timo.menzel
/
test_pie
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
test_pie/external/alglib-3.16.0/interpolation.h

9958 lines
415 KiB
C++
Executable File
Raw Blame History

/*************************************************************************
ALGLIB 3.16.0 (source code generated 2019-12-19)
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 >>>
*************************************************************************/
#ifndef _interpolation_pkg_h
#define _interpolation_pkg_h
#include "ap.h"
#include "alglibinternal.h"
#include "alglibmisc.h"
#include "linalg.h"
#include "optimization.h"
#include "solvers.h"
#include "specialfunctions.h"
#include "integration.h"
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES)
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
#if defined(AE_COMPILE_IDW) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_vector x;
ae_vector y;
ae_vector tsyw;
ae_vector tsw;
ae_matrix tsxy;
ae_vector tsdist;
kdtreerequestbuffer requestbuffer;
} idwcalcbuffer;
typedef struct
{
ae_int_t nx;
ae_int_t ny;
ae_vector globalprior;
ae_int_t algotype;
ae_int_t nlayers;
double r0;
double rdecay;
double lambda0;
double lambdalast;
double lambdadecay;
double shepardp;
kdtree tree;
ae_int_t npoints;
ae_vector shepardxy;
idwcalcbuffer buffer;
} idwmodel;
typedef struct
{
ae_int_t priortermtype;
ae_vector priortermval;
ae_int_t algotype;
ae_int_t nlayers;
double r0;
double rdecay;
double lambda0;
double lambdalast;
double lambdadecay;
double shepardp;
ae_vector xy;
ae_int_t npoints;
ae_int_t nx;
ae_int_t ny;
ae_matrix tmpxy;
ae_matrix tmplayers;
ae_vector tmptags;
ae_vector tmpdist;
ae_vector tmpx;
ae_vector tmpwy;
ae_vector tmpw;
kdtree tmptree;
ae_vector tmpmean;
} idwbuilder;
typedef struct
{
double rmserror;
double avgerror;
double maxerror;
double r2;
} idwreport;
#endif
#if defined(AE_COMPILE_RATINT) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_int_t n;
double sy;
ae_vector x;
ae_vector y;
ae_vector w;
} barycentricinterpolant;
#endif
#if defined(AE_COMPILE_FITSPHERE) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_int_t nfev;
ae_int_t iterationscount;
} fitsphereinternalreport;
#endif
#if defined(AE_COMPILE_INTFITSERV) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_SPLINE1D) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_bool periodic;
ae_int_t n;
ae_int_t k;
ae_int_t continuity;
ae_vector x;
ae_vector c;
} spline1dinterpolant;
typedef struct
{
double taskrcond;
double rmserror;
double avgerror;
double avgrelerror;
double maxerror;
} spline1dfitreport;
#endif
#if defined(AE_COMPILE_PARAMETRIC) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_int_t n;
ae_bool periodic;
ae_vector p;
spline1dinterpolant x;
spline1dinterpolant y;
} pspline2interpolant;
typedef struct
{
ae_int_t n;
ae_bool periodic;
ae_vector p;
spline1dinterpolant x;
spline1dinterpolant y;
spline1dinterpolant z;
} pspline3interpolant;
#endif
#if defined(AE_COMPILE_SPLINE3D) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_int_t k;
ae_int_t stype;
ae_int_t n;
ae_int_t m;
ae_int_t l;
ae_int_t d;
ae_vector x;
ae_vector y;
ae_vector z;
ae_vector f;
} spline3dinterpolant;
#endif
#if defined(AE_COMPILE_POLINT) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_LSFIT) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
double taskrcond;
double rmserror;
double avgerror;
double avgrelerror;
double maxerror;
} polynomialfitreport;
typedef struct
{
double taskrcond;
ae_int_t dbest;
double rmserror;
double avgerror;
double avgrelerror;
double maxerror;
} barycentricfitreport;
typedef struct
{
double taskrcond;
ae_int_t iterationscount;
ae_int_t varidx;
double rmserror;
double avgerror;
double avgrelerror;
double maxerror;
double wrmserror;
ae_matrix covpar;
ae_vector errpar;
ae_vector errcurve;
ae_vector noise;
double r2;
} lsfitreport;
typedef struct
{
ae_int_t optalgo;
ae_int_t m;
ae_int_t k;
double epsx;
ae_int_t maxits;
double stpmax;
ae_bool xrep;
ae_vector c0;
ae_vector c1;
ae_vector s;
ae_vector bndl;
ae_vector bndu;
ae_matrix taskx;
ae_vector tasky;
ae_int_t npoints;
ae_vector taskw;
ae_int_t nweights;
ae_int_t wkind;
ae_int_t wits;
double diffstep;
double teststep;
ae_matrix cleic;
ae_int_t nec;
ae_int_t nic;
ae_bool xupdated;
ae_bool needf;
ae_bool needfg;
ae_bool needfgh;
ae_int_t pointindex;
ae_vector x;
ae_vector c;
double f;
ae_vector g;
ae_matrix h;
ae_vector wcur;
ae_vector tmpct;
ae_vector tmp;
ae_vector tmpf;
ae_matrix tmpjac;
ae_matrix tmpjacw;
double tmpnoise;
matinvreport invrep;
ae_int_t repiterationscount;
ae_int_t repterminationtype;
ae_int_t repvaridx;
double reprmserror;
double repavgerror;
double repavgrelerror;
double repmaxerror;
double repwrmserror;
lsfitreport rep;
minlmstate optstate;
minlmreport optrep;
ae_int_t prevnpt;
ae_int_t prevalgo;
rcommstate rstate;
} lsfitstate;
#endif
#if defined(AE_COMPILE_RBFV2) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_vector x;
ae_vector curboxmin;
ae_vector curboxmax;
double curdist2;
ae_vector x123;
ae_vector y123;
} rbfv2calcbuffer;
typedef struct
{
ae_int_t ny;
ae_int_t nx;
ae_int_t bf;
ae_int_t nh;
ae_vector ri;
ae_vector s;
ae_vector kdroots;
ae_vector kdnodes;
ae_vector kdsplits;
ae_vector kdboxmin;
ae_vector kdboxmax;
ae_vector cw;
ae_matrix v;
double lambdareg;
ae_int_t maxits;
double supportr;
ae_int_t basisfunction;
rbfv2calcbuffer calcbuf;
} rbfv2model;
typedef struct
{
rbfv2calcbuffer calcbuf;
ae_vector cx;
ae_vector rx;
ae_vector ry;
ae_vector tx;
ae_vector ty;
ae_vector rf;
} rbfv2gridcalcbuffer;
typedef struct
{
ae_int_t terminationtype;
double maxerror;
double rmserror;
} rbfv2report;
#endif
#if defined(AE_COMPILE_SPLINE2D) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_int_t stype;
ae_int_t n;
ae_int_t m;
ae_int_t d;
ae_vector x;
ae_vector y;
ae_vector f;
} spline2dinterpolant;
typedef struct
{
ae_int_t priorterm;
double priortermval;
ae_int_t areatype;
double xa;
double xb;
double ya;
double yb;
ae_int_t gridtype;
ae_int_t kx;
ae_int_t ky;
double smoothing;
ae_int_t nlayers;
ae_int_t solvertype;
double lambdabase;
ae_vector xy;
ae_int_t npoints;
ae_int_t d;
double sx;
double sy;
ae_bool adddegreeoffreedom;
ae_int_t interfacesize;
ae_int_t lsqrcnt;
ae_int_t maxcoresize;
} spline2dbuilder;
typedef struct
{
double rmserror;
double avgerror;
double maxerror;
double r2;
} spline2dfitreport;
typedef struct
{
ae_int_t blockwidth;
ae_int_t kx;
ae_int_t ky;
ae_int_t npoints;
ae_int_t nrows;
ae_int_t ndenserows;
ae_int_t ndensebatches;
ae_int_t d;
ae_int_t maxbatch;
ae_matrix vals;
ae_vector batches;
ae_vector batchbases;
double lambdareg;
ae_vector tmp0;
ae_vector tmp1;
ae_matrix tmp2;
} spline2dxdesignmatrix;
typedef struct
{
linlsqrstate solver;
linlsqrreport solverrep;
ae_matrix blockata;
ae_matrix trsmbuf2;
ae_matrix cholbuf2;
ae_vector cholbuf1;
ae_vector tmp0;
ae_vector tmp1;
} spline2dblockllsbuf;
typedef struct
{
spline2dxdesignmatrix xdesignmatrix;
ae_vector tmp0;
ae_vector tmpz;
spline2dfitreport dummyrep;
spline2dinterpolant localmodel;
spline2dblockllsbuf blockllsbuf;
} spline2dfastddmbuf;
#endif
#if defined(AE_COMPILE_RBFV1) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_vector calcbufxcx;
ae_matrix calcbufx;
ae_vector calcbuftags;
kdtreerequestbuffer requestbuffer;
} rbfv1calcbuffer;
typedef struct
{
ae_int_t ny;
ae_int_t nx;
ae_int_t nc;
ae_int_t nl;
kdtree tree;
ae_matrix xc;
ae_matrix wr;
double rmax;
ae_matrix v;
ae_vector calcbufxcx;
ae_matrix calcbufx;
ae_vector calcbuftags;
} rbfv1model;
typedef struct
{
ae_vector tx;
ae_vector cx;
ae_vector ty;
ae_vector flag0;
ae_vector flag1;
ae_vector flag2;
ae_vector flag12;
ae_vector expbuf0;
ae_vector expbuf1;
ae_vector expbuf2;
kdtreerequestbuffer requestbuf;
ae_matrix calcbufx;
ae_vector calcbuftags;
} gridcalc3v1buf;
typedef struct
{
ae_int_t arows;
ae_int_t acols;
ae_int_t annz;
ae_int_t iterationscount;
ae_int_t nmv;
ae_int_t terminationtype;
} rbfv1report;
#endif
#if defined(AE_COMPILE_RBF) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
ae_int_t modelversion;
rbfv1calcbuffer bufv1;
rbfv2calcbuffer bufv2;
} rbfcalcbuffer;
typedef struct
{
ae_int_t nx;
ae_int_t ny;
ae_int_t modelversion;
rbfv1model model1;
rbfv2model model2;
double lambdav;
double radvalue;
double radzvalue;
ae_int_t nlayers;
ae_int_t aterm;
ae_int_t algorithmtype;
double epsort;
double epserr;
ae_int_t maxits;
ae_int_t nnmaxits;
ae_int_t n;
ae_matrix x;
ae_matrix y;
ae_bool hasscale;
ae_vector s;
ae_int_t progress10000;
ae_bool terminationrequest;
} rbfmodel;
typedef struct
{
double rmserror;
double maxerror;
ae_int_t arows;
ae_int_t acols;
ae_int_t annz;
ae_int_t iterationscount;
ae_int_t nmv;
ae_int_t terminationtype;
} rbfreport;
#endif
#if defined(AE_COMPILE_INTCOMP) || !defined(AE_PARTIAL_BUILD)
#endif
}
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS C++ INTERFACE
//
/////////////////////////////////////////////////////////////////////////
namespace alglib
{
#if defined(AE_COMPILE_IDW) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Buffer object which is used to perform evaluation requests in the
multithreaded mode (multiple threads working with same IDW object).
This object should be created with idwcreatecalcbuffer().
*************************************************************************/
class _idwcalcbuffer_owner
{
public:
_idwcalcbuffer_owner();
_idwcalcbuffer_owner(const _idwcalcbuffer_owner &rhs);
_idwcalcbuffer_owner& operator=(const _idwcalcbuffer_owner &rhs);
virtual ~_idwcalcbuffer_owner();
alglib_impl::idwcalcbuffer* c_ptr();
alglib_impl::idwcalcbuffer* c_ptr() const;
protected:
alglib_impl::idwcalcbuffer *p_struct;
};
class idwcalcbuffer : public _idwcalcbuffer_owner
{
public:
idwcalcbuffer();
idwcalcbuffer(const idwcalcbuffer &rhs);
idwcalcbuffer& operator=(const idwcalcbuffer &rhs);
virtual ~idwcalcbuffer();
};
/*************************************************************************
IDW (Inverse Distance Weighting) model object.
*************************************************************************/
class _idwmodel_owner
{
public:
_idwmodel_owner();
_idwmodel_owner(const _idwmodel_owner &rhs);
_idwmodel_owner& operator=(const _idwmodel_owner &rhs);
virtual ~_idwmodel_owner();
alglib_impl::idwmodel* c_ptr();
alglib_impl::idwmodel* c_ptr() const;
protected:
alglib_impl::idwmodel *p_struct;
};
class idwmodel : public _idwmodel_owner
{
public:
idwmodel();
idwmodel(const idwmodel &rhs);
idwmodel& operator=(const idwmodel &rhs);
virtual ~idwmodel();
};
/*************************************************************************
Builder object used to generate IDW (Inverse Distance Weighting) model.
*************************************************************************/
class _idwbuilder_owner
{
public:
_idwbuilder_owner();
_idwbuilder_owner(const _idwbuilder_owner &rhs);
_idwbuilder_owner& operator=(const _idwbuilder_owner &rhs);
virtual ~_idwbuilder_owner();
alglib_impl::idwbuilder* c_ptr();
alglib_impl::idwbuilder* c_ptr() const;
protected:
alglib_impl::idwbuilder *p_struct;
};
class idwbuilder : public _idwbuilder_owner
{
public:
idwbuilder();
idwbuilder(const idwbuilder &rhs);
idwbuilder& operator=(const idwbuilder &rhs);
virtual ~idwbuilder();
};
/*************************************************************************
IDW fitting report:
rmserror RMS error
avgerror average error
maxerror maximum error
r2 coefficient of determination, R-squared, 1-RSS/TSS
*************************************************************************/
class _idwreport_owner
{
public:
_idwreport_owner();
_idwreport_owner(const _idwreport_owner &rhs);
_idwreport_owner& operator=(const _idwreport_owner &rhs);
virtual ~_idwreport_owner();
alglib_impl::idwreport* c_ptr();
alglib_impl::idwreport* c_ptr() const;
protected:
alglib_impl::idwreport *p_struct;
};
class idwreport : public _idwreport_owner
{
public:
idwreport();
idwreport(const idwreport &rhs);
idwreport& operator=(const idwreport &rhs);
virtual ~idwreport();
double &rmserror;
double &avgerror;
double &maxerror;
double &r2;
};
#endif
#if defined(AE_COMPILE_RATINT) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Barycentric interpolant.
*************************************************************************/
class _barycentricinterpolant_owner
{
public:
_barycentricinterpolant_owner();
_barycentricinterpolant_owner(const _barycentricinterpolant_owner &rhs);
_barycentricinterpolant_owner& operator=(const _barycentricinterpolant_owner &rhs);
virtual ~_barycentricinterpolant_owner();
alglib_impl::barycentricinterpolant* c_ptr();
alglib_impl::barycentricinterpolant* c_ptr() const;
protected:
alglib_impl::barycentricinterpolant *p_struct;
};
class barycentricinterpolant : public _barycentricinterpolant_owner
{
public:
barycentricinterpolant();
barycentricinterpolant(const barycentricinterpolant &rhs);
barycentricinterpolant& operator=(const barycentricinterpolant &rhs);
virtual ~barycentricinterpolant();
};
#endif
#if defined(AE_COMPILE_FITSPHERE) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_INTFITSERV) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_SPLINE1D) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
1-dimensional spline interpolant
*************************************************************************/
class _spline1dinterpolant_owner
{
public:
_spline1dinterpolant_owner();
_spline1dinterpolant_owner(const _spline1dinterpolant_owner &rhs);
_spline1dinterpolant_owner& operator=(const _spline1dinterpolant_owner &rhs);
virtual ~_spline1dinterpolant_owner();
alglib_impl::spline1dinterpolant* c_ptr();
alglib_impl::spline1dinterpolant* c_ptr() const;
protected:
alglib_impl::spline1dinterpolant *p_struct;
};
class spline1dinterpolant : public _spline1dinterpolant_owner
{
public:
spline1dinterpolant();
spline1dinterpolant(const spline1dinterpolant &rhs);
spline1dinterpolant& operator=(const spline1dinterpolant &rhs);
virtual ~spline1dinterpolant();
};
/*************************************************************************
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
*************************************************************************/
class _spline1dfitreport_owner
{
public:
_spline1dfitreport_owner();
_spline1dfitreport_owner(const _spline1dfitreport_owner &rhs);
_spline1dfitreport_owner& operator=(const _spline1dfitreport_owner &rhs);
virtual ~_spline1dfitreport_owner();
alglib_impl::spline1dfitreport* c_ptr();
alglib_impl::spline1dfitreport* c_ptr() const;
protected:
alglib_impl::spline1dfitreport *p_struct;
};
class spline1dfitreport : public _spline1dfitreport_owner
{
public:
spline1dfitreport();
spline1dfitreport(const spline1dfitreport &rhs);
spline1dfitreport& operator=(const spline1dfitreport &rhs);
virtual ~spline1dfitreport();
double &taskrcond;
double &rmserror;
double &avgerror;
double &avgrelerror;
double &maxerror;
};
#endif
#if defined(AE_COMPILE_PARAMETRIC) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Parametric spline inteprolant: 2-dimensional curve.
You should not try to access its members directly - use PSpline2XXXXXXXX()
functions instead.
*************************************************************************/
class _pspline2interpolant_owner
{
public:
_pspline2interpolant_owner();
_pspline2interpolant_owner(const _pspline2interpolant_owner &rhs);
_pspline2interpolant_owner& operator=(const _pspline2interpolant_owner &rhs);
virtual ~_pspline2interpolant_owner();
alglib_impl::pspline2interpolant* c_ptr();
alglib_impl::pspline2interpolant* c_ptr() const;
protected:
alglib_impl::pspline2interpolant *p_struct;
};
class pspline2interpolant : public _pspline2interpolant_owner
{
public:
pspline2interpolant();
pspline2interpolant(const pspline2interpolant &rhs);
pspline2interpolant& operator=(const pspline2interpolant &rhs);
virtual ~pspline2interpolant();
};
/*************************************************************************
Parametric spline inteprolant: 3-dimensional curve.
You should not try to access its members directly - use PSpline3XXXXXXXX()
functions instead.
*************************************************************************/
class _pspline3interpolant_owner
{
public:
_pspline3interpolant_owner();
_pspline3interpolant_owner(const _pspline3interpolant_owner &rhs);
_pspline3interpolant_owner& operator=(const _pspline3interpolant_owner &rhs);
virtual ~_pspline3interpolant_owner();
alglib_impl::pspline3interpolant* c_ptr();
alglib_impl::pspline3interpolant* c_ptr() const;
protected:
alglib_impl::pspline3interpolant *p_struct;
};
class pspline3interpolant : public _pspline3interpolant_owner
{
public:
pspline3interpolant();
pspline3interpolant(const pspline3interpolant &rhs);
pspline3interpolant& operator=(const pspline3interpolant &rhs);
virtual ~pspline3interpolant();
};
#endif
#if defined(AE_COMPILE_SPLINE3D) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
3-dimensional spline inteprolant
*************************************************************************/
class _spline3dinterpolant_owner
{
public:
_spline3dinterpolant_owner();
_spline3dinterpolant_owner(const _spline3dinterpolant_owner &rhs);
_spline3dinterpolant_owner& operator=(const _spline3dinterpolant_owner &rhs);
virtual ~_spline3dinterpolant_owner();
alglib_impl::spline3dinterpolant* c_ptr();
alglib_impl::spline3dinterpolant* c_ptr() const;
protected:
alglib_impl::spline3dinterpolant *p_struct;
};
class spline3dinterpolant : public _spline3dinterpolant_owner
{
public:
spline3dinterpolant();
spline3dinterpolant(const spline3dinterpolant &rhs);
spline3dinterpolant& operator=(const spline3dinterpolant &rhs);
virtual ~spline3dinterpolant();
};
#endif
#if defined(AE_COMPILE_POLINT) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_LSFIT) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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
*************************************************************************/
class _polynomialfitreport_owner
{
public:
_polynomialfitreport_owner();
_polynomialfitreport_owner(const _polynomialfitreport_owner &rhs);
_polynomialfitreport_owner& operator=(const _polynomialfitreport_owner &rhs);
virtual ~_polynomialfitreport_owner();
alglib_impl::polynomialfitreport* c_ptr();
alglib_impl::polynomialfitreport* c_ptr() const;
protected:
alglib_impl::polynomialfitreport *p_struct;
};
class polynomialfitreport : public _polynomialfitreport_owner
{
public:
polynomialfitreport();
polynomialfitreport(const polynomialfitreport &rhs);
polynomialfitreport& operator=(const polynomialfitreport &rhs);
virtual ~polynomialfitreport();
double &taskrcond;
double &rmserror;
double &avgerror;
double &avgrelerror;
double &maxerror;
};
/*************************************************************************
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
*************************************************************************/
class _barycentricfitreport_owner
{
public:
_barycentricfitreport_owner();
_barycentricfitreport_owner(const _barycentricfitreport_owner &rhs);
_barycentricfitreport_owner& operator=(const _barycentricfitreport_owner &rhs);
virtual ~_barycentricfitreport_owner();
alglib_impl::barycentricfitreport* c_ptr();
alglib_impl::barycentricfitreport* c_ptr() const;
protected:
alglib_impl::barycentricfitreport *p_struct;
};
class barycentricfitreport : public _barycentricfitreport_owner
{
public:
barycentricfitreport();
barycentricfitreport(const barycentricfitreport &rhs);
barycentricfitreport& operator=(const barycentricfitreport &rhs);
virtual ~barycentricfitreport();
double &taskrcond;
ae_int_t &dbest;
double &rmserror;
double &avgerror;
double &avgrelerror;
double &maxerror;
};
/*************************************************************************
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.
*************************************************************************/
class _lsfitreport_owner
{
public:
_lsfitreport_owner();
_lsfitreport_owner(const _lsfitreport_owner &rhs);
_lsfitreport_owner& operator=(const _lsfitreport_owner &rhs);
virtual ~_lsfitreport_owner();
alglib_impl::lsfitreport* c_ptr();
alglib_impl::lsfitreport* c_ptr() const;
protected:
alglib_impl::lsfitreport *p_struct;
};
class lsfitreport : public _lsfitreport_owner
{
public:
lsfitreport();
lsfitreport(const lsfitreport &rhs);
lsfitreport& operator=(const lsfitreport &rhs);
virtual ~lsfitreport();
double &taskrcond;
ae_int_t &iterationscount;
ae_int_t &varidx;
double &rmserror;
double &avgerror;
double &avgrelerror;
double &maxerror;
double &wrmserror;
real_2d_array covpar;
real_1d_array errpar;
real_1d_array errcurve;
real_1d_array noise;
double &r2;
};
/*************************************************************************
Nonlinear fitter.
You should use ALGLIB functions to work with fitter.
Never try to access its fields directly!
*************************************************************************/
class _lsfitstate_owner
{
public:
_lsfitstate_owner();
_lsfitstate_owner(const _lsfitstate_owner &rhs);
_lsfitstate_owner& operator=(const _lsfitstate_owner &rhs);
virtual ~_lsfitstate_owner();
alglib_impl::lsfitstate* c_ptr();
alglib_impl::lsfitstate* c_ptr() const;
protected:
alglib_impl::lsfitstate *p_struct;
};
class lsfitstate : public _lsfitstate_owner
{
public:
lsfitstate();
lsfitstate(const lsfitstate &rhs);
lsfitstate& operator=(const lsfitstate &rhs);
virtual ~lsfitstate();
ae_bool &needf;
ae_bool &needfg;
ae_bool &needfgh;
ae_bool &xupdated;
real_1d_array c;
double &f;
real_1d_array g;
real_2d_array h;
real_1d_array x;
};
#endif
#if defined(AE_COMPILE_RBFV2) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_SPLINE2D) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
2-dimensional spline inteprolant
*************************************************************************/
class _spline2dinterpolant_owner
{
public:
_spline2dinterpolant_owner();
_spline2dinterpolant_owner(const _spline2dinterpolant_owner &rhs);
_spline2dinterpolant_owner& operator=(const _spline2dinterpolant_owner &rhs);
virtual ~_spline2dinterpolant_owner();
alglib_impl::spline2dinterpolant* c_ptr();
alglib_impl::spline2dinterpolant* c_ptr() const;
protected:
alglib_impl::spline2dinterpolant *p_struct;
};
class spline2dinterpolant : public _spline2dinterpolant_owner
{
public:
spline2dinterpolant();
spline2dinterpolant(const spline2dinterpolant &rhs);
spline2dinterpolant& operator=(const spline2dinterpolant &rhs);
virtual ~spline2dinterpolant();
};
/*************************************************************************
Nonlinear least squares solver used to fit 2D splines to data
*************************************************************************/
class _spline2dbuilder_owner
{
public:
_spline2dbuilder_owner();
_spline2dbuilder_owner(const _spline2dbuilder_owner &rhs);
_spline2dbuilder_owner& operator=(const _spline2dbuilder_owner &rhs);
virtual ~_spline2dbuilder_owner();
alglib_impl::spline2dbuilder* c_ptr();
alglib_impl::spline2dbuilder* c_ptr() const;
protected:
alglib_impl::spline2dbuilder *p_struct;
};
class spline2dbuilder : public _spline2dbuilder_owner
{
public:
spline2dbuilder();
spline2dbuilder(const spline2dbuilder &rhs);
spline2dbuilder& operator=(const spline2dbuilder &rhs);
virtual ~spline2dbuilder();
};
/*************************************************************************
Spline 2D fitting report:
rmserror RMS error
avgerror average error
maxerror maximum error
r2 coefficient of determination, R-squared, 1-RSS/TSS
*************************************************************************/
class _spline2dfitreport_owner
{
public:
_spline2dfitreport_owner();
_spline2dfitreport_owner(const _spline2dfitreport_owner &rhs);
_spline2dfitreport_owner& operator=(const _spline2dfitreport_owner &rhs);
virtual ~_spline2dfitreport_owner();
alglib_impl::spline2dfitreport* c_ptr();
alglib_impl::spline2dfitreport* c_ptr() const;
protected:
alglib_impl::spline2dfitreport *p_struct;
};
class spline2dfitreport : public _spline2dfitreport_owner
{
public:
spline2dfitreport();
spline2dfitreport(const spline2dfitreport &rhs);
spline2dfitreport& operator=(const spline2dfitreport &rhs);
virtual ~spline2dfitreport();
double &rmserror;
double &avgerror;
double &maxerror;
double &r2;
};
#endif
#if defined(AE_COMPILE_RBFV1) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_RBF) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Buffer object which is used to perform nearest neighbor requests in the
multithreaded mode (multiple threads working with same KD-tree object).
This object should be created with KDTreeCreateBuffer().
*************************************************************************/
class _rbfcalcbuffer_owner
{
public:
_rbfcalcbuffer_owner();
_rbfcalcbuffer_owner(const _rbfcalcbuffer_owner &rhs);
_rbfcalcbuffer_owner& operator=(const _rbfcalcbuffer_owner &rhs);
virtual ~_rbfcalcbuffer_owner();
alglib_impl::rbfcalcbuffer* c_ptr();
alglib_impl::rbfcalcbuffer* c_ptr() const;
protected:
alglib_impl::rbfcalcbuffer *p_struct;
};
class rbfcalcbuffer : public _rbfcalcbuffer_owner
{
public:
rbfcalcbuffer();
rbfcalcbuffer(const rbfcalcbuffer &rhs);
rbfcalcbuffer& operator=(const rbfcalcbuffer &rhs);
virtual ~rbfcalcbuffer();
};
/*************************************************************************
RBF model.
Never try to directly work with fields of this object - always use ALGLIB
functions to use this object.
*************************************************************************/
class _rbfmodel_owner
{
public:
_rbfmodel_owner();
_rbfmodel_owner(const _rbfmodel_owner &rhs);
_rbfmodel_owner& operator=(const _rbfmodel_owner &rhs);
virtual ~_rbfmodel_owner();
alglib_impl::rbfmodel* c_ptr();
alglib_impl::rbfmodel* c_ptr() const;
protected:
alglib_impl::rbfmodel *p_struct;
};
class rbfmodel : public _rbfmodel_owner
{
public:
rbfmodel();
rbfmodel(const rbfmodel &rhs);
rbfmodel& operator=(const rbfmodel &rhs);
virtual ~rbfmodel();
};
/*************************************************************************
RBF solution report:
* TerminationType - termination type, positive values - success,
non-positive - failure.
Fields which are set by modern RBF solvers (hierarchical):
* RMSError - root-mean-square error; NAN for old solvers (ML, QNN)
* MaxError - maximum error; NAN for old solvers (ML, QNN)
*************************************************************************/
class _rbfreport_owner
{
public:
_rbfreport_owner();
_rbfreport_owner(const _rbfreport_owner &rhs);
_rbfreport_owner& operator=(const _rbfreport_owner &rhs);
virtual ~_rbfreport_owner();
alglib_impl::rbfreport* c_ptr();
alglib_impl::rbfreport* c_ptr() const;
protected:
alglib_impl::rbfreport *p_struct;
};
class rbfreport : public _rbfreport_owner
{
public:
rbfreport();
rbfreport(const rbfreport &rhs);
rbfreport& operator=(const rbfreport &rhs);
virtual ~rbfreport();
double &rmserror;
double &maxerror;
ae_int_t &arows;
ae_int_t &acols;
ae_int_t &annz;
ae_int_t &iterationscount;
ae_int_t &nmv;
ae_int_t &terminationtype;
};
#endif
#if defined(AE_COMPILE_INTCOMP) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_IDW) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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 idwserialize(idwmodel &obj, std::string &s_out);
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void idwunserialize(const std::string &s_in, idwmodel &obj);
/*************************************************************************
This function serializes data structure to C++ stream.
Data stream generated by this function is same as string representation
generated by string version of serializer - alphanumeric characters,
dots, underscores, minus signs, which are grouped into words separated by
spaces and CR+LF.
We recommend you to read comments on string version of serializer to find
out more about serialization of AlGLIB objects.
*************************************************************************/
void idwserialize(idwmodel &obj, std::ostream &s_out);
/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void idwunserialize(const std::istream &s_in, idwmodel &obj);
/*************************************************************************
This function creates buffer structure which can be used to perform
parallel IDW model evaluations (with one IDW model instance being
used from multiple threads, as long as different threads use different
instances of buffer).
This buffer object can be used with idwtscalcbuf() function (here "ts"
stands for "thread-safe", "buf" is a suffix which denotes function which
reuses previously allocated output space).
How to use it:
* create IDW model structure or load it from file
* call idwcreatecalcbuffer(), once per thread working with IDW model (you
should call this function only AFTER model initialization, see below for
more information)
* call idwtscalcbuf() from different threads, with each thread working
with its own copy of buffer object.
INPUT PARAMETERS
S - IDW model
OUTPUT PARAMETERS
Buf - external buffer.
IMPORTANT: buffer object should be used only with IDW model object which
was used to initialize buffer. Any attempt to use buffer with
different object is dangerous - you may get memory violation
error because sizes of internal arrays do not fit to dimensions
of the IDW structure.
IMPORTANT: you should call this function only for model which was built
with model builder (or unserialized from file). Sizes of some
internal structures are determined only after model is built,
so buffer object created before model construction stage will
be useless (and any attempt to use it will result in exception).
-- ALGLIB --
Copyright 22.10.2018 by Sergey Bochkanov
*************************************************************************/
void idwcreatecalcbuffer(const idwmodel &s, idwcalcbuffer &buf, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This subroutine creates builder object used to generate IDW model from
irregularly sampled (scattered) dataset. Multidimensional scalar/vector-
-valued are supported.
Builder object is used to fit model to data as follows:
* builder object is created with idwbuildercreate() function
* dataset is added with idwbuildersetpoints() function
* one of the modern IDW algorithms is chosen with either:
* idwbuildersetalgomstab() - Multilayer STABilized algorithm (interpolation)
Alternatively, one of the textbook algorithms can be chosen (not recommended):
* idwbuildersetalgotextbookshepard() - textbook Shepard algorithm
* idwbuildersetalgotextbookmodshepard()-textbook modified Shepard algorithm
* finally, model construction is performed with idwfit() function.
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
!
! 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:
NX - dimensionality of the argument, NX>=1
NY - dimensionality of the function being modeled, NY>=1;
NY=1 corresponds to classic scalar function, NY>=1 corresponds
to vector-valued function.
OUTPUT PARAMETERS:
State- builder object
-- ALGLIB PROJECT --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildercreate(const ae_int_t nx, const ae_int_t ny, idwbuilder &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function changes number of layers used by IDW-MSTAB algorithm.
The more layers you have, the finer details can be reproduced with IDW
model. The less layers you have, the less memory and CPU time is consumed
by the model.
Memory consumption grows linearly with layers count, running time grows
sub-linearly.
The default number of layers is 16, which allows you to reproduce details
at distance down to SRad/65536. You will rarely need to change it.
INPUT PARAMETERS:
State - builder object
NLayers - NLayers>=1, the number of layers used by the model.
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildersetnlayers(const idwbuilder &state, const ae_int_t nlayers, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function adds dataset to the builder object.
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:
State - builder object
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
specified, in this case only leading [N,NX+NY] elements
will be used.
N - number of points in the dataset, N>=0.
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildersetpoints(const idwbuilder &state, const real_2d_array &xy, const ae_int_t n, const xparams _xparams = alglib::xdefault);
void idwbuildersetpoints(const idwbuilder &state, const real_2d_array &xy, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets IDW model construction algorithm to the Multilayer
Stabilized IDW method (IDW-MSTAB), a latest incarnation of the inverse
distance weighting interpolation which fixes shortcomings of the original
and modified Shepard's variants.
The distinctive features of IDW-MSTAB are:
1) exact interpolation is pursued (as opposed to fitting and noise
suppression)
2) improved robustness when compared with that of other algorithms:
* MSTAB shows almost no strange fitting artifacts like ripples and
sharp spikes (unlike N-dimensional splines and HRBFs)
* MSTAB does not return function values far from the interval spanned
by the dataset; say, if all your points have |f|<=1, you can be sure
that model value won't deviate too much from [-1,+1]
3) good model construction time competing with that of HRBFs and bicubic
splines
4) ability to work with any number of dimensions, starting from NX=1
The drawbacks of IDW-MSTAB (and all IDW algorithms in general) are:
1) dependence of the model evaluation time on the search radius
2) bad extrapolation properties, models built by this method are usually
conservative in their predictions
Thus, IDW-MSTAB is a good "default" option if you want to perform
scattered multidimensional interpolation. Although it has its drawbacks,
it is easy to use and robust, which makes it a good first step.
INPUT PARAMETERS:
State - builder object
SRad - initial search radius, SRad>0 is required. A model value
is obtained by "smart" averaging of the dataset points
within search radius.
NOTE 1: IDW interpolation can correctly handle ANY dataset, including
datasets with non-distinct points. In case non-distinct points are
found, an average value for this point will be calculated.
NOTE 2: the memory requirements for model storage are O(NPoints*NLayers).
The model construction needs twice as much memory as model storage.
NOTE 3: by default 16 IDW layers are built which is enough for most cases.
You can change this parameter with idwbuildersetnlayers() method.
Larger values may be necessary if you need to reproduce extrafine
details at distances smaller than SRad/65536. Smaller value may
be necessary if you have to save memory and computing time, and
ready to sacrifice some model quality.
ALGORITHM DESCRIPTION
ALGLIB implementation of IDW is somewhat similar to the modified Shepard's
method (one with search radius R) but overcomes several of its drawbacks,
namely:
1) a tendency to show stepwise behavior for uniform datasets
2) a tendency to show terrible interpolation properties for highly
nonuniform datasets which often arise in geospatial tasks
(function values are densely sampled across multiple separated
"tracks")
IDW-MSTAB method performs several passes over dataset and builds a sequence
of progressively refined IDW models (layers), which starts from one with
largest search radius SRad and continues to smaller search radii until
required number of layers is built. Highest layers reproduce global
behavior of the target function at larger distances whilst lower layers
reproduce fine details at smaller distances.
Each layer is an IDW model built with following modifications:
* weights go to zero when distance approach to the current search radius
* an additional regularizing term is added to the distance: w=1/(d^2+lambda)
* an additional fictional term with unit weight and zero function value is
added in order to promote continuity properties at the isolated and
boundary points
By default, 16 layers is built, which is enough for most cases. You can
change this parameter with idwbuildersetnlayers() method.
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildersetalgomstab(const idwbuilder &state, const double srad, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets IDW model construction algorithm to the textbook
Shepard's algorithm with custom (user-specified) power parameter.
IMPORTANT: we do NOT recommend using textbook IDW algorithms because they
have terrible interpolation properties. Use MSTAB in all cases.
INPUT PARAMETERS:
State - builder object
P - power parameter, P>0; good value to start with is 2.0
NOTE 1: IDW interpolation can correctly handle ANY dataset, including
datasets with non-distinct points. In case non-distinct points are
found, an average value for this point will be calculated.
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildersetalgotextbookshepard(const idwbuilder &state, const double p, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets IDW model construction algorithm to the 'textbook'
modified Shepard's algorithm with user-specified search radius.
IMPORTANT: we do NOT recommend using textbook IDW algorithms because they
have terrible interpolation properties. Use MSTAB in all cases.
INPUT PARAMETERS:
State - builder object
R - search radius
NOTE 1: IDW interpolation can correctly handle ANY dataset, including
datasets with non-distinct points. In case non-distinct points are
found, an average value for this point will be calculated.
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildersetalgotextbookmodshepard(const idwbuilder &state, const double r, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets prior term (model value at infinity) as user-specified
value.
INPUT PARAMETERS:
S - spline builder
V - value for user-defined prior
NOTE: for vector-valued models all components of the prior are set to same
user-specified value
-- ALGLIB --
Copyright 29.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildersetuserterm(const idwbuilder &state, const double v, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets constant prior term (model value at infinity).
Constant prior term is determined as mean value over dataset.
INPUT PARAMETERS:
S - spline builder
-- ALGLIB --
Copyright 29.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildersetconstterm(const idwbuilder &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets zero prior term (model value at infinity).
INPUT PARAMETERS:
S - spline builder
-- ALGLIB --
Copyright 29.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwbuildersetzeroterm(const idwbuilder &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
IDW interpolation: scalar target, 1-dimensional argument
NOTE: this function modifies internal temporaries of the IDW model, thus
IT IS NOT THREAD-SAFE! If you want to perform parallel model
evaluation from the multiple threads, use idwtscalcbuf() with per-
thread buffer object.
INPUT PARAMETERS:
S - IDW interpolant built with IDW builder
X0 - argument value
Result:
IDW interpolant S(X0)
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
double idwcalc1(const idwmodel &s, const double x0, const xparams _xparams = alglib::xdefault);
/*************************************************************************
IDW interpolation: scalar target, 2-dimensional argument
NOTE: this function modifies internal temporaries of the IDW model, thus
IT IS NOT THREAD-SAFE! If you want to perform parallel model
evaluation from the multiple threads, use idwtscalcbuf() with per-
thread buffer object.
INPUT PARAMETERS:
S - IDW interpolant built with IDW builder
X0, X1 - argument value
Result:
IDW interpolant S(X0,X1)
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
double idwcalc2(const idwmodel &s, const double x0, const double x1, const xparams _xparams = alglib::xdefault);
/*************************************************************************
IDW interpolation: scalar target, 3-dimensional argument
NOTE: this function modifies internal temporaries of the IDW model, thus
IT IS NOT THREAD-SAFE! If you want to perform parallel model
evaluation from the multiple threads, use idwtscalcbuf() with per-
thread buffer object.
INPUT PARAMETERS:
S - IDW interpolant built with IDW builder
X0,X1,X2- argument value
Result:
IDW interpolant S(X0,X1,X2)
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
double idwcalc3(const idwmodel &s, const double x0, const double x1, const double x2, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the IDW 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 idwcalc1(),
idwcalc2() or idwcalc3().
NOTE: this function modifies internal temporaries of the IDW model, thus
IT IS NOT THREAD-SAFE! If you want to perform parallel model
evaluation from the multiple threads, use idwtscalcbuf() with per-
thread buffer object.
INPUT PARAMETERS:
S - IDW 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 will be
reallocated after call to this function. In case you want
to reuse previously allocated Y, you may use idwcalcbuf(),
which reallocates Y only when it is too small.
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwcalc(const idwmodel &s, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the IDW model at the given point.
Same as idwcalc(), but does not reallocate Y when in is large enough to
store function values.
NOTE: this function modifies internal temporaries of the IDW model, thus
IT IS NOT THREAD-SAFE! If you want to perform parallel model
evaluation from the multiple threads, use idwtscalcbuf() with per-
thread buffer object.
INPUT PARAMETERS:
S - IDW 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 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwcalcbuf(const idwmodel &s, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the IDW model at the given point, using
external buffer object (internal temporaries of IDW model are not
modified).
This function allows to use same IDW model object in different threads,
assuming that different threads use different instances of the buffer
structure.
INPUT PARAMETERS:
S - IDW model, may be shared between different threads
Buf - buffer object created for this particular instance of IDW
model with idwcreatecalcbuffer().
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 idwtscalcbuf(const idwmodel &s, const idwcalcbuffer &buf, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function fits IDW model to the dataset using current IDW construction
algorithm. A model being built and fitting report are returned.
INPUT PARAMETERS:
State - builder object
OUTPUT PARAMETERS:
Model - an IDW model built with current algorithm
Rep - model fitting report, fields of this structure contain
information about average fitting errors.
NOTE: although IDW-MSTAB algorithm is an interpolation method, i.e. it
tries to fit the model exactly, it can handle datasets with non-
distinct points which can not be fit exactly; in such cases least-
squares fitting is performed.
-- ALGLIB --
Copyright 22.10.2018 by Bochkanov Sergey
*************************************************************************/
void idwfit(const idwbuilder &state, idwmodel &model, idwreport &rep, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_RATINT) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_FITSPHERE) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Fits least squares (LS) circle (or NX-dimensional sphere) to data (a set
of points in NX-dimensional space).
Least squares circle minimizes sum of squared deviations between distances
from points to the center and some "candidate" radius, which is also
fitted to the data.
INPUT PARAMETERS:
XY - array[NPoints,NX] (or larger), contains dataset.
One row = one point in NX-dimensional space.
NPoints - dataset size, NPoints>0
NX - space dimensionality, NX>0 (1, 2, 3, 4, 5 and so on)
OUTPUT PARAMETERS:
CX - central point for a sphere
R - radius
-- ALGLIB --
Copyright 07.05.2018 by Bochkanov Sergey
*************************************************************************/
void fitspherels(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, real_1d_array &cx, double &r, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Fits minimum circumscribed (MC) circle (or NX-dimensional sphere) to data
(a set of points in NX-dimensional space).
INPUT PARAMETERS:
XY - array[NPoints,NX] (or larger), contains dataset.
One row = one point in NX-dimensional space.
NPoints - dataset size, NPoints>0
NX - space dimensionality, NX>0 (1, 2, 3, 4, 5 and so on)
OUTPUT PARAMETERS:
CX - central point for a sphere
RHi - radius
NOTE: this function is an easy-to-use wrapper around more powerful "expert"
function fitspherex().
This wrapper is optimized for ease of use and stability - at the
cost of somewhat lower performance (we have to use very tight
stopping criteria for inner optimizer because we want to make sure
that it will converge on any dataset).
If you are ready to experiment with settings of "expert" function,
you can achieve ~2-4x speedup over standard "bulletproof" settings.
-- ALGLIB --
Copyright 14.04.2017 by Bochkanov Sergey
*************************************************************************/
void fitspheremc(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, real_1d_array &cx, double &rhi, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Fits maximum inscribed circle (or NX-dimensional sphere) to data (a set of
points in NX-dimensional space).
INPUT PARAMETERS:
XY - array[NPoints,NX] (or larger), contains dataset.
One row = one point in NX-dimensional space.
NPoints - dataset size, NPoints>0
NX - space dimensionality, NX>0 (1, 2, 3, 4, 5 and so on)
OUTPUT PARAMETERS:
CX - central point for a sphere
RLo - radius
NOTE: this function is an easy-to-use wrapper around more powerful "expert"
function fitspherex().
This wrapper is optimized for ease of use and stability - at the
cost of somewhat lower performance (we have to use very tight
stopping criteria for inner optimizer because we want to make sure
that it will converge on any dataset).
If you are ready to experiment with settings of "expert" function,
you can achieve ~2-4x speedup over standard "bulletproof" settings.
-- ALGLIB --
Copyright 14.04.2017 by Bochkanov Sergey
*************************************************************************/
void fitspheremi(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, real_1d_array &cx, double &rlo, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Fits minimum zone circle (or NX-dimensional sphere) to data (a set of
points in NX-dimensional space).
INPUT PARAMETERS:
XY - array[NPoints,NX] (or larger), contains dataset.
One row = one point in NX-dimensional space.
NPoints - dataset size, NPoints>0
NX - space dimensionality, NX>0 (1, 2, 3, 4, 5 and so on)
OUTPUT PARAMETERS:
CX - central point for a sphere
RLo - radius of inscribed circle
RHo - radius of circumscribed circle
NOTE: this function is an easy-to-use wrapper around more powerful "expert"
function fitspherex().
This wrapper is optimized for ease of use and stability - at the
cost of somewhat lower performance (we have to use very tight
stopping criteria for inner optimizer because we want to make sure
that it will converge on any dataset).
If you are ready to experiment with settings of "expert" function,
you can achieve ~2-4x speedup over standard "bulletproof" settings.
-- ALGLIB --
Copyright 14.04.2017 by Bochkanov Sergey
*************************************************************************/
void fitspheremz(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, real_1d_array &cx, double &rlo, double &rhi, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Fitting minimum circumscribed, maximum inscribed or minimum zone circles
(or NX-dimensional spheres) to data (a set of points in NX-dimensional
space).
This is expert function which allows to tweak many parameters of
underlying nonlinear solver:
* stopping criteria for inner iterations
* number of outer iterations
* penalty coefficient used to handle nonlinear constraints (we convert
unconstrained nonsmooth optimization problem ivolving max() and/or min()
operations to quadratically constrained smooth one).
You may tweak all these parameters or only some of them, leaving other
ones at their default state - just specify zero value, and solver will
fill it with appropriate default one.
These comments also include some discussion of approach used to handle
such unusual fitting problem, its stability, drawbacks of alternative
methods, and convergence properties.
INPUT PARAMETERS:
XY - array[NPoints,NX] (or larger), contains dataset.
One row = one point in NX-dimensional space.
NPoints - dataset size, NPoints>0
NX - space dimensionality, NX>0 (1, 2, 3, 4, 5 and so on)
ProblemType-used to encode problem type:
* 0 for least squares circle
* 1 for minimum circumscribed circle/sphere fitting (MC)
* 2 for maximum inscribed circle/sphere fitting (MI)
* 3 for minimum zone circle fitting (difference between
Rhi and Rlo is minimized), denoted as MZ
EpsX - stopping condition for NLC optimizer:
* must be non-negative
* use 0 to choose default value (1.0E-12 is used by default)
* you may specify larger values, up to 1.0E-6, if you want
to speed-up solver; NLC solver performs several
preconditioned outer iterations, so final result
typically has precision much better than EpsX.
AULIts - number of outer iterations performed by NLC optimizer:
* must be non-negative
* use 0 to choose default value (20 is used by default)
* you may specify values smaller than 20 if you want to
speed up solver; 10 often results in good combination of
precision and speed; sometimes you may get good results
with just 6 outer iterations.
Ignored for ProblemType=0.
Penalty - penalty coefficient for NLC optimizer:
* must be non-negative
* use 0 to choose default value (1.0E6 in current version)
* it should be really large, 1.0E6...1.0E7 is a good value
to start from;
* generally, default value is good enough
Ignored for ProblemType=0.
OUTPUT PARAMETERS:
CX - central point for a sphere
RLo - radius:
* for ProblemType=2,3, radius of the inscribed sphere
* for ProblemType=0 - radius of the least squares sphere
* for ProblemType=1 - zero
RHo - radius:
* for ProblemType=1,3, radius of the circumscribed sphere
* for ProblemType=0 - radius of the least squares sphere
* for ProblemType=2 - zero
NOTE: ON THE UNIQUENESS OF SOLUTIONS
ALGLIB provides solution to several related circle fitting problems: MC
(minimum circumscribed), MI (maximum inscribed) and MZ (minimum zone)
fitting, LS (least squares) fitting.
It is important to note that among these problems only MC and LS are
convex and have unique solution independently from starting point.
As for MI, it may (or may not, depending on dataset properties) have
multiple solutions, and it always has one degenerate solution C=infinity
which corresponds to infinitely large radius. Thus, there are no guarantees
that solution to MI returned by this solver will be the best one (and no
one can provide you with such guarantee because problem is NP-hard). The
only guarantee you have is that this solution is locally optimal, i.e. it
can not be improved by infinitesimally small tweaks in the parameters.
It is also possible to "run away" to infinity when started from bad
initial point located outside of point cloud (or when point cloud does not
span entire circumference/surface of the sphere).
Finally, MZ (minimum zone circle) stands somewhere between MC and MI in
stability. It is somewhat regularized by "circumscribed" term of the merit
function; however, solutions to MZ may be non-unique, and in some unlucky
cases it is also possible to "run away to infinity".
NOTE: ON THE NONLINEARLY CONSTRAINED PROGRAMMING APPROACH
The problem formulation for MC (minimum circumscribed circle; for the
sake of simplicity we omit MZ and MI here) is:
[ [ ]2 ]
min [ max [ XY[i]-C ] ]
C [ i [ ] ]
i.e. it is unconstrained nonsmooth optimization problem of finding "best"
central point, with radius R being unambiguously determined from C. In
order to move away from non-smoothness we use following reformulation:
[ ] [ ]2
min [ R ] subject to R>=0, [ XY[i]-C ] <= R^2
C,R [ ] [ ]
i.e. it becomes smooth quadratically constrained optimization problem with
linear target function. Such problem statement is 100% equivalent to the
original nonsmooth one, but much easier to approach. We solve it with
MinNLC solver provided by ALGLIB.
NOTE: ON INSTABILITY OF SEQUENTIAL LINEARIZATION APPROACH
ALGLIB has nonlinearly constrained solver which proved to be stable on
such problems. However, some authors proposed to linearize constraints in
the vicinity of current approximation (Ci,Ri) and to get next approximate
solution (Ci+1,Ri+1) as solution to linear programming problem. Obviously,
LP problems are easier than nonlinearly constrained ones.
Indeed, such approach to MC/MI/MZ resulted in ~10-20x increase in
performance (when compared with NLC solver). However, it turned out that
in some cases linearized model fails to predict correct direction for next
step and tells us that we converged to solution even when we are still 2-4
digits of precision away from it.
It is important that it is not failure of LP solver - it is failure of the
linear model; even when solved exactly, it fails to handle subtle
nonlinearities which arise near the solution. We validated it by comparing
results returned by ALGLIB linear solver with that of MATLAB.
In our experiments with linearization:
* MC failed most often, at both realistic and synthetic datasets
* MI sometimes failed, but sometimes succeeded
* MZ often succeeded; our guess is that presence of two independent sets
of constraints (one set for Rlo and another one for Rhi) and two terms
in the target function (Rlo and Rhi) regularizes task, so when linear
model fails to handle nonlinearities from Rlo, it uses Rhi as a hint
(and vice versa).
Because linearization approach failed to achieve stable results, we do not
include it in ALGLIB.
-- ALGLIB --
Copyright 14.04.2017 by Bochkanov Sergey
*************************************************************************/
void fitspherex(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, const ae_int_t problemtype, const double epsx, const ae_int_t aulits, const double penalty, real_1d_array &cx, double &rlo, double &rhi, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_INTFITSERV) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_SPLINE1D) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dbuildlinear(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dbuildcubic(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dgriddiffcubic(const real_1d_array &x, const real_1d_array &y, real_1d_array &d, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dgriddiff2cubic(const real_1d_array &x, const real_1d_array &y, real_1d_array &d1, real_1d_array &d2, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dconvcubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dbuildcatmullrom(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dbuildhermite(const real_1d_array &x, const real_1d_array &y, const real_1d_array &d, spline1dinterpolant &c, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dbuildakima(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Fitting by smoothing (penalized) cubic spline.
This function approximates N scattered points (some of X[] may be equal to
each other) by cubic spline with M nodes at equidistant grid spanning
interval [min(x,xc),max(x,xc)].
The problem is regularized by adding nonlinearity penalty to usual least
squares penalty function:
MERIT_FUNC = F_LS + F_NL
where F_LS is a least squares error term, and F_NL is a nonlinearity
penalty which is roughly proportional to LambdaNS*integral{ S''(x)^2*dx }.
Algorithm applies automatic renormalization of F_NL which makes penalty
term roughly invariant to scaling of X[] and changes in M.
This function is a new edition of penalized regression spline fitting,
a fast and compact one which needs much less resources that its previous
version: just O(maxMN) memory and O(maxMN*log(maxMN)) time.
NOTE: it is OK to run this function with both M<<N and M>>N; say, it is
possible to process 100 points with 1000-node spline.
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 lengths
M - number of basis functions ( = number_of_nodes), M>=4.
LambdaNS - LambdaNS>=0, regularization constant passed by user.
It penalizes nonlinearity in the regression spline.
Possible values to start from are 0.00001, 0.1, 1
OUTPUT PARAMETERS:
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
-- ALGLIB PROJECT --
Copyright 27.08.2019 by Bochkanov Sergey
*************************************************************************/
void spline1dfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, const double lambdans, spline1dinterpolant &s, spline1dfitreport &rep, const xparams _xparams = alglib::xdefault);
void spline1dfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, const double lambdans, spline1dinterpolant &s, spline1dfitreport &rep, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void spline1dbuildmonotone(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_PARAMETRIC) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_SPLINE3D) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_POLINT) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void polynomialcheb2bar(const real_1d_array &t, const double a, const double b, barycentricinterpolant &p, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void polynomialbar2pow(const barycentricinterpolant &p, real_1d_array &a, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void polynomialpow2bar(const real_1d_array &a, barycentricinterpolant &p, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void polynomialbuild(const real_1d_array &x, const real_1d_array &y, barycentricinterpolant &p, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void polynomialbuildeqdist(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void polynomialbuildcheb1(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void polynomialbuildcheb2(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
double polynomialcalceqdist(const double a, const double b, const real_1d_array &f, const double t, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
double polynomialcalccheb1(const double a, const double b, const real_1d_array &f, const double t, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
double polynomialcalccheb2(const double a, const double b, const real_1d_array &f, const double t, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_LSFIT) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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()
NOTES:
you can convert P from barycentric form to the power or Chebyshev
basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from
POLINT subpackage.
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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
-- 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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()
NOTES:
you can convert P from barycentric form to the power or Chebyshev
basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from
POLINT subpackage.
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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.
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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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: for stability reasons the B parameter is restricted by [1/1000,1000]
range. It prevents algorithm from making trial steps deep into the
area of bad parameters.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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: for stability reasons the B parameter is restricted by [1/1000,1000]
range. It prevents algorithm from making trial steps deep into the
area of bad parameters.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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: for better stability B parameter is restricted by [+-1/1000,+-1000]
range, and G is restricted by [1/10,10] range. It prevents algorithm
from making trial steps deep into the area of bad parameters.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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: for better stability B parameter is restricted by [+-1/1000,+-1000]
range, and G is restricted by [1/10,10] range. It prevents algorithm
from making trial steps deep into the area of bad parameters.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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: for better stability B parameter is restricted by [+-1/1000,+-1000]
range, and G is restricted by [1/10,10] range. It prevents algorithm
from making trial steps deep into the area of bad parameters.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
void lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void lsfitcreatef(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const double diffstep, lsfitstate &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void lsfitcreatefg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const bool cheapfg, lsfitstate &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void lsfitcreatewfgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, lsfitstate &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
void lsfitcreatefgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, lsfitstate &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
Stopping conditions for nonlinear least squares fitting.
INPUT PARAMETERS:
State - structure which stores algorithm state
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 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 epsx, const ae_int_t maxits, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets linear constraints for underlying optimizer
Linear constraints are inactive by default (after initial creation).
They are preserved until explicitly turned off with another SetLC() call.
INPUT PARAMETERS:
State - structure stores algorithm state
C - linear constraints, array[K,N+1].
Each row of C represents one constraint, either equality
or inequality (see below):
* first N elements correspond to coefficients,
* last element corresponds to the right part.
All elements of C (including right part) must be finite.
CT - type of constraints, array[K]:
* if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1]
* if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1]
* if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1]
K - number of equality/inequality constraints, K>=0:
* if given, only leading K elements of C/CT are used
* if not given, automatically determined from sizes of C/CT
IMPORTANT: if you have linear constraints, it is strongly recommended to
set scale of variables with lsfitsetscale(). QP solver which is
used to calculate linearly constrained steps heavily relies on
good scaling of input problems.
NOTE: linear (non-box) constraints are satisfied only approximately -
there always exists some violation due to numerical errors and
algorithmic limitations.
NOTE: general linear constraints add significant overhead to solution
process. Although solver performs roughly same amount of iterations
(when compared with similar box-only constrained problem), each
iteration now involves solution of linearly constrained QP
subproblem, which requires ~3-5 times more Cholesky decompositions.
Thus, if you can reformulate your problem in such way this it has
only box constraints, it may be beneficial to do so.
-- ALGLIB --
Copyright 29.04.2017 by Bochkanov Sergey
*************************************************************************/
void lsfitsetlc(const lsfitstate &state, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k, const xparams _xparams = alglib::xdefault);
void lsfitsetlc(const lsfitstate &state, const real_2d_array &c, const integer_1d_array &ct, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This family of functions is used to launcn iterations of nonlinear fitter
These functions accept following parameters:
state - algorithm state
func - callback which calculates function (or merit function)
value func at given point x
grad - callback which calculates function (or merit function)
value func and gradient grad at given point x
hess - callback which calculates function (or merit function)
value func, gradient grad and Hessian hess at given point x
rep - optional callback which is called after each iteration
can be NULL
ptr - optional pointer which is passed to func/grad/hess/jac/rep
can be NULL
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
*************************************************************************/
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) = NULL,
void *ptr = NULL,
const xparams _xparams = alglib::xdefault);
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) = NULL,
void *ptr = NULL,
const xparams _xparams = alglib::xdefault);
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) = NULL,
void *ptr = NULL,
const xparams _xparams = alglib::xdefault);
/*************************************************************************
Nonlinear least squares fitting results.
Called after return from LSFitFit().
INPUT PARAMETERS:
State - algorithm state
OUTPUT PARAMETERS:
Info - completion code:
* -8 optimizer detected NAN/INF in the target
function and/or gradient
* -7 gradient verification failed.
See LSFitSetGradientCheck() for more information.
* -3 inconsistent constraints
* 2 relative step is no more than EpsX.
* 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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_RBFV2) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_SPLINE2D) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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 spline2dserialize(spline2dinterpolant &obj, std::string &s_out);
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void spline2dunserialize(const std::string &s_in, spline2dinterpolant &obj);
/*************************************************************************
This function serializes data structure to C++ stream.
Data stream generated by this function is same as string representation
generated by string version of serializer - alphanumeric characters,
dots, underscores, minus signs, which are grouped into words separated by
spaces and CR+LF.
We recommend you to read comments on string version of serializer to find
out more about serialization of AlGLIB objects.
*************************************************************************/
void spline2dserialize(spline2dinterpolant &obj, std::ostream &s_out);
/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void spline2dunserialize(const std::istream &s_in, spline2dinterpolant &obj);
/*************************************************************************
This subroutine calculates the value of the bilinear or bicubic spline at
the given point X.
Input parameters:
C - 2D spline object.
Built by spline2dbuildbilinearv or spline2dbuildbicubicv.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This subroutine calculates bilinear or bicubic vector-valued spline at the
given point (X,Y).
If you need just some specific component of vector-valued spline, you can
use spline2dcalcvi() function.
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 01.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dcalcvbuf(const spline2dinterpolant &c, const double x, const double y, real_1d_array &f, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This subroutine calculates specific component of vector-valued bilinear or
bicubic spline at the given point (X,Y).
INPUT PARAMETERS:
C - spline interpolant.
X, Y- point
I - component index, in [0,D). An exception is generated for out
of range values.
RESULT:
value of I-th component
-- ALGLIB PROJECT --
Copyright 01.02.2018 by Bochkanov Sergey
*************************************************************************/
double spline2dcalcvi(const spline2dinterpolant &c, const double x, const double y, const ae_int_t i, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This subroutine calculates value of specific component of bilinear or
bicubic vector-valued spline and its derivatives.
Input parameters:
C - spline interpolant.
X, Y- point
I - component index, in [0,D)
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 spline2ddiffvi(const spline2dinterpolant &c, const double x, const double y, const ae_int_t i, double &f, double &fx, double &fy, double &fxy, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This subroutine creates least squares solver used to fit 2D splines to
irregularly sampled (scattered) data.
Solver object is used to perform spline fits as follows:
* solver object is created with spline2dbuildercreate() function
* dataset is added with spline2dbuildersetpoints() function
* fit area is chosen:
* spline2dbuildersetarea() - for user-defined area
* spline2dbuildersetareaauto() - for automatically chosen area
* number of grid nodes is chosen with spline2dbuildersetgrid()
* prior term is chosen with one of the following functions:
* spline2dbuildersetlinterm() to set linear prior
* spline2dbuildersetconstterm() to set constant prior
* spline2dbuildersetzeroterm() to set zero prior
* spline2dbuildersetuserterm() to set user-defined constant prior
* solver algorithm is chosen with either:
* spline2dbuildersetalgoblocklls() - BlockLLS algorithm, medium-scale problems
* spline2dbuildersetalgofastddm() - FastDDM algorithm, large-scale problems
* finally, fitting itself is performed with spline2dfit() function.
Most of the steps above can be omitted, solver is configured with good
defaults. The minimum is to call:
* spline2dbuildercreate() to create solver object
* spline2dbuildersetpoints() to specify dataset
* spline2dbuildersetgrid() to tell how many nodes you need
* spline2dfit() to perform fit
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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:
D - positive number, number of Y-components: D=1 for simple scalar
fit, D>1 for vector-valued spline fitting.
OUTPUT PARAMETERS:
S - solver object
-- ALGLIB PROJECT --
Copyright 29.01.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildercreate(const ae_int_t d, spline2dbuilder &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets constant prior term (model is a sum of bicubic spline
and global prior, which can be linear, constant, user-defined constant or
zero).
Constant prior term is determined by least squares fitting.
INPUT PARAMETERS:
S - spline builder
V - value for user-defined prior
-- ALGLIB --
Copyright 01.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetuserterm(const spline2dbuilder &state, const double v, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets linear prior term (model is a sum of bicubic spline and
global prior, which can be linear, constant, user-defined constant or
zero).
Linear prior term is determined by least squares fitting.
INPUT PARAMETERS:
S - spline builder
-- ALGLIB --
Copyright 01.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetlinterm(const spline2dbuilder &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets constant prior term (model is a sum of bicubic spline
and global prior, which can be linear, constant, user-defined constant or
zero).
Constant prior term is determined by least squares fitting.
INPUT PARAMETERS:
S - spline builder
-- ALGLIB --
Copyright 01.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetconstterm(const spline2dbuilder &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets zero prior term (model is a sum of bicubic spline and
global prior, which can be linear, constant, user-defined constant or
zero).
INPUT PARAMETERS:
S - spline builder
-- ALGLIB --
Copyright 01.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetzeroterm(const spline2dbuilder &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function adds dataset to the builder object.
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 - spline 2D builder object
XY - points, array[N,2+D]. One row corresponds to one point
in the dataset. First 2 elements are coordinates, next
D elements are function values. Array may be larger than
specified, in this case only leading [N,NX+NY] elements
will be used.
N - number of points in the dataset
-- ALGLIB --
Copyright 05.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetpoints(const spline2dbuilder &state, const real_2d_array &xy, const ae_int_t n, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets area where 2D spline interpolant is built. "Auto" means
that area extent is determined automatically from dataset extent.
INPUT PARAMETERS:
S - spline 2D builder object
-- ALGLIB --
Copyright 05.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetareaauto(const spline2dbuilder &state, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets area where 2D spline interpolant is built to
user-defined one: [XA,XB]*[YA,YB]
INPUT PARAMETERS:
S - spline 2D builder object
XA,XB - spatial extent in the first (X) dimension, XA<XB
YA,YB - spatial extent in the second (Y) dimension, YA<YB
-- ALGLIB --
Copyright 05.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetarea(const spline2dbuilder &state, const double xa, const double xb, const double ya, const double yb, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets nodes count for 2D spline interpolant. Fitting is
performed on area defined with one of the "setarea" functions; this one
sets number of nodes placed upon the fitting area.
INPUT PARAMETERS:
S - spline 2D builder object
KX - nodes count for the first (X) dimension; fitting interval
[XA,XB] is separated into KX-1 subintervals, with KX nodes
created at the boundaries.
KY - nodes count for the first (Y) dimension; fitting interval
[YA,YB] is separated into KY-1 subintervals, with KY nodes
created at the boundaries.
NOTE: at least 4 nodes is created in each dimension, so KX and KY are
silently increased if needed.
-- ALGLIB --
Copyright 05.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetgrid(const spline2dbuilder &state, const ae_int_t kx, const ae_int_t ky, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function allows you to choose least squares solver used to perform
fitting. This function sets solver algorithm to "FastDDM", which performs
fast parallel fitting by splitting problem into smaller chunks and merging
results together.
This solver is optimized for large-scale problems, starting from 256x256
grids, and up to 10000x10000 grids. Of course, it will work for smaller
grids too.
More detailed description of the algorithm is given below:
* algorithm generates hierarchy of nested grids, ranging from ~16x16
(topmost "layer" of the model) to ~KX*KY one (final layer). Upper layers
model global behavior of the function, lower layers are used to model
fine details. Moving from layer to layer doubles grid density.
* fitting is started from topmost layer, subsequent layers are fitted
using residuals from previous ones.
* user may choose to skip generation of upper layers and generate only a
few bottom ones, which will result in much better performance and
parallelization efficiency, at the cost of algorithm inability to "patch"
large holes in the dataset.
* every layer is regularized using progressively increasing regularization
coefficient; thus, increasing LambdaV penalizes fine details first,
leaving lower frequencies almost intact for a while.
* after fitting is done, all layers are merged together into one bicubic
spline
IMPORTANT: regularization coefficient used by this solver is different
from the one used by BlockLLS. Latter utilizes nonlinearity
penalty, which is global in nature (large regularization
results in global linear trend being extracted); this solver
uses another, localized form of penalty, which is suitable for
parallel processing.
Notes on memory and performance:
* memory requirements: most memory is consumed during modeling of the
higher layers; ~[512*NPoints] bytes is required for a model with full
hierarchy of grids being generated. However, if you skip a few topmost
layers, you will get nearly constant (wrt. points count and grid size)
memory consumption.
* serial running time: O(K*K)+O(NPoints) for a KxK grid
* parallelism potential: good. You may get nearly linear speed-up when
performing fitting with just a few layers. Adding more layers results in
model becoming more global, which somewhat reduces efficiency of the
parallel code.
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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:
S - spline 2D builder object
NLayers - number of layers in the model:
* NLayers>=1 means that up to chosen number of bottom
layers is fitted
* NLayers=0 means that maximum number of layers is chosen
(according to current grid size)
* NLayers<=-1 means that up to |NLayers| topmost layers is
skipped
Recommendations:
* good "default" value is 2 layers
* you may need more layers, if your dataset is very
irregular and you want to "patch" large holes. For a
grid step H (equal to AreaWidth/GridSize) you may expect
that last layer reproduces variations at distance H (and
can patch holes that wide); that higher layers operate
at distances 2*H, 4*H, 8*H and so on.
* good value for "bullletproof" mode is NLayers=0, which
results in complete hierarchy of layers being generated.
LambdaV - regularization coefficient, chosen in such a way that it
penalizes bottom layers (fine details) first.
LambdaV>=0, zero value means that no penalty is applied.
-- ALGLIB --
Copyright 05.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetalgofastddm(const spline2dbuilder &state, const ae_int_t nlayers, const double lambdav, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function allows you to choose least squares solver used to perform
fitting. This function sets solver algorithm to "BlockLLS", which performs
least squares fitting with fast sparse direct solver, with optional
nonsmoothness penalty being applied.
Nonlinearity penalty has the following form:
[ ]
P() ~ Lambda* integral[ (d2S/dx2)^2 + 2*(d2S/dxdy)^2 + (d2S/dy2)^2 ]dxdy
[ ]
here integral is calculated over entire grid, and "~" means "proportional"
because integral is normalized after calcilation. Extremely large values
of Lambda result in linear fit being performed.
NOTE: this algorithm is the most robust and controllable one, but it is
limited by 512x512 grids and (say) up to 1.000.000 points. However,
ALGLIB has one more spline solver: FastDDM algorithm, which is
intended for really large-scale problems (in 10M-100M range). FastDDM
algorithm also has better parallelism properties.
More information on BlockLLS solver:
* memory requirements: ~[32*K^3+256*NPoints] bytes for KxK grid with
NPoints-sized dataset
* serial running time: O(K^4+NPoints)
* parallelism potential: limited. You may get some sublinear gain when
working with large grids (K's in 256..512 range)
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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:
S - spline 2D builder object
LambdaNS- non-negative value:
* positive value means that some smoothing is applied
* zero value means that no smoothing is applied, and
corresponding entries of design matrix are numerically
zero and dropped from consideration.
-- ALGLIB --
Copyright 05.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetalgoblocklls(const spline2dbuilder &state, const double lambdans, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function allows you to choose least squares solver used to perform
fitting. This function sets solver algorithm to "NaiveLLS".
IMPORTANT: NaiveLLS is NOT intended to be used in real life code! This
algorithm solves problem by generated dense (K^2)x(K^2+NPoints)
matrix and solves linear least squares problem with dense
solver.
It is here just to test BlockLLS against reference solver
(and maybe for someone trying to compare well optimized solver
against straightforward approach to the LLS problem).
More information on naive LLS solver:
* memory requirements: ~[8*K^4+256*NPoints] bytes for KxK grid.
* serial running time: O(K^6+NPoints) for KxK grid
* when compared with BlockLLS, NaiveLLS has ~K larger memory demand and
~K^2 larger running time.
INPUT PARAMETERS:
S - spline 2D builder object
LambdaNS- nonsmoothness penalty
-- ALGLIB --
Copyright 05.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dbuildersetalgonaivells(const spline2dbuilder &state, const double lambdans, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function fits bicubic spline to current dataset, using current area/
grid and current LLS solver.
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
! * hardware vendor (Intel) implementations of linear algebra primitives
! (C++ and C# versions, x86/x64 platform)
!
! 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:
State - spline 2D builder object
OUTPUT PARAMETERS:
S - 2D spline, fit result
Rep - fitting report, which provides some additional info about
errors, R2 coefficient and so on.
-- ALGLIB --
Copyright 05.02.2018 by Bochkanov Sergey
*************************************************************************/
void spline2dfit(const spline2dbuilder &state, spline2dinterpolant &s, spline2dfitreport &rep, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_RBFV1) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_RBF) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
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);
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void rbfunserialize(const std::string &s_in, rbfmodel &obj);
/*************************************************************************
This function serializes data structure to C++ stream.
Data stream generated by this function is same as string representation
generated by string version of serializer - alphanumeric characters,
dots, underscores, minus signs, which are grouped into words separated by
spaces and CR+LF.
We recommend you to read comments on string version of serializer to find
out more about serialization of AlGLIB objects.
*************************************************************************/
void rbfserialize(rbfmodel &obj, std::ostream &s_out);
/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void rbfunserialize(const std::istream &s_in, rbfmodel &obj);
/*************************************************************************
This function creates RBF model for a scalar (NY=1) or vector (NY>1)
function in a NX-dimensional space (NX>=1).
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) or rbfsetpointsandscales().
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 tweaks algorithm properties with rbfsetalgohierarchical() method
(or chooses one of the legacy algorithms - 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.
IMPORTANT: we recommend you to use latest model construction algorithm -
hierarchical RBFs, which is activated by rbfsetalgohierarchical()
function. This algorithm is the fastest one, and most memory-
efficient.
However, it is incompatible with older versions of ALGLIB
(pre-3.11). So, if you serialize hierarchical model, you will
be unable to load it in pre-3.11 ALGLIB. Other model types (QNN
and RBF-ML) are still backward-compatible.
INPUT PARAMETERS:
NX - dimension of the space, NX>=1
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. Some additional memory
is allocated during model construction, but most of this memory is
freed after model coefficients are calculated. Amount of this
additional memory depends on model construction algorithm being
used.
NOTE 2: prior to ALGLIB version 3.11, RBF models supported only NX=2 or
NX=3. Any attempt to create single-dimensional or more than
3-dimensional RBF model resulted in exception.
ALGLIB 3.11 supports any NX>0, but models created with NX!=2 and
NX!=3 are incompatible with (a) older versions of ALGLIB, (b) old
model construction algorithms (QNN or RBF-ML).
So, if you create a model with NX=2 or NX=3, then, depending on
specific model construction algorithm being chosen, you will (QNN
and RBF-ML) or will not (HierarchicalRBF) get backward compatibility
with older versions of ALGLIB. You have a choice here.
However, if you create a model with NX neither 2 nor 3, you have
no backward compatibility from the start, and you are forced to
use hierarchical RBFs and ALGLIB 3.11 or later.
-- ALGLIB --
Copyright 13.12.2011, 20.06.2016 by Bochkanov Sergey
*************************************************************************/
void rbfcreate(const ae_int_t nx, const ae_int_t ny, rbfmodel &s, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function creates buffer structure which can be used to perform
parallel RBF model evaluations (with one RBF model instance being
used from multiple threads, as long as different threads use different
instances of buffer).
This buffer object can be used with rbftscalcbuf() function (here "ts"
stands for "thread-safe", "buf" is a suffix which denotes function which
reuses previously allocated output space).
How to use it:
* create RBF model structure with rbfcreate()
* load data, tune parameters
* call rbfbuildmodel()
* call rbfcreatecalcbuffer(), once per thread working with RBF model (you
should call this function only AFTER call to rbfbuildmodel(), see below
for more information)
* call rbftscalcbuf() from different threads, with each thread working
with its own copy of buffer object.
INPUT PARAMETERS
S - RBF model
OUTPUT PARAMETERS
Buf - external buffer.
IMPORTANT: buffer object should be used only with RBF model object which
was used to initialize buffer. Any attempt to use buffer with
different object is dangerous - you may get memory violation
error because sizes of internal arrays do not fit to dimensions
of RBF structure.
IMPORTANT: you should call this function only for model which was built
with rbfbuildmodel() function, after successful invocation of
rbfbuildmodel(). Sizes of some internal structures are
determined only after model is built, so buffer object created
before model construction stage will be useless (and any
attempt to use it will result in exception).
-- ALGLIB --
Copyright 02.04.2016 by Sergey Bochkanov
*************************************************************************/
void rbfcreatecalcbuffer(const rbfmodel &s, rbfcalcbuffer &buf, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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.
IMPORTANT: ALGLIB version 3.11 and later allows you to specify a set of
per-dimension scales. Interpolation radii are multiplied by the
scale vector. It may be useful if you have mixed spatio-temporal
data (say, a set of 3D slices recorded at different times).
You should call rbfsetpointsandscales() function to use this
feature.
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
specified, 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: dataset added by this function is not saved during model serialization.
MODEL ITSELF is serialized, but data used to build it are not.
So, if you 1) add dataset to empty RBF model, 2) serialize and
unserialize it, then you will get an empty RBF model with no dataset
being attached.
From the other side, if you call rbfbuildmodel() between (1) and (2),
then after (2) you will get your fully constructed RBF model - but
again with no dataset attached, so subsequent calls to rbfbuildmodel()
will produce empty model.
-- ALGLIB --
Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
void rbfsetpoints(const rbfmodel &s, const real_2d_array &xy, const ae_int_t n, const xparams _xparams = alglib::xdefault);
void rbfsetpoints(const rbfmodel &s, const real_2d_array &xy, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function adds dataset and a vector of per-dimension scales.
It may be useful if you have mixed spatio-temporal data - say, a set of 3D
slices recorded at different times. Such data typically require different
RBF radii for spatial and temporal dimensions. ALGLIB solves this problem
by specifying single RBF radius, which is (optionally) multiplied by the
scale vector.
This function overrides results of the previous calls, i.e. multiple calls
of this function will result in only the last set being added.
IMPORTANT: only HierarchicalRBF algorithm can work with scaled points. So,
using this function results in RBF models which can be used in
ALGLIB 3.11 or later. Previous versions of the library will be
unable to unserialize models produced by HierarchicalRBF algo.
Any attempt to use this function with RBF-ML or QNN algorithms
will result in -3 error code being returned (incorrect
algorithm).
INPUT PARAMETERS:
R - 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
specified, in this case only leading [N,NX+NY] elements
will be used.
N - number of points in the dataset
S - array[NX], scale vector, S[i]>0.
After you've added dataset and (optionally) tuned algorithm settings you
should call rbfbuildmodel() in order to build a model for you.
NOTE: dataset added by this function is not saved during model serialization.
MODEL ITSELF is serialized, but data used to build it are not.
So, if you 1) add dataset to empty RBF model, 2) serialize and
unserialize it, then you will get an empty RBF model with no dataset
being attached.
From the other side, if you call rbfbuildmodel() between (1) and (2),
then after (2) you will get your fully constructed RBF model - but
again with no dataset attached, so subsequent calls to rbfbuildmodel()
will produce empty model.
-- ALGLIB --
Copyright 20.06.2016 by Bochkanov Sergey
*************************************************************************/
void rbfsetpointsandscales(const rbfmodel &r, const real_2d_array &xy, const ae_int_t n, const real_1d_array &s, const xparams _xparams = alglib::xdefault);
void rbfsetpointsandscales(const rbfmodel &r, const real_2d_array &xy, const real_1d_array &s, const xparams _xparams = alglib::xdefault);
/*************************************************************************
DEPRECATED:since version 3.11 ALGLIB includes new RBF model construction
algorithm, Hierarchical RBF. This algorithm is faster and
requires less memory than QNN and RBF-ML. It is especially good
for large-scale interpolation problems. So, we recommend you to
consider Hierarchical RBF as default option.
==========================================================================
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, const xparams _xparams = alglib::xdefault);
void rbfsetalgoqnn(const rbfmodel &s, const xparams _xparams = alglib::xdefault);
/*************************************************************************
DEPRECATED:since version 3.11 ALGLIB includes new RBF model construction
algorithm, Hierarchical RBF. This algorithm is faster and
requires less memory than QNN and RBF-ML. It is especially good
for large-scale interpolation problems. So, we recommend you to
consider Hierarchical RBF as default option.
==========================================================================
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, const xparams _xparams = alglib::xdefault);
void rbfsetalgomultilayer(const rbfmodel &s, const double rbase, const ae_int_t nlayers, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets RBF interpolation algorithm. ALGLIB supports several
RBF algorithms with different properties.
This algorithm is called Hierarchical RBF. It similar to its previous
incarnation, RBF-ML, i.e. it also builds a sequence of models with
decreasing radii. However, it uses more economical way of building upper
layers (ones with large radii), which results in faster model construction
and evaluation, as well as smaller memory footprint during construction.
This algorithm has following important features:
* ability to handle millions of points
* controllable smoothing via nonlinearity penalization
* support for NX-dimensional models with NX=1 or NX>3 (unlike QNN or RBF-ML)
* support for specification of per-dimensional radii via scale vector,
which is set by means of rbfsetpointsandscales() function. This feature
is useful if you solve spatio-temporal interpolation problems, where
different radii are required for spatial and temporal dimensions.
Running times are roughly proportional to:
* N*log(N)*NLayers - for model construction
* N*NLayers - for model evaluation
You may see that running time does not depend on search radius or points
density, just on number of layers in the hierarchy.
IMPORTANT: this model construction algorithm was introduced in ALGLIB 3.11
and produces models which are INCOMPATIBLE with previous
versions of ALGLIB. You can not unserialize models produced
with this function in ALGLIB 3.10 or earlier.
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.
LambdaNS- >=0, nonlinearity penalty coefficient, negative values are
not allowed. This parameter adds controllable smoothing to
the problem, which may reduce noise. Specification of non-
zero lambda means that in addition to fitting error solver
will also minimize LambdaNS*|S''(x)|^2 (appropriately
generalized to multiple dimensions.
Specification of exactly zero value means that no penalty
is added (we do not even evaluate matrix of second
derivatives which is necessary for smoothing).
Calculation of nonlinearity penalty is costly - it results
in several-fold increase of model construction time.
Evaluation time remains the same.
Optimal lambda is problem-dependent and requires trial
and error. Good value to start from is 1e-5...1e-6,
which corresponds to slightly noticeable smoothing of the
function. Value 1e-2 usually means that quite heavy
smoothing is applied.
TUNING ALGORITHM
In order to use this algorithm you have to choose three parameters:
* initial radius RBase
* number of layers in the model NLayers
* penalty coefficient LambdaNS
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 (or specify
non-zero LambdaNS).
TYPICAL ERRORS
1. 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.
2. 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.
-- ALGLIB --
Copyright 20.06.2016 by Bochkanov Sergey
*************************************************************************/
void rbfsetalgohierarchical(const rbfmodel &s, const double rbase, const ae_int_t nlayers, const double lambdans, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets basis function type, which can be:
* 0 for classic Gaussian
* 1 for fast and compact bell-like basis function, which becomes exactly
zero at distance equal to 3*R (default option).
INPUT PARAMETERS:
S - RBF model, initialized by RBFCreate() call
BF - basis function type:
* 0 - classic Gaussian
* 1 - fast and compact one
-- ALGLIB --
Copyright 01.02.2017 by Bochkanov Sergey
*************************************************************************/
void rbfsetv2bf(const rbfmodel &s, const ae_int_t bf, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets stopping criteria of the underlying linear solver for
hierarchical (version 2) RBF constructor.
INPUT PARAMETERS:
S - RBF model, initialized by RBFCreate() call
MaxIts - this criterion will stop algorithm after MaxIts iterations.
Typically a few hundreds iterations is required, with 400
being a good default value to start experimentation.
Zero value means that default value will be selected.
-- ALGLIB --
Copyright 01.02.2017 by Bochkanov Sergey
*************************************************************************/
void rbfsetv2its(const rbfmodel &s, const ae_int_t maxits, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function sets support radius parameter of hierarchical (version 2)
RBF constructor.
Hierarchical RBF model achieves great speed-up by removing from the model
excessive (too dense) nodes. Say, if you have RBF radius equal to 1 meter,
and two nodes are just 1 millimeter apart, you may remove one of them
without reducing model quality.
Support radius parameter is used to justify which points need removal, and
which do not. If two points are less than SUPPORT_R*CUR_RADIUS units of
distance apart, one of them is removed from the model. The larger support
radius is, the faster model construction AND evaluation are. However,
too large values result in "bumpy" models.
INPUT PARAMETERS:
S - RBF model, initialized by RBFCreate() call
R - support radius coefficient, >=0.
Recommended values are [0.1,0.4] range, with 0.1 being
default value.
-- ALGLIB --
Copyright 01.02.2017 by Bochkanov Sergey
*************************************************************************/
void rbfsetv2supportr(const rbfmodel &s, const double r, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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; only QNN returns this
error code, other algorithms can handle non-
distinct nodes.
* -4 - nonconvergence of the internal SVD solver
* -3 incorrect model construction algorithm was chosen:
QNN or RBF-ML, combined with one of the incompatible
features - NX=1 or NX>3; points with per-dimension
scales.
* 1 - successful termination
* 8 - a termination request was submitted via
rbfrequesttermination() function.
Fields which are set only by modern RBF solvers (hierarchical
or nonnegative; older solvers like QNN and ML initialize these
fields by NANs):
* rep.rmserror - root-mean-square error at nodes
* rep.maxerror - maximum error at nodes
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the RBF model in the given point.
IMPORTANT: this function works only with modern (hierarchical) RBFs. It
can not be used with legacy (version 1) RBFs because older RBF
code does not support 1-dimensional models.
This function should be used when we have NY=1 (scalar function) and NX=1
(1-dimensional space). If you have 3-dimensional space, use rbfcalc3(). If
you have 2-dimensional space, use rbfcalc3(). If you have general
situation (NX-dimensional space, NY-dimensional function) you should use
generic rbfcalc().
If you want to perform parallel model evaluation from multiple threads,
use rbftscalcbuf() with per-thread buffer object.
This function returns 0.0 when:
* model is not initialized
* NX<>1
* NY<>1
INPUT PARAMETERS:
S - RBF model
X0 - X-coordinate, finite number
RESULT:
value of the model or 0.0 (as defined above)
-- ALGLIB --
Copyright 13.12.2011 by Bochkanov Sergey
*************************************************************************/
double rbfcalc1(const rbfmodel &s, const double x0, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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 generic rbfcalc().
If you want to calculate function values many times, consider using
rbfgridcalc2v(), which is far more efficient than many subsequent calls to
rbfcalc2().
If you want to perform parallel model evaluation from multiple threads,
use rbftscalcbuf() with per-thread buffer object.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates value 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 generic rbfcalc().
If you want to calculate function values many times, consider using
rbfgridcalc3v(), which is far more efficient than many subsequent calls to
rbfcalc3().
If you want to perform parallel model evaluation from multiple threads,
use rbftscalcbuf() with per-thread buffer object.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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().
If you want to perform parallel model evaluation from multiple threads,
use rbftscalcbuf() with per-thread buffer object.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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.
If you want to perform parallel model evaluation from multiple threads,
use rbftscalcbuf() with per-thread buffer object.
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the RBF model at the given point, using
external buffer object (internal temporaries of RBF model are not
modified).
This function allows to use same RBF model object in different threads,
assuming that different threads use different instances of buffer
structure.
INPUT PARAMETERS:
S - RBF model, may be shared between different threads
Buf - buffer object created for this particular instance of RBF
model with rbfcreatecalcbuffer().
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 rbftscalcbuf(const rbfmodel &s, const rbfcalcbuffer &buf, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This is legacy function for gridded calculation of RBF model.
It is superseded by rbfgridcalc2v() and rbfgridcalc2vsubset() functions.
-- 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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the RBF model at the regular grid,
which has N0*N1 points, with Point[I,J] = (X0[I], X1[J]). Vector-valued
RBF models are supported.
This function returns 0.0 when:
* model is not initialized
* NX<>2
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
!
! 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.
NOTE: Parallel processing is implemented only for modern (hierarchical)
RBFs. Legacy version 1 RBFs (created by QNN or RBF-ML) are still
processed serially.
INPUT PARAMETERS:
S - RBF model, used in read-only mode, can be shared between
multiple invocations of this function from multiple
threads.
X0 - array of grid nodes, first coordinates, array[N0].
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N0 - grid size (number of nodes) in the first dimension
X1 - array of grid nodes, second coordinates, array[N1]
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N1 - grid size (number of nodes) in the second dimension
OUTPUT PARAMETERS:
Y - function values, array[NY*N0*N1], where NY is a number of
"output" vector values (this function supports vector-
valued RBF models). Y is out-variable and is reallocated
by this function.
Y[K+NY*(I0+I1*N0)]=F_k(X0[I0],X1[I1]), for:
* K=0...NY-1
* I0=0...N0-1
* I1=0...N1-1
NOTE: this function supports weakly ordered grid nodes, i.e. you may have
X[i]=X[i+1] for some i. It does not provide you any performance
benefits due to duplication of points, just convenience and
flexibility.
NOTE: this function is re-entrant, i.e. you may use same rbfmodel
structure in multiple threads calling this function for different
grids.
NOTE: if you need function values on some subset of regular grid, which
may be described as "several compact and dense islands", you may
use rbfgridcalc2vsubset().
-- ALGLIB --
Copyright 27.01.2017 by Bochkanov Sergey
*************************************************************************/
void rbfgridcalc2v(const rbfmodel &s, const real_1d_array &x0, const ae_int_t n0, const real_1d_array &x1, const ae_int_t n1, real_1d_array &y, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the RBF model at some subset of regular
grid:
* grid has N0*N1 points, with Point[I,J] = (X0[I], X1[J])
* only values at some subset of this grid are required
Vector-valued RBF models are supported.
This function returns 0.0 when:
* model is not initialized
* NX<>2
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
!
! 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.
NOTE: Parallel processing is implemented only for modern (hierarchical)
RBFs. Legacy version 1 RBFs (created by QNN or RBF-ML) are still
processed serially.
INPUT PARAMETERS:
S - RBF model, used in read-only mode, can be shared between
multiple invocations of this function from multiple
threads.
X0 - array of grid nodes, first coordinates, array[N0].
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N0 - grid size (number of nodes) in the first dimension
X1 - array of grid nodes, second coordinates, array[N1]
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N1 - grid size (number of nodes) in the second dimension
FlagY - array[N0*N1]:
* Y[I0+I1*N0] corresponds to node (X0[I0],X1[I1])
* it is a "bitmap" array which contains False for nodes
which are NOT calculated, and True for nodes which are
required.
OUTPUT PARAMETERS:
Y - function values, array[NY*N0*N1*N2], where NY is a number
of "output" vector values (this function supports vector-
valued RBF models):
* Y[K+NY*(I0+I1*N0)]=F_k(X0[I0],X1[I1]),
for K=0...NY-1, I0=0...N0-1, I1=0...N1-1.
* elements of Y[] which correspond to FlagY[]=True are
loaded by model values (which may be exactly zero for
some nodes).
* elements of Y[] which correspond to FlagY[]=False MAY be
initialized by zeros OR may be calculated. This function
processes grid as a hierarchy of nested blocks and
micro-rows. If just one element of micro-row is required,
entire micro-row (up to 8 nodes in the current version,
but no promises) is calculated.
NOTE: this function supports weakly ordered grid nodes, i.e. you may have
X[i]=X[i+1] for some i. It does not provide you any performance
benefits due to duplication of points, just convenience and
flexibility.
NOTE: this function is re-entrant, i.e. you may use same rbfmodel
structure in multiple threads calling this function for different
grids.
-- ALGLIB --
Copyright 04.03.2016 by Bochkanov Sergey
*************************************************************************/
void rbfgridcalc2vsubset(const rbfmodel &s, const real_1d_array &x0, const ae_int_t n0, const real_1d_array &x1, const ae_int_t n1, const boolean_1d_array &flagy, real_1d_array &y, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the RBF model at the regular grid,
which has N0*N1*N2 points, with Point[I,J,K] = (X0[I], X1[J], X2[K]).
Vector-valued RBF models are supported.
This function returns 0.0 when:
* model is not initialized
* NX<>3
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
!
! 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.
NOTE: Parallel processing is implemented only for modern (hierarchical)
RBFs. Legacy version 1 RBFs (created by QNN or RBF-ML) are still
processed serially.
INPUT PARAMETERS:
S - RBF model, used in read-only mode, can be shared between
multiple invocations of this function from multiple
threads.
X0 - array of grid nodes, first coordinates, array[N0].
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N0 - grid size (number of nodes) in the first dimension
X1 - array of grid nodes, second coordinates, array[N1]
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N1 - grid size (number of nodes) in the second dimension
X2 - array of grid nodes, third coordinates, array[N2]
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N2 - grid size (number of nodes) in the third dimension
OUTPUT PARAMETERS:
Y - function values, array[NY*N0*N1*N2], where NY is a number
of "output" vector values (this function supports vector-
valued RBF models). Y is out-variable and is reallocated
by this function.
Y[K+NY*(I0+I1*N0+I2*N0*N1)]=F_k(X0[I0],X1[I1],X2[I2]), for:
* K=0...NY-1
* I0=0...N0-1
* I1=0...N1-1
* I2=0...N2-1
NOTE: this function supports weakly ordered grid nodes, i.e. you may have
X[i]=X[i+1] for some i. It does not provide you any performance
benefits due to duplication of points, just convenience and
flexibility.
NOTE: this function is re-entrant, i.e. you may use same rbfmodel
structure in multiple threads calling this function for different
grids.
NOTE: if you need function values on some subset of regular grid, which
may be described as "several compact and dense islands", you may
use rbfgridcalc3vsubset().
-- ALGLIB --
Copyright 04.03.2016 by Bochkanov Sergey
*************************************************************************/
void rbfgridcalc3v(const rbfmodel &s, const real_1d_array &x0, const ae_int_t n0, const real_1d_array &x1, const ae_int_t n1, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function calculates values of the RBF model at some subset of regular
grid:
* grid has N0*N1*N2 points, with Point[I,J,K] = (X0[I], X1[J], X2[K])
* only values at some subset of this grid are required
Vector-valued RBF models are supported.
This function returns 0.0 when:
* model is not initialized
* NX<>3
! COMMERCIAL EDITION OF ALGLIB:
!
! Commercial Edition of ALGLIB includes following important improvements
! of this function:
! * high-performance native backend with same C# interface (C# version)
! * multithreading support (C++ and C# versions)
!
! 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.
NOTE: Parallel processing is implemented only for modern (hierarchical)
RBFs. Legacy version 1 RBFs (created by QNN or RBF-ML) are still
processed serially.
INPUT PARAMETERS:
S - RBF model, used in read-only mode, can be shared between
multiple invocations of this function from multiple
threads.
X0 - array of grid nodes, first coordinates, array[N0].
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N0 - grid size (number of nodes) in the first dimension
X1 - array of grid nodes, second coordinates, array[N1]
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N1 - grid size (number of nodes) in the second dimension
X2 - array of grid nodes, third coordinates, array[N2]
Must be ordered by ascending. Exception is generated
if the array is not correctly ordered.
N2 - grid size (number of nodes) in the third dimension
FlagY - array[N0*N1*N2]:
* Y[I0+I1*N0+I2*N0*N1] corresponds to node (X0[I0],X1[I1],X2[I2])
* it is a "bitmap" array which contains False for nodes
which are NOT calculated, and True for nodes which are
required.
OUTPUT PARAMETERS:
Y - function values, array[NY*N0*N1*N2], where NY is a number
of "output" vector values (this function supports vector-
valued RBF models):
* Y[K+NY*(I0+I1*N0+I2*N0*N1)]=F_k(X0[I0],X1[I1],X2[I2]),
for K=0...NY-1, I0=0...N0-1, I1=0...N1-1, I2=0...N2-1.
* elements of Y[] which correspond to FlagY[]=True are
loaded by model values (which may be exactly zero for
some nodes).
* elements of Y[] which correspond to FlagY[]=False MAY be
initialized by zeros OR may be calculated. This function
processes grid as a hierarchy of nested blocks and
micro-rows. If just one element of micro-row is required,
entire micro-row (up to 8 nodes in the current version,
but no promises) is calculated.
NOTE: this function supports weakly ordered grid nodes, i.e. you may have
X[i]=X[i+1] for some i. It does not provide you any performance
benefits due to duplication of points, just convenience and
flexibility.
NOTE: this function is re-entrant, i.e. you may use same rbfmodel
structure in multiple threads calling this function for different
grids.
-- ALGLIB --
Copyright 04.03.2016 by Bochkanov Sergey
*************************************************************************/
void rbfgridcalc3vsubset(const rbfmodel &s, const real_1d_array &x0, const ae_int_t n0, const real_1d_array &x1, const ae_int_t n1, const real_1d_array &x2, const ae_int_t n2, const boolean_1d_array &flagy, real_1d_array &y, const xparams _xparams = alglib::xdefault);
/*************************************************************************
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
For ModelVersion=1:
* last column - radius, same for all dimensions of
the function being modelled
For ModelVersion=2:
* last NX columns - radii, one per dimension
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.
ModelVersion-version of the RBF model:
* 1 - for models created by QNN and RBF-ML algorithms,
compatible with ALGLIB 3.10 or earlier.
* 2 - for models created by HierarchicalRBF, requires
ALGLIB 3.11 or later
-- 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, ae_int_t &modelversion, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function returns model version.
INPUT PARAMETERS:
S - RBF model
RESULT:
* 1 - for models created by QNN and RBF-ML algorithms,
compatible with ALGLIB 3.10 or earlier.
* 2 - for models created by HierarchicalRBF, requires
ALGLIB 3.11 or later
-- ALGLIB --
Copyright 06.07.2016 by Bochkanov Sergey
*************************************************************************/
ae_int_t rbfgetmodelversion(const rbfmodel &s, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function is used to peek into hierarchical RBF construction process
from some other thread and get current progress indicator. It returns
value in [0,1].
IMPORTANT: only HRBFs (hierarchical RBFs) support peeking into progress
indicator. Legacy RBF-ML and RBF-QNN do not support it. You
will always get 0 value.
INPUT PARAMETERS:
S - RBF model object
RESULT:
progress value, in [0,1]
-- ALGLIB --
Copyright 17.11.2018 by Bochkanov Sergey
*************************************************************************/
double rbfpeekprogress(const rbfmodel &s, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function is used to submit a request for termination of the
hierarchical RBF construction process from some other thread. As result,
RBF construction is terminated smoothly (with proper deallocation of all
necessary resources) and resultant model is filled by zeros.
A rep.terminationtype=8 will be returned upon receiving such request.
IMPORTANT: only HRBFs (hierarchical RBFs) support termination requests.
Legacy RBF-ML and RBF-QNN do not support it. An attempt to
terminate their construction will be ignored.
IMPORTANT: termination request flag is cleared when the model construction
starts. Thus, any pre-construction termination requests will be
silently ignored - only ones submitted AFTER construction has
actually began will be handled.
INPUT PARAMETERS:
S - RBF model object
-- ALGLIB --
Copyright 17.11.2018 by Bochkanov Sergey
*************************************************************************/
void rbfrequesttermination(const rbfmodel &s, const xparams _xparams = alglib::xdefault);
#endif
#if defined(AE_COMPILE_INTCOMP) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
This function is left for backward compatibility.
Use fitspheremc() instead.
-- ALGLIB --
Copyright 14.04.2017 by Bochkanov Sergey
*************************************************************************/
void nsfitspheremcc(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, real_1d_array &cx, double &rhi, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function is left for backward compatibility.
Use fitspheremi() instead.
-- ALGLIB --
Copyright 14.04.2017 by Bochkanov Sergey
*************************************************************************/
void nsfitspheremic(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, real_1d_array &cx, double &rlo, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function is left for backward compatibility.
Use fitspheremz() instead.
-- ALGLIB --
Copyright 14.04.2017 by Bochkanov Sergey
*************************************************************************/
void nsfitspheremzc(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, real_1d_array &cx, double &rlo, double &rhi, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function is left for backward compatibility.
Use fitspherex() instead.
-- ALGLIB --
Copyright 14.04.2017 by Bochkanov Sergey
*************************************************************************/
void nsfitspherex(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nx, const ae_int_t problemtype, const double epsx, const ae_int_t aulits, const double penalty, real_1d_array &cx, double &rlo, double &rhi, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function is an obsolete and deprecated version of fitting by
penalized cubic spline.
It was superseded by spline1dfit(), which is an orders of magnitude faster
and more memory-efficient implementation.
Do NOT use this function in the new code!
-- 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
/*************************************************************************
This function is an obsolete and deprecated version of fitting by
penalized cubic spline.
It was superseded by spline1dfit(), which is an orders of magnitude faster
and more memory-efficient implementation.
Do NOT use this function in the new code!
-- 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, const xparams _xparams = alglib::xdefault);
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, const xparams _xparams = alglib::xdefault);
#endif
}
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS)
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
#if defined(AE_COMPILE_IDW) || !defined(AE_PARTIAL_BUILD)
void idwcreatecalcbuffer(idwmodel* s,
idwcalcbuffer* buf,
ae_state *_state);
void idwbuildercreate(ae_int_t nx,
ae_int_t ny,
idwbuilder* state,
ae_state *_state);
void idwbuildersetnlayers(idwbuilder* state,
ae_int_t nlayers,
ae_state *_state);
void idwbuildersetpoints(idwbuilder* state,
/* Real */ ae_matrix* xy,
ae_int_t n,
ae_state *_state);
void idwbuildersetalgomstab(idwbuilder* state,
double srad,
ae_state *_state);
void idwbuildersetalgotextbookshepard(idwbuilder* state,
double p,
ae_state *_state);
void idwbuildersetalgotextbookmodshepard(idwbuilder* state,
double r,
ae_state *_state);
void idwbuildersetuserterm(idwbuilder* state, double v, ae_state *_state);
void idwbuildersetconstterm(idwbuilder* state, ae_state *_state);
void idwbuildersetzeroterm(idwbuilder* state, ae_state *_state);
double idwcalc1(idwmodel* s, double x0, ae_state *_state);
double idwcalc2(idwmodel* s, double x0, double x1, ae_state *_state);
double idwcalc3(idwmodel* s,
double x0,
double x1,
double x2,
ae_state *_state);
void idwcalc(idwmodel* s,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void idwcalcbuf(idwmodel* s,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void idwtscalcbuf(idwmodel* s,
idwcalcbuffer* buf,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void idwfit(idwbuilder* state,
idwmodel* model,
idwreport* rep,
ae_state *_state);
void idwalloc(ae_serializer* s, idwmodel* model, ae_state *_state);
void idwserialize(ae_serializer* s, idwmodel* model, ae_state *_state);
void idwunserialize(ae_serializer* s, idwmodel* model, ae_state *_state);
void _idwcalcbuffer_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _idwcalcbuffer_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _idwcalcbuffer_clear(void* _p);
void _idwcalcbuffer_destroy(void* _p);
void _idwmodel_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _idwmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _idwmodel_clear(void* _p);
void _idwmodel_destroy(void* _p);
void _idwbuilder_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _idwbuilder_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _idwbuilder_clear(void* _p);
void _idwbuilder_destroy(void* _p);
void _idwreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _idwreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _idwreport_clear(void* _p);
void _idwreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_RATINT) || !defined(AE_PARTIAL_BUILD)
double barycentriccalc(barycentricinterpolant* b,
double t,
ae_state *_state);
void barycentricdiff1(barycentricinterpolant* b,
double t,
double* f,
double* df,
ae_state *_state);
void barycentricdiff2(barycentricinterpolant* b,
double t,
double* f,
double* df,
double* d2f,
ae_state *_state);
void barycentriclintransx(barycentricinterpolant* b,
double ca,
double cb,
ae_state *_state);
void barycentriclintransy(barycentricinterpolant* b,
double ca,
double cb,
ae_state *_state);
void barycentricunpack(barycentricinterpolant* b,
ae_int_t* n,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
/* Real */ ae_vector* w,
ae_state *_state);
void barycentricbuildxyw(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
/* Real */ ae_vector* w,
ae_int_t n,
barycentricinterpolant* b,
ae_state *_state);
void barycentricbuildfloaterhormann(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_int_t n,
ae_int_t d,
barycentricinterpolant* b,
ae_state *_state);
void barycentriccopy(barycentricinterpolant* b,
barycentricinterpolant* b2,
ae_state *_state);
void _barycentricinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _barycentricinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _barycentricinterpolant_clear(void* _p);
void _barycentricinterpolant_destroy(void* _p);
#endif
#if defined(AE_COMPILE_FITSPHERE) || !defined(AE_PARTIAL_BUILD)
void fitspherels(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
/* Real */ ae_vector* cx,
double* r,
ae_state *_state);
void fitspheremc(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
/* Real */ ae_vector* cx,
double* rhi,
ae_state *_state);
void fitspheremi(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
/* Real */ ae_vector* cx,
double* rlo,
ae_state *_state);
void fitspheremz(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
/* Real */ ae_vector* cx,
double* rlo,
double* rhi,
ae_state *_state);
void fitspherex(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
ae_int_t problemtype,
double epsx,
ae_int_t aulits,
double penalty,
/* Real */ ae_vector* cx,
double* rlo,
double* rhi,
ae_state *_state);
void fitsphereinternal(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
ae_int_t problemtype,
ae_int_t solvertype,
double epsx,
ae_int_t aulits,
double penalty,
/* Real */ ae_vector* cx,
double* rlo,
double* rhi,
fitsphereinternalreport* rep,
ae_state *_state);
void _fitsphereinternalreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _fitsphereinternalreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _fitsphereinternalreport_clear(void* _p);
void _fitsphereinternalreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_INTFITSERV) || !defined(AE_PARTIAL_BUILD)
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);
void buildpriorterm(/* Real */ ae_matrix* xy,
ae_int_t n,
ae_int_t nx,
ae_int_t ny,
ae_int_t modeltype,
double priorval,
/* Real */ ae_matrix* v,
ae_state *_state);
void buildpriorterm1(/* Real */ ae_vector* xy1,
ae_int_t n,
ae_int_t nx,
ae_int_t ny,
ae_int_t modeltype,
double priorval,
/* Real */ ae_matrix* v,
ae_state *_state);
#endif
#if defined(AE_COMPILE_SPLINE1D) || !defined(AE_PARTIAL_BUILD)
void spline1dbuildlinear(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_int_t n,
spline1dinterpolant* c,
ae_state *_state);
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);
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);
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);
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);
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);
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);
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);
void spline1dbuildhermite(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
/* Real */ ae_vector* d,
ae_int_t n,
spline1dinterpolant* c,
ae_state *_state);
void spline1dbuildakima(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_int_t n,
spline1dinterpolant* c,
ae_state *_state);
double spline1dcalc(spline1dinterpolant* c, double x, ae_state *_state);
void spline1ddiff(spline1dinterpolant* c,
double x,
double* s,
double* ds,
double* d2s,
ae_state *_state);
void spline1dcopy(spline1dinterpolant* c,
spline1dinterpolant* cc,
ae_state *_state);
void spline1dunpack(spline1dinterpolant* c,
ae_int_t* n,
/* Real */ ae_matrix* tbl,
ae_state *_state);
void spline1dlintransx(spline1dinterpolant* c,
double a,
double b,
ae_state *_state);
void spline1dlintransy(spline1dinterpolant* c,
double a,
double b,
ae_state *_state);
double spline1dintegrate(spline1dinterpolant* c,
double x,
ae_state *_state);
void spline1dfit(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_int_t n,
ae_int_t m,
double lambdans,
spline1dinterpolant* s,
spline1dfitreport* rep,
ae_state *_state);
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);
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);
void heapsortdpoints(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
/* Real */ ae_vector* d,
ae_int_t n,
ae_state *_state);
void solvepolinom2(double p0,
double m0,
double p1,
double m1,
double* x0,
double* x1,
ae_int_t* nr,
ae_state *_state);
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 bisectmethod(double pa,
double ma,
double pb,
double mb,
double a,
double b,
double* x,
ae_state *_state);
void spline1dbuildmonotone(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_int_t n,
spline1dinterpolant* c,
ae_state *_state);
void _spline1dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline1dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline1dinterpolant_clear(void* _p);
void _spline1dinterpolant_destroy(void* _p);
void _spline1dfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline1dfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline1dfitreport_clear(void* _p);
void _spline1dfitreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_PARAMETRIC) || !defined(AE_PARTIAL_BUILD)
void pspline2build(/* Real */ ae_matrix* xy,
ae_int_t n,
ae_int_t st,
ae_int_t pt,
pspline2interpolant* p,
ae_state *_state);
void pspline3build(/* Real */ ae_matrix* xy,
ae_int_t n,
ae_int_t st,
ae_int_t pt,
pspline3interpolant* p,
ae_state *_state);
void pspline2buildperiodic(/* Real */ ae_matrix* xy,
ae_int_t n,
ae_int_t st,
ae_int_t pt,
pspline2interpolant* p,
ae_state *_state);
void pspline3buildperiodic(/* Real */ ae_matrix* xy,
ae_int_t n,
ae_int_t st,
ae_int_t pt,
pspline3interpolant* p,
ae_state *_state);
void pspline2parametervalues(pspline2interpolant* p,
ae_int_t* n,
/* Real */ ae_vector* t,
ae_state *_state);
void pspline3parametervalues(pspline3interpolant* p,
ae_int_t* n,
/* Real */ ae_vector* t,
ae_state *_state);
void pspline2calc(pspline2interpolant* p,
double t,
double* x,
double* y,
ae_state *_state);
void pspline3calc(pspline3interpolant* p,
double t,
double* x,
double* y,
double* z,
ae_state *_state);
void pspline2tangent(pspline2interpolant* p,
double t,
double* x,
double* y,
ae_state *_state);
void pspline3tangent(pspline3interpolant* p,
double t,
double* x,
double* y,
double* z,
ae_state *_state);
void pspline2diff(pspline2interpolant* p,
double t,
double* x,
double* dx,
double* y,
double* dy,
ae_state *_state);
void pspline3diff(pspline3interpolant* p,
double t,
double* x,
double* dx,
double* y,
double* dy,
double* z,
double* dz,
ae_state *_state);
void pspline2diff2(pspline2interpolant* p,
double t,
double* x,
double* dx,
double* d2x,
double* y,
double* dy,
double* d2y,
ae_state *_state);
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);
double pspline2arclength(pspline2interpolant* p,
double a,
double b,
ae_state *_state);
double pspline3arclength(pspline3interpolant* p,
double a,
double b,
ae_state *_state);
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);
void _pspline2interpolant_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _pspline2interpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _pspline2interpolant_clear(void* _p);
void _pspline2interpolant_destroy(void* _p);
void _pspline3interpolant_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _pspline3interpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _pspline3interpolant_clear(void* _p);
void _pspline3interpolant_destroy(void* _p);
#endif
#if defined(AE_COMPILE_SPLINE3D) || !defined(AE_PARTIAL_BUILD)
double spline3dcalc(spline3dinterpolant* c,
double x,
double y,
double z,
ae_state *_state);
void spline3dlintransxyz(spline3dinterpolant* c,
double ax,
double bx,
double ay,
double by,
double az,
double bz,
ae_state *_state);
void spline3dlintransf(spline3dinterpolant* c,
double a,
double b,
ae_state *_state);
void spline3dcopy(spline3dinterpolant* c,
spline3dinterpolant* cc,
ae_state *_state);
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);
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);
void spline3dcalcvbuf(spline3dinterpolant* c,
double x,
double y,
double z,
/* Real */ ae_vector* f,
ae_state *_state);
void spline3dcalcv(spline3dinterpolant* c,
double x,
double y,
double z,
/* Real */ ae_vector* f,
ae_state *_state);
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);
void _spline3dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline3dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline3dinterpolant_clear(void* _p);
void _spline3dinterpolant_destroy(void* _p);
#endif
#if defined(AE_COMPILE_POLINT) || !defined(AE_PARTIAL_BUILD)
void polynomialbar2cheb(barycentricinterpolant* p,
double a,
double b,
/* Real */ ae_vector* t,
ae_state *_state);
void polynomialcheb2bar(/* Real */ ae_vector* t,
ae_int_t n,
double a,
double b,
barycentricinterpolant* p,
ae_state *_state);
void polynomialbar2pow(barycentricinterpolant* p,
double c,
double s,
/* Real */ ae_vector* a,
ae_state *_state);
void polynomialpow2bar(/* Real */ ae_vector* a,
ae_int_t n,
double c,
double s,
barycentricinterpolant* p,
ae_state *_state);
void polynomialbuild(/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_int_t n,
barycentricinterpolant* p,
ae_state *_state);
void polynomialbuildeqdist(double a,
double b,
/* Real */ ae_vector* y,
ae_int_t n,
barycentricinterpolant* p,
ae_state *_state);
void polynomialbuildcheb1(double a,
double b,
/* Real */ ae_vector* y,
ae_int_t n,
barycentricinterpolant* p,
ae_state *_state);
void polynomialbuildcheb2(double a,
double b,
/* Real */ ae_vector* y,
ae_int_t n,
barycentricinterpolant* p,
ae_state *_state);
double polynomialcalceqdist(double a,
double b,
/* Real */ ae_vector* f,
ae_int_t n,
double t,
ae_state *_state);
double polynomialcalccheb1(double a,
double b,
/* Real */ ae_vector* f,
ae_int_t n,
double t,
ae_state *_state);
double polynomialcalccheb2(double a,
double b,
/* Real */ ae_vector* f,
ae_int_t n,
double t,
ae_state *_state);
#endif
#if defined(AE_COMPILE_LSFIT) || !defined(AE_PARTIAL_BUILD)
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);
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);
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);
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);
double logisticcalc4(double x,
double a,
double b,
double c,
double d,
ae_state *_state);
double logisticcalc5(double x,
double a,
double b,
double c,
double d,
double g,
ae_state *_state);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
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);
void lsfitsetcond(lsfitstate* state,
double epsx,
ae_int_t maxits,
ae_state *_state);
void lsfitsetstpmax(lsfitstate* state, double stpmax, ae_state *_state);
void lsfitsetxrep(lsfitstate* state, ae_bool needxrep, ae_state *_state);
void lsfitsetscale(lsfitstate* state,
/* Real */ ae_vector* s,
ae_state *_state);
void lsfitsetbc(lsfitstate* state,
/* Real */ ae_vector* bndl,
/* Real */ ae_vector* bndu,
ae_state *_state);
void lsfitsetlc(lsfitstate* state,
/* Real */ ae_matrix* c,
/* Integer */ ae_vector* ct,
ae_int_t k,
ae_state *_state);
ae_bool lsfititeration(lsfitstate* state, ae_state *_state);
void lsfitresults(lsfitstate* state,
ae_int_t* info,
/* Real */ ae_vector* c,
lsfitreport* rep,
ae_state *_state);
void lsfitsetgradientcheck(lsfitstate* state,
double teststep,
ae_state *_state);
void _polynomialfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _polynomialfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _polynomialfitreport_clear(void* _p);
void _polynomialfitreport_destroy(void* _p);
void _barycentricfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _barycentricfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _barycentricfitreport_clear(void* _p);
void _barycentricfitreport_destroy(void* _p);
void _lsfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _lsfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _lsfitreport_clear(void* _p);
void _lsfitreport_destroy(void* _p);
void _lsfitstate_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _lsfitstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _lsfitstate_clear(void* _p);
void _lsfitstate_destroy(void* _p);
#endif
#if defined(AE_COMPILE_RBFV2) || !defined(AE_PARTIAL_BUILD)
void rbfv2create(ae_int_t nx,
ae_int_t ny,
rbfv2model* s,
ae_state *_state);
void rbfv2createcalcbuffer(rbfv2model* s,
rbfv2calcbuffer* buf,
ae_state *_state);
void rbfv2buildhierarchical(/* Real */ ae_matrix* x,
/* Real */ ae_matrix* y,
ae_int_t n,
/* Real */ ae_vector* scalevec,
ae_int_t aterm,
ae_int_t nh,
double rbase,
double lambdans,
rbfv2model* s,
ae_int_t* progress10000,
ae_bool* terminationrequest,
rbfv2report* rep,
ae_state *_state);
void rbfv2alloc(ae_serializer* s, rbfv2model* model, ae_state *_state);
void rbfv2serialize(ae_serializer* s, rbfv2model* model, ae_state *_state);
void rbfv2unserialize(ae_serializer* s,
rbfv2model* model,
ae_state *_state);
double rbfv2farradius(ae_int_t bf, ae_state *_state);
double rbfv2nearradius(ae_int_t bf, ae_state *_state);
double rbfv2basisfunc(ae_int_t bf, double d2, ae_state *_state);
void rbfv2basisfuncdiff2(ae_int_t bf,
double d2,
double* f,
double* df,
double* d2f,
ae_state *_state);
double rbfv2calc1(rbfv2model* s, double x0, ae_state *_state);
double rbfv2calc2(rbfv2model* s, double x0, double x1, ae_state *_state);
double rbfv2calc3(rbfv2model* s,
double x0,
double x1,
double x2,
ae_state *_state);
void rbfv2calcbuf(rbfv2model* s,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfv2tscalcbuf(rbfv2model* s,
rbfv2calcbuffer* buf,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfv2gridcalc2(rbfv2model* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_matrix* y,
ae_state *_state);
void rbfv2gridcalcvx(rbfv2model* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* x2,
ae_int_t n2,
/* Real */ ae_vector* x3,
ae_int_t n3,
/* Boolean */ ae_vector* flagy,
ae_bool sparsey,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfv2partialgridcalcrec(rbfv2model* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* x2,
ae_int_t n2,
/* Real */ ae_vector* x3,
ae_int_t n3,
/* Integer */ ae_vector* blocks0,
ae_int_t block0a,
ae_int_t block0b,
/* Integer */ ae_vector* blocks1,
ae_int_t block1a,
ae_int_t block1b,
/* Integer */ ae_vector* blocks2,
ae_int_t block2a,
ae_int_t block2b,
/* Integer */ ae_vector* blocks3,
ae_int_t block3a,
ae_int_t block3b,
/* Boolean */ ae_vector* flagy,
ae_bool sparsey,
ae_int_t levelidx,
double avgfuncpernode,
ae_shared_pool* bufpool,
/* Real */ ae_vector* y,
ae_state *_state);
ae_bool _trypexec_rbfv2partialgridcalcrec(rbfv2model* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* x2,
ae_int_t n2,
/* Real */ ae_vector* x3,
ae_int_t n3,
/* Integer */ ae_vector* blocks0,
ae_int_t block0a,
ae_int_t block0b,
/* Integer */ ae_vector* blocks1,
ae_int_t block1a,
ae_int_t block1b,
/* Integer */ ae_vector* blocks2,
ae_int_t block2a,
ae_int_t block2b,
/* Integer */ ae_vector* blocks3,
ae_int_t block3a,
ae_int_t block3b,
/* Boolean */ ae_vector* flagy,
ae_bool sparsey,
ae_int_t levelidx,
double avgfuncpernode,
ae_shared_pool* bufpool,
/* Real */ ae_vector* y, ae_state *_state);
void rbfv2unpack(rbfv2model* s,
ae_int_t* nx,
ae_int_t* ny,
/* Real */ ae_matrix* xwr,
ae_int_t* nc,
/* Real */ ae_matrix* v,
ae_state *_state);
void _rbfv2calcbuffer_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfv2calcbuffer_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfv2calcbuffer_clear(void* _p);
void _rbfv2calcbuffer_destroy(void* _p);
void _rbfv2model_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfv2model_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfv2model_clear(void* _p);
void _rbfv2model_destroy(void* _p);
void _rbfv2gridcalcbuffer_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfv2gridcalcbuffer_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfv2gridcalcbuffer_clear(void* _p);
void _rbfv2gridcalcbuffer_destroy(void* _p);
void _rbfv2report_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfv2report_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfv2report_clear(void* _p);
void _rbfv2report_destroy(void* _p);
#endif
#if defined(AE_COMPILE_SPLINE2D) || !defined(AE_PARTIAL_BUILD)
double spline2dcalc(spline2dinterpolant* c,
double x,
double y,
ae_state *_state);
void spline2ddiff(spline2dinterpolant* c,
double x,
double y,
double* f,
double* fx,
double* fy,
double* fxy,
ae_state *_state);
void spline2dcalcvbuf(spline2dinterpolant* c,
double x,
double y,
/* Real */ ae_vector* f,
ae_state *_state);
double spline2dcalcvi(spline2dinterpolant* c,
double x,
double y,
ae_int_t i,
ae_state *_state);
void spline2dcalcv(spline2dinterpolant* c,
double x,
double y,
/* Real */ ae_vector* f,
ae_state *_state);
void spline2ddiffvi(spline2dinterpolant* c,
double x,
double y,
ae_int_t i,
double* f,
double* fx,
double* fy,
double* fxy,
ae_state *_state);
void spline2dlintransxy(spline2dinterpolant* c,
double ax,
double bx,
double ay,
double by,
ae_state *_state);
void spline2dlintransf(spline2dinterpolant* c,
double a,
double b,
ae_state *_state);
void spline2dcopy(spline2dinterpolant* c,
spline2dinterpolant* cc,
ae_state *_state);
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);
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);
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);
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);
void spline2dunpackv(spline2dinterpolant* c,
ae_int_t* m,
ae_int_t* n,
ae_int_t* d,
/* Real */ ae_matrix* tbl,
ae_state *_state);
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);
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);
void spline2dunpack(spline2dinterpolant* c,
ae_int_t* m,
ae_int_t* n,
/* Real */ ae_matrix* tbl,
ae_state *_state);
void spline2dbuildercreate(ae_int_t d,
spline2dbuilder* state,
ae_state *_state);
void spline2dbuildersetuserterm(spline2dbuilder* state,
double v,
ae_state *_state);
void spline2dbuildersetlinterm(spline2dbuilder* state, ae_state *_state);
void spline2dbuildersetconstterm(spline2dbuilder* state, ae_state *_state);
void spline2dbuildersetzeroterm(spline2dbuilder* state, ae_state *_state);
void spline2dbuildersetpoints(spline2dbuilder* state,
/* Real */ ae_matrix* xy,
ae_int_t n,
ae_state *_state);
void spline2dbuildersetareaauto(spline2dbuilder* state, ae_state *_state);
void spline2dbuildersetarea(spline2dbuilder* state,
double xa,
double xb,
double ya,
double yb,
ae_state *_state);
void spline2dbuildersetgrid(spline2dbuilder* state,
ae_int_t kx,
ae_int_t ky,
ae_state *_state);
void spline2dbuildersetalgofastddm(spline2dbuilder* state,
ae_int_t nlayers,
double lambdav,
ae_state *_state);
void spline2dbuildersetalgoblocklls(spline2dbuilder* state,
double lambdans,
ae_state *_state);
void spline2dbuildersetalgonaivells(spline2dbuilder* state,
double lambdans,
ae_state *_state);
void spline2dfit(spline2dbuilder* state,
spline2dinterpolant* s,
spline2dfitreport* rep,
ae_state *_state);
void spline2dalloc(ae_serializer* s,
spline2dinterpolant* spline,
ae_state *_state);
void spline2dserialize(ae_serializer* s,
spline2dinterpolant* spline,
ae_state *_state);
void spline2dunserialize(ae_serializer* s,
spline2dinterpolant* spline,
ae_state *_state);
void _spline2dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline2dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline2dinterpolant_clear(void* _p);
void _spline2dinterpolant_destroy(void* _p);
void _spline2dbuilder_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline2dbuilder_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline2dbuilder_clear(void* _p);
void _spline2dbuilder_destroy(void* _p);
void _spline2dfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline2dfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline2dfitreport_clear(void* _p);
void _spline2dfitreport_destroy(void* _p);
void _spline2dxdesignmatrix_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline2dxdesignmatrix_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline2dxdesignmatrix_clear(void* _p);
void _spline2dxdesignmatrix_destroy(void* _p);
void _spline2dblockllsbuf_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline2dblockllsbuf_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline2dblockllsbuf_clear(void* _p);
void _spline2dblockllsbuf_destroy(void* _p);
void _spline2dfastddmbuf_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _spline2dfastddmbuf_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _spline2dfastddmbuf_clear(void* _p);
void _spline2dfastddmbuf_destroy(void* _p);
#endif
#if defined(AE_COMPILE_RBFV1) || !defined(AE_PARTIAL_BUILD)
void rbfv1create(ae_int_t nx,
ae_int_t ny,
rbfv1model* s,
ae_state *_state);
void rbfv1createcalcbuffer(rbfv1model* s,
rbfv1calcbuffer* buf,
ae_state *_state);
void rbfv1buildmodel(/* Real */ ae_matrix* x,
/* Real */ ae_matrix* y,
ae_int_t n,
ae_int_t aterm,
ae_int_t algorithmtype,
ae_int_t nlayers,
double radvalue,
double radzvalue,
double lambdav,
double epsort,
double epserr,
ae_int_t maxits,
rbfv1model* s,
rbfv1report* rep,
ae_state *_state);
void rbfv1alloc(ae_serializer* s, rbfv1model* model, ae_state *_state);
void rbfv1serialize(ae_serializer* s, rbfv1model* model, ae_state *_state);
void rbfv1unserialize(ae_serializer* s,
rbfv1model* model,
ae_state *_state);
double rbfv1calc2(rbfv1model* s, double x0, double x1, ae_state *_state);
double rbfv1calc3(rbfv1model* s,
double x0,
double x1,
double x2,
ae_state *_state);
void rbfv1calcbuf(rbfv1model* s,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfv1tscalcbuf(rbfv1model* s,
rbfv1calcbuffer* buf,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfv1gridcalc2(rbfv1model* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_matrix* y,
ae_state *_state);
void rbfv1gridcalc3vrec(rbfv1model* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* x2,
ae_int_t n2,
/* Integer */ ae_vector* blocks0,
ae_int_t block0a,
ae_int_t block0b,
/* Integer */ ae_vector* blocks1,
ae_int_t block1a,
ae_int_t block1b,
/* Integer */ ae_vector* blocks2,
ae_int_t block2a,
ae_int_t block2b,
/* Boolean */ ae_vector* flagy,
ae_bool sparsey,
double searchradius,
double avgfuncpernode,
ae_shared_pool* bufpool,
/* Real */ ae_vector* y,
ae_state *_state);
ae_bool _trypexec_rbfv1gridcalc3vrec(rbfv1model* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* x2,
ae_int_t n2,
/* Integer */ ae_vector* blocks0,
ae_int_t block0a,
ae_int_t block0b,
/* Integer */ ae_vector* blocks1,
ae_int_t block1a,
ae_int_t block1b,
/* Integer */ ae_vector* blocks2,
ae_int_t block2a,
ae_int_t block2b,
/* Boolean */ ae_vector* flagy,
ae_bool sparsey,
double searchradius,
double avgfuncpernode,
ae_shared_pool* bufpool,
/* Real */ ae_vector* y, ae_state *_state);
void rbfv1unpack(rbfv1model* s,
ae_int_t* nx,
ae_int_t* ny,
/* Real */ ae_matrix* xwr,
ae_int_t* nc,
/* Real */ ae_matrix* v,
ae_state *_state);
void _rbfv1calcbuffer_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfv1calcbuffer_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfv1calcbuffer_clear(void* _p);
void _rbfv1calcbuffer_destroy(void* _p);
void _rbfv1model_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfv1model_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfv1model_clear(void* _p);
void _rbfv1model_destroy(void* _p);
void _gridcalc3v1buf_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _gridcalc3v1buf_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _gridcalc3v1buf_clear(void* _p);
void _gridcalc3v1buf_destroy(void* _p);
void _rbfv1report_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfv1report_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfv1report_clear(void* _p);
void _rbfv1report_destroy(void* _p);
#endif
#if defined(AE_COMPILE_RBF) || !defined(AE_PARTIAL_BUILD)
void rbfcreate(ae_int_t nx, ae_int_t ny, rbfmodel* s, ae_state *_state);
void rbfcreatecalcbuffer(rbfmodel* s,
rbfcalcbuffer* buf,
ae_state *_state);
void rbfsetpoints(rbfmodel* s,
/* Real */ ae_matrix* xy,
ae_int_t n,
ae_state *_state);
void rbfsetpointsandscales(rbfmodel* r,
/* Real */ ae_matrix* xy,
ae_int_t n,
/* Real */ ae_vector* s,
ae_state *_state);
void rbfsetalgoqnn(rbfmodel* s, double q, double z, ae_state *_state);
void rbfsetalgomultilayer(rbfmodel* s,
double rbase,
ae_int_t nlayers,
double lambdav,
ae_state *_state);
void rbfsetalgohierarchical(rbfmodel* s,
double rbase,
ae_int_t nlayers,
double lambdans,
ae_state *_state);
void rbfsetlinterm(rbfmodel* s, ae_state *_state);
void rbfsetconstterm(rbfmodel* s, ae_state *_state);
void rbfsetzeroterm(rbfmodel* s, ae_state *_state);
void rbfsetv2bf(rbfmodel* s, ae_int_t bf, ae_state *_state);
void rbfsetv2its(rbfmodel* s, ae_int_t maxits, ae_state *_state);
void rbfsetv2supportr(rbfmodel* s, double r, ae_state *_state);
void rbfsetcond(rbfmodel* s,
double epsort,
double epserr,
ae_int_t maxits,
ae_state *_state);
void rbfbuildmodel(rbfmodel* s, rbfreport* rep, ae_state *_state);
double rbfcalc1(rbfmodel* s, double x0, ae_state *_state);
double rbfcalc2(rbfmodel* s, double x0, double x1, ae_state *_state);
double rbfcalc3(rbfmodel* s,
double x0,
double x1,
double x2,
ae_state *_state);
void rbfcalc(rbfmodel* s,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfcalcbuf(rbfmodel* s,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void rbftscalcbuf(rbfmodel* s,
rbfcalcbuffer* buf,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
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);
void rbfgridcalc2v(rbfmodel* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfgridcalc2vsubset(rbfmodel* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Boolean */ ae_vector* flagy,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfgridcalc3v(rbfmodel* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* x2,
ae_int_t n2,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfgridcalc3vsubset(rbfmodel* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* x2,
ae_int_t n2,
/* Boolean */ ae_vector* flagy,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfgridcalc2vx(rbfmodel* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Boolean */ ae_vector* flagy,
ae_bool sparsey,
/* Real */ ae_vector* y,
ae_state *_state);
void rbfgridcalc3vx(rbfmodel* s,
/* Real */ ae_vector* x0,
ae_int_t n0,
/* Real */ ae_vector* x1,
ae_int_t n1,
/* Real */ ae_vector* x2,
ae_int_t n2,
/* Boolean */ ae_vector* flagy,
ae_bool sparsey,
/* Real */ ae_vector* y,
ae_state *_state);
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_int_t* modelversion,
ae_state *_state);
ae_int_t rbfgetmodelversion(rbfmodel* s, ae_state *_state);
double rbfpeekprogress(rbfmodel* s, ae_state *_state);
void rbfrequesttermination(rbfmodel* s, ae_state *_state);
void rbfalloc(ae_serializer* s, rbfmodel* model, ae_state *_state);
void rbfserialize(ae_serializer* s, rbfmodel* model, ae_state *_state);
void rbfunserialize(ae_serializer* s, rbfmodel* model, ae_state *_state);
void _rbfcalcbuffer_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfcalcbuffer_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfcalcbuffer_clear(void* _p);
void _rbfcalcbuffer_destroy(void* _p);
void _rbfmodel_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfmodel_clear(void* _p);
void _rbfmodel_destroy(void* _p);
void _rbfreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _rbfreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _rbfreport_clear(void* _p);
void _rbfreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_INTCOMP) || !defined(AE_PARTIAL_BUILD)
void nsfitspheremcc(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
/* Real */ ae_vector* cx,
double* rhi,
ae_state *_state);
void nsfitspheremic(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
/* Real */ ae_vector* cx,
double* rlo,
ae_state *_state);
void nsfitspheremzc(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
/* Real */ ae_vector* cx,
double* rlo,
double* rhi,
ae_state *_state);
void nsfitspherex(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nx,
ae_int_t problemtype,
double epsx,
ae_int_t aulits,
double penalty,
/* Real */ ae_vector* cx,
double* rlo,
double* rhi,
ae_state *_state);
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);
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);
#endif
}
#endif
Reference in New Issue View Git Blame Copy Permalink