_state *_state)
{


    rep->taskrcond = (double)(0);
    rep->iterationscount = 0;
    rep->varidx = -1;
    rep->rmserror = (double)(0);
    rep->avgerror = (double)(0);
    rep->avgrelerror = (double)(0);
    rep->maxerror = (double)(0);
    rep->wrmserror = (double)(0);
    rep->r2 = (double)(0);
    ae_matrix_set_length(&rep->covpar, 0, 0, _state);
    ae_vector_set_length(&rep->errpar, 0, _state);
    ae_vector_set_length(&rep->errcurve, 0, _state);
    ae_vector_set_length(&rep->noise, 0, _state);
}


/*************************************************************************
This internal function estimates covariance matrix and other error-related
information for linear/nonlinear least squares model.

It has a bit awkward interface, but it can be used  for  both  linear  and
nonlinear problems.

INPUT PARAMETERS:
    F1  -   array[0..N-1,0..K-1]:
            * for linear problems - matrix of function values
            * for nonlinear problems - Jacobian matrix
    F0  -   array[0..N-1]:
            * for linear problems - must be filled with zeros
            * for nonlinear problems - must store values of function being
              fitted
    Y   -   array[0..N-1]:
            * for linear and nonlinear problems - must store target values
    W   -   weights, array[0..N-1]:
            * for linear and nonlinear problems - weights
    X   -   array[0..K-1]:
            * for linear and nonlinear problems - current solution
    S   -   array[0..K-1]:
            * its components should be strictly positive
            * squared inverse of this diagonal matrix is used as damping
              factor for covariance matrix (linear and nonlinear problems)
            * for nonlinear problems, when scale of the variables is usually
              explicitly given by user, you may use scale vector for this
              parameter
            * for linear problems you may set this parameter to
              S=sqrt(1/diag(F'*F))
            * this parameter is automatically rescaled by this function,
              only relative magnitudes of its components (with respect to
              each other) matter.
    N   -   number of points, N>0.
    K   -   number of dimensions
    Rep -   structure which is used to store results
    Z   -   additional matrix which, depending on ZKind, may contain some
            information used to accelerate calculations - or just can be
            temporary buffer:
            * for ZKind=0       Z contains no information, just temporary
                                buffer which can be resized and used as needed
            * for ZKind=1       Z contains triangular matrix from QR
                                decomposition of W*F1. This matrix can be used
                                to speedup calculation of covariance matrix.
                                It should not be changed by algorithm.
    ZKind-  contents of Z

OUTPUT PARAMETERS:

* Rep.CovPar        covariance matrix for parameters, array[K,K].
* Rep.ErrPar        errors in parameters, array[K],
                    errpar = sqrt(diag(CovPar))
* Rep.ErrCurve      vector of fit errors - standard deviations of empirical
                    best-fit curve from "ideal" best-fit curve built  with
                    infinite number of samples, array[N].
                    errcurve = sqrt(diag(J*CovPar*J')),
                    where J is Jacobian matrix.
* Rep.Noise         vector of per-point estimates of noise, array[N]
* Rep.R2            coefficient of determination (non-weighted)

Other fields of Rep are not changed.

IMPORTANT:  errors  in  parameters  are  calculated  without  taking  into
            account boundary/linear constraints! Presence  of  constraints
            changes distribution of errors, but there is no  easy  way  to
            account for constraints when you calculate covariance matrix.
            
NOTE:       noise in the data is estimated as follows:
            * for fitting without user-supplied  weights  all  points  are
              assumed to have same level of noise, which is estimated from
              the data
            * for fitting with user-supplied weights we assume that  noise
              level in I-th point is inversely proportional to Ith weight.
              Coefficient of proportionality is estimated from the data.
            
NOTE:       we apply small amount of regularization when we invert squared
            Jacobian and calculate covariance matrix. It  guarantees  that
            algorithm won't divide by zero  during  inversion,  but  skews
            error estimates a bit (fractional error is about 10^-9).
            
            However, we believe that this difference is insignificant  for
            all practical purposes except for the situation when you  want
            to compare ALGLIB results with "reference"  implementation  up
            to the last significant digit.

  -- ALGLIB PROJECT --
     Copyright 10.12.2009 by Bochkanov Sergey
*************************************************************************/
static void lsfit_estimateerrors(/* Real    */ ae_matrix* f1,
     /* Real    */ ae_vector* f0,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_vector* w,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* s,
     ae_int_t n,
     ae_int_t k,
     lsfitreport* rep,
     /* Real    */ ae_matrix* z,
     ae_int_t zkind,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _s;
    ae_int_t i;
    ae_int_t j;
    ae_int_t j1;
    double v;
    double noisec;
    ae_int_t info;
    matinvreport invrep;
    ae_int_t nzcnt;
    double avg;
    double rss;
    double tss;
    double sz;
    double ss;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_s, s, _state);
    s = &_s;
    _matinvreport_init(&invrep, _state);

    
    /*
     * Compute NZCnt - count of non-zero weights
     */
    nzcnt = 0;
    for(i=0; i<=n-1; i++)
    {
        if( ae_fp_neq(w->ptr.p_double[i],(double)(0)) )
        {
            nzcnt = nzcnt+1;
        }
    }
    
    /*
     * Compute R2
     */
    if( nzcnt>0 )
    {
        avg = 0.0;
        for(i=0; i<=n-1; i++)
        {
            if( ae_fp_neq(w->ptr.p_double[i],(double)(0)) )
            {
                avg = avg+y->ptr.p_double[i];
            }
        }
        avg = avg/nzcnt;
        rss = 0.0;
        tss = 0.0;
        for(i=0; i<=n-1; i++)
        {
            if( ae_fp_neq(w->ptr.p_double[i],(double)(0)) )
            {
                v = ae_v_dotproduct(&f1->ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,k-1));
                v = v+f0->ptr.p_double[i];
                rss = rss+ae_sqr(v-y->ptr.p_double[i], _state);
                tss = tss+ae_sqr(y->ptr.p_double[i]-avg, _state);
            }
        }
        if( ae_fp_neq(tss,(double)(0)) )
        {
            rep->r2 = ae_maxreal(1.0-rss/tss, 0.0, _state);
        }
        else
        {
            rep->r2 = 1.0;
        }
    }
    else
    {
        rep->r2 = (double)(0);
    }
    
    /*
     * Compute estimate of proportionality between noise in the data and weights:
     *     NoiseC = mean(per-point-noise*per-point-weight)
     * Noise level (standard deviation) at each point is equal to NoiseC/W[I].
     */
    if( nzcnt>k )
    {
        noisec = 0.0;
        for(i=0; i<=n-1; i++)
        {
            if( ae_fp_neq(w->ptr.p_double[i],(double)(0)) )
            {
                v = ae_v_dotproduct(&f1->ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,k-1));
                v = v+f0->ptr.p_double[i];
                noisec = noisec+ae_sqr((v-y->ptr.p_double[i])*w->ptr.p_double[i], _state);
            }
        }
        noisec = ae_sqrt(noisec/(nzcnt-k), _state);
    }
    else
    {
        noisec = 0.0;
    }
    
    /*
     * Two branches on noise level:
     * * NoiseC>0   normal situation
     * * NoiseC=0   degenerate case CovPar is filled by zeros
     */
    rmatrixsetlengthatleast(&rep->covpar, k, k, _state);
    if( ae_fp_greater(noisec,(double)(0)) )
    {
        
        /*
         * Normal situation: non-zero noise level
         */
        ae_assert(zkind==0||zkind==1, "LSFit: internal error in EstimateErrors() function", _state);
        if( zkind==0 )
        {
            
            /*
             * Z contains no additional information which can be used to speed up
             * calculations. We have to calculate covariance matrix on our own:
             * * Compute scaled Jacobian N*J, where N[i,i]=WCur[I]/NoiseC, store in Z
             * * Compute Z'*Z, store in CovPar
             * * Apply moderate regularization to CovPar and compute matrix inverse.
             *   In case inverse failed, increase regularization parameter and try
             *   again.
             */
            rmatrixsetlengthatleast(z, n, k, _state);
            for(i=0; i<=n-1; i++)
            {
                v = w->ptr.p_double[i]/noisec;
                ae_v_moved(&z->ptr.pp_double[i][0], 1, &f1->ptr.pp_double[i][0], 1, ae_v_len(0,k-1), v);
            }
            
            /*
             * Convert S to automatically scaled damped matrix:
             * * calculate SZ - sum of diagonal elements of Z'*Z
             * * calculate SS - sum of diagonal elements of S^(-2)
             * * overwrite S by (SZ/SS)*S^(-2)
             * * now S has approximately same magnitude as giagonal of Z'*Z
             */
            sz = (double)(0);
            for(i=0; i<=n-1; i++)
            {
                for(j=0; j<=k-1; j++)
                {
                    sz = sz+z->ptr.pp_double[i][j]*z->ptr.pp_double[i][j];
                }
            }
            if( ae_fp_eq(sz,(double)(0)) )
            {
                sz = (double)(1);
            }
            ss = (double)(0);
            for(j=0; j<=k-1; j++)
            {
                ss = ss+1/ae_sqr(s->ptr.p_double[j], _state);
            }
            for(j=0; j<=k-1; j++)
            {
                s->ptr.p_double[j] = sz/ss/ae_sqr(s->ptr.p_double[j], _state);
            }
            
            /*
             * Calculate damped inverse inv(Z'*Z+S).
             * We increase damping factor V until Z'*Z become well-conditioned.
             */
            v = 1.0E3*ae_machineepsilon;
            do
            {
                rmatrixsyrk(k, n, 1.0, z, 0, 0, 2, 0.0, &rep->covpar, 0, 0, ae_true, _state);
                for(i=0; i<=k-1; i++)
                {
                    rep->covpar.ptr.pp_double[i][i] = rep->covpar.ptr.pp_double[i][i]+v*s->ptr.p_double[i];
                }
                spdmatrixinverse(&rep->covpar, k, ae_true, &info, &invrep, _state);
                v = 10*v;
            }
            while(info<=0);
            for(i=0; i<=k-1; i++)
            {
                for(j=i+1; j<=k-1; j++)
                {
                    rep->covpar.ptr.pp_double[j][i] = rep->covpar.ptr.pp_double[i][j];
                }
            }
        }
        if( zkind==1 )
        {
            
            /*
             * We can reuse additional information:
             * * Z contains R matrix from QR decomposition of W*F1 
             * * After multiplication by 1/NoiseC we get Z_mod = N*F1, where diag(N)=w[i]/NoiseC
             * * Such triangular Z_mod is a Cholesky factor from decomposition of J'*N'*N*J.
             *   Thus, we can calculate covariance matrix as inverse of the matrix given by
             *   its Cholesky decomposition. It allow us to avoid time-consuming calculation
             *   of J'*N'*N*J in CovPar - complexity is reduced from O(N*K^2) to O(K^3), which
             *   is quite good because K is usually orders of magnitude smaller than N.
             *
             * First, convert S to automatically scaled damped matrix:
             * * calculate SZ - sum of magnitudes of diagonal elements of Z/NoiseC
             * * calculate SS - sum of diagonal elements of S^(-1)
             * * overwrite S by (SZ/SS)*S^(-1)
             * * now S has approximately same magnitude as giagonal of Z'*Z
             */
            sz = (double)(0);
            for(j=0; j<=k-1; j++)
            {
                sz = sz+ae_fabs(z->ptr.pp_double[j][j]/noisec, _state);
            }
            if( ae_fp_eq(sz,(double)(0)) )
            {
                sz = (double)(1);
            }
            ss = (double)(0);
            for(j=0; j<=k-1; j++)
            {
                ss = ss+1/s->ptr.p_double[j];
            }
            for(j=0; j<=k-1; j++)
            {
                s->ptr.p_double[j] = sz/ss/s->ptr.p_double[j];
            }
            
            /*
             * Calculate damped inverse of inv((Z+v*S)'*(Z+v*S))
             * We increase damping factor V until matrix become well-conditioned.
             */
            v = 1.0E3*ae_machineepsilon;
            do
            {
                for(i=0; i<=k-1; i++)
                {
                    for(j=i; j<=k-1; j++)
                    {
                        rep->covpar.ptr.pp_double[i][j] = z->ptr.pp_double[i][j]/noisec;
                    }
                    rep->covpar.ptr.pp_double[i][i] = rep->covpar.ptr.pp_double[i][i]+v*s->ptr.p_double[i];
                }
                spdmatrixcholeskyinverse(&rep->covpar, k, ae_true, &info, &invrep, _state);
                v = 10*v;
            }
            while(info<=0);
            for(i=0; i<=k-1; i++)
            {
                for(j=i+1; j<=k-1; j++)
                {
                    rep->covpar.ptr.pp_double[j][i] = rep->covpar.ptr.pp_double[i][j];
                }
            }
        }
    }
    else
    {
        
        /*
         * Degenerate situation: zero noise level, covariance matrix is zero.
         */
        for(i=0; i<=k-1; i++)
        {
            for(j=0; j<=k-1; j++)
            {
                rep->covpar.ptr.pp_double[j][i] = (double)(0);
            }
        }
    }
    
    /*
     * Estimate erorrs in parameters, curve and per-point noise
     */
    rvectorsetlengthatleast(&rep->errpar, k, _state);
    rvectorsetlengthatleast(&rep->errcurve, n, _state);
    rvectorsetlengthatleast(&rep->noise, n, _state);
    for(i=0; i<=k-1; i++)
    {
        rep->errpar.ptr.p_double[i] = ae_sqrt(rep->covpar.ptr.pp_double[i][i], _state);
    }
    for(i=0; i<=n-1; i++)
    {
        
        /*
         * ErrCurve[I] is sqrt(P[i,i]) where P=J*CovPar*J'
         */
        v = 0.0;
        for(j=0; j<=k-1; j++)
        {
            for(j1=0; j1<=k-1; j1++)
            {
                v = v+f1->ptr.pp_double[i][j]*rep->covpar.ptr.pp_double[j][j1]*f1->ptr.pp_double[i][j1];
            }
        }
        rep->errcurve.ptr.p_double[i] = ae_sqrt(v, _state);
        
        /*
         * Noise[i] is filled using weights and current estimate of noise level
         */
        if( ae_fp_neq(w->ptr.p_double[i],(double)(0)) )
        {
            rep->noise.ptr.p_double[i] = noisec/w->ptr.p_double[i];
        }
        else
        {
            rep->noise.ptr.p_double[i] = (double)(0);
        }
    }
    ae_frame_leave(_state);
}


void _polynomialfitreport_init(void* _p, ae_state *_state)
{
    polynomialfitreport *p = (polynomialfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _polynomialfitreport_init_copy(void* _dst, void* _src, ae_state *_state)
{
    polynomialfitreport *dst = (polynomialfitreport*)_dst;
    polynomialfitreport *src = (polynomialfitreport*)_src;
    dst->taskrcond = src->taskrcond;
    dst->rmserror = src->rmserror;
    dst->avgerror = src->avgerror;
    dst->avgrelerror = src->avgrelerror;
    dst->maxerror = src->maxerror;
}


void _polynomialfitreport_clear(void* _p)
{
    polynomialfitreport *p = (polynomialfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _polynomialfitreport_destroy(void* _p)
{
    polynomialfitreport *p = (polynomialfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _barycentricfitreport_init(void* _p, ae_state *_state)
{
    barycentricfitreport *p = (barycentricfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _barycentricfitreport_init_copy(void* _dst, void* _src, ae_state *_state)
{
    barycentricfitreport *dst = (barycentricfitreport*)_dst;
    barycentricfitreport *src = (barycentricfitreport*)_src;
    dst->taskrcond = src->taskrcond;
    dst->dbest = src->dbest;
    dst->rmserror = src->rmserror;
    dst->avgerror = src->avgerror;
    dst->avgrelerror = src->avgrelerror;
    dst->maxerror = src->maxerror;
}


void _barycentricfitreport_clear(void* _p)
{
    barycentricfitreport *p = (barycentricfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _barycentricfitreport_destroy(void* _p)
{
    barycentricfitreport *p = (barycentricfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _spline1dfitreport_init(void* _p, ae_state *_state)
{
    spline1dfitreport *p = (spline1dfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _spline1dfitreport_init_copy(void* _dst, void* _src, ae_state *_state)
{
    spline1dfitreport *dst = (spline1dfitreport*)_dst;
    spline1dfitreport *src = (spline1dfitreport*)_src;
    dst->taskrcond = src->taskrcond;
    dst->rmserror = src->rmserror;
    dst->avgerror = src->avgerror;
    dst->avgrelerror = src->avgrelerror;
    dst->maxerror = src->maxerror;
}


void _spline1dfitreport_clear(void* _p)
{
    spline1dfitreport *p = (spline1dfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _spline1dfitreport_destroy(void* _p)
{
    spline1dfitreport *p = (spline1dfitreport*)_p;
    ae_touch_ptr((void*)p);
}


void _lsfitreport_init(void* _p, ae_state *_state)
{
    lsfitreport *p = (lsfitreport*)_p;
    ae_touch_ptr((void*)p);
    ae_matrix_init(&p->covpar, 0, 0, DT_REAL, _state);
    ae_vector_init(&p->errpar, 0, DT_REAL, _state);
    ae_vector_init(&p->errcurve, 0, DT_REAL, _state);
    ae_vector_init(&p->noise, 0, DT_REAL, _state);
}


void _lsfitreport_init_copy(void* _dst, void* _src, ae_state *_state)
{
    lsfitreport *dst = (lsfitreport*)_dst;
    lsfitreport *src = (lsfitreport*)_src;
    dst->taskrcond = src->taskrcond;
    dst->iterationscount = src->iterationscount;
    dst->varidx = src->varidx;
    dst->rmserror = src->rmserror;
    dst->avgerror = src->avgerror;
    dst->avgrelerror = src->avgrelerror;
    dst->maxerror = src->maxerror;
    dst->wrmserror = src->wrmserror;
    ae_matrix_init_copy(&dst->covpar, &src->covpar, _state);
    ae_vector_init_copy(&dst->errpar, &src->errpar, _state);
    ae_vector_init_copy(&dst->errcurve, &src->errcurve, _state);
    ae_vector_init_copy(&dst->noise, &src->noise, _state);
    dst->r2 = src->r2;
}


void _lsfitreport_clear(void* _p)
{
    lsfitreport *p = (lsfitreport*)_p;
    ae_touch_ptr((void*)p);
    ae_matrix_clear(&p->covpar);
    ae_vector_clear(&p->errpar);
    ae_vector_clear(&p->errcurve);
    ae_vector_clear(&p->noise);
}


void _lsfitreport_destroy(void* _p)
{
    lsfitreport *p = (lsfitreport*)_p;
    ae_touch_ptr((void*)p);
    ae_matrix_destroy(&p->covpar);
    ae_vector_destroy(&p->errpar);
    ae_vector_destroy(&p->errcurve);
    ae_vector_destroy(&p->noise);
}


void _lsfitstate_init(void* _p, ae_state *_state)
{
    lsfitstate *p = (lsfitstate*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_init(&p->s, 0, DT_REAL, _state);
    ae_vector_init(&p->bndl, 0, DT_REAL, _state);
    ae_vector_init(&p->bndu, 0, DT_REAL, _state);
    ae_matrix_init(&p->taskx, 0, 0, DT_REAL, _state);
    ae_vector_init(&p->tasky, 0, DT_REAL, _state);
    ae_vector_init(&p->taskw, 0, DT_REAL, _state);
    ae_vector_init(&p->x, 0, DT_REAL, _state);
    ae_vector_init(&p->c, 0, DT_REAL, _state);
    ae_vector_init(&p->g, 0, DT_REAL, _state);
    ae_matrix_init(&p->h, 0, 0, DT_REAL, _state);
    ae_vector_init(&p->wcur, 0, DT_REAL, _state);
    ae_vector_init(&p->tmp, 0, DT_REAL, _state);
    ae_vector_init(&p->tmpf, 0, DT_REAL, _state);
    ae_matrix_init(&p->tmpjac, 0, 0, DT_REAL, _state);
    ae_matrix_init(&p->tmpjacw, 0, 0, DT_REAL, _state);
    _matinvreport_init(&p->invrep, _state);
    _lsfitreport_init(&p->rep, _state);
    _minlmstate_init(&p->optstate, _state);
    _minlmreport_init(&p->optrep, _state);
    _rcommstate_init(&p->rstate, _state);
}


void _lsfitstate_init_copy(void* _dst, void* _src, ae_state *_state)
{
    lsfitstate *dst = (lsfitstate*)_dst;
    lsfitstate *src = (lsfitstate*)_src;
    dst->optalgo = src->optalgo;
    dst->m = src->m;
    dst->k = src->k;
    dst->epsf = src->epsf;
    dst->epsx = src->epsx;
    dst->maxits = src->maxits;
    dst->stpmax = src->stpmax;
    dst->xrep = src->xrep;
    ae_vector_init_copy(&dst->s, &src->s, _state);
    ae_vector_init_copy(&dst->bndl, &src->bndl, _state);
    ae_vector_init_copy(&dst->bndu, &src->bndu, _state);
    ae_matrix_init_copy(&dst->taskx, &src->taskx, _state);
    ae_vector_init_copy(&dst->tasky, &src->tasky, _state);
    dst->npoints = src->npoints;
    ae_vector_init_copy(&dst->taskw, &src->taskw, _state);
    dst->nweights = src->nweights;
    dst->wkind = src->wkind;
    dst->wits = src->wits;
    dst->diffstep = src->diffstep;
    dst->teststep = src->teststep;
    dst->xupdated = src->xupdated;
    dst->needf = src->needf;
    dst->needfg = src->needfg;
    dst->needfgh = src->needfgh;
    dst->pointindex = src->pointindex;
    ae_vector_init_copy(&dst->x, &src->x, _state);
    ae_vector_init_copy(&dst->c, &src->c, _state);
    dst->f = src->f;
    ae_vector_init_copy(&dst->g, &src->g, _state);
    ae_matrix_init_copy(&dst->h, &src->h, _state);
    ae_vector_init_copy(&dst->wcur, &src->wcur, _state);
    ae_vector_init_copy(&dst->tmp, &src->tmp, _state);
    ae_vector_init_copy(&dst->tmpf, &src->tmpf, _state);
    ae_matrix_init_copy(&dst->tmpjac, &src->tmpjac, _state);
    ae_matrix_init_copy(&dst->tmpjacw, &src->tmpjacw, _state);
    dst->tmpnoise = src->tmpnoise;
    _matinvreport_init_copy(&dst->invrep, &src->invrep, _state);
    dst->repiterationscount = src->repiterationscount;
    dst->repterminationtype = src->repterminationtype;
    dst->repvaridx = src->repvaridx;
    dst->reprmserror = src->reprmserror;
    dst->repavgerror = src->repavgerror;
    dst->repavgrelerror = src->repavgrelerror;
    dst->repmaxerror = src->repmaxerror;
    dst->repwrmserror = src->repwrmserror;
    _lsfitreport_init_copy(&dst->rep, &src->rep, _state);
    _minlmstate_init_copy(&dst->optstate, &src->optstate, _state);
    _minlmreport_init_copy(&dst->optrep, &src->optrep, _state);
    dst->prevnpt = src->prevnpt;
    dst->prevalgo = src->prevalgo;
    _rcommstate_init_copy(&dst->rstate, &src->rstate, _state);
}


void _lsfitstate_clear(void* _p)
{
    lsfitstate *p = (lsfitstate*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_clear(&p->s);
    ae_vector_clear(&p->bndl);
    ae_vector_clear(&p->bndu);
    ae_matrix_clear(&p->taskx);
    ae_vector_clear(&p->tasky);
    ae_vector_clear(&p->taskw);
    ae_vector_clear(&p->x);
    ae_vector_clear(&p->c);
    ae_vector_clear(&p->g);
    ae_matrix_clear(&p->h);
    ae_vector_clear(&p->wcur);
    ae_vector_clear(&p->tmp);
    ae_vector_clear(&p->tmpf);
    ae_matrix_clear(&p->tmpjac);
    ae_matrix_clear(&p->tmpjacw);
    _matinvreport_clear(&p->invrep);
    _lsfitreport_clear(&p->rep);
    _minlmstate_clear(&p->optstate);
    _minlmreport_clear(&p->optrep);
    _rcommstate_clear(&p->rstate);
}


void _lsfitstate_destroy(void* _p)
{
    lsfitstate *p = (lsfitstate*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_destroy(&p->s);
    ae_vector_destroy(&p->bndl);
    ae_vector_destroy(&p->bndu);
    ae_matrix_destroy(&p->taskx);
    ae_vector_destroy(&p->tasky);
    ae_vector_destroy(&p->taskw);
    ae_vector_destroy(&p->x);
    ae_vector_destroy(&p->c);
    ae_vector_destroy(&p->g);
    ae_matrix_destroy(&p->h);
    ae_vector_destroy(&p->wcur);
    ae_vector_destroy(&p->tmp);
    ae_vector_destroy(&p->tmpf);
    ae_matrix_destroy(&p->tmpjac);
    ae_matrix_destroy(&p->tmpjacw);
    _matinvreport_destroy(&p->invrep);
    _lsfitreport_destroy(&p->rep);
    _minlmstate_destroy(&p->optstate);
    _minlmreport_destroy(&p->optrep);
    _rcommstate_destroy(&p->rstate);
}




/*************************************************************************
This function  builds  non-periodic 2-dimensional parametric spline  which
starts at (X[0],Y[0]) and ends at (X[N-1],Y[N-1]).

INPUT PARAMETERS:
    XY  -   points, array[0..N-1,0..1].
            XY[I,0:1] corresponds to the Ith point.
            Order of points is important!
    N   -   points count, N>=5 for Akima splines, N>=2 for other types  of
            splines.
    ST  -   spline type:
            * 0     Akima spline
            * 1     parabolically terminated Catmull-Rom spline (Tension=0)
            * 2     parabolically terminated cubic spline
    PT  -   parameterization type:
            * 0     uniform
            * 1     chord length
            * 2     centripetal

OUTPUT PARAMETERS:
    P   -   parametric spline interpolant


NOTES:
* this function  assumes  that  there all consequent points  are distinct.
  I.e. (x0,y0)<>(x1,y1),  (x1,y1)<>(x2,y2),  (x2,y2)<>(x3,y3)  and  so on.
  However, non-consequent points may coincide, i.e. we can  have  (x0,y0)=
  =(x2,y2).

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2build(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t st,
     ae_int_t pt,
     pspline2interpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_matrix _xy;
    ae_vector tmp;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_init_copy(&_xy, xy, _state);
    xy = &_xy;
    _pspline2interpolant_clear(p);
    ae_vector_init(&tmp, 0, DT_REAL, _state);

    ae_assert(st>=0&&st<=2, "PSpline2Build: incorrect spline type!", _state);
    ae_assert(pt>=0&&pt<=2, "PSpline2Build: incorrect parameterization type!", _state);
    if( st==0 )
    {
        ae_assert(n>=5, "PSpline2Build: N<5 (minimum value for Akima splines)!", _state);
    }
    else
    {
        ae_assert(n>=2, "PSpline2Build: N<2!", _state);
    }
    
    /*
     * Prepare
     */
    p->n = n;
    p->periodic = ae_false;
    ae_vector_set_length(&tmp, n, _state);
    
    /*
     * Build parameterization, check that all parameters are distinct
     */
    parametric_pspline2par(xy, n, pt, &p->p, _state);
    ae_assert(aredistinct(&p->p, n, _state), "PSpline2Build: consequent points are too close!", _state);
    
    /*
     * Build splines
     */
    if( st==0 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1));
        spline1dbuildakima(&p->p, &tmp, n, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1));
        spline1dbuildakima(&p->p, &tmp, n, &p->y, _state);
    }
    if( st==1 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->y, _state);
    }
    if( st==2 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->y, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This function  builds  non-periodic 3-dimensional parametric spline  which
starts at (X[0],Y[0],Z[0]) and ends at (X[N-1],Y[N-1],Z[N-1]).

Same as PSpline2Build() function, but for 3D, so we  won't  duplicate  its
description here.

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3build(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t st,
     ae_int_t pt,
     pspline3interpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_matrix _xy;
    ae_vector tmp;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_init_copy(&_xy, xy, _state);
    xy = &_xy;
    _pspline3interpolant_clear(p);
    ae_vector_init(&tmp, 0, DT_REAL, _state);

    ae_assert(st>=0&&st<=2, "PSpline3Build: incorrect spline type!", _state);
    ae_assert(pt>=0&&pt<=2, "PSpline3Build: incorrect parameterization type!", _state);
    if( st==0 )
    {
        ae_assert(n>=5, "PSpline3Build: N<5 (minimum value for Akima splines)!", _state);
    }
    else
    {
        ae_assert(n>=2, "PSpline3Build: N<2!", _state);
    }
    
    /*
     * Prepare
     */
    p->n = n;
    p->periodic = ae_false;
    ae_vector_set_length(&tmp, n, _state);
    
    /*
     * Build parameterization, check that all parameters are distinct
     */
    parametric_pspline3par(xy, n, pt, &p->p, _state);
    ae_assert(aredistinct(&p->p, n, _state), "PSpline3Build: consequent points are too close!", _state);
    
    /*
     * Build splines
     */
    if( st==0 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1));
        spline1dbuildakima(&p->p, &tmp, n, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1));
        spline1dbuildakima(&p->p, &tmp, n, &p->y, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][2], xy->stride, ae_v_len(0,n-1));
        spline1dbuildakima(&p->p, &tmp, n, &p->z, _state);
    }
    if( st==1 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->y, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][2], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->z, _state);
    }
    if( st==2 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->y, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][2], xy->stride, ae_v_len(0,n-1));
        spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->z, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This  function  builds  periodic  2-dimensional  parametric  spline  which
starts at (X[0],Y[0]), goes through all points to (X[N-1],Y[N-1]) and then
back to (X[0],Y[0]).

INPUT PARAMETERS:
    XY  -   points, array[0..N-1,0..1].
            XY[I,0:1] corresponds to the Ith point.
            XY[N-1,0:1] must be different from XY[0,0:1].
            Order of points is important!
    N   -   points count, N>=3 for other types of splines.
    ST  -   spline type:
            * 1     Catmull-Rom spline (Tension=0) with cyclic boundary conditions
            * 2     cubic spline with cyclic boundary conditions
    PT  -   parameterization type:
            * 0     uniform
            * 1     chord length
            * 2     centripetal

OUTPUT PARAMETERS:
    P   -   parametric spline interpolant


NOTES:
* this function  assumes  that there all consequent points  are  distinct.
  I.e. (x0,y0)<>(x1,y1), (x1,y1)<>(x2,y2),  (x2,y2)<>(x3,y3)  and  so  on.
  However, non-consequent points may coincide, i.e. we can  have  (x0,y0)=
  =(x2,y2).
* last point of sequence is NOT equal to the first  point.  You  shouldn't
  make curve "explicitly periodic" by making them equal.

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2buildperiodic(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t st,
     ae_int_t pt,
     pspline2interpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_matrix _xy;
    ae_matrix xyp;
    ae_vector tmp;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_init_copy(&_xy, xy, _state);
    xy = &_xy;
    _pspline2interpolant_clear(p);
    ae_matrix_init(&xyp, 0, 0, DT_REAL, _state);
    ae_vector_init(&tmp, 0, DT_REAL, _state);

    ae_assert(st>=1&&st<=2, "PSpline2BuildPeriodic: incorrect spline type!", _state);
    ae_assert(pt>=0&&pt<=2, "PSpline2BuildPeriodic: incorrect parameterization type!", _state);
    ae_assert(n>=3, "PSpline2BuildPeriodic: N<3!", _state);
    
    /*
     * Prepare
     */
    p->n = n;
    p->periodic = ae_true;
    ae_vector_set_length(&tmp, n+1, _state);
    ae_matrix_set_length(&xyp, n+1, 2, _state);
    ae_v_move(&xyp.ptr.pp_double[0][0], xyp.stride, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1));
    ae_v_move(&xyp.ptr.pp_double[0][1], xyp.stride, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1));
    ae_v_move(&xyp.ptr.pp_double[n][0], 1, &xy->ptr.pp_double[0][0], 1, ae_v_len(0,1));
    
    /*
     * Build parameterization, check that all parameters are distinct
     */
    parametric_pspline2par(&xyp, n+1, pt, &p->p, _state);
    ae_assert(aredistinct(&p->p, n+1, _state), "PSpline2BuildPeriodic: consequent (or first and last) points are too close!", _state);
    
    /*
     * Build splines
     */
    if( st==1 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][0], xyp.stride, ae_v_len(0,n));
        spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][1], xyp.stride, ae_v_len(0,n));
        spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->y, _state);
    }
    if( st==2 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][0], xyp.stride, ae_v_len(0,n));
        spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][1], xyp.stride, ae_v_len(0,n));
        spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->y, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This  function  builds  periodic  3-dimensional  parametric  spline  which
starts at (X[0],Y[0],Z[0]), goes through all points to (X[N-1],Y[N-1],Z[N-1])
and then back to (X[0],Y[0],Z[0]).

Same as PSpline2Build() function, but for 3D, so we  won't  duplicate  its
description here.

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3buildperiodic(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t st,
     ae_int_t pt,
     pspline3interpolant* p,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_matrix _xy;
    ae_matrix xyp;
    ae_vector tmp;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_init_copy(&_xy, xy, _state);
    xy = &_xy;
    _pspline3interpolant_clear(p);
    ae_matrix_init(&xyp, 0, 0, DT_REAL, _state);
    ae_vector_init(&tmp, 0, DT_REAL, _state);

    ae_assert(st>=1&&st<=2, "PSpline3BuildPeriodic: incorrect spline type!", _state);
    ae_assert(pt>=0&&pt<=2, "PSpline3BuildPeriodic: incorrect parameterization type!", _state);
    ae_assert(n>=3, "PSpline3BuildPeriodic: N<3!", _state);
    
    /*
     * Prepare
     */
    p->n = n;
    p->periodic = ae_true;
    ae_vector_set_length(&tmp, n+1, _state);
    ae_matrix_set_length(&xyp, n+1, 3, _state);
    ae_v_move(&xyp.ptr.pp_double[0][0], xyp.stride, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1));
    ae_v_move(&xyp.ptr.pp_double[0][1], xyp.stride, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1));
    ae_v_move(&xyp.ptr.pp_double[0][2], xyp.stride, &xy->ptr.pp_double[0][2], xy->stride, ae_v_len(0,n-1));
    ae_v_move(&xyp.ptr.pp_double[n][0], 1, &xy->ptr.pp_double[0][0], 1, ae_v_len(0,2));
    
    /*
     * Build parameterization, check that all parameters are distinct
     */
    parametric_pspline3par(&xyp, n+1, pt, &p->p, _state);
    ae_assert(aredistinct(&p->p, n+1, _state), "PSplineBuild2Periodic: consequent (or first and last) points are too close!", _state);
    
    /*
     * Build splines
     */
    if( st==1 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][0], xyp.stride, ae_v_len(0,n));
        spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][1], xyp.stride, ae_v_len(0,n));
        spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->y, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][2], xyp.stride, ae_v_len(0,n));
        spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->z, _state);
    }
    if( st==2 )
    {
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][0], xyp.stride, ae_v_len(0,n));
        spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->x, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][1], xyp.stride, ae_v_len(0,n));
        spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->y, _state);
        ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][2], xyp.stride, ae_v_len(0,n));
        spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->z, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This function returns vector of parameter values correspoding to points.

I.e. for P created from (X[0],Y[0])...(X[N-1],Y[N-1]) and U=TValues(P)  we
have
    (X[0],Y[0]) = PSpline2Calc(P,U[0]),
    (X[1],Y[1]) = PSpline2Calc(P,U[1]),
    (X[2],Y[2]) = PSpline2Calc(P,U[2]),
    ...

INPUT PARAMETERS:
    P   -   parametric spline interpolant

OUTPUT PARAMETERS:
    N   -   array size
    T   -   array[0..N-1]


NOTES:
* for non-periodic splines U[0]=0, U[0]<U[1]<...<U[N-1], U[N-1]=1
* for periodic splines     U[0]=0, U[0]<U[1]<...<U[N-1], U[N-1]<1

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2parametervalues(pspline2interpolant* p,
     ae_int_t* n,
     /* Real    */ ae_vector* t,
     ae_state *_state)
{

    *n = 0;
    ae_vector_clear(t);

    ae_assert(p->n>=2, "PSpline2ParameterValues: internal error!", _state);
    *n = p->n;
    ae_vector_set_length(t, *n, _state);
    ae_v_move(&t->ptr.p_double[0], 1, &p->p.ptr.p_double[0], 1, ae_v_len(0,*n-1));
    t->ptr.p_double[0] = (double)(0);
    if( !p->periodic )
    {
        t->ptr.p_double[*n-1] = (double)(1);
    }
}


/*************************************************************************
This function returns vector of parameter values correspoding to points.

Same as PSpline2ParameterValues(), but for 3D.

  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3parametervalues(pspline3interpolant* p,
     ae_int_t* n,
     /* Real    */ ae_vector* t,
     ae_state *_state)
{

    *n = 0;
    ae_vector_clear(t);

    ae_assert(p->n>=2, "PSpline3ParameterValues: internal error!", _state);
    *n = p->n;
    ae_vector_set_length(t, *n, _state);
    ae_v_move(&t->ptr.p_double[0], 1, &p->p.ptr.p_double[0], 1, ae_v_len(0,*n-1));
    t->ptr.p_double[0] = (double)(0);
    if( !p->periodic )
    {
        t->ptr.p_double[*n-1] = (double)(1);
    }
}


/*************************************************************************
This function  calculates  the value of the parametric spline for a  given
value of parameter T

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-position
    Y   -   Y-position


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2calc(pspline2interpolant* p,
     double t,
     double* x,
     double* y,
     ae_state *_state)
{

    *x = 0;
    *y = 0;

    if( p->periodic )
    {
        t = t-ae_ifloor(t, _state);
    }
    *x = spline1dcalc(&p->x, t, _state);
    *y = spline1dcalc(&p->y, t, _state);
}


/*************************************************************************
This function  calculates  the value of the parametric spline for a  given
value of parameter T.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-position
    Y   -   Y-position
    Z   -   Z-position


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3calc(pspline3interpolant* p,
     double t,
     double* x,
     double* y,
     double* z,
     ae_state *_state)
{

    *x = 0;
    *y = 0;
    *z = 0;

    if( p->periodic )
    {
        t = t-ae_ifloor(t, _state);
    }
    *x = spline1dcalc(&p->x, t, _state);
    *y = spline1dcalc(&p->y, t, _state);
    *z = spline1dcalc(&p->z, t, _state);
}


/*************************************************************************
This function  calculates  tangent vector for a given value of parameter T

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X    -   X-component of tangent vector (normalized)
    Y    -   Y-component of tangent vector (normalized)
    
NOTE:
    X^2+Y^2 is either 1 (for non-zero tangent vector) or 0.


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2tangent(pspline2interpolant* p,
     double t,
     double* x,
     double* y,
     ae_state *_state)
{
    double v;
    double v0;
    double v1;

    *x = 0;
    *y = 0;

    if( p->periodic )
    {
        t = t-ae_ifloor(t, _state);
    }
    pspline2diff(p, t, &v0, x, &v1, y, _state);
    if( ae_fp_neq(*x,(double)(0))||ae_fp_neq(*y,(double)(0)) )
    {
        
        /*
         * this code is a bit more complex than X^2+Y^2 to avoid
         * overflow for large values of X and Y.
         */
        v = safepythag2(*x, *y, _state);
        *x = *x/v;
        *y = *y/v;
    }
}


/*************************************************************************
This function  calculates  tangent vector for a given value of parameter T

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X    -   X-component of tangent vector (normalized)
    Y    -   Y-component of tangent vector (normalized)
    Z    -   Z-component of tangent vector (normalized)

NOTE:
    X^2+Y^2+Z^2 is either 1 (for non-zero tangent vector) or 0.


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3tangent(pspline3interpolant* p,
     double t,
     double* x,
     double* y,
     double* z,
     ae_state *_state)
{
    double v;
    double v0;
    double v1;
    double v2;

    *x = 0;
    *y = 0;
    *z = 0;

    if( p->periodic )
    {
        t = t-ae_ifloor(t, _state);
    }
    pspline3diff(p, t, &v0, x, &v1, y, &v2, z, _state);
    if( (ae_fp_neq(*x,(double)(0))||ae_fp_neq(*y,(double)(0)))||ae_fp_neq(*z,(double)(0)) )
    {
        v = safepythag3(*x, *y, *z, _state);
        *x = *x/v;
        *y = *y/v;
        *z = *z/v;
    }
}


/*************************************************************************
This function calculates derivative, i.e. it returns (dX/dT,dY/dT).

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-value
    DX  -   X-derivative
    Y   -   Y-value
    DY  -   Y-derivative


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2diff(pspline2interpolant* p,
     double t,
     double* x,
     double* dx,
     double* y,
     double* dy,
     ae_state *_state)
{
    double d2s;

    *x = 0;
    *dx = 0;
    *y = 0;
    *dy = 0;

    if( p->periodic )
    {
        t = t-ae_ifloor(t, _state);
    }
    spline1ddiff(&p->x, t, x, dx, &d2s, _state);
    spline1ddiff(&p->y, t, y, dy, &d2s, _state);
}


/*************************************************************************
This function calculates derivative, i.e. it returns (dX/dT,dY/dT,dZ/dT).

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-value
    DX  -   X-derivative
    Y   -   Y-value
    DY  -   Y-derivative
    Z   -   Z-value
    DZ  -   Z-derivative


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3diff(pspline3interpolant* p,
     double t,
     double* x,
     double* dx,
     double* y,
     double* dy,
     double* z,
     double* dz,
     ae_state *_state)
{
    double d2s;

    *x = 0;
    *dx = 0;
    *y = 0;
    *dy = 0;
    *z = 0;
    *dz = 0;

    if( p->periodic )
    {
        t = t-ae_ifloor(t, _state);
    }
    spline1ddiff(&p->x, t, x, dx, &d2s, _state);
    spline1ddiff(&p->y, t, y, dy, &d2s, _state);
    spline1ddiff(&p->z, t, z, dz, &d2s, _state);
}


/*************************************************************************
This function calculates first and second derivative with respect to T.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-value
    DX  -   derivative
    D2X -   second derivative
    Y   -   Y-value
    DY  -   derivative
    D2Y -   second derivative


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline2diff2(pspline2interpolant* p,
     double t,
     double* x,
     double* dx,
     double* d2x,
     double* y,
     double* dy,
     double* d2y,
     ae_state *_state)
{

    *x = 0;
    *dx = 0;
    *d2x = 0;
    *y = 0;
    *dy = 0;
    *d2y = 0;

    if( p->periodic )
    {
        t = t-ae_ifloor(t, _state);
    }
    spline1ddiff(&p->x, t, x, dx, d2x, _state);
    spline1ddiff(&p->y, t, y, dy, d2y, _state);
}


/*************************************************************************
This function calculates first and second derivative with respect to T.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    T   -   point:
            * T in [0,1] corresponds to interval spanned by points
            * for non-periodic splines T<0 (or T>1) correspond to parts of
              the curve before the first (after the last) point
            * for periodic splines T<0 (or T>1) are projected  into  [0,1]
              by making T=T-floor(T).

OUTPUT PARAMETERS:
    X   -   X-value
    DX  -   derivative
    D2X -   second derivative
    Y   -   Y-value
    DY  -   derivative
    D2Y -   second derivative
    Z   -   Z-value
    DZ  -   derivative
    D2Z -   second derivative


  -- ALGLIB PROJECT --
     Copyright 28.05.2010 by Bochkanov Sergey
*************************************************************************/
void pspline3diff2(pspline3interpolant* p,
     double t,
     double* x,
     double* dx,
     double* d2x,
     double* y,
     double* dy,
     double* d2y,
     double* z,
     double* dz,
     double* d2z,
     ae_state *_state)
{

    *x = 0;
    *dx = 0;
    *d2x = 0;
    *y = 0;
    *dy = 0;
    *d2y = 0;
    *z = 0;
    *dz = 0;
    *d2z = 0;

    if( p->periodic )
    {
        t = t-ae_ifloor(t, _state);
    }
    spline1ddiff(&p->x, t, x, dx, d2x, _state);
    spline1ddiff(&p->y, t, y, dy, d2y, _state);
    spline1ddiff(&p->z, t, z, dz, d2z, _state);
}


/*************************************************************************
This function  calculates  arc length, i.e. length of  curve  between  t=a
and t=b.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    A,B -   parameter values corresponding to arc ends:
            * B>A will result in positive length returned
            * B<A will result in negative length returned

RESULT:
    length of arc starting at T=A and ending at T=B.


  -- ALGLIB PROJECT --
     Copyright 30.05.2010 by Bochkanov Sergey
*************************************************************************/
double pspline2arclength(pspline2interpolant* p,
     double a,
     double b,
     ae_state *_state)
{
    ae_frame _frame_block;
    autogkstate state;
    autogkreport rep;
    double sx;
    double dsx;
    double d2sx;
    double sy;
    double dsy;
    double d2sy;
    double result;

    ae_frame_make(_state, &_frame_block);
    _autogkstate_init(&state, _state);
    _autogkreport_init(&rep, _state);

    autogksmooth(a, b, &state, _state);
    while(autogkiteration(&state, _state))
    {
        spline1ddiff(&p->x, state.x, &sx, &dsx, &d2sx, _state);
        spline1ddiff(&p->y, state.x, &sy, &dsy, &d2sy, _state);
        state.f = safepythag2(dsx, dsy, _state);
    }
    autogkresults(&state, &result, &rep, _state);
    ae_assert(rep.terminationtype>0, "PSpline2ArcLength: internal error!", _state);
    ae_frame_leave(_state);
    return result;
}


/*************************************************************************
This function  calculates  arc length, i.e. length of  curve  between  t=a
and t=b.

INPUT PARAMETERS:
    P   -   parametric spline interpolant
    A,B -   parameter values corresponding to arc ends:
            * B>A will result in positive length returned
            * B<A will result in negative length returned

RESULT:
    length of arc starting at T=A and ending at T=B.


  -- ALGLIB PROJECT --
     Copyright 30.05.2010 by Bochkanov Sergey
*************************************************************************/
double pspline3arclength(pspline3interpolant* p,
     double a,
     double b,
     ae_state *_state)
{
    ae_frame _frame_block;
    autogkstate state;
    autogkreport rep;
    double sx;
    double dsx;
    double d2sx;
    double sy;
    double dsy;
    double d2sy;
    double sz;
    double dsz;
    double d2sz;
    double result;

    ae_frame_make(_state, &_frame_block);
    _autogkstate_init(&state, _state);
    _autogkreport_init(&rep, _state);

    autogksmooth(a, b, &state, _state);
    while(autogkiteration(&state, _state))
    {
        spline1ddiff(&p->x, state.x, &sx, &dsx, &d2sx, _state);
        spline1ddiff(&p->y, state.x, &sy, &dsy, &d2sy, _state);
        spline1ddiff(&p->z, state.x, &sz, &dsz, &d2sz, _state);
        state.f = safepythag3(dsx, dsy, dsz, _state);
    }
    autogkresults(&state, &result, &rep, _state);
    ae_assert(rep.terminationtype>0, "PSpline3ArcLength: internal error!", _state);
    ae_frame_leave(_state);
    return result;
}


/*************************************************************************
This  subroutine fits piecewise linear curve to points with Ramer-Douglas-
Peucker algorithm. This  function  performs PARAMETRIC fit, i.e. it can be
used to fit curves like circles.

On  input  it  accepts dataset which describes parametric multidimensional
curve X(t), with X being vector, and t taking values in [0,N), where N  is
a number of points in dataset. As result, it returns reduced  dataset  X2,
which can be used to build  parametric  curve  X2(t),  which  approximates
X(t) with desired precision (or has specified number of sections).


INPUT PARAMETERS:
    X       -   array of multidimensional points:
                * at least N elements, leading N elements are used if more
                  than N elements were specified
                * order of points is IMPORTANT because  it  is  parametric
                  fit
                * each row of array is one point which has D coordinates
    N       -   number of elements in X
    D       -   number of dimensions (elements per row of X)
    StopM   -   stopping condition - desired number of sections:
                * at most M sections are generated by this function
                * less than M sections can be generated if we have N<M
                  (or some X are non-distinct).
                * zero StopM means that algorithm does not stop after
                  achieving some pre-specified section count
    StopEps -   stopping condition - desired precision:
                * algorithm stops after error in each section is at most Eps
                * zero Eps means that algorithm does not stop after
                  achieving some pre-specified precision

OUTPUT PARAMETERS:
    X2      -   array of corner points for piecewise approximation,
                has length NSections+1 or zero (for NSections=0).
    Idx2    -   array of indexes (parameter values):
                * has length NSections+1 or zero (for NSections=0).
                * each element of Idx2 corresponds to same-numbered
                  element of X2
                * each element of Idx2 is index of  corresponding  element
                  of X2 at original array X, i.e. I-th  row  of  X2  is
                  Idx2[I]-th row of X.
                * elements of Idx2 can be treated as parameter values
                  which should be used when building new parametric curve
                * Idx2[0]=0, Idx2[NSections]=N-1
    NSections-  number of sections found by algorithm, NSections<=M,
                NSections can be zero for degenerate datasets
                (N<=1 or all X[] are non-distinct).

NOTE: algorithm stops after:
      a) dividing curve into StopM sections 
      b) achieving required precision StopEps
      c) dividing curve into N-1 sections
      If both StopM and StopEps are non-zero, algorithm is stopped by  the
      FIRST criterion which is satisfied. In case both StopM  and  StopEps
      are zero, algorithm stops because of (c).
                
  -- ALGLIB --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
void parametricrdpfixed(/* Real    */ ae_matrix* x,
     ae_int_t n,
     ae_int_t d,
     ae_int_t stopm,
     double stopeps,
     /* Real    */ ae_matrix* x2,
     /* Integer */ ae_vector* idx2,
     ae_int_t* nsections,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_bool allsame;
    ae_int_t k0;
    ae_int_t k1;
    ae_int_t k2;
    double e0;
    double e1;
    ae_int_t idx0;
    ae_int_t idx1;
    ae_int_t worstidx;
    double worsterror;
    ae_matrix sections;
    ae_vector heaperrors;
    ae_vector heaptags;
    ae_vector buf0;
    ae_vector buf1;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_clear(x2);
    ae_vector_clear(idx2);
    *nsections = 0;
    ae_matrix_init(&sections, 0, 0, DT_REAL, _state);
    ae_vector_init(&heaperrors, 0, DT_REAL, _state);
    ae_vector_init(&heaptags, 0, DT_INT, _state);
    ae_vector_init(&buf0, 0, DT_REAL, _state);
    ae_vector_init(&buf1, 0, DT_REAL, _state);

    ae_assert(n>=0, "LSTFitPiecewiseLinearParametricRDP: N<0", _state);
    ae_assert(d>=1, "LSTFitPiecewiseLinearParametricRDP: D<=0", _state);
    ae_assert(stopm>=0, "LSTFitPiecewiseLinearParametricRDP: StopM<1", _state);
    ae_assert(ae_isfinite(stopeps, _state)&&ae_fp_greater_eq(stopeps,(double)(0)), "LSTFitPiecewiseLinearParametricRDP: StopEps<0 or is infinite", _state);
    ae_assert(x->rows>=n, "LSTFitPiecewiseLinearParametricRDP: Rows(X)<N", _state);
    ae_assert(x->cols>=d, "LSTFitPiecewiseLinearParametricRDP: Cols(X)<D", _state);
    ae_assert(apservisfinitematrix(x, n, d, _state), "LSTFitPiecewiseLinearParametricRDP: X contains infinite/NAN values", _state);
    
    /*
     * Handle degenerate cases
     */
    if( n<=1 )
    {
        *nsections = 0;
        ae_frame_leave(_state);
        return;
    }
    allsame = ae_true;
    for(i=1; i<=n-1; i++)
    {
        for(j=0; j<=d-1; j++)
        {
            allsame = allsame&&ae_fp_eq(x->ptr.pp_double[i][j],x->ptr.pp_double[0][j]);
        }
    }
    if( allsame )
    {
        *nsections = 0;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Prepare first section
     */
    parametric_rdpanalyzesectionpar(x, 0, n-1, d, &worstidx, &worsterror, _state);
    ae_matrix_set_length(&sections, n, 4, _state);
    ae_vector_set_length(&heaperrors, n, _state);
    ae_vector_set_length(&heaptags, n, _state);
    *nsections = 1;
    sections.ptr.pp_double[0][0] = (double)(0);
    sections.ptr.pp_double[0][1] = (double)(n-1);
    sections.ptr.pp_double[0][2] = (double)(worstidx);
    sections.ptr.pp_double[0][3] = worsterror;
    heaperrors.ptr.p_double[0] = worsterror;
    heaptags.ptr.p_int[0] = 0;
    ae_assert(ae_fp_eq(sections.ptr.pp_double[0][1],(double)(n-1)), "RDP algorithm: integrity check failed", _state);
    
    /*
     * Main loop.
     * Repeatedly find section with worst error and divide it.
     * Terminate after M-th section, or because of other reasons (see loop internals).
     */
    for(;;)
    {
        
        /*
         * Break loop if one of the stopping conditions was met.
         * Store index of worst section to K.
         */
        if( ae_fp_eq(heaperrors.ptr.p_double[0],(double)(0)) )
        {
            break;
        }
        if( ae_fp_greater(stopeps,(double)(0))&&ae_fp_less_eq(heaperrors.ptr.p_double[0],stopeps) )
        {
            break;
        }
        if( stopm>0&&*nsections>=stopm )
        {
            break;
        }
        k = heaptags.ptr.p_int[0];
        
        /*
         * K-th section is divided in two:
         * * first  one spans interval from X[Sections[K,0]] to X[Sections[K,2]]
         * * second one spans interval from X[Sections[K,2]] to X[Sections[K,1]]
         *
         * First section is stored at K-th position, second one is appended to the table.
         * Then we update heap which stores pairs of (error,section_index)
         */
        k0 = ae_round(sections.ptr.pp_double[k][0], _state);
        k1 = ae_round(sections.ptr.pp_double[k][1], _state);
        k2 = ae_round(sections.ptr.pp_double[k][2], _state);
        parametric_rdpanalyzesectionpar(x, k0, k2, d, &idx0, &e0, _state);
        parametric_rdpanalyzesectionpar(x, k2, k1, d, &idx1, &e1, _state);
        sections.ptr.pp_double[k][0] = (double)(k0);
        sections.ptr.pp_double[k][1] = (double)(k2);
        sections.ptr.pp_double[k][2] = (double)(idx0);
        sections.ptr.pp_double[k][3] = e0;
        tagheapreplacetopi(&heaperrors, &heaptags, *nsections, e0, k, _state);
        sections.ptr.pp_double[*nsections][0] = (double)(k2);
        sections.ptr.pp_double[*nsections][1] = (double)(k1);
        sections.ptr.pp_double[*nsections][2] = (double)(idx1);
        sections.ptr.pp_double[*nsections][3] = e1;
        tagheappushi(&heaperrors, &heaptags, nsections, e1, *nsections, _state);
    }
    
    /*
     * Convert from sections to indexes
     */
    ae_vector_set_length(&buf0, *nsections+1, _state);
    for(i=0; i<=*nsections-1; i++)
    {
        buf0.ptr.p_double[i] = (double)(ae_round(sections.ptr.pp_double[i][0], _state));
    }
    buf0.ptr.p_double[*nsections] = (double)(n-1);
    tagsortfast(&buf0, &buf1, *nsections+1, _state);
    ae_vector_set_length(idx2, *nsections+1, _state);
    for(i=0; i<=*nsections; i++)
    {
        idx2->ptr.p_int[i] = ae_round(buf0.ptr.p_double[i], _state);
    }
    ae_assert(idx2->ptr.p_int[0]==0, "RDP algorithm: integrity check failed", _state);
    ae_assert(idx2->ptr.p_int[*nsections]==n-1, "RDP algorithm: integrity check failed", _state);
    
    /*
     * Output sections:
     * * first NSection elements of X2/Y2 are filled by x/y at left boundaries of sections
     * * last element of X2/Y2 is filled by right boundary of rightmost section
     * * X2/Y2 is sorted by ascending of X2
     */
    ae_matrix_set_length(x2, *nsections+1, d, _state);
    for(i=0; i<=*nsections; i++)
    {
        for(j=0; j<=d-1; j++)
        {
            x2->ptr.pp_double[i][j] = x->ptr.pp_double[idx2->ptr.p_int[i]][j];
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Builds non-periodic parameterization for 2-dimensional spline
*************************************************************************/
static void parametric_pspline2par(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t pt,
     /* Real    */ ae_vector* p,
     ae_state *_state)
{
    double v;
    ae_int_t i;

    ae_vector_clear(p);

    ae_assert(pt>=0&&pt<=2, "PSpline2Par: internal error!", _state);
    
    /*
     * Build parameterization:
     * * fill by non-normalized values
     * * normalize them so we have P[0]=0, P[N-1]=1.
     */
    ae_vector_set_length(p, n, _state);
    if( pt==0 )
    {
        for(i=0; i<=n-1; i++)
        {
            p->ptr.p_double[i] = (double)(i);
        }
    }
    if( pt==1 )
    {
        p->ptr.p_double[0] = (double)(0);
        for(i=1; i<=n-1; i++)
        {
            p->ptr.p_double[i] = p->ptr.p_double[i-1]+safepythag2(xy->ptr.pp_double[i][0]-xy->ptr.pp_double[i-1][0], xy->ptr.pp_double[i][1]-xy->ptr.pp_double[i-1][1], _state);
        }
    }
    if( pt==2 )
    {
        p->ptr.p_double[0] = (double)(0);
        for(i=1; i<=n-1; i++)
        {
            p->ptr.p_double[i] = p->ptr.p_double[i-1]+ae_sqrt(safepythag2(xy->ptr.pp_double[i][0]-xy->ptr.pp_double[i-1][0], xy->ptr.pp_double[i][1]-xy->ptr.pp_double[i-1][1], _state), _state);
        }
    }
    v = 1/p->ptr.p_double[n-1];
    ae_v_muld(&p->ptr.p_double[0], 1, ae_v_len(0,n-1), v);
}


/*************************************************************************
Builds non-periodic parameterization for 3-dimensional spline
*************************************************************************/
static void parametric_pspline3par(/* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_int_t pt,
     /* Real    */ ae_vector* p,
     ae_state *_state)
{
    double v;
    ae_int_t i;

    ae_vector_clear(p);

    ae_assert(pt>=0&&pt<=2, "PSpline3Par: internal error!", _state);
    
    /*
     * Build parameterization:
     * * fill by non-normalized values
     * * normalize them so we have P[0]=0, P[N-1]=1.
     */
    ae_vector_set_length(p, n, _state);
    if( pt==0 )
    {
        for(i=0; i<=n-1; i++)
        {
            p->ptr.p_double[i] = (double)(i);
        }
    }
    if( pt==1 )
    {
        p->ptr.p_double[0] = (double)(0);
        for(i=1; i<=n-1; i++)
        {
            p->ptr.p_double[i] = p->ptr.p_double[i-1]+safepythag3(xy->ptr.pp_double[i][0]-xy->ptr.pp_double[i-1][0], xy->ptr.pp_double[i][1]-xy->ptr.pp_double[i-1][1], xy->ptr.pp_double[i][2]-xy->ptr.pp_double[i-1][2], _state);
        }
    }
    if( pt==2 )
    {
        p->ptr.p_double[0] = (double)(0);
        for(i=1; i<=n-1; i++)
        {
            p->ptr.p_double[i] = p->ptr.p_double[i-1]+ae_sqrt(safepythag3(xy->ptr.pp_double[i][0]-xy->ptr.pp_double[i-1][0], xy->ptr.pp_double[i][1]-xy->ptr.pp_double[i-1][1], xy->ptr.pp_double[i][2]-xy->ptr.pp_double[i-1][2], _state), _state);
        }
    }
    v = 1/p->ptr.p_double[n-1];
    ae_v_muld(&p->ptr.p_double[0], 1, ae_v_len(0,n-1), v);
}


/*************************************************************************
This function analyzes section of curve for processing by RDP algorithm:
given set of points X,Y with indexes [I0,I1] it returns point with
worst deviation from linear model (PARAMETRIC version which sees curve
as X(t) with vector X).

Input parameters:
    XY          -   array
    I0,I1       -   interval (boundaries included) to process
    D           -   number of dimensions
    
OUTPUT PARAMETERS:
    WorstIdx    -   index of worst point
    WorstError  -   error at worst point
    
NOTE: this function guarantees that it returns exactly zero for a section
      with less than 3 points.

  -- ALGLIB PROJECT --
     Copyright 02.10.2014 by Bochkanov Sergey
*************************************************************************/
static void parametric_rdpanalyzesectionpar(/* Real    */ ae_matrix* xy,
     ae_int_t i0,
     ae_int_t i1,
     ae_int_t d,
     ae_int_t* worstidx,
     double* worsterror,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;
    double v;
    double d2;
    double ts;
    double vv;

    *worstidx = 0;
    *worsterror = 0;

    
    /*
     * Quick exit for 0, 1, 2 points
     */
    if( i1-i0+1<3 )
    {
        *worstidx = i0;
        *worsterror = 0.0;
        return;
    }
    
    /*
     * Estimate D2 - squared distance between XY[I1] and XY[I0].
     * In case D2=0 handle it as special case.
     */
    d2 = 0.0;
    for(j=0; j<=d-1; j++)
    {
        d2 = d2+ae_sqr(xy->ptr.pp_double[i1][j]-xy->ptr.pp_double[i0][j], _state);
    }
    if( ae_fp_eq(d2,(double)(0)) )
    {
        
        /*
         * First and last points are equal, interval evaluation is
         * trivial - we just calculate distance from all points to
         * the first/last one.
         */
        *worstidx = i0;
        *worsterror = 0.0;
        for(i=i0+1; i<=i1-1; i++)
        {
            vv = 0.0;
            for(j=0; j<=d-1; j++)
            {
                v = xy->ptr.pp_double[i][j]-xy->ptr.pp_double[i0][j];
                vv = vv+v*v;
            }
            vv = ae_sqrt(vv, _state);
            if( ae_fp_greater(vv,*worsterror) )
            {
                *worsterror = vv;
                *worstidx = i;
            }
        }
        return;
    }
    
    /*
     * General case
     *
     * Current section of curve is modeled as x(t) = d*t+c, where
     *     d = XY[I1]-XY[I0]
     *     c = XY[I0]
     *     t is in [0,1]
     */
    *worstidx = i0;
    *worsterror = 0.0;
    for(i=i0+1; i<=i1-1; i++)
    {
        
        /*
         * Determine t_s - parameter value for projected point.
         */
        ts = (double)(i-i0)/(double)(i1-i0);
        
        /*
         * Estimate error norm
         */
        vv = 0.0;
        for(j=0; j<=d-1; j++)
        {
            v = (xy->ptr.pp_double[i1][j]-xy->ptr.pp_double[i0][j])*ts-(xy->ptr.pp_double[i][j]-xy->ptr.pp_double[i0][j]);
            vv = vv+ae_sqr(v, _state);
        }
        vv = ae_sqrt(vv, _state);
        if( ae_fp_greater(vv,*worsterror) )
        {
            *worsterror = vv;
            *worstidx = i;
        }
    }
}


void _pspline2interpolant_init(void* _p, ae_state *_state)
{
    pspline2interpolant *p = (pspline2interpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_init(&p->p, 0, DT_REAL, _state);
    _spline1dinterpolant_init(&p->x, _state);
    _spline1dinterpolant_init(&p->y, _state);
}


void _pspline2interpolant_init_copy(void* _dst, void* _src, ae_state *_state)
{
    pspline2interpolant *dst = (pspline2interpolant*)_dst;
    pspline2interpolant *src = (pspline2interpolant*)_src;
    dst->n = src->n;
    dst->periodic = src->periodic;
    ae_vector_init_copy(&dst->p, &src->p, _state);
    _spline1dinterpolant_init_copy(&dst->x, &src->x, _state);
    _spline1dinterpolant_init_copy(&dst->y, &src->y, _state);
}


void _pspline2interpolant_clear(void* _p)
{
    pspline2interpolant *p = (pspline2interpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_clear(&p->p);
    _spline1dinterpolant_clear(&p->x);
    _spline1dinterpolant_clear(&p->y);
}


void _pspline2interpolant_destroy(void* _p)
{
    pspline2interpolant *p = (pspline2interpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_destroy(&p->p);
    _spline1dinterpolant_destroy(&p->x);
    _spline1dinterpolant_destroy(&p->y);
}


void _pspline3interpolant_init(void* _p, ae_state *_state)
{
    pspline3interpolant *p = (pspline3interpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_init(&p->p, 0, DT_REAL, _state);
    _spline1dinterpolant_init(&p->x, _state);
    _spline1dinterpolant_init(&p->y, _state);
    _spline1dinterpolant_init(&p->z, _state);
}


void _pspline3interpolant_init_copy(void* _dst, void* _src, ae_state *_state)
{
    pspline3interpolant *dst = (pspline3interpolant*)_dst;
    pspline3interpolant *src = (pspline3interpolant*)_src;
    dst->n = src->n;
    dst->periodic = src->periodic;
    ae_vector_init_copy(&dst->p, &src->p, _state);
    _spline1dinterpolant_init_copy(&dst->x, &src->x, _state);
    _spline1dinterpolant_init_copy(&dst->y, &src->y, _state);
    _spline1dinterpolant_init_copy(&dst->z, &src->z, _state);
}


void _pspline3interpolant_clear(void* _p)
{
    pspline3interpolant *p = (pspline3interpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_clear(&p->p);
    _spline1dinterpolant_clear(&p->x);
    _spline1dinterpolant_clear(&p->y);
    _spline1dinterpolant_clear(&p->z);
}


void _pspline3interpolant_destroy(void* _p)
{
    pspline3interpolant *p = (pspline3interpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_destroy(&p->p);
    _spline1dinterpolant_destroy(&p->x);
    _spline1dinterpolant_destroy(&p->y);
    _spline1dinterpolant_destroy(&p->z);
}




/*************************************************************************
This function creates RBF  model  for  a  scalar (NY=1)  or  vector (NY>1)
function in a NX-dimensional space (NX=2 or NX=3).

Newly created model is empty. It can be used for interpolation right after
creation, but it just returns zeros. You have to add points to the  model,
tune interpolation settings, and then  call  model  construction  function
RBFBuildModel() which will update model according to your specification.

USAGE:
1. User creates model with RBFCreate()
2. User adds dataset with RBFSetPoints() (points do NOT have to  be  on  a
   regular grid)
3. (OPTIONAL) User chooses polynomial term by calling:
   * RBFLinTerm() to set linear term
   * RBFConstTerm() to set constant term
   * RBFZeroTerm() to set zero term
   By default, linear term is used.
4. User chooses specific RBF algorithm to use: either QNN (RBFSetAlgoQNN)
   or ML (RBFSetAlgoMultiLayer).
5. User calls RBFBuildModel() function which rebuilds model  according  to
   the specification
6. User may call RBFCalc() to calculate model value at the specified point,
   RBFGridCalc() to  calculate   model  values at the points of the regular
   grid. User may extract model coefficients with RBFUnpack() call.
   
INPUT PARAMETERS:
    NX      -   dimension of the space, NX=2 or NX=3
    NY      -   function dimension, NY>=1

OUTPUT PARAMETERS:
    S       -   RBF model (initially equals to zero)

NOTE 1: memory requirements. RBF models require amount of memory  which is
        proportional  to  the  number  of data points. Memory is allocated 
        during model construction, but most of this memory is freed  after
        model coefficients are calculated.
        
        Some approximate estimates for N centers with default settings are
        given below:
        * about 250*N*(sizeof(double)+2*sizeof(int)) bytes  of  memory  is
          needed during model construction stage.
        * about 15*N*sizeof(double) bytes is needed after model is built.
        For example, for N=100000 we may need 0.6 GB of memory  to  build
        model, but just about 0.012 GB to store it.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfcreate(ae_int_t nx, ae_int_t ny, rbfmodel* s, ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;

    _rbfmodel_clear(s);

    ae_assert(nx==2||nx==3, "RBFCreate: NX<>2 and NX<>3", _state);
    ae_assert(ny>=1, "RBFCreate: NY<1", _state);
    s->nx = nx;
    s->ny = ny;
    s->nl = 0;
    s->nc = 0;
    ae_matrix_set_length(&s->v, ny, rbf_mxnx+1, _state);
    for(i=0; i<=ny-1; i++)
    {
        for(j=0; j<=rbf_mxnx; j++)
        {
            s->v.ptr.pp_double[i][j] = (double)(0);
        }
    }
    s->n = 0;
    s->rmax = (double)(0);
    s->gridtype = 2;
    s->fixrad = ae_false;
    s->radvalue = (double)(1);
    s->radzvalue = (double)(5);
    s->aterm = 1;
    s->algorithmtype = 1;
    
    /*
     * stopping criteria
     */
    s->epsort = rbf_eps;
    s->epserr = rbf_eps;
    s->maxits = 0;
}


/*************************************************************************
This function adds dataset.

This function overrides results of the previous calls, i.e. multiple calls
of this function will result in only the last set being added.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call.
    XY      -   points, array[N,NX+NY]. One row corresponds to  one  point
                in the dataset. First NX elements  are  coordinates,  next
                NY elements are function values. Array may  be larger than 
                specific,  in  this  case  only leading [N,NX+NY] elements 
                will be used.
    N       -   number of points in the dataset

After you've added dataset and (optionally) tuned algorithm  settings  you
should call RBFBuildModel() in order to build a model for you.

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.
      

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetpoints(rbfmodel* s,
     /* Real    */ ae_matrix* xy,
     ae_int_t n,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;


    ae_assert(n>0, "RBFSetPoints: N<0", _state);
    ae_assert(xy->rows>=n, "RBFSetPoints: Rows(XY)<N", _state);
    ae_assert(xy->cols>=s->nx+s->ny, "RBFSetPoints: Cols(XY)<NX+NY", _state);
    s->n = n;
    ae_matrix_set_length(&s->x, s->n, rbf_mxnx, _state);
    ae_matrix_set_length(&s->y, s->n, s->ny, _state);
    for(i=0; i<=s->n-1; i++)
    {
        for(j=0; j<=rbf_mxnx-1; j++)
        {
            s->x.ptr.pp_double[i][j] = (double)(0);
        }
        for(j=0; j<=s->nx-1; j++)
        {
            s->x.ptr.pp_double[i][j] = xy->ptr.pp_double[i][j];
        }
        for(j=0; j<=s->ny-1; j++)
        {
            s->y.ptr.pp_double[i][j] = xy->ptr.pp_double[i][j+s->nx];
        }
    }
}


/*************************************************************************
This  function  sets  RBF interpolation algorithm. ALGLIB supports several
RBF algorithms with different properties.

This algorithm is called RBF-QNN and  it  is  good  for  point  sets  with
following properties:
a) all points are distinct
b) all points are well separated.
c) points  distribution  is  approximately  uniform.  There is no "contour
   lines", clusters of points, or other small-scale structures.

Algorithm description:
1) interpolation centers are allocated to data points
2) interpolation radii are calculated as distances to the  nearest centers
   times Q coefficient (where Q is a value from [0.75,1.50]).
3) after  performing (2) radii are transformed in order to avoid situation
   when single outlier has very large radius and  influences  many  points
   across all dataset. Transformation has following form:
       new_r[i] = min(r[i],Z*median(r[]))
   where r[i] is I-th radius, median()  is a median  radius across  entire
   dataset, Z is user-specified value which controls amount  of  deviation
   from median radius.

When (a) is violated,  we  will  be unable to build RBF model. When (b) or
(c) are violated, model will be built, but interpolation quality  will  be
low. See http://www.alglib.net/interpolation/ for more information on this
subject.

This algorithm is used by default.

Additional Q parameter controls smoothness properties of the RBF basis:
* Q<0.75 will give perfectly conditioned basis,  but  terrible  smoothness
  properties (RBF interpolant will have sharp peaks around function values)
* Q around 1.0 gives good balance between smoothness and condition number
* Q>1.5 will lead to badly conditioned systems and slow convergence of the
  underlying linear solver (although smoothness will be very good)
* Q>2.0 will effectively make optimizer useless because it won't  converge
  within reasonable amount of iterations. It is possible to set such large
  Q, but it is advised not to do so.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    Q       -   Q parameter, Q>0, recommended value - 1.0
    Z       -   Z parameter, Z>0, recommended value - 5.0

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.


  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetalgoqnn(rbfmodel* s, double q, double z, ae_state *_state)
{


    ae_assert(ae_isfinite(q, _state), "RBFSetAlgoQNN: Q is infinite or NAN", _state);
    ae_assert(ae_fp_greater(q,(double)(0)), "RBFSetAlgoQNN: Q<=0", _state);
    rbf_rbfgridpoints(s, _state);
    rbf_rbfradnn(s, q, z, _state);
    s->algorithmtype = 1;
}


/*************************************************************************
This  function  sets  RBF interpolation algorithm. ALGLIB supports several
RBF algorithms with different properties.

This  algorithm is called RBF-ML. It builds  multilayer  RBF  model,  i.e.
model with subsequently decreasing  radii,  which  allows  us  to  combine
smoothness (due to  large radii of  the first layers) with  exactness (due
to small radii of the last layers) and fast convergence.

Internally RBF-ML uses many different  means  of acceleration, from sparse
matrices  to  KD-trees,  which  results in algorithm whose working time is
roughly proportional to N*log(N)*Density*RBase^2*NLayers,  where  N  is  a
number of points, Density is an average density if points per unit of  the
interpolation space, RBase is an initial radius, NLayers is  a  number  of
layers.

RBF-ML is good for following kinds of interpolation problems:
1. "exact" problems (perfect fit) with well separated points
2. least squares problems with arbitrary distribution of points (algorithm
   gives  perfect  fit  where it is possible, and resorts to least squares
   fit in the hard areas).
3. noisy problems where  we  want  to  apply  some  controlled  amount  of
   smoothing.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    RBase   -   RBase parameter, RBase>0
    NLayers -   NLayers parameter, NLayers>0, recommended value  to  start
                with - about 5.
    LambdaV -   regularization value, can be useful when  solving  problem
                in the least squares sense.  Optimal  lambda  is  problem-
                dependent and require trial and error. In our  experience,
                good lambda can be as large as 0.1, and you can use  0.001
                as initial guess.
                Default  value  - 0.01, which is used when LambdaV is  not
                given.  You  can  specify  zero  value,  but  it  is   not
                recommended to do so.

TUNING ALGORITHM

In order to use this algorithm you have to choose three parameters:
* initial radius RBase
* number of layers in the model NLayers
* regularization coefficient LambdaV

Initial radius is easy to choose - you can pick any number  several  times
larger  than  the  average  distance between points. Algorithm won't break
down if you choose radius which is too large (model construction time will
increase, but model will be built correctly).

Choose such number of layers that RLast=RBase/2^(NLayers-1)  (radius  used
by  the  last  layer)  will  be  smaller than the typical distance between
points.  In  case  model  error  is  too large, you can increase number of
layers.  Having  more  layers  will make model construction and evaluation
proportionally slower, but it will allow you to have model which precisely
fits your data. From the other side, if you want to  suppress  noise,  you
can DECREASE number of layers to make your model less flexible.

Regularization coefficient LambdaV controls smoothness of  the  individual
models built for each layer. We recommend you to use default value in case
you don't want to tune this parameter,  because  having  non-zero  LambdaV
accelerates and stabilizes internal iterative algorithm. In case you  want
to suppress noise you can use  LambdaV  as  additional  parameter  (larger
value = more smoothness) to tune.

TYPICAL ERRORS

1. Using  initial  radius  which is too large. Memory requirements  of the
   RBF-ML are roughly proportional to N*Density*RBase^2 (where Density  is
   an average density of points per unit of the interpolation  space).  In
   the extreme case of the very large RBase we will need O(N^2)  units  of
   memory - and many layers in order to decrease radius to some reasonably
   small value.

2. Using too small number of layers - RBF models with large radius are not
   flexible enough to reproduce small variations in the  target  function.
   You  need  many  layers  with  different radii, from large to small, in
   order to have good model.

3. Using  initial  radius  which  is  too  small.  You will get model with
   "holes" in the areas which are too far away from interpolation centers.
   However, algorithm will work correctly (and quickly) in this case.

4. Using too many layers - you will get too large and too slow model. This
   model  will  perfectly  reproduce  your function, but maybe you will be
   able to achieve similar results with less layers (and less memory).
   
  -- ALGLIB --
     Copyright 02.03.2012 by Bochkanov Sergey
*************************************************************************/
void rbfsetalgomultilayer(rbfmodel* s,
     double rbase,
     ae_int_t nlayers,
     double lambdav,
     ae_state *_state)
{


    ae_assert(ae_isfinite(rbase, _state), "RBFSetAlgoMultiLayer: RBase is infinite or NaN", _state);
    ae_assert(ae_fp_greater(rbase,(double)(0)), "RBFSetAlgoMultiLayer: RBase<=0", _state);
    ae_assert(nlayers>=0, "RBFSetAlgoMultiLayer: NLayers<0", _state);
    ae_assert(ae_isfinite(lambdav, _state), "RBFSetAlgoMultiLayer: LambdaV is infinite or NAN", _state);
    ae_assert(ae_fp_greater_eq(lambdav,(double)(0)), "RBFSetAlgoMultiLayer: LambdaV<0", _state);
    s->radvalue = rbase;
    s->nlayers = nlayers;
    s->algorithmtype = 2;
    s->lambdav = lambdav;
}


/*************************************************************************
This function sets linear term (model is a sum of radial  basis  functions
plus linear polynomial). This function won't have effect until  next  call 
to RBFBuildModel().

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetlinterm(rbfmodel* s, ae_state *_state)
{


    s->aterm = 1;
}


/*************************************************************************
This function sets constant term (model is a sum of radial basis functions
plus constant).  This  function  won't  have  effect  until  next  call to 
RBFBuildModel().

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetconstterm(rbfmodel* s, ae_state *_state)
{


    s->aterm = 2;
}


/*************************************************************************
This  function  sets  zero  term (model is a sum of radial basis functions 
without polynomial term). This function won't have effect until next  call
to RBFBuildModel().

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetzeroterm(rbfmodel* s, ae_state *_state)
{


    s->aterm = 3;
}


/*************************************************************************
This function sets stopping criteria of the underlying linear solver.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    EpsOrt  -   orthogonality stopping criterion, EpsOrt>=0. Algorithm will
                stop when ||A'*r||<=EpsOrt where A' is a transpose of  the 
                system matrix, r is a residual vector.
                Recommended value of EpsOrt is equal to 1E-6.
                This criterion will stop algorithm when we have "bad fit"
                situation, i.e. when we should stop in a point with large,
                nonzero residual.
    EpsErr  -   residual stopping  criterion.  Algorithm  will  stop  when
                ||r||<=EpsErr*||b||, where r is a residual vector, b is  a
                right part of the system (function values).
                Recommended value of EpsErr is equal to 1E-3 or 1E-6.
                This  criterion  will  stop  algorithm  in  a  "good  fit" 
                situation when we have near-zero residual near the desired
                solution.
    MaxIts  -   this criterion will stop algorithm after MaxIts iterations.
                It should be used for debugging purposes only!
                Zero MaxIts means that no limit is placed on the number of
                iterations.

We  recommend  to  set  moderate  non-zero  values   EpsOrt   and   EpsErr 
simultaneously. Values equal to 10E-6 are good to start with. In case  you
need high performance and do not need high precision ,  you  may  decrease
EpsErr down to 0.001. However, we do not recommend decreasing EpsOrt.

As for MaxIts, we recommend to leave it zero unless you know what you do.

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetcond(rbfmodel* s,
     double epsort,
     double epserr,
     ae_int_t maxits,
     ae_state *_state)
{


    ae_assert(ae_isfinite(epsort, _state)&&ae_fp_greater_eq(epsort,(double)(0)), "RBFSetCond: EpsOrt is negative, INF or NAN", _state);
    ae_assert(ae_isfinite(epserr, _state)&&ae_fp_greater_eq(epserr,(double)(0)), "RBFSetCond: EpsB is negative, INF or NAN", _state);
    ae_assert(maxits>=0, "RBFSetCond: MaxIts is negative", _state);
    if( (ae_fp_eq(epsort,(double)(0))&&ae_fp_eq(epserr,(double)(0)))&&maxits==0 )
    {
        s->epsort = rbf_eps;
        s->epserr = rbf_eps;
        s->maxits = 0;
    }
    else
    {
        s->epsort = epsort;
        s->epserr = epserr;
        s->maxits = maxits;
    }
}


/*************************************************************************
This   function  builds  RBF  model  and  returns  report  (contains  some 
information which can be used for evaluation of the algorithm properties).

Call to this function modifies RBF model by calculating its centers/radii/
weights  and  saving  them  into  RBFModel  structure.  Initially RBFModel 
contain zero coefficients, but after call to this function  we  will  have
coefficients which were calculated in order to fit our dataset.

After you called this function you can call RBFCalc(),  RBFGridCalc()  and
other model calculation functions.

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    Rep     -   report:
                * Rep.TerminationType:
                  * -5 - non-distinct basis function centers were detected,
                         interpolation aborted
                  * -4 - nonconvergence of the internal SVD solver
                  *  1 - successful termination
                Fields are used for debugging purposes:
                * Rep.IterationsCount - iterations count of the LSQR solver
                * Rep.NMV - number of matrix-vector products
                * Rep.ARows - rows count for the system matrix
                * Rep.ACols - columns count for the system matrix
                * Rep.ANNZ - number of significantly non-zero elements
                  (elements above some algorithm-determined threshold)

NOTE:  failure  to  build  model will leave current state of the structure
unchanged.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfbuildmodel(rbfmodel* s, rbfreport* rep, ae_state *_state)
{
    ae_frame _frame_block;
    kdtree tree;
    kdtree ctree;
    ae_vector dist;
    ae_vector xcx;
    ae_matrix a;
    ae_matrix v;
    ae_matrix omega;
    ae_vector y;
    ae_matrix residualy;
    ae_vector radius;
    ae_matrix xc;
    ae_vector mnx;
    ae_vector mxx;
    ae_vector edge;
    ae_vector mxsteps;
    ae_int_t nc;
    double rmax;
    ae_vector tags;
    ae_vector ctags;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t k2;
    ae_int_t snnz;
    ae_vector tmp0;
    ae_vector tmp1;
    ae_int_t layerscnt;

    ae_frame_make(_state, &_frame_block);
    _rbfreport_clear(rep);
    _kdtree_init(&tree, _state);
    _kdtree_init(&ctree, _state);
    ae_vector_init(&dist, 0, DT_REAL, _state);
    ae_vector_init(&xcx, 0, DT_REAL, _state);
    ae_matrix_init(&a, 0, 0, DT_REAL, _state);
    ae_matrix_init(&v, 0, 0, DT_REAL, _state);
    ae_matrix_init(&omega, 0, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_matrix_init(&residualy, 0, 0, DT_REAL, _state);
    ae_vector_init(&radius, 0, DT_REAL, _state);
    ae_matrix_init(&xc, 0, 0, DT_REAL, _state);
    ae_vector_init(&mnx, 0, DT_REAL, _state);
    ae_vector_init(&mxx, 0, DT_REAL, _state);
    ae_vector_init(&edge, 0, DT_REAL, _state);
    ae_vector_init(&mxsteps, 0, DT_INT, _state);
    ae_vector_init(&tags, 0, DT_INT, _state);
    ae_vector_init(&ctags, 0, DT_INT, _state);
    ae_vector_init(&tmp0, 0, DT_REAL, _state);
    ae_vector_init(&tmp1, 0, DT_REAL, _state);

    ae_assert(s->nx==2||s->nx==3, "RBFBuildModel: S.NX<>2 or S.NX<>3!", _state);
    
    /*
     * Quick exit when we have no points
     */
    if( s->n==0 )
    {
        rep->terminationtype = 1;
        rep->iterationscount = 0;
        rep->nmv = 0;
        rep->arows = 0;
        rep->acols = 0;
        kdtreebuildtagged(&s->xc, &tags, 0, rbf_mxnx, 0, 2, &s->tree, _state);
        ae_matrix_set_length(&s->xc, 0, 0, _state);
        ae_matrix_set_length(&s->wr, 0, 0, _state);
        s->nc = 0;
        s->rmax = (double)(0);
        ae_matrix_set_length(&s->v, s->ny, rbf_mxnx+1, _state);
        for(i=0; i<=s->ny-1; i++)
        {
            for(j=0; j<=rbf_mxnx; j++)
            {
                s->v.ptr.pp_double[i][j] = (double)(0);
            }
        }
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * General case, N>0
     */
    rep->annz = 0;
    rep->iterationscount = 0;
    rep->nmv = 0;
    ae_vector_set_length(&xcx, rbf_mxnx, _state);
    
    /*
     * First model in a sequence - linear model.
     * Residuals from linear regression are stored in the ResidualY variable
     * (used later to build RBF models).
     */
    ae_matrix_set_length(&residualy, s->n, s->ny, _state);
    for(i=0; i<=s->n-1; i++)
    {
        for(j=0; j<=s->ny-1; j++)
        {
            residualy.ptr.pp_double[i][j] = s->y.ptr.pp_double[i][j];
        }
    }
    if( !rbf_buildlinearmodel(&s->x, &residualy, s->n, s->ny, s->aterm, &v, _state) )
    {
        rep->terminationtype = -5;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Handle special case: multilayer model with NLayers=0.
     * Quick exit.
     */
    if( s->algorithmtype==2&&s->nlayers==0 )
    {
        rep->terminationtype = 1;
        rep->iterationscount = 0;
        rep->nmv = 0;
        rep->arows = 0;
        rep->acols = 0;
        kdtreebuildtagged(&s->xc, &tags, 0, rbf_mxnx, 0, 2, &s->tree, _state);
        ae_matrix_set_length(&s->xc, 0, 0, _state);
        ae_matrix_set_length(&s->wr, 0, 0, _state);
        s->nc = 0;
        s->rmax = (double)(0);
        ae_matrix_set_length(&s->v, s->ny, rbf_mxnx+1, _state);
        for(i=0; i<=s->ny-1; i++)
        {
            for(j=0; j<=rbf_mxnx; j++)
            {
                s->v.ptr.pp_double[i][j] = v.ptr.pp_double[i][j];
            }
        }
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Second model in a sequence - RBF term.
     *
     * NOTE: assignments below are not necessary, but without them
     *       MSVC complains about unitialized variables.
     */
    nc = 0;
    rmax = (double)(0);
    layerscnt = 0;
    if( s->algorithmtype==1 )
    {
        
        /*
         * Add RBF model.
         * This model uses local KD-trees to speed-up nearest neighbor searches.
         */
        if( s->gridtype==1 )
        {
            ae_vector_set_length(&mxx, s->nx, _state);
            ae_vector_set_length(&mnx, s->nx, _state);
            ae_vector_set_length(&mxsteps, s->nx, _state);
            ae_vector_set_length(&edge, s->nx, _state);
            for(i=0; i<=s->nx-1; i++)
            {
                mxx.ptr.p_double[i] = s->x.ptr.pp_double[0][i];
                mnx.ptr.p_double[i] = s->x.ptr.pp_double[0][i];
            }
            for(i=0; i<=s->n-1; i++)
            {
                for(j=0; j<=s->nx-1; j++)
                {
                    if( ae_fp_less(mxx.ptr.p_double[j],s->x.ptr.pp_double[i][j]) )
                    {
                        mxx.ptr.p_double[j] = s->x.ptr.pp_double[i][j];
                    }
                    if( ae_fp_greater(mnx.ptr.p_double[j],s->x.ptr.pp_double[i][j]) )
                    {
                        mnx.ptr.p_double[j] = s->x.ptr.pp_double[i][j];
                    }
                }
            }
            for(i=0; i<=s->nx-1; i++)
            {
                mxsteps.ptr.p_int[i] = ae_trunc((mxx.ptr.p_double[i]-mnx.ptr.p_double[i])/(2*s->h), _state)+1;
                edge.ptr.p_double[i] = (mxx.ptr.p_double[i]+mnx.ptr.p_double[i])/2-s->h*mxsteps.ptr.p_int[i];
            }
            nc = 1;
            for(i=0; i<=s->nx-1; i++)
            {
                mxsteps.ptr.p_int[i] = 2*mxsteps.ptr.p_int[i]+1;
                nc = nc*mxsteps.ptr.p_int[i];
            }
            ae_matrix_set_length(&xc, nc, rbf_mxnx, _state);
            if( s->nx==2 )
            {
                for(i=0; i<=mxsteps.ptr.p_int[0]-1; i++)
                {
                    for(j=0; j<=mxsteps.ptr.p_int[1]-1; j++)
                    {
                        for(k2=0; k2<=rbf_mxnx-1; k2++)
                        {
                            xc.ptr.pp_double[i*mxsteps.ptr.p_int[1]+j][k2] = (double)(0);
                        }
                        xc.ptr.pp_double[i*mxsteps.ptr.p_int[1]+j][0] = edge.ptr.p_double[0]+s->h*i;
                        xc.ptr.pp_double[i*mxsteps.ptr.p_int[1]+j][1] = edge.ptr.p_double[1]+s->h*j;
                    }
                }
            }
            if( s->nx==3 )
            {
                for(i=0; i<=mxsteps.ptr.p_int[0]-1; i++)
                {
                    for(j=0; j<=mxsteps.ptr.p_int[1]-1; j++)
                    {
                        for(k=0; k<=mxsteps.ptr.p_int[2]-1; k++)
                        {
                            for(k2=0; k2<=rbf_mxnx-1; k2++)
                            {
                                xc.ptr.pp_double[i*mxsteps.ptr.p_int[1]+j][k2] = (double)(0);
                            }
                            xc.ptr.pp_double[(i*mxsteps.ptr.p_int[1]+j)*mxsteps.ptr.p_int[2]+k][0] = edge.ptr.p_double[0]+s->h*i;
                            xc.ptr.pp_double[(i*mxsteps.ptr.p_int[1]+j)*mxsteps.ptr.p_int[2]+k][1] = edge.ptr.p_double[1]+s->h*j;
                            xc.ptr.pp_double[(i*mxsteps.ptr.p_int[1]+j)*mxsteps.ptr.p_int[2]+k][2] = edge.ptr.p_double[2]+s->h*k;
                        }
                    }
                }
            }
        }
        else
        {
            if( s->gridtype==2 )
            {
                nc = s->n;
                ae_matrix_set_length(&xc, nc, rbf_mxnx, _state);
                for(i=0; i<=nc-1; i++)
                {
                    for(j=0; j<=rbf_mxnx-1; j++)
                    {
                        xc.ptr.pp_double[i][j] = s->x.ptr.pp_double[i][j];
                    }
                }
            }
            else
            {
                if( s->gridtype==3 )
                {
                    nc = s->nc;
                    ae_matrix_set_length(&xc, nc, rbf_mxnx, _state);
                    for(i=0; i<=nc-1; i++)
                    {
                        for(j=0; j<=rbf_mxnx-1; j++)
                        {
                            xc.ptr.pp_double[i][j] = s->xc.ptr.pp_double[i][j];
                        }
                    }
                }
                else
                {
                    ae_assert(ae_false, "RBFBuildModel: either S.GridType<1 or S.GridType>3!", _state);
                }
            }
        }
        rmax = (double)(0);
        ae_vector_set_length(&radius, nc, _state);
        ae_vector_set_length(&ctags, nc, _state);
        for(i=0; i<=nc-1; i++)
        {
            ctags.ptr.p_int[i] = i;
        }
        kdtreebuildtagged(&xc, &ctags, nc, rbf_mxnx, 0, 2, &ctree, _state);
        if( s->fixrad )
        {
            
            /*
             * Fixed radius
             */
            for(i=0; i<=nc-1; i++)
            {
                radius.ptr.p_double[i] = s->radvalue;
            }
            rmax = radius.ptr.p_double[0];
        }
        else
        {
            
            /*
             * Dynamic radius
             */
            if( nc==0 )
            {
                rmax = (double)(1);
            }
            else
            {
                if( nc==1 )
                {
                    radius.ptr.p_double[0] = s->radvalue;
                    rmax = radius.ptr.p_double[0];
                }
                else
                {
                    
                    /*
                     * NC>1, calculate radii using distances to nearest neigbors
                     */
                    for(i=0; i<=nc-1; i++)
                    {
                        for(j=0; j<=rbf_mxnx-1; j++)
                        {
                            xcx.ptr.p_double[j] = xc.ptr.pp_double[i][j];
                        }
                        if( kdtreequeryknn(&ctree, &xcx, 1, ae_false, _state)>0 )
                        {
                            kdtreequeryresultsdistances(&ctree, &dist, _state);
                            radius.ptr.p_double[i] = s->radvalue*dist.ptr.p_double[0];
                        }
                        else
                        {
                            
                            /*
                             * No neighbors found (it will happen when we have only one center).
                             * Initialize radius with default value.
                             */
                            radius.ptr.p_double[i] = 1.0;
                        }
                    }
                    
                    /*
                     * Apply filtering
                     */
                    rvectorsetlengthatleast(&tmp0, nc, _state);
                    for(i=0; i<=nc-1; i++)
                    {
                        tmp0.ptr.p_double[i] = radius.ptr.p_double[i];
                    }
                    tagsortfast(&tmp0, &tmp1, nc, _state);
                    for(i=0; i<=nc-1; i++)
                    {
                        radius.ptr.p_double[i] = ae_minreal(radius.ptr.p_double[i], s->radzvalue*tmp0.ptr.p_double[nc/2], _state);
                    }
                    
                    /*
                     * Calculate RMax, check that all radii are non-zero
                     */
                    for(i=0; i<=nc-1; i++)
                    {
                        rmax = ae_maxreal(rmax, radius.ptr.p_double[i], _state);
                    }
                    for(i=0; i<=nc-1; i++)
                    {
                        if( ae_fp_eq(radius.ptr.p_double[i],(double)(0)) )
                        {
                            rep->terminationtype = -5;
                            ae_frame_leave(_state);
                            return;
                        }
                    }
                }
            }
        }
        ivectorsetlengthatleast(&tags, s->n, _state);
        for(i=0; i<=s->n-1; i++)
        {
            tags.ptr.p_int[i] = i;
        }
        kdtreebuildtagged(&s->x, &tags, s->n, rbf_mxnx, 0, 2, &tree, _state);
        rbf_buildrbfmodellsqr(&s->x, &residualy, &xc, &radius, s->n, nc, s->ny, &tree, &ctree, s->epsort, s->epserr, s->maxits, &rep->annz, &snnz, &omega, &rep->terminationtype, &rep->iterationscount, &rep->nmv, _state);
        layerscnt = 1;
    }
    else
    {
        if( s->algorithmtype==2 )
        {
            rmax = s->radvalue;
            rbf_buildrbfmlayersmodellsqr(&s->x, &residualy, &xc, s->radvalue, &radius, s->n, &nc, s->ny, s->nlayers, &ctree, 1.0E-6, 1.0E-6, 50, s->lambdav, &rep->annz, &omega, &rep->terminationtype, &rep->iterationscount, &rep->nmv, _state);
            layerscnt = s->nlayers;
        }
        else
        {
            ae_assert(ae_false, "RBFBuildModel: internal error(AlgorithmType neither 1 nor 2)", _state);
        }
    }
    if( rep->terminationtype<=0 )
    {
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Model is built
     */
    s->nc = nc/layerscnt;
    s->rmax = rmax;
    s->nl = layerscnt;
    ae_matrix_set_length(&s->xc, s->nc, rbf_mxnx, _state);
    ae_matrix_set_length(&s->wr, s->nc, 1+s->nl*s->ny, _state);
    ae_matrix_set_length(&s->v, s->ny, rbf_mxnx+1, _state);
    for(i=0; i<=s->nc-1; i++)
    {
        for(j=0; j<=rbf_mxnx-1; j++)
        {
            s->xc.ptr.pp_double[i][j] = xc.ptr.pp_double[i][j];
        }
    }
    ivectorsetlengthatleast(&tags, s->nc, _state);
    for(i=0; i<=s->nc-1; i++)
    {
        tags.ptr.p_int[i] = i;
    }
    kdtreebuildtagged(&s->xc, &tags, s->nc, rbf_mxnx, 0, 2, &s->tree, _state);
    for(i=0; i<=s->nc-1; i++)
    {
        s->wr.ptr.pp_double[i][0] = radius.ptr.p_double[i];
        for(k=0; k<=layerscnt-1; k++)
        {
            for(j=0; j<=s->ny-1; j++)
            {
                s->wr.ptr.pp_double[i][1+k*s->ny+j] = omega.ptr.pp_double[k*s->nc+i][j];
            }
        }
    }
    for(i=0; i<=s->ny-1; i++)
    {
        for(j=0; j<=rbf_mxnx; j++)
        {
            s->v.ptr.pp_double[i][j] = v.ptr.pp_double[i][j];
        }
    }
    rep->terminationtype = 1;
    rep->arows = s->n;
    rep->acols = s->nc;
    ae_frame_leave(_state);
}


/*************************************************************************
This function calculates values of the RBF model in the given point.

This function should be used when we have NY=1 (scalar function) and  NX=2
(2-dimensional space). If you have 3-dimensional space, use RBFCalc3(). If
you have general situation (NX-dimensional space, NY-dimensional function)
you should use general, less efficient implementation RBFCalc().

If  you  want  to  calculate  function  values  many times, consider using 
RBFGridCalc2(), which is far more efficient than many subsequent calls  to
RBFCalc2().

This function returns 0.0 when:
* model is not initialized
* NX<>2
 *NY<>1

INPUT PARAMETERS:
    S       -   RBF model
    X0      -   first coordinate, finite number
    X1      -   second coordinate, finite number

RESULT:
    value of the model or 0.0 (as defined above)

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
double rbfcalc2(rbfmodel* s, double x0, double x1, ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;
    ae_int_t lx;
    ae_int_t tg;
    double d2;
    double t;
    double bfcur;
    double rcur;
    double result;


    ae_assert(ae_isfinite(x0, _state), "RBFCalc2: invalid value for X0 (X0 is Inf)!", _state);
    ae_assert(ae_isfinite(x1, _state), "RBFCalc2: invalid value for X1 (X1 is Inf)!", _state);
    if( s->ny!=1||s->nx!=2 )
    {
        result = (double)(0);
        return result;
    }
    result = s->v.ptr.pp_double[0][0]*x0+s->v.ptr.pp_double[0][1]*x1+s->v.ptr.pp_double[0][rbf_mxnx];
    if( s->nc==0 )
    {
        return result;
    }
    rvectorsetlengthatleast(&s->calcbufxcx, rbf_mxnx, _state);
    for(i=0; i<=rbf_mxnx-1; i++)
    {
        s->calcbufxcx.ptr.p_double[i] = 0.0;
    }
    s->calcbufxcx.ptr.p_double[0] = x0;
    s->calcbufxcx.ptr.p_double[1] = x1;
    lx = kdtreequeryrnn(&s->tree, &s->calcbufxcx, s->rmax*rbf_rbffarradius, ae_true, _state);
    kdtreequeryresultsx(&s->tree, &s->calcbufx, _state);
    kdtreequeryresultstags(&s->tree, &s->calcbuftags, _state);
    for(i=0; i<=lx-1; i++)
    {
        tg = s->calcbuftags.ptr.p_int[i];
        d2 = ae_sqr(x0-s->calcbufx.ptr.pp_double[i][0], _state)+ae_sqr(x1-s->calcbufx.ptr.pp_double[i][1], _state);
        rcur = s->wr.ptr.pp_double[tg][0];
        bfcur = ae_exp(-d2/(rcur*rcur), _state);
        for(j=0; j<=s->nl-1; j++)
        {
            result = result+bfcur*s->wr.ptr.pp_double[tg][1+j];
            rcur = 0.5*rcur;
            t = bfcur*bfcur;
            bfcur = t*t;
        }
    }
    return result;
}


/*************************************************************************
This function calculates values of the RBF model in the given point.

This function should be used when we have NY=1 (scalar function) and  NX=3
(3-dimensional space). If you have 2-dimensional space, use RBFCalc2(). If
you have general situation (NX-dimensional space, NY-dimensional function)
you should use general, less efficient implementation RBFCalc().

This function returns 0.0 when:
* model is not initialized
* NX<>3
 *NY<>1

INPUT PARAMETERS:
    S       -   RBF model
    X0      -   first coordinate, finite number
    X1      -   second coordinate, finite number
    X2      -   third coordinate, finite number

RESULT:
    value of the model or 0.0 (as defined above)

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
double rbfcalc3(rbfmodel* s,
     double x0,
     double x1,
     double x2,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;
    ae_int_t lx;
    ae_int_t tg;
    double t;
    double rcur;
    double bf;
    double result;


    ae_assert(ae_isfinite(x0, _state), "RBFCalc3: invalid value for X0 (X0 is Inf or NaN)!", _state);
    ae_assert(ae_isfinite(x1, _state), "RBFCalc3: invalid value for X1 (X1 is Inf or NaN)!", _state);
    ae_assert(ae_isfinite(x2, _state), "RBFCalc3: invalid value for X2 (X2 is Inf or NaN)!", _state);
    if( s->ny!=1||s->nx!=3 )
    {
        result = (double)(0);
        return result;
    }
    result = s->v.ptr.pp_double[0][0]*x0+s->v.ptr.pp_double[0][1]*x1+s->v.ptr.pp_double[0][2]*x2+s->v.ptr.pp_double[0][rbf_mxnx];
    if( s->nc==0 )
    {
        return result;
    }
    
    /*
     * calculating value for F(X)
     */
    rvectorsetlengthatleast(&s->calcbufxcx, rbf_mxnx, _state);
    for(i=0; i<=rbf_mxnx-1; i++)
    {
        s->calcbufxcx.ptr.p_double[i] = 0.0;
    }
    s->calcbufxcx.ptr.p_double[0] = x0;
    s->calcbufxcx.ptr.p_double[1] = x1;
    s->calcbufxcx.ptr.p_double[2] = x2;
    lx = kdtreequeryrnn(&s->tree, &s->calcbufxcx, s->rmax*rbf_rbffarradius, ae_true, _state);
    kdtreequeryresultsx(&s->tree, &s->calcbufx, _state);
    kdtreequeryresultstags(&s->tree, &s->calcbuftags, _state);
    for(i=0; i<=lx-1; i++)
    {
        tg = s->calcbuftags.ptr.p_int[i];
        rcur = s->wr.ptr.pp_double[tg][0];
        bf = ae_exp(-(ae_sqr(x0-s->calcbufx.ptr.pp_double[i][0], _state)+ae_sqr(x1-s->calcbufx.ptr.pp_double[i][1], _state)+ae_sqr(x2-s->calcbufx.ptr.pp_double[i][2], _state))/ae_sqr(rcur, _state), _state);
        for(j=0; j<=s->nl-1; j++)
        {
            result = result+bf*s->wr.ptr.pp_double[tg][1+j];
            t = bf*bf;
            bf = t*t;
        }
    }
    return result;
}


/*************************************************************************
This function calculates values of the RBF model at the given point.

This is general function which can be used for arbitrary NX (dimension  of 
the space of arguments) and NY (dimension of the function itself). However
when  you  have  NY=1  you  may  find more convenient to use RBFCalc2() or 
RBFCalc3().

This function returns 0.0 when model is not initialized.

INPUT PARAMETERS:
    S       -   RBF model
    X       -   coordinates, array[NX].
                X may have more than NX elements, in this case only 
                leading NX will be used.

OUTPUT PARAMETERS:
    Y       -   function value, array[NY]. Y is out-parameter and 
                reallocated after call to this function. In case you  want
                to reuse previously allocated Y, you may use RBFCalcBuf(),
                which reallocates Y only when it is too small.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfcalc(rbfmodel* s,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state)
{

    ae_vector_clear(y);

    ae_assert(x->cnt>=s->nx, "RBFCalc: Length(X)<NX", _state);
    ae_assert(isfinitevector(x, s->nx, _state), "RBFCalc: X contains infinite or NaN values", _state);
    rbfcalcbuf(s, x, y, _state);
}


/*************************************************************************
This function calculates values of the RBF model at the given point.

Same as RBFCalc(), but does not reallocate Y when in is large enough to 
store function values.

INPUT PARAMETERS:
    S       -   RBF model
    X       -   coordinates, array[NX].
                X may have more than NX elements, in this case only 
                leading NX will be used.
    Y       -   possibly preallocated array

OUTPUT PARAMETERS:
    Y       -   function value, array[NY]. Y is not reallocated when it
                is larger than NY.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfcalcbuf(rbfmodel* s,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t lx;
    ae_int_t tg;
    double t;
    double rcur;
    double bf;


    ae_assert(x->cnt>=s->nx, "RBFCalcBuf: Length(X)<NX", _state);
    ae_assert(isfinitevector(x, s->nx, _state), "RBFCalcBuf: X contains infinite or NaN values", _state);
    if( y->cnt<s->ny )
    {
        ae_vector_set_length(y, s->ny, _state);
    }
    for(i=0; i<=s->ny-1; i++)
    {
        y->ptr.p_double[i] = s->v.ptr.pp_double[i][rbf_mxnx];
        for(j=0; j<=s->nx-1; j++)
        {
            y->ptr.p_double[i] = y->ptr.p_double[i]+s->v.ptr.pp_double[i][j]*x->ptr.p_double[j];
        }
    }
    if( s->nc==0 )
    {
        return;
    }
    rvectorsetlengthatleast(&s->calcbufxcx, rbf_mxnx, _state);
    for(i=0; i<=rbf_mxnx-1; i++)
    {
        s->calcbufxcx.ptr.p_double[i] = 0.0;
    }
    for(i=0; i<=s->nx-1; i++)
    {
        s->calcbufxcx.ptr.p_double[i] = x->ptr.p_double[i];
    }
    lx = kdtreequeryrnn(&s->tree, &s->calcbufxcx, s->rmax*rbf_rbffarradius, ae_true, _state);
    kdtreequeryresultsx(&s->tree, &s->calcbufx, _state);
    kdtreequeryresultstags(&s->tree, &s->calcbuftags, _state);
    for(i=0; i<=s->ny-1; i++)
    {
        for(j=0; j<=lx-1; j++)
        {
            tg = s->calcbuftags.ptr.p_int[j];
            rcur = s->wr.ptr.pp_double[tg][0];
            bf = ae_exp(-(ae_sqr(s->calcbufxcx.ptr.p_double[0]-s->calcbufx.ptr.pp_double[j][0], _state)+ae_sqr(s->calcbufxcx.ptr.p_double[1]-s->calcbufx.ptr.pp_double[j][1], _state)+ae_sqr(s->calcbufxcx.ptr.p_double[2]-s->calcbufx.ptr.pp_double[j][2], _state))/ae_sqr(rcur, _state), _state);
            for(k=0; k<=s->nl-1; k++)
            {
                y->ptr.p_double[i] = y->ptr.p_double[i]+bf*s->wr.ptr.pp_double[tg][1+k*s->ny+i];
                t = bf*bf;
                bf = t*t;
            }
        }
    }
}


/*************************************************************************
This function calculates values of the RBF model at the regular grid.

Grid have N0*N1 points, with Point[I,J] = (X0[I], X1[J])

This function returns 0.0 when:
* model is not initialized
* NX<>2
 *NY<>1

INPUT PARAMETERS:
    S       -   RBF model
    X0      -   array of grid nodes, first coordinates, array[N0]
    N0      -   grid size (number of nodes) in the first dimension
    X1      -   array of grid nodes, second coordinates, array[N1]
    N1      -   grid size (number of nodes) in the second dimension

OUTPUT PARAMETERS:
    Y       -   function values, array[N0,N1]. Y is out-variable and 
                is reallocated by this function.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfgridcalc2(rbfmodel* s,
     /* Real    */ ae_vector* x0,
     ae_int_t n0,
     /* Real    */ ae_vector* x1,
     ae_int_t n1,
     /* Real    */ ae_matrix* y,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector cpx0;
    ae_vector cpx1;
    ae_vector p01;
    ae_vector p11;
    ae_vector p2;
    double rlimit;
    double xcnorm2;
    ae_int_t hp01;
    double hcpx0;
    double xc0;
    double xc1;
    double omega;
    double radius;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t d;
    ae_int_t i00;
    ae_int_t i01;
    ae_int_t i10;
    ae_int_t i11;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_clear(y);
    ae_vector_init(&cpx0, 0, DT_REAL, _state);
    ae_vector_init(&cpx1, 0, DT_REAL, _state);
    ae_vector_init(&p01, 0, DT_INT, _state);
    ae_vector_init(&p11, 0, DT_INT, _state);
    ae_vector_init(&p2, 0, DT_INT, _state);

    ae_assert(n0>0, "RBFGridCalc2: invalid value for N0 (N0<=0)!", _state);
    ae_assert(n1>0, "RBFGridCalc2: invalid value for N1 (N1<=0)!", _state);
    ae_assert(x0->cnt>=n0, "RBFGridCalc2: Length(X0)<N0", _state);
    ae_assert(x1->cnt>=n1, "RBFGridCalc2: Length(X1)<N1", _state);
    ae_assert(isfinitevector(x0, n0, _state), "RBFGridCalc2: X0 contains infinite or NaN values!", _state);
    ae_assert(isfinitevector(x1, n1, _state), "RBFGridCalc2: X1 contains infinite or NaN values!", _state);
    ae_matrix_set_length(y, n0, n1, _state);
    for(i=0; i<=n0-1; i++)
    {
        for(j=0; j<=n1-1; j++)
        {
            y->ptr.pp_double[i][j] = (double)(0);
        }
    }
    if( (s->ny!=1||s->nx!=2)||s->nc==0 )
    {
        ae_frame_leave(_state);
        return;
    }
    
    /*
     *create and sort arrays
     */
    ae_vector_set_length(&cpx0, n0, _state);
    for(i=0; i<=n0-1; i++)
    {
        cpx0.ptr.p_double[i] = x0->ptr.p_double[i];
    }
    tagsort(&cpx0, n0, &p01, &p2, _state);
    ae_vector_set_length(&cpx1, n1, _state);
    for(i=0; i<=n1-1; i++)
    {
        cpx1.ptr.p_double[i] = x1->ptr.p_double[i];
    }
    tagsort(&cpx1, n1, &p11, &p2, _state);
    
    /*
     *calculate function's value
     */
    for(i=0; i<=s->nc-1; i++)
    {
        radius = s->wr.ptr.pp_double[i][0];
        for(d=0; d<=s->nl-1; d++)
        {
            omega = s->wr.ptr.pp_double[i][1+d];
            rlimit = radius*rbf_rbffarradius;
            
            /*
             *search lower and upper indexes
             */
            i00 = lowerbound(&cpx0, n0, s->xc.ptr.pp_double[i][0]-rlimit, _state);
            i01 = upperbound(&cpx0, n0, s->xc.ptr.pp_double[i][0]+rlimit, _state);
            i10 = lowerbound(&cpx1, n1, s->xc.ptr.pp_double[i][1]-rlimit, _state);
            i11 = upperbound(&cpx1, n1, s->xc.ptr.pp_double[i][1]+rlimit, _state);
            xc0 = s->xc.ptr.pp_double[i][0];
            xc1 = s->xc.ptr.pp_double[i][1];
            for(j=i00; j<=i01-1; j++)
            {
                hcpx0 = cpx0.ptr.p_double[j];
                hp01 = p01.ptr.p_int[j];
                for(k=i10; k<=i11-1; k++)
                {
                    xcnorm2 = ae_sqr(hcpx0-xc0, _state)+ae_sqr(cpx1.ptr.p_double[k]-xc1, _state);
                    if( ae_fp_less_eq(xcnorm2,rlimit*rlimit) )
                    {
                        y->ptr.pp_double[hp01][p11.ptr.p_int[k]] = y->ptr.pp_double[hp01][p11.ptr.p_int[k]]+ae_exp(-xcnorm2/ae_sqr(radius, _state), _state)*omega;
                    }
                }
            }
            radius = 0.5*radius;
        }
    }
    
    /*
     *add linear term
     */
    for(i=0; i<=n0-1; i++)
    {
        for(j=0; j<=n1-1; j++)
        {
            y->ptr.pp_double[i][j] = y->ptr.pp_double[i][j]+s->v.ptr.pp_double[0][0]*x0->ptr.p_double[i]+s->v.ptr.pp_double[0][1]*x1->ptr.p_double[j]+s->v.ptr.pp_double[0][rbf_mxnx];
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This function "unpacks" RBF model by extracting its coefficients.

INPUT PARAMETERS:
    S       -   RBF model

OUTPUT PARAMETERS:
    NX      -   dimensionality of argument
    NY      -   dimensionality of the target function
    XWR     -   model information, array[NC,NX+NY+1].
                One row of the array corresponds to one basis function:
                * first NX columns  - coordinates of the center 
                * next NY columns   - weights, one per dimension of the 
                                      function being modelled
                * last column       - radius, same for all dimensions of
                                      the function being modelled
    NC      -   number of the centers
    V       -   polynomial  term , array[NY,NX+1]. One row per one 
                dimension of the function being modelled. First NX 
                elements are linear coefficients, V[NX] is equal to the 
                constant part.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfunpack(rbfmodel* s,
     ae_int_t* nx,
     ae_int_t* ny,
     /* Real    */ ae_matrix* xwr,
     ae_int_t* nc,
     /* Real    */ ae_matrix* v,
     ae_state *_state)
{
    ae_int_t i;
    ae_int_t j;
    double rcur;

    *nx = 0;
    *ny = 0;
    ae_matrix_clear(xwr);
    *nc = 0;
    ae_matrix_clear(v);

    *nx = s->nx;
    *ny = s->ny;
    *nc = s->nc;
    
    /*
     * Fill V
     */
    ae_matrix_set_length(v, s->ny, s->nx+1, _state);
    for(i=0; i<=s->ny-1; i++)
    {
        ae_v_move(&v->ptr.pp_double[i][0], 1, &s->v.ptr.pp_double[i][0], 1, ae_v_len(0,s->nx-1));
        v->ptr.pp_double[i][s->nx] = s->v.ptr.pp_double[i][rbf_mxnx];
    }
    
    /*
     * Fill XWR and V
     */
    if( *nc*s->nl>0 )
    {
        ae_matrix_set_length(xwr, s->nc*s->nl, s->nx+s->ny+1, _state);
        for(i=0; i<=s->nc-1; i++)
        {
            rcur = s->wr.ptr.pp_double[i][0];
            for(j=0; j<=s->nl-1; j++)
            {
                ae_v_move(&xwr->ptr.pp_double[i*s->nl+j][0], 1, &s->xc.ptr.pp_double[i][0], 1, ae_v_len(0,s->nx-1));
                ae_v_move(&xwr->ptr.pp_double[i*s->nl+j][s->nx], 1, &s->wr.ptr.pp_double[i][1+j*s->ny], 1, ae_v_len(s->nx,s->nx+s->ny-1));
                xwr->ptr.pp_double[i*s->nl+j][s->nx+s->ny] = rcur;
                rcur = 0.5*rcur;
            }
        }
    }
}


/*************************************************************************
Serializer: allocation

  -- ALGLIB --
     Copyright 02.02.2012 by Bochkanov Sergey
*************************************************************************/
void rbfalloc(ae_serializer* s, rbfmodel* model, ae_state *_state)
{


    
    /*
     * Header
     */
    ae_serializer_alloc_entry(s);
    ae_serializer_alloc_entry(s);
    
    /*
     * Data
     */
    ae_serializer_alloc_entry(s);
    ae_serializer_alloc_entry(s);
    ae_serializer_alloc_entry(s);
    ae_serializer_alloc_entry(s);
    kdtreealloc(s, &model->tree, _state);
    allocrealmatrix(s, &model->xc, -1, -1, _state);
    allocrealmatrix(s, &model->wr, -1, -1, _state);
    ae_serializer_alloc_entry(s);
    allocrealmatrix(s, &model->v, -1, -1, _state);
}


/*************************************************************************
Serializer: serialization

  -- ALGLIB --
     Copyright 02.02.2012 by Bochkanov Sergey
*************************************************************************/
void rbfserialize(ae_serializer* s, rbfmodel* model, ae_state *_state)
{


    
    /*
     * Header
     */
    ae_serializer_serialize_int(s, getrbfserializationcode(_state), _state);
    ae_serializer_serialize_int(s, rbf_rbffirstversion, _state);
    
    /*
     * Data
     */
    ae_serializer_serialize_int(s, model->nx, _state);
    ae_serializer_serialize_int(s, model->ny, _state);
    ae_serializer_serialize_int(s, model->nc, _state);
    ae_serializer_serialize_int(s, model->nl, _state);
    kdtreeserialize(s, &model->tree, _state);
    serializerealmatrix(s, &model->xc, -1, -1, _state);
    serializerealmatrix(s, &model->wr, -1, -1, _state);
    ae_serializer_serialize_double(s, model->rmax, _state);
    serializerealmatrix(s, &model->v, -1, -1, _state);
}


/*************************************************************************
Serializer: unserialization

  -- ALGLIB --
     Copyright 02.02.2012 by Bochkanov Sergey
*************************************************************************/
void rbfunserialize(ae_serializer* s, rbfmodel* model, ae_state *_state)
{
    ae_int_t i0;
    ae_int_t i1;
    ae_int_t nx;
    ae_int_t ny;

    _rbfmodel_clear(model);

    
    /*
     * Header
     */
    ae_serializer_unserialize_int(s, &i0, _state);
    ae_assert(i0==getrbfserializationcode(_state), "RBFUnserialize: stream header corrupted", _state);
    ae_serializer_unserialize_int(s, &i1, _state);
    ae_assert(i1==rbf_rbffirstversion, "RBFUnserialize: stream header corrupted", _state);
    
    /*
     * Unserialize primary model parameters, initialize model.
     *
     * It is necessary to call RBFCreate() because some internal fields
     * which are NOT unserialized will need initialization.
     */
    ae_serializer_unserialize_int(s, &nx, _state);
    ae_serializer_unserialize_int(s, &ny, _state);
    rbfcreate(nx, ny, model, _state);
    ae_serializer_unserialize_int(s, &model->nc, _state);
    ae_serializer_unserialize_int(s, &model->nl, _state);
    kdtreeunserialize(s, &model->tree, _state);
    unserializerealmatrix(s, &model->xc, _state);
    unserializerealmatrix(s, &model->wr, _state);
    ae_serializer_unserialize_double(s, &model->rmax, _state);
    unserializerealmatrix(s, &model->v, _state);
}


/*************************************************************************
This function changes centers allocation algorithm to one which  allocates
centers exactly at the dataset points (one input point = one center). This
function won't have effect until next call to RBFBuildModel().

INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
static void rbf_rbfgridpoints(rbfmodel* s, ae_state *_state)
{


    s->gridtype = 2;
}


/*************************************************************************
This function changes radii  calculation  algorithm  to  one  which  makes
radius for I-th node equal to R[i]=DistNN[i]*Q, where:
* R[i] is a radius calculated by the algorithm
* DistNN[i] is distance from I-th center to its nearest neighbor center
* Q is a scale parameter, which should be within [0.75,1.50], with
  recommended value equal to 1.0
* after performing radii calculation, radii are transformed  in  order  to
  avoid situation when single outlier has very large radius and influences
  many points across entire dataset. Transformation has following form:
       new_r[i] = min(r[i],Z*median(r[]))
   where r[i] is I-th radius, median()  is  a median  radius across entire
   dataset, Z is user-specified value which controls amount  of  deviation
   from median radius.

This function won't have effect until next call to RBFBuildModel().

The idea behind this algorithm is to choose radii corresponding  to  basis
functions is such way that I-th radius is approximately equal to  distance
from I-th center to its nearest neighbor. In this case  interactions  with
distant points will be insignificant, and we  will  get  well  conditioned
basis.

Properties of this basis depend on the value of Q:
* Q<0.75 will give perfectly conditioned basis,  but  terrible  smoothness
  properties (RBF interpolant will have sharp peaks around function values)
* Q>1.5 will lead to badly conditioned systems and slow convergence of the
  underlying linear solver (although smoothness will be very good)
* Q around 1.0 gives good balance between smoothness and condition number


INPUT PARAMETERS:
    S       -   RBF model, initialized by RBFCreate() call
    Q       -   radius coefficient, Q>0
    Z       -   z-parameter, Z>0
    
Default value of Q is equal to 1.0
Default value of Z is equal to 5.0

NOTE: this   function  has   some   serialization-related  subtleties.  We
      recommend you to study serialization examples from ALGLIB  Reference
      Manual if you want to perform serialization of your models.

  -- ALGLIB --
     Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
static void rbf_rbfradnn(rbfmodel* s,
     double q,
     double z,
     ae_state *_state)
{


    ae_assert(ae_isfinite(q, _state)&&ae_fp_greater(q,(double)(0)), "RBFRadNN: Q<=0, infinite or NAN", _state);
    ae_assert(ae_isfinite(z, _state)&&ae_fp_greater(z,(double)(0)), "RBFRadNN: Z<=0, infinite or NAN", _state);
    s->fixrad = ae_false;
    s->radvalue = q;
    s->radzvalue = z;
}


static ae_bool rbf_buildlinearmodel(/* Real    */ ae_matrix* x,
     /* Real    */ ae_matrix* y,
     ae_int_t n,
     ae_int_t ny,
     ae_int_t modeltype,
     /* Real    */ ae_matrix* v,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector tmpy;
    ae_matrix a;
    double scaling;
    ae_vector shifting;
    double mn;
    double mx;
    ae_vector c;
    lsfitreport rep;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t info;
    ae_bool result;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_clear(v);
    ae_vector_init(&tmpy, 0, DT_REAL, _state);
    ae_matrix_init(&a, 0, 0, DT_REAL, _state);
    ae_vector_init(&shifting, 0, DT_REAL, _state);
    ae_vector_init(&c, 0, DT_REAL, _state);
    _lsfitreport_init(&rep, _state);

    ae_assert(n>=0, "BuildLinearModel: N<0", _state);
    ae_assert(ny>0, "BuildLinearModel: NY<=0", _state);
    
    /*
     * Handle degenerate case (N=0)
     */
    result = ae_true;
    ae_matrix_set_length(v, ny, rbf_mxnx+1, _state);
    if( n==0 )
    {
        for(j=0; j<=rbf_mxnx; j++)
        {
            for(i=0; i<=ny-1; i++)
            {
                v->ptr.pp_double[i][j] = (double)(0);
            }
        }
        ae_frame_leave(_state);
        return result;
    }
    
    /*
     * Allocate temporaries
     */
    ae_vector_set_length(&tmpy, n, _state);
    
    /*
     * General linear model.
     */
    if( modeltype==1 )
    {
        
        /*
         * Calculate scaling/shifting, transform variables, prepare LLS problem
         */
        ae_matrix_set_length(&a, n, rbf_mxnx+1, _state);
        ae_vector_set_length(&shifting, rbf_mxnx, _state);
        scaling = (double)(0);
        for(i=0; i<=rbf_mxnx-1; i++)
        {
            mn = x->ptr.pp_double[0][i];
            mx = mn;
            for(j=1; j<=n-1; j++)
            {
                if( ae_fp_greater(mn,x->ptr.pp_double[j][i]) )
                {
                    mn = x->ptr.pp_double[j][i];
                }
                if( ae_fp_less(mx,x->ptr.pp_double[j][i]) )
                {
                    mx = x->ptr.pp_double[j][i];
                }
            }
            scaling = ae_maxreal(scaling, mx-mn, _state);
            shifting.ptr.p_double[i] = 0.5*(mx+mn);
        }
        if( ae_fp_eq(scaling,(double)(0)) )
        {
            scaling = (double)(1);
        }
        else
        {
            scaling = 0.5*scaling;
        }
        for(i=0; i<=n-1; i++)
        {
            for(j=0; j<=rbf_mxnx-1; j++)
            {
                a.ptr.pp_double[i][j] = (x->ptr.pp_double[i][j]-shifting.ptr.p_double[j])/scaling;
            }
        }
        for(i=0; i<=n-1; i++)
        {
            a.ptr.pp_double[i][rbf_mxnx] = (double)(1);
        }
        
        /*
         * Solve linear system in transformed variables, make backward 
         */
        for(i=0; i<=ny-1; i++)
        {
            for(j=0; j<=n-1; j++)
            {
                tmpy.ptr.p_double[j] = y->ptr.pp_double[j][i];
            }
            lsfitlinear(&tmpy, &a, n, rbf_mxnx+1, &info, &c, &rep, _state);
            if( info<=0 )
            {
                result = ae_false;
                ae_frame_leave(_state);
                return result;
            }
            for(j=0; j<=rbf_mxnx-1; j++)
            {
                v->ptr.pp_double[i][j] = c.ptr.p_double[j]/scaling;
            }
            v->ptr.pp_double[i][rbf_mxnx] = c.ptr.p_double[rbf_mxnx];
            for(j=0; j<=rbf_mxnx-1; j++)
            {
                v->ptr.pp_double[i][rbf_mxnx] = v->ptr.pp_double[i][rbf_mxnx]-shifting.ptr.p_double[j]*v->ptr.pp_double[i][j];
            }
            for(j=0; j<=n-1; j++)
            {
                for(k=0; k<=rbf_mxnx-1; k++)
                {
                    y->ptr.pp_double[j][i] = y->ptr.pp_double[j][i]-x->ptr.pp_double[j][k]*v->ptr.pp_double[i][k];
                }
                y->ptr.pp_double[j][i] = y->ptr.pp_double[j][i]-v->ptr.pp_double[i][rbf_mxnx];
            }
        }
        ae_frame_leave(_state);
        return result;
    }
    
    /*
     * Constant model, very simple
     */
    if( modeltype==2 )
    {
        for(i=0; i<=ny-1; i++)
        {
            for(j=0; j<=rbf_mxnx; j++)
            {
                v->ptr.pp_double[i][j] = (double)(0);
            }
            for(j=0; j<=n-1; j++)
            {
                v->ptr.pp_double[i][rbf_mxnx] = v->ptr.pp_double[i][rbf_mxnx]+y->ptr.pp_double[j][i];
            }
            if( n>0 )
            {
                v->ptr.pp_double[i][rbf_mxnx] = v->ptr.pp_double[i][rbf_mxnx]/n;
            }
            for(j=0; j<=n-1; j++)
            {
                y->ptr.pp_double[j][i] = y->ptr.pp_double[j][i]-v->ptr.pp_double[i][rbf_mxnx];
            }
        }
        ae_frame_leave(_state);
        return result;
    }
    
    /*
     * Zero model
     */
    ae_assert(modeltype==3, "BuildLinearModel: unknown model type", _state);
    for(i=0; i<=ny-1; i++)
    {
        for(j=0; j<=rbf_mxnx; j++)
        {
            v->ptr.pp_double[i][j] = (double)(0);
        }
    }
    ae_frame_leave(_state);
    return result;
}


static void rbf_buildrbfmodellsqr(/* Real    */ ae_matrix* x,
     /* Real    */ ae_matrix* y,
     /* Real    */ ae_matrix* xc,
     /* Real    */ ae_vector* r,
     ae_int_t n,
     ae_int_t nc,
     ae_int_t ny,
     kdtree* pointstree,
     kdtree* centerstree,
     double epsort,
     double epserr,
     ae_int_t maxits,
     ae_int_t* gnnz,
     ae_int_t* snnz,
     /* Real    */ ae_matrix* w,
     ae_int_t* info,
     ae_int_t* iterationscount,
     ae_int_t* nmv,
     ae_state *_state)
{
    ae_frame _frame_block;
    linlsqrstate state;
    linlsqrreport lsqrrep;
    sparsematrix spg;
    sparsematrix sps;
    ae_vector nearcenterscnt;
    ae_vector nearpointscnt;
    ae_vector skipnearpointscnt;
    ae_vector farpointscnt;
    ae_int_t maxnearcenterscnt;
    ae_int_t maxnearpointscnt;
    ae_int_t maxfarpointscnt;
    ae_int_t sumnearcenterscnt;
    ae_int_t sumnearpointscnt;
    ae_int_t sumfarpointscnt;
    double maxrad;
    ae_vector pointstags;
    ae_vector centerstags;
    ae_matrix nearpoints;
    ae_matrix nearcenters;
    ae_matrix farpoints;
    ae_int_t tmpi;
    ae_int_t pointscnt;
    ae_int_t centerscnt;
    ae_vector xcx;
    ae_vector tmpy;
    ae_vector tc;
    ae_vector g;
    ae_vector c;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t sind;
    ae_matrix a;
    double vv;
    double vx;
    double vy;
    double vz;
    double vr;
    double gnorm2;
    ae_vector tmp0;
    ae_vector tmp1;
    ae_vector tmp2;
    double fx;
    ae_matrix xx;
    ae_matrix cx;
    double mrad;

    ae_frame_make(_state, &_frame_block);
    *gnnz = 0;
    *snnz = 0;
    ae_matrix_clear(w);
    *info = 0;
    *iterationscount = 0;
    *nmv = 0;
    _linlsqrstate_init(&state, _state);
    _linlsqrreport_init(&lsqrrep, _state);
    _sparsematrix_init(&spg, _state);
    _sparsematrix_init(&sps, _state);
    ae_vector_init(&nearcenterscnt, 0, DT_INT, _state);
    ae_vector_init(&nearpointscnt, 0, DT_INT, _state);
    ae_vector_init(&skipnearpointscnt, 0, DT_INT, _state);
    ae_vector_init(&farpointscnt, 0, DT_INT, _state);
    ae_vector_init(&pointstags, 0, DT_INT, _state);
    ae_vector_init(&centerstags, 0, DT_INT, _state);
    ae_matrix_init(&nearpoints, 0, 0, DT_REAL, _state);
    ae_matrix_init(&nearcenters, 0, 0, DT_REAL, _state);
    ae_matrix_init(&farpoints, 0, 0, DT_REAL, _state);
    ae_vector_init(&xcx, 0, DT_REAL, _state);
    ae_vector_init(&tmpy, 0, DT_REAL, _state);
    ae_vector_init(&tc, 0, DT_REAL, _state);
    ae_vector_init(&g, 0, DT_REAL, _state);
    ae_vector_init(&c, 0, DT_REAL, _state);
    ae_matrix_init(&a, 0, 0, DT_REAL, _state);
    ae_vector_init(&tmp0, 0, DT_REAL, _state);
    ae_vector_init(&tmp1, 0, DT_REAL, _state);
    ae_vector_init(&tmp2, 0, DT_REAL, _state);
    ae_matrix_init(&xx, 0, 0, DT_REAL, _state);
    ae_matrix_init(&cx, 0, 0, DT_REAL, _state);

    
    /*
     * Handle special cases: NC=0
     */
    if( nc==0 )
    {
        *info = 1;
        *iterationscount = 0;
        *nmv = 0;
        ae_frame_leave(_state);
        return;
    }
    
    /*
     * Prepare for general case, NC>0
     */
    ae_vector_set_length(&xcx, rbf_mxnx, _state);
    ae_vector_set_length(&pointstags, n, _state);
    ae_vector_set_length(&centerstags, nc, _state);
    *info = -1;
    *iterationscount = 0;
    *nmv = 0;
    
    /*
     * This block prepares quantities used to compute approximate cardinal basis functions (ACBFs):
     * * NearCentersCnt[]   -   array[NC], whose elements store number of near centers used to build ACBF
     * * NearPointsCnt[]    -   array[NC], number of near points used to build ACBF
     * * FarPointsCnt[]     -   array[NC], number of far points (ones where ACBF is nonzero)
     * * MaxNearCentersCnt  -   max(NearCentersCnt)
     * * MaxNearPointsCnt   -   max(NearPointsCnt)
     * * SumNearCentersCnt  -   sum(NearCentersCnt)
     * * SumNearPointsCnt   -   sum(NearPointsCnt)
     * * SumFarPointsCnt    -   sum(FarPointsCnt)
     */
    ae_vector_set_length(&nearcenterscnt, nc, _state);
    ae_vector_set_length(&nearpointscnt, nc, _state);
    ae_vector_set_length(&skipnearpointscnt, nc, _state);
    ae_vector_set_length(&farpointscnt, nc, _state);
    maxnearcenterscnt = 0;
    maxnearpointscnt = 0;
    maxfarpointscnt = 0;
    sumnearcenterscnt = 0;
    sumnearpointscnt = 0;
    sumfarpointscnt = 0;
    for(i=0; i<=nc-1; i++)
    {
        for(j=0; j<=rbf_mxnx-1; j++)
        {
            xcx.ptr.p_double[j] = xc->ptr.pp_double[i][j];
        }
        
        /*
         * Determine number of near centers and maximum radius of near centers
         */
        nearcenterscnt.ptr.p_int[i] = kdtreequeryrnn(centerstree, &xcx, r->ptr.p_double[i]*rbf_rbfnearradius, ae_true, _state);
        kdtreequeryresultstags(centerstree, &centerstags, _state);
        maxrad = (double)(0);
        for(j=0; j<=nearcenterscnt.ptr.p_int[i]-1; j++)
        {
            maxrad = ae_maxreal(maxrad, ae_fabs(r->ptr.p_double[centerstags.ptr.p_int[j]], _state), _state);
        }
        
        /*
         * Determine number of near points (ones which used to build ACBF)
         * and skipped points (the most near points which are NOT used to build ACBF
         * and are NOT included in the near points count
         */
        skipnearpointscnt.ptr.p_int[i] = kdtreequeryrnn(pointstree, &xcx, 0.1*r->ptr.p_double[i], ae_true, _state);
        nearpointscnt.ptr.p_int[i] = kdtreequeryrnn(pointstree, &xcx, (r->ptr.p_double[i]+maxrad)*rbf_rbfnearradius, ae_true, _state)-skipnearpointscnt.ptr.p_int[i];
        ae_assert(nearpointscnt.ptr.p_int[i]>=0, "BuildRBFModelLSQR: internal error", _state);
        
        /*
         * Determine number of far points
         */
        farpointscnt.ptr.p_int[i] = kdtreequeryrnn(pointstree, &xcx, ae_maxreal(r->ptr.p_double[i]*rbf_rbfnearradius+maxrad*rbf_rbffarradius, r->ptr.p_double[i]*rbf_rbffarradius, _state), ae_true, _state);
        
        /*
         * calculate sum and max, make some basic checks
         */
        ae_assert(nearcenterscnt.ptr.p_int[i]>0, "BuildRBFModelLSQR: internal error", _state);
        maxnearcenterscnt = ae_maxint(maxnearcenterscnt, nearcenterscnt.ptr.p_int[i], _state);
        maxnearpointscnt = ae_maxint(maxnearpointscnt, nearpointscnt.ptr.p_int[i], _state);
        maxfarpointscnt = ae_maxint(maxfarpointscnt, farpointscnt.ptr.p_int[i], _state);
        sumnearcenterscnt = sumnearcenterscnt+nearcenterscnt.ptr.p_int[i];
        sumnearpointscnt = sumnearpointscnt+nearpointscnt.ptr.p_int[i];
        sumfarpointscnt = sumfarpointscnt+farpointscnt.ptr.p_int[i];
    }
    *snnz = sumnearcenterscnt;
    *gnnz = sumfarpointscnt;
    ae_assert(maxnearcenterscnt>0, "BuildRBFModelLSQR: internal error", _state);
    
    /*
     * Allocate temporaries.
     *
     * NOTE: we want to avoid allocation of zero-size arrays, so we
     *       use max(desired_size,1) instead of desired_size when performing
     *       memory allocation.
     */
    ae_matrix_set_length(&a, maxnearpointscnt+maxnearcenterscnt, maxnearcenterscnt, _state);
    ae_vector_set_length(&tmpy, maxnearpointscnt+maxnearcenterscnt, _state);
    ae_vector_set_length(&g, maxnearcenterscnt, _state);
    ae_vector_set_length(&c, maxnearcenterscnt, _state);
    ae_matrix_set_length(&nearcenters, maxnearcenterscnt, rbf_mxnx, _state);
    ae_matrix_set_length(&nearpoints, ae_maxint(maxnearpointscnt, 1, _state), rbf_mxnx, _state);
    ae_matrix_set_length(&farpoints, ae_maxint(maxfarpointscnt, 1, _state), rbf_mxnx, _state);
    
    /*
     * fill matrix SpG
     */
    sparsecreate(n, nc, *gnnz, &spg, _state);
    sparsecreate(nc, nc, *snnz, &sps, _state);
    for(i=0; i<=nc-1; i++)
    {
        centerscnt = nearcenterscnt.ptr.p_int[i];
        
        /*
         * main center
         */
        for(j=0; j<=rbf_mxnx-1; j++)
        {
            xcx.ptr.p_double[j] = xc->ptr.pp_double[i][j];
        }
        
        /*
         * center's tree
         */
        tmpi = kdtreequeryknn(centerstree, &xcx, centerscnt, ae_true, _state);
        ae_assert(tmpi==centerscnt, "BuildRBFModelLSQR: internal error", _state);
        kdtreequeryresultsx(centerstree, &cx, _state);
        kdtreequeryresultstags(centerstree, &centerstags, _state);
        
        /*
         * point's tree
         */
        mrad = (double)(0);
        for(j=0; j<=centerscnt-1; j++)
        {
            mrad = ae_maxreal(mrad, r->ptr.p_double[centerstags.ptr.p_int[j]], _state);
        }
        
        /*
         * we need to be sure that 'CTree' contains
         * at least one side center
         */
        sparseset(&sps, i, i, (double)(1), _state);
        c.ptr.p_double[0] = 1.0;
        for(j=1; j<=centerscnt-1; j++)
        {
            c.ptr.p_double[j] = 0.0;
        }
        if( centerscnt>1&&nearpointscnt.ptr.p_int[i]>0 )
        {
            
            /*
             * first KDTree request for points
             */
            pointscnt = nearpointscnt.ptr.p_int[i];
            tmpi = kdtreequeryknn(pointstree, &xcx, skipnearpointscnt.ptr.p_int[i]+nearpointscnt.ptr.p_int[i], ae_true, _state);
            ae_assert(tmpi==skipnearpointscnt.ptr.p_int[i]+nearpointscnt.ptr.p_int[i], "BuildRBFModelLSQR: internal error", _state);
            kdtreequeryresultsx(pointstree, &xx, _state);
            sind = skipnearpointscnt.ptr.p_int[i];
            for(j=0; j<=pointscnt-1; j++)
            {
                vx = xx.ptr.pp_double[sind+j][0];
                vy = xx.ptr.pp_double[sind+j][1];
                vz = xx.ptr.pp_double[sind+j][2];
                for(k=0; k<=centerscnt-1; k++)
                {
                    vr = 0.0;
                    vv = vx-cx.ptr.pp_double[k][0];
                    vr = vr+vv*vv;
                    vv = vy-cx.ptr.pp_double[k][1];
                    vr = vr+vv*vv;
                    vv = vz-cx.ptr.pp_double[k][2];
                    vr = vr+vv*vv;
                    vv = r->ptr.p_double[centerstags.ptr.p_int[k]];
                    a.ptr.pp_double[j][k] = ae_exp(-vr/(vv*vv), _state);
                }
            }
            for(j=0; j<=centerscnt-1; j++)
            {
                g.ptr.p_double[j] = ae_exp(-(ae_sqr(xcx.ptr.p_double[0]-cx.ptr.pp_double[j][0], _state)+ae_sqr(xcx.ptr.p_double[1]-cx.ptr.pp_double[j][1], _state)+ae_sqr(xcx.ptr.p_double[2]-cx.ptr.pp_double[j][2], _state))/ae_sqr(r->ptr.p_double[centerstags.ptr.p_int[j]], _state), _state);
            }
            
            /*
             * calculate the problem
             */
            gnorm2 = ae_v_dotproduct(&g.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1));
            for(j=0; j<=pointscnt-1; j++)
            {
                vv = ae_v_dotproduct(&a.ptr.pp_double[j][0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1));
                vv = vv/gnorm2;
                tmpy.ptr.p_double[j] = -vv;
                ae_v_subd(&a.ptr.pp_double[j][0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1), vv);
            }
            for(j=pointscnt; j<=pointscnt+centerscnt-1; j++)
            {
                for(k=0; k<=centerscnt-1; k++)
                {
                    a.ptr.pp_double[j][k] = 0.0;
                }
                a.ptr.pp_double[j][j-pointscnt] = 1.0E-6;
                tmpy.ptr.p_double[j] = 0.0;
            }
            fblssolvels(&a, &tmpy, pointscnt+centerscnt, centerscnt, &tmp0, &tmp1, &tmp2, _state);
            ae_v_move(&c.ptr.p_double[0], 1, &tmpy.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1));
            vv = ae_v_dotproduct(&g.ptr.p_double[0], 1, &c.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1));
            vv = vv/gnorm2;
            ae_v_subd(&c.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1), vv);
            vv = 1/gnorm2;
            ae_v_addd(&c.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1), vv);
            for(j=0; j<=centerscnt-1; j++)
            {
                sparseset(&sps, i, centerstags.ptr.p_int[j], c.ptr.p_double[j], _state);
            }
        }
        
        /*
         * second KDTree request for points
         */
        pointscnt = farpointscnt.ptr.p_int[i];
        tmpi = kdtreequeryknn(pointstree, &xcx, pointscnt, ae_true, _state);
        ae_assert(tmpi==pointscnt, "BuildRBFModelLSQR: internal error", _state);
        kdtreequeryresultsx(pointstree, &xx, _state);
        kdtreequeryresultstags(pointstree, &pointstags, _state);
        
        /*
         *fill SpG matrix
         */
        for(j=0; j<=pointscnt-1; j++)
        {
            fx = (double)(0);
            vx = xx.ptr.pp_double[j][0];
            vy = xx.ptr.pp_double[j][1];
            vz = xx.ptr.pp_double[j][2];
            for(k=0; k<=centerscnt-1; k++)
            {
                vr = 0.0;
                vv = vx-cx.ptr.pp_double[k][0];
                vr = vr+vv*vv;
                vv = vy-cx.ptr.pp_double[k][1];
                vr = vr+vv*vv;
                vv = vz-cx.ptr.pp_double[k][2];
                vr = vr+vv*vv;
                vv = r->ptr.p_double[centerstags.ptr.p_int[k]];
                vv = vv*vv;
                fx = fx+c.ptr.p_double[k]*ae_exp(-vr/vv, _state);
            }
            sparseset(&spg, pointstags.ptr.p_int[j], i, fx, _state);
        }
    }
    sparseconverttocrs(&spg, _state);
    sparseconverttocrs(&sps, _state);
    
    /*
     * solve by LSQR method
     */
    ae_vector_set_length(&tmpy, n, _state);
    ae_vector_set_length(&tc, nc, _state);
    ae_matrix_set_length(w, nc, ny, _state);
    linlsqrcreate(n, nc, &state, _state);
    linlsqrsetcond(&state, epsort, epserr, maxits, _state);
    for(i=0; i<=ny-1; i++)
    {
        for(j=0; j<=n-1; j++)
        {
            tmpy.ptr.p_double[j] = y->ptr.pp_double[j][i];
        }
        linlsqrsolvesparse(&state, &spg, &tmpy, _state);
        linlsqrresults(&state, &c, &lsqrrep, _state);
        if( lsqrrep.terminationtype<=0 )
        {
            *info = -4;
            ae_frame_leave(_state);
            return;
        }
        sparsemtv(&sps, &c, &tc, _state);
        for(j=0; j<=nc-1; j++)
        {
            w->ptr.pp_double[j][i] = tc.ptr.p_double[j];
        }
        *iterationscount = *iterationscount+lsqrrep.iterationscount;
        *nmv = *nmv+lsqrrep.nmv;
    }
    *info = 1;
    ae_frame_leave(_state);
}


static void rbf_buildrbfmlayersmodellsqr(/* Real    */ ae_matrix* x,
     /* Real    */ ae_matrix* y,
     /* Real    */ ae_matrix* xc,
     double rval,
     /* Real    */ ae_vector* r,
     ae_int_t n,
     ae_int_t* nc,
     ae_int_t ny,
     ae_int_t nlayers,
     kdtree* centerstree,
     double epsort,
     double epserr,
     ae_int_t maxits,
     double lambdav,
     ae_int_t* annz,
     /* Real    */ ae_matrix* w,
     ae_int_t* info,
     ae_int_t* iterationscount,
     ae_int_t* nmv,
     ae_state *_state)
{
    ae_frame _frame_block;
    linlsqrstate state;
    linlsqrreport lsqrrep;
    sparsematrix spa;
    double anorm;
    ae_vector omega;
    ae_vector xx;
    ae_vector tmpy;
    ae_matrix cx;
    double yval;
    ae_int_t nec;
    ae_vector centerstags;
    ae_int_t layer;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    double v;
    double rmaxbefore;
    double rmaxafter;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_clear(xc);
    ae_vector_clear(r);
    *nc = 0;
    *annz = 0;
    ae_matrix_clear(w);
    *info = 0;
    *iterationscount = 0;
    *nmv = 0;
    _linlsqrstate_init(&state, _state);
    _linlsqrreport_init(&lsqrrep, _state);
    _sparsematrix_init(&spa, _state);
    ae_vector_init(&omega, 0, DT_REAL, _state);
    ae_vector_init(&xx, 0, DT_REAL, _state);
    ae_vector_init(&tmpy, 0, DT_REAL, _state);
    ae_matrix_init(&cx, 0, 0, DT_REAL, _state);
    ae_vector_init(&centerstags, 0, DT_INT, _state);

    ae_assert(nlayers>=0, "BuildRBFMLayersModelLSQR: invalid argument(NLayers<0)", _state);
    ae_assert(n>=0, "BuildRBFMLayersModelLSQR: invalid argument(N<0)", _state);
    ae_assert(rbf_mxnx>0&&rbf_mxnx<=3, "BuildRBFMLayersModelLSQR: internal error(invalid global const MxNX: either MxNX<=0 or MxNX>3)", _state);
    *annz = 0;
    if( n==0||nlayers==0 )
    {
        *info = 1;
        *iterationscount = 0;
        *nmv = 0;
        ae_frame_leave(_state);
        return;
    }
    *nc = n*nlayers;
    ae_vector_set_length(&xx, rbf_mxnx, _state);
    ae_vector_set_length(&centerstags, n, _state);
    ae_matrix_set_length(xc, *nc, rbf_mxnx, _state);
    ae_vector_set_length(r, *nc, _state);
    for(i=0; i<=*nc-1; i++)
    {
        for(j=0; j<=rbf_mxnx-1; j++)
        {
            xc->ptr.pp_double[i][j] = x->ptr.pp_double[i%n][j];
        }
    }
    for(i=0; i<=*nc-1; i++)
    {
        r->ptr.p_double[i] = rval/ae_pow((double)(2), (double)(i/n), _state);
    }
    for(i=0; i<=n-1; i++)
    {
        centerstags.ptr.p_int[i] = i;
    }
    kdtreebuildtagged(xc, &centerstags, n, rbf_mxnx, 0, 2, centerstree, _state);
    ae_vector_set_length(&omega, n, _state);
    ae_vector_set_length(&tmpy, n, _state);
    ae_matrix_set_length(w, *nc, ny, _state);
    *info = -1;
    *iterationscount = 0;
    *nmv = 0;
    linlsqrcreate(n, n, &state, _state);
    linlsqrsetcond(&state, epsort, epserr, maxits, _state);
    linlsqrsetlambdai(&state, 1.0E-6, _state);
    
    /*
     * calculate number of non-zero elements for sparse matrix
     */
    for(i=0; i<=n-1; i++)
    {
        for(j=0; j<=rbf_mxnx-1; j++)
        {
            xx.ptr.p_double[j] = x->ptr.pp_double[i][j];
        }
        *annz = *annz+kdtreequeryrnn(centerstree, &xx, r->ptr.p_double[0]*rbf_rbfmlradius, ae_true, _state);
    }
    for(layer=0; layer<=nlayers-1; layer++)
    {
        
        /*
         * Fill sparse matrix, calculate norm(A)
         */
        anorm = 0.0;
        sparsecreate(n, n, *annz, &spa, _state);
        for(i=0; i<=n-1; i++)
        {
            for(j=0; j<=rbf_mxnx-1; j++)
            {
                xx.ptr.p_double[j] = x->ptr.pp_double[i][j];
            }
            nec = kdtreequeryrnn(centerstree, &xx, r->ptr.p_double[layer*n]*rbf_rbfmlradius, ae_true, _state);
            kdtreequeryresultsx(centerstree, &cx, _state);
            kdtreequeryresultstags(centerstree, &centerstags, _state);
            for(j=0; j<=nec-1; j++)
            {
                v = ae_exp(-(ae_sqr(xx.ptr.p_double[0]-cx.ptr.pp_double[j][0], _state)+ae_sqr(xx.ptr.p_double[1]-cx.ptr.pp_double[j][1], _state)+ae_sqr(xx.ptr.p_double[2]-cx.ptr.pp_double[j][2], _state))/ae_sqr(r->ptr.p_double[layer*n+centerstags.ptr.p_int[j]], _state), _state);
                sparseset(&spa, i, centerstags.ptr.p_int[j], v, _state);
                anorm = anorm+ae_sqr(v, _state);
            }
        }
        anorm = ae_sqrt(anorm, _state);
        sparseconverttocrs(&spa, _state);
        
        /*
         * Calculate maximum residual before adding new layer.
         * This value is not used by algorithm, the only purpose is to make debugging easier.
         */
        rmaxbefore = 0.0;
        for(j=0; j<=n-1; j++)
        {
            for(i=0; i<=ny-1; i++)
            {
                rmaxbefore = ae_maxreal(rmaxbefore, ae_fabs(y->ptr.pp_double[j][i], _state), _state);
            }
        }
        
        /*
         * Process NY dimensions of the target function
         */
        for(i=0; i<=ny-1; i++)
        {
            for(j=0; j<=n-1; j++)
            {
                tmpy.ptr.p_double[j] = y->ptr.pp_double[j][i];
            }
            
            /*
             * calculate Omega for current layer
             */
            linlsqrsetlambdai(&state, lambdav*anorm/n, _state);
            linlsqrsolvesparse(&state, &spa, &tmpy, _state);
            linlsqrresults(&state, &omega, &lsqrrep, _state);
            if( lsqrrep.terminationtype<=0 )
            {
                *info = -4;
                ae_frame_leave(_state);
                return;
            }
            
            /*
             * calculate error for current layer
             */
            for(j=0; j<=n-1; j++)
            {
                yval = (double)(0);
                for(k=0; k<=rbf_mxnx-1; k++)
                {
                    xx.ptr.p_double[k] = x->ptr.pp_double[j][k];
                }
                nec = kdtreequeryrnn(centerstree, &xx, r->ptr.p_double[layer*n]*rbf_rbffarradius, ae_true, _state);
                kdtreequeryresultsx(centerstree, &cx, _state);
                kdtreequeryresultstags(centerstree, &centerstags, _state);
                for(k=0; k<=nec-1; k++)
                {
                    yval = yval+omega.ptr.p_double[centerstags.ptr.p_int[k]]*ae_exp(-(ae_sqr(xx.ptr.p_double[0]-cx.ptr.pp_double[k][0], _state)+ae_sqr(xx.ptr.p_double[1]-cx.ptr.pp_double[k][1], _state)+ae_sqr(xx.ptr.p_double[2]-cx.ptr.pp_double[k][2], _state))/ae_sqr(r->ptr.p_double[layer*n+centerstags.ptr.p_int[k]], _state), _state);
                }
                y->ptr.pp_double[j][i] = y->ptr.pp_double[j][i]-yval;
            }
            
            /*
             * write Omega in out parameter W
             */
            for(j=0; j<=n-1; j++)
            {
                w->ptr.pp_double[layer*n+j][i] = omega.ptr.p_double[j];
            }
            *iterationscount = *iterationscount+lsqrrep.iterationscount;
            *nmv = *nmv+lsqrrep.nmv;
        }
        
        /*
         * Calculate maximum residual before adding new layer.
         * This value is not used by algorithm, the only purpose is to make debugging easier.
         */
        rmaxafter = 0.0;
        for(j=0; j<=n-1; j++)
        {
            for(i=0; i<=ny-1; i++)
            {
                rmaxafter = ae_maxreal(rmaxafter, ae_fabs(y->ptr.pp_double[j][i], _state), _state);
            }
        }
    }
    *info = 1;
    ae_frame_leave(_state);
}


void _rbfmodel_init(void* _p, ae_state *_state)
{
    rbfmodel *p = (rbfmodel*)_p;
    ae_touch_ptr((void*)p);
    _kdtree_init(&p->tree, _state);
    ae_matrix_init(&p->xc, 0, 0, DT_REAL, _state);
    ae_matrix_init(&p->wr, 0, 0, DT_REAL, _state);
    ae_matrix_init(&p->v, 0, 0, DT_REAL, _state);
    ae_matrix_init(&p->x, 0, 0, DT_REAL, _state);
    ae_matrix_init(&p->y, 0, 0, DT_REAL, _state);
    ae_vector_init(&p->calcbufxcx, 0, DT_REAL, _state);
    ae_matrix_init(&p->calcbufx, 0, 0, DT_REAL, _state);
    ae_vector_init(&p->calcbuftags, 0, DT_INT, _state);
}


void _rbfmodel_init_copy(void* _dst, void* _src, ae_state *_state)
{
    rbfmodel *dst = (rbfmodel*)_dst;
    rbfmodel *src = (rbfmodel*)_src;
    dst->ny = src->ny;
    dst->nx = src->nx;
    dst->nc = src->nc;
    dst->nl = src->nl;
    _kdtree_init_copy(&dst->tree, &src->tree, _state);
    ae_matrix_init_copy(&dst->xc, &src->xc, _state);
    ae_matrix_init_copy(&dst->wr, &src->wr, _state);
    dst->rmax = src->rmax;
    ae_matrix_init_copy(&dst->v, &src->v, _state);
    dst->gridtype = src->gridtype;
    dst->fixrad = src->fixrad;
    dst->lambdav = src->lambdav;
    dst->radvalue = src->radvalue;
    dst->radzvalue = src->radzvalue;
    dst->nlayers = src->nlayers;
    dst->aterm = src->aterm;
    dst->algorithmtype = src->algorithmtype;
    dst->epsort = src->epsort;
    dst->epserr = src->epserr;
    dst->maxits = src->maxits;
    dst->h = src->h;
    dst->n = src->n;
    ae_matrix_init_copy(&dst->x, &src->x, _state);
    ae_matrix_init_copy(&dst->y, &src->y, _state);
    ae_vector_init_copy(&dst->calcbufxcx, &src->calcbufxcx, _state);
    ae_matrix_init_copy(&dst->calcbufx, &src->calcbufx, _state);
    ae_vector_init_copy(&dst->calcbuftags, &src->calcbuftags, _state);
}


void _rbfmodel_clear(void* _p)
{
    rbfmodel *p = (rbfmodel*)_p;
    ae_touch_ptr((void*)p);
    _kdtree_clear(&p->tree);
    ae_matrix_clear(&p->xc);
    ae_matrix_clear(&p->wr);
    ae_matrix_clear(&p->v);
    ae_matrix_clear(&p->x);
    ae_matrix_clear(&p->y);
    ae_vector_clear(&p->calcbufxcx);
    ae_matrix_clear(&p->calcbufx);
    ae_vector_clear(&p->calcbuftags);
}


void _rbfmodel_destroy(void* _p)
{
    rbfmodel *p = (rbfmodel*)_p;
    ae_touch_ptr((void*)p);
    _kdtree_destroy(&p->tree);
    ae_matrix_destroy(&p->xc);
    ae_matrix_destroy(&p->wr);
    ae_matrix_destroy(&p->v);
    ae_matrix_destroy(&p->x);
    ae_matrix_destroy(&p->y);
    ae_vector_destroy(&p->calcbufxcx);
    ae_matrix_destroy(&p->calcbufx);
    ae_vector_destroy(&p->calcbuftags);
}


void _rbfreport_init(void* _p, ae_state *_state)
{
    rbfreport *p = (rbfreport*)_p;
    ae_touch_ptr((void*)p);
}


void _rbfreport_init_copy(void* _dst, void* _src, ae_state *_state)
{
    rbfreport *dst = (rbfreport*)_dst;
    rbfreport *src = (rbfreport*)_src;
    dst->arows = src->arows;
    dst->acols = src->acols;
    dst->annz = src->annz;
    dst->iterationscount = src->iterationscount;
    dst->nmv = src->nmv;
    dst->terminationtype = src->terminationtype;
}


void _rbfreport_clear(void* _p)
{
    rbfreport *p = (rbfreport*)_p;
    ae_touch_ptr((void*)p);
}


void _rbfreport_destroy(void* _p)
{
    rbfreport *p = (rbfreport*)_p;
    ae_touch_ptr((void*)p);
}




/*************************************************************************
This subroutine calculates the value of the bilinear or bicubic spline  at
the given point X.

Input parameters:
    C   -   coefficients table.
            Built by BuildBilinearSpline or BuildBicubicSpline.
    X, Y-   point

Result:
    S(x,y)

  -- ALGLIB PROJECT --
     Copyright 05.07.2007 by Bochkanov Sergey
*************************************************************************/
double spline2dcalc(spline2dinterpolant* c,
     double x,
     double y,
     ae_state *_state)
{
    double v;
    double vx;
    double vy;
    double vxy;
    double result;


    ae_assert(c->stype==-1||c->stype==-3, "Spline2DCalc: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline2DCalc: X or Y contains NaN or Infinite value", _state);
    if( c->d!=1 )
    {
        result = (double)(0);
        return result;
    }
    spline2ddiff(c, x, y, &v, &vx, &vy, &vxy, _state);
    result = v;
    return result;
}


/*************************************************************************
This subroutine calculates the value of the bilinear or bicubic spline  at
the given point X and its derivatives.

Input parameters:
    C   -   spline interpolant.
    X, Y-   point

Output parameters:
    F   -   S(x,y)
    FX  -   dS(x,y)/dX
    FY  -   dS(x,y)/dY
    FXY -   d2S(x,y)/dXdY

  -- ALGLIB PROJECT --
     Copyright 05.07.2007 by Bochkanov Sergey
*************************************************************************/
void spline2ddiff(spline2dinterpolant* c,
     double x,
     double y,
     double* f,
     double* fx,
     double* fy,
     double* fxy,
     ae_state *_state)
{
    double t;
    double dt;
    double u;
    double du;
    ae_int_t ix;
    ae_int_t iy;
    ae_int_t l;
    ae_int_t r;
    ae_int_t h;
    ae_int_t s1;
    ae_int_t s2;
    ae_int_t s3;
    ae_int_t s4;
    ae_int_t sfx;
    ae_int_t sfy;
    ae_int_t sfxy;
    double y1;
    double y2;
    double y3;
    double y4;
    double v;
    double t0;
    double t1;
    double t2;
    double t3;
    double u0;
    double u1;
    double u2;
    double u3;

    *f = 0;
    *fx = 0;
    *fy = 0;
    *fxy = 0;

    ae_assert(c->stype==-1||c->stype==-3, "Spline2DDiff: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline2DDiff: X or Y contains NaN or Infinite value", _state);
    
    /*
     * Prepare F, dF/dX, dF/dY, d2F/dXdY
     */
    *f = (double)(0);
    *fx = (double)(0);
    *fy = (double)(0);
    *fxy = (double)(0);
    if( c->d!=1 )
    {
        return;
    }
    
    /*
     * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included)
     */
    l = 0;
    r = c->n-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->x.ptr.p_double[h],x) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    t = (x-c->x.ptr.p_double[l])/(c->x.ptr.p_double[l+1]-c->x.ptr.p_double[l]);
    dt = 1.0/(c->x.ptr.p_double[l+1]-c->x.ptr.p_double[l]);
    ix = l;
    
    /*
     * Binary search in the [ y[0], ..., y[m-2] ] (y[m-1] is not included)
     */
    l = 0;
    r = c->m-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->y.ptr.p_double[h],y) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    u = (y-c->y.ptr.p_double[l])/(c->y.ptr.p_double[l+1]-c->y.ptr.p_double[l]);
    du = 1.0/(c->y.ptr.p_double[l+1]-c->y.ptr.p_double[l]);
    iy = l;
    
    /*
     * Bilinear interpolation
     */
    if( c->stype==-1 )
    {
        y1 = c->f.ptr.p_double[c->n*iy+ix];
        y2 = c->f.ptr.p_double[c->n*iy+(ix+1)];
        y3 = c->f.ptr.p_double[c->n*(iy+1)+(ix+1)];
        y4 = c->f.ptr.p_double[c->n*(iy+1)+ix];
        *f = (1-t)*(1-u)*y1+t*(1-u)*y2+t*u*y3+(1-t)*u*y4;
        *fx = (-(1-u)*y1+(1-u)*y2+u*y3-u*y4)*dt;
        *fy = (-(1-t)*y1-t*y2+t*y3+(1-t)*y4)*du;
        *fxy = (y1-y2+y3-y4)*du*dt;
        return;
    }
    
    /*
     * Bicubic interpolation
     */
    if( c->stype==-3 )
    {
        
        /*
         * Prepare info
         */
        t0 = (double)(1);
        t1 = t;
        t2 = ae_sqr(t, _state);
        t3 = t*t2;
        u0 = (double)(1);
        u1 = u;
        u2 = ae_sqr(u, _state);
        u3 = u*u2;
        sfx = c->n*c->m;
        sfy = 2*c->n*c->m;
        sfxy = 3*c->n*c->m;
        s1 = c->n*iy+ix;
        s2 = c->n*iy+(ix+1);
        s3 = c->n*(iy+1)+(ix+1);
        s4 = c->n*(iy+1)+ix;
        
        /*
         * Calculate
         */
        v = c->f.ptr.p_double[s1];
        *f = *f+v*t0*u0;
        v = c->f.ptr.p_double[sfy+s1]/du;
        *f = *f+v*t0*u1;
        *fy = *fy+v*t0*u0*du;
        v = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s4]-2*c->f.ptr.p_double[sfy+s1]/du-c->f.ptr.p_double[sfy+s4]/du;
        *f = *f+v*t0*u2;
        *fy = *fy+2*v*t0*u1*du;
        v = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s4]+c->f.ptr.p_double[sfy+s1]/du+c->f.ptr.p_double[sfy+s4]/du;
        *f = *f+v*t0*u3;
        *fy = *fy+3*v*t0*u2*du;
        v = c->f.ptr.p_double[sfx+s1]/dt;
        *f = *f+v*t1*u0;
        *fx = *fx+v*t0*u0*dt;
        v = c->f.ptr.p_double[sfxy+s1]/(dt*du);
        *f = *f+v*t1*u1;
        *fx = *fx+v*t0*u1*dt;
        *fy = *fy+v*t1*u0*du;
        *fxy = *fxy+v*t0*u0*dt*du;
        v = -3*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du);
        *f = *f+v*t1*u2;
        *fx = *fx+v*t0*u2*dt;
        *fy = *fy+2*v*t1*u1*du;
        *fxy = *fxy+2*v*t0*u1*dt*du;
        v = 2*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du);
        *f = *f+v*t1*u3;
        *fx = *fx+v*t0*u3*dt;
        *fy = *fy+3*v*t1*u2*du;
        *fxy = *fxy+3*v*t0*u2*dt*du;
        v = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s2]-2*c->f.ptr.p_double[sfx+s1]/dt-c->f.ptr.p_double[sfx+s2]/dt;
        *f = *f+v*t2*u0;
        *fx = *fx+2*v*t1*u0*dt;
        v = -3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du);
        *f = *f+v*t2*u1;
        *fx = *fx+2*v*t1*u1*dt;
        *fy = *fy+v*t2*u0*du;
        *fxy = *fxy+2*v*t1*u0*dt*du;
        v = 9*c->f.ptr.p_double[s1]-9*c->f.ptr.p_double[s2]+9*c->f.ptr.p_double[s3]-9*c->f.ptr.p_double[s4]+6*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s2]/dt-3*c->f.ptr.p_double[sfx+s3]/dt-6*c->f.ptr.p_double[sfx+s4]/dt+6*c->f.ptr.p_double[sfy+s1]/du-6*c->f.ptr.p_double[sfy+s2]/du-3*c->f.ptr.p_double[sfy+s3]/du+3*c->f.ptr.p_double[sfy+s4]/du+4*c->f.ptr.p_double[sfxy+s1]/(dt*du)+2*c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+2*c->f.ptr.p_double[sfxy+s4]/(dt*du);
        *f = *f+v*t2*u2;
        *fx = *fx+2*v*t1*u2*dt;
        *fy = *fy+2*v*t2*u1*du;
        *fxy = *fxy+4*v*t1*u1*dt*du;
        v = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-4*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s2]/dt+2*c->f.ptr.p_double[sfx+s3]/dt+4*c->f.ptr.p_double[sfx+s4]/dt-3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du+3*c->f.ptr.p_double[sfy+s3]/du-3*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-2*c->f.ptr.p_double[sfxy+s4]/(dt*du);
        *f = *f+v*t2*u3;
        *fx = *fx+2*v*t1*u3*dt;
        *fy = *fy+3*v*t2*u2*du;
        *fxy = *fxy+6*v*t1*u2*dt*du;
        v = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s2]+c->f.ptr.p_double[sfx+s1]/dt+c->f.ptr.p_double[sfx+s2]/dt;
        *f = *f+v*t3*u0;
        *fx = *fx+3*v*t2*u0*dt;
        v = 2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du);
        *f = *f+v*t3*u1;
        *fx = *fx+3*v*t2*u1*dt;
        *fy = *fy+v*t3*u0*du;
        *fxy = *fxy+3*v*t2*u0*dt*du;
        v = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-3*c->f.ptr.p_double[sfx+s1]/dt-3*c->f.ptr.p_double[sfx+s2]/dt+3*c->f.ptr.p_double[sfx+s3]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-4*c->f.ptr.p_double[sfy+s1]/du+4*c->f.ptr.p_double[sfy+s2]/du+2*c->f.ptr.p_double[sfy+s3]/du-2*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-2*c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du);
        *f = *f+v*t3*u2;
        *fx = *fx+3*v*t2*u2*dt;
        *fy = *fy+2*v*t3*u1*du;
        *fxy = *fxy+6*v*t2*u1*dt*du;
        v = 4*c->f.ptr.p_double[s1]-4*c->f.ptr.p_double[s2]+4*c->f.ptr.p_double[s3]-4*c->f.ptr.p_double[s4]+2*c->f.ptr.p_double[sfx+s1]/dt+2*c->f.ptr.p_double[sfx+s2]/dt-2*c->f.ptr.p_double[sfx+s3]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfy+s3]/du+2*c->f.ptr.p_double[sfy+s4]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du);
        *f = *f+v*t3*u3;
        *fx = *fx+3*v*t2*u3*dt;
        *fy = *fy+3*v*t3*u2*du;
        *fxy = *fxy+9*v*t2*u2*dt*du;
        return;
    }
}


/*************************************************************************
This subroutine performs linear transformation of the spline argument.

Input parameters:
    C       -   spline interpolant
    AX, BX  -   transformation coefficients: x = A*t + B
    AY, BY  -   transformation coefficients: y = A*u + B
Result:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 30.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dlintransxy(spline2dinterpolant* c,
     double ax,
     double bx,
     double ay,
     double by,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector x;
    ae_vector y;
    ae_vector f;
    ae_vector v;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&x, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_vector_init(&f, 0, DT_REAL, _state);
    ae_vector_init(&v, 0, DT_REAL, _state);

    ae_assert(c->stype==-3||c->stype==-1, "Spline2DLinTransXY: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert(ae_isfinite(ax, _state), "Spline2DLinTransXY: AX is infinite or NaN", _state);
    ae_assert(ae_isfinite(bx, _state), "Spline2DLinTransXY: BX is infinite or NaN", _state);
    ae_assert(ae_isfinite(ay, _state), "Spline2DLinTransXY: AY is infinite or NaN", _state);
    ae_assert(ae_isfinite(by, _state), "Spline2DLinTransXY: BY is infinite or NaN", _state);
    ae_vector_set_length(&x, c->n, _state);
    ae_vector_set_length(&y, c->m, _state);
    ae_vector_set_length(&f, c->m*c->n*c->d, _state);
    for(j=0; j<=c->n-1; j++)
    {
        x.ptr.p_double[j] = c->x.ptr.p_double[j];
    }
    for(i=0; i<=c->m-1; i++)
    {
        y.ptr.p_double[i] = c->y.ptr.p_double[i];
    }
    for(i=0; i<=c->m-1; i++)
    {
        for(j=0; j<=c->n-1; j++)
        {
            for(k=0; k<=c->d-1; k++)
            {
                f.ptr.p_double[c->d*(i*c->n+j)+k] = c->f.ptr.p_double[c->d*(i*c->n+j)+k];
            }
        }
    }
    
    /*
     * Handle different combinations of AX/AY
     */
    if( ae_fp_eq(ax,(double)(0))&&ae_fp_neq(ay,(double)(0)) )
    {
        for(i=0; i<=c->m-1; i++)
        {
            spline2dcalcvbuf(c, bx, y.ptr.p_double[i], &v, _state);
            y.ptr.p_double[i] = (y.ptr.p_double[i]-by)/ay;
            for(j=0; j<=c->n-1; j++)
            {
                for(k=0; k<=c->d-1; k++)
                {
                    f.ptr.p_double[c->d*(i*c->n+j)+k] = v.ptr.p_double[k];
                }
            }
        }
    }
    if( ae_fp_neq(ax,(double)(0))&&ae_fp_eq(ay,(double)(0)) )
    {
        for(j=0; j<=c->n-1; j++)
        {
            spline2dcalcvbuf(c, x.ptr.p_double[j], by, &v, _state);
            x.ptr.p_double[j] = (x.ptr.p_double[j]-bx)/ax;
            for(i=0; i<=c->m-1; i++)
            {
                for(k=0; k<=c->d-1; k++)
                {
                    f.ptr.p_double[c->d*(i*c->n+j)+k] = v.ptr.p_double[k];
                }
            }
        }
    }
    if( ae_fp_neq(ax,(double)(0))&&ae_fp_neq(ay,(double)(0)) )
    {
        for(j=0; j<=c->n-1; j++)
        {
            x.ptr.p_double[j] = (x.ptr.p_double[j]-bx)/ax;
        }
        for(i=0; i<=c->m-1; i++)
        {
            y.ptr.p_double[i] = (y.ptr.p_double[i]-by)/ay;
        }
    }
    if( ae_fp_eq(ax,(double)(0))&&ae_fp_eq(ay,(double)(0)) )
    {
        spline2dcalcvbuf(c, bx, by, &v, _state);
        for(i=0; i<=c->m-1; i++)
        {
            for(j=0; j<=c->n-1; j++)
            {
                for(k=0; k<=c->d-1; k++)
                {
                    f.ptr.p_double[c->d*(i*c->n+j)+k] = v.ptr.p_double[k];
                }
            }
        }
    }
    
    /*
     * Rebuild spline
     */
    if( c->stype==-3 )
    {
        spline2dbuildbicubicv(&x, c->n, &y, c->m, &f, c->d, c, _state);
    }
    if( c->stype==-1 )
    {
        spline2dbuildbilinearv(&x, c->n, &y, c->m, &f, c->d, c, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine performs linear transformation of the spline.

Input parameters:
    C   -   spline interpolant.
    A, B-   transformation coefficients: S2(x,y) = A*S(x,y) + B
    
Output parameters:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 30.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dlintransf(spline2dinterpolant* c,
     double a,
     double b,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector x;
    ae_vector y;
    ae_vector f;
    ae_int_t i;
    ae_int_t j;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&x, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_vector_init(&f, 0, DT_REAL, _state);

    ae_assert(c->stype==-3||c->stype==-1, "Spline2DLinTransF: incorrect C (incorrect parameter C.SType)", _state);
    ae_vector_set_length(&x, c->n, _state);
    ae_vector_set_length(&y, c->m, _state);
    ae_vector_set_length(&f, c->m*c->n*c->d, _state);
    for(j=0; j<=c->n-1; j++)
    {
        x.ptr.p_double[j] = c->x.ptr.p_double[j];
    }
    for(i=0; i<=c->m-1; i++)
    {
        y.ptr.p_double[i] = c->y.ptr.p_double[i];
    }
    for(i=0; i<=c->m*c->n*c->d-1; i++)
    {
        f.ptr.p_double[i] = a*c->f.ptr.p_double[i]+b;
    }
    if( c->stype==-3 )
    {
        spline2dbuildbicubicv(&x, c->n, &y, c->m, &f, c->d, c, _state);
    }
    if( c->stype==-1 )
    {
        spline2dbuildbilinearv(&x, c->n, &y, c->m, &f, c->d, c, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine makes the copy of the spline model.

Input parameters:
    C   -   spline interpolant

Output parameters:
    CC  -   spline copy

  -- ALGLIB PROJECT --
     Copyright 29.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dcopy(spline2dinterpolant* c,
     spline2dinterpolant* cc,
     ae_state *_state)
{
    ae_int_t tblsize;

    _spline2dinterpolant_clear(cc);

    ae_assert(c->k==1||c->k==3, "Spline2DCopy: incorrect C (incorrect parameter C.K)", _state);
    cc->k = c->k;
    cc->n = c->n;
    cc->m = c->m;
    cc->d = c->d;
    cc->stype = c->stype;
    tblsize = -1;
    if( c->stype==-3 )
    {
        tblsize = 4*c->n*c->m*c->d;
    }
    if( c->stype==-1 )
    {
        tblsize = c->n*c->m*c->d;
    }
    ae_assert(tblsize>0, "Spline2DCopy: internal error", _state);
    ae_vector_set_length(&cc->x, cc->n, _state);
    ae_vector_set_length(&cc->y, cc->m, _state);
    ae_vector_set_length(&cc->f, tblsize, _state);
    ae_v_move(&cc->x.ptr.p_double[0], 1, &c->x.ptr.p_double[0], 1, ae_v_len(0,cc->n-1));
    ae_v_move(&cc->y.ptr.p_double[0], 1, &c->y.ptr.p_double[0], 1, ae_v_len(0,cc->m-1));
    ae_v_move(&cc->f.ptr.p_double[0], 1, &c->f.ptr.p_double[0], 1, ae_v_len(0,tblsize-1));
}


/*************************************************************************
Bicubic spline resampling

Input parameters:
    A           -   function values at the old grid,
                    array[0..OldHeight-1, 0..OldWidth-1]
    OldHeight   -   old grid height, OldHeight>1
    OldWidth    -   old grid width, OldWidth>1
    NewHeight   -   new grid height, NewHeight>1
    NewWidth    -   new grid width, NewWidth>1
    
Output parameters:
    B           -   function values at the new grid,
                    array[0..NewHeight-1, 0..NewWidth-1]

  -- ALGLIB routine --
     15 May, 2007
     Copyright by Bochkanov Sergey
*************************************************************************/
void spline2dresamplebicubic(/* Real    */ ae_matrix* a,
     ae_int_t oldheight,
     ae_int_t oldwidth,
     /* Real    */ ae_matrix* b,
     ae_int_t newheight,
     ae_int_t newwidth,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_matrix buf;
    ae_vector x;
    ae_vector y;
    spline1dinterpolant c;
    ae_int_t mw;
    ae_int_t mh;
    ae_int_t i;
    ae_int_t j;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_clear(b);
    ae_matrix_init(&buf, 0, 0, DT_REAL, _state);
    ae_vector_init(&x, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    _spline1dinterpolant_init(&c, _state);

    ae_assert(oldwidth>1&&oldheight>1, "Spline2DResampleBicubic: width/height less than 1", _state);
    ae_assert(newwidth>1&&newheight>1, "Spline2DResampleBicubic: width/height less than 1", _state);
    
    /*
     * Prepare
     */
    mw = ae_maxint(oldwidth, newwidth, _state);
    mh = ae_maxint(oldheight, newheight, _state);
    ae_matrix_set_length(b, newheight, newwidth, _state);
    ae_matrix_set_length(&buf, oldheight, newwidth, _state);
    ae_vector_set_length(&x, ae_maxint(mw, mh, _state), _state);
    ae_vector_set_length(&y, ae_maxint(mw, mh, _state), _state);
    
    /*
     * Horizontal interpolation
     */
    for(i=0; i<=oldheight-1; i++)
    {
        
        /*
         * Fill X, Y
         */
        for(j=0; j<=oldwidth-1; j++)
        {
            x.ptr.p_double[j] = (double)j/(double)(oldwidth-1);
            y.ptr.p_double[j] = a->ptr.pp_double[i][j];
        }
        
        /*
         * Interpolate and place result into temporary matrix
         */
        spline1dbuildcubic(&x, &y, oldwidth, 0, 0.0, 0, 0.0, &c, _state);
        for(j=0; j<=newwidth-1; j++)
        {
            buf.ptr.pp_double[i][j] = spline1dcalc(&c, (double)j/(double)(newwidth-1), _state);
        }
    }
    
    /*
     * Vertical interpolation
     */
    for(j=0; j<=newwidth-1; j++)
    {
        
        /*
         * Fill X, Y
         */
        for(i=0; i<=oldheight-1; i++)
        {
            x.ptr.p_double[i] = (double)i/(double)(oldheight-1);
            y.ptr.p_double[i] = buf.ptr.pp_double[i][j];
        }
        
        /*
         * Interpolate and place result into B
         */
        spline1dbuildcubic(&x, &y, oldheight, 0, 0.0, 0, 0.0, &c, _state);
        for(i=0; i<=newheight-1; i++)
        {
            b->ptr.pp_double[i][j] = spline1dcalc(&c, (double)i/(double)(newheight-1), _state);
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
Bilinear spline resampling

Input parameters:
    A           -   function values at the old grid,
                    array[0..OldHeight-1, 0..OldWidth-1]
    OldHeight   -   old grid height, OldHeight>1
    OldWidth    -   old grid width, OldWidth>1
    NewHeight   -   new grid height, NewHeight>1
    NewWidth    -   new grid width, NewWidth>1

Output parameters:
    B           -   function values at the new grid,
                    array[0..NewHeight-1, 0..NewWidth-1]

  -- ALGLIB routine --
     09.07.2007
     Copyright by Bochkanov Sergey
*************************************************************************/
void spline2dresamplebilinear(/* Real    */ ae_matrix* a,
     ae_int_t oldheight,
     ae_int_t oldwidth,
     /* Real    */ ae_matrix* b,
     ae_int_t newheight,
     ae_int_t newwidth,
     ae_state *_state)
{
    ae_int_t l;
    ae_int_t c;
    double t;
    double u;
    ae_int_t i;
    ae_int_t j;

    ae_matrix_clear(b);

    ae_assert(oldwidth>1&&oldheight>1, "Spline2DResampleBilinear: width/height less than 1", _state);
    ae_assert(newwidth>1&&newheight>1, "Spline2DResampleBilinear: width/height less than 1", _state);
    ae_matrix_set_length(b, newheight, newwidth, _state);
    for(i=0; i<=newheight-1; i++)
    {
        for(j=0; j<=newwidth-1; j++)
        {
            l = i*(oldheight-1)/(newheight-1);
            if( l==oldheight-1 )
            {
                l = oldheight-2;
            }
            u = (double)i/(double)(newheight-1)*(oldheight-1)-l;
            c = j*(oldwidth-1)/(newwidth-1);
            if( c==oldwidth-1 )
            {
                c = oldwidth-2;
            }
            t = (double)(j*(oldwidth-1))/(double)(newwidth-1)-c;
            b->ptr.pp_double[i][j] = (1-t)*(1-u)*a->ptr.pp_double[l][c]+t*(1-u)*a->ptr.pp_double[l][c+1]+t*u*a->ptr.pp_double[l+1][c+1]+(1-t)*u*a->ptr.pp_double[l+1][c];
        }
    }
}


/*************************************************************************
This subroutine builds bilinear vector-valued spline.

Input parameters:
    X   -   spline abscissas, array[0..N-1]
    Y   -   spline ordinates, array[0..M-1]
    F   -   function values, array[0..M*N*D-1]:
            * first D elements store D values at (X[0],Y[0])
            * next D elements store D values at (X[1],Y[0])
            * general form - D function values at (X[i],Y[j]) are stored
              at F[D*(J*N+I)...D*(J*N+I)+D-1].
    M,N -   grid size, M>=2, N>=2
    D   -   vector dimension, D>=1

Output parameters:
    C   -   spline interpolant

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildbilinearv(/* Real    */ ae_vector* x,
     ae_int_t n,
     /* Real    */ ae_vector* y,
     ae_int_t m,
     /* Real    */ ae_vector* f,
     ae_int_t d,
     spline2dinterpolant* c,
     ae_state *_state)
{
    double t;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t i0;

    _spline2dinterpolant_clear(c);

    ae_assert(n>=2, "Spline2DBuildBilinearV: N is less then 2", _state);
    ae_assert(m>=2, "Spline2DBuildBilinearV: M is less then 2", _state);
    ae_assert(d>=1, "Spline2DBuildBilinearV: invalid argument D (D<1)", _state);
    ae_assert(x->cnt>=n&&y->cnt>=m, "Spline2DBuildBilinearV: length of X or Y is too short (Length(X/Y)<N/M)", _state);
    ae_assert(isfinitevector(x, n, _state)&&isfinitevector(y, m, _state), "Spline2DBuildBilinearV: X or Y contains NaN or Infinite value", _state);
    k = n*m*d;
    ae_assert(f->cnt>=k, "Spline2DBuildBilinearV: length of F is too short (Length(F)<N*M*D)", _state);
    ae_assert(isfinitevector(f, k, _state), "Spline2DBuildBilinearV: F contains NaN or Infinite value", _state);
    
    /*
     * Fill interpolant
     */
    c->k = 1;
    c->n = n;
    c->m = m;
    c->d = d;
    c->stype = -1;
    ae_vector_set_length(&c->x, c->n, _state);
    ae_vector_set_length(&c->y, c->m, _state);
    ae_vector_set_length(&c->f, k, _state);
    for(i=0; i<=c->n-1; i++)
    {
        c->x.ptr.p_double[i] = x->ptr.p_double[i];
    }
    for(i=0; i<=c->m-1; i++)
    {
        c->y.ptr.p_double[i] = y->ptr.p_double[i];
    }
    for(i=0; i<=k-1; i++)
    {
        c->f.ptr.p_double[i] = f->ptr.p_double[i];
    }
    
    /*
     * Sort points
     */
    for(j=0; j<=c->n-1; j++)
    {
        k = j;
        for(i=j+1; i<=c->n-1; i++)
        {
            if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) )
            {
                k = i;
            }
        }
        if( k!=j )
        {
            for(i=0; i<=c->m-1; i++)
            {
                for(i0=0; i0<=c->d-1; i0++)
                {
                    t = c->f.ptr.p_double[c->d*(i*c->n+j)+i0];
                    c->f.ptr.p_double[c->d*(i*c->n+j)+i0] = c->f.ptr.p_double[c->d*(i*c->n+k)+i0];
                    c->f.ptr.p_double[c->d*(i*c->n+k)+i0] = t;
                }
            }
            t = c->x.ptr.p_double[j];
            c->x.ptr.p_double[j] = c->x.ptr.p_double[k];
            c->x.ptr.p_double[k] = t;
        }
    }
    for(i=0; i<=c->m-1; i++)
    {
        k = i;
        for(j=i+1; j<=c->m-1; j++)
        {
            if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) )
            {
                k = j;
            }
        }
        if( k!=i )
        {
            for(j=0; j<=c->n-1; j++)
            {
                for(i0=0; i0<=c->d-1; i0++)
                {
                    t = c->f.ptr.p_double[c->d*(i*c->n+j)+i0];
                    c->f.ptr.p_double[c->d*(i*c->n+j)+i0] = c->f.ptr.p_double[c->d*(k*c->n+j)+i0];
                    c->f.ptr.p_double[c->d*(k*c->n+j)+i0] = t;
                }
            }
            t = c->y.ptr.p_double[i];
            c->y.ptr.p_double[i] = c->y.ptr.p_double[k];
            c->y.ptr.p_double[k] = t;
        }
    }
}


/*************************************************************************
This subroutine builds bicubic vector-valued spline.

Input parameters:
    X   -   spline abscissas, array[0..N-1]
    Y   -   spline ordinates, array[0..M-1]
    F   -   function values, array[0..M*N*D-1]:
            * first D elements store D values at (X[0],Y[0])
            * next D elements store D values at (X[1],Y[0])
            * general form - D function values at (X[i],Y[j]) are stored
              at F[D*(J*N+I)...D*(J*N+I)+D-1].
    M,N -   grid size, M>=2, N>=2
    D   -   vector dimension, D>=1

Output parameters:
    C   -   spline interpolant

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildbicubicv(/* Real    */ ae_vector* x,
     ae_int_t n,
     /* Real    */ ae_vector* y,
     ae_int_t m,
     /* Real    */ ae_vector* f,
     ae_int_t d,
     spline2dinterpolant* c,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector _f;
    ae_matrix tf;
    ae_matrix dx;
    ae_matrix dy;
    ae_matrix dxy;
    double t;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t di;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init_copy(&_f, f, _state);
    f = &_f;
    _spline2dinterpolant_clear(c);
    ae_matrix_init(&tf, 0, 0, DT_REAL, _state);
    ae_matrix_init(&dx, 0, 0, DT_REAL, _state);
    ae_matrix_init(&dy, 0, 0, DT_REAL, _state);
    ae_matrix_init(&dxy, 0, 0, DT_REAL, _state);

    ae_assert(n>=2, "Spline2DBuildBicubicV: N is less than 2", _state);
    ae_assert(m>=2, "Spline2DBuildBicubicV: M is less than 2", _state);
    ae_assert(d>=1, "Spline2DBuildBicubicV: invalid argument D (D<1)", _state);
    ae_assert(x->cnt>=n&&y->cnt>=m, "Spline2DBuildBicubicV: length of X or Y is too short (Length(X/Y)<N/M)", _state);
    ae_assert(isfinitevector(x, n, _state)&&isfinitevector(y, m, _state), "Spline2DBuildBicubicV: X or Y contains NaN or Infinite value", _state);
    k = n*m*d;
    ae_assert(f->cnt>=k, "Spline2DBuildBicubicV: length of F is too short (Length(F)<N*M*D)", _state);
    ae_assert(isfinitevector(f, k, _state), "Spline2DBuildBicubicV: F contains NaN or Infinite value", _state);
    
    /*
     * Fill interpolant:
     *  F[0]...F[N*M*D-1]:
     *      f(i,j) table. f(0,0), f(0, 1), f(0,2) and so on...
     *  F[N*M*D]...F[2*N*M*D-1]:
     *      df(i,j)/dx table.
     *  F[2*N*M*D]...F[3*N*M*D-1]:
     *      df(i,j)/dy table.
     *  F[3*N*M*D]...F[4*N*M*D-1]:
     *      d2f(i,j)/dxdy table.
     */
    c->k = 3;
    c->d = d;
    c->n = n;
    c->m = m;
    c->stype = -3;
    k = 4*k;
    ae_vector_set_length(&c->x, c->n, _state);
    ae_vector_set_length(&c->y, c->m, _state);
    ae_vector_set_length(&c->f, k, _state);
    ae_matrix_set_length(&tf, c->m, c->n, _state);
    for(i=0; i<=c->n-1; i++)
    {
        c->x.ptr.p_double[i] = x->ptr.p_double[i];
    }
    for(i=0; i<=c->m-1; i++)
    {
        c->y.ptr.p_double[i] = y->ptr.p_double[i];
    }
    
    /*
     * Sort points
     */
    for(j=0; j<=c->n-1; j++)
    {
        k = j;
        for(i=j+1; i<=c->n-1; i++)
        {
            if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) )
            {
                k = i;
            }
        }
        if( k!=j )
        {
            for(i=0; i<=c->m-1; i++)
            {
                for(di=0; di<=c->d-1; di++)
                {
                    t = f->ptr.p_double[c->d*(i*c->n+j)+di];
                    f->ptr.p_double[c->d*(i*c->n+j)+di] = f->ptr.p_double[c->d*(i*c->n+k)+di];
                    f->ptr.p_double[c->d*(i*c->n+k)+di] = t;
                }
            }
            t = c->x.ptr.p_double[j];
            c->x.ptr.p_double[j] = c->x.ptr.p_double[k];
            c->x.ptr.p_double[k] = t;
        }
    }
    for(i=0; i<=c->m-1; i++)
    {
        k = i;
        for(j=i+1; j<=c->m-1; j++)
        {
            if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) )
            {
                k = j;
            }
        }
        if( k!=i )
        {
            for(j=0; j<=c->n-1; j++)
            {
                for(di=0; di<=c->d-1; di++)
                {
                    t = f->ptr.p_double[c->d*(i*c->n+j)+di];
                    f->ptr.p_double[c->d*(i*c->n+j)+di] = f->ptr.p_double[c->d*(k*c->n+j)+di];
                    f->ptr.p_double[c->d*(k*c->n+j)+di] = t;
                }
            }
            t = c->y.ptr.p_double[i];
            c->y.ptr.p_double[i] = c->y.ptr.p_double[k];
            c->y.ptr.p_double[k] = t;
        }
    }
    for(di=0; di<=c->d-1; di++)
    {
        for(i=0; i<=c->m-1; i++)
        {
            for(j=0; j<=c->n-1; j++)
            {
                tf.ptr.pp_double[i][j] = f->ptr.p_double[c->d*(i*c->n+j)+di];
            }
        }
        spline2d_bicubiccalcderivatives(&tf, &c->x, &c->y, c->m, c->n, &dx, &dy, &dxy, _state);
        for(i=0; i<=c->m-1; i++)
        {
            for(j=0; j<=c->n-1; j++)
            {
                k = c->d*(i*c->n+j)+di;
                c->f.ptr.p_double[k] = tf.ptr.pp_double[i][j];
                c->f.ptr.p_double[c->n*c->m*c->d+k] = dx.ptr.pp_double[i][j];
                c->f.ptr.p_double[2*c->n*c->m*c->d+k] = dy.ptr.pp_double[i][j];
                c->f.ptr.p_double[3*c->n*c->m*c->d+k] = dxy.ptr.pp_double[i][j];
            }
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine calculates bilinear or bicubic vector-valued spline at the
given point (X,Y).

INPUT PARAMETERS:
    C   -   spline interpolant.
    X, Y-   point
    F   -   output buffer, possibly preallocated array. In case array size
            is large enough to store result, it is not reallocated.  Array
            which is too short will be reallocated

OUTPUT PARAMETERS:
    F   -   array[D] (or larger) which stores function values

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dcalcvbuf(spline2dinterpolant* c,
     double x,
     double y,
     /* Real    */ ae_vector* f,
     ae_state *_state)
{
    double t;
    double dt;
    double u;
    double du;
    ae_int_t ix;
    ae_int_t iy;
    ae_int_t l;
    ae_int_t r;
    ae_int_t h;
    ae_int_t s1;
    ae_int_t s2;
    ae_int_t s3;
    ae_int_t s4;
    ae_int_t sfx;
    ae_int_t sfy;
    ae_int_t sfxy;
    double y1;
    double y2;
    double y3;
    double y4;
    double v;
    double t0;
    double t1;
    double t2;
    double t3;
    double u0;
    double u1;
    double u2;
    double u3;
    ae_int_t i;


    ae_assert(c->stype==-1||c->stype==-3, "Spline2DCalcVBuf: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline2DCalcVBuf: either X=NaN/Infinite or Y=NaN/Infinite", _state);
    rvectorsetlengthatleast(f, c->d, _state);
    
    /*
     * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included)
     */
    l = 0;
    r = c->n-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->x.ptr.p_double[h],x) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    t = (x-c->x.ptr.p_double[l])/(c->x.ptr.p_double[l+1]-c->x.ptr.p_double[l]);
    dt = 1.0/(c->x.ptr.p_double[l+1]-c->x.ptr.p_double[l]);
    ix = l;
    
    /*
     * Binary search in the [ y[0], ..., y[m-2] ] (y[m-1] is not included)
     */
    l = 0;
    r = c->m-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->y.ptr.p_double[h],y) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    u = (y-c->y.ptr.p_double[l])/(c->y.ptr.p_double[l+1]-c->y.ptr.p_double[l]);
    du = 1.0/(c->y.ptr.p_double[l+1]-c->y.ptr.p_double[l]);
    iy = l;
    
    /*
     * Bilinear interpolation
     */
    if( c->stype==-1 )
    {
        for(i=0; i<=c->d-1; i++)
        {
            y1 = c->f.ptr.p_double[c->d*(c->n*iy+ix)+i];
            y2 = c->f.ptr.p_double[c->d*(c->n*iy+(ix+1))+i];
            y3 = c->f.ptr.p_double[c->d*(c->n*(iy+1)+(ix+1))+i];
            y4 = c->f.ptr.p_double[c->d*(c->n*(iy+1)+ix)+i];
            f->ptr.p_double[i] = (1-t)*(1-u)*y1+t*(1-u)*y2+t*u*y3+(1-t)*u*y4;
        }
        return;
    }
    
    /*
     * Bicubic interpolation
     */
    if( c->stype==-3 )
    {
        
        /*
         * Prepare info
         */
        t0 = (double)(1);
        t1 = t;
        t2 = ae_sqr(t, _state);
        t3 = t*t2;
        u0 = (double)(1);
        u1 = u;
        u2 = ae_sqr(u, _state);
        u3 = u*u2;
        sfx = c->n*c->m*c->d;
        sfy = 2*c->n*c->m*c->d;
        sfxy = 3*c->n*c->m*c->d;
        for(i=0; i<=c->d-1; i++)
        {
            
            /*
             * Prepare F, dF/dX, dF/dY, d2F/dXdY
             */
            f->ptr.p_double[i] = (double)(0);
            s1 = c->d*(c->n*iy+ix)+i;
            s2 = c->d*(c->n*iy+(ix+1))+i;
            s3 = c->d*(c->n*(iy+1)+(ix+1))+i;
            s4 = c->d*(c->n*(iy+1)+ix)+i;
            
            /*
             * Calculate
             */
            v = c->f.ptr.p_double[s1];
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t0*u0;
            v = c->f.ptr.p_double[sfy+s1]/du;
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t0*u1;
            v = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s4]-2*c->f.ptr.p_double[sfy+s1]/du-c->f.ptr.p_double[sfy+s4]/du;
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t0*u2;
            v = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s4]+c->f.ptr.p_double[sfy+s1]/du+c->f.ptr.p_double[sfy+s4]/du;
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t0*u3;
            v = c->f.ptr.p_double[sfx+s1]/dt;
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t1*u0;
            v = c->f.ptr.p_double[sfxy+s1]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t1*u1;
            v = -3*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t1*u2;
            v = 2*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t1*u3;
            v = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s2]-2*c->f.ptr.p_double[sfx+s1]/dt-c->f.ptr.p_double[sfx+s2]/dt;
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t2*u0;
            v = -3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t2*u1;
            v = 9*c->f.ptr.p_double[s1]-9*c->f.ptr.p_double[s2]+9*c->f.ptr.p_double[s3]-9*c->f.ptr.p_double[s4]+6*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s2]/dt-3*c->f.ptr.p_double[sfx+s3]/dt-6*c->f.ptr.p_double[sfx+s4]/dt+6*c->f.ptr.p_double[sfy+s1]/du-6*c->f.ptr.p_double[sfy+s2]/du-3*c->f.ptr.p_double[sfy+s3]/du+3*c->f.ptr.p_double[sfy+s4]/du+4*c->f.ptr.p_double[sfxy+s1]/(dt*du)+2*c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+2*c->f.ptr.p_double[sfxy+s4]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t2*u2;
            v = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-4*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s2]/dt+2*c->f.ptr.p_double[sfx+s3]/dt+4*c->f.ptr.p_double[sfx+s4]/dt-3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du+3*c->f.ptr.p_double[sfy+s3]/du-3*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-2*c->f.ptr.p_double[sfxy+s4]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t2*u3;
            v = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s2]+c->f.ptr.p_double[sfx+s1]/dt+c->f.ptr.p_double[sfx+s2]/dt;
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t3*u0;
            v = 2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t3*u1;
            v = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-3*c->f.ptr.p_double[sfx+s1]/dt-3*c->f.ptr.p_double[sfx+s2]/dt+3*c->f.ptr.p_double[sfx+s3]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-4*c->f.ptr.p_double[sfy+s1]/du+4*c->f.ptr.p_double[sfy+s2]/du+2*c->f.ptr.p_double[sfy+s3]/du-2*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-2*c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t3*u2;
            v = 4*c->f.ptr.p_double[s1]-4*c->f.ptr.p_double[s2]+4*c->f.ptr.p_double[s3]-4*c->f.ptr.p_double[s4]+2*c->f.ptr.p_double[sfx+s1]/dt+2*c->f.ptr.p_double[sfx+s2]/dt-2*c->f.ptr.p_double[sfx+s3]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfy+s3]/du+2*c->f.ptr.p_double[sfy+s4]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du);
            f->ptr.p_double[i] = f->ptr.p_double[i]+v*t3*u3;
        }
        return;
    }
}


/*************************************************************************
This subroutine calculates bilinear or bicubic vector-valued spline at the
given point (X,Y).

INPUT PARAMETERS:
    C   -   spline interpolant.
    X, Y-   point

OUTPUT PARAMETERS:
    F   -   array[D] which stores function values.  F is out-parameter and
            it  is  reallocated  after  call to this function. In case you
            want  to    reuse  previously  allocated  F,   you   may   use
            Spline2DCalcVBuf(),  which  reallocates  F only when it is too
            small.

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dcalcv(spline2dinterpolant* c,
     double x,
     double y,
     /* Real    */ ae_vector* f,
     ae_state *_state)
{

    ae_vector_clear(f);

    ae_assert(c->stype==-1||c->stype==-3, "Spline2DCalcV: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline2DCalcV: either X=NaN/Infinite or Y=NaN/Infinite", _state);
    ae_vector_set_length(f, c->d, _state);
    spline2dcalcvbuf(c, x, y, f, _state);
}


/*************************************************************************
This subroutine unpacks two-dimensional spline into the coefficients table

Input parameters:
    C   -   spline interpolant.

Result:
    M, N-   grid size (x-axis and y-axis)
    D   -   number of components
    Tbl -   coefficients table, unpacked format,
            D - components: [0..(N-1)*(M-1)*D-1, 0..19].
            For T=0..D-1 (component index), I = 0...N-2 (x index),
            J=0..M-2 (y index):
                K :=  T + I*D + J*D*(N-1)
                
                K-th row stores decomposition for T-th component of the
                vector-valued function
                
                Tbl[K,0] = X[i]
                Tbl[K,1] = X[i+1]
                Tbl[K,2] = Y[j]
                Tbl[K,3] = Y[j+1]
                Tbl[K,4] = C00
                Tbl[K,5] = C01
                Tbl[K,6] = C02
                Tbl[K,7] = C03
                Tbl[K,8] = C10
                Tbl[K,9] = C11
                ...
                Tbl[K,19] = C33
            On each grid square spline is equals to:
                S(x) = SUM(c[i,j]*(t^i)*(u^j), i=0..3, j=0..3)
                t = x-x[j]
                u = y-y[i]

  -- ALGLIB PROJECT --
     Copyright 16.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline2dunpackv(spline2dinterpolant* c,
     ae_int_t* m,
     ae_int_t* n,
     ae_int_t* d,
     /* Real    */ ae_matrix* tbl,
     ae_state *_state)
{
    ae_int_t k;
    ae_int_t p;
    ae_int_t ci;
    ae_int_t cj;
    ae_int_t s1;
    ae_int_t s2;
    ae_int_t s3;
    ae_int_t s4;
    ae_int_t sfx;
    ae_int_t sfy;
    ae_int_t sfxy;
    double y1;
    double y2;
    double y3;
    double y4;
    double dt;
    double du;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k0;

    *m = 0;
    *n = 0;
    *d = 0;
    ae_matrix_clear(tbl);

    ae_assert(c->stype==-3||c->stype==-1, "Spline2DUnpackV: incorrect C (incorrect parameter C.SType)", _state);
    *n = c->n;
    *m = c->m;
    *d = c->d;
    ae_matrix_set_length(tbl, (*n-1)*(*m-1)*(*d), 20, _state);
    sfx = *n*(*m)*(*d);
    sfy = 2*(*n)*(*m)*(*d);
    sfxy = 3*(*n)*(*m)*(*d);
    for(i=0; i<=*m-2; i++)
    {
        for(j=0; j<=*n-2; j++)
        {
            for(k=0; k<=*d-1; k++)
            {
                p = *d*(i*(*n-1)+j)+k;
                tbl->ptr.pp_double[p][0] = c->x.ptr.p_double[j];
                tbl->ptr.pp_double[p][1] = c->x.ptr.p_double[j+1];
                tbl->ptr.pp_double[p][2] = c->y.ptr.p_double[i];
                tbl->ptr.pp_double[p][3] = c->y.ptr.p_double[i+1];
                dt = 1/(tbl->ptr.pp_double[p][1]-tbl->ptr.pp_double[p][0]);
                du = 1/(tbl->ptr.pp_double[p][3]-tbl->ptr.pp_double[p][2]);
                
                /*
                 * Bilinear interpolation
                 */
                if( c->stype==-1 )
                {
                    for(k0=4; k0<=19; k0++)
                    {
                        tbl->ptr.pp_double[p][k0] = (double)(0);
                    }
                    y1 = c->f.ptr.p_double[*d*(*n*i+j)+k];
                    y2 = c->f.ptr.p_double[*d*(*n*i+(j+1))+k];
                    y3 = c->f.ptr.p_double[*d*(*n*(i+1)+(j+1))+k];
                    y4 = c->f.ptr.p_double[*d*(*n*(i+1)+j)+k];
                    tbl->ptr.pp_double[p][4] = y1;
                    tbl->ptr.pp_double[p][4+1*4+0] = y2-y1;
                    tbl->ptr.pp_double[p][4+0*4+1] = y4-y1;
                    tbl->ptr.pp_double[p][4+1*4+1] = y3-y2-y4+y1;
                }
                
                /*
                 * Bicubic interpolation
                 */
                if( c->stype==-3 )
                {
                    s1 = *d*(*n*i+j)+k;
                    s2 = *d*(*n*i+(j+1))+k;
                    s3 = *d*(*n*(i+1)+(j+1))+k;
                    s4 = *d*(*n*(i+1)+j)+k;
                    tbl->ptr.pp_double[p][4+0*4+0] = c->f.ptr.p_double[s1];
                    tbl->ptr.pp_double[p][4+0*4+1] = c->f.ptr.p_double[sfy+s1]/du;
                    tbl->ptr.pp_double[p][4+0*4+2] = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s4]-2*c->f.ptr.p_double[sfy+s1]/du-c->f.ptr.p_double[sfy+s4]/du;
                    tbl->ptr.pp_double[p][4+0*4+3] = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s4]+c->f.ptr.p_double[sfy+s1]/du+c->f.ptr.p_double[sfy+s4]/du;
                    tbl->ptr.pp_double[p][4+1*4+0] = c->f.ptr.p_double[sfx+s1]/dt;
                    tbl->ptr.pp_double[p][4+1*4+1] = c->f.ptr.p_double[sfxy+s1]/(dt*du);
                    tbl->ptr.pp_double[p][4+1*4+2] = -3*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du);
                    tbl->ptr.pp_double[p][4+1*4+3] = 2*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du);
                    tbl->ptr.pp_double[p][4+2*4+0] = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s2]-2*c->f.ptr.p_double[sfx+s1]/dt-c->f.ptr.p_double[sfx+s2]/dt;
                    tbl->ptr.pp_double[p][4+2*4+1] = -3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du);
                    tbl->ptr.pp_double[p][4+2*4+2] = 9*c->f.ptr.p_double[s1]-9*c->f.ptr.p_double[s2]+9*c->f.ptr.p_double[s3]-9*c->f.ptr.p_double[s4]+6*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s2]/dt-3*c->f.ptr.p_double[sfx+s3]/dt-6*c->f.ptr.p_double[sfx+s4]/dt+6*c->f.ptr.p_double[sfy+s1]/du-6*c->f.ptr.p_double[sfy+s2]/du-3*c->f.ptr.p_double[sfy+s3]/du+3*c->f.ptr.p_double[sfy+s4]/du+4*c->f.ptr.p_double[sfxy+s1]/(dt*du)+2*c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+2*c->f.ptr.p_double[sfxy+s4]/(dt*du);
                    tbl->ptr.pp_double[p][4+2*4+3] = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-4*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s2]/dt+2*c->f.ptr.p_double[sfx+s3]/dt+4*c->f.ptr.p_double[sfx+s4]/dt-3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du+3*c->f.ptr.p_double[sfy+s3]/du-3*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-2*c->f.ptr.p_double[sfxy+s4]/(dt*du);
                    tbl->ptr.pp_double[p][4+3*4+0] = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s2]+c->f.ptr.p_double[sfx+s1]/dt+c->f.ptr.p_double[sfx+s2]/dt;
                    tbl->ptr.pp_double[p][4+3*4+1] = 2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du);
                    tbl->ptr.pp_double[p][4+3*4+2] = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-3*c->f.ptr.p_double[sfx+s1]/dt-3*c->f.ptr.p_double[sfx+s2]/dt+3*c->f.ptr.p_double[sfx+s3]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-4*c->f.ptr.p_double[sfy+s1]/du+4*c->f.ptr.p_double[sfy+s2]/du+2*c->f.ptr.p_double[sfy+s3]/du-2*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-2*c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du);
                    tbl->ptr.pp_double[p][4+3*4+3] = 4*c->f.ptr.p_double[s1]-4*c->f.ptr.p_double[s2]+4*c->f.ptr.p_double[s3]-4*c->f.ptr.p_double[s4]+2*c->f.ptr.p_double[sfx+s1]/dt+2*c->f.ptr.p_double[sfx+s2]/dt-2*c->f.ptr.p_double[sfx+s3]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfy+s3]/du+2*c->f.ptr.p_double[sfy+s4]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du);
                }
                
                /*
                 * Rescale Cij
                 */
                for(ci=0; ci<=3; ci++)
                {
                    for(cj=0; cj<=3; cj++)
                    {
                        tbl->ptr.pp_double[p][4+ci*4+cj] = tbl->ptr.pp_double[p][4+ci*4+cj]*ae_pow(dt, (double)(ci), _state)*ae_pow(du, (double)(cj), _state);
                    }
                }
            }
        }
    }
}


/*************************************************************************
This subroutine was deprecated in ALGLIB 3.6.0

We recommend you to switch  to  Spline2DBuildBilinearV(),  which  is  more
flexible and accepts its arguments in more convenient order.

  -- ALGLIB PROJECT --
     Copyright 05.07.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildbilinear(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_matrix* f,
     ae_int_t m,
     ae_int_t n,
     spline2dinterpolant* c,
     ae_state *_state)
{
    double t;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;

    _spline2dinterpolant_clear(c);

    ae_assert(n>=2, "Spline2DBuildBilinear: N<2", _state);
    ae_assert(m>=2, "Spline2DBuildBilinear: M<2", _state);
    ae_assert(x->cnt>=n&&y->cnt>=m, "Spline2DBuildBilinear: length of X or Y is too short (Length(X/Y)<N/M)", _state);
    ae_assert(isfinitevector(x, n, _state)&&isfinitevector(y, m, _state), "Spline2DBuildBilinear: X or Y contains NaN or Infinite value", _state);
    ae_assert(f->rows>=m&&f->cols>=n, "Spline2DBuildBilinear: size of F is too small (rows(F)<M or cols(F)<N)", _state);
    ae_assert(apservisfinitematrix(f, m, n, _state), "Spline2DBuildBilinear: F contains NaN or Infinite value", _state);
    
    /*
     * Fill interpolant
     */
    c->k = 1;
    c->n = n;
    c->m = m;
    c->d = 1;
    c->stype = -1;
    ae_vector_set_length(&c->x, c->n, _state);
    ae_vector_set_length(&c->y, c->m, _state);
    ae_vector_set_length(&c->f, c->n*c->m, _state);
    for(i=0; i<=c->n-1; i++)
    {
        c->x.ptr.p_double[i] = x->ptr.p_double[i];
    }
    for(i=0; i<=c->m-1; i++)
    {
        c->y.ptr.p_double[i] = y->ptr.p_double[i];
    }
    for(i=0; i<=c->m-1; i++)
    {
        for(j=0; j<=c->n-1; j++)
        {
            c->f.ptr.p_double[i*c->n+j] = f->ptr.pp_double[i][j];
        }
    }
    
    /*
     * Sort points
     */
    for(j=0; j<=c->n-1; j++)
    {
        k = j;
        for(i=j+1; i<=c->n-1; i++)
        {
            if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) )
            {
                k = i;
            }
        }
        if( k!=j )
        {
            for(i=0; i<=c->m-1; i++)
            {
                t = c->f.ptr.p_double[i*c->n+j];
                c->f.ptr.p_double[i*c->n+j] = c->f.ptr.p_double[i*c->n+k];
                c->f.ptr.p_double[i*c->n+k] = t;
            }
            t = c->x.ptr.p_double[j];
            c->x.ptr.p_double[j] = c->x.ptr.p_double[k];
            c->x.ptr.p_double[k] = t;
        }
    }
    for(i=0; i<=c->m-1; i++)
    {
        k = i;
        for(j=i+1; j<=c->m-1; j++)
        {
            if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) )
            {
                k = j;
            }
        }
        if( k!=i )
        {
            for(j=0; j<=c->n-1; j++)
            {
                t = c->f.ptr.p_double[i*c->n+j];
                c->f.ptr.p_double[i*c->n+j] = c->f.ptr.p_double[k*c->n+j];
                c->f.ptr.p_double[k*c->n+j] = t;
            }
            t = c->y.ptr.p_double[i];
            c->y.ptr.p_double[i] = c->y.ptr.p_double[k];
            c->y.ptr.p_double[k] = t;
        }
    }
}


/*************************************************************************
This subroutine was deprecated in ALGLIB 3.6.0

We recommend you to switch  to  Spline2DBuildBicubicV(),  which  is  more
flexible and accepts its arguments in more convenient order.

  -- ALGLIB PROJECT --
     Copyright 05.07.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildbicubic(/* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     /* Real    */ ae_matrix* f,
     ae_int_t m,
     ae_int_t n,
     spline2dinterpolant* c,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_matrix _f;
    ae_int_t sfx;
    ae_int_t sfy;
    ae_int_t sfxy;
    ae_matrix dx;
    ae_matrix dy;
    ae_matrix dxy;
    double t;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_init_copy(&_f, f, _state);
    f = &_f;
    _spline2dinterpolant_clear(c);
    ae_matrix_init(&dx, 0, 0, DT_REAL, _state);
    ae_matrix_init(&dy, 0, 0, DT_REAL, _state);
    ae_matrix_init(&dxy, 0, 0, DT_REAL, _state);

    ae_assert(n>=2, "Spline2DBuildBicubicSpline: N<2", _state);
    ae_assert(m>=2, "Spline2DBuildBicubicSpline: M<2", _state);
    ae_assert(x->cnt>=n&&y->cnt>=m, "Spline2DBuildBicubic: length of X or Y is too short (Length(X/Y)<N/M)", _state);
    ae_assert(isfinitevector(x, n, _state)&&isfinitevector(y, m, _state), "Spline2DBuildBicubic: X or Y contains NaN or Infinite value", _state);
    ae_assert(f->rows>=m&&f->cols>=n, "Spline2DBuildBicubic: size of F is too small (rows(F)<M or cols(F)<N)", _state);
    ae_assert(apservisfinitematrix(f, m, n, _state), "Spline2DBuildBicubic: F contains NaN or Infinite value", _state);
    
    /*
     * Fill interpolant:
     *  F[0]...F[N*M-1]:
     *      f(i,j) table. f(0,0), f(0, 1), f(0,2) and so on...
     *  F[N*M]...F[2*N*M-1]:
     *      df(i,j)/dx table.
     *  F[2*N*M]...F[3*N*M-1]:
     *      df(i,j)/dy table.
     *  F[3*N*M]...F[4*N*M-1]:
     *      d2f(i,j)/dxdy table.
     */
    c->k = 3;
    c->d = 1;
    c->n = n;
    c->m = m;
    c->stype = -3;
    sfx = c->n*c->m;
    sfy = 2*c->n*c->m;
    sfxy = 3*c->n*c->m;
    ae_vector_set_length(&c->x, c->n, _state);
    ae_vector_set_length(&c->y, c->m, _state);
    ae_vector_set_length(&c->f, 4*c->n*c->m, _state);
    for(i=0; i<=c->n-1; i++)
    {
        c->x.ptr.p_double[i] = x->ptr.p_double[i];
    }
    for(i=0; i<=c->m-1; i++)
    {
        c->y.ptr.p_double[i] = y->ptr.p_double[i];
    }
    
    /*
     * Sort points
     */
    for(j=0; j<=c->n-1; j++)
    {
        k = j;
        for(i=j+1; i<=c->n-1; i++)
        {
            if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) )
            {
                k = i;
            }
        }
        if( k!=j )
        {
            for(i=0; i<=c->m-1; i++)
            {
                t = f->ptr.pp_double[i][j];
                f->ptr.pp_double[i][j] = f->ptr.pp_double[i][k];
                f->ptr.pp_double[i][k] = t;
            }
            t = c->x.ptr.p_double[j];
            c->x.ptr.p_double[j] = c->x.ptr.p_double[k];
            c->x.ptr.p_double[k] = t;
        }
    }
    for(i=0; i<=c->m-1; i++)
    {
        k = i;
        for(j=i+1; j<=c->m-1; j++)
        {
            if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) )
            {
                k = j;
            }
        }
        if( k!=i )
        {
            for(j=0; j<=c->n-1; j++)
            {
                t = f->ptr.pp_double[i][j];
                f->ptr.pp_double[i][j] = f->ptr.pp_double[k][j];
                f->ptr.pp_double[k][j] = t;
            }
            t = c->y.ptr.p_double[i];
            c->y.ptr.p_double[i] = c->y.ptr.p_double[k];
            c->y.ptr.p_double[k] = t;
        }
    }
    spline2d_bicubiccalcderivatives(f, &c->x, &c->y, c->m, c->n, &dx, &dy, &dxy, _state);
    for(i=0; i<=c->m-1; i++)
    {
        for(j=0; j<=c->n-1; j++)
        {
            k = i*c->n+j;
            c->f.ptr.p_double[k] = f->ptr.pp_double[i][j];
            c->f.ptr.p_double[sfx+k] = dx.ptr.pp_double[i][j];
            c->f.ptr.p_double[sfy+k] = dy.ptr.pp_double[i][j];
            c->f.ptr.p_double[sfxy+k] = dxy.ptr.pp_double[i][j];
        }
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine was deprecated in ALGLIB 3.6.0

We recommend you to switch  to  Spline2DUnpackV(),  which is more flexible
and accepts its arguments in more convenient order.

  -- ALGLIB PROJECT --
     Copyright 29.06.2007 by Bochkanov Sergey
*************************************************************************/
void spline2dunpack(spline2dinterpolant* c,
     ae_int_t* m,
     ae_int_t* n,
     /* Real    */ ae_matrix* tbl,
     ae_state *_state)
{
    ae_int_t k;
    ae_int_t p;
    ae_int_t ci;
    ae_int_t cj;
    ae_int_t s1;
    ae_int_t s2;
    ae_int_t s3;
    ae_int_t s4;
    ae_int_t sfx;
    ae_int_t sfy;
    ae_int_t sfxy;
    double y1;
    double y2;
    double y3;
    double y4;
    double dt;
    double du;
    ae_int_t i;
    ae_int_t j;

    *m = 0;
    *n = 0;
    ae_matrix_clear(tbl);

    ae_assert(c->stype==-3||c->stype==-1, "Spline2DUnpack: incorrect C (incorrect parameter C.SType)", _state);
    if( c->d!=1 )
    {
        *n = 0;
        *m = 0;
        return;
    }
    *n = c->n;
    *m = c->m;
    ae_matrix_set_length(tbl, (*n-1)*(*m-1), 20, _state);
    sfx = *n*(*m);
    sfy = 2*(*n)*(*m);
    sfxy = 3*(*n)*(*m);
    
    /*
     * Fill
     */
    for(i=0; i<=*m-2; i++)
    {
        for(j=0; j<=*n-2; j++)
        {
            p = i*(*n-1)+j;
            tbl->ptr.pp_double[p][0] = c->x.ptr.p_double[j];
            tbl->ptr.pp_double[p][1] = c->x.ptr.p_double[j+1];
            tbl->ptr.pp_double[p][2] = c->y.ptr.p_double[i];
            tbl->ptr.pp_double[p][3] = c->y.ptr.p_double[i+1];
            dt = 1/(tbl->ptr.pp_double[p][1]-tbl->ptr.pp_double[p][0]);
            du = 1/(tbl->ptr.pp_double[p][3]-tbl->ptr.pp_double[p][2]);
            
            /*
             * Bilinear interpolation
             */
            if( c->stype==-1 )
            {
                for(k=4; k<=19; k++)
                {
                    tbl->ptr.pp_double[p][k] = (double)(0);
                }
                y1 = c->f.ptr.p_double[*n*i+j];
                y2 = c->f.ptr.p_double[*n*i+(j+1)];
                y3 = c->f.ptr.p_double[*n*(i+1)+(j+1)];
                y4 = c->f.ptr.p_double[*n*(i+1)+j];
                tbl->ptr.pp_double[p][4] = y1;
                tbl->ptr.pp_double[p][4+1*4+0] = y2-y1;
                tbl->ptr.pp_double[p][4+0*4+1] = y4-y1;
                tbl->ptr.pp_double[p][4+1*4+1] = y3-y2-y4+y1;
            }
            
            /*
             * Bicubic interpolation
             */
            if( c->stype==-3 )
            {
                s1 = *n*i+j;
                s2 = *n*i+(j+1);
                s3 = *n*(i+1)+(j+1);
                s4 = *n*(i+1)+j;
                tbl->ptr.pp_double[p][4+0*4+0] = c->f.ptr.p_double[s1];
                tbl->ptr.pp_double[p][4+0*4+1] = c->f.ptr.p_double[sfy+s1]/du;
                tbl->ptr.pp_double[p][4+0*4+2] = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s4]-2*c->f.ptr.p_double[sfy+s1]/du-c->f.ptr.p_double[sfy+s4]/du;
                tbl->ptr.pp_double[p][4+0*4+3] = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s4]+c->f.ptr.p_double[sfy+s1]/du+c->f.ptr.p_double[sfy+s4]/du;
                tbl->ptr.pp_double[p][4+1*4+0] = c->f.ptr.p_double[sfx+s1]/dt;
                tbl->ptr.pp_double[p][4+1*4+1] = c->f.ptr.p_double[sfxy+s1]/(dt*du);
                tbl->ptr.pp_double[p][4+1*4+2] = -3*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du);
                tbl->ptr.pp_double[p][4+1*4+3] = 2*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du);
                tbl->ptr.pp_double[p][4+2*4+0] = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s2]-2*c->f.ptr.p_double[sfx+s1]/dt-c->f.ptr.p_double[sfx+s2]/dt;
                tbl->ptr.pp_double[p][4+2*4+1] = -3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du);
                tbl->ptr.pp_double[p][4+2*4+2] = 9*c->f.ptr.p_double[s1]-9*c->f.ptr.p_double[s2]+9*c->f.ptr.p_double[s3]-9*c->f.ptr.p_double[s4]+6*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s2]/dt-3*c->f.ptr.p_double[sfx+s3]/dt-6*c->f.ptr.p_double[sfx+s4]/dt+6*c->f.ptr.p_double[sfy+s1]/du-6*c->f.ptr.p_double[sfy+s2]/du-3*c->f.ptr.p_double[sfy+s3]/du+3*c->f.ptr.p_double[sfy+s4]/du+4*c->f.ptr.p_double[sfxy+s1]/(dt*du)+2*c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+2*c->f.ptr.p_double[sfxy+s4]/(dt*du);
                tbl->ptr.pp_double[p][4+2*4+3] = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-4*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s2]/dt+2*c->f.ptr.p_double[sfx+s3]/dt+4*c->f.ptr.p_double[sfx+s4]/dt-3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du+3*c->f.ptr.p_double[sfy+s3]/du-3*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-2*c->f.ptr.p_double[sfxy+s4]/(dt*du);
                tbl->ptr.pp_double[p][4+3*4+0] = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s2]+c->f.ptr.p_double[sfx+s1]/dt+c->f.ptr.p_double[sfx+s2]/dt;
                tbl->ptr.pp_double[p][4+3*4+1] = 2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du);
                tbl->ptr.pp_double[p][4+3*4+2] = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-3*c->f.ptr.p_double[sfx+s1]/dt-3*c->f.ptr.p_double[sfx+s2]/dt+3*c->f.ptr.p_double[sfx+s3]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-4*c->f.ptr.p_double[sfy+s1]/du+4*c->f.ptr.p_double[sfy+s2]/du+2*c->f.ptr.p_double[sfy+s3]/du-2*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-2*c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du);
                tbl->ptr.pp_double[p][4+3*4+3] = 4*c->f.ptr.p_double[s1]-4*c->f.ptr.p_double[s2]+4*c->f.ptr.p_double[s3]-4*c->f.ptr.p_double[s4]+2*c->f.ptr.p_double[sfx+s1]/dt+2*c->f.ptr.p_double[sfx+s2]/dt-2*c->f.ptr.p_double[sfx+s3]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfy+s3]/du+2*c->f.ptr.p_double[sfy+s4]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du);
            }
            
            /*
             * Rescale Cij
             */
            for(ci=0; ci<=3; ci++)
            {
                for(cj=0; cj<=3; cj++)
                {
                    tbl->ptr.pp_double[p][4+ci*4+cj] = tbl->ptr.pp_double[p][4+ci*4+cj]*ae_pow(dt, (double)(ci), _state)*ae_pow(du, (double)(cj), _state);
                }
            }
        }
    }
}


/*************************************************************************
Internal subroutine.
Calculation of the first derivatives and the cross-derivative.
*************************************************************************/
static void spline2d_bicubiccalcderivatives(/* Real    */ ae_matrix* a,
     /* Real    */ ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_int_t m,
     ae_int_t n,
     /* Real    */ ae_matrix* dx,
     /* Real    */ ae_matrix* dy,
     /* Real    */ ae_matrix* dxy,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_int_t i;
    ae_int_t j;
    ae_vector xt;
    ae_vector ft;
    double s;
    double ds;
    double d2s;
    spline1dinterpolant c;

    ae_frame_make(_state, &_frame_block);
    ae_matrix_clear(dx);
    ae_matrix_clear(dy);
    ae_matrix_clear(dxy);
    ae_vector_init(&xt, 0, DT_REAL, _state);
    ae_vector_init(&ft, 0, DT_REAL, _state);
    _spline1dinterpolant_init(&c, _state);

    ae_matrix_set_length(dx, m, n, _state);
    ae_matrix_set_length(dy, m, n, _state);
    ae_matrix_set_length(dxy, m, n, _state);
    
    /*
     * dF/dX
     */
    ae_vector_set_length(&xt, n, _state);
    ae_vector_set_length(&ft, n, _state);
    for(i=0; i<=m-1; i++)
    {
        for(j=0; j<=n-1; j++)
        {
            xt.ptr.p_double[j] = x->ptr.p_double[j];
            ft.ptr.p_double[j] = a->ptr.pp_double[i][j];
        }
        spline1dbuildcubic(&xt, &ft, n, 0, 0.0, 0, 0.0, &c, _state);
        for(j=0; j<=n-1; j++)
        {
            spline1ddiff(&c, x->ptr.p_double[j], &s, &ds, &d2s, _state);
            dx->ptr.pp_double[i][j] = ds;
        }
    }
    
    /*
     * dF/dY
     */
    ae_vector_set_length(&xt, m, _state);
    ae_vector_set_length(&ft, m, _state);
    for(j=0; j<=n-1; j++)
    {
        for(i=0; i<=m-1; i++)
        {
            xt.ptr.p_double[i] = y->ptr.p_double[i];
            ft.ptr.p_double[i] = a->ptr.pp_double[i][j];
        }
        spline1dbuildcubic(&xt, &ft, m, 0, 0.0, 0, 0.0, &c, _state);
        for(i=0; i<=m-1; i++)
        {
            spline1ddiff(&c, y->ptr.p_double[i], &s, &ds, &d2s, _state);
            dy->ptr.pp_double[i][j] = ds;
        }
    }
    
    /*
     * d2F/dXdY
     */
    ae_vector_set_length(&xt, n, _state);
    ae_vector_set_length(&ft, n, _state);
    for(i=0; i<=m-1; i++)
    {
        for(j=0; j<=n-1; j++)
        {
            xt.ptr.p_double[j] = x->ptr.p_double[j];
            ft.ptr.p_double[j] = dy->ptr.pp_double[i][j];
        }
        spline1dbuildcubic(&xt, &ft, n, 0, 0.0, 0, 0.0, &c, _state);
        for(j=0; j<=n-1; j++)
        {
            spline1ddiff(&c, x->ptr.p_double[j], &s, &ds, &d2s, _state);
            dxy->ptr.pp_double[i][j] = ds;
        }
    }
    ae_frame_leave(_state);
}


void _spline2dinterpolant_init(void* _p, ae_state *_state)
{
    spline2dinterpolant *p = (spline2dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_init(&p->x, 0, DT_REAL, _state);
    ae_vector_init(&p->y, 0, DT_REAL, _state);
    ae_vector_init(&p->f, 0, DT_REAL, _state);
}


void _spline2dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state)
{
    spline2dinterpolant *dst = (spline2dinterpolant*)_dst;
    spline2dinterpolant *src = (spline2dinterpolant*)_src;
    dst->k = src->k;
    dst->stype = src->stype;
    dst->n = src->n;
    dst->m = src->m;
    dst->d = src->d;
    ae_vector_init_copy(&dst->x, &src->x, _state);
    ae_vector_init_copy(&dst->y, &src->y, _state);
    ae_vector_init_copy(&dst->f, &src->f, _state);
}


void _spline2dinterpolant_clear(void* _p)
{
    spline2dinterpolant *p = (spline2dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_clear(&p->x);
    ae_vector_clear(&p->y);
    ae_vector_clear(&p->f);
}


void _spline2dinterpolant_destroy(void* _p)
{
    spline2dinterpolant *p = (spline2dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_destroy(&p->x);
    ae_vector_destroy(&p->y);
    ae_vector_destroy(&p->f);
}




/*************************************************************************
This subroutine calculates the value of the trilinear or tricubic spline at
the given point (X,Y,Z).

INPUT PARAMETERS:
    C   -   coefficients table.
            Built by BuildBilinearSpline or BuildBicubicSpline.
    X, Y,
    Z   -   point

Result:
    S(x,y,z)

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
double spline3dcalc(spline3dinterpolant* c,
     double x,
     double y,
     double z,
     ae_state *_state)
{
    double v;
    double vx;
    double vy;
    double vxy;
    double result;


    ae_assert(c->stype==-1||c->stype==-3, "Spline3DCalc: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert((ae_isfinite(x, _state)&&ae_isfinite(y, _state))&&ae_isfinite(z, _state), "Spline3DCalc: X=NaN/Infinite, Y=NaN/Infinite or Z=NaN/Infinite", _state);
    if( c->d!=1 )
    {
        result = (double)(0);
        return result;
    }
    spline3d_spline3ddiff(c, x, y, z, &v, &vx, &vy, &vxy, _state);
    result = v;
    return result;
}


/*************************************************************************
This subroutine performs linear transformation of the spline argument.

INPUT PARAMETERS:
    C       -   spline interpolant
    AX, BX  -   transformation coefficients: x = A*u + B
    AY, BY  -   transformation coefficients: y = A*v + B
    AZ, BZ  -   transformation coefficients: z = A*w + B
    
OUTPUT PARAMETERS:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dlintransxyz(spline3dinterpolant* c,
     double ax,
     double bx,
     double ay,
     double by,
     double az,
     double bz,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector x;
    ae_vector y;
    ae_vector z;
    ae_vector f;
    ae_vector v;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t di;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&x, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_vector_init(&z, 0, DT_REAL, _state);
    ae_vector_init(&f, 0, DT_REAL, _state);
    ae_vector_init(&v, 0, DT_REAL, _state);

    ae_assert(c->stype==-3||c->stype==-1, "Spline3DLinTransXYZ: incorrect C (incorrect parameter C.SType)", _state);
    ae_vector_set_length(&x, c->n, _state);
    ae_vector_set_length(&y, c->m, _state);
    ae_vector_set_length(&z, c->l, _state);
    ae_vector_set_length(&f, c->m*c->n*c->l*c->d, _state);
    for(j=0; j<=c->n-1; j++)
    {
        x.ptr.p_double[j] = c->x.ptr.p_double[j];
    }
    for(i=0; i<=c->m-1; i++)
    {
        y.ptr.p_double[i] = c->y.ptr.p_double[i];
    }
    for(i=0; i<=c->l-1; i++)
    {
        z.ptr.p_double[i] = c->z.ptr.p_double[i];
    }
    
    /*
     * Handle different combinations of zero/nonzero AX/AY/AZ
     */
    if( (ae_fp_neq(ax,(double)(0))&&ae_fp_neq(ay,(double)(0)))&&ae_fp_neq(az,(double)(0)) )
    {
        ae_v_move(&f.ptr.p_double[0], 1, &c->f.ptr.p_double[0], 1, ae_v_len(0,c->m*c->n*c->l*c->d-1));
    }
    if( (ae_fp_eq(ax,(double)(0))&&ae_fp_neq(ay,(double)(0)))&&ae_fp_neq(az,(double)(0)) )
    {
        for(i=0; i<=c->m-1; i++)
        {
            for(j=0; j<=c->l-1; j++)
            {
                spline3dcalcv(c, bx, y.ptr.p_double[i], z.ptr.p_double[j], &v, _state);
                for(k=0; k<=c->n-1; k++)
                {
                    for(di=0; di<=c->d-1; di++)
                    {
                        f.ptr.p_double[c->d*(c->n*(c->m*j+i)+k)+di] = v.ptr.p_double[di];
                    }
                }
            }
        }
        ax = (double)(1);
        bx = (double)(0);
    }
    if( (ae_fp_neq(ax,(double)(0))&&ae_fp_eq(ay,(double)(0)))&&ae_fp_neq(az,(double)(0)) )
    {
        for(i=0; i<=c->n-1; i++)
        {
            for(j=0; j<=c->l-1; j++)
            {
                spline3dcalcv(c, x.ptr.p_double[i], by, z.ptr.p_double[j], &v, _state);
                for(k=0; k<=c->m-1; k++)
                {
                    for(di=0; di<=c->d-1; di++)
                    {
                        f.ptr.p_double[c->d*(c->n*(c->m*j+k)+i)+di] = v.ptr.p_double[di];
                    }
                }
            }
        }
        ay = (double)(1);
        by = (double)(0);
    }
    if( (ae_fp_neq(ax,(double)(0))&&ae_fp_neq(ay,(double)(0)))&&ae_fp_eq(az,(double)(0)) )
    {
        for(i=0; i<=c->n-1; i++)
        {
            for(j=0; j<=c->m-1; j++)
            {
                spline3dcalcv(c, x.ptr.p_double[i], y.ptr.p_double[j], bz, &v, _state);
                for(k=0; k<=c->l-1; k++)
                {
                    for(di=0; di<=c->d-1; di++)
                    {
                        f.ptr.p_double[c->d*(c->n*(c->m*k+j)+i)+di] = v.ptr.p_double[di];
                    }
                }
            }
        }
        az = (double)(1);
        bz = (double)(0);
    }
    if( (ae_fp_eq(ax,(double)(0))&&ae_fp_eq(ay,(double)(0)))&&ae_fp_neq(az,(double)(0)) )
    {
        for(i=0; i<=c->l-1; i++)
        {
            spline3dcalcv(c, bx, by, z.ptr.p_double[i], &v, _state);
            for(k=0; k<=c->m-1; k++)
            {
                for(j=0; j<=c->n-1; j++)
                {
                    for(di=0; di<=c->d-1; di++)
                    {
                        f.ptr.p_double[c->d*(c->n*(c->m*i+k)+j)+di] = v.ptr.p_double[di];
                    }
                }
            }
        }
        ax = (double)(1);
        bx = (double)(0);
        ay = (double)(1);
        by = (double)(0);
    }
    if( (ae_fp_eq(ax,(double)(0))&&ae_fp_neq(ay,(double)(0)))&&ae_fp_eq(az,(double)(0)) )
    {
        for(i=0; i<=c->m-1; i++)
        {
            spline3dcalcv(c, bx, y.ptr.p_double[i], bz, &v, _state);
            for(k=0; k<=c->l-1; k++)
            {
                for(j=0; j<=c->n-1; j++)
                {
                    for(di=0; di<=c->d-1; di++)
                    {
                        f.ptr.p_double[c->d*(c->n*(c->m*k+i)+j)+di] = v.ptr.p_double[di];
                    }
                }
            }
        }
        ax = (double)(1);
        bx = (double)(0);
        az = (double)(1);
        bz = (double)(0);
    }
    if( (ae_fp_neq(ax,(double)(0))&&ae_fp_eq(ay,(double)(0)))&&ae_fp_eq(az,(double)(0)) )
    {
        for(i=0; i<=c->n-1; i++)
        {
            spline3dcalcv(c, x.ptr.p_double[i], by, bz, &v, _state);
            for(k=0; k<=c->l-1; k++)
            {
                for(j=0; j<=c->m-1; j++)
                {
                    for(di=0; di<=c->d-1; di++)
                    {
                        f.ptr.p_double[c->d*(c->n*(c->m*k+j)+i)+di] = v.ptr.p_double[di];
                    }
                }
            }
        }
        ay = (double)(1);
        by = (double)(0);
        az = (double)(1);
        bz = (double)(0);
    }
    if( (ae_fp_eq(ax,(double)(0))&&ae_fp_eq(ay,(double)(0)))&&ae_fp_eq(az,(double)(0)) )
    {
        spline3dcalcv(c, bx, by, bz, &v, _state);
        for(k=0; k<=c->l-1; k++)
        {
            for(j=0; j<=c->m-1; j++)
            {
                for(i=0; i<=c->n-1; i++)
                {
                    for(di=0; di<=c->d-1; di++)
                    {
                        f.ptr.p_double[c->d*(c->n*(c->m*k+j)+i)+di] = v.ptr.p_double[di];
                    }
                }
            }
        }
        ax = (double)(1);
        bx = (double)(0);
        ay = (double)(1);
        by = (double)(0);
        az = (double)(1);
        bz = (double)(0);
    }
    
    /*
     * General case: AX<>0, AY<>0, AZ<>0
     * Unpack, scale and pack again.
     */
    for(i=0; i<=c->n-1; i++)
    {
        x.ptr.p_double[i] = (x.ptr.p_double[i]-bx)/ax;
    }
    for(i=0; i<=c->m-1; i++)
    {
        y.ptr.p_double[i] = (y.ptr.p_double[i]-by)/ay;
    }
    for(i=0; i<=c->l-1; i++)
    {
        z.ptr.p_double[i] = (z.ptr.p_double[i]-bz)/az;
    }
    if( c->stype==-1 )
    {
        spline3dbuildtrilinearv(&x, c->n, &y, c->m, &z, c->l, &f, c->d, c, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine performs linear transformation of the spline.

INPUT PARAMETERS:
    C   -   spline interpolant.
    A, B-   transformation coefficients: S2(x,y) = A*S(x,y,z) + B
    
OUTPUT PARAMETERS:
    C   -   transformed spline

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dlintransf(spline3dinterpolant* c,
     double a,
     double b,
     ae_state *_state)
{
    ae_frame _frame_block;
    ae_vector x;
    ae_vector y;
    ae_vector z;
    ae_vector f;
    ae_int_t i;
    ae_int_t j;

    ae_frame_make(_state, &_frame_block);
    ae_vector_init(&x, 0, DT_REAL, _state);
    ae_vector_init(&y, 0, DT_REAL, _state);
    ae_vector_init(&z, 0, DT_REAL, _state);
    ae_vector_init(&f, 0, DT_REAL, _state);

    ae_assert(c->stype==-3||c->stype==-1, "Spline3DLinTransF: incorrect C (incorrect parameter C.SType)", _state);
    ae_vector_set_length(&x, c->n, _state);
    ae_vector_set_length(&y, c->m, _state);
    ae_vector_set_length(&z, c->l, _state);
    ae_vector_set_length(&f, c->m*c->n*c->l*c->d, _state);
    for(j=0; j<=c->n-1; j++)
    {
        x.ptr.p_double[j] = c->x.ptr.p_double[j];
    }
    for(i=0; i<=c->m-1; i++)
    {
        y.ptr.p_double[i] = c->y.ptr.p_double[i];
    }
    for(i=0; i<=c->l-1; i++)
    {
        z.ptr.p_double[i] = c->z.ptr.p_double[i];
    }
    for(i=0; i<=c->m*c->n*c->l*c->d-1; i++)
    {
        f.ptr.p_double[i] = a*c->f.ptr.p_double[i]+b;
    }
    if( c->stype==-1 )
    {
        spline3dbuildtrilinearv(&x, c->n, &y, c->m, &z, c->l, &f, c->d, c, _state);
    }
    ae_frame_leave(_state);
}


/*************************************************************************
This subroutine makes the copy of the spline model.

INPUT PARAMETERS:
    C   -   spline interpolant

OUTPUT PARAMETERS:
    CC  -   spline copy

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dcopy(spline3dinterpolant* c,
     spline3dinterpolant* cc,
     ae_state *_state)
{
    ae_int_t tblsize;

    _spline3dinterpolant_clear(cc);

    ae_assert(c->k==1||c->k==3, "Spline3DCopy: incorrect C (incorrect parameter C.K)", _state);
    cc->k = c->k;
    cc->n = c->n;
    cc->m = c->m;
    cc->l = c->l;
    cc->d = c->d;
    tblsize = c->n*c->m*c->l*c->d;
    cc->stype = c->stype;
    ae_vector_set_length(&cc->x, cc->n, _state);
    ae_vector_set_length(&cc->y, cc->m, _state);
    ae_vector_set_length(&cc->z, cc->l, _state);
    ae_vector_set_length(&cc->f, tblsize, _state);
    ae_v_move(&cc->x.ptr.p_double[0], 1, &c->x.ptr.p_double[0], 1, ae_v_len(0,cc->n-1));
    ae_v_move(&cc->y.ptr.p_double[0], 1, &c->y.ptr.p_double[0], 1, ae_v_len(0,cc->m-1));
    ae_v_move(&cc->z.ptr.p_double[0], 1, &c->z.ptr.p_double[0], 1, ae_v_len(0,cc->l-1));
    ae_v_move(&cc->f.ptr.p_double[0], 1, &c->f.ptr.p_double[0], 1, ae_v_len(0,tblsize-1));
}


/*************************************************************************
Trilinear spline resampling

INPUT PARAMETERS:
    A           -   array[0..OldXCount*OldYCount*OldZCount-1], function
                    values at the old grid, :
                        A[0]        x=0,y=0,z=0
                        A[1]        x=1,y=0,z=0
                        A[..]       ...
                        A[..]       x=oldxcount-1,y=0,z=0
                        A[..]       x=0,y=1,z=0
                        A[..]       ...
                        ...
    OldZCount   -   old Z-count, OldZCount>1
    OldYCount   -   old Y-count, OldYCount>1
    OldXCount   -   old X-count, OldXCount>1
    NewZCount   -   new Z-count, NewZCount>1
    NewYCount   -   new Y-count, NewYCount>1
    NewXCount   -   new X-count, NewXCount>1

OUTPUT PARAMETERS:
    B           -   array[0..NewXCount*NewYCount*NewZCount-1], function
                    values at the new grid:
                        B[0]        x=0,y=0,z=0
                        B[1]        x=1,y=0,z=0
                        B[..]       ...
                        B[..]       x=newxcount-1,y=0,z=0
                        B[..]       x=0,y=1,z=0
                        B[..]       ...
                        ...

  -- ALGLIB routine --
     26.04.2012
     Copyright by Bochkanov Sergey
*************************************************************************/
void spline3dresampletrilinear(/* Real    */ ae_vector* a,
     ae_int_t oldzcount,
     ae_int_t oldycount,
     ae_int_t oldxcount,
     ae_int_t newzcount,
     ae_int_t newycount,
     ae_int_t newxcount,
     /* Real    */ ae_vector* b,
     ae_state *_state)
{
    double xd;
    double yd;
    double zd;
    double c0;
    double c1;
    double c2;
    double c3;
    ae_int_t ix;
    ae_int_t iy;
    ae_int_t iz;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;

    ae_vector_clear(b);

    ae_assert((oldycount>1&&oldzcount>1)&&oldxcount>1, "Spline3DResampleTrilinear: length/width/height less than 1", _state);
    ae_assert((newycount>1&&newzcount>1)&&newxcount>1, "Spline3DResampleTrilinear: length/width/height less than 1", _state);
    ae_assert(a->cnt>=oldycount*oldzcount*oldxcount, "Spline3DResampleTrilinear: length/width/height less than 1", _state);
    ae_vector_set_length(b, newxcount*newycount*newzcount, _state);
    for(i=0; i<=newxcount-1; i++)
    {
        for(j=0; j<=newycount-1; j++)
        {
            for(k=0; k<=newzcount-1; k++)
            {
                ix = i*(oldxcount-1)/(newxcount-1);
                if( ix==oldxcount-1 )
                {
                    ix = oldxcount-2;
                }
                xd = (double)(i*(oldxcount-1))/(double)(newxcount-1)-ix;
                iy = j*(oldycount-1)/(newycount-1);
                if( iy==oldycount-1 )
                {
                    iy = oldycount-2;
                }
                yd = (double)(j*(oldycount-1))/(double)(newycount-1)-iy;
                iz = k*(oldzcount-1)/(newzcount-1);
                if( iz==oldzcount-1 )
                {
                    iz = oldzcount-2;
                }
                zd = (double)(k*(oldzcount-1))/(double)(newzcount-1)-iz;
                c0 = a->ptr.p_double[oldxcount*(oldycount*iz+iy)+ix]*(1-xd)+a->ptr.p_double[oldxcount*(oldycount*iz+iy)+(ix+1)]*xd;
                c1 = a->ptr.p_double[oldxcount*(oldycount*iz+(iy+1))+ix]*(1-xd)+a->ptr.p_double[oldxcount*(oldycount*iz+(iy+1))+(ix+1)]*xd;
                c2 = a->ptr.p_double[oldxcount*(oldycount*(iz+1)+iy)+ix]*(1-xd)+a->ptr.p_double[oldxcount*(oldycount*(iz+1)+iy)+(ix+1)]*xd;
                c3 = a->ptr.p_double[oldxcount*(oldycount*(iz+1)+(iy+1))+ix]*(1-xd)+a->ptr.p_double[oldxcount*(oldycount*(iz+1)+(iy+1))+(ix+1)]*xd;
                c0 = c0*(1-yd)+c1*yd;
                c1 = c2*(1-yd)+c3*yd;
                b->ptr.p_double[newxcount*(newycount*k+j)+i] = c0*(1-zd)+c1*zd;
            }
        }
    }
}


/*************************************************************************
This subroutine builds trilinear vector-valued spline.

INPUT PARAMETERS:
    X   -   spline abscissas,  array[0..N-1]
    Y   -   spline ordinates,  array[0..M-1]
    Z   -   spline applicates, array[0..L-1] 
    F   -   function values, array[0..M*N*L*D-1]:
            * first D elements store D values at (X[0],Y[0],Z[0])
            * next D elements store D values at (X[1],Y[0],Z[0])
            * next D elements store D values at (X[2],Y[0],Z[0])
            * ...
            * next D elements store D values at (X[0],Y[1],Z[0])
            * next D elements store D values at (X[1],Y[1],Z[0])
            * next D elements store D values at (X[2],Y[1],Z[0])
            * ...
            * next D elements store D values at (X[0],Y[0],Z[1])
            * next D elements store D values at (X[1],Y[0],Z[1])
            * next D elements store D values at (X[2],Y[0],Z[1])
            * ...
            * general form - D function values at (X[i],Y[j]) are stored
              at F[D*(N*(M*K+J)+I)...D*(N*(M*K+J)+I)+D-1].
    M,N,
    L   -   grid size, M>=2, N>=2, L>=2
    D   -   vector dimension, D>=1

OUTPUT PARAMETERS:
    C   -   spline interpolant

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dbuildtrilinearv(/* Real    */ ae_vector* x,
     ae_int_t n,
     /* Real    */ ae_vector* y,
     ae_int_t m,
     /* Real    */ ae_vector* z,
     ae_int_t l,
     /* Real    */ ae_vector* f,
     ae_int_t d,
     spline3dinterpolant* c,
     ae_state *_state)
{
    double t;
    ae_int_t tblsize;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t i0;
    ae_int_t j0;

    _spline3dinterpolant_clear(c);

    ae_assert(m>=2, "Spline3DBuildTrilinearV: M<2", _state);
    ae_assert(n>=2, "Spline3DBuildTrilinearV: N<2", _state);
    ae_assert(l>=2, "Spline3DBuildTrilinearV: L<2", _state);
    ae_assert(d>=1, "Spline3DBuildTrilinearV: D<1", _state);
    ae_assert((x->cnt>=n&&y->cnt>=m)&&z->cnt>=l, "Spline3DBuildTrilinearV: length of X, Y or Z is too short (Length(X/Y/Z)<N/M/L)", _state);
    ae_assert((isfinitevector(x, n, _state)&&isfinitevector(y, m, _state))&&isfinitevector(z, l, _state), "Spline3DBuildTrilinearV: X, Y or Z contains NaN or Infinite value", _state);
    tblsize = n*m*l*d;
    ae_assert(f->cnt>=tblsize, "Spline3DBuildTrilinearV: length of F is too short (Length(F)<N*M*L*D)", _state);
    ae_assert(isfinitevector(f, tblsize, _state), "Spline3DBuildTrilinearV: F contains NaN or Infinite value", _state);
    
    /*
     * Fill interpolant
     */
    c->k = 1;
    c->n = n;
    c->m = m;
    c->l = l;
    c->d = d;
    c->stype = -1;
    ae_vector_set_length(&c->x, c->n, _state);
    ae_vector_set_length(&c->y, c->m, _state);
    ae_vector_set_length(&c->z, c->l, _state);
    ae_vector_set_length(&c->f, tblsize, _state);
    for(i=0; i<=c->n-1; i++)
    {
        c->x.ptr.p_double[i] = x->ptr.p_double[i];
    }
    for(i=0; i<=c->m-1; i++)
    {
        c->y.ptr.p_double[i] = y->ptr.p_double[i];
    }
    for(i=0; i<=c->l-1; i++)
    {
        c->z.ptr.p_double[i] = z->ptr.p_double[i];
    }
    for(i=0; i<=tblsize-1; i++)
    {
        c->f.ptr.p_double[i] = f->ptr.p_double[i];
    }
    
    /*
     * Sort points:
     *  * sort x;
     *  * sort y;
     *  * sort z.
     */
    for(j=0; j<=c->n-1; j++)
    {
        k = j;
        for(i=j+1; i<=c->n-1; i++)
        {
            if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) )
            {
                k = i;
            }
        }
        if( k!=j )
        {
            for(i=0; i<=c->m-1; i++)
            {
                for(j0=0; j0<=c->l-1; j0++)
                {
                    for(i0=0; i0<=c->d-1; i0++)
                    {
                        t = c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+j)+i0];
                        c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+j)+i0] = c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+k)+i0];
                        c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+k)+i0] = t;
                    }
                }
            }
            t = c->x.ptr.p_double[j];
            c->x.ptr.p_double[j] = c->x.ptr.p_double[k];
            c->x.ptr.p_double[k] = t;
        }
    }
    for(i=0; i<=c->m-1; i++)
    {
        k = i;
        for(j=i+1; j<=c->m-1; j++)
        {
            if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) )
            {
                k = j;
            }
        }
        if( k!=i )
        {
            for(j=0; j<=c->n-1; j++)
            {
                for(j0=0; j0<=c->l-1; j0++)
                {
                    for(i0=0; i0<=c->d-1; i0++)
                    {
                        t = c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+j)+i0];
                        c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+j)+i0] = c->f.ptr.p_double[c->d*(c->n*(c->m*j0+k)+j)+i0];
                        c->f.ptr.p_double[c->d*(c->n*(c->m*j0+k)+j)+i0] = t;
                    }
                }
            }
            t = c->y.ptr.p_double[i];
            c->y.ptr.p_double[i] = c->y.ptr.p_double[k];
            c->y.ptr.p_double[k] = t;
        }
    }
    for(k=0; k<=c->l-1; k++)
    {
        i = k;
        for(j=i+1; j<=c->l-1; j++)
        {
            if( ae_fp_less(c->z.ptr.p_double[j],c->z.ptr.p_double[i]) )
            {
                i = j;
            }
        }
        if( i!=k )
        {
            for(j=0; j<=c->m-1; j++)
            {
                for(j0=0; j0<=c->n-1; j0++)
                {
                    for(i0=0; i0<=c->d-1; i0++)
                    {
                        t = c->f.ptr.p_double[c->d*(c->n*(c->m*k+j)+j0)+i0];
                        c->f.ptr.p_double[c->d*(c->n*(c->m*k+j)+j0)+i0] = c->f.ptr.p_double[c->d*(c->n*(c->m*i+j)+j0)+i0];
                        c->f.ptr.p_double[c->d*(c->n*(c->m*i+j)+j0)+i0] = t;
                    }
                }
            }
            t = c->z.ptr.p_double[k];
            c->z.ptr.p_double[k] = c->z.ptr.p_double[i];
            c->z.ptr.p_double[i] = t;
        }
    }
}


/*************************************************************************
This subroutine calculates bilinear or bicubic vector-valued spline at the
given point (X,Y,Z).

INPUT PARAMETERS:
    C   -   spline interpolant.
    X, Y,
    Z   -   point
    F   -   output buffer, possibly preallocated array. In case array size
            is large enough to store result, it is not reallocated.  Array
            which is too short will be reallocated

OUTPUT PARAMETERS:
    F   -   array[D] (or larger) which stores function values

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dcalcvbuf(spline3dinterpolant* c,
     double x,
     double y,
     double z,
     /* Real    */ ae_vector* f,
     ae_state *_state)
{
    double xd;
    double yd;
    double zd;
    double c0;
    double c1;
    double c2;
    double c3;
    ae_int_t ix;
    ae_int_t iy;
    ae_int_t iz;
    ae_int_t l;
    ae_int_t r;
    ae_int_t h;
    ae_int_t i;


    ae_assert(c->stype==-1||c->stype==-3, "Spline3DCalcVBuf: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert((ae_isfinite(x, _state)&&ae_isfinite(y, _state))&&ae_isfinite(z, _state), "Spline3DCalcVBuf: X, Y or Z contains NaN/Infinite", _state);
    rvectorsetlengthatleast(f, c->d, _state);
    
    /*
     * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included)
     */
    l = 0;
    r = c->n-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->x.ptr.p_double[h],x) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    ix = l;
    
    /*
     * Binary search in the [ y[0], ..., y[n-2] ] (y[n-1] is not included)
     */
    l = 0;
    r = c->m-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->y.ptr.p_double[h],y) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    iy = l;
    
    /*
     * Binary search in the [ z[0], ..., z[n-2] ] (z[n-1] is not included)
     */
    l = 0;
    r = c->l-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->z.ptr.p_double[h],z) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    iz = l;
    xd = (x-c->x.ptr.p_double[ix])/(c->x.ptr.p_double[ix+1]-c->x.ptr.p_double[ix]);
    yd = (y-c->y.ptr.p_double[iy])/(c->y.ptr.p_double[iy+1]-c->y.ptr.p_double[iy]);
    zd = (z-c->z.ptr.p_double[iz])/(c->z.ptr.p_double[iz+1]-c->z.ptr.p_double[iz]);
    for(i=0; i<=c->d-1; i++)
    {
        
        /*
         * Trilinear interpolation
         */
        if( c->stype==-1 )
        {
            c0 = c->f.ptr.p_double[c->d*(c->n*(c->m*iz+iy)+ix)+i]*(1-xd)+c->f.ptr.p_double[c->d*(c->n*(c->m*iz+iy)+(ix+1))+i]*xd;
            c1 = c->f.ptr.p_double[c->d*(c->n*(c->m*iz+(iy+1))+ix)+i]*(1-xd)+c->f.ptr.p_double[c->d*(c->n*(c->m*iz+(iy+1))+(ix+1))+i]*xd;
            c2 = c->f.ptr.p_double[c->d*(c->n*(c->m*(iz+1)+iy)+ix)+i]*(1-xd)+c->f.ptr.p_double[c->d*(c->n*(c->m*(iz+1)+iy)+(ix+1))+i]*xd;
            c3 = c->f.ptr.p_double[c->d*(c->n*(c->m*(iz+1)+(iy+1))+ix)+i]*(1-xd)+c->f.ptr.p_double[c->d*(c->n*(c->m*(iz+1)+(iy+1))+(ix+1))+i]*xd;
            c0 = c0*(1-yd)+c1*yd;
            c1 = c2*(1-yd)+c3*yd;
            f->ptr.p_double[i] = c0*(1-zd)+c1*zd;
        }
    }
}


/*************************************************************************
This subroutine calculates trilinear or tricubic vector-valued spline at the
given point (X,Y,Z).

INPUT PARAMETERS:
    C   -   spline interpolant.
    X, Y,
    Z   -   point

OUTPUT PARAMETERS:
    F   -   array[D] which stores function values.  F is out-parameter and
            it  is  reallocated  after  call to this function. In case you
            want  to    reuse  previously  allocated  F,   you   may   use
            Spline2DCalcVBuf(),  which  reallocates  F only when it is too
            small.

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dcalcv(spline3dinterpolant* c,
     double x,
     double y,
     double z,
     /* Real    */ ae_vector* f,
     ae_state *_state)
{

    ae_vector_clear(f);

    ae_assert(c->stype==-1||c->stype==-3, "Spline3DCalcV: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert((ae_isfinite(x, _state)&&ae_isfinite(y, _state))&&ae_isfinite(z, _state), "Spline3DCalcV: X=NaN/Infinite, Y=NaN/Infinite or Z=NaN/Infinite", _state);
    ae_vector_set_length(f, c->d, _state);
    spline3dcalcvbuf(c, x, y, z, f, _state);
}


/*************************************************************************
This subroutine unpacks tri-dimensional spline into the coefficients table

INPUT PARAMETERS:
    C   -   spline interpolant.

Result:
    N   -   grid size (X)
    M   -   grid size (Y)
    L   -   grid size (Z)
    D   -   number of components
    SType-  spline type. Currently, only one spline type is supported:
            trilinear spline, as indicated by SType=1.
    Tbl -   spline coefficients: [0..(N-1)*(M-1)*(L-1)*D-1, 0..13].
            For T=0..D-1 (component index), I = 0...N-2 (x index),
            J=0..M-2 (y index), K=0..L-2 (z index):
                Q := T + I*D + J*D*(N-1) + K*D*(N-1)*(M-1),
                
                Q-th row stores decomposition for T-th component of the
                vector-valued function
                
                Tbl[Q,0] = X[i]
                Tbl[Q,1] = X[i+1]
                Tbl[Q,2] = Y[j]
                Tbl[Q,3] = Y[j+1]
                Tbl[Q,4] = Z[k]
                Tbl[Q,5] = Z[k+1]
                
                Tbl[Q,6] = C000
                Tbl[Q,7] = C100
                Tbl[Q,8] = C010
                Tbl[Q,9] = C110
                Tbl[Q,10]= C001
                Tbl[Q,11]= C101
                Tbl[Q,12]= C011
                Tbl[Q,13]= C111
            On each grid square spline is equals to:
                S(x) = SUM(c[i,j,k]*(x^i)*(y^j)*(z^k), i=0..1, j=0..1, k=0..1)
                t = x-x[j]
                u = y-y[i]
                v = z-z[k]
            
            NOTE: format of Tbl is given for SType=1. Future versions of
                  ALGLIB can use different formats for different values of
                  SType.

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
void spline3dunpackv(spline3dinterpolant* c,
     ae_int_t* n,
     ae_int_t* m,
     ae_int_t* l,
     ae_int_t* d,
     ae_int_t* stype,
     /* Real    */ ae_matrix* tbl,
     ae_state *_state)
{
    ae_int_t p;
    ae_int_t ci;
    ae_int_t cj;
    ae_int_t ck;
    double du;
    double dv;
    double dw;
    ae_int_t i;
    ae_int_t j;
    ae_int_t k;
    ae_int_t di;
    ae_int_t i0;

    *n = 0;
    *m = 0;
    *l = 0;
    *d = 0;
    *stype = 0;
    ae_matrix_clear(tbl);

    ae_assert(c->stype==-1, "Spline3DUnpackV: incorrect C (incorrect parameter C.SType)", _state);
    *n = c->n;
    *m = c->m;
    *l = c->l;
    *d = c->d;
    *stype = ae_iabs(c->stype, _state);
    ae_matrix_set_length(tbl, (*n-1)*(*m-1)*(*l-1)*(*d), 14, _state);
    
    /*
     * Fill
     */
    for(i=0; i<=*n-2; i++)
    {
        for(j=0; j<=*m-2; j++)
        {
            for(k=0; k<=*l-2; k++)
            {
                for(di=0; di<=*d-1; di++)
                {
                    p = *d*((*n-1)*((*m-1)*k+j)+i)+di;
                    tbl->ptr.pp_double[p][0] = c->x.ptr.p_double[i];
                    tbl->ptr.pp_double[p][1] = c->x.ptr.p_double[i+1];
                    tbl->ptr.pp_double[p][2] = c->y.ptr.p_double[j];
                    tbl->ptr.pp_double[p][3] = c->y.ptr.p_double[j+1];
                    tbl->ptr.pp_double[p][4] = c->z.ptr.p_double[k];
                    tbl->ptr.pp_double[p][5] = c->z.ptr.p_double[k+1];
                    du = 1/(tbl->ptr.pp_double[p][1]-tbl->ptr.pp_double[p][0]);
                    dv = 1/(tbl->ptr.pp_double[p][3]-tbl->ptr.pp_double[p][2]);
                    dw = 1/(tbl->ptr.pp_double[p][5]-tbl->ptr.pp_double[p][4]);
                    
                    /*
                     * Trilinear interpolation
                     */
                    if( c->stype==-1 )
                    {
                        for(i0=6; i0<=13; i0++)
                        {
                            tbl->ptr.pp_double[p][i0] = (double)(0);
                        }
                        tbl->ptr.pp_double[p][6+2*(2*0+0)+0] = c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di];
                        tbl->ptr.pp_double[p][6+2*(2*0+0)+1] = c->f.ptr.p_double[*d*(*n*(*m*k+j)+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di];
                        tbl->ptr.pp_double[p][6+2*(2*0+1)+0] = c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di];
                        tbl->ptr.pp_double[p][6+2*(2*0+1)+1] = c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+(i+1))+di]+c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di];
                        tbl->ptr.pp_double[p][6+2*(2*1+0)+0] = c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di];
                        tbl->ptr.pp_double[p][6+2*(2*1+0)+1] = c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+(i+1))+di]+c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di];
                        tbl->ptr.pp_double[p][6+2*(2*1+1)+0] = c->f.ptr.p_double[*d*(*n*(*m*(k+1)+(j+1))+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+i)+di]+c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di];
                        tbl->ptr.pp_double[p][6+2*(2*1+1)+1] = c->f.ptr.p_double[*d*(*n*(*m*(k+1)+(j+1))+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*(k+1)+(j+1))+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+(i+1))+di]+c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+(i+1))+di]+c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+i)+di]+c->f.ptr.p_double[*d*(*n*(*m*k+j)+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di];
                    }
                    
                    /*
                     * Rescale Cij
                     */
                    for(ci=0; ci<=1; ci++)
                    {
                        for(cj=0; cj<=1; cj++)
                        {
                            for(ck=0; ck<=1; ck++)
                            {
                                tbl->ptr.pp_double[p][6+2*(2*ck+cj)+ci] = tbl->ptr.pp_double[p][6+2*(2*ck+cj)+ci]*ae_pow(du, (double)(ci), _state)*ae_pow(dv, (double)(cj), _state)*ae_pow(dw, (double)(ck), _state);
                            }
                        }
                    }
                }
            }
        }
    }
}


/*************************************************************************
This subroutine calculates the value of the trilinear(or tricubic;possible
will be later) spline  at the given point X(and its derivatives; possible
will be later).

INPUT PARAMETERS:
    C       -   spline interpolant.
    X, Y, Z -   point

OUTPUT PARAMETERS:
    F   -   S(x,y,z)
    FX  -   dS(x,y,z)/dX
    FY  -   dS(x,y,z)/dY
    FXY -   d2S(x,y,z)/dXdY

  -- ALGLIB PROJECT --
     Copyright 26.04.2012 by Bochkanov Sergey
*************************************************************************/
static void spline3d_spline3ddiff(spline3dinterpolant* c,
     double x,
     double y,
     double z,
     double* f,
     double* fx,
     double* fy,
     double* fxy,
     ae_state *_state)
{
    double xd;
    double yd;
    double zd;
    double c0;
    double c1;
    double c2;
    double c3;
    ae_int_t ix;
    ae_int_t iy;
    ae_int_t iz;
    ae_int_t l;
    ae_int_t r;
    ae_int_t h;

    *f = 0;
    *fx = 0;
    *fy = 0;
    *fxy = 0;

    ae_assert(c->stype==-1||c->stype==-3, "Spline3DDiff: incorrect C (incorrect parameter C.SType)", _state);
    ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline3DDiff: X or Y contains NaN or Infinite value", _state);
    
    /*
     * Prepare F, dF/dX, dF/dY, d2F/dXdY
     */
    *f = (double)(0);
    *fx = (double)(0);
    *fy = (double)(0);
    *fxy = (double)(0);
    if( c->d!=1 )
    {
        return;
    }
    
    /*
     * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included)
     */
    l = 0;
    r = c->n-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->x.ptr.p_double[h],x) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    ix = l;
    
    /*
     * Binary search in the [ y[0], ..., y[n-2] ] (y[n-1] is not included)
     */
    l = 0;
    r = c->m-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->y.ptr.p_double[h],y) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    iy = l;
    
    /*
     * Binary search in the [ z[0], ..., z[n-2] ] (z[n-1] is not included)
     */
    l = 0;
    r = c->l-1;
    while(l!=r-1)
    {
        h = (l+r)/2;
        if( ae_fp_greater_eq(c->z.ptr.p_double[h],z) )
        {
            r = h;
        }
        else
        {
            l = h;
        }
    }
    iz = l;
    xd = (x-c->x.ptr.p_double[ix])/(c->x.ptr.p_double[ix+1]-c->x.ptr.p_double[ix]);
    yd = (y-c->y.ptr.p_double[iy])/(c->y.ptr.p_double[iy+1]-c->y.ptr.p_double[iy]);
    zd = (z-c->z.ptr.p_double[iz])/(c->z.ptr.p_double[iz+1]-c->z.ptr.p_double[iz]);
    
    /*
     * Trilinear interpolation
     */
    if( c->stype==-1 )
    {
        c0 = c->f.ptr.p_double[c->n*(c->m*iz+iy)+ix]*(1-xd)+c->f.ptr.p_double[c->n*(c->m*iz+iy)+(ix+1)]*xd;
        c1 = c->f.ptr.p_double[c->n*(c->m*iz+(iy+1))+ix]*(1-xd)+c->f.ptr.p_double[c->n*(c->m*iz+(iy+1))+(ix+1)]*xd;
        c2 = c->f.ptr.p_double[c->n*(c->m*(iz+1)+iy)+ix]*(1-xd)+c->f.ptr.p_double[c->n*(c->m*(iz+1)+iy)+(ix+1)]*xd;
        c3 = c->f.ptr.p_double[c->n*(c->m*(iz+1)+(iy+1))+ix]*(1-xd)+c->f.ptr.p_double[c->n*(c->m*(iz+1)+(iy+1))+(ix+1)]*xd;
        c0 = c0*(1-yd)+c1*yd;
        c1 = c2*(1-yd)+c3*yd;
        *f = c0*(1-zd)+c1*zd;
    }
}


void _spline3dinterpolant_init(void* _p, ae_state *_state)
{
    spline3dinterpolant *p = (spline3dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_init(&p->x, 0, DT_REAL, _state);
    ae_vector_init(&p->y, 0, DT_REAL, _state);
    ae_vector_init(&p->z, 0, DT_REAL, _state);
    ae_vector_init(&p->f, 0, DT_REAL, _state);
}


void _spline3dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state)
{
    spline3dinterpolant *dst = (spline3dinterpolant*)_dst;
    spline3dinterpolant *src = (spline3dinterpolant*)_src;
    dst->k = src->k;
    dst->stype = src->stype;
    dst->n = src->n;
    dst->m = src->m;
    dst->l = src->l;
    dst->d = src->d;
    ae_vector_init_copy(&dst->x, &src->x, _state);
    ae_vector_init_copy(&dst->y, &src->y, _state);
    ae_vector_init_copy(&dst->z, &src->z, _state);
    ae_vector_init_copy(&dst->f, &src->f, _state);
}


void _spline3dinterpolant_clear(void* _p)
{
    spline3dinterpolant *p = (spline3dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_clear(&p->x);
    ae_vector_clear(&p->y);
    ae_vector_clear(&p->z);
    ae_vector_clear(&p->f);
}


void _spline3dinterpolant_destroy(void* _p)
{
    spline3dinterpolant *p = (spline3dinterpolant*)_p;
    ae_touch_ptr((void*)p);
    ae_vector_destroy(&p->x);
    ae_vector_destroy(&p->y);
    ae_vector_destroy(&p->z);
    ae_vector_destroy(&p->f);
}



}

