/*************************************************************************
ALGLIB 4.07.0 (source code generated 2025-12-29)
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 _dataanalysis_pkg_h
#define _dataanalysis_pkg_h
#include "ap.h"
#include "alglibinternal.h"
#include "alglibmisc.h"
#include "linalg.h"
#include "statistics.h"
#include "specialfunctions.h"
#include "optimization.h"
#include "solvers.h"

/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES)
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
#if defined(AE_COMPILE_PCA) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_BDSS) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    double relclserror;
    double avgce;
    double rmserror;
    double avgerror;
    double avgrelerror;
} cvreport;
#endif
#if defined(AE_COMPILE_MLPBASE) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    double relclserror;
    double avgce;
    double rmserror;
    double avgerror;
    double avgrelerror;
} modelerrors;
typedef struct
{
    double f;
    ae_vector g;
} smlpgrad;
typedef struct
{
    ae_int_t hlnetworktype;
    ae_int_t hlnormtype;
    ae_vector hllayersizes;
    ae_vector hlconnections;
    ae_vector hlneurons;
    ae_vector structinfo;
    ae_vector weights;
    ae_vector columnmeans;
    ae_vector columnsigmas;
    ae_vector neurons;
    ae_vector dfdnet;
    ae_vector derror;
    ae_vector x;
    ae_vector y;
    ae_matrix xy;
    ae_vector xyrow;
    ae_vector nwbuf;
    ae_vector integerbuf;
    modelerrors err;
    ae_vector rndbuf;
    ae_shared_pool buf;
    ae_shared_pool gradbuf;
    ae_matrix dummydxy;
    sparsematrix dummysxy;
    ae_vector dummyidx;
    ae_shared_pool dummypool;
} multilayerperceptron;
#endif
#if defined(AE_COMPILE_MLPE) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    ae_int_t ensemblesize;
    ae_vector weights;
    ae_vector columnmeans;
    ae_vector columnsigmas;
    multilayerperceptron network;
    ae_vector y;
} mlpensemble;
#endif
#if defined(AE_COMPILE_CLUSTERING) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    ae_matrix ct;
    ae_matrix ctbest;
    ae_vector xycbest;
    ae_vector xycprev;
    ae_vector d2;
    ae_vector csizes;
    apbuffers initbuf;
    ae_shared_pool updatepool;
} kmeansbuffers;
typedef struct
{
    ae_int_t npoints;
    ae_int_t nfeatures;
    ae_int_t disttype;
    ae_matrix xy;
    ae_matrix d;
    ae_int_t ahcalgo;
    ae_int_t kmeansrestarts;
    ae_int_t kmeansmaxits;
    ae_int_t kmeansinitalgo;
    ae_bool kmeansdbgnoits;
    ae_int_t seed;
    ae_matrix tmpd;
    apbuffers distbuf;
    kmeansbuffers kmeanstmp;
} clusterizerstate;
typedef struct
{
    ae_int_t terminationtype;
    ae_int_t npoints;
    ae_vector p;
    ae_matrix z;
    ae_matrix pz;
    ae_matrix pm;
    ae_vector mergedist;
} ahcreport;
typedef struct
{
    ae_int_t npoints;
    ae_int_t nfeatures;
    ae_int_t terminationtype;
    ae_int_t iterationscount;
    double energy;
    ae_int_t k;
    ae_matrix c;
    ae_vector cidx;
} kmeansreport;
#endif
#if defined(AE_COMPILE_DFOREST) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    ae_int_t dstype;
    ae_int_t npoints;
    ae_int_t nvars;
    ae_int_t nclasses;
    ae_vector dsdata;
    ae_vector dsrval;
    ae_vector dsival;
    ae_int_t rdfalgo;
    double rdfratio;
    double rdfvars;
    ae_int_t rdfglobalseed;
    ae_int_t rdfsplitstrength;
    ae_int_t rdfimportance;
    ae_vector dsmin;
    ae_vector dsmax;
    ae_vector dsbinary;
    double dsravg;
    ae_vector dsctotals;
    ae_int_t rdfprogress;
    ae_int_t rdftotal;
    ae_shared_pool workpool;
    ae_shared_pool votepool;
    ae_shared_pool treepool;
    ae_shared_pool treefactory;
    ae_bool neediobmatrix;
    ae_matrix iobmatrix;
    ae_vector varimpshuffle2;
} decisionforestbuilder;
typedef struct
{
    ae_vector classpriors;
    ae_vector varpool;
    ae_int_t varpoolsize;
    ae_vector trnset;
    ae_int_t trnsize;
    ae_vector trnlabelsr;
    ae_vector trnlabelsi;
    ae_vector oobset;
    ae_int_t oobsize;
    ae_vector ooblabelsr;
    ae_vector ooblabelsi;
    ae_vector treebuf;
    ae_vector curvals;
    ae_vector bestvals;
    ae_vector tmp0i;
    ae_vector tmp1i;
    ae_vector tmp0r;
    ae_vector tmp1r;
    ae_vector tmp2r;
    ae_vector tmp3r;
    ae_vector tmpnrms2;
    ae_vector classtotals0;
    ae_vector classtotals1;
    ae_vector classtotals01;
} dfworkbuf;
typedef struct
{
    ae_vector trntotals;
    ae_vector oobtotals;
    ae_vector trncounts;
    ae_vector oobcounts;
    ae_vector giniimportances;
} dfvotebuf;
typedef struct
{
    ae_vector losses;
    ae_vector xraw;
    ae_vector xdist;
    ae_vector xcur;
    ae_vector y;
    ae_vector yv;
    ae_vector targety;
    ae_vector startnodes;
} dfpermimpbuf;
typedef struct
{
    ae_vector treebuf;
    ae_int_t treeidx;
} dftreebuf;
typedef struct
{
    ae_vector x;
    ae_vector y;
} decisionforestbuffer;
typedef struct
{
    ae_int_t forestformat;
    ae_bool usemantissa8;
    ae_int_t nvars;
    ae_int_t nclasses;
    ae_int_t ntrees;
    ae_int_t bufsize;
    ae_vector trees;
    decisionforestbuffer buffer;
    ae_vector trees8;
} decisionforest;
typedef struct
{
    double relclserror;
    double avgce;
    double rmserror;
    double avgerror;
    double avgrelerror;
    double oobrelclserror;
    double oobavgce;
    double oobrmserror;
    double oobavgerror;
    double oobavgrelerror;
    ae_vector topvars;
    ae_vector varimportances;
} dfreport;
typedef struct
{
    ae_vector treebuf;
    ae_vector idxbuf;
    ae_vector tmpbufr;
    ae_vector tmpbufr2;
    ae_vector tmpbufi;
    ae_vector classibuf;
    ae_vector sortrbuf;
    ae_vector sortrbuf2;
    ae_vector sortibuf;
    ae_vector varpool;
    ae_vector evsbin;
    ae_vector evssplits;
} dfinternalbuffers;
#endif
#if defined(AE_COMPILE_LINREG) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    ae_vector w;
} linearmodel;
typedef struct
{
    ae_matrix c;
    double rmserror;
    double avgerror;
    double avgrelerror;
    double cvrmserror;
    double cvavgerror;
    double cvavgrelerror;
    ae_int_t ncvdefects;
    ae_vector cvdefects;
} lrreport;
#endif
#if defined(AE_COMPILE_FILTERS) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_SSA) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    ae_int_t nsequences;
    ae_vector sequenceidx;
    ae_vector sequencedata;
    ae_int_t algotype;
    ae_int_t windowwidth;
    ae_int_t rtpowerup;
    ae_int_t topk;
    ae_int_t precomputedwidth;
    ae_int_t precomputednbasis;
    ae_matrix precomputedbasis;
    ae_int_t defaultsubspaceits;
    ae_int_t memorylimit;
    ae_bool arebasisandsolvervalid;
    ae_matrix basis;
    ae_matrix basist;
    ae_vector sv;
    ae_vector forecasta;
    ae_int_t nbasis;
    eigsubspacestate solver;
    ae_matrix xxt;
    hqrndstate rs;
    ae_int_t rngseed;
    ae_vector rtqueue;
    ae_int_t rtqueuecnt;
    ae_int_t rtqueuechunk;
    ae_int_t dbgcntevd;
    ae_vector tmp0;
    ae_vector tmp1;
    eigsubspacereport solverrep;
    ae_vector alongtrend;
    ae_vector alongnoise;
    ae_matrix aseqtrajectory;
    ae_matrix aseqtbproduct;
    ae_vector aseqcounts;
    ae_vector fctrend;
    ae_vector fcnoise;
    ae_matrix fctrendm;
    ae_matrix uxbatch;
    ae_int_t uxbatchwidth;
    ae_int_t uxbatchsize;
    ae_int_t uxbatchlimit;
} ssamodel;
#endif
#if defined(AE_COMPILE_LDA) || !defined(AE_PARTIAL_BUILD)
#endif
#if defined(AE_COMPILE_MCPD) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    ae_int_t n;
    ae_vector states;
    ae_int_t npairs;
    ae_matrix data;
    ae_matrix ec;
    ae_matrix bndl;
    ae_matrix bndu;
    ae_matrix c;
    ae_vector ct;
    ae_int_t ccnt;
    ae_vector pw;
    ae_matrix priorp;
    double regterm;
    minbleicstate bs;
    ae_int_t repinneriterationscount;
    ae_int_t repouteriterationscount;
    ae_int_t repnfev;
    ae_int_t repterminationtype;
    minbleicreport br;
    ae_vector tmpp;
    ae_vector effectivew;
    ae_vector effectivebndl;
    ae_vector effectivebndu;
    ae_matrix effectivec;
    ae_vector effectivect;
    ae_vector h;
    ae_matrix p;
} mcpdstate;
typedef struct
{
    ae_int_t inneriterationscount;
    ae_int_t outeriterationscount;
    ae_int_t nfev;
    ae_int_t terminationtype;
} mcpdreport;
#endif
#if defined(AE_COMPILE_LOGIT) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    ae_vector w;
} logitmodel;
typedef struct
{
    ae_bool brackt;
    ae_bool stage1;
    ae_int_t infoc;
    double dg;
    double dgm;
    double dginit;
    double dgtest;
    double dgx;
    double dgxm;
    double dgy;
    double dgym;
    double finit;
    double ftest1;
    double fm;
    double fx;
    double fxm;
    double fy;
    double fym;
    double stx;
    double sty;
    double stmin;
    double stmax;
    double width;
    double width1;
    double xtrapf;
} logitmcstate;
typedef struct
{
    ae_int_t ngrad;
    ae_int_t nhess;
} mnlreport;
#endif
#if defined(AE_COMPILE_KNN) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    kdtreerequestbuffer treebuf;
    ae_vector x;
    ae_vector y;
    ae_vector tags;
    ae_matrix xy;
} knnbuffer;
typedef struct
{
    ae_int_t dstype;
    ae_int_t npoints;
    ae_int_t nvars;
    ae_bool iscls;
    ae_int_t nout;
    ae_matrix dsdata;
    ae_vector dsrval;
    ae_vector dsival;
    ae_int_t knnnrm;
} knnbuilder;
typedef struct
{
    ae_int_t nvars;
    ae_int_t nout;
    ae_int_t k;
    double eps;
    ae_bool iscls;
    ae_bool isdummy;
    kdtree tree;
    knnbuffer buffer;
} knnmodel;
typedef struct
{
    double relclserror;
    double avgce;
    double rmserror;
    double avgerror;
    double avgrelerror;
} knnreport;
#endif
#if defined(AE_COMPILE_MLPTRAIN) || !defined(AE_PARTIAL_BUILD)
typedef struct
{
    double relclserror;
    double avgce;
    double rmserror;
    double avgerror;
    double avgrelerror;
    ae_int_t ngrad;
    ae_int_t nhess;
    ae_int_t ncholesky;
} mlpreport;
typedef struct
{
    double relclserror;
    double avgce;
    double rmserror;
    double avgerror;
    double avgrelerror;
} mlpcvreport;
typedef struct
{
    ae_vector bestparameters;
    double bestrmserror;
    ae_bool randomizenetwork;
    multilayerperceptron network;
    minlbfgsstate optimizer;
    minlbfgsreport optimizerrep;
    ae_vector wbuf0;
    ae_vector wbuf1;
    ae_vector allminibatches;
    ae_vector currentminibatch;
    rcommstate rstate;
    ae_int_t algoused;
    ae_int_t minibatchsize;
    hqrndstate generator;
} smlptrnsession;
typedef struct
{
    ae_vector trnsubset;
    ae_vector valsubset;
    ae_shared_pool mlpsessions;
    mlpreport mlprep;
    multilayerperceptron network;
} mlpetrnsession;
typedef struct
{
    ae_int_t nin;
    ae_int_t nout;
    ae_bool rcpar;
    ae_int_t lbfgsfactor;
    double decay;
    double wstep;
    ae_int_t maxits;
    ae_int_t datatype;
    ae_int_t npoints;
    ae_matrix densexy;
    sparsematrix sparsexy;
    smlptrnsession session;
    ae_int_t ngradbatch;
    ae_vector subset;
    ae_int_t subsetsize;
    ae_vector valsubset;
    ae_int_t valsubsetsize;
    ae_int_t algokind;
    ae_int_t minibatchsize;
} mlptrainer;
typedef struct
{
    multilayerperceptron network;
    mlpreport rep;
    ae_vector subset;
    ae_int_t subsetsize;
    ae_vector xyrow;
    ae_vector y;
    ae_int_t ngrad;
    ae_shared_pool trnpool;
} mlpparallelizationcv;
#endif
#if defined(AE_COMPILE_DATACOMP) || !defined(AE_PARTIAL_BUILD)
#endif

}

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

#if defined(AE_COMPILE_PCA) || !defined(AE_PARTIAL_BUILD)

#endif

#if defined(AE_COMPILE_BDSS) || !defined(AE_PARTIAL_BUILD)

#endif

#if defined(AE_COMPILE_MLPBASE) || !defined(AE_PARTIAL_BUILD)
class _modelerrors_owner;
class modelerrors;
class _multilayerperceptron_owner;
class multilayerperceptron;


/*************************************************************************
Model's errors:
    * RelCLSError   -   fraction of misclassified cases.
    * AvgCE         -   acerage cross-entropy
    * RMSError      -   root-mean-square error
    * AvgError      -   average error
    * AvgRelError   -   average relative error

NOTE 1: RelCLSError/AvgCE are zero on regression problems.

NOTE 2: on classification problems  RMSError/AvgError/AvgRelError  contain
        errors in prediction of posterior probabilities
*************************************************************************/
class _modelerrors_owner
{
public:
    _modelerrors_owner();
    _modelerrors_owner(alglib_impl::modelerrors *attach_to);
    _modelerrors_owner(const _modelerrors_owner &rhs);
    _modelerrors_owner& operator=(const _modelerrors_owner &rhs);
    virtual ~_modelerrors_owner();
    alglib_impl::modelerrors* c_ptr();
    const alglib_impl::modelerrors* c_ptr() const;
protected:
    alglib_impl::modelerrors *p_struct;
    bool is_attached;
};
class modelerrors : public _modelerrors_owner
{
public:
    modelerrors();
    modelerrors(alglib_impl::modelerrors *attach_to);
    modelerrors(const modelerrors &rhs);
    modelerrors& operator=(const modelerrors &rhs);
    virtual ~modelerrors();
    double &relclserror;
    double &avgce;
    double &rmserror;
    double &avgerror;
    double &avgrelerror;


};


/*************************************************************************

*************************************************************************/
class _multilayerperceptron_owner
{
public:
    _multilayerperceptron_owner();
    _multilayerperceptron_owner(alglib_impl::multilayerperceptron *attach_to);
    _multilayerperceptron_owner(const _multilayerperceptron_owner &rhs);
    _multilayerperceptron_owner& operator=(const _multilayerperceptron_owner &rhs);
    virtual ~_multilayerperceptron_owner();
    alglib_impl::multilayerperceptron* c_ptr();
    const alglib_impl::multilayerperceptron* c_ptr() const;
protected:
    alglib_impl::multilayerperceptron *p_struct;
    bool is_attached;
};
class multilayerperceptron : public _multilayerperceptron_owner
{
public:
    multilayerperceptron();
    multilayerperceptron(alglib_impl::multilayerperceptron *attach_to);
    multilayerperceptron(const multilayerperceptron &rhs);
    multilayerperceptron& operator=(const multilayerperceptron &rhs);
    virtual ~multilayerperceptron();


};
#endif

#if defined(AE_COMPILE_MLPE) || !defined(AE_PARTIAL_BUILD)
class _mlpensemble_owner;
class mlpensemble;


/*************************************************************************
Neural networks ensemble
*************************************************************************/
class _mlpensemble_owner
{
public:
    _mlpensemble_owner();
    _mlpensemble_owner(alglib_impl::mlpensemble *attach_to);
    _mlpensemble_owner(const _mlpensemble_owner &rhs);
    _mlpensemble_owner& operator=(const _mlpensemble_owner &rhs);
    virtual ~_mlpensemble_owner();
    alglib_impl::mlpensemble* c_ptr();
    const alglib_impl::mlpensemble* c_ptr() const;
protected:
    alglib_impl::mlpensemble *p_struct;
    bool is_attached;
};
class mlpensemble : public _mlpensemble_owner
{
public:
    mlpensemble();
    mlpensemble(alglib_impl::mlpensemble *attach_to);
    mlpensemble(const mlpensemble &rhs);
    mlpensemble& operator=(const mlpensemble &rhs);
    virtual ~mlpensemble();


};
#endif

#if defined(AE_COMPILE_CLUSTERING) || !defined(AE_PARTIAL_BUILD)
class _clusterizerstate_owner;
class clusterizerstate;
class _ahcreport_owner;
class ahcreport;
class _kmeansreport_owner;
class kmeansreport;


/*************************************************************************
This structure is a clusterization engine.

You should not try to access its fields directly.
Use ALGLIB functions in order to work with this object.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
class _clusterizerstate_owner
{
public:
    _clusterizerstate_owner();
    _clusterizerstate_owner(alglib_impl::clusterizerstate *attach_to);
    _clusterizerstate_owner(const _clusterizerstate_owner &rhs);
    _clusterizerstate_owner& operator=(const _clusterizerstate_owner &rhs);
    virtual ~_clusterizerstate_owner();
    alglib_impl::clusterizerstate* c_ptr();
    const alglib_impl::clusterizerstate* c_ptr() const;
protected:
    alglib_impl::clusterizerstate *p_struct;
    bool is_attached;
};
class clusterizerstate : public _clusterizerstate_owner
{
public:
    clusterizerstate();
    clusterizerstate(alglib_impl::clusterizerstate *attach_to);
    clusterizerstate(const clusterizerstate &rhs);
    clusterizerstate& operator=(const clusterizerstate &rhs);
    virtual ~clusterizerstate();


};


/*************************************************************************
This structure  is used to store results of the agglomerative hierarchical
clustering (AHC).

Following information is returned:

* TerminationType - completion code:
  * 1   for successful completion of algorithm
  * -5  inappropriate combination of  clustering  algorithm  and  distance
        function was used. As for now, it  is  possible  only when  Ward's
        method is called for dataset with non-Euclidean distance function.
  In case negative completion code is returned,  other  fields  of  report
  structure are invalid and should not be used.

* NPoints contains number of points in the original dataset

* Z contains information about merges performed  (see below).  Z  contains
  indexes from the original (unsorted) dataset and it can be used when you
  need to know what points were merged. However, it is not convenient when
  you want to build a dendrograd (see below).

* if  you  want  to  build  dendrogram, you  can use Z, but it is not good
  option, because Z contains  indexes from  unsorted  dataset.  Dendrogram
  built from such dataset is likely to have intersections. So, you have to
  reorder you points before building dendrogram.
  Permutation which reorders point is returned in P. Another representation
  of  merges,  which  is  more  convenient for dendorgram construction, is
  returned in PM.

* more information on format of Z, P and PM can be found below and in the
  examples from ALGLIB Reference Manual.

FORMAL DESCRIPTION OF FIELDS:
    NPoints         number of points
    Z               array[NPoints-1,2],  contains   indexes   of  clusters
                    linked in pairs to  form  clustering  tree.  I-th  row
                    corresponds to I-th merge:
                    * Z[I,0] - index of the first cluster to merge
                    * Z[I,1] - index of the second cluster to merge
                    * Z[I,0]<Z[I,1]
                    * clusters are  numbered  from 0 to 2*NPoints-2,  with
                      indexes from 0 to NPoints-1 corresponding to  points
                      of the original dataset, and indexes from NPoints to
                      2*NPoints-2  correspond  to  clusters  generated  by
                      subsequent  merges  (I-th  row  of Z creates cluster
                      with index NPoints+I).

                    IMPORTANT: indexes in Z[] are indexes in the ORIGINAL,
                    unsorted dataset. In addition to  Z algorithm  outputs
                    permutation which rearranges points in such  way  that
                    subsequent merges are  performed  on  adjacent  points
                    (such order is needed if you want to build dendrogram).
                    However,  indexes  in  Z  are  related  to   original,
                    unrearranged sequence of points.

    P               array[NPoints], permutation which reorders points  for
                    dendrogram  construction.  P[i] contains  index of the
                    position  where  we  should  move  I-th  point  of the
                    original dataset in order to apply merges PZ/PM.

    PZ              same as Z, but for permutation of points given  by  P.
                    The  only  thing  which  changed  are  indexes  of the
                    original points; indexes of clusters remained same.

    MergeDist       array[NPoints-1], contains distances between  clusters
                    being merged (MergeDist[i] correspond to merge  stored
                    in Z[i,...]):
                    * CLINK, SLINK and  average  linkage algorithms report
                      "raw", unmodified distance metric.
                    * Ward's   method   reports   weighted   intra-cluster
                      variance, which is equal to ||Ca-Cb||^2 * Sa*Sb/(Sa+Sb).
                      Here  A  and  B  are  clusters being merged, Ca is a
                      center of A, Cb is a center of B, Sa is a size of A,
                      Sb is a size of B.

    PM              array[NPoints-1,6], another representation of  merges,
                    which is suited for dendrogram construction. It  deals
                    with rearranged points (permutation P is applied)  and
                    represents merges in a form which different  from  one
                    used by Z.
                    For each I from 0 to NPoints-2, I-th row of PM represents
                    merge performed on two clusters C0 and C1. Here:
                    * C0 contains points with indexes PM[I,0]...PM[I,1]
                    * C1 contains points with indexes PM[I,2]...PM[I,3]
                    * indexes stored in PM are given for dataset sorted
                      according to permutation P
                    * PM[I,1]=PM[I,2]-1 (only adjacent clusters are merged)
                    * PM[I,0]<=PM[I,1], PM[I,2]<=PM[I,3], i.e. both
                      clusters contain at least one point
                    * heights of "subdendrograms" corresponding  to  C0/C1
                      are stored in PM[I,4]  and  PM[I,5].  Subdendrograms
                      corresponding   to   single-point   clusters    have
                      height=0. Dendrogram of the merge result has  height
                      H=max(H0,H1)+1.

NOTE: there is one-to-one correspondence between merges described by Z and
      PM. I-th row of Z describes same merge of clusters as I-th row of PM,
      with "left" cluster from Z corresponding to the "left" one from PM.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
class _ahcreport_owner
{
public:
    _ahcreport_owner();
    _ahcreport_owner(alglib_impl::ahcreport *attach_to);
    _ahcreport_owner(const _ahcreport_owner &rhs);
    _ahcreport_owner& operator=(const _ahcreport_owner &rhs);
    virtual ~_ahcreport_owner();
    alglib_impl::ahcreport* c_ptr();
    const alglib_impl::ahcreport* c_ptr() const;
protected:
    alglib_impl::ahcreport *p_struct;
    bool is_attached;
};
class ahcreport : public _ahcreport_owner
{
public:
    ahcreport();
    ahcreport(alglib_impl::ahcreport *attach_to);
    ahcreport(const ahcreport &rhs);
    ahcreport& operator=(const ahcreport &rhs);
    virtual ~ahcreport();
    ae_int_t &terminationtype;
    ae_int_t &npoints;
    integer_1d_array p;
    integer_2d_array z;
    integer_2d_array pz;
    integer_2d_array pm;
    real_1d_array mergedist;


};


/*************************************************************************
This  structure   is  used  to  store  results of the  k-means  clustering
algorithm.

Following information is always returned:
* NPoints contains number of points in the original dataset
* TerminationType contains completion code, negative on failure, positive
  on success
* K contains number of clusters

For positive TerminationType we return:
* NFeatures contains number of variables in the original dataset
* C, which contains centers found by algorithm
* CIdx, which maps points of the original dataset to clusters

FORMAL DESCRIPTION OF FIELDS:
    NPoints         number of points, >=0
    NFeatures       number of variables, >=1
    TerminationType completion code:
                    * -5 if  distance  type  is  anything  different  from
                         Euclidean metric
                    * -3 for degenerate dataset: a) less  than  K  distinct
                         points, b) K=0 for non-empty dataset.
                    * +1 for successful completion
    K               number of clusters
    C               array[K,NFeatures], rows of the array store centers
    CIdx            array[NPoints], which contains cluster indexes
    IterationsCount actual number of iterations performed by clusterizer.
                    If algorithm performed more than one random restart,
                    total number of iterations is returned.
    Energy          merit function, "energy", sum  of  squared  deviations
                    from cluster centers

  -- ALGLIB --
     Copyright 27.11.2012 by Bochkanov Sergey
*************************************************************************/
class _kmeansreport_owner
{
public:
    _kmeansreport_owner();
    _kmeansreport_owner(alglib_impl::kmeansreport *attach_to);
    _kmeansreport_owner(const _kmeansreport_owner &rhs);
    _kmeansreport_owner& operator=(const _kmeansreport_owner &rhs);
    virtual ~_kmeansreport_owner();
    alglib_impl::kmeansreport* c_ptr();
    const alglib_impl::kmeansreport* c_ptr() const;
protected:
    alglib_impl::kmeansreport *p_struct;
    bool is_attached;
};
class kmeansreport : public _kmeansreport_owner
{
public:
    kmeansreport();
    kmeansreport(alglib_impl::kmeansreport *attach_to);
    kmeansreport(const kmeansreport &rhs);
    kmeansreport& operator=(const kmeansreport &rhs);
    virtual ~kmeansreport();
    ae_int_t &npoints;
    ae_int_t &nfeatures;
    ae_int_t &terminationtype;
    ae_int_t &iterationscount;
    double &energy;
    ae_int_t &k;
    real_2d_array c;
    integer_1d_array cidx;


};
#endif

#if defined(AE_COMPILE_DFOREST) || !defined(AE_PARTIAL_BUILD)
class _decisionforestbuilder_owner;
class decisionforestbuilder;
class _decisionforestbuffer_owner;
class decisionforestbuffer;
class _decisionforest_owner;
class decisionforest;
class _dfreport_owner;
class dfreport;


/*************************************************************************
A random forest (decision forest) builder object.

Used to store dataset and specify decision forest training algorithm settings.
*************************************************************************/
class _decisionforestbuilder_owner
{
public:
    _decisionforestbuilder_owner();
    _decisionforestbuilder_owner(alglib_impl::decisionforestbuilder *attach_to);
    _decisionforestbuilder_owner(const _decisionforestbuilder_owner &rhs);
    _decisionforestbuilder_owner& operator=(const _decisionforestbuilder_owner &rhs);
    virtual ~_decisionforestbuilder_owner();
    alglib_impl::decisionforestbuilder* c_ptr();
    const alglib_impl::decisionforestbuilder* c_ptr() const;
protected:
    alglib_impl::decisionforestbuilder *p_struct;
    bool is_attached;
};
class decisionforestbuilder : public _decisionforestbuilder_owner
{
public:
    decisionforestbuilder();
    decisionforestbuilder(alglib_impl::decisionforestbuilder *attach_to);
    decisionforestbuilder(const decisionforestbuilder &rhs);
    decisionforestbuilder& operator=(const decisionforestbuilder &rhs);
    virtual ~decisionforestbuilder();


};


/*************************************************************************
Buffer object which is used to perform  various  requests  (usually  model
inference) in the multithreaded mode (multiple threads working  with  same
DF object).

This object should be created with DFCreateBuffer().
*************************************************************************/
class _decisionforestbuffer_owner
{
public:
    _decisionforestbuffer_owner();
    _decisionforestbuffer_owner(alglib_impl::decisionforestbuffer *attach_to);
    _decisionforestbuffer_owner(const _decisionforestbuffer_owner &rhs);
    _decisionforestbuffer_owner& operator=(const _decisionforestbuffer_owner &rhs);
    virtual ~_decisionforestbuffer_owner();
    alglib_impl::decisionforestbuffer* c_ptr();
    const alglib_impl::decisionforestbuffer* c_ptr() const;
protected:
    alglib_impl::decisionforestbuffer *p_struct;
    bool is_attached;
};
class decisionforestbuffer : public _decisionforestbuffer_owner
{
public:
    decisionforestbuffer();
    decisionforestbuffer(alglib_impl::decisionforestbuffer *attach_to);
    decisionforestbuffer(const decisionforestbuffer &rhs);
    decisionforestbuffer& operator=(const decisionforestbuffer &rhs);
    virtual ~decisionforestbuffer();


};


/*************************************************************************
Decision forest (random forest) model.
*************************************************************************/
class _decisionforest_owner
{
public:
    _decisionforest_owner();
    _decisionforest_owner(alglib_impl::decisionforest *attach_to);
    _decisionforest_owner(const _decisionforest_owner &rhs);
    _decisionforest_owner& operator=(const _decisionforest_owner &rhs);
    virtual ~_decisionforest_owner();
    alglib_impl::decisionforest* c_ptr();
    const alglib_impl::decisionforest* c_ptr() const;
protected:
    alglib_impl::decisionforest *p_struct;
    bool is_attached;
};
class decisionforest : public _decisionforest_owner
{
public:
    decisionforest();
    decisionforest(alglib_impl::decisionforest *attach_to);
    decisionforest(const decisionforest &rhs);
    decisionforest& operator=(const decisionforest &rhs);
    virtual ~decisionforest();


};


/*************************************************************************
Decision forest training report.

=== training/oob errors ==================================================

Following fields store training set errors:
* relclserror           -   fraction of misclassified cases, [0,1]
* avgce                 -   average cross-entropy in bits per symbol
* rmserror              -   root-mean-square error
* avgerror              -   average error
* avgrelerror           -   average relative error

Out-of-bag estimates are stored in fields with same names, but "oob" prefix.

For classification problems:
* RMS, AVG and AVGREL errors are calculated for posterior probabilities

For regression problems:
* RELCLS and AVGCE errors are zero

=== variable importance ==================================================

Following fields are used to store variable importance information:

* topvars               -   variables ordered from the most  important  to
                            less  important  ones  (according  to  current
                            choice of importance raiting).
                            For example, topvars[0] contains index of  the
                            most important variable, and topvars[0:2]  are
                            indexes of 3 most important ones and so on.

* varimportances        -   array[nvars], ratings (the  larger,  the  more
                            important the variable  is,  always  in  [0,1]
                            range).
                            By default, filled  by  zeros  (no  importance
                            ratings are  provided  unless  you  explicitly
                            request them).
                            Zero rating means that variable is not important,
                            however you will rarely encounter such a thing,
                            in many cases  unimportant  variables  produce
                            nearly-zero (but nonzero) ratings.

Variable importance report must be EXPLICITLY requested by calling:
* dfbuildersetimportancegini() function, if you need out-of-bag Gini-based
  importance rating also known as MDI  (fast to  calculate,  resistant  to
  overfitting  issues,   but   has   some   bias  towards  continuous  and
  high-cardinality categorical variables)
* dfbuildersetimportancetrngini() function, if you need training set Gini-
  -based importance rating (what other packages typically report).
* dfbuildersetimportancepermutation() function, if you  need  permutation-
  based importance rating also known as MDA (slower to calculate, but less
  biased)
* dfbuildersetimportancenone() function,  if  you  do  not  need  importance
  ratings - ratings will be zero, topvars[] will be [0,1,2,...]

Different importance ratings (Gini or permutation) produce  non-comparable
values. Although in all cases rating values lie in [0,1] range, there  are
exist differences:
* informally speaking, Gini importance rating tends to divide "unit amount
  of importance"  between  several  important  variables, i.e. it produces
  estimates which roughly sum to 1.0 (or less than 1.0, if your  task  can
  not be solved exactly). If all variables  are  equally  important,  they
  will have same rating,  roughly  1/NVars,  even  if  every  variable  is
  critically important.
* from the other side, permutation importance tells us what percentage  of
  the model predictive power will be ruined  by  permuting  this  specific
  variable. It does not produce estimates which  sum  to  one.  Critically
  important variable will have rating close  to  1.0,  and  you  may  have
  multiple variables with such a rating.

More information on variable importance ratings can be found  in  comments
on the dfbuildersetimportancegini() and dfbuildersetimportancepermutation()
functions.
*************************************************************************/
class _dfreport_owner
{
public:
    _dfreport_owner();
    _dfreport_owner(alglib_impl::dfreport *attach_to);
    _dfreport_owner(const _dfreport_owner &rhs);
    _dfreport_owner& operator=(const _dfreport_owner &rhs);
    virtual ~_dfreport_owner();
    alglib_impl::dfreport* c_ptr();
    const alglib_impl::dfreport* c_ptr() const;
protected:
    alglib_impl::dfreport *p_struct;
    bool is_attached;
};
class dfreport : public _dfreport_owner
{
public:
    dfreport();
    dfreport(alglib_impl::dfreport *attach_to);
    dfreport(const dfreport &rhs);
    dfreport& operator=(const dfreport &rhs);
    virtual ~dfreport();
    double &relclserror;
    double &avgce;
    double &rmserror;
    double &avgerror;
    double &avgrelerror;
    double &oobrelclserror;
    double &oobavgce;
    double &oobrmserror;
    double &oobavgerror;
    double &oobavgrelerror;
    integer_1d_array topvars;
    real_1d_array varimportances;


};
#endif

#if defined(AE_COMPILE_LINREG) || !defined(AE_PARTIAL_BUILD)
class _linearmodel_owner;
class linearmodel;
class _lrreport_owner;
class lrreport;


/*************************************************************************

*************************************************************************/
class _linearmodel_owner
{
public:
    _linearmodel_owner();
    _linearmodel_owner(alglib_impl::linearmodel *attach_to);
    _linearmodel_owner(const _linearmodel_owner &rhs);
    _linearmodel_owner& operator=(const _linearmodel_owner &rhs);
    virtual ~_linearmodel_owner();
    alglib_impl::linearmodel* c_ptr();
    const alglib_impl::linearmodel* c_ptr() const;
protected:
    alglib_impl::linearmodel *p_struct;
    bool is_attached;
};
class linearmodel : public _linearmodel_owner
{
public:
    linearmodel();
    linearmodel(alglib_impl::linearmodel *attach_to);
    linearmodel(const linearmodel &rhs);
    linearmodel& operator=(const linearmodel &rhs);
    virtual ~linearmodel();


};


/*************************************************************************
LRReport structure contains additional information about linear model:
* C             -   covariation matrix,  array[0..NVars,0..NVars].
                    C[i,j] = Cov(A[i],A[j])
* RMSError      -   root mean square error on a training set
* AvgError      -   average error on a training set
* AvgRelError   -   average relative error on a training set (excluding
                    observations with zero function value).
* CVRMSError    -   leave-one-out cross-validation estimate of
                    generalization error. Calculated using fast algorithm
                    with O(NVars*NPoints) complexity.
* CVAvgError    -   cross-validation estimate of average error
* CVAvgRelError -   cross-validation estimate of average relative error

All other fields of the structure are intended for internal use and should
not be used outside ALGLIB.
*************************************************************************/
class _lrreport_owner
{
public:
    _lrreport_owner();
    _lrreport_owner(alglib_impl::lrreport *attach_to);
    _lrreport_owner(const _lrreport_owner &rhs);
    _lrreport_owner& operator=(const _lrreport_owner &rhs);
    virtual ~_lrreport_owner();
    alglib_impl::lrreport* c_ptr();
    const alglib_impl::lrreport* c_ptr() const;
protected:
    alglib_impl::lrreport *p_struct;
    bool is_attached;
};
class lrreport : public _lrreport_owner
{
public:
    lrreport();
    lrreport(alglib_impl::lrreport *attach_to);
    lrreport(const lrreport &rhs);
    lrreport& operator=(const lrreport &rhs);
    virtual ~lrreport();
    real_2d_array c;
    double &rmserror;
    double &avgerror;
    double &avgrelerror;
    double &cvrmserror;
    double &cvavgerror;
    double &cvavgrelerror;
    ae_int_t &ncvdefects;
    integer_1d_array cvdefects;


};
#endif

#if defined(AE_COMPILE_FILTERS) || !defined(AE_PARTIAL_BUILD)

#endif

#if defined(AE_COMPILE_SSA) || !defined(AE_PARTIAL_BUILD)
class _ssamodel_owner;
class ssamodel;


/*************************************************************************
This object stores state of the SSA model.

You should use ALGLIB functions to work with this object.
*************************************************************************/
class _ssamodel_owner
{
public:
    _ssamodel_owner();
    _ssamodel_owner(alglib_impl::ssamodel *attach_to);
    _ssamodel_owner(const _ssamodel_owner &rhs);
    _ssamodel_owner& operator=(const _ssamodel_owner &rhs);
    virtual ~_ssamodel_owner();
    alglib_impl::ssamodel* c_ptr();
    const alglib_impl::ssamodel* c_ptr() const;
protected:
    alglib_impl::ssamodel *p_struct;
    bool is_attached;
};
class ssamodel : public _ssamodel_owner
{
public:
    ssamodel();
    ssamodel(alglib_impl::ssamodel *attach_to);
    ssamodel(const ssamodel &rhs);
    ssamodel& operator=(const ssamodel &rhs);
    virtual ~ssamodel();


};
#endif

#if defined(AE_COMPILE_LDA) || !defined(AE_PARTIAL_BUILD)

#endif

#if defined(AE_COMPILE_MCPD) || !defined(AE_PARTIAL_BUILD)
class _mcpdstate_owner;
class mcpdstate;
class _mcpdreport_owner;
class mcpdreport;


/*************************************************************************
This structure is a MCPD (Markov Chains for Population Data) solver.

You should use ALGLIB functions in order to work with this object.

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
class _mcpdstate_owner
{
public:
    _mcpdstate_owner();
    _mcpdstate_owner(alglib_impl::mcpdstate *attach_to);
    _mcpdstate_owner(const _mcpdstate_owner &rhs);
    _mcpdstate_owner& operator=(const _mcpdstate_owner &rhs);
    virtual ~_mcpdstate_owner();
    alglib_impl::mcpdstate* c_ptr();
    const alglib_impl::mcpdstate* c_ptr() const;
protected:
    alglib_impl::mcpdstate *p_struct;
    bool is_attached;
};
class mcpdstate : public _mcpdstate_owner
{
public:
    mcpdstate();
    mcpdstate(alglib_impl::mcpdstate *attach_to);
    mcpdstate(const mcpdstate &rhs);
    mcpdstate& operator=(const mcpdstate &rhs);
    virtual ~mcpdstate();


};


/*************************************************************************
This structure is a MCPD training report:
    InnerIterationsCount    -   number of inner iterations of the
                                underlying optimization algorithm
    OuterIterationsCount    -   number of outer iterations of the
                                underlying optimization algorithm
    NFEV                    -   number of merit function evaluations
    TerminationType         -   termination type
                                (same as for MinBLEIC optimizer, positive
                                values denote success, negative ones -
                                failure)

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
class _mcpdreport_owner
{
public:
    _mcpdreport_owner();
    _mcpdreport_owner(alglib_impl::mcpdreport *attach_to);
    _mcpdreport_owner(const _mcpdreport_owner &rhs);
    _mcpdreport_owner& operator=(const _mcpdreport_owner &rhs);
    virtual ~_mcpdreport_owner();
    alglib_impl::mcpdreport* c_ptr();
    const alglib_impl::mcpdreport* c_ptr() const;
protected:
    alglib_impl::mcpdreport *p_struct;
    bool is_attached;
};
class mcpdreport : public _mcpdreport_owner
{
public:
    mcpdreport();
    mcpdreport(alglib_impl::mcpdreport *attach_to);
    mcpdreport(const mcpdreport &rhs);
    mcpdreport& operator=(const mcpdreport &rhs);
    virtual ~mcpdreport();
    ae_int_t &inneriterationscount;
    ae_int_t &outeriterationscount;
    ae_int_t &nfev;
    ae_int_t &terminationtype;


};
#endif

#if defined(AE_COMPILE_LOGIT) || !defined(AE_PARTIAL_BUILD)
class _logitmodel_owner;
class logitmodel;
class _mnlreport_owner;
class mnlreport;


/*************************************************************************

*************************************************************************/
class _logitmodel_owner
{
public:
    _logitmodel_owner();
    _logitmodel_owner(alglib_impl::logitmodel *attach_to);
    _logitmodel_owner(const _logitmodel_owner &rhs);
    _logitmodel_owner& operator=(const _logitmodel_owner &rhs);
    virtual ~_logitmodel_owner();
    alglib_impl::logitmodel* c_ptr();
    const alglib_impl::logitmodel* c_ptr() const;
protected:
    alglib_impl::logitmodel *p_struct;
    bool is_attached;
};
class logitmodel : public _logitmodel_owner
{
public:
    logitmodel();
    logitmodel(alglib_impl::logitmodel *attach_to);
    logitmodel(const logitmodel &rhs);
    logitmodel& operator=(const logitmodel &rhs);
    virtual ~logitmodel();


};


/*************************************************************************
MNLReport structure contains information about training process:
* NGrad     -   number of gradient calculations
* NHess     -   number of Hessian calculations
*************************************************************************/
class _mnlreport_owner
{
public:
    _mnlreport_owner();
    _mnlreport_owner(alglib_impl::mnlreport *attach_to);
    _mnlreport_owner(const _mnlreport_owner &rhs);
    _mnlreport_owner& operator=(const _mnlreport_owner &rhs);
    virtual ~_mnlreport_owner();
    alglib_impl::mnlreport* c_ptr();
    const alglib_impl::mnlreport* c_ptr() const;
protected:
    alglib_impl::mnlreport *p_struct;
    bool is_attached;
};
class mnlreport : public _mnlreport_owner
{
public:
    mnlreport();
    mnlreport(alglib_impl::mnlreport *attach_to);
    mnlreport(const mnlreport &rhs);
    mnlreport& operator=(const mnlreport &rhs);
    virtual ~mnlreport();
    ae_int_t &ngrad;
    ae_int_t &nhess;


};
#endif

#if defined(AE_COMPILE_KNN) || !defined(AE_PARTIAL_BUILD)
class _knnbuffer_owner;
class knnbuffer;
class _knnbuilder_owner;
class knnbuilder;
class _knnmodel_owner;
class knnmodel;
class _knnreport_owner;
class knnreport;


/*************************************************************************
Buffer object which is used to perform  various  requests  (usually  model
inference) in the multithreaded mode (multiple threads working  with  same
KNN object).

This object should be created with KNNCreateBuffer().
*************************************************************************/
class _knnbuffer_owner
{
public:
    _knnbuffer_owner();
    _knnbuffer_owner(alglib_impl::knnbuffer *attach_to);
    _knnbuffer_owner(const _knnbuffer_owner &rhs);
    _knnbuffer_owner& operator=(const _knnbuffer_owner &rhs);
    virtual ~_knnbuffer_owner();
    alglib_impl::knnbuffer* c_ptr();
    const alglib_impl::knnbuffer* c_ptr() const;
protected:
    alglib_impl::knnbuffer *p_struct;
    bool is_attached;
};
class knnbuffer : public _knnbuffer_owner
{
public:
    knnbuffer();
    knnbuffer(alglib_impl::knnbuffer *attach_to);
    knnbuffer(const knnbuffer &rhs);
    knnbuffer& operator=(const knnbuffer &rhs);
    virtual ~knnbuffer();


};


/*************************************************************************
A KNN builder object; this object encapsulates  dataset  and  all  related
settings, it is used to create an actual instance of KNN model.
*************************************************************************/
class _knnbuilder_owner
{
public:
    _knnbuilder_owner();
    _knnbuilder_owner(alglib_impl::knnbuilder *attach_to);
    _knnbuilder_owner(const _knnbuilder_owner &rhs);
    _knnbuilder_owner& operator=(const _knnbuilder_owner &rhs);
    virtual ~_knnbuilder_owner();
    alglib_impl::knnbuilder* c_ptr();
    const alglib_impl::knnbuilder* c_ptr() const;
protected:
    alglib_impl::knnbuilder *p_struct;
    bool is_attached;
};
class knnbuilder : public _knnbuilder_owner
{
public:
    knnbuilder();
    knnbuilder(alglib_impl::knnbuilder *attach_to);
    knnbuilder(const knnbuilder &rhs);
    knnbuilder& operator=(const knnbuilder &rhs);
    virtual ~knnbuilder();


};


/*************************************************************************
KNN model, can be used for classification or regression
*************************************************************************/
class _knnmodel_owner
{
public:
    _knnmodel_owner();
    _knnmodel_owner(alglib_impl::knnmodel *attach_to);
    _knnmodel_owner(const _knnmodel_owner &rhs);
    _knnmodel_owner& operator=(const _knnmodel_owner &rhs);
    virtual ~_knnmodel_owner();
    alglib_impl::knnmodel* c_ptr();
    const alglib_impl::knnmodel* c_ptr() const;
protected:
    alglib_impl::knnmodel *p_struct;
    bool is_attached;
};
class knnmodel : public _knnmodel_owner
{
public:
    knnmodel();
    knnmodel(alglib_impl::knnmodel *attach_to);
    knnmodel(const knnmodel &rhs);
    knnmodel& operator=(const knnmodel &rhs);
    virtual ~knnmodel();


};


/*************************************************************************
KNN training report.

Following fields store training set errors:
* relclserror       -   fraction of misclassified cases, [0,1]
* avgce             -   average cross-entropy in bits per symbol
* rmserror          -   root-mean-square error
* avgerror          -   average error
* avgrelerror       -   average relative error

For classification problems:
* RMS, AVG and AVGREL errors are calculated for posterior probabilities

For regression problems:
* RELCLS and AVGCE errors are zero
*************************************************************************/
class _knnreport_owner
{
public:
    _knnreport_owner();
    _knnreport_owner(alglib_impl::knnreport *attach_to);
    _knnreport_owner(const _knnreport_owner &rhs);
    _knnreport_owner& operator=(const _knnreport_owner &rhs);
    virtual ~_knnreport_owner();
    alglib_impl::knnreport* c_ptr();
    const alglib_impl::knnreport* c_ptr() const;
protected:
    alglib_impl::knnreport *p_struct;
    bool is_attached;
};
class knnreport : public _knnreport_owner
{
public:
    knnreport();
    knnreport(alglib_impl::knnreport *attach_to);
    knnreport(const knnreport &rhs);
    knnreport& operator=(const knnreport &rhs);
    virtual ~knnreport();
    double &relclserror;
    double &avgce;
    double &rmserror;
    double &avgerror;
    double &avgrelerror;


};
#endif

#if defined(AE_COMPILE_MLPTRAIN) || !defined(AE_PARTIAL_BUILD)
class _mlpreport_owner;
class mlpreport;
class _mlpcvreport_owner;
class mlpcvreport;
class _mlptrainer_owner;
class mlptrainer;


/*************************************************************************
Training report:
    * RelCLSError   -   fraction of misclassified cases.
    * AvgCE         -   acerage cross-entropy
    * RMSError      -   root-mean-square error
    * AvgError      -   average error
    * AvgRelError   -   average relative error
    * NGrad         -   number of gradient calculations
    * NHess         -   number of Hessian calculations
    * NCholesky     -   number of Cholesky decompositions

NOTE 1: RelCLSError/AvgCE are zero on regression problems.

NOTE 2: on classification problems  RMSError/AvgError/AvgRelError  contain
        errors in prediction of posterior probabilities
*************************************************************************/
class _mlpreport_owner
{
public:
    _mlpreport_owner();
    _mlpreport_owner(alglib_impl::mlpreport *attach_to);
    _mlpreport_owner(const _mlpreport_owner &rhs);
    _mlpreport_owner& operator=(const _mlpreport_owner &rhs);
    virtual ~_mlpreport_owner();
    alglib_impl::mlpreport* c_ptr();
    const alglib_impl::mlpreport* c_ptr() const;
protected:
    alglib_impl::mlpreport *p_struct;
    bool is_attached;
};
class mlpreport : public _mlpreport_owner
{
public:
    mlpreport();
    mlpreport(alglib_impl::mlpreport *attach_to);
    mlpreport(const mlpreport &rhs);
    mlpreport& operator=(const mlpreport &rhs);
    virtual ~mlpreport();
    double &relclserror;
    double &avgce;
    double &rmserror;
    double &avgerror;
    double &avgrelerror;
    ae_int_t &ngrad;
    ae_int_t &nhess;
    ae_int_t &ncholesky;


};


/*************************************************************************
Cross-validation estimates of generalization error
*************************************************************************/
class _mlpcvreport_owner
{
public:
    _mlpcvreport_owner();
    _mlpcvreport_owner(alglib_impl::mlpcvreport *attach_to);
    _mlpcvreport_owner(const _mlpcvreport_owner &rhs);
    _mlpcvreport_owner& operator=(const _mlpcvreport_owner &rhs);
    virtual ~_mlpcvreport_owner();
    alglib_impl::mlpcvreport* c_ptr();
    const alglib_impl::mlpcvreport* c_ptr() const;
protected:
    alglib_impl::mlpcvreport *p_struct;
    bool is_attached;
};
class mlpcvreport : public _mlpcvreport_owner
{
public:
    mlpcvreport();
    mlpcvreport(alglib_impl::mlpcvreport *attach_to);
    mlpcvreport(const mlpcvreport &rhs);
    mlpcvreport& operator=(const mlpcvreport &rhs);
    virtual ~mlpcvreport();
    double &relclserror;
    double &avgce;
    double &rmserror;
    double &avgerror;
    double &avgrelerror;


};


/*************************************************************************
Trainer object for neural network.

You should not try to access fields of this object directly -  use  ALGLIB
functions to work with this object.
*************************************************************************/
class _mlptrainer_owner
{
public:
    _mlptrainer_owner();
    _mlptrainer_owner(alglib_impl::mlptrainer *attach_to);
    _mlptrainer_owner(const _mlptrainer_owner &rhs);
    _mlptrainer_owner& operator=(const _mlptrainer_owner &rhs);
    virtual ~_mlptrainer_owner();
    alglib_impl::mlptrainer* c_ptr();
    const alglib_impl::mlptrainer* c_ptr() const;
protected:
    alglib_impl::mlptrainer *p_struct;
    bool is_attached;
};
class mlptrainer : public _mlptrainer_owner
{
public:
    mlptrainer();
    mlptrainer(alglib_impl::mlptrainer *attach_to);
    mlptrainer(const mlptrainer &rhs);
    mlptrainer& operator=(const mlptrainer &rhs);
    virtual ~mlptrainer();


};
#endif

#if defined(AE_COMPILE_DATACOMP) || !defined(AE_PARTIAL_BUILD)

#endif

#if defined(AE_COMPILE_PCA) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Principal components analysis

This function builds orthogonal basis  where  first  axis  corresponds  to
direction with maximum variance, second axis  maximizes  variance  in  the
subspace orthogonal to first axis and so on.

This function builds FULL basis, i.e. returns N vectors  corresponding  to
ALL directions, no matter how informative. If you need  just a  few  (say,
10 or 50) of the most important directions, you may find it faster to  use
one of the reduced versions:
* pcatruncatedsubspace() - for subspace iteration based method

It should be noted that, unlike LDA, PCA does not use class labels.

INPUT PARAMETERS:
    X           -   dataset, array[NPoints,NVars].
                    matrix contains ONLY INDEPENDENT VARIABLES.
    NPoints     -   dataset size, NPoints>=0
    NVars       -   number of independent variables, NVars>=1

OUTPUT PARAMETERS:
    S2          -   array[NVars]. variance values corresponding
                    to basis vectors.
    V           -   array[NVars,NVars]
                    matrix, whose columns store basis vectors.

  ! FREE EDITION OF ALGLIB:
  !
  ! Free Edition of ALGLIB supports following important features for  this
  ! function:
  ! * C++ version: x64 SIMD support using C++ intrinsics
  ! * C#  version: x64 SIMD support using NET5/NetCore hardware intrinsics
  !
  ! We  recommend  you  to  read  'Compiling ALGLIB' section of the ALGLIB
  ! Reference Manual in order  to  find  out  how to activate SIMD support
  ! in ALGLIB.

  ! 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, ARM) implementations of linear algebra and
  !   other primitives (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.

  -- ALGLIB --
     Copyright 25.08.2008 by Bochkanov Sergey
*************************************************************************/
void pcabuildbasis(const real_2d_array &x, const ae_int_t npoints, const ae_int_t nvars, real_1d_array &s2, real_2d_array &v, const xparams _xparams = alglib::xdefault);
void pcabuildbasis(const real_2d_array &x, real_1d_array &s2, real_2d_array &v, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Principal components analysis

This function performs truncated PCA, i.e. returns just a few most important
directions.

Internally it uses iterative eigensolver which is very efficient when only
a minor fraction of full basis is required. Thus, if you need full  basis,
it is better to use pcabuildbasis() function.

It should be noted that, unlike LDA, PCA does not use class labels.

INPUT PARAMETERS:
    X           -   dataset, array[0..NPoints-1,0..NVars-1].
                    matrix contains ONLY INDEPENDENT VARIABLES.
    NPoints     -   dataset size, NPoints>=0
    NVars       -   number of independent variables, NVars>=1
    NNeeded     -   number of requested components, in [1,NVars] range;
                    this function is efficient only for NNeeded<<NVars.
    Eps         -   desired  precision  of  vectors  returned;  underlying
                    solver will stop iterations as soon as absolute  error
                    in corresponding singular values  reduces  to  roughly
                    eps*MAX(lambda[]), with lambda[] being array of  eigen
                    values.
                    Zero value means that  algorithm  performs  number  of
                    iterations  specified  by  maxits  parameter,  without
                    paying attention to precision.
    MaxIts      -   number of iterations performed by  subspace  iteration
                    method. Zero value means that no  limit  on  iteration
                    count is placed (eps-based stopping condition is used).


OUTPUT PARAMETERS:
    S2          -   array[NNeeded]. Variance values corresponding
                    to basis vectors.
    V           -   array[NVars,NNeeded]
                    matrix, whose columns store basis vectors.

NOTE: passing eps=0 and maxits=0 results in small eps  being  selected  as
stopping condition. Exact value of automatically selected eps is  version-
-dependent.

  ! FREE EDITION OF ALGLIB:
  !
  ! Free Edition of ALGLIB supports following important features for  this
  ! function:
  ! * C++ version: x64 SIMD support using C++ intrinsics
  ! * C#  version: x64 SIMD support using NET5/NetCore hardware intrinsics
  !
  ! We  recommend  you  to  read  'Compiling ALGLIB' section of the ALGLIB
  ! Reference Manual in order  to  find  out  how to activate SIMD support
  ! in ALGLIB.

  ! 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, ARM) implementations of linear algebra and
  !   other primitives (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.

  -- ALGLIB --
     Copyright 10.01.2017 by Bochkanov Sergey
*************************************************************************/
void pcatruncatedsubspace(const real_2d_array &x, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nneeded, const double eps, const ae_int_t maxits, real_1d_array &s2, real_2d_array &v, const xparams _xparams = alglib::xdefault);
void pcatruncatedsubspace(const real_2d_array &x, const ae_int_t nneeded, const double eps, const ae_int_t maxits, real_1d_array &s2, real_2d_array &v, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Sparse truncated principal components analysis

This function performs sparse truncated PCA, i.e. returns just a few  most
important principal components for a sparse input X.

Internally it uses iterative eigensolver which is very efficient when only
a minor fraction of full basis is required.

It should be noted that, unlike LDA, PCA does not use class labels.

INPUT PARAMETERS:
    X           -   sparse dataset, sparse  npoints*nvars  matrix.  It  is
                    recommended to use CRS sparse storage format;  non-CRS
                    input will be internally converted to CRS.
                    Matrix contains ONLY INDEPENDENT VARIABLES,  and  must
                    be EXACTLY npoints*nvars.
    NPoints     -   dataset size, NPoints>=0
    NVars       -   number of independent variables, NVars>=1
    NNeeded     -   number of requested components, in [1,NVars] range;
                    this function is efficient only for NNeeded<<NVars.
    Eps         -   desired  precision  of  vectors  returned;  underlying
                    solver will stop iterations as soon as absolute  error
                    in corresponding singular values  reduces  to  roughly
                    eps*MAX(lambda[]), with lambda[] being array of  eigen
                    values.
                    Zero value means that  algorithm  performs  number  of
                    iterations  specified  by  maxits  parameter,  without
                    paying attention to precision.
    MaxIts      -   number of iterations performed by  subspace  iteration
                    method. Zero value means that no  limit  on  iteration
                    count is placed (eps-based stopping condition is used).


OUTPUT PARAMETERS:
    S2          -   array[NNeeded]. Variance values corresponding
                    to basis vectors.
    V           -   array[NVars,NNeeded]
                    matrix, whose columns store basis vectors.

NOTE: passing eps=0 and maxits=0 results in small eps  being  selected  as
      a stopping condition. Exact value of automatically selected  eps  is
      version-dependent.

NOTE: zero  MaxIts  is  silently  replaced  by some reasonable value which
      prevents eternal loops (possible when inputs are degenerate and  too
      stringent stopping criteria are specified). In  current  version  it
      is 50+2*NVars.

  ! FREE EDITION OF ALGLIB:
  !
  ! Free Edition of ALGLIB supports following important features for  this
  ! function:
  ! * C++ version: x64 SIMD support using C++ intrinsics
  ! * C#  version: x64 SIMD support using NET5/NetCore hardware intrinsics
  !
  ! We  recommend  you  to  read  'Compiling ALGLIB' section of the ALGLIB
  ! Reference Manual in order  to  find  out  how to activate SIMD support
  ! in ALGLIB.

  ! 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, ARM) implementations of linear algebra and
  !   other primitives (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.

  -- ALGLIB --
     Copyright 10.01.2017 by Bochkanov Sergey
*************************************************************************/
void pcatruncatedsubspacesparse(const sparsematrix &x, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nneeded, const double eps, const ae_int_t maxits, real_1d_array &s2, real_2d_array &v, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_BDSS) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Optimal binary classification

Algorithms finds optimal (=with minimal cross-entropy) binary partition.
Internal subroutine.

INPUT PARAMETERS:
    A       -   array[0..N-1], variable
    C       -   array[0..N-1], class numbers (0 or 1).
    N       -   array size

OUTPUT PARAMETERS:
    Info    -   completetion code:
                * -3, all values of A[] are same (partition is impossible)
                * -2, one of C[] is incorrect (<0, >1)
                * -1, incorrect pararemets were passed (N<=0).
                *  1, OK
    Threshold-  partiton boundary. Left part contains values which are
                strictly less than Threshold. Right part contains values
                which are greater than or equal to Threshold.
    PAL, PBL-   probabilities P(0|v<Threshold) and P(1|v<Threshold)
    PAR, PBR-   probabilities P(0|v>=Threshold) and P(1|v>=Threshold)
    CVE     -   cross-validation estimate of cross-entropy

  -- ALGLIB --
     Copyright 22.05.2008 by Bochkanov Sergey
*************************************************************************/
void dsoptimalsplit2(const real_1d_array &a, const integer_1d_array &c, const ae_int_t n, ae_int_t &info, double &threshold, double &pal, double &pbl, double &par, double &pbr, double &cve, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Optimal partition, internal subroutine. Fast version.

Accepts:
    A       array[0..N-1]       array of attributes     array[0..N-1]
    C       array[0..N-1]       array of class labels
    TiesBuf array[0..N]         temporaries (ties)
    CntBuf  array[0..2*NC-1]    temporaries (counts)
    Alpha                       centering factor (0<=alpha<=1, recommended value - 0.05)
    BufR    array[0..N-1]       temporaries
    BufI    array[0..N-1]       temporaries

Output:
    Info    error code (">0"=OK, "<0"=bad)
    RMS     training set RMS error
    CVRMS   leave-one-out RMS error

Note:
    content of all arrays is changed by subroutine;
    it doesn't allocate temporaries.

  -- ALGLIB --
     Copyright 11.12.2008 by Bochkanov Sergey
*************************************************************************/
void dsoptimalsplit2fast(real_1d_array &a, integer_1d_array &c, integer_1d_array &tiesbuf, integer_1d_array &cntbuf, real_1d_array &bufr, integer_1d_array &bufi, const ae_int_t n, const ae_int_t nc, const double alpha, ae_int_t &info, double &threshold, double &rms, double &cvrms, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_MLPBASE) || !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 a text or XML file. 
  But you should not insert separators into the middle of the "words"
  nor should you change the 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 it in C# one, 
  and vice versa.
*************************************************************************/
void mlpserialize(const multilayerperceptron &obj, std::string &s_out);


/*************************************************************************
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 mlpserialize(const multilayerperceptron &obj, std::ostream &s_out);


/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void mlpunserialize(const std::string &s_in, multilayerperceptron &obj);


/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void mlpunserialize(const std::istream &s_in, multilayerperceptron &obj);


/*************************************************************************
Creates  neural  network  with  NIn  inputs,  NOut outputs, without hidden
layers, with linear output layer. Network weights are  filled  with  small
random values.

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcreate0(const ae_int_t nin, const ae_int_t nout, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Same  as  MLPCreate0,  but  with  one  hidden  layer  (NHid  neurons) with
non-linear activation function. Output layer is linear.

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcreate1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Same as MLPCreate0, but with two hidden layers (NHid1 and  NHid2  neurons)
with non-linear activation function. Output layer is linear.
 $ALL

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcreate2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Creates  neural  network  with  NIn  inputs,  NOut outputs, without hidden
layers with non-linear output layer. Network weights are filled with small
random values.

Activation function of the output layer takes values:

    (B, +INF), if D>=0

or

    (-INF, B), if D<0.


  -- ALGLIB --
     Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreateb0(const ae_int_t nin, const ae_int_t nout, const double b, const double d, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Same as MLPCreateB0 but with non-linear hidden layer.

  -- ALGLIB --
     Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreateb1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double b, const double d, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Same as MLPCreateB0 but with two non-linear hidden layers.

  -- ALGLIB --
     Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreateb2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double b, const double d, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Creates  neural  network  with  NIn  inputs,  NOut outputs, without hidden
layers with non-linear output layer. Network weights are filled with small
random values. Activation function of the output layer takes values [A,B].

  -- ALGLIB --
     Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreater0(const ae_int_t nin, const ae_int_t nout, const double a, const double b, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Same as MLPCreateR0, but with non-linear hidden layer.

  -- ALGLIB --
     Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreater1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double a, const double b, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Same as MLPCreateR0, but with two non-linear hidden layers.

  -- ALGLIB --
     Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreater2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double a, const double b, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Creates classifier network with NIn  inputs  and  NOut  possible  classes.
Network contains no hidden layers and linear output  layer  with  SOFTMAX-
normalization  (so  outputs  sums  up  to  1.0  and  converge to posterior
probabilities).

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcreatec0(const ae_int_t nin, const ae_int_t nout, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Same as MLPCreateC0, but with one non-linear hidden layer.

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcreatec1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Same as MLPCreateC0, but with two non-linear hidden layers.

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcreatec2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Copying of neural network

INPUT PARAMETERS:
    Network1 -   original

OUTPUT PARAMETERS:
    Network2 -   copy

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcopy(const multilayerperceptron &network1, multilayerperceptron &network2, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function copies tunable  parameters (weights/means/sigmas)  from  one
network to another with same architecture. It  performs  some  rudimentary
checks that architectures are same, and throws exception if check fails.

It is intended for fast copying of states between two  network  which  are
known to have same geometry.

INPUT PARAMETERS:
    Network1 -   source, must be correctly initialized
    Network2 -   target, must have same architecture

OUTPUT PARAMETERS:
    Network2 -   network state is copied from source to target

  -- ALGLIB --
     Copyright 20.06.2013 by Bochkanov Sergey
*************************************************************************/
void mlpcopytunableparameters(const multilayerperceptron &network1, multilayerperceptron &network2, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Randomization of neural network weights

  -- ALGLIB --
     Copyright 06.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlprandomize(multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Randomization of neural network weights and standartisator

  -- ALGLIB --
     Copyright 10.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlprandomizefull(multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Internal subroutine.

  -- ALGLIB --
     Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpinitpreprocessor(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Returns information about initialized network: number of inputs, outputs,
weights.

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpproperties(const multilayerperceptron &network, ae_int_t &nin, ae_int_t &nout, ae_int_t &wcount, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Returns number of inputs.

  -- ALGLIB --
     Copyright 19.10.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpgetinputscount(const multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Returns number of outputs.

  -- ALGLIB --
     Copyright 19.10.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpgetoutputscount(const multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Returns number of weights.

  -- ALGLIB --
     Copyright 19.10.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpgetweightscount(const multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Tells whether network is SOFTMAX-normalized (i.e. classifier) or not.

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
bool mlpissoftmax(const multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns total number of layers (including input, hidden and
output layers).

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpgetlayerscount(const multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns size of K-th layer.

K=0 corresponds to input layer, K=CNT-1 corresponds to output layer.

Size of the output layer is always equal to the number of outputs, although
when we have softmax-normalized network, last neuron doesn't have any
connections - it is just zero.

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpgetlayersize(const multilayerperceptron &network, const ae_int_t k, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns offset/scaling coefficients for I-th input of the
network.

INPUT PARAMETERS:
    Network     -   network
    I           -   input index

OUTPUT PARAMETERS:
    Mean        -   mean term
    Sigma       -   sigma term, guaranteed to be nonzero.

I-th input is passed through linear transformation
    IN[i] = (IN[i]-Mean)/Sigma
before feeding to the network

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
void mlpgetinputscaling(const multilayerperceptron &network, const ae_int_t i, double &mean, double &sigma, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns offset/scaling coefficients for I-th output of the
network.

INPUT PARAMETERS:
    Network     -   network
    I           -   input index

OUTPUT PARAMETERS:
    Mean        -   mean term
    Sigma       -   sigma term, guaranteed to be nonzero.

I-th output is passed through linear transformation
    OUT[i] = OUT[i]*Sigma+Mean
before returning it to user. In case we have SOFTMAX-normalized network,
we return (Mean,Sigma)=(0.0,1.0).

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
void mlpgetoutputscaling(const multilayerperceptron &network, const ae_int_t i, double &mean, double &sigma, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns information about Ith neuron of Kth layer

INPUT PARAMETERS:
    Network     -   network
    K           -   layer index
    I           -   neuron index (within layer)

OUTPUT PARAMETERS:
    FKind       -   activation function type (used by MLPActivationFunction())
                    this value is zero for input or linear neurons
    Threshold   -   also called offset, bias
                    zero for input neurons

NOTE: this function throws exception if layer or neuron with  given  index
do not exists.

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
void mlpgetneuroninfo(multilayerperceptron &network, const ae_int_t k, const ae_int_t i, ae_int_t &fkind, double &threshold, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns information about connection from I0-th neuron of
K0-th layer to I1-th neuron of K1-th layer.

INPUT PARAMETERS:
    Network     -   network
    K0          -   layer index
    I0          -   neuron index (within layer)
    K1          -   layer index
    I1          -   neuron index (within layer)

RESULT:
    connection weight (zero for non-existent connections)

This function:
1. throws exception if layer or neuron with given index do not exists.
2. returns zero if neurons exist, but there is no connection between them

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
double mlpgetweight(multilayerperceptron &network, const ae_int_t k0, const ae_int_t i0, const ae_int_t k1, const ae_int_t i1, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets offset/scaling coefficients for I-th input of the
network.

INPUT PARAMETERS:
    Network     -   network
    I           -   input index
    Mean        -   mean term
    Sigma       -   sigma term (if zero, will be replaced by 1.0)

NTE: I-th input is passed through linear transformation
    IN[i] = (IN[i]-Mean)/Sigma
before feeding to the network. This function sets Mean and Sigma.

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
void mlpsetinputscaling(multilayerperceptron &network, const ae_int_t i, const double mean, const double sigma, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets offset/scaling coefficients for I-th output of the
network.

INPUT PARAMETERS:
    Network     -   network
    I           -   input index
    Mean        -   mean term
    Sigma       -   sigma term (if zero, will be replaced by 1.0)

OUTPUT PARAMETERS:

NOTE: I-th output is passed through linear transformation
    OUT[i] = OUT[i]*Sigma+Mean
before returning it to user. This function sets Sigma/Mean. In case we
have SOFTMAX-normalized network, you can not set (Sigma,Mean) to anything
other than(0.0,1.0) - this function will throw exception.

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
void mlpsetoutputscaling(multilayerperceptron &network, const ae_int_t i, const double mean, const double sigma, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function modifies information about Ith neuron of Kth layer

INPUT PARAMETERS:
    Network     -   network
    K           -   layer index
    I           -   neuron index (within layer)
    FKind       -   activation function type (used by MLPActivationFunction())
                    this value must be zero for input neurons
                    (you can not set activation function for input neurons)
    Threshold   -   also called offset, bias
                    this value must be zero for input neurons
                    (you can not set threshold for input neurons)

NOTES:
1. this function throws exception if layer or neuron with given index do
   not exists.
2. this function also throws exception when you try to set non-linear
   activation function for input neurons (any kind of network) or for output
   neurons of classifier network.
3. this function throws exception when you try to set non-zero threshold for
   input neurons (any kind of network).

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
void mlpsetneuroninfo(multilayerperceptron &network, const ae_int_t k, const ae_int_t i, const ae_int_t fkind, const double threshold, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function modifies information about connection from I0-th neuron of
K0-th layer to I1-th neuron of K1-th layer.

INPUT PARAMETERS:
    Network     -   network
    K0          -   layer index
    I0          -   neuron index (within layer)
    K1          -   layer index
    I1          -   neuron index (within layer)
    W           -   connection weight (must be zero for non-existent
                    connections)

This function:
1. throws exception if layer or neuron with given index do not exists.
2. throws exception if you try to set non-zero weight for non-existent
   connection

  -- ALGLIB --
     Copyright 25.03.2011 by Bochkanov Sergey
*************************************************************************/
void mlpsetweight(multilayerperceptron &network, const ae_int_t k0, const ae_int_t i0, const ae_int_t k1, const ae_int_t i1, const double w, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Neural network activation function

INPUT PARAMETERS:
    NET         -   neuron input
    K           -   function index (zero for linear function)

OUTPUT PARAMETERS:
    F           -   function
    DF          -   its derivative
    D2F         -   its second derivative

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpactivationfunction(const double net, const ae_int_t k, double &f, double &df, double &d2f, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Procesing

INPUT PARAMETERS:
    Network -   neural network
    X       -   input vector,  array[0..NIn-1].

OUTPUT PARAMETERS:
    Y       -   result. Regression estimate when solving regression  task,
                vector of posterior probabilities for classification task.

See also MLPProcessI

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpprocess(multilayerperceptron &network, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
'interactive'  variant  of  MLPProcess  for  languages  like  Python which
support constructs like "Y = MLPProcess(NN,X)" and interactive mode of the
interpreter

This function allocates new array on each call,  so  it  is  significantly
slower than its 'non-interactive' counterpart, but it is  more  convenient
when you call it from command line.

  -- ALGLIB --
     Copyright 21.09.2010 by Bochkanov Sergey
*************************************************************************/
void mlpprocessi(multilayerperceptron &network, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Error of the neural network on dataset.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format;
    NPoints     -   points count.

RESULT:
    sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2)

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
double mlperror(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Error of the neural network on dataset given by sparse matrix.

  ! 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:
    Network     -   neural network
    XY          -   training  set,  see  below  for  information  on   the
                    training set format. This function checks  correctness
                    of  the  dataset  (no  NANs/INFs,  class  numbers  are
                    correct) and throws exception when  incorrect  dataset
                    is passed.  Sparse  matrix  must  use  CRS  format for
                    storage.
    NPoints     -   points count, >=0

RESULT:
    sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2)

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
double mlperrorsparse(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Natural error function for neural network, internal subroutine.

NOTE: this function is single-threaded. Unlike other  error  function,  it
receives no speed-up from being executed in SMP mode.

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
double mlperrorn(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Classification error of the neural network on dataset.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format;
    NPoints     -   points count.

RESULT:
    classification error (number of misclassified cases)

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpclserror(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Relative classification error on the test set.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format;
    NPoints     -   points count.

RESULT:
Percent   of incorrectly   classified  cases.  Works  both  for classifier
networks and general purpose networks used as classifiers.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 25.12.2008 by Bochkanov Sergey
*************************************************************************/
double mlprelclserror(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Relative classification error on the test set given by sparse matrix.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format. Sparse matrix must use CRS format
                    for storage.
    NPoints     -   points count, >=0.

RESULT:
Percent   of incorrectly   classified  cases.  Works  both  for classifier
networks and general purpose networks used as classifiers.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 09.08.2012 by Bochkanov Sergey
*************************************************************************/
double mlprelclserrorsparse(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average cross-entropy  (in bits  per element) on the test set.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format;
    NPoints     -   points count.

RESULT:
CrossEntropy/(NPoints*LN(2)).
Zero if network solves regression task.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 08.01.2009 by Bochkanov Sergey
*************************************************************************/
double mlpavgce(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average  cross-entropy  (in bits  per element)  on the  test set  given by
sparse matrix.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format. This function checks  correctness
                    of  the  dataset  (no  NANs/INFs,  class  numbers  are
                    correct) and throws exception when  incorrect  dataset
                    is passed.  Sparse  matrix  must  use  CRS  format for
                    storage.
    NPoints     -   points count, >=0.

RESULT:
CrossEntropy/(NPoints*LN(2)).
Zero if network solves regression task.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 9.08.2012 by Bochkanov Sergey
*************************************************************************/
double mlpavgcesparse(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
RMS error on the test set given.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format;
    NPoints     -   points count.

RESULT:
Root mean  square error. Its meaning for regression task is obvious. As for
classification  task,  RMS  error  means  error  when estimating  posterior
probabilities.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
double mlprmserror(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
RMS error on the test set given by sparse matrix.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format. This function checks  correctness
                    of  the  dataset  (no  NANs/INFs,  class  numbers  are
                    correct) and throws exception when  incorrect  dataset
                    is passed.  Sparse  matrix  must  use  CRS  format for
                    storage.
    NPoints     -   points count, >=0.

RESULT:
Root mean  square error. Its meaning for regression task is obvious. As for
classification  task,  RMS  error  means  error  when estimating  posterior
probabilities.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 09.08.2012 by Bochkanov Sergey
*************************************************************************/
double mlprmserrorsparse(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average absolute error on the test set.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format;
    NPoints     -   points count.

RESULT:
Its meaning for regression task is obvious. As for classification task, it
means average error when estimating posterior probabilities.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 11.03.2008 by Bochkanov Sergey
*************************************************************************/
double mlpavgerror(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average absolute error on the test set given by sparse matrix.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format. This function checks  correctness
                    of  the  dataset  (no  NANs/INFs,  class  numbers  are
                    correct) and throws exception when  incorrect  dataset
                    is passed.  Sparse  matrix  must  use  CRS  format for
                    storage.
    NPoints     -   points count, >=0.

RESULT:
Its meaning for regression task is obvious. As for classification task, it
means average error when estimating posterior probabilities.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 09.08.2012 by Bochkanov Sergey
*************************************************************************/
double mlpavgerrorsparse(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average relative error on the test set.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format;
    NPoints     -   points count.

RESULT:
Its meaning for regression task is obvious. As for classification task, it
means  average  relative  error  when  estimating posterior probability of
belonging to the correct class.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 11.03.2008 by Bochkanov Sergey
*************************************************************************/
double mlpavgrelerror(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average relative error on the test set given by sparse matrix.

  ! 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:
    Network     -   neural network;
    XY          -   training  set,  see  below  for  information  on   the
                    training set format. This function checks  correctness
                    of  the  dataset  (no  NANs/INFs,  class  numbers  are
                    correct) and throws exception when  incorrect  dataset
                    is passed.  Sparse  matrix  must  use  CRS  format for
                    storage.
    NPoints     -   points count, >=0.

RESULT:
Its meaning for regression task is obvious. As for classification task, it
means  average  relative  error  when  estimating posterior probability of
belonging to the correct class.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 09.08.2012 by Bochkanov Sergey
*************************************************************************/
double mlpavgrelerrorsparse(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Gradient calculation

INPUT PARAMETERS:
    Network -   network initialized with one of the network creation funcs
    X       -   input vector, length of array must be at least NIn
    DesiredY-   desired outputs, length of array must be at least NOut
    Grad    -   possibly preallocated array. If size of array is smaller
                than WCount, it will be reallocated. It is recommended to
                reuse previously allocated array to reduce allocation
                overhead.

OUTPUT PARAMETERS:
    E       -   error function, SUM(sqr(y[i]-desiredy[i])/2,i)
    Grad    -   gradient of E with respect to weights of network, array[WCount]

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpgrad(multilayerperceptron &network, const real_1d_array &x, const real_1d_array &desiredy, double &e, real_1d_array &grad, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Gradient calculation (natural error function is used)

INPUT PARAMETERS:
    Network -   network initialized with one of the network creation funcs
    X       -   input vector, length of array must be at least NIn
    DesiredY-   desired outputs, length of array must be at least NOut
    Grad    -   possibly preallocated array. If size of array is smaller
                than WCount, it will be reallocated. It is recommended to
                reuse previously allocated array to reduce allocation
                overhead.

OUTPUT PARAMETERS:
    E       -   error function, sum-of-squares for regression networks,
                cross-entropy for classification networks.
    Grad    -   gradient of E with respect to weights of network, array[WCount]

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpgradn(multilayerperceptron &network, const real_1d_array &x, const real_1d_array &desiredy, double &e, real_1d_array &grad, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Batch gradient calculation for a set of inputs/outputs

  ! 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:
    Network -   network initialized with one of the network creation funcs
    XY      -   original dataset in dense format; one sample = one row:
                * first NIn columns contain inputs,
                * for regression problem, next NOut columns store
                  desired outputs.
                * for classification problem, next column (just one!)
                  stores class number.
    SSize   -   number of elements in XY
    Grad    -   possibly preallocated array. If size of array is smaller
                than WCount, it will be reallocated. It is recommended to
                reuse previously allocated array to reduce allocation
                overhead.

OUTPUT PARAMETERS:
    E       -   error function, SUM(sqr(y[i]-desiredy[i])/2,i)
    Grad    -   gradient of E with respect to weights of network, array[WCount]

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpgradbatch(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Batch gradient calculation for a set  of inputs/outputs  given  by  sparse
matrices

  ! 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:
    Network -   network initialized with one of the network creation funcs
    XY      -   original dataset in sparse format; one sample = one row:
                * MATRIX MUST BE STORED IN CRS FORMAT
                * first NIn columns contain inputs.
                * for regression problem, next NOut columns store
                  desired outputs.
                * for classification problem, next column (just one!)
                  stores class number.
    SSize   -   number of elements in XY
    Grad    -   possibly preallocated array. If size of array is smaller
                than WCount, it will be reallocated. It is recommended to
                reuse previously allocated array to reduce allocation
                overhead.

OUTPUT PARAMETERS:
    E       -   error function, SUM(sqr(y[i]-desiredy[i])/2,i)
    Grad    -   gradient of E with respect to weights of network, array[WCount]

  -- ALGLIB --
     Copyright 26.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpgradbatchsparse(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t ssize, double &e, real_1d_array &grad, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Batch gradient calculation for a subset of dataset

  ! 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:
    Network -   network initialized with one of the network creation funcs
    XY      -   original dataset in dense format; one sample = one row:
                * first NIn columns contain inputs,
                * for regression problem, next NOut columns store
                  desired outputs.
                * for classification problem, next column (just one!)
                  stores class number.
    SetSize -   real size of XY, SetSize>=0;
    Idx     -   subset of SubsetSize elements, array[SubsetSize]:
                * Idx[I] stores row index in the original dataset which is
                  given by XY. Gradient is calculated with respect to rows
                  whose indexes are stored in Idx[].
                * Idx[]  must store correct indexes; this function  throws
                  an  exception  in  case  incorrect index (less than 0 or
                  larger than rows(XY)) is given
                * Idx[]  may  store  indexes  in  any  order and even with
                  repetitions.
    SubsetSize- number of elements in Idx[] array:
                * positive value means that subset given by Idx[] is processed
                * zero value results in zero gradient
                * negative value means that full dataset is processed
    Grad      - possibly  preallocated array. If size of array is  smaller
                than WCount, it will be reallocated. It is  recommended to
                reuse  previously  allocated  array  to  reduce allocation
                overhead.

OUTPUT PARAMETERS:
    E         - error function, SUM(sqr(y[i]-desiredy[i])/2,i)
    Grad      - gradient  of  E  with  respect   to  weights  of  network,
                array[WCount]

  -- ALGLIB --
     Copyright 26.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpgradbatchsubset(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Batch gradient calculation for a set of inputs/outputs  for  a  subset  of
dataset given by set of indexes.

  ! 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:
    Network -   network initialized with one of the network creation funcs
    XY      -   original dataset in sparse format; one sample = one row:
                * MATRIX MUST BE STORED IN CRS FORMAT
                * first NIn columns contain inputs,
                * for regression problem, next NOut columns store
                  desired outputs.
                * for classification problem, next column (just one!)
                  stores class number.
    SetSize -   real size of XY, SetSize>=0;
    Idx     -   subset of SubsetSize elements, array[SubsetSize]:
                * Idx[I] stores row index in the original dataset which is
                  given by XY. Gradient is calculated with respect to rows
                  whose indexes are stored in Idx[].
                * Idx[]  must store correct indexes; this function  throws
                  an  exception  in  case  incorrect index (less than 0 or
                  larger than rows(XY)) is given
                * Idx[]  may  store  indexes  in  any  order and even with
                  repetitions.
    SubsetSize- number of elements in Idx[] array:
                * positive value means that subset given by Idx[] is processed
                * zero value results in zero gradient
                * negative value means that full dataset is processed
    Grad      - possibly  preallocated array. If size of array is  smaller
                than WCount, it will be reallocated. It is  recommended to
                reuse  previously  allocated  array  to  reduce allocation
                overhead.

OUTPUT PARAMETERS:
    E       -   error function, SUM(sqr(y[i]-desiredy[i])/2,i)
    Grad    -   gradient  of  E  with  respect   to  weights  of  network,
                array[WCount]

NOTE: when  SubsetSize<0 is used full dataset by call MLPGradBatchSparse
      function.

  -- ALGLIB --
     Copyright 26.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpgradbatchsparsesubset(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Batch gradient calculation for a set of inputs/outputs
(natural error function is used)

INPUT PARAMETERS:
    Network -   network initialized with one of the network creation funcs
    XY      -   set of inputs/outputs; one sample = one row;
                first NIn columns contain inputs,
                next NOut columns - desired outputs.
    SSize   -   number of elements in XY
    Grad    -   possibly preallocated array. If size of array is smaller
                than WCount, it will be reallocated. It is recommended to
                reuse previously allocated array to reduce allocation
                overhead.

OUTPUT PARAMETERS:
    E       -   error function, sum-of-squares for regression networks,
                cross-entropy for classification networks.
    Grad    -   gradient of E with respect to weights of network, array[WCount]

  -- ALGLIB --
     Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpgradnbatch(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Batch Hessian calculation (natural error function) using R-algorithm.
Internal subroutine.

  -- ALGLIB --
     Copyright 26.01.2008 by Bochkanov Sergey.

     Hessian calculation based on R-algorithm described in
     "Fast Exact Multiplication by the Hessian",
     B. A. Pearlmutter,
     Neural Computation, 1994.
*************************************************************************/
void mlphessiannbatch(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, real_2d_array &h, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Batch Hessian calculation using R-algorithm.
Internal subroutine.

  -- ALGLIB --
     Copyright 26.01.2008 by Bochkanov Sergey.

     Hessian calculation based on R-algorithm described in
     "Fast Exact Multiplication by the Hessian",
     B. A. Pearlmutter,
     Neural Computation, 1994.
*************************************************************************/
void mlphessianbatch(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, real_2d_array &h, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Calculation of all types of errors on subset of dataset.

  ! 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:
    Network -   network initialized with one of the network creation funcs
    XY      -   original dataset; one sample = one row;
                first NIn columns contain inputs,
                next NOut columns - desired outputs.
    SetSize -   real size of XY, SetSize>=0;
    Subset  -   subset of SubsetSize elements, array[SubsetSize];
    SubsetSize- number of elements in Subset[] array:
                * if SubsetSize>0, rows of XY with indices Subset[0]...
                  ...Subset[SubsetSize-1] are processed
                * if SubsetSize=0, zeros are returned
                * if SubsetSize<0, entire dataset is  processed;  Subset[]
                  array is ignored in this case.

OUTPUT PARAMETERS:
    Rep     -   it contains all type of errors.

  -- ALGLIB --
     Copyright 04.09.2012 by Bochkanov Sergey
*************************************************************************/
void mlpallerrorssubset(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Calculation of all types of errors on subset of dataset.

  ! 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:
    Network -   network initialized with one of the network creation funcs
    XY      -   original dataset given by sparse matrix;
                one sample = one row;
                first NIn columns contain inputs,
                next NOut columns - desired outputs.
    SetSize -   real size of XY, SetSize>=0;
    Subset  -   subset of SubsetSize elements, array[SubsetSize];
    SubsetSize- number of elements in Subset[] array:
                * if SubsetSize>0, rows of XY with indices Subset[0]...
                  ...Subset[SubsetSize-1] are processed
                * if SubsetSize=0, zeros are returned
                * if SubsetSize<0, entire dataset is  processed;  Subset[]
                  array is ignored in this case.

OUTPUT PARAMETERS:
    Rep     -   it contains all type of errors.


  -- ALGLIB --
     Copyright 04.09.2012 by Bochkanov Sergey
*************************************************************************/
void mlpallerrorssparsesubset(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Error of the neural network on subset of dataset.

  ! 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:
    Network   -     neural network;
    XY        -     training  set,  see  below  for  information  on   the
                    training set format;
    SetSize   -     real size of XY, SetSize>=0;
    Subset    -     subset of SubsetSize elements, array[SubsetSize];
    SubsetSize-     number of elements in Subset[] array:
                    * if SubsetSize>0, rows of XY with indices Subset[0]...
                      ...Subset[SubsetSize-1] are processed
                    * if SubsetSize=0, zeros are returned
                    * if SubsetSize<0, entire dataset is  processed;  Subset[]
                      array is ignored in this case.

RESULT:
    sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2)

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 04.09.2012 by Bochkanov Sergey
*************************************************************************/
double mlperrorsubset(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Error of the neural network on subset of sparse dataset.

  ! 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:
    Network   -     neural network;
    XY        -     training  set,  see  below  for  information  on   the
                    training set format. This function checks  correctness
                    of  the  dataset  (no  NANs/INFs,  class  numbers  are
                    correct) and throws exception when  incorrect  dataset
                    is passed.  Sparse  matrix  must  use  CRS  format for
                    storage.
    SetSize   -     real size of XY, SetSize>=0;
                    it is used when SubsetSize<0;
    Subset    -     subset of SubsetSize elements, array[SubsetSize];
    SubsetSize-     number of elements in Subset[] array:
                    * if SubsetSize>0, rows of XY with indices Subset[0]...
                      ...Subset[SubsetSize-1] are processed
                    * if SubsetSize=0, zeros are returned
                    * if SubsetSize<0, entire dataset is  processed;  Subset[]
                      array is ignored in this case.

RESULT:
    sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2)

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
dataset format is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 04.09.2012 by Bochkanov Sergey
*************************************************************************/
double mlperrorsparsesubset(multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_MLPE) || !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 a text or XML file. 
  But you should not insert separators into the middle of the "words"
  nor should you change the 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 it in C# one, 
  and vice versa.
*************************************************************************/
void mlpeserialize(const mlpensemble &obj, std::string &s_out);


/*************************************************************************
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 mlpeserialize(const mlpensemble &obj, std::ostream &s_out);


/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void mlpeunserialize(const std::string &s_in, mlpensemble &obj);


/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void mlpeunserialize(const std::istream &s_in, mlpensemble &obj);


/*************************************************************************
Like MLPCreate0, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreate0(const ae_int_t nin, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreate1, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreate1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreate2, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreate2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateB0, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreateb0(const ae_int_t nin, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateB1, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreateb1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateB2, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreateb2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateR0, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreater0(const ae_int_t nin, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateR1, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreater1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateR2, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreater2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateC0, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreatec0(const ae_int_t nin, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateC1, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreatec1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like MLPCreateC2, but for ensembles.

  -- ALGLIB --
     Copyright 18.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreatec2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Creates ensemble from network. Only network geometry is copied.

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpecreatefromnetwork(const multilayerperceptron &network, const ae_int_t ensemblesize, mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Randomization of MLP ensemble

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlperandomize(mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Return ensemble properties (number of inputs and outputs).

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpeproperties(const mlpensemble &ensemble, ae_int_t &nin, ae_int_t &nout, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Return normalization type (whether ensemble is SOFTMAX-normalized or not).

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
bool mlpeissoftmax(const mlpensemble &ensemble, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Procesing

INPUT PARAMETERS:
    Ensemble-   neural networks ensemble
    X       -   input vector,  array[0..NIn-1].
    Y       -   (possibly) preallocated buffer; if size of Y is less than
                NOut, it will be reallocated. If it is large enough, it
                is NOT reallocated, so we can save some time on reallocation.


OUTPUT PARAMETERS:
    Y       -   result. Regression estimate when solving regression  task,
                vector of posterior probabilities for classification task.

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpeprocess(mlpensemble &ensemble, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
'interactive'  variant  of  MLPEProcess  for  languages  like Python which
support constructs like "Y = MLPEProcess(LM,X)" and interactive mode of the
interpreter

This function allocates new array on each call,  so  it  is  significantly
slower than its 'non-interactive' counterpart, but it is  more  convenient
when you call it from command line.

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpeprocessi(mlpensemble &ensemble, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Relative classification error on the test set

INPUT PARAMETERS:
    Ensemble-   ensemble
    XY      -   test set
    NPoints -   test set size

RESULT:
    percent of incorrectly classified cases.
    Works both for classifier betwork and for regression networks which
are used as classifiers.

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
double mlperelclserror(mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average cross-entropy (in bits per element) on the test set

INPUT PARAMETERS:
    Ensemble-   ensemble
    XY      -   test set
    NPoints -   test set size

RESULT:
    CrossEntropy/(NPoints*LN(2)).
    Zero if ensemble solves regression task.

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
double mlpeavgce(mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
RMS error on the test set

INPUT PARAMETERS:
    Ensemble-   ensemble
    XY      -   test set
    NPoints -   test set size

RESULT:
    root mean square error.
    Its meaning for regression task is obvious. As for classification task
RMS error means error when estimating posterior probabilities.

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
double mlpermserror(mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average error on the test set

INPUT PARAMETERS:
    Ensemble-   ensemble
    XY      -   test set
    NPoints -   test set size

RESULT:
    Its meaning for regression task is obvious. As for classification task
it means average error when estimating posterior probabilities.

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
double mlpeavgerror(mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average relative error on the test set

INPUT PARAMETERS:
    Ensemble-   ensemble
    XY      -   test set
    NPoints -   test set size

RESULT:
    Its meaning for regression task is obvious. As for classification task
it means average relative error when estimating posterior probabilities.

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
double mlpeavgrelerror(mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_CLUSTERING) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
This function initializes clusterizer object. Newly initialized object  is
empty, i.e. it does not contain dataset. You should use it as follows:
1. creation
2. dataset is added with ClusterizerSetPoints()
3. additional parameters are set
3. clusterization is performed with one of the clustering functions

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizercreate(clusterizerstate &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function adds dataset to the clusterizer structure.

This function overrides all previous calls  of  ClusterizerSetPoints()  or
ClusterizerSetDistances().

INPUT PARAMETERS:
    S       -   clusterizer state, initialized by ClusterizerCreate()
    XY      -   array[NPoints,NFeatures], dataset
    NPoints -   number of points, >=0
    NFeatures-  number of features, >=1
    DistType-   distance function:
                *  0    Chebyshev distance  (L-inf norm)
                *  1    city block distance (L1 norm)
                *  2    Euclidean distance  (L2 norm), non-squared
                * 10    Pearson correlation:
                        dist(a,b) = 1-corr(a,b)
                * 11    Absolute Pearson correlation:
                        dist(a,b) = 1-|corr(a,b)|
                * 12    Uncentered Pearson correlation (cosine of the angle):
                        dist(a,b) = a'*b/(|a|*|b|)
                * 13    Absolute uncentered Pearson correlation
                        dist(a,b) = |a'*b|/(|a|*|b|)
                * 20    Spearman rank correlation:
                        dist(a,b) = 1-rankcorr(a,b)
                * 21    Absolute Spearman rank correlation
                        dist(a,b) = 1-|rankcorr(a,b)|

NOTE 1: different distance functions have different performance penalty:
        * Euclidean or Pearson correlation distances are the fastest ones
        * Spearman correlation distance function is a bit slower
        * city block and Chebyshev distances are order of magnitude slower

        The reason behing difference in performance is that correlation-based
        distance functions are computed using optimized linear algebra kernels,
        while Chebyshev and city block distance functions are computed using
        simple nested loops with two branches at each iteration.

NOTE 2: different clustering algorithms have different limitations:
        * agglomerative hierarchical clustering algorithms may be used with
          any kind of distance metric
        * k-means++ clustering algorithm may be used only  with  Euclidean
          distance function
        Thus, list of specific clustering algorithms you may  use  depends
        on distance function you specify when you set your dataset.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizersetpoints(clusterizerstate &s, const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype, const xparams _xparams = alglib::xdefault);
void clusterizersetpoints(clusterizerstate &s, const real_2d_array &xy, const ae_int_t disttype, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function adds dataset given by distance  matrix  to  the  clusterizer
structure. It is important that dataset is not  given  explicitly  -  only
distance matrix is given.

This function overrides all previous calls  of  ClusterizerSetPoints()  or
ClusterizerSetDistances().

INPUT PARAMETERS:
    S       -   clusterizer state, initialized by ClusterizerCreate()
    D       -   array[NPoints,NPoints], distance matrix given by its upper
                or lower triangle (main diagonal is  ignored  because  its
                entries are expected to be zero).
    NPoints -   number of points
    IsUpper -   whether upper or lower triangle of D is given.

NOTE 1: different clustering algorithms have different limitations:
        * agglomerative hierarchical clustering algorithms may be used with
          any kind of distance metric, including one  which  is  given  by
          distance matrix
        * k-means++ clustering algorithm may be used only  with  Euclidean
          distance function and explicitly given points - it  can  not  be
          used with dataset given by distance matrix
        Thus, if you call this function, you will be unable to use k-means
        clustering algorithm to process your problem.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizersetdistances(clusterizerstate &s, const real_2d_array &d, const ae_int_t npoints, const bool isupper, const xparams _xparams = alglib::xdefault);
void clusterizersetdistances(clusterizerstate &s, const real_2d_array &d, const bool isupper, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets agglomerative hierarchical clustering algorithm

INPUT PARAMETERS:
    S       -   clusterizer state, initialized by ClusterizerCreate()
    Algo    -   algorithm type:
                * 0     complete linkage (default algorithm)
                * 1     single linkage
                * 2     unweighted average linkage
                * 3     weighted average linkage
                * 4     Ward's method

NOTE: Ward's method works correctly only with Euclidean  distance,  that's
      why algorithm will return negative termination  code  (failure)  for
      any other distance type.

      It is possible, however,  to  use  this  method  with  user-supplied
      distance matrix. It  is  your  responsibility  to pass one which was
      calculated with Euclidean distance function.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizersetahcalgo(clusterizerstate &s, const ae_int_t algo, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  sets k-means properties:  number  of  restarts and maximum
number of iterations per one run.

INPUT PARAMETERS:
    S       -   clusterizer state, initialized by ClusterizerCreate()
    Restarts-   restarts count, >=1.
                k-means++ algorithm performs several restarts and  chooses
                best set of centers (one with minimum squared distance).
    MaxIts  -   maximum number of k-means iterations performed during  one
                run. >=0, zero value means that algorithm performs unlimited
                number of iterations.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizersetkmeanslimits(clusterizerstate &s, const ae_int_t restarts, const ae_int_t maxits, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets k-means  initialization  algorithm.  Several  different
algorithms can be chosen, including k-means++.

INPUT PARAMETERS:
    S       -   clusterizer state, initialized by ClusterizerCreate()
    InitAlgo-   initialization algorithm:
                * 0  automatic selection ( different  versions  of  ALGLIB
                     may select different algorithms)
                * 1  random initialization
                * 2  k-means++ initialization  (best  quality  of  initial
                     centers, but long  non-parallelizable  initialization
                     phase with bad cache locality)
                * 3  "fast-greedy"  algorithm  with  efficient,  easy   to
                     parallelize initialization. Quality of initial centers
                     is  somewhat  worse  than  that  of  k-means++.  This
                     algorithm is a default one in the current version  of
                     ALGLIB.
                *-1  "debug" algorithm which always selects first  K  rows
                     of dataset; this algorithm is used for debug purposes
                     only. Do not use it in the industrial code!

  -- ALGLIB --
     Copyright 21.01.2015 by Bochkanov Sergey
*************************************************************************/
void clusterizersetkmeansinit(clusterizerstate &s, const ae_int_t initalgo, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  sets  seed  which  is  used to initialize internal RNG. By
default, deterministic seed is used - same for each run of clusterizer. If
you specify non-deterministic  seed  value,  then  some  algorithms  which
depend on random initialization (in current version: k-means)  may  return
slightly different results after each run.

INPUT PARAMETERS:
    S       -   clusterizer state, initialized by ClusterizerCreate()
    Seed    -   seed:
                * positive values = use deterministic seed for each run of
                  algorithms which depend on random initialization
                * zero or negative values = use non-deterministic seed

  -- ALGLIB --
     Copyright 08.06.2017 by Bochkanov Sergey
*************************************************************************/
void clusterizersetseed(clusterizerstate &s, const ae_int_t seed, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function performs agglomerative hierarchical clustering

NOTE: Agglomerative  hierarchical  clustering  algorithm  has two  phases:
      distance matrix calculation and clustering  itself. Only first phase
      (distance matrix calculation) is accelerated by SIMD and SMP.  Thus,
      acceleration is significant  only  for  medium  or  high-dimensional
      problems.

      Although activating multithreading gives some speedup  over  single-
      threaded execution, you  should  not  expect  nearly-linear  scaling
      with respect to cores count.

INPUT PARAMETERS:
    S       -   clusterizer state, initialized by ClusterizerCreate()

OUTPUT PARAMETERS:
    Rep     -   clustering results; see description of AHCReport
                structure for more information.

NOTE 1: hierarchical clustering algorithms require large amounts of memory.
        In particular, this implementation needs  sizeof(double)*NPoints^2
        bytes, which are used to store distance matrix. In  case  we  work
        with user-supplied matrix, this amount is multiplied by 2 (we have
        to store original matrix and to work with its copy).

        For example, problem with 10000 points  would require 800M of RAM,
        even when working in a 1-dimensional space.

  ! FREE EDITION OF ALGLIB:
  !
  ! Free Edition of ALGLIB supports following important features for  this
  ! function:
  ! * C++ version: x64 SIMD support using C++ intrinsics
  ! * C#  version: x64 SIMD support using NET5/NetCore hardware intrinsics
  !
  ! We  recommend  you  to  read  'Compiling ALGLIB' section of the ALGLIB
  ! Reference Manual in order  to  find  out  how to activate SIMD support
  ! in ALGLIB.

  ! 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, ARM) implementations of linear algebra and
  !   other primitives (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.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizerrunahc(clusterizerstate &s, ahcreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function performs clustering by k-means++ algorithm.

You may change algorithm properties by calling:
* ClusterizerSetKMeansLimits() to change number of restarts or iterations
* ClusterizerSetKMeansInit() to change initialization algorithm

By  default,  one  restart  and  unlimited number of iterations are  used.
Initialization algorithm is chosen automatically.

NOTE: k-means clustering  algorithm has two  phases:  selection of initial
      centers and clustering  itself.  ALGLIB  parallelizes  both  phases.
      Parallel version is optimized for the following  scenario: medium or
      high-dimensional problem (8 or more dimensions) with large number of
      points and clusters. However, some speed-up  can  be  obtained  even
      when assumptions above are violated.

INPUT PARAMETERS:
    S       -   clusterizer state, initialized by ClusterizerCreate()
    K       -   number of clusters, K>=0.
                K  can  be  zero only when algorithm is called  for  empty
                dataset,  in   this   case   completion  code  is  set  to
                success (+1).
                If  K=0  and  dataset  size  is  non-zero,  we   can   not
                meaningfully assign points to some center  (there  are  no
                centers because K=0) and  return  -3  as  completion  code
                (failure).

OUTPUT PARAMETERS:
    Rep     -   clustering results; see description of KMeansReport
                structure for more information.

NOTE 1: k-means  clustering  can  be  performed  only  for  datasets  with
        Euclidean  distance  function.  Algorithm  will  return   negative
        completion code in Rep.TerminationType in case dataset  was  added
        to clusterizer with DistType other than Euclidean (or dataset  was
        specified by distance matrix instead of explicitly given points).

NOTE 2: by default, k-means uses non-deterministic seed to initialize  RNG
        which is used to select initial centers. As  result,  each  run of
        algorithm may return different values. If you  need  deterministic
        behavior, use ClusterizerSetSeed() function.

  ! FREE EDITION OF ALGLIB:
  !
  ! Free Edition of ALGLIB supports following important features for  this
  ! function:
  ! * C++ version: x64 SIMD support using C++ intrinsics
  ! * C#  version: x64 SIMD support using NET5/NetCore hardware intrinsics
  !
  ! We  recommend  you  to  read  'Compiling ALGLIB' section of the ALGLIB
  ! Reference Manual in order  to  find  out  how to activate SIMD support
  ! in ALGLIB.

  ! 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, ARM) implementations of linear algebra and
  !   other primitives (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.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizerrunkmeans(clusterizerstate &s, const ae_int_t k, kmeansreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns distance matrix for dataset

INPUT PARAMETERS:
    XY      -   array[NPoints,NFeatures], dataset
    NPoints -   number of points, >=0
    NFeatures-  number of features, >=1
    DistType-   distance function:
                *  0    Chebyshev distance  (L-inf norm)
                *  1    city block distance (L1 norm)
                *  2    Euclidean distance  (L2 norm, non-squared)
                * 10    Pearson correlation:
                        dist(a,b) = 1-corr(a,b)
                * 11    Absolute Pearson correlation:
                        dist(a,b) = 1-|corr(a,b)|
                * 12    Uncentered Pearson correlation (cosine of the angle):
                        dist(a,b) = a'*b/(|a|*|b|)
                * 13    Absolute uncentered Pearson correlation
                        dist(a,b) = |a'*b|/(|a|*|b|)
                * 20    Spearman rank correlation:
                        dist(a,b) = 1-rankcorr(a,b)
                * 21    Absolute Spearman rank correlation
                        dist(a,b) = 1-|rankcorr(a,b)|

OUTPUT PARAMETERS:
    D       -   array[NPoints,NPoints], distance matrix
                (full matrix is returned, with lower and upper triangles)

NOTE:  different distance functions have different performance penalty:
       * Euclidean or Pearson correlation distances are the fastest ones
       * Spearman correlation distance function is a bit slower
       * city block and Chebyshev distances are order of magnitude slower

       The reason behing difference in performance is that correlation-based
       distance functions are computed using optimized linear algebra kernels,
       while Chebyshev and city block distance functions are computed using
       simple nested loops with two branches at each iteration.

  ! FREE EDITION OF ALGLIB:
  !
  ! Free Edition of ALGLIB supports following important features for  this
  ! function:
  ! * C++ version: x64 SIMD support using C++ intrinsics
  ! * C#  version: x64 SIMD support using NET5/NetCore hardware intrinsics
  !
  ! We  recommend  you  to  read  'Compiling ALGLIB' section of the ALGLIB
  ! Reference Manual in order  to  find  out  how to activate SIMD support
  ! in ALGLIB.

  ! 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, ARM) implementations of linear algebra and
  !   other primitives (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.

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizergetdistances(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype, real_2d_array &d, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function takes as input clusterization report Rep,  desired  clusters
count K, and builds top K clusters from hierarchical clusterization  tree.
It returns assignment of points to clusters (array of cluster indexes).

INPUT PARAMETERS:
    Rep     -   report from ClusterizerRunAHC() performed on XY
    K       -   desired number of clusters, 1<=K<=NPoints.
                K can be zero only when NPoints=0.

OUTPUT PARAMETERS:
    CIdx    -   array[NPoints], I-th element contains cluster index  (from
                0 to K-1) for I-th point of the dataset.
    CZ      -   array[K]. This array allows  to  convert  cluster  indexes
                returned by this function to indexes used by  Rep.Z.  J-th
                cluster returned by this function corresponds to  CZ[J]-th
                cluster stored in Rep.Z/PZ/PM.
                It is guaranteed that CZ[I]<CZ[I+1].

NOTE: K clusters built by this subroutine are assumed to have no hierarchy.
      Although  they  were  obtained  by  manipulation with top K nodes of
      dendrogram  (i.e.  hierarchical  decomposition  of  dataset),   this
      function does not return information about hierarchy.  Each  of  the
      clusters stand on its own.

NOTE: Cluster indexes returned by this function  does  not  correspond  to
      indexes returned in Rep.Z/PZ/PM. Either you work  with  hierarchical
      representation of the dataset (dendrogram), or you work with  "flat"
      representation returned by this function.  Each  of  representations
      has its own clusters indexing system (former uses [0, 2*NPoints-2]),
      while latter uses [0..K-1]), although  it  is  possible  to  perform
      conversion from one system to another by means of CZ array, returned
      by this function, which allows you to convert indexes stored in CIdx
      to the numeration system used by Rep.Z.

NOTE: this subroutine is optimized for moderate values of K. Say, for  K=5
      it will perform many times faster than  for  K=100.  Its  worst-case
      performance is O(N*K), although in average case  it  perform  better
      (up to O(N*log(K))).

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizergetkclusters(const ahcreport &rep, const ae_int_t k, integer_1d_array &cidx, integer_1d_array &cz, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  accepts  AHC  report  Rep,  desired  minimum  intercluster
distance and returns top clusters from  hierarchical  clusterization  tree
which are separated by distance R or HIGHER.

It returns assignment of points to clusters (array of cluster indexes).

There is one more function with similar name - ClusterizerSeparatedByCorr,
which returns clusters with intercluster correlation equal to R  or  LOWER
(note: higher for distance, lower for correlation).

INPUT PARAMETERS:
    Rep     -   report from ClusterizerRunAHC() performed on XY
    R       -   desired minimum intercluster distance, R>=0

OUTPUT PARAMETERS:
    K       -   number of clusters, 1<=K<=NPoints
    CIdx    -   array[NPoints], I-th element contains cluster index  (from
                0 to K-1) for I-th point of the dataset.
    CZ      -   array[K]. This array allows  to  convert  cluster  indexes
                returned by this function to indexes used by  Rep.Z.  J-th
                cluster returned by this function corresponds to  CZ[J]-th
                cluster stored in Rep.Z/PZ/PM.
                It is guaranteed that CZ[I]<CZ[I+1].

NOTE: K clusters built by this subroutine are assumed to have no hierarchy.
      Although  they  were  obtained  by  manipulation with top K nodes of
      dendrogram  (i.e.  hierarchical  decomposition  of  dataset),   this
      function does not return information about hierarchy.  Each  of  the
      clusters stand on its own.

NOTE: Cluster indexes returned by this function  does  not  correspond  to
      indexes returned in Rep.Z/PZ/PM. Either you work  with  hierarchical
      representation of the dataset (dendrogram), or you work with  "flat"
      representation returned by this function.  Each  of  representations
      has its own clusters indexing system (former uses [0, 2*NPoints-2]),
      while latter uses [0..K-1]), although  it  is  possible  to  perform
      conversion from one system to another by means of CZ array, returned
      by this function, which allows you to convert indexes stored in CIdx
      to the numeration system used by Rep.Z.

NOTE: this subroutine is optimized for moderate values of K. Say, for  K=5
      it will perform many times faster than  for  K=100.  Its  worst-case
      performance is O(N*K), although in average case  it  perform  better
      (up to O(N*log(K))).

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizerseparatedbydist(const ahcreport &rep, const double r, ae_int_t &k, integer_1d_array &cidx, integer_1d_array &cz, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  accepts  AHC  report  Rep,  desired  maximum  intercluster
correlation and returns top clusters from hierarchical clusterization tree
which are separated by correlation R or LOWER.

It returns assignment of points to clusters (array of cluster indexes).

There is one more function with similar name - ClusterizerSeparatedByDist,
which returns clusters with intercluster distance equal  to  R  or  HIGHER
(note: higher for distance, lower for correlation).

INPUT PARAMETERS:
    Rep     -   report from ClusterizerRunAHC() performed on XY
    R       -   desired maximum intercluster correlation, -1<=R<=+1

OUTPUT PARAMETERS:
    K       -   number of clusters, 1<=K<=NPoints
    CIdx    -   array[NPoints], I-th element contains cluster index  (from
                0 to K-1) for I-th point of the dataset.
    CZ      -   array[K]. This array allows  to  convert  cluster  indexes
                returned by this function to indexes used by  Rep.Z.  J-th
                cluster returned by this function corresponds to  CZ[J]-th
                cluster stored in Rep.Z/PZ/PM.
                It is guaranteed that CZ[I]<CZ[I+1].

NOTE: K clusters built by this subroutine are assumed to have no hierarchy.
      Although  they  were  obtained  by  manipulation with top K nodes of
      dendrogram  (i.e.  hierarchical  decomposition  of  dataset),   this
      function does not return information about hierarchy.  Each  of  the
      clusters stand on its own.

NOTE: Cluster indexes returned by this function  does  not  correspond  to
      indexes returned in Rep.Z/PZ/PM. Either you work  with  hierarchical
      representation of the dataset (dendrogram), or you work with  "flat"
      representation returned by this function.  Each  of  representations
      has its own clusters indexing system (former uses [0, 2*NPoints-2]),
      while latter uses [0..K-1]), although  it  is  possible  to  perform
      conversion from one system to another by means of CZ array, returned
      by this function, which allows you to convert indexes stored in CIdx
      to the numeration system used by Rep.Z.

NOTE: this subroutine is optimized for moderate values of K. Say, for  K=5
      it will perform many times faster than  for  K=100.  Its  worst-case
      performance is O(N*K), although in average case  it  perform  better
      (up to O(N*log(K))).

  -- ALGLIB --
     Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizerseparatedbycorr(const ahcreport &rep, const double r, ae_int_t &k, integer_1d_array &cidx, integer_1d_array &cz, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_DFOREST) || !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 a text or XML file. 
  But you should not insert separators into the middle of the "words"
  nor should you change the 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 it in C# one, 
  and vice versa.
*************************************************************************/
void dfserialize(const decisionforest &obj, std::string &s_out);


/*************************************************************************
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 dfserialize(const decisionforest &obj, std::ostream &s_out);


/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void dfunserialize(const std::string &s_in, decisionforest &obj);


/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void dfunserialize(const std::istream &s_in, decisionforest &obj);


/*************************************************************************
This function creates buffer  structure  which  can  be  used  to  perform
parallel inference requests.

DF subpackage  provides two sets of computing functions - ones  which  use
internal buffer of DF model  (these  functions are single-threaded because
they use same buffer, which can not  shared  between  threads),  and  ones
which use external buffer.

This function is used to initialize external buffer.

INPUT PARAMETERS
    Model       -   DF model which is associated with newly created buffer

OUTPUT PARAMETERS
    Buf         -   external buffer.


IMPORTANT: buffer object should be used only with model which was used  to
           initialize buffer. Any attempt to  use  buffer  with  different
           object is dangerous - you  may   get  integrity  check  failure
           (exception) because sizes of internal  arrays  do  not  fit  to
           dimensions of the model structure.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void dfcreatebuffer(const decisionforest &model, decisionforestbuffer &buf, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This subroutine creates DecisionForestBuilder  object  which  is  used  to
train decision forests.

By default, new builder stores empty dataset and some  reasonable  default
settings. At the very least, you should specify dataset prior to  building
decision forest. You can also tweak settings of  the  forest  construction
algorithm (recommended, although default setting should work well).

Following actions are mandatory:
* calling dfbuildersetdataset() to specify dataset
* calling dfbuilderbuildrandomforest()  to  build  decision  forest  using
  current dataset and default settings

Additionally, you may call:
* dfbuildersetrndvars() or dfbuildersetrndvarsratio() to specify number of
  variables randomly chosen for each split
* dfbuildersetsubsampleratio() to specify fraction of the dataset randomly
  subsampled to build each tree
* dfbuildersetseed() to control random seed chosen for tree construction

INPUT PARAMETERS:
    none

OUTPUT PARAMETERS:
    S           -   decision forest builder

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildercreate(decisionforestbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This subroutine adds dense dataset to the internal storage of the  builder
object. Specifying your dataset in the dense format means that  the  dense
version of the forest construction algorithm will be invoked.

INPUT PARAMETERS:
    S           -   decision forest builder object
    XY          -   array[NPoints,NVars+1] (minimum size; actual size  can
                    be larger, only leading part is used anyway), dataset:
                    * first NVars elements of each row store values of the
                      independent variables
                    * last  column  store class number (in 0...NClasses-1)
                      or real value of the dependent variable
    NPoints     -   number of rows in the dataset, NPoints>=1
    NVars       -   number of independent variables, NVars>=1
    NClasses    -   indicates type of the problem being solved:
                    * NClasses>=2 means  that  classification  problem  is
                      solved  (last  column  of  the  dataset stores class
                      number)
                    * NClasses=1 means that regression problem  is  solved
                      (last column of the dataset stores variable value)

OUTPUT PARAMETERS:
    S           -   decision forest builder

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetdataset(decisionforestbuilder &s, const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets number  of  variables  (in  [1,NVars]  range)  used  by
decision forest construction algorithm.

The default option is to use roughly sqrt(NVars) variables.

INPUT PARAMETERS:
    S           -   decision forest builder object
    RndVars     -   number of randomly selected variables; values  outside
                    of [1,NVars] range are silently clipped.

OUTPUT PARAMETERS:
    S           -   decision forest builder

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetrndvars(decisionforestbuilder &s, const ae_int_t rndvars, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets number of variables used by decision forest construction
algorithm as a fraction of total variable count (0,1) range.

The default option is to use roughly sqrt(NVars) variables.

INPUT PARAMETERS:
    S           -   decision forest builder object
    F           -   round(NVars*F) variables are selected

OUTPUT PARAMETERS:
    S           -   decision forest builder

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetrndvarsratio(decisionforestbuilder &s, const double f, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function tells decision forest builder to automatically choose number
of  variables  used  by  decision forest construction  algorithm.  Roughly
sqrt(NVars) variables will be used.

INPUT PARAMETERS:
    S           -   decision forest builder object

OUTPUT PARAMETERS:
    S           -   decision forest builder

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetrndvarsauto(decisionforestbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets size of dataset subsample generated the decision forest
construction algorithm. Size is specified as a fraction of  total  dataset
size.

The default option is to use 50% of the dataset for training, 50% for  the
OOB estimates. You can decrease fraction F down to 10%, 1% or  even  below
in order to reduce overfitting.

INPUT PARAMETERS:
    S           -   decision forest builder object
    F           -   fraction of the dataset to use, in (0,1] range. Values
                    outside of this range will  be  silently  clipped.  At
                    least one element is always selected for the  training
                    set.

OUTPUT PARAMETERS:
    S           -   decision forest builder

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetsubsampleratio(decisionforestbuilder &s, const double f, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets seed used by internal RNG for  random  subsampling  and
random selection of variable subsets.

By default random seed is used, i.e. every time you build decision forest,
we seed generator with new value  obtained  from  system-wide  RNG.  Thus,
decision forest builder returns non-deterministic results. You can  change
such behavior by specyfing fixed positive seed value.

INPUT PARAMETERS:
    S           -   decision forest builder object
    SeedVal     -   seed value:
                    * positive values are used for seeding RNG with fixed
                      seed, i.e. subsequent runs on same data will return
                      same decision forests
                    * non-positive seed means that random seed is used
                      for every run of builder, i.e. subsequent  runs  on
                      same  datasets  will  return   slightly   different
                      decision forests

OUTPUT PARAMETERS:
    S           -   decision forest builder, see

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetseed(decisionforestbuilder &s, const ae_int_t seedval, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets random decision forest construction algorithm.

As for now, only one decision forest construction algorithm is supported -
a dense "baseline" RDF algorithm.

INPUT PARAMETERS:
    S           -   decision forest builder object
    AlgoType    -   algorithm type:
                    * 0 = baseline dense RDF

OUTPUT PARAMETERS:
    S           -   decision forest builder, see

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetrdfalgo(decisionforestbuilder &s, const ae_int_t algotype, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  sets  split  selection  algorithm used by decision  forest
classifier. You may choose several algorithms, with  different  speed  and
quality of the results.

INPUT PARAMETERS:
    S           -   decision forest builder object
    SplitStrength-  split type:
                    * 0 = split at the random position, fastest one
                    * 1 = split at the middle of the range
                    * 2 = strong split at the best point of the range (default)

OUTPUT PARAMETERS:
    S           -   decision forest builder, see

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetrdfsplitstrength(decisionforestbuilder &s, const ae_int_t splitstrength, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  tells  decision  forest  construction  algorithm  to   use
Gini impurity based variable importance estimation (also known as MDI).

This version of importance estimation algorithm analyzes mean decrease  in
impurity (MDI) on training sample during  splits.  The result  is  divided
by impurity at the root node in order to produce estimate in [0,1] range.

Such estimates are fast to calculate and beautifully  normalized  (sum  to
one) but have following downsides:
* They ALWAYS sum to 1.0, even if output is completely unpredictable. I.e.
  MDI allows to order variables by importance, but does not  tell us about
  "absolute" importances of variables
* there exist some bias towards continuous and high-cardinality categorical
  variables

NOTE: informally speaking, MDA (permutation importance) rating answers the
      question  "what  part  of  the  model  predictive power is ruined by
      permuting k-th variable?" while MDI tells us "what part of the model
      predictive power was achieved due to usage of k-th variable".

      Thus, MDA rates each variable independently at "0 to 1"  scale while
      MDI (and OOB-MDI too) tends to divide "unit  amount  of  importance"
      between several important variables.

      If  all  variables  are  equally  important,  they  will  have  same
      MDI/OOB-MDI rating, equal (for OOB-MDI: roughly equal)  to  1/NVars.
      However, roughly  same  picture  will  be  produced   for  the  "all
      variables provide information no one is critical" situation  and for
      the "all variables are critical, drop any one, everything is ruined"
      situation.

      Contrary to that, MDA will rate critical variable as ~1.0 important,
      and important but non-critical variable will  have  less  than  unit
      rating.

NOTE: quite an often MDA and MDI return same results. It generally happens
      on problems with low test set error (a few  percents  at  most)  and
      large enough training set to avoid overfitting.

      The difference between MDA, MDI and OOB-MDI becomes  important  only
      on "hard" tasks with high test set error and/or small training set.

INPUT PARAMETERS:
    S           -   decision forest builder object

OUTPUT PARAMETERS:
    S           -   decision forest builder object. Next call to the forest
                    construction function will produce:
                    * importance estimates in rep.varimportances field
                    * variable ranks in rep.topvars field

  -- ALGLIB --
     Copyright 29.07.2019 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetimportancetrngini(decisionforestbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  tells  decision  forest  construction  algorithm  to   use
out-of-bag version of Gini variable importance estimation (also  known  as
OOB-MDI).

This version of importance estimation algorithm analyzes mean decrease  in
impurity (MDI) on out-of-bag sample during splits. The result  is  divided
by impurity at the root node in order to produce estimate in [0,1] range.

Such estimates are fast to calculate and resistant to  overfitting  issues
(thanks to the  out-of-bag  estimates  used). However, OOB Gini rating has
following downsides:
* there exist some bias towards continuous and high-cardinality categorical
  variables
* Gini rating allows us to order variables by importance, but it  is  hard
  to define importance of the variable by itself.

NOTE: informally speaking, MDA (permutation importance) rating answers the
      question  "what  part  of  the  model  predictive power is ruined by
      permuting k-th variable?" while MDI tells us "what part of the model
      predictive power was achieved due to usage of k-th variable".

      Thus, MDA rates each variable independently at "0 to 1"  scale while
      MDI (and OOB-MDI too) tends to divide "unit  amount  of  importance"
      between several important variables.

      If  all  variables  are  equally  important,  they  will  have  same
      MDI/OOB-MDI rating, equal (for OOB-MDI: roughly equal)  to  1/NVars.
      However, roughly  same  picture  will  be  produced   for  the  "all
      variables provide information no one is critical" situation  and for
      the "all variables are critical, drop any one, everything is ruined"
      situation.

      Contrary to that, MDA will rate critical variable as ~1.0 important,
      and important but non-critical variable will  have  less  than  unit
      rating.

NOTE: quite an often MDA and MDI return same results. It generally happens
      on problems with low test set error (a few  percents  at  most)  and
      large enough training set to avoid overfitting.

      The difference between MDA, MDI and OOB-MDI becomes  important  only
      on "hard" tasks with high test set error and/or small training set.

INPUT PARAMETERS:
    S           -   decision forest builder object

OUTPUT PARAMETERS:
    S           -   decision forest builder object. Next call to the forest
                    construction function will produce:
                    * importance estimates in rep.varimportances field
                    * variable ranks in rep.topvars field

  -- ALGLIB --
     Copyright 29.07.2019 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetimportanceoobgini(decisionforestbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  tells  decision  forest  construction  algorithm  to   use
permutation variable importance estimator (also known as MDA).

This version of importance estimation algorithm analyzes mean increase  in
out-of-bag sum of squared  residuals  after  random  permutation  of  J-th
variable. The result is divided by error computed with all variables being
perturbed in order to produce R-squared-like estimate in [0,1] range.

Such estimate  is  slower to calculate than Gini-based rating  because  it
needs multiple inference runs for each of variables being studied.

ALGLIB uses parallelized and highly  optimized  algorithm  which  analyzes
path through the decision tree and allows  to  handle  most  perturbations
in O(1) time; nevertheless, requesting MDA importances may increase forest
construction time from 10% to 200% (or more,  if  you  have  thousands  of
variables).

However, MDA rating has following benefits over Gini-based ones:
* no bias towards specific variable types
* ability to directly evaluate "absolute" importance of some  variable  at
  "0 to 1" scale (contrary to Gini-based rating, which returns comparative
  importances).

NOTE: informally speaking, MDA (permutation importance) rating answers the
      question  "what  part  of  the  model  predictive power is ruined by
      permuting k-th variable?" while MDI tells us "what part of the model
      predictive power was achieved due to usage of k-th variable".

      Thus, MDA rates each variable independently at "0 to 1"  scale while
      MDI (and OOB-MDI too) tends to divide "unit  amount  of  importance"
      between several important variables.

      If  all  variables  are  equally  important,  they  will  have  same
      MDI/OOB-MDI rating, equal (for OOB-MDI: roughly equal)  to  1/NVars.
      However, roughly  same  picture  will  be  produced   for  the  "all
      variables provide information no one is critical" situation  and for
      the "all variables are critical, drop any one, everything is ruined"
      situation.

      Contrary to that, MDA will rate critical variable as ~1.0 important,
      and important but non-critical variable will  have  less  than  unit
      rating.

NOTE: quite an often MDA and MDI return same results. It generally happens
      on problems with low test set error (a few  percents  at  most)  and
      large enough training set to avoid overfitting.

      The difference between MDA, MDI and OOB-MDI becomes  important  only
      on "hard" tasks with high test set error and/or small training set.

INPUT PARAMETERS:
    S           -   decision forest builder object

OUTPUT PARAMETERS:
    S           -   decision forest builder object. Next call to the forest
                    construction function will produce:
                    * importance estimates in rep.varimportances field
                    * variable ranks in rep.topvars field

  -- ALGLIB --
     Copyright 29.07.2019 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetimportancepermutation(decisionforestbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  tells  decision  forest  construction  algorithm  to  skip
variable importance estimation.

INPUT PARAMETERS:
    S           -   decision forest builder object

OUTPUT PARAMETERS:
    S           -   decision forest builder object. Next call to the forest
                    construction function will result in forest being built
                    without variable importance estimation.

  -- ALGLIB --
     Copyright 29.07.2019 by Bochkanov Sergey
*************************************************************************/
void dfbuildersetimportancenone(decisionforestbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is an alias for dfbuilderpeekprogress(), left in ALGLIB  for
backward compatibility reasons.

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
double dfbuildergetprogress(const decisionforestbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is used to peek into  decision  forest  construction process
from some other thread and get current progress indicator.

It returns value in [0,1].

INPUT PARAMETERS:
    S           -   decision forest builder object used  to  build  forest
                    in some other thread

RESULT:
    progress value, in [0,1]

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
double dfbuilderpeekprogress(const decisionforestbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This subroutine builds decision forest according to current settings using
dataset internally stored in the builder object. Dense algorithm is used.

NOTE: this   function   uses   dense  algorithm  for  forest  construction
      independently from the dataset format (dense or sparse).

NOTE: forest built with this function is  stored  in-memory  using  64-bit
      data structures for offsets/indexes/split values. It is possible  to
      convert  forest  into  more  memory-efficient   compressed    binary
      representation.  Depending  on  the  problem  properties,  3.7x-5.7x
      compression factors are possible.

      The downsides of compression are (a) slight reduction in  the  model
      accuracy and (b) ~1.5x reduction in  the  inference  speed  (due  to
      increased complexity of the storage format).

      See comments on dfbinarycompression() for more info.

Default settings are used by the algorithm; you can tweak  them  with  the
help of the following functions:
* dfbuildersetrfactor() - to control a fraction of the  dataset  used  for
  subsampling
* dfbuildersetrandomvars() - to control number of variables randomly chosen
  for decision rule creation

  ! 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:
    S           -   decision forest builder object
    NTrees      -   NTrees>=1, number of trees to train

OUTPUT PARAMETERS:
    DF          -   decision forest. You can compress this forest to  more
                    compact 16-bit representation with dfbinarycompression()
    Rep         -   report, see below for information on its fields.

=== report information produced by forest construction function ==========

Decision forest training report includes following information:
* training set errors
* out-of-bag estimates of errors
* variable importance ratings

Following fields are used to store information:
* training set errors are stored in rep.relclserror, rep.avgce, rep.rmserror,
  rep.avgerror and rep.avgrelerror
* out-of-bag estimates of errors are stored in rep.oobrelclserror, rep.oobavgce,
  rep.oobrmserror, rep.oobavgerror and rep.oobavgrelerror

Variable importance reports, if requested by dfbuildersetimportancegini(),
dfbuildersetimportancetrngini() or dfbuildersetimportancepermutation()
call, are stored in:
* rep.varimportances field stores importance ratings
* rep.topvars stores variable indexes ordered from the most important to
  less important ones

You can find more information about report fields in:
* comments on dfreport structure
* comments on dfbuildersetimportancegini function
* comments on dfbuildersetimportancetrngini function
* comments on dfbuildersetimportancepermutation function

  -- ALGLIB --
     Copyright 21.05.2018 by Bochkanov Sergey
*************************************************************************/
void dfbuilderbuildrandomforest(decisionforestbuilder &s, const ae_int_t ntrees, decisionforest &df, dfreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function performs binary compression of the decision forest.

Original decision forest produced by the  forest  builder  is stored using
64-bit representation for all numbers - offsets, variable  indexes,  split
points.

It is possible to significantly reduce model size by means of:
* using compressed  dynamic encoding for integers  (offsets  and  variable
  indexes), which uses just 1 byte to store small ints  (less  than  128),
  just 2 bytes for larger values (less than 128^2) and so on
* storing floating point numbers using 8-bit exponent and 16-bit mantissa

As  result,  model  needs  significantly  less  memory (compression factor
depends on  variable and class counts). In particular:
* NVars<128   and NClasses<128 result in 4.4x-5.7x model size reduction
* NVars<16384 and NClasses<128 result in 3.7x-4.5x model size reduction

Such storage format performs lossless compression  of  all  integers,  but
compression of floating point values (split values) is lossy, with roughly
0.01% relative error introduced during rounding. Thus, we recommend you to
re-evaluate model accuracy after compression.

Another downside  of  compression  is  ~1.5x reduction  in  the  inference
speed due to necessity of dynamic decompression of the compressed model.

INPUT PARAMETERS:
    DF      -   decision forest built by forest builder

OUTPUT PARAMETERS:
    DF      -   replaced by compressed forest

RESULT:
    compression factor (in-RAM size of the compressed model vs than of the
    uncompressed one), positive number larger than 1.0

  -- ALGLIB --
     Copyright 22.07.2019 by Bochkanov Sergey
*************************************************************************/
double dfbinarycompression(decisionforest &df, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Inference using decision forest

IMPORTANT: this  function  is  thread-unsafe  and  may   modify   internal
           structures of the model! You can not use same model  object for
           parallel evaluation from several threads.

           Use dftsprocess()  with  independent  thread-local  buffers  if
           you need thread-safe evaluation.

INPUT PARAMETERS:
    DF      -   decision forest model
    X       -   input vector,  array[NVars]
    Y       -   possibly preallocated buffer, reallocated if too small

OUTPUT PARAMETERS:
    Y       -   result. Regression estimate when solving regression  task,
                vector of posterior probabilities for classification task.

See also DFProcessI.


  -- ALGLIB --
     Copyright 16.02.2009 by Bochkanov Sergey
*************************************************************************/
void dfprocess(const decisionforest &df, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
'interactive' variant of DFProcess for languages like Python which support
constructs like "Y = DFProcessI(DF,X)" and interactive mode of interpreter

This function allocates new array on each call,  so  it  is  significantly
slower than its 'non-interactive' counterpart, but it is  more  convenient
when you call it from command line.

IMPORTANT: this  function  is  thread-unsafe  and  may   modify   internal
           structures of the model! You can not use same model  object for
           parallel evaluation from several threads.

           Use dftsprocess()  with  independent  thread-local  buffers  if
           you need thread-safe evaluation.

  -- ALGLIB --
     Copyright 28.02.2010 by Bochkanov Sergey
*************************************************************************/
void dfprocessi(const decisionforest &df, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns first component of the  inferred  vector  (i.e.  one
with index #0).

It is a convenience wrapper for dfprocess() intended for either:
* 1-dimensional regression problems
* 2-class classification problems

In the former case this function returns inference result as scalar, which
is definitely more convenient that wrapping it as vector.  In  the  latter
case it returns probability of object belonging to class #0.

If you call it for anything different from two cases above, it  will  work
as defined, i.e. return y[0], although it is of less use in such cases.

IMPORTANT: this function is thread-unsafe and modifies internal structures
           of the model! You can not use same model  object  for  parallel
           evaluation from several threads.

           Use dftsprocess() with  independent  thread-local  buffers,  if
           you need thread-safe evaluation.

INPUT PARAMETERS:
    Model   -   DF model
    X       -   input vector,  array[0..NVars-1].

RESULT:
    Y[0]

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
double dfprocess0(decisionforest &model, const real_1d_array &x, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns most probable class number for an  input  X.  It  is
same as calling  dfprocess(model,x,y), then determining i=argmax(y[i]) and
returning i.

A class number in [0,NOut) range in returned for classification  problems,
-1 is returned when this function is called for regression problems.

IMPORTANT: this function is thread-unsafe and modifies internal structures
           of the model! You can not use same model  object  for  parallel
           evaluation from several threads.

           Use dftsprocess()  with independent  thread-local  buffers,  if
           you need thread-safe evaluation.

INPUT PARAMETERS:
    Model   -   decision forest model
    X       -   input vector,  array[0..NVars-1].

RESULT:
    class number, -1 for regression tasks

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
ae_int_t dfclassify(decisionforest &model, const real_1d_array &x, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Inference using decision forest

Thread-safe procesing using external buffer for temporaries.

This function is thread-safe (i.e .  you  can  use  same  DF   model  from
multiple threads) as long as you use different buffer objects for different
threads.

INPUT PARAMETERS:
    DF      -   decision forest model
    Buf     -   buffer object, must be  allocated  specifically  for  this
                model with dfcreatebuffer().
    X       -   input vector,  array[NVars]
    Y       -   possibly preallocated buffer, reallocated if too small

OUTPUT PARAMETERS:
    Y       -   result. Regression estimate when solving regression  task,
                vector of posterior probabilities for classification task.

See also DFProcessI.


  -- ALGLIB --
     Copyright 16.02.2009 by Bochkanov Sergey
*************************************************************************/
void dftsprocess(const decisionforest &df, decisionforestbuffer &buf, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Relative classification error on the test set

INPUT PARAMETERS:
    DF      -   decision forest model
    XY      -   test set
    NPoints -   test set size

RESULT:
    percent of incorrectly classified cases.
    Zero if model solves regression task.

  -- ALGLIB --
     Copyright 16.02.2009 by Bochkanov Sergey
*************************************************************************/
double dfrelclserror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average cross-entropy (in bits per element) on the test set

INPUT PARAMETERS:
    DF      -   decision forest model
    XY      -   test set
    NPoints -   test set size

RESULT:
    CrossEntropy/(NPoints*LN(2)).
    Zero if model solves regression task.

  -- ALGLIB --
     Copyright 16.02.2009 by Bochkanov Sergey
*************************************************************************/
double dfavgce(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
RMS error on the test set

INPUT PARAMETERS:
    DF      -   decision forest model
    XY      -   test set
    NPoints -   test set size

RESULT:
    root mean square error.
    Its meaning for regression task is obvious. As for
    classification task, RMS error means error when estimating posterior
    probabilities.

  -- ALGLIB --
     Copyright 16.02.2009 by Bochkanov Sergey
*************************************************************************/
double dfrmserror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average error on the test set

INPUT PARAMETERS:
    DF      -   decision forest model
    XY      -   test set
    NPoints -   test set size

RESULT:
    Its meaning for regression task is obvious. As for
    classification task, it means average error when estimating posterior
    probabilities.

  -- ALGLIB --
     Copyright 16.02.2009 by Bochkanov Sergey
*************************************************************************/
double dfavgerror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average relative error on the test set

INPUT PARAMETERS:
    DF      -   decision forest model
    XY      -   test set
    NPoints -   test set size

RESULT:
    Its meaning for regression task is obvious. As for
    classification task, it means average relative error when estimating
    posterior probability of belonging to the correct class.

  -- ALGLIB --
     Copyright 16.02.2009 by Bochkanov Sergey
*************************************************************************/
double dfavgrelerror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This subroutine builds random decision forest.

--------- DEPRECATED VERSION! USE DECISION FOREST BUILDER OBJECT ---------

  -- ALGLIB --
     Copyright 19.02.2009 by Bochkanov Sergey
*************************************************************************/
void dfbuildrandomdecisionforest(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, const ae_int_t ntrees, const double r, ae_int_t &info, decisionforest &df, dfreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This subroutine builds random decision forest.

--------- DEPRECATED VERSION! USE DECISION FOREST BUILDER OBJECT ---------

  -- ALGLIB --
     Copyright 19.02.2009 by Bochkanov Sergey
*************************************************************************/
void dfbuildrandomdecisionforestx1(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, const ae_int_t ntrees, const ae_int_t nrndvars, const double r, ae_int_t &info, decisionforest &df, dfreport &rep, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_LINREG) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Linear regression

Subroutine builds model:

    Y = A(0)*X[0] + ... + A(N-1)*X[N-1] + A(N)

and model found in ALGLIB format, covariation matrix, training set  errors
(rms,  average,  average  relative)   and  leave-one-out  cross-validation
estimate of the generalization error. CV  estimate calculated  using  fast
algorithm with O(NPoints*NVars) complexity.

When  covariation  matrix  is  calculated  standard deviations of function
values are assumed to be equal to RMS error on the training set.

INPUT PARAMETERS:
    XY          -   training set, array [0..NPoints-1,0..NVars]:
                    * NVars columns - independent variables
                    * last column - dependent variable
    NPoints     -   training set size, NPoints>NVars+1. An exception is
                    generated otherwise.
    NVars       -   number of independent variables

OUTPUT PARAMETERS:
    LM          -   linear model in the ALGLIB format. Use subroutines of
                    this unit to work with the model.
    Rep         -   additional results, see comments on LRReport structure.

  -- ALGLIB --
     Copyright 02.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuild(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, linearmodel &lm, lrreport &rep, const xparams _xparams = alglib::xdefault);
void lrbuild(const real_2d_array &xy, linearmodel &lm, lrreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Linear regression

Variant of LRBuild which uses vector of standatd deviations (errors in
function values).

INPUT PARAMETERS:
    XY          -   training set, array [0..NPoints-1,0..NVars]:
                    * NVars columns - independent variables
                    * last column - dependent variable
    S           -   standard deviations (errors in function values)
                    array[NPoints], S[i]>0.
    NPoints     -   training set size, NPoints>NVars+1
    NVars       -   number of independent variables

OUTPUT PARAMETERS:
    LM          -   linear model in the ALGLIB format. Use subroutines of
                    this unit to work with the model.
    Rep         -   additional results, see comments on LRReport structure.

  -- ALGLIB --
     Copyright 02.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuilds(const real_2d_array &xy, const real_1d_array &s, const ae_int_t npoints, const ae_int_t nvars, linearmodel &lm, lrreport &rep, const xparams _xparams = alglib::xdefault);
void lrbuilds(const real_2d_array &xy, const real_1d_array &s, linearmodel &lm, lrreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like LRBuildS, but builds model

    Y = A(0)*X[0] + ... + A(N-1)*X[N-1]

i.e. with zero constant term.

  -- ALGLIB --
     Copyright 30.10.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuildzs(const real_2d_array &xy, const real_1d_array &s, const ae_int_t npoints, const ae_int_t nvars, linearmodel &lm, lrreport &rep, const xparams _xparams = alglib::xdefault);
void lrbuildzs(const real_2d_array &xy, const real_1d_array &s, linearmodel &lm, lrreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Like LRBuild but builds model

    Y = A(0)*X[0] + ... + A(N-1)*X[N-1]

i.e. with zero constant term.

  -- ALGLIB --
     Copyright 30.10.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuildz(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, linearmodel &lm, lrreport &rep, const xparams _xparams = alglib::xdefault);
void lrbuildz(const real_2d_array &xy, linearmodel &lm, lrreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Unpacks coefficients of linear model.

INPUT PARAMETERS:
    LM          -   linear model in ALGLIB format

OUTPUT PARAMETERS:
    V           -   coefficients, array[0..NVars]
                    constant term (intercept) is stored in the V[NVars].
    NVars       -   number of independent variables (one less than number
                    of coefficients)

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrunpack(const linearmodel &lm, real_1d_array &v, ae_int_t &nvars, const xparams _xparams = alglib::xdefault);


/*************************************************************************
"Packs" coefficients and creates linear model in ALGLIB format (LRUnpack
reversed).

INPUT PARAMETERS:
    V           -   coefficients, array[0..NVars]
    NVars       -   number of independent variables

OUTPUT PAREMETERS:
    LM          -   linear model.

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrpack(const real_1d_array &v, const ae_int_t nvars, linearmodel &lm, const xparams _xparams = alglib::xdefault);
void lrpack(const real_1d_array &v, linearmodel &lm, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Procesing

INPUT PARAMETERS:
    LM      -   linear model
    X       -   input vector,  array[0..NVars-1].

Result:
    value of linear model regression estimate

  -- ALGLIB --
     Copyright 03.09.2008 by Bochkanov Sergey
*************************************************************************/
double lrprocess(const linearmodel &lm, const real_1d_array &x, const xparams _xparams = alglib::xdefault);


/*************************************************************************
RMS error on the test set

INPUT PARAMETERS:
    LM      -   linear model
    XY      -   test set
    NPoints -   test set size

RESULT:
    root mean square error.

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lrrmserror(const linearmodel &lm, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average error on the test set

INPUT PARAMETERS:
    LM      -   linear model
    XY      -   test set
    NPoints -   test set size

RESULT:
    average error.

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lravgerror(const linearmodel &lm, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
RMS error on the test set

INPUT PARAMETERS:
    LM      -   linear model
    XY      -   test set
    NPoints -   test set size

RESULT:
    average relative error.

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lravgrelerror(const linearmodel &lm, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_FILTERS) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Filters: simple moving averages (unsymmetric).

This filter replaces array by results of SMA(K) filter. SMA(K) is defined
as filter which averages at most K previous points (previous - not points
AROUND central point) - or less, in case of the first K-1 points.

INPUT PARAMETERS:
    X           -   array[N], array to process. It can be larger than N,
                    in this case only first N points are processed.
    N           -   points count, N>=0
    K           -   K>=1 (K can be larger than N ,  such  cases  will  be
                    correctly handled). Window width. K=1 corresponds  to
                    identity transformation (nothing changes).

OUTPUT PARAMETERS:
    X           -   array, whose first N elements were processed with SMA(K)

NOTE 1: this function uses efficient in-place  algorithm  which  does not
        allocate temporary arrays.

NOTE 2: this algorithm makes only one pass through array and uses running
        sum  to speed-up calculation of the averages. Additional measures
        are taken to ensure that running sum on a long sequence  of  zero
        elements will be correctly reset to zero even in the presence  of
        round-off error.

NOTE 3: this  is  unsymmetric version of the algorithm,  which  does  NOT
        averages points after the current one. Only X[i], X[i-1], ... are
        used when calculating new value of X[i]. We should also note that
        this algorithm uses BOTH previous points and  current  one,  i.e.
        new value of X[i] depends on BOTH previous point and X[i] itself.

  -- ALGLIB --
     Copyright 25.10.2011 by Bochkanov Sergey
*************************************************************************/
void filtersma(real_1d_array &x, const ae_int_t n, const ae_int_t k, const xparams _xparams = alglib::xdefault);
void filtersma(real_1d_array &x, const ae_int_t k, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Filters: exponential moving averages.

This filter replaces array by results of EMA(alpha) filter. EMA(alpha) is
defined as filter which replaces X[] by S[]:
    S[0] = X[0]
    S[t] = alpha*X[t] + (1-alpha)*S[t-1]

INPUT PARAMETERS:
    X           -   array[N], array to process. It can be larger than N,
                    in this case only first N points are processed.
    N           -   points count, N>=0
    alpha       -   0<alpha<=1, smoothing parameter.

OUTPUT PARAMETERS:
    X           -   array, whose first N elements were processed
                    with EMA(alpha)

NOTE 1: this function uses efficient in-place  algorithm  which  does not
        allocate temporary arrays.

NOTE 2: this algorithm uses BOTH previous points and  current  one,  i.e.
        new value of X[i] depends on BOTH previous point and X[i] itself.

NOTE 3: technical analytis users quite often work  with  EMA  coefficient
        expressed in DAYS instead of fractions. If you want to  calculate
        EMA(N), where N is a number of days, you can use alpha=2/(N+1).

  -- ALGLIB --
     Copyright 25.10.2011 by Bochkanov Sergey
*************************************************************************/
void filterema(real_1d_array &x, const ae_int_t n, const double alpha, const xparams _xparams = alglib::xdefault);
void filterema(real_1d_array &x, const double alpha, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Filters: linear regression moving averages.

This filter replaces array by results of LRMA(K) filter.

LRMA(K) is defined as filter which, for each data  point,  builds  linear
regression  model  using  K  prevous  points (point itself is included in
these K points) and calculates value of this linear model at the point in
question.

INPUT PARAMETERS:
    X           -   array[N], array to process. It can be larger than N,
                    in this case only first N points are processed.
    N           -   points count, N>=0
    K           -   K>=1 (K can be larger than N ,  such  cases  will  be
                    correctly handled). Window width. K=1 corresponds  to
                    identity transformation (nothing changes).

OUTPUT PARAMETERS:
    X           -   array, whose first N elements were processed with LRMA(K)

NOTE 1: this function uses efficient in-place  algorithm  which  does not
        allocate temporary arrays.

NOTE 2: this algorithm makes only one pass through array and uses running
        sum  to speed-up calculation of the averages. Additional measures
        are taken to ensure that running sum on a long sequence  of  zero
        elements will be correctly reset to zero even in the presence  of
        round-off error.

NOTE 3: this  is  unsymmetric version of the algorithm,  which  does  NOT
        averages points after the current one. Only X[i], X[i-1], ... are
        used when calculating new value of X[i]. We should also note that
        this algorithm uses BOTH previous points and  current  one,  i.e.
        new value of X[i] depends on BOTH previous point and X[i] itself.

  -- ALGLIB --
     Copyright 25.10.2011 by Bochkanov Sergey
*************************************************************************/
void filterlrma(real_1d_array &x, const ae_int_t n, const ae_int_t k, const xparams _xparams = alglib::xdefault);
void filterlrma(real_1d_array &x, const ae_int_t k, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_SSA) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
This function creates SSA model object.  Right after creation model is  in
"dummy" mode - you can add data,  but   analyzing/prediction  will  return
just zeros (it assumes that basis is empty).

HOW TO USE SSA MODEL:

1. create model with ssacreate()
2. add data with one/many ssaaddsequence() calls
3. choose SSA algorithm with one of ssasetalgo...() functions:
   * ssasetalgotopkdirect() for direct one-run analysis
   * ssasetalgotopkrealtime() for algorithm optimized for many  subsequent
     runs with warm-start capabilities
   * ssasetalgoprecomputed() for user-supplied basis
4. set window width with ssasetwindow()
5. perform one of the analysis-related activities:
   a) call ssagetbasis() to get basis
   b) call ssaanalyzelast() ssaanalyzesequence() or ssaanalyzelastwindow()
      to perform analysis (trend/noise separation)
   c) call  one  of   the   forecasting   functions  (ssaforecastlast() or
      ssaforecastsequence()) to perform prediction; alternatively, you can
      extract linear recurrence coefficients with ssagetlrr().
   SSA analysis will be performed during first  call  to  analysis-related
   function. SSA model is smart enough to track all changes in the dataset
   and  model  settings,  to  cache  previously  computed  basis  and   to
   re-evaluate basis only when necessary.

Additionally, if your setting involves constant stream  of  incoming data,
you can perform quick update already calculated  model  with  one  of  the
incremental   append-and-update   functions:  ssaappendpointandupdate() or
ssaappendsequenceandupdate().

NOTE: steps (2), (3), (4) can be performed in arbitrary order.

INPUT PARAMETERS:
    none

OUTPUT PARAMETERS:
    S               -   structure which stores model state

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssacreate(ssamodel &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets window width for SSA model. You should call  it  before
analysis phase. Default window width is 1 (not for real use).

Special notes:
* this function call can be performed at any moment before  first call  to
  analysis-related functions
* changing window width invalidates internally stored basis; if you change
  window width AFTER you call analysis-related  function,  next  analysis
  phase will require re-calculation of  the  basis  according  to  current
  algorithm.
* calling this function with exactly  same window width as current one has
  no effect
* if you specify window width larger  than any data sequence stored in the
  model, analysis will return zero basis.

INPUT PARAMETERS:
    S               -   SSA model created with ssacreate()
    WindowWidth     -   >=1, new window width

OUTPUT PARAMETERS:
    S               -   SSA model, updated

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssasetwindow(ssamodel &s, const ae_int_t windowwidth, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  sets  seed  which  is used to initialize internal RNG when
we make pseudorandom decisions on model updates.

By default, deterministic seed is used - which results in same sequence of
pseudorandom decisions every time you run SSA model. If you  specify  non-
deterministic seed value, then SSA  model  may  return  slightly different
results after each run.

This function can be useful when you have several SSA models updated  with
sseappendpointandupdate() called with 0<UpdateIts<1 (fractional value) and
due to performance limitations want them to perform updates  at  different
moments.

INPUT PARAMETERS:
    S       -   SSA model
    Seed    -   seed:
                * positive values = use deterministic seed for each run of
                  algorithms which depend on random initialization
                * zero or negative values = use non-deterministic seed

  -- ALGLIB --
     Copyright 03.11.2017 by Bochkanov Sergey
*************************************************************************/
void ssasetseed(ssamodel &s, const ae_int_t seed, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets length of power-up cycle for real-time algorithm.

By default, this algorithm performs costly O(N*WindowWidth^2)  init  phase
followed by full run of truncated  EVD.  However,  if  you  are  ready  to
live with a bit lower-quality basis during first few iterations,  you  can
split this O(N*WindowWidth^2) initialization  between  several  subsequent
append-and-update rounds. It results in better latency of the algorithm.

This function invalidates basis/solver, next analysis call will result  in
full recalculation of everything.

INPUT PARAMETERS:
    S       -   SSA model
    PWLen   -   length of the power-up stage:
                * 0 means that no power-up is requested
                * 1 is the same as 0
                * >1 means that delayed power-up is performed

  -- ALGLIB --
     Copyright 03.11.2017 by Bochkanov Sergey
*************************************************************************/
void ssasetpoweruplength(ssamodel &s, const ae_int_t pwlen, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets memory limit of SSA analysis.

Straightforward SSA with sequence length T and window width W needs O(T*W)
memory. It is possible to reduce memory consumption by splitting task into
smaller chunks.

Thus function allows you to specify approximate memory limit (measured  in
double precision numbers used for buffers). Actual memory consumption will
be comparable to the number specified by you.

Default memory limit is 50.000.000 (400Mbytes) in current version.

INPUT PARAMETERS:
    S       -   SSA model
    MemLimit-   memory limit, >=0. Zero value means no limit.

  -- ALGLIB --
     Copyright 20.12.2017 by Bochkanov Sergey
*************************************************************************/
void ssasetmemorylimit(ssamodel &s, const ae_int_t memlimit, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function adds data sequence to SSA  model.  Only   single-dimensional
sequences are supported.

What is a sequences? Following definitions/requirements apply:
* a sequence  is  an  array of  values  measured  in  subsequent,  equally
  separated time moments (ticks).
* you may have many sequences  in your  dataset;  say,  one  sequence  may
  correspond to one trading session.
* sequence length should be larger  than current  window  length  (shorter
  sequences will be ignored during analysis).
* analysis is performed within a  sequence; different  sequences  are  NOT
  stacked together to produce one large contiguous stream of data.
* analysis is performed for all  sequences at once, i.e. same set of basis
  vectors is computed for all sequences

INCREMENTAL ANALYSIS

This function is non intended for  incremental updates of previously found
SSA basis. Calling it invalidates  all previous analysis results (basis is
reset and will be recalculated from zero during next analysis).

If  you  want  to  perform   incremental/real-time  SSA,  consider   using
following functions:
* ssaappendpointandupdate() for appending one point
* ssaappendsequenceandupdate() for appending new sequence

INPUT PARAMETERS:
    S               -   SSA model created with ssacreate()
    X               -   array[N], data, can be larger (additional values
                        are ignored)
    N               -   data length, can be automatically determined from
                        the array length. N>=0.

OUTPUT PARAMETERS:
    S               -   SSA model, updated

NOTE: you can clear dataset with ssacleardata()

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaaddsequence(ssamodel &s, const real_1d_array &x, const ae_int_t n, const xparams _xparams = alglib::xdefault);
void ssaaddsequence(ssamodel &s, const real_1d_array &x, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function appends single point to last data sequence stored in the SSA
model and tries to update model in the  incremental  manner  (if  possible
with current algorithm).

If you want to add more than one point at once:
* if you want to add M points to the same sequence, perform M-1 calls with
  UpdateIts parameter set to 0.0, and last call with non-zero UpdateIts.
* if you want to add new sequence, use ssaappendsequenceandupdate()

Running time of this function does NOT depend on  dataset  size,  only  on
window width and number of singular vectors. Depending on algorithm  being
used, incremental update has complexity:
* for top-K real time   - O(UpdateIts*K*Width^2), with fractional UpdateIts
* for top-K direct      - O(Width^3) for any non-zero UpdateIts
* for precomputed basis - O(1), no update is performed

INPUT PARAMETERS:
    S               -   SSA model created with ssacreate()
    X               -   new point
    UpdateIts       -   >=0,  floating  point (!)  value,  desired  update
                        frequency:
                        * zero value means that point is  stored,  but  no
                          update is performed
                        * integer part of the value means  that  specified
                          number of iterations is always performed
                        * fractional part of  the  value  means  that  one
                          iteration is performed with this probability.

                        Recommended value: 0<UpdateIts<=1.  Values  larger
                        than 1 are VERY seldom  needed.  If  your  dataset
                        changes slowly, you can set it  to  0.1  and  skip
                        90% of updates.

                        In any case, no information is lost even with zero
                        value of UpdateIts! It will be  incorporated  into
                        model, sooner or later.

OUTPUT PARAMETERS:
    S               -   SSA model, updated

NOTE: this function uses internal  RNG  to  handle  fractional  values  of
      UpdateIts. By default it  is  initialized  with  fixed  seed  during
      initial calculation of basis. Thus subsequent calls to this function
      will result in the same sequence of pseudorandom decisions.

      However, if  you  have  several  SSA  models  which  are  calculated
      simultaneously, and if you want to reduce computational  bottlenecks
      by performing random updates at random moments, then fixed  seed  is
      not an option - all updates will fire at same moments.

      You may change it with ssasetseed() function.

NOTE: this function throws an exception if called for empty dataset (there
      is no "last" sequence to modify).

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaappendpointandupdate(ssamodel &s, const double x, const double updateits, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function appends new sequence to dataset stored in the SSA  model and
tries to update model in the incremental manner (if possible  with current
algorithm).

Notes:
* if you want to add M sequences at once, perform M-1 calls with UpdateIts
  parameter set to 0.0, and last call with non-zero UpdateIts.
* if you want to add just one point, use ssaappendpointandupdate()

Running time of this function does NOT depend on  dataset  size,  only  on
sequence length, window width and number of singular vectors. Depending on
algorithm being used, incremental update has complexity:
* for top-K real time   - O(UpdateIts*K*Width^2+(NTicks-Width)*Width^2)
* for top-K direct      - O(Width^3+(NTicks-Width)*Width^2)
* for precomputed basis - O(1), no update is performed

INPUT PARAMETERS:
    S               -   SSA model created with ssacreate()
    X               -   new sequence, array[NTicks] or larget
    NTicks          -   >=1, number of ticks in the sequence
    UpdateIts       -   >=0,  floating  point (!)  value,  desired  update
                        frequency:
                        * zero value means that point is  stored,  but  no
                          update is performed
                        * integer part of the value means  that  specified
                          number of iterations is always performed
                        * fractional part of  the  value  means  that  one
                          iteration is performed with this probability.

                        Recommended value: 0<UpdateIts<=1.  Values  larger
                        than 1 are VERY seldom  needed.  If  your  dataset
                        changes slowly, you can set it  to  0.1  and  skip
                        90% of updates.

                        In any case, no information is lost even with zero
                        value of UpdateIts! It will be  incorporated  into
                        model, sooner or later.

OUTPUT PARAMETERS:
    S               -   SSA model, updated

NOTE: this function uses internal  RNG  to  handle  fractional  values  of
      UpdateIts. By default it  is  initialized  with  fixed  seed  during
      initial calculation of basis. Thus subsequent calls to this function
      will result in the same sequence of pseudorandom decisions.

      However, if  you  have  several  SSA  models  which  are  calculated
      simultaneously, and if you want to reduce computational  bottlenecks
      by performing random updates at random moments, then fixed  seed  is
      not an option - all updates will fire at same moments.

      You may change it with ssasetseed() function.

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaappendsequenceandupdate(ssamodel &s, const real_1d_array &x, const ae_int_t nticks, const double updateits, const xparams _xparams = alglib::xdefault);
void ssaappendsequenceandupdate(ssamodel &s, const real_1d_array &x, const double updateits, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function sets SSA algorithm to "precomputed vectors" algorithm.

This  algorithm  uses  precomputed  set  of  orthonormal  (orthogonal  AND
normalized) basis vectors supplied by user. Thus, basis calculation  phase
is not performed -  we  already  have  our  basis  -  and  only  analysis/
forecasting phase requires actual calculations.

This algorithm may handle "append" requests which add just  one/few  ticks
to the end of the last sequence in O(1) time.

NOTE: this algorithm accepts both basis and window  width,  because  these
      two parameters are naturally aligned.  Calling  this  function  sets
      window width; if you call ssasetwindow() with  other  window  width,
      then during analysis stage algorithm will detect conflict and  reset
      to zero basis.

INPUT PARAMETERS:
    S               -   SSA model
    A               -   array[WindowWidth,NBasis], orthonormalized  basis;
                        this function does NOT control  orthogonality  and
                        does NOT perform any kind of  renormalization.  It
                        is your responsibility to provide it with  correct
                        basis.
    WindowWidth     -   window width, >=1
    NBasis          -   number of basis vectors, 1<=NBasis<=WindowWidth

OUTPUT PARAMETERS:
    S               -   updated model

NOTE: calling this function invalidates basis in all cases.

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssasetalgoprecomputed(ssamodel &s, const real_2d_array &a, const ae_int_t windowwidth, const ae_int_t nbasis, const xparams _xparams = alglib::xdefault);
void ssasetalgoprecomputed(ssamodel &s, const real_2d_array &a, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function sets SSA algorithm to "direct top-K" algorithm.

"Direct top-K" algorithm performs full  SVD  of  the  N*WINDOW  trajectory
matrix (hence its name - direct solver  is  used),  then  extracts  top  K
components. Overall running time is O(N*WINDOW^2), where N is a number  of
ticks in the dataset, WINDOW is window width.

This algorithm may handle "append" requests which add just  one/few  ticks
to the end of the last sequence in O(WINDOW^3) time,  which  is  ~N/WINDOW
times faster than re-computing everything from scratch.

INPUT PARAMETERS:
    S               -   SSA model
    TopK            -   number of components to analyze; TopK>=1.

OUTPUT PARAMETERS:
    S               -   updated model


NOTE: TopK>WindowWidth is silently decreased to WindowWidth during analysis
      phase

NOTE: calling this function invalidates basis, except  for  the  situation
      when this algorithm was already set with same parameters.

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssasetalgotopkdirect(ssamodel &s, const ae_int_t topk, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets SSA algorithm to "top-K real time algorithm". This algo
extracts K components with largest singular values.

It  is  real-time  version  of  top-K  algorithm  which  is  optimized for
incremental processing and  fast  start-up. Internally  it  uses  subspace
eigensolver for truncated SVD. It results  in  ability  to  perform  quick
updates of the basis when only a few points/sequences is added to dataset.

Performance profile of the algorithm is given below:
* O(K*WindowWidth^2) running time for incremental update  of  the  dataset
  with one of the "append-and-update" functions (ssaappendpointandupdate()
  or ssaappendsequenceandupdate()).
* O(N*WindowWidth^2) running time for initial basis evaluation (N=size  of
  dataset)
* ability  to  split  costly  initialization  across  several  incremental
  updates of the basis (so called "Power-Up" functionality,  activated  by
  ssasetpoweruplength() function)

INPUT PARAMETERS:
    S               -   SSA model
    TopK            -   number of components to analyze; TopK>=1.

OUTPUT PARAMETERS:
    S               -   updated model

NOTE: this  algorithm  is  optimized  for  large-scale  tasks  with  large
      datasets. On toy problems with just  5-10 points it can return basis
      which is slightly different from that returned by  direct  algorithm
      (ssasetalgotopkdirect() function). However, the  difference  becomes
      negligible as dataset grows.

NOTE: TopK>WindowWidth is silently decreased to WindowWidth during analysis
      phase

NOTE: calling this function invalidates basis, except  for  the  situation
      when this algorithm was already set with same parameters.

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssasetalgotopkrealtime(ssamodel &s, const ae_int_t topk, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function clears all data stored in the  model  and  invalidates  all
basis components found so far.

INPUT PARAMETERS:
    S               -   SSA model created with ssacreate()

OUTPUT PARAMETERS:
    S               -   SSA model, updated

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssacleardata(ssamodel &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function executes SSA on internally stored dataset and returns  basis
found by current method.

INPUT PARAMETERS:
    S               -   SSA model

OUTPUT PARAMETERS:
    A               -   array[WindowWidth,NBasis],   basis;  vectors  are
                        stored in matrix columns, by descreasing variance
    SV              -   array[NBasis]:
                        * zeros - for model initialized with SSASetAlgoPrecomputed()
                        * singular values - for other algorithms
    WindowWidth     -   current window
    NBasis          -   basis size


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.


HANDLING OF DEGENERATE CASES

Calling  this  function  in  degenerate  cases  (no  data  or all data are
shorter than window size; no algorithm is specified)  returns  basis  with
just one zero vector.

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssagetbasis(ssamodel &s, real_2d_array &a, real_1d_array &sv, ae_int_t &windowwidth, ae_int_t &nbasis, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns linear recurrence relation (LRR) coefficients  found
by current SSA algorithm.

INPUT PARAMETERS:
    S               -   SSA model

OUTPUT PARAMETERS:
    A               -   array[WindowWidth-1]. Coefficients  of  the
                        linear recurrence of the form:
                        X[W-1] = X[W-2]*A[W-2] + X[W-3]*A[W-3] + ... + X[0]*A[0].
                        Empty array for WindowWidth=1.
    WindowWidth     -   current window width


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.


HANDLING OF DEGENERATE CASES

Calling  this  function  in  degenerate  cases  (no  data  or all data are
shorter than window size; no algorithm is specified) returns zeros.

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssagetlrr(ssamodel &s, real_1d_array &a, ae_int_t &windowwidth, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  executes  SSA  on  internally  stored  dataset and returns
analysis  for  the  last  window  of  the  last sequence. Such analysis is
an lightweight alternative for full scale reconstruction (see below).

Typical use case for this function is  real-time  setting,  when  you  are
interested in quick-and-dirty (very quick and very  dirty)  processing  of
just a few last ticks of the trend.

IMPORTANT: full  scale  SSA  involves  analysis  of  the  ENTIRE  dataset,
           with reconstruction being done for  all  positions  of  sliding
           window with subsequent hankelization  (diagonal  averaging)  of
           the resulting matrix.

           Such analysis requires O((DataLen-Window)*Window*NBasis)  FLOPs
           and can be quite costly. However, it has  nice  noise-canceling
           effects due to averaging.

           This function performs REDUCED analysis of the last window.  It
           is much faster - just O(Window*NBasis),  but  its  results  are
           DIFFERENT from that of ssaanalyzelast(). In  particular,  first
           few points of the trend are much more prone to noise.

INPUT PARAMETERS:
    S               -   SSA model

OUTPUT PARAMETERS:
    Trend           -   array[WindowSize], reconstructed trend line
    Noise           -   array[WindowSize], the rest of the signal;
                        it holds that ActualData = Trend+Noise.
    NTicks          -   current WindowSize


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.

In  any  case,  only  basis  is  reused. Reconstruction is performed  from
scratch every time you call this function.


HANDLING OF DEGENERATE CASES

Following degenerate cases may happen:
* dataset is empty (no analysis can be done)
* all sequences are shorter than the window length,no analysis can be done
* no algorithm is specified (no analysis can be done)
* last sequence is shorter than the window length (analysis can  be  done,
  but we can not perform reconstruction on the last sequence)

Calling this function in degenerate cases returns following result:
* in any case, WindowWidth ticks is returned
* trend is assumed to be zero
* noise is initialized by the last sequence; if last sequence  is  shorter
  than the window size, it is moved to  the  end  of  the  array, and  the
  beginning of the noise array is filled by zeros

No analysis is performed in degenerate cases (we immediately return  dummy
values, no basis is constructed).

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaanalyzelastwindow(ssamodel &s, real_1d_array &trend, real_1d_array &noise, ae_int_t &nticks, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function:
* builds SSA basis using internally stored (entire) dataset
* returns reconstruction for the last NTicks of the last sequence

If you want to analyze some other sequence, use ssaanalyzesequence().

Reconstruction phase involves  generation  of  NTicks-WindowWidth  sliding
windows, their decomposition using empirical orthogonal functions found by
SSA, followed by averaging of each data point across  several  overlapping
windows. Thus, every point in the output trend is reconstructed  using  up
to WindowWidth overlapping  windows  (WindowWidth windows exactly  in  the
inner points, just one window at the extremal points).

IMPORTANT: due to averaging this function returns  different  results  for
           different values of NTicks. It is expected and not a bug.

           For example:
           * Trend[NTicks-1] is always same because it is not averaged  in
             any case (same applies to Trend[0]).
           * Trend[NTicks-2] has different values  for  NTicks=WindowWidth
             and NTicks=WindowWidth+1 because former  case  means that  no
             averaging is performed, and latter  case means that averaging
             using two sliding windows  is  performed.  Larger  values  of
             NTicks produce same results as NTicks=WindowWidth+1.
           * ...and so on...

PERFORMANCE: this  function has O((NTicks-WindowWidth)*WindowWidth*NBasis)
             running time. If you work  in  time-constrained  setting  and
             have to analyze just a few last ticks, choosing NTicks  equal
             to WindowWidth+SmoothingLen, with SmoothingLen=1...WindowWidth
             will result in good compromise between noise cancellation and
             analysis speed.

INPUT PARAMETERS:
    S               -   SSA model
    NTicks          -   number of ticks to analyze, Nticks>=1.
                        * special case of NTicks<=WindowWidth  is  handled
                          by analyzing last window and  returning   NTicks
                          last ticks.
                        * special case NTicks>LastSequenceLen  is  handled
                          by prepending result with NTicks-LastSequenceLen
                          zeros.

OUTPUT PARAMETERS:
    Trend           -   array[NTicks], reconstructed trend line
    Noise           -   array[NTicks], the rest of the signal;
                        it holds that ActualData = Trend+Noise.


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.

In  any  case,  only  basis  is  reused. Reconstruction is performed  from
scratch every time you call this function.


HANDLING OF DEGENERATE CASES

Following degenerate cases may happen:
* dataset is empty (no analysis can be done)
* all sequences are shorter than the window length,no analysis can be done
* no algorithm is specified (no analysis can be done)
* last sequence is shorter than the window length (analysis  can  be done,
  but we can not perform reconstruction on the last sequence)

Calling this function in degenerate cases returns following result:
* in any case, NTicks ticks is returned
* trend is assumed to be zero
* noise is initialized by the last sequence; if last sequence  is  shorter
  than the window size, it is moved to  the  end  of  the  array, and  the
  beginning of the noise array is filled by zeros

No analysis is performed in degenerate cases (we immediately return  dummy
values, no basis is constructed).

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaanalyzelast(ssamodel &s, const ae_int_t nticks, real_1d_array &trend, real_1d_array &noise, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function:
* builds SSA basis using internally stored (entire) dataset
* returns reconstruction for the sequence being passed to this function

If  you  want  to  analyze  last  sequence  stored  in   the   model,  use
ssaanalyzelast().

Reconstruction phase involves  generation  of  NTicks-WindowWidth  sliding
windows, their decomposition using empirical orthogonal functions found by
SSA, followed by averaging of each data point across  several  overlapping
windows. Thus, every point in the output trend is reconstructed  using  up
to WindowWidth overlapping  windows  (WindowWidth windows exactly  in  the
inner points, just one window at the extremal points).

PERFORMANCE: this  function has O((NTicks-WindowWidth)*WindowWidth*NBasis)
             running time. If you work  in  time-constrained  setting  and
             have to analyze just a few last ticks, choosing NTicks  equal
             to WindowWidth+SmoothingLen, with SmoothingLen=1...WindowWidth
             will result in good compromise between noise cancellation and
             analysis speed.

INPUT PARAMETERS:
    S               -   SSA model
    Data            -   array[NTicks], can be larger (only NTicks  leading
                        elements will be used)
    NTicks          -   number of ticks to analyze, Nticks>=1.
                        * special case of NTicks<WindowWidth  is   handled
                          by returning zeros as trend, and signal as noise

OUTPUT PARAMETERS:
    Trend           -   array[NTicks], reconstructed trend line
    Noise           -   array[NTicks], the rest of the signal;
                        it holds that ActualData = Trend+Noise.


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.

In  any  case,  only  basis  is  reused. Reconstruction is performed  from
scratch every time you call this function.


HANDLING OF DEGENERATE CASES

Following degenerate cases may happen:
* dataset is empty (no analysis can be done)
* all sequences are shorter than the window length,no analysis can be done
* no algorithm is specified (no analysis can be done)
* sequence being passed is shorter than the window length

Calling this function in degenerate cases returns following result:
* in any case, NTicks ticks is returned
* trend is assumed to be zero
* noise is initialized by the sequence.

No analysis is performed in degenerate cases (we immediately return  dummy
values, no basis is constructed).

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaanalyzesequence(ssamodel &s, const real_1d_array &data, const ae_int_t nticks, real_1d_array &trend, real_1d_array &noise, const xparams _xparams = alglib::xdefault);
void ssaanalyzesequence(ssamodel &s, const real_1d_array &data, real_1d_array &trend, real_1d_array &noise, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function builds SSA basis and performs forecasting  for  a  specified
number of ticks, returning value of trend.

Forecast is performed as follows:
* SSA  trend  extraction  is  applied  to last WindowWidth elements of the
  internally stored dataset; this step is basically a noise reduction.
* linear recurrence relation is applied to extracted trend

This function has following running time:
* O(NBasis*WindowWidth) for trend extraction phase (always performed)
* O(WindowWidth*NTicks) for forecast phase

NOTE: noise reduction is ALWAYS applied by this algorithm; if you want  to
      apply recurrence relation  to  raw  unprocessed  data,  use  another
      function - ssaforecastsequence() which allows to  turn  on  and  off
      noise reduction phase.

NOTE: this algorithm performs prediction using only one - last  -  sliding
      window.  Predictions  produced   by   such   approach   are   smooth
      continuations of the reconstructed  trend  line,  but  they  can  be
      easily corrupted by noise. If you need  noise-resistant  prediction,
      use ssaforecastavglast() function, which averages predictions  built
      using several sliding windows.

INPUT PARAMETERS:
    S               -   SSA model
    NTicks          -   number of ticks to forecast, NTicks>=1

OUTPUT PARAMETERS:
    Trend           -   array[NTicks], predicted trend line


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.


HANDLING OF DEGENERATE CASES

Following degenerate cases may happen:
* dataset is empty (no analysis can be done)
* all sequences are shorter than the window length,no analysis can be done
* no algorithm is specified (no analysis can be done)
* last sequence is shorter than the WindowWidth   (analysis  can  be done,
  but we can not perform forecasting on the last sequence)
* window lentgh is 1 (impossible to use for forecasting)
* SSA analysis algorithm is  configured  to  extract  basis  whose size is
  equal to window length (impossible to use for  forecasting;  only  basis
  whose size is less than window length can be used).

Calling this function in degenerate cases returns following result:
* NTicks  copies  of  the  last  value is returned for non-empty task with
  large enough dataset, but with overcomplete  basis  (window  width=1  or
  basis size is equal to window width)
* zero trend with length=NTicks is returned for empty task

No analysis is performed in degenerate cases (we immediately return  dummy
values, no basis is ever constructed).

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaforecastlast(ssamodel &s, const ae_int_t nticks, real_1d_array &trend, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function builds SSA  basis  and  performs  forecasting  for  a  user-
specified sequence, returning value of trend.

Forecasting is done in two stages:
* first,  we  extract  trend  from the WindowWidth  last  elements of  the
  sequence. This stage is optional, you  can  turn  it  off  if  you  pass
  data which are already processed with SSA. Of course, you  can  turn  it
  off even for raw data, but it is not recommended - noise suppression  is
  very important for correct prediction.
* then, we apply LRR for last  WindowWidth-1  elements  of  the  extracted
  trend.

This function has following running time:
* O(NBasis*WindowWidth) for trend extraction phase
* O(WindowWidth*NTicks) for forecast phase

NOTE: this algorithm performs prediction using only one - last  -  sliding
      window.  Predictions  produced   by   such   approach   are   smooth
      continuations of the reconstructed  trend  line,  but  they  can  be
      easily corrupted by noise. If you need  noise-resistant  prediction,
      use ssaforecastavgsequence() function,  which  averages  predictions
      built using several sliding windows.

INPUT PARAMETERS:
    S               -   SSA model
    Data            -   array[NTicks], data to forecast
    DataLen         -   number of ticks in the data, DataLen>=1
    ForecastLen     -   number of ticks to predict, ForecastLen>=1
    ApplySmoothing  -   whether to apply smoothing trend extraction or not;
                        if you do not know what to specify, pass True.

OUTPUT PARAMETERS:
    Trend           -   array[ForecastLen], forecasted trend


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.


HANDLING OF DEGENERATE CASES

Following degenerate cases may happen:
* dataset is empty (no analysis can be done)
* all sequences are shorter than the window length,no analysis can be done
* no algorithm is specified (no analysis can be done)
* data sequence is shorter than the WindowWidth   (analysis  can  be done,
  but we can not perform forecasting on the last sequence)
* window lentgh is 1 (impossible to use for forecasting)
* SSA analysis algorithm is  configured  to  extract  basis  whose size is
  equal to window length (impossible to use for  forecasting;  only  basis
  whose size is less than window length can be used).

Calling this function in degenerate cases returns following result:
* ForecastLen copies of the last value is returned for non-empty task with
  large enough dataset, but with overcomplete  basis  (window  width=1  or
  basis size is equal to window width)
* zero trend with length=ForecastLen is returned for empty task

No analysis is performed in degenerate cases (we immediately return  dummy
values, no basis is ever constructed).

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaforecastsequence(ssamodel &s, const real_1d_array &data, const ae_int_t datalen, const ae_int_t forecastlen, const bool applysmoothing, real_1d_array &trend, const xparams _xparams = alglib::xdefault);
void ssaforecastsequence(ssamodel &s, const real_1d_array &data, const ae_int_t forecastlen, real_1d_array &trend, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function builds SSA basis and performs forecasting  for  a  specified
number of ticks, returning value of trend.

Forecast is performed as follows:
* SSA  trend  extraction  is  applied to last  M  sliding windows  of  the
  internally stored dataset
* for each of M sliding windows, M predictions are built
* average value of M predictions is returned

This function has following running time:
* O(NBasis*WindowWidth*M) for trend extraction phase (always performed)
* O(WindowWidth*NTicks*M) for forecast phase

NOTE: noise reduction is ALWAYS applied by this algorithm; if you want  to
      apply recurrence relation  to  raw  unprocessed  data,  use  another
      function - ssaforecastsequence() which allows to  turn  on  and  off
      noise reduction phase.

NOTE: combination of several predictions results in lesser sensitivity  to
      noise, but it may produce undesirable discontinuities  between  last
      point of the trend and first point of the prediction. The reason  is
      that  last  point  of  the  trend is usually corrupted by noise, but
      average  value of  several  predictions  is less sensitive to noise,
      thus discontinuity appears. It is not a bug.

INPUT PARAMETERS:
    S               -   SSA model
    M               -   number  of  sliding  windows  to combine, M>=1. If
                        your dataset has less than M sliding windows, this
                        parameter will be silently reduced.
    NTicks          -   number of ticks to forecast, NTicks>=1

OUTPUT PARAMETERS:
    Trend           -   array[NTicks], predicted trend line


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.


HANDLING OF DEGENERATE CASES

Following degenerate cases may happen:
* dataset is empty (no analysis can be done)
* all sequences are shorter than the window length,no analysis can be done
* no algorithm is specified (no analysis can be done)
* last sequence is shorter than the WindowWidth   (analysis  can  be done,
  but we can not perform forecasting on the last sequence)
* window lentgh is 1 (impossible to use for forecasting)
* SSA analysis algorithm is  configured  to  extract  basis  whose size is
  equal to window length (impossible to use for  forecasting;  only  basis
  whose size is less than window length can be used).

Calling this function in degenerate cases returns following result:
* NTicks  copies  of  the  last  value is returned for non-empty task with
  large enough dataset, but with overcomplete  basis  (window  width=1  or
  basis size is equal to window width)
* zero trend with length=NTicks is returned for empty task

No analysis is performed in degenerate cases (we immediately return  dummy
values, no basis is ever constructed).

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaforecastavglast(ssamodel &s, const ae_int_t m, const ae_int_t nticks, real_1d_array &trend, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function builds SSA  basis  and  performs  forecasting  for  a  user-
specified sequence, returning value of trend.

Forecasting is done in two stages:
* first,  we  extract  trend  from M last sliding windows of the sequence.
  This stage is optional, you can  turn  it  off  if  you  pass data which
  are already processed with SSA. Of course, you  can  turn  it  off  even
  for raw data, but it is not recommended  -  noise  suppression  is  very
  important for correct prediction.
* then, we apply LRR independently for M sliding windows
* average of M predictions is returned

This function has following running time:
* O(NBasis*WindowWidth*M) for trend extraction phase
* O(WindowWidth*NTicks*M) for forecast phase

NOTE: combination of several predictions results in lesser sensitivity  to
      noise, but it may produce undesirable discontinuities  between  last
      point of the trend and first point of the prediction. The reason  is
      that  last  point  of  the  trend is usually corrupted by noise, but
      average  value of  several  predictions  is less sensitive to noise,
      thus discontinuity appears. It is not a bug.

INPUT PARAMETERS:
    S               -   SSA model
    Data            -   array[NTicks], data to forecast
    DataLen         -   number of ticks in the data, DataLen>=1
    M               -   number  of  sliding  windows  to combine, M>=1. If
                        your dataset has less than M sliding windows, this
                        parameter will be silently reduced.
    ForecastLen     -   number of ticks to predict, ForecastLen>=1
    ApplySmoothing  -   whether to apply smoothing trend extraction or not.
                        if you do not know what to specify, pass true.

OUTPUT PARAMETERS:
    Trend           -   array[ForecastLen], forecasted trend


CACHING/REUSE OF THE BASIS

Caching/reuse of previous results is performed:
* first call performs full run of SSA; basis is stored in the cache
* subsequent calls reuse previously cached basis
* if you call any function which changes model properties (window  length,
  algorithm, dataset), internal basis will be invalidated.
* the only calls which do NOT invalidate basis are listed below:
  a) ssasetwindow() with same window length
  b) ssaappendpointandupdate()
  c) ssaappendsequenceandupdate()
  d) ssasetalgotopk...() with exactly same K
  Calling these functions will result in reuse of previously found basis.


HANDLING OF DEGENERATE CASES

Following degenerate cases may happen:
* dataset is empty (no analysis can be done)
* all sequences are shorter than the window length,no analysis can be done
* no algorithm is specified (no analysis can be done)
* data sequence is shorter than the WindowWidth   (analysis  can  be done,
  but we can not perform forecasting on the last sequence)
* window lentgh is 1 (impossible to use for forecasting)
* SSA analysis algorithm is  configured  to  extract  basis  whose size is
  equal to window length (impossible to use for  forecasting;  only  basis
  whose size is less than window length can be used).

Calling this function in degenerate cases returns following result:
* ForecastLen copies of the last value is returned for non-empty task with
  large enough dataset, but with overcomplete  basis  (window  width=1  or
  basis size is equal to window width)
* zero trend with length=ForecastLen is returned for empty task

No analysis is performed in degenerate cases (we immediately return  dummy
values, no basis is ever constructed).

  -- ALGLIB --
     Copyright 30.10.2017 by Bochkanov Sergey
*************************************************************************/
void ssaforecastavgsequence(ssamodel &s, const real_1d_array &data, const ae_int_t datalen, const ae_int_t m, const ae_int_t forecastlen, const bool applysmoothing, real_1d_array &trend, const xparams _xparams = alglib::xdefault);
void ssaforecastavgsequence(ssamodel &s, const real_1d_array &data, const ae_int_t m, const ae_int_t forecastlen, real_1d_array &trend, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_LDA) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Multiclass Fisher LDA

The function finds coefficients of a linear  combination  which  optimally
separates training set. Most suited for 2-class problems, see fisherldan()
for an variant that returns N-dimensional basis.

INPUT PARAMETERS:
    XY          -   training set, array[NPoints,NVars+1].
                    First NVars columns store values of independent
                    variables, the next column stores class index (from 0
                    to NClasses-1) which dataset element belongs to.
                    Fractional values are rounded to the nearest integer.
                    The class index must be in the [0,NClasses-1] range,
                    an exception is generated otherwise.
    NPoints     -   training set size, NPoints>=0
    NVars       -   number of independent variables, NVars>=1
    NClasses    -   number of classes, NClasses>=2


OUTPUT PARAMETERS:
    W           -   linear combination coefficients, array[NVars]

  ! FREE EDITION OF ALGLIB:
  !
  ! Free Edition of ALGLIB supports following important features for  this
  ! function:
  ! * C++ version: x64 SIMD support using C++ intrinsics
  ! * C#  version: x64 SIMD support using NET5/NetCore hardware intrinsics
  !
  ! We  recommend  you  to  read  'Compiling ALGLIB' section of the ALGLIB
  ! Reference Manual in order  to  find  out  how to activate SIMD support
  ! in ALGLIB.

  ! 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, ARM) implementations of linear algebra and
  !   other primitives (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.

  -- ALGLIB --
     Copyright 31.05.2008 by Bochkanov Sergey
*************************************************************************/
void fisherlda(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, real_1d_array &w, const xparams _xparams = alglib::xdefault);
void fisherlda(const real_2d_array &xy, const ae_int_t nclasses, real_1d_array &w, const xparams _xparams = alglib::xdefault);


/*************************************************************************
N-dimensional multiclass Fisher LDA

Subroutine finds coefficients of linear combinations which optimally separates
training set on classes. It returns N-dimensional basis whose vector are sorted
by quality of training set separation (in descending order).

INPUT PARAMETERS:
    XY          -   training set, array[NPoints,NVars+1].
                    First NVars columns store values of independent
                    variables, the next column stores class index (from 0
                    to NClasses-1) which dataset element belongs to.
                    Fractional values are rounded to the nearest integer.
                    The class index must be in the [0,NClasses-1] range,
                    an exception is generated otherwise.
    NPoints     -   training set size, NPoints>=0
    NVars       -   number of independent variables, NVars>=1
    NClasses    -   number of classes, NClasses>=2


OUTPUT PARAMETERS:
    W           -   basis, array[NVars,NVars]
                    columns of matrix stores basis vectors, sorted by
                    quality of training set separation (in descending order)

  ! FREE EDITION OF ALGLIB:
  !
  ! Free Edition of ALGLIB supports following important features for  this
  ! function:
  ! * C++ version: x64 SIMD support using C++ intrinsics
  ! * C#  version: x64 SIMD support using NET5/NetCore hardware intrinsics
  !
  ! We  recommend  you  to  read  'Compiling ALGLIB' section of the ALGLIB
  ! Reference Manual in order  to  find  out  how to activate SIMD support
  ! in ALGLIB.

  ! 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, ARM) implementations of linear algebra and
  !   other primitives (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.

  -- ALGLIB --
     Copyright 31.05.2008 by Bochkanov Sergey
*************************************************************************/
void fisherldan(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, real_2d_array &w, const xparams _xparams = alglib::xdefault);
void fisherldan(const real_2d_array &xy, const ae_int_t nclasses, real_2d_array &w, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_MCPD) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
DESCRIPTION:

This function creates MCPD (Markov Chains for Population Data) solver.

This  solver  can  be  used  to find transition matrix P for N-dimensional
prediction  problem  where transition from X[i] to X[i+1] is  modelled  as
    X[i+1] = P*X[i]
where X[i] and X[i+1] are N-dimensional population vectors (components  of
each X are non-negative), and P is a N*N transition matrix (elements of  P
are non-negative, each column sums to 1.0).

Such models arise when when:
* there is some population of individuals
* individuals can have different states
* individuals can transit from one state to another
* population size is constant, i.e. there is no new individuals and no one
  leaves population
* you want to model transitions of individuals from one state into another

USAGE:

Here we give very brief outline of the MCPD. We strongly recommend you  to
read examples in the ALGLIB Reference Manual and to read ALGLIB User Guide
on data analysis which is available at http://www.alglib.net/dataanalysis/

1. User initializes algorithm state with MCPDCreate() call

2. User  adds  one  or  more  tracks -  sequences of states which describe
   evolution of a system being modelled from different starting conditions

3. User may add optional boundary, equality  and/or  linear constraints on
   the coefficients of P by calling one of the following functions:
   * MCPDSetEC() to set equality constraints
   * MCPDSetBC() to set bound constraints
   * MCPDSetLC() to set linear constraints

4. Optionally,  user  may  set  custom  weights  for prediction errors (by
   default, algorithm assigns non-equal, automatically chosen weights  for
   errors in the prediction of different components of X). It can be  done
   with a call of MCPDSetPredictionWeights() function.

5. User calls MCPDSolve() function which takes algorithm  state and
   pointer (delegate, etc.) to callback function which calculates F/G.

6. User calls MCPDResults() to get solution

INPUT PARAMETERS:
    N       -   problem dimension, N>=1

OUTPUT PARAMETERS:
    State   -   structure stores algorithm state

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdcreate(const ae_int_t n, mcpdstate &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
DESCRIPTION:

This function is a specialized version of MCPDCreate()  function,  and  we
recommend  you  to read comments for this function for general information
about MCPD solver.

This  function  creates  MCPD (Markov Chains for Population  Data)  solver
for "Entry-state" model,  i.e. model  where transition from X[i] to X[i+1]
is modelled as
    X[i+1] = P*X[i]
where
    X[i] and X[i+1] are N-dimensional state vectors
    P is a N*N transition matrix
and  one  selected component of X[] is called "entry" state and is treated
in a special way:
    system state always transits from "entry" state to some another state
    system state can not transit from any state into "entry" state
Such conditions basically mean that row of P which corresponds to  "entry"
state is zero.

Such models arise when:
* there is some population of individuals
* individuals can have different states
* individuals can transit from one state to another
* population size is NOT constant -  at every moment of time there is some
  (unpredictable) amount of "new" individuals, which can transit into  one
  of the states at the next turn, but still no one leaves population
* you want to model transitions of individuals from one state into another
* but you do NOT want to predict amount of "new"  individuals  because  it
  does not depends on individuals already present (hence  system  can  not
  transit INTO entry state - it can only transit FROM it).

This model is discussed  in  more  details  in  the ALGLIB User Guide (see
http://www.alglib.net/dataanalysis/ for more data).

INPUT PARAMETERS:
    N       -   problem dimension, N>=2
    EntryState- index of entry state, in 0..N-1

OUTPUT PARAMETERS:
    State   -   structure stores algorithm state

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdcreateentry(const ae_int_t n, const ae_int_t entrystate, mcpdstate &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
DESCRIPTION:

This function is a specialized version of MCPDCreate()  function,  and  we
recommend  you  to read comments for this function for general information
about MCPD solver.

This  function  creates  MCPD (Markov Chains for Population  Data)  solver
for "Exit-state" model,  i.e. model  where  transition from X[i] to X[i+1]
is modelled as
    X[i+1] = P*X[i]
where
    X[i] and X[i+1] are N-dimensional state vectors
    P is a N*N transition matrix
and  one  selected component of X[] is called "exit"  state and is treated
in a special way:
    system state can transit from any state into "exit" state
    system state can not transit from "exit" state into any other state
    transition operator discards "exit" state (makes it zero at each turn)
Such  conditions  basically  mean  that  column  of P which corresponds to
"exit" state is zero. Multiplication by such P may decrease sum of  vector
components.

Such models arise when:
* there is some population of individuals
* individuals can have different states
* individuals can transit from one state to another
* population size is NOT constant - individuals can move into "exit" state
  and leave population at the next turn, but there are no new individuals
* amount of individuals which leave population can be predicted
* you want to model transitions of individuals from one state into another
  (including transitions into the "exit" state)

This model is discussed  in  more  details  in  the ALGLIB User Guide (see
http://www.alglib.net/dataanalysis/ for more data).

INPUT PARAMETERS:
    N       -   problem dimension, N>=2
    ExitState-  index of exit state, in 0..N-1

OUTPUT PARAMETERS:
    State   -   structure stores algorithm state

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdcreateexit(const ae_int_t n, const ae_int_t exitstate, mcpdstate &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
DESCRIPTION:

This function is a specialized version of MCPDCreate()  function,  and  we
recommend  you  to read comments for this function for general information
about MCPD solver.

This  function  creates  MCPD (Markov Chains for Population  Data)  solver
for "Entry-Exit-states" model, i.e. model where  transition  from  X[i] to
X[i+1] is modelled as
    X[i+1] = P*X[i]
where
    X[i] and X[i+1] are N-dimensional state vectors
    P is a N*N transition matrix
one selected component of X[] is called "entry" state and is treated in  a
special way:
    system state always transits from "entry" state to some another state
    system state can not transit from any state into "entry" state
and another one component of X[] is called "exit" state and is treated  in
a special way too:
    system state can transit from any state into "exit" state
    system state can not transit from "exit" state into any other state
    transition operator discards "exit" state (makes it zero at each turn)
Such conditions basically mean that:
    row of P which corresponds to "entry" state is zero
    column of P which corresponds to "exit" state is zero
Multiplication by such P may decrease sum of vector components.

Such models arise when:
* there is some population of individuals
* individuals can have different states
* individuals can transit from one state to another
* population size is NOT constant
* at every moment of time there is some (unpredictable)  amount  of  "new"
  individuals, which can transit into one of the states at the next turn
* some  individuals  can  move  (predictably)  into "exit" state and leave
  population at the next turn
* you want to model transitions of individuals from one state into another,
  including transitions from the "entry" state and into the "exit" state.
* but you do NOT want to predict amount of "new"  individuals  because  it
  does not depends on individuals already present (hence  system  can  not
  transit INTO entry state - it can only transit FROM it).

This model is discussed  in  more  details  in  the ALGLIB User Guide (see
http://www.alglib.net/dataanalysis/ for more data).

INPUT PARAMETERS:
    N       -   problem dimension, N>=2
    EntryState- index of entry state, in 0..N-1
    ExitState-  index of exit state, in 0..N-1

OUTPUT PARAMETERS:
    State   -   structure stores algorithm state

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdcreateentryexit(const ae_int_t n, const ae_int_t entrystate, const ae_int_t exitstate, mcpdstate &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  is  used to add a track - sequence of system states at the
different moments of its evolution.

You  may  add  one  or several tracks to the MCPD solver. In case you have
several tracks, they won't overwrite each other. For example,  if you pass
two tracks, A1-A2-A3 (system at t=A+1, t=A+2 and t=A+3) and B1-B2-B3, then
solver will try to model transitions from t=A+1 to t=A+2, t=A+2 to  t=A+3,
t=B+1 to t=B+2, t=B+2 to t=B+3. But it WONT mix these two tracks - i.e. it
wont try to model transition from t=A+3 to t=B+1.

INPUT PARAMETERS:
    S       -   solver
    XY      -   track, array[K,N]:
                * I-th row is a state at t=I
                * elements of XY must be non-negative (exception will be
                  thrown on negative elements)
    K       -   number of points in a track
                * if given, only leading K rows of XY are used
                * if not given, automatically determined from size of XY

NOTES:

1. Track may contain either proportional or population data:
   * with proportional data all rows of XY must sum to 1.0, i.e. we have
     proportions instead of absolute population values
   * with population data rows of XY contain population counts and generally
     do not sum to 1.0 (although they still must be non-negative)

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdaddtrack(mcpdstate &s, const real_2d_array &xy, const ae_int_t k, const xparams _xparams = alglib::xdefault);
void mcpdaddtrack(mcpdstate &s, const real_2d_array &xy, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is used to add equality constraints on the elements  of  the
transition matrix P.

MCPD solver has four types of constraints which can be placed on P:
* user-specified equality constraints (optional)
* user-specified bound constraints (optional)
* user-specified general linear constraints (optional)
* basic constraints (always present):
  * non-negativity: P[i,j]>=0
  * consistency: every column of P sums to 1.0

Final  constraints  which  are  passed  to  the  underlying  optimizer are
calculated  as  intersection  of all present constraints. For example, you
may specify boundary constraint on P[0,0] and equality one:
    0.1<=P[0,0]<=0.9
    P[0,0]=0.5
Such  combination  of  constraints  will  be  silently  reduced  to  their
intersection, which is P[0,0]=0.5.

This  function  can  be  used  to  place equality constraints on arbitrary
subset of elements of P. Set of constraints is specified by EC, which  may
contain either NAN's or finite numbers from [0,1]. NAN denotes absence  of
constraint, finite number denotes equality constraint on specific  element
of P.

You can also  use  MCPDAddEC()  function  which  allows  to  ADD  equality
constraint  for  one  element  of P without changing constraints for other
elements.

These functions (MCPDSetEC and MCPDAddEC) interact as follows:
* there is internal matrix of equality constraints which is stored in  the
  MCPD solver
* MCPDSetEC() replaces this matrix by another one (SET)
* MCPDAddEC() modifies one element of this matrix and  leaves  other  ones
  unchanged (ADD)
* thus  MCPDAddEC()  call  preserves  all  modifications  done by previous
  calls,  while  MCPDSetEC()  completely discards all changes  done to the
  equality constraints.

INPUT PARAMETERS:
    S       -   solver
    EC      -   equality constraints, array[N,N]. Elements of  EC  can  be
                either NAN's or finite  numbers from  [0,1].  NAN  denotes
                absence  of  constraints,  while  finite  value    denotes
                equality constraint on the corresponding element of P.

NOTES:

1. infinite values of EC will lead to exception being thrown. Values  less
than 0.0 or greater than 1.0 will lead to error code being returned  after
call to MCPDSolve().

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdsetec(mcpdstate &s, const real_2d_array &ec, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is used to add equality constraints on the elements  of  the
transition matrix P.

MCPD solver has four types of constraints which can be placed on P:
* user-specified equality constraints (optional)
* user-specified bound constraints (optional)
* user-specified general linear constraints (optional)
* basic constraints (always present):
  * non-negativity: P[i,j]>=0
  * consistency: every column of P sums to 1.0

Final  constraints  which  are  passed  to  the  underlying  optimizer are
calculated  as  intersection  of all present constraints. For example, you
may specify boundary constraint on P[0,0] and equality one:
    0.1<=P[0,0]<=0.9
    P[0,0]=0.5
Such  combination  of  constraints  will  be  silently  reduced  to  their
intersection, which is P[0,0]=0.5.

This function can be used to ADD equality constraint for one element of  P
without changing constraints for other elements.

You  can  also  use  MCPDSetEC()  function  which  allows  you  to specify
arbitrary set of equality constraints in one call.

These functions (MCPDSetEC and MCPDAddEC) interact as follows:
* there is internal matrix of equality constraints which is stored in the
  MCPD solver
* MCPDSetEC() replaces this matrix by another one (SET)
* MCPDAddEC() modifies one element of this matrix and leaves  other  ones
  unchanged (ADD)
* thus  MCPDAddEC()  call  preserves  all  modifications done by previous
  calls,  while  MCPDSetEC()  completely discards all changes done to the
  equality constraints.

INPUT PARAMETERS:
    S       -   solver
    I       -   row index of element being constrained
    J       -   column index of element being constrained
    C       -   value (constraint for P[I,J]).  Can  be  either  NAN  (no
                constraint) or finite value from [0,1].

NOTES:

1. infinite values of C  will lead to exception being thrown. Values  less
than 0.0 or greater than 1.0 will lead to error code being returned  after
call to MCPDSolve().

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdaddec(mcpdstate &s, const ae_int_t i, const ae_int_t j, const double c, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is used to add bound constraints  on  the  elements  of  the
transition matrix P.

MCPD solver has four types of constraints which can be placed on P:
* user-specified equality constraints (optional)
* user-specified bound constraints (optional)
* user-specified general linear constraints (optional)
* basic constraints (always present):
  * non-negativity: P[i,j]>=0
  * consistency: every column of P sums to 1.0

Final  constraints  which  are  passed  to  the  underlying  optimizer are
calculated  as  intersection  of all present constraints. For example, you
may specify boundary constraint on P[0,0] and equality one:
    0.1<=P[0,0]<=0.9
    P[0,0]=0.5
Such  combination  of  constraints  will  be  silently  reduced  to  their
intersection, which is P[0,0]=0.5.

This  function  can  be  used  to  place bound   constraints  on arbitrary
subset  of  elements  of  P.  Set of constraints is specified by BndL/BndU
matrices, which may contain arbitrary combination  of  finite  numbers  or
infinities (like -INF<x<=0.5 or 0.1<=x<+INF).

You can also use MCPDAddBC() function which allows to ADD bound constraint
for one element of P without changing constraints for other elements.

These functions (MCPDSetBC and MCPDAddBC) interact as follows:
* there is internal matrix of bound constraints which is stored in the
  MCPD solver
* MCPDSetBC() replaces this matrix by another one (SET)
* MCPDAddBC() modifies one element of this matrix and  leaves  other  ones
  unchanged (ADD)
* thus  MCPDAddBC()  call  preserves  all  modifications  done by previous
  calls,  while  MCPDSetBC()  completely discards all changes  done to the
  equality constraints.

INPUT PARAMETERS:
    S       -   solver
    BndL    -   lower bounds constraints, array[N,N]. Elements of BndL can
                be finite numbers or -INF.
    BndU    -   upper bounds constraints, array[N,N]. Elements of BndU can
                be finite numbers or +INF.

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdsetbc(mcpdstate &s, const real_2d_array &bndl, const real_2d_array &bndu, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is used to add bound constraints  on  the  elements  of  the
transition matrix P.

MCPD solver has four types of constraints which can be placed on P:
* user-specified equality constraints (optional)
* user-specified bound constraints (optional)
* user-specified general linear constraints (optional)
* basic constraints (always present):
  * non-negativity: P[i,j]>=0
  * consistency: every column of P sums to 1.0

Final  constraints  which  are  passed  to  the  underlying  optimizer are
calculated  as  intersection  of all present constraints. For example, you
may specify boundary constraint on P[0,0] and equality one:
    0.1<=P[0,0]<=0.9
    P[0,0]=0.5
Such  combination  of  constraints  will  be  silently  reduced  to  their
intersection, which is P[0,0]=0.5.

This  function  can  be  used to ADD bound constraint for one element of P
without changing constraints for other elements.

You  can  also  use  MCPDSetBC()  function  which  allows to  place  bound
constraints  on arbitrary subset of elements of P.   Set of constraints is
specified  by  BndL/BndU matrices, which may contain arbitrary combination
of finite numbers or infinities (like -INF<x<=0.5 or 0.1<=x<+INF).

These functions (MCPDSetBC and MCPDAddBC) interact as follows:
* there is internal matrix of bound constraints which is stored in the
  MCPD solver
* MCPDSetBC() replaces this matrix by another one (SET)
* MCPDAddBC() modifies one element of this matrix and  leaves  other  ones
  unchanged (ADD)
* thus  MCPDAddBC()  call  preserves  all  modifications  done by previous
  calls,  while  MCPDSetBC()  completely discards all changes  done to the
  equality constraints.

INPUT PARAMETERS:
    S       -   solver
    I       -   row index of element being constrained
    J       -   column index of element being constrained
    BndL    -   lower bound
    BndU    -   upper bound

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdaddbc(mcpdstate &s, const ae_int_t i, const ae_int_t j, const double bndl, const double bndu, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is used to set linear equality/inequality constraints on the
elements of the transition matrix P.

This function can be used to set one or several general linear constraints
on the elements of P. Two types of constraints are supported:
* equality constraints
* inequality constraints (both less-or-equal and greater-or-equal)

Coefficients  of  constraints  are  specified  by  matrix  C (one  of  the
parameters).  One  row  of  C  corresponds  to  one  constraint.   Because
transition  matrix P has N*N elements,  we  need  N*N columns to store all
coefficients  (they  are  stored row by row), and one more column to store
right part - hence C has N*N+1 columns.  Constraint  kind is stored in the
CT array.

Thus, I-th linear constraint is
    P[0,0]*C[I,0] + P[0,1]*C[I,1] + .. + P[0,N-1]*C[I,N-1] +
        + P[1,0]*C[I,N] + P[1,1]*C[I,N+1] + ... +
        + P[N-1,N-1]*C[I,N*N-1]  ?=?  C[I,N*N]
where ?=? can be either "=" (CT[i]=0), "<=" (CT[i]<0) or ">=" (CT[i]>0).

Your constraint may involve only some subset of P (less than N*N elements).
For example it can be something like
    P[0,0] + P[0,1] = 0.5
In this case you still should pass matrix  with N*N+1 columns, but all its
elements (except for C[0,0], C[0,1] and C[0,N*N-1]) will be zero.

INPUT PARAMETERS:
    S       -   solver
    C       -   array[K,N*N+1] - coefficients of constraints
                (see above for complete description)
    CT      -   array[K] - constraint types
                (see above for complete description)
    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

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdsetlc(mcpdstate &s, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k, const xparams _xparams = alglib::xdefault);
void mcpdsetlc(mcpdstate &s, const real_2d_array &c, const integer_1d_array &ct, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function allows to  tune  amount  of  Tikhonov  regularization  being
applied to your problem.

By default, regularizing term is equal to r*||P-prior_P||^2, where r is  a
small non-zero value,  P is transition matrix, prior_P is identity matrix,
||X||^2 is a sum of squared elements of X.

This  function  allows  you to change coefficient r. You can  also  change
prior values with MCPDSetPrior() function.

INPUT PARAMETERS:
    S       -   solver
    V       -   regularization  coefficient, finite non-negative value. It
                is  not  recommended  to specify zero value unless you are
                pretty sure that you want it.

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdsettikhonovregularizer(mcpdstate &s, const double v, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This  function  allows to set prior values used for regularization of your
problem.

By default, regularizing term is equal to r*||P-prior_P||^2, where r is  a
small non-zero value,  P is transition matrix, prior_P is identity matrix,
||X||^2 is a sum of squared elements of X.

This  function  allows  you to change prior values prior_P. You  can  also
change r with MCPDSetTikhonovRegularizer() function.

INPUT PARAMETERS:
    S       -   solver
    PP      -   array[N,N], matrix of prior values:
                1. elements must be real numbers from [0,1]
                2. columns must sum to 1.0.
                First property is checked (exception is thrown otherwise),
                while second one is not checked/enforced.

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdsetprior(mcpdstate &s, const real_2d_array &pp, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is used to change prediction weights

MCPD solver scales prediction errors as follows
    Error(P) = ||W*(y-P*x)||^2
where
    x is a system state at time t
    y is a system state at time t+1
    P is a transition matrix
    W is a diagonal scaling matrix

By default, weights are chosen in order  to  minimize  relative prediction
error instead of absolute one. For example, if one component of  state  is
about 0.5 in magnitude and another one is about 0.05, then algorithm  will
make corresponding weights equal to 2.0 and 20.0.

INPUT PARAMETERS:
    S       -   solver
    PW      -   array[N], weights:
                * must be non-negative values (exception will be thrown otherwise)
                * zero values will be replaced by automatically chosen values

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdsetpredictionweights(mcpdstate &s, const real_1d_array &pw, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function is used to start solution of the MCPD problem.

After return from this function, you can use MCPDResults() to get solution
and completion code.

  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdsolve(mcpdstate &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
MCPD results

INPUT PARAMETERS:
    State   -   algorithm state

OUTPUT PARAMETERS:
    P       -   array[N,N], transition matrix
    Rep     -   optimization report. You should check Rep.TerminationType
                in  order  to  distinguish  successful  termination  from
                unsuccessful one. Speaking short, positive values  denote
                success, negative ones are failures.
                More information about fields of this  structure  can  be
                found in the comments on MCPDReport datatype.


  -- ALGLIB --
     Copyright 23.05.2010 by Bochkanov Sergey
*************************************************************************/
void mcpdresults(const mcpdstate &s, real_2d_array &p, mcpdreport &rep, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_LOGIT) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
This subroutine trains logit model.

INPUT PARAMETERS:
    XY          -   training set, array[0..NPoints-1,0..NVars]
                    First NVars columns store values of independent
                    variables, next column stores number of class (from 0
                    to NClasses-1) which dataset element belongs to. Fractional
                    values are rounded to nearest integer.
    NPoints     -   training set size, NPoints>=1
    NVars       -   number of independent variables, NVars>=1
    NClasses    -   number of classes, NClasses>=2

OUTPUT PARAMETERS:
    Info        -   return code:
                    * -2, if there is a point with class number
                          outside of [0..NClasses-1].
                    * -1, if incorrect parameters was passed
                          (NPoints<NVars+2, NVars<1, NClasses<2).
                    *  1, if task has been solved
    LM          -   model built
    Rep         -   training report

  -- ALGLIB --
     Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
void mnltrainh(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, ae_int_t &info, logitmodel &lm, mnlreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Procesing

INPUT PARAMETERS:
    LM      -   logit model, passed by non-constant reference
                (some fields of structure are used as temporaries
                when calculating model output).
    X       -   input vector,  array[0..NVars-1].
    Y       -   (possibly) preallocated buffer; if size of Y is less than
                NClasses, it will be reallocated.If it is large enough, it
                is NOT reallocated, so we can save some time on reallocation.

OUTPUT PARAMETERS:
    Y       -   result, array[0..NClasses-1]
                Vector of posterior probabilities for classification task.

  -- ALGLIB --
     Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
void mnlprocess(logitmodel &lm, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
'interactive'  variant  of  MNLProcess  for  languages  like  Python which
support constructs like "Y = MNLProcess(LM,X)" and interactive mode of the
interpreter

This function allocates new array on each call,  so  it  is  significantly
slower than its 'non-interactive' counterpart, but it is  more  convenient
when you call it from command line.

  -- ALGLIB --
     Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
void mnlprocessi(logitmodel &lm, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Unpacks coefficients of logit model. Logit model have form:

    P(class=i) = S(i) / (S(0) + S(1) + ... +S(M-1))
          S(i) = Exp(A[i,0]*X[0] + ... + A[i,N-1]*X[N-1] + A[i,N]), when i<M-1
        S(M-1) = 1

INPUT PARAMETERS:
    LM          -   logit model in ALGLIB format

OUTPUT PARAMETERS:
    V           -   coefficients, array[0..NClasses-2,0..NVars]
    NVars       -   number of independent variables
    NClasses    -   number of classes

  -- ALGLIB --
     Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
void mnlunpack(const logitmodel &lm, real_2d_array &a, ae_int_t &nvars, ae_int_t &nclasses, const xparams _xparams = alglib::xdefault);


/*************************************************************************
"Packs" coefficients and creates logit model in ALGLIB format (MNLUnpack
reversed).

INPUT PARAMETERS:
    A           -   model (see MNLUnpack)
    NVars       -   number of independent variables
    NClasses    -   number of classes

OUTPUT PARAMETERS:
    LM          -   logit model.

  -- ALGLIB --
     Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
void mnlpack(const real_2d_array &a, const ae_int_t nvars, const ae_int_t nclasses, logitmodel &lm, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average cross-entropy (in bits per element) on the test set

INPUT PARAMETERS:
    LM      -   logit model
    XY      -   test set
    NPoints -   test set size

RESULT:
    CrossEntropy/(NPoints*ln(2)).

  -- ALGLIB --
     Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
double mnlavgce(logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Relative classification error on the test set

INPUT PARAMETERS:
    LM      -   logit model
    XY      -   test set
    NPoints -   test set size

RESULT:
    percent of incorrectly classified cases.

  -- ALGLIB --
     Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
double mnlrelclserror(logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
RMS error on the test set

INPUT PARAMETERS:
    LM      -   logit model
    XY      -   test set
    NPoints -   test set size

RESULT:
    root mean square error (error when estimating posterior probabilities).

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double mnlrmserror(logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average error on the test set

INPUT PARAMETERS:
    LM      -   logit model
    XY      -   test set
    NPoints -   test set size

RESULT:
    average error (error when estimating posterior probabilities).

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double mnlavgerror(logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average relative error on the test set

INPUT PARAMETERS:
    LM      -   logit model
    XY      -   test set
    NPoints -   test set size

RESULT:
    average relative error (error when estimating posterior probabilities).

  -- ALGLIB --
     Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double mnlavgrelerror(logitmodel &lm, const real_2d_array &xy, const ae_int_t ssize, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Classification error on test set = MNLRelClsError*NPoints

  -- ALGLIB --
     Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
ae_int_t mnlclserror(logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_KNN) || !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 a text or XML file. 
  But you should not insert separators into the middle of the "words"
  nor should you change the 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 it in C# one, 
  and vice versa.
*************************************************************************/
void knnserialize(const knnmodel &obj, std::string &s_out);


/*************************************************************************
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 knnserialize(const knnmodel &obj, std::ostream &s_out);


/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void knnunserialize(const std::string &s_in, knnmodel &obj);


/*************************************************************************
This function unserializes data structure from stream.
*************************************************************************/
void knnunserialize(const std::istream &s_in, knnmodel &obj);


/*************************************************************************
This function creates buffer  structure  which  can  be  used  to  perform
parallel KNN requests.

KNN subpackage provides two sets of computing functions - ones  which  use
internal buffer of KNN model (these  functions are single-threaded because
they use same buffer, which can not  shared  between  threads),  and  ones
which use external buffer.

This function is used to initialize external buffer.

INPUT PARAMETERS
    Model       -   KNN model which is associated with newly created buffer

OUTPUT PARAMETERS
    Buf         -   external buffer.


IMPORTANT: buffer object should be used only with model which was used  to
           initialize buffer. Any attempt to  use  buffer  with  different
           object is dangerous - you  may   get  integrity  check  failure
           (exception) because sizes of internal  arrays  do  not  fit  to
           dimensions of the model structure.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knncreatebuffer(const knnmodel &model, knnbuffer &buf, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This subroutine creates KNNBuilder object which is used to train KNN models.

By default, new builder stores empty dataset and some  reasonable  default
settings. At the very least, you should specify dataset prior to  building
KNN model. You can also tweak settings of the model construction algorithm
(recommended, although default settings should work well).

Following actions are mandatory:
* calling knnbuildersetdataset() to specify dataset
* calling knnbuilderbuildknnmodel() to build KNN model using current
  dataset and default settings

Additionally, you may call:
* knnbuildersetnorm() to change norm being used

INPUT PARAMETERS:
    none

OUTPUT PARAMETERS:
    S           -   KNN builder

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnbuildercreate(knnbuilder &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Specifies regression problem (one or more continuous  output variables are
predicted). There also exists "classification" version of this function.

This subroutine adds dense dataset to the internal storage of the  builder
object. Specifying your dataset in the dense format means that  the  dense
version of the KNN construction algorithm will be invoked.

INPUT PARAMETERS:
    S           -   KNN builder object
    XY          -   array[NPoints,NVars+NOut] (note: actual  size  can  be
                    larger, only leading part is used anyway), dataset:
                    * first NVars elements of each row store values of the
                      independent variables
                    * next NOut elements store  values  of  the  dependent
                      variables
    NPoints     -   number of rows in the dataset, NPoints>=1
    NVars       -   number of independent variables, NVars>=1
    NOut        -   number of dependent variables, NOut>=1

OUTPUT PARAMETERS:
    S           -   KNN builder

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnbuildersetdatasetreg(knnbuilder &s, const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nout, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Specifies classification problem (two  or  more  classes  are  predicted).
There also exists "regression" version of this function.

This subroutine adds dense dataset to the internal storage of the  builder
object. Specifying your dataset in the dense format means that  the  dense
version of the KNN construction algorithm will be invoked.

INPUT PARAMETERS:
    S           -   KNN builder object
    XY          -   array[NPoints,NVars+1] (note:   actual   size  can  be
                    larger, only leading part is used anyway), dataset:
                    * first NVars elements of each row store values of the
                      independent variables
                    * next element stores class index, in [0,NClasses)
    NPoints     -   number of rows in the dataset, NPoints>=1
    NVars       -   number of independent variables, NVars>=1
    NClasses    -   number of classes, NClasses>=2

OUTPUT PARAMETERS:
    S           -   KNN builder

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnbuildersetdatasetcls(knnbuilder &s, const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets norm type used for neighbor search.

INPUT PARAMETERS:
    S           -   decision forest builder object
    NormType    -   norm type:
                    * 0      inf-norm
                    * 1      1-norm
                    * 2      Euclidean norm (default)

OUTPUT PARAMETERS:
    S           -   decision forest builder

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnbuildersetnorm(knnbuilder &s, const ae_int_t nrmtype, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This subroutine builds KNN model  according  to  current  settings,  using
dataset internally stored in the builder object.

The model being built performs inference using Eps-approximate  K  nearest
neighbors search algorithm, with:
* K=1,  Eps=0 corresponding to the "nearest neighbor algorithm"
* K>1,  Eps=0 corresponding to the "K nearest neighbors algorithm"
* K>=1, Eps>0 corresponding to "approximate nearest neighbors algorithm"

An approximate KNN is a good option for high-dimensional  datasets  (exact
KNN works slowly when dimensions count grows).

An ALGLIB implementation of kd-trees is used to perform k-nn searches.

  ! 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:
    S       -   KNN builder object
    K       -   number of neighbors to search for, K>=1
    Eps     -   approximation factor:
                * Eps=0 means that exact kNN search is performed
                * Eps>0 means that (1+Eps)-approximate search is performed

OUTPUT PARAMETERS:
    Model       -   KNN model
    Rep         -   report

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnbuilderbuildknnmodel(knnbuilder &s, const ae_int_t k, const double eps, knnmodel &model, knnreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Changing search settings of KNN model.

K and EPS parameters of KNN  (AKNN)  search  are  specified  during  model
construction. However, plain KNN algorithm with Euclidean distance  allows
you to change them at any moment.

NOTE: future versions of KNN model may support advanced versions  of  KNN,
      such as NCA or LMNN. It is possible that such algorithms won't allow
      you to change search settings on the fly. If you call this  function
      for an algorithm which does not support on-the-fly changes, it  will
      throw an exception.

INPUT PARAMETERS:
    Model   -   KNN model
    K       -   K>=1, neighbors count
    EPS     -   accuracy of the EPS-approximate NN search. Set to 0.0,  if
                you want to perform "classic" KNN search.  Specify  larger
                values  if  you  need  to  speed-up  high-dimensional  KNN
                queries.

OUTPUT PARAMETERS:
    nothing on success, exception on failure

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnrewritekeps(knnmodel &model, const ae_int_t k, const double eps, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Inference using KNN model.

See also knnprocess0(), knnprocessi() and knnclassify() for options with a
bit more convenient interface.

IMPORTANT: this function is thread-unsafe and modifies internal structures
           of the model! You can not use same model  object  for  parallel
           evaluation from several threads.

           Use knntsprocess() with independent  thread-local  buffers,  if
           you need thread-safe evaluation.

INPUT PARAMETERS:
    Model   -   KNN model
    X       -   input vector,  array[0..NVars-1].
    Y       -   possible preallocated buffer. Reused if long enough.

OUTPUT PARAMETERS:
    Y       -   result. Regression estimate when solving regression  task,
                vector of posterior probabilities for classification task.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnprocess(knnmodel &model, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns first component of the  inferred  vector  (i.e.  one
with index #0).

It is a convenience wrapper for knnprocess() intended for either:
* 1-dimensional regression problems
* 2-class classification problems

In the former case this function returns inference result as scalar, which
is definitely more convenient that wrapping it as vector.  In  the  latter
case it returns probability of object belonging to class #0.

If you call it for anything different from two cases above, it  will  work
as defined, i.e. return y[0], although it is of less use in such cases.

IMPORTANT: this function is thread-unsafe and modifies internal structures
           of the model! You can not use same model  object  for  parallel
           evaluation from several threads.

           Use knntsprocess() with independent  thread-local  buffers,  if
           you need thread-safe evaluation.

INPUT PARAMETERS:
    Model   -   KNN model
    X       -   input vector,  array[0..NVars-1].

RESULT:
    Y[0]

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
double knnprocess0(knnmodel &model, const real_1d_array &x, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function returns most probable class number for an  input  X.  It  is
same as calling knnprocess(model,x,y), then determining i=argmax(y[i]) and
returning i.

A class number in [0,NOut) range in returned for classification  problems,
-1 is returned when this function is called for regression problems.

IMPORTANT: this function is thread-unsafe and modifies internal structures
           of the model! You can not use same model  object  for  parallel
           evaluation from several threads.

           Use knntsprocess() with independent  thread-local  buffers,  if
           you need thread-safe evaluation.

INPUT PARAMETERS:
    Model   -   KNN model
    X       -   input vector,  array[0..NVars-1].

RESULT:
    class number, -1 for regression tasks

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
ae_int_t knnclassify(knnmodel &model, const real_1d_array &x, const xparams _xparams = alglib::xdefault);


/*************************************************************************
'interactive' variant of knnprocess()  for  languages  like  Python  which
support constructs like "y = knnprocessi(model,x)" and interactive mode of
the interpreter.

This function allocates new array on each call,  so  it  is  significantly
slower than its 'non-interactive' counterpart, but it is  more  convenient
when you call it from command line.

IMPORTANT: this  function  is  thread-unsafe  and  may   modify   internal
           structures of the model! You can not use same model  object for
           parallel evaluation from several threads.

           Use knntsprocess()  with  independent  thread-local  buffers if
           you need thread-safe evaluation.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnprocessi(knnmodel &model, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Thread-safe procesing using external buffer for temporaries.

This function is thread-safe (i.e .  you  can  use  same  KNN  model  from
multiple threads) as long as you use different buffer objects for different
threads.

INPUT PARAMETERS:
    Model   -   KNN model
    Buf     -   buffer object, must be  allocated  specifically  for  this
                model with knncreatebuffer().
    X       -   input vector,  array[NVars]

OUTPUT PARAMETERS:
    Y       -   result, array[NOut].   Regression  estimate  when  solving
                regression task,  vector  of  posterior  probabilities for
                a classification task.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knntsprocess(const knnmodel &model, knnbuffer &buf, const real_1d_array &x, real_1d_array &y, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Relative classification error on the test set

INPUT PARAMETERS:
    Model   -   KNN model
    XY      -   test set
    NPoints -   test set size

RESULT:
    percent of incorrectly classified cases.
    Zero if model solves regression task.

NOTE: if  you  need several different kinds of error metrics, it is better
      to use knnallerrors() which computes all error metric  with just one
      pass over dataset.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
double knnrelclserror(const knnmodel &model, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average cross-entropy (in bits per element) on the test set

INPUT PARAMETERS:
    Model   -   KNN model
    XY      -   test set
    NPoints -   test set size

RESULT:
    CrossEntropy/NPoints.
    Zero if model solves regression task.

NOTE: the cross-entropy metric is too unstable when used to  evaluate  KNN
      models (such models can report exactly  zero probabilities),  so  we
      do not recommend using it.

NOTE: if  you  need several different kinds of error metrics, it is better
      to use knnallerrors() which computes all error metric  with just one
      pass over dataset.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
double knnavgce(const knnmodel &model, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
RMS error on the test set.

Its meaning for regression task is obvious. As for classification problems,
RMS error means error when estimating posterior probabilities.

INPUT PARAMETERS:
    Model   -   KNN model
    XY      -   test set
    NPoints -   test set size

RESULT:
    root mean square error.

NOTE: if  you  need several different kinds of error metrics, it is better
      to use knnallerrors() which computes all error metric  with just one
      pass over dataset.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
double knnrmserror(const knnmodel &model, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average error on the test set

Its meaning for regression task is obvious. As for classification problems,
average error means error when estimating posterior probabilities.

INPUT PARAMETERS:
    Model   -   KNN model
    XY      -   test set
    NPoints -   test set size

RESULT:
    average error

NOTE: if  you  need several different kinds of error metrics, it is better
      to use knnallerrors() which computes all error metric  with just one
      pass over dataset.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
double knnavgerror(const knnmodel &model, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Average relative error on the test set

Its meaning for regression task is obvious. As for classification problems,
average relative error means error when estimating posterior probabilities.

INPUT PARAMETERS:
    Model   -   KNN model
    XY      -   test set
    NPoints -   test set size

RESULT:
    average relative error

NOTE: if  you  need several different kinds of error metrics, it is better
      to use knnallerrors() which computes all error metric  with just one
      pass over dataset.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
double knnavgrelerror(const knnmodel &model, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Calculates all kinds of errors for the model in one call.

INPUT PARAMETERS:
    Model   -   KNN model
    XY      -   test set:
                * one row per point
                * first NVars columns store independent variables
                * depending on problem type:
                  * next column stores class number in [0,NClasses) -  for
                    classification problems
                  * next NOut columns  store  dependent  variables  -  for
                    regression problems
    NPoints -   test set size, NPoints>=0

OUTPUT PARAMETERS:
    Rep     -   following fields are loaded with errors for both regression
                and classification models:
                * rep.rmserror - RMS error for the output
                * rep.avgerror - average error
                * rep.avgrelerror - average relative error
                following fields are set only  for classification  models,
                zero for regression ones:
                * relclserror   - relative classification error, in [0,1]
                * avgce - average cross-entropy in bits per dataset entry

NOTE: the cross-entropy metric is too unstable when used to  evaluate  KNN
      models (such models can report exactly  zero probabilities),  so  we
      do not recommend using it.

  -- ALGLIB --
     Copyright 15.02.2019 by Bochkanov Sergey
*************************************************************************/
void knnallerrors(const knnmodel &model, const real_2d_array &xy, const ae_int_t npoints, knnreport &rep, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_MLPTRAIN) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
Neural network training  using  modified  Levenberg-Marquardt  with  exact
Hessian calculation and regularization. Subroutine trains  neural  network
with restarts from random positions. Algorithm is well  suited  for  small
and medium scale problems (hundreds of weights).

INPUT PARAMETERS:
    Network     -   neural network with initialized geometry
    XY          -   training set
    NPoints     -   training set size
    Decay       -   weight decay constant, >=0.001
                    Decay term 'Decay*||Weights||^2' is added to error
                    function.
                    If you don't know what Decay to choose, use 0.001.
    Restarts    -   number of restarts from random position, >0.
                    If you don't know what Restarts to choose, use 2.

OUTPUT PARAMETERS:
    Network     -   trained neural network.
    Info        -   return code:
                    * -9, if internal matrix inverse subroutine failed
                    * -2, if there is a point with class number
                          outside of [0..NOut-1].
                    * -1, if wrong parameters specified
                          (NPoints<0, Restarts<1).
                    *  2, if task has been solved.
    Rep         -   training report

  -- ALGLIB --
     Copyright 10.03.2009 by Bochkanov Sergey
*************************************************************************/
void mlptrainlm(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Neural  network  training  using  L-BFGS  algorithm  with  regularization.
Subroutine  trains  neural  network  with  restarts from random positions.
Algorithm  is  well  suited  for  problems  of  any dimensionality (memory
requirements and step complexity are linear by weights number).

INPUT PARAMETERS:
    Network     -   neural network with initialized geometry
    XY          -   training set
    NPoints     -   training set size
    Decay       -   weight decay constant, >=0.001
                    Decay term 'Decay*||Weights||^2' is added to error
                    function.
                    If you don't know what Decay to choose, use 0.001.
    Restarts    -   number of restarts from random position, >0.
                    If you don't know what Restarts to choose, use 2.
    WStep       -   stopping criterion. Algorithm stops if  step  size  is
                    less than WStep. Recommended value - 0.01.  Zero  step
                    size means stopping after MaxIts iterations.
    MaxIts      -   stopping   criterion.  Algorithm  stops  after  MaxIts
                    iterations (NOT gradient  calculations).  Zero  MaxIts
                    means stopping when step is sufficiently small.

OUTPUT PARAMETERS:
    Network     -   trained neural network.
    Info        -   return code:
                    * -8, if both WStep=0 and MaxIts=0
                    * -2, if there is a point with class number
                          outside of [0..NOut-1].
                    * -1, if wrong parameters specified
                          (NPoints<0, Restarts<1).
                    *  2, if task has been solved.
    Rep         -   training report

  -- ALGLIB --
     Copyright 09.12.2007 by Bochkanov Sergey
*************************************************************************/
void mlptrainlbfgs(multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, ae_int_t &info, mlpreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Neural network training using early stopping (base algorithm - L-BFGS with
regularization).

INPUT PARAMETERS:
    Network     -   neural network with initialized geometry
    TrnXY       -   training set
    TrnSize     -   training set size, TrnSize>0
    ValXY       -   validation set
    ValSize     -   validation set size, ValSize>0
    Decay       -   weight decay constant, >=0.001
                    Decay term 'Decay*||Weights||^2' is added to error
                    function.
                    If you don't know what Decay to choose, use 0.001.
    Restarts    -   number of restarts, either:
                    * strictly positive number - algorithm make specified
                      number of restarts from random position.
                    * -1, in which case algorithm makes exactly one run
                      from the initial state of the network (no randomization).
                    If you don't know what Restarts to choose, choose one
                    one the following:
                    * -1 (deterministic start)
                    * +1 (one random restart)
                    * +5 (moderate amount of random restarts)

OUTPUT PARAMETERS:
    Network     -   trained neural network.
    Info        -   return code:
                    * -2, if there is a point with class number
                          outside of [0..NOut-1].
                    * -1, if wrong parameters specified
                          (NPoints<0, Restarts<1, ...).
                    *  2, task has been solved, stopping  criterion  met -
                          sufficiently small step size.  Not expected  (we
                          use  EARLY  stopping)  but  possible  and not an
                          error.
                    *  6, task has been solved, stopping  criterion  met -
                          increasing of validation set error.
    Rep         -   training report

NOTE:

Algorithm stops if validation set error increases for  a  long  enough  or
step size is small enought  (there  are  task  where  validation  set  may
decrease for eternity). In any case solution returned corresponds  to  the
minimum of validation set error.

  -- ALGLIB --
     Copyright 10.03.2009 by Bochkanov Sergey
*************************************************************************/
void mlptraines(multilayerperceptron &network, const real_2d_array &trnxy, const ae_int_t trnsize, const real_2d_array &valxy, const ae_int_t valsize, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Cross-validation estimate of generalization error.

Base algorithm - L-BFGS.

INPUT PARAMETERS:
    Network     -   neural network with initialized geometry.   Network is
                    not changed during cross-validation -  it is used only
                    as a representative of its architecture.
    XY          -   training set.
    SSize       -   training set size
    Decay       -   weight  decay, same as in MLPTrainLBFGS
    Restarts    -   number of restarts, >0.
                    restarts are counted for each partition separately, so
                    total number of restarts will be Restarts*FoldsCount.
    WStep       -   stopping criterion, same as in MLPTrainLBFGS
    MaxIts      -   stopping criterion, same as in MLPTrainLBFGS
    FoldsCount  -   number of folds in k-fold cross-validation,
                    2<=FoldsCount<=SSize.
                    recommended value: 10.

OUTPUT PARAMETERS:
    Info        -   return code, same as in MLPTrainLBFGS
    Rep         -   report, same as in MLPTrainLM/MLPTrainLBFGS
    CVRep       -   generalization error estimates

  -- ALGLIB --
     Copyright 09.12.2007 by Bochkanov Sergey
*************************************************************************/
void mlpkfoldcvlbfgs(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, const ae_int_t foldscount, ae_int_t &info, mlpreport &rep, mlpcvreport &cvrep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Cross-validation estimate of generalization error.

Base algorithm - Levenberg-Marquardt.

INPUT PARAMETERS:
    Network     -   neural network with initialized geometry.   Network is
                    not changed during cross-validation -  it is used only
                    as a representative of its architecture.
    XY          -   training set.
    SSize       -   training set size
    Decay       -   weight  decay, same as in MLPTrainLBFGS
    Restarts    -   number of restarts, >0.
                    restarts are counted for each partition separately, so
                    total number of restarts will be Restarts*FoldsCount.
    FoldsCount  -   number of folds in k-fold cross-validation,
                    2<=FoldsCount<=SSize.
                    recommended value: 10.

OUTPUT PARAMETERS:
    Info        -   return code, same as in MLPTrainLBFGS
    Rep         -   report, same as in MLPTrainLM/MLPTrainLBFGS
    CVRep       -   generalization error estimates

  -- ALGLIB --
     Copyright 09.12.2007 by Bochkanov Sergey
*************************************************************************/
void mlpkfoldcvlm(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const ae_int_t foldscount, ae_int_t &info, mlpreport &rep, mlpcvreport &cvrep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function estimates generalization error using cross-validation on the
current dataset with current training settings.

  ! 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:
    S           -   trainer object
    Network     -   neural network. It must have same number of inputs and
                    output/classes as was specified during creation of the
                    trainer object. Network is not changed  during  cross-
                    validation and is not trained - it  is  used  only  as
                    representative of its architecture. I.e., we  estimate
                    generalization properties of  ARCHITECTURE,  not  some
                    specific network.
    NRestarts   -   number of restarts, >=0:
                    * NRestarts>0  means  that  for  each cross-validation
                      round   specified  number   of  random  restarts  is
                      performed,  with  best  network  being  chosen after
                      training.
                    * NRestarts=0 is same as NRestarts=1
    FoldsCount  -   number of folds in k-fold cross-validation:
                    * 2<=FoldsCount<=size of dataset
                    * recommended value: 10.
                    * values larger than dataset size will be silently
                      truncated down to dataset size

OUTPUT PARAMETERS:
    Rep         -   structure which contains cross-validation estimates:
                    * Rep.RelCLSError - fraction of misclassified cases.
                    * Rep.AvgCE - acerage cross-entropy
                    * Rep.RMSError - root-mean-square error
                    * Rep.AvgError - average error
                    * Rep.AvgRelError - average relative error

NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(),
      or subset with only one point  was  given,  zeros  are  returned  as
      estimates.

NOTE: this method performs FoldsCount cross-validation  rounds,  each  one
      with NRestarts random starts.  Thus,  FoldsCount*NRestarts  networks
      are trained in total.

NOTE: Rep.RelCLSError/Rep.AvgCE are zero on regression problems.

NOTE: on classification problems Rep.RMSError/Rep.AvgError/Rep.AvgRelError
      contain errors in prediction of posterior probabilities.

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpkfoldcv(mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, const ae_int_t foldscount, mlpreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Creation of the network trainer object for regression networks

INPUT PARAMETERS:
    NIn         -   number of inputs, NIn>=1
    NOut        -   number of outputs, NOut>=1

OUTPUT PARAMETERS:
    S           -   neural network trainer object.
                    This structure can be used to train any regression
                    network with NIn inputs and NOut outputs.

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpcreatetrainer(const ae_int_t nin, const ae_int_t nout, mlptrainer &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Creation of the network trainer object for classification networks

INPUT PARAMETERS:
    NIn         -   number of inputs, NIn>=1
    NClasses    -   number of classes, NClasses>=2

OUTPUT PARAMETERS:
    S           -   neural network trainer object.
                    This structure can be used to train any classification
                    network with NIn inputs and NOut outputs.

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpcreatetrainercls(const ae_int_t nin, const ae_int_t nclasses, mlptrainer &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets "current dataset" of the trainer object to  one  passed
by user.

INPUT PARAMETERS:
    S           -   trainer object
    XY          -   training  set,  see  below  for  information  on   the
                    training set format. This function checks  correctness
                    of  the  dataset  (no  NANs/INFs,  class  numbers  are
                    correct) and throws exception when  incorrect  dataset
                    is passed.
    NPoints     -   points count, >=0.

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
datasetformat is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpsetdataset(mlptrainer &s, const real_2d_array &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets "current dataset" of the trainer object to  one  passed
by user (sparse matrix is used to store dataset).

INPUT PARAMETERS:
    S           -   trainer object
    XY          -   training  set,  see  below  for  information  on   the
                    training set format. This function checks  correctness
                    of  the  dataset  (no  NANs/INFs,  class  numbers  are
                    correct) and throws exception when  incorrect  dataset
                    is passed. Any  sparse  storage  format  can be  used:
                    Hash-table, CRS...
    NPoints     -   points count, >=0

DATASET FORMAT:

This  function  uses  two  different  dataset formats - one for regression
networks, another one for classification networks.

For regression networks with NIn inputs and NOut outputs following dataset
format is used:
* dataset is given by NPoints*(NIn+NOut) matrix
* each row corresponds to one example
* first NIn columns are inputs, next NOut columns are outputs

For classification networks with NIn inputs and NClasses clases  following
datasetformat is used:
* dataset is given by NPoints*(NIn+1) matrix
* each row corresponds to one example
* first NIn columns are inputs, last column stores class number (from 0 to
  NClasses-1).

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpsetsparsedataset(mlptrainer &s, const sparsematrix &xy, const ae_int_t npoints, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets weight decay coefficient which is used for training.

INPUT PARAMETERS:
    S           -   trainer object
    Decay       -   weight  decay  coefficient,  >=0.  Weight  decay  term
                    'Decay*||Weights||^2' is added to error  function.  If
                    you don't know what Decay to choose, use 1.0E-3.
                    Weight decay can be set to zero,  in this case network
                    is trained without weight decay.

NOTE: by default network uses some small nonzero value for weight decay.

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpsetdecay(mlptrainer &s, const double decay, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets stopping criteria for the optimizer.

INPUT PARAMETERS:
    S           -   trainer object
    WStep       -   stopping criterion. Algorithm stops if  step  size  is
                    less than WStep. Recommended value - 0.01.  Zero  step
                    size means stopping after MaxIts iterations.
                    WStep>=0.
    MaxIts      -   stopping   criterion.  Algorithm  stops  after  MaxIts
                    epochs (full passes over entire dataset).  Zero MaxIts
                    means stopping when step is sufficiently small.
                    MaxIts>=0.

NOTE: by default, WStep=0.005 and MaxIts=0 are used. These values are also
      used when MLPSetCond() is called with WStep=0 and MaxIts=0.

NOTE: these stopping criteria are used for all kinds of neural training  -
      from "conventional" networks to early stopping ensembles. When  used
      for "conventional" networks, they are  used  as  the  only  stopping
      criteria. When combined with early stopping, they used as ADDITIONAL
      stopping criteria which can terminate early stopping algorithm.

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpsetcond(mlptrainer &s, const double wstep, const ae_int_t maxits, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function sets training algorithm: batch training using L-BFGS will be
used.

This algorithm:
* the most robust for small-scale problems, but may be too slow for  large
  scale ones.
* perfoms full pass through the dataset before performing step
* uses conditions specified by MLPSetCond() for stopping
* is default one used by trainer object

INPUT PARAMETERS:
    S           -   trainer object

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpsetalgobatch(mlptrainer &s, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function trains neural network passed to this function, using current
dataset (one which was passed to MLPSetDataset() or MLPSetSparseDataset())
and current training settings. Training  from  NRestarts  random  starting
positions is performed, best network is chosen.

Training is performed using current training algorithm.

  ! 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:
    S           -   trainer object
    Network     -   neural network. It must have same number of inputs and
                    output/classes as was specified during creation of the
                    trainer object.
    NRestarts   -   number of restarts, >=0:
                    * NRestarts>0 means that specified  number  of  random
                      restarts are performed, best network is chosen after
                      training
                    * NRestarts=0 means that current state of the  network
                      is used for training.

OUTPUT PARAMETERS:
    Network     -   trained network

NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(),
      network  is  filled  by zero  values.  Same  behavior  for functions
      MLPStartTraining and MLPContinueTraining.

NOTE: this method uses sum-of-squares error function for training.

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlptrainnetwork(mlptrainer &s, multilayerperceptron &network, const ae_int_t nrestarts, mlpreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
IMPORTANT: this is an "expert" version of the MLPTrain() function.  We  do
           not recommend you to use it unless you are pretty sure that you
           need ability to monitor training progress.

This function performs step-by-step training of the neural  network.  Here
"step-by-step" means that training  starts  with  MLPStartTraining() call,
and then user subsequently calls MLPContinueTraining() to perform one more
iteration of the training.

After call to this function trainer object remembers network and  is ready
to  train  it.  However,  no  training  is  performed  until first call to
MLPContinueTraining() function. Subsequent calls  to MLPContinueTraining()
will advance training progress one iteration further.

EXAMPLE:
    >
    > ...initialize network and trainer object....
    >
    > MLPStartTraining(Trainer, Network, True)
    > while MLPContinueTraining(Trainer, Network) do
    >     ...visualize training progress...
    >

INPUT PARAMETERS:
    S           -   trainer object
    Network     -   neural network. It must have same number of inputs and
                    output/classes as was specified during creation of the
                    trainer object.
    RandomStart -   randomize network before training or not:
                    * True  means  that  network  is  randomized  and  its
                      initial state (one which was passed to  the  trainer
                      object) is lost.
                    * False  means  that  training  is  started  from  the
                      current state of the network

OUTPUT PARAMETERS:
    Network     -   neural network which is ready to training (weights are
                    initialized, preprocessor is initialized using current
                    training set)

NOTE: this method uses sum-of-squares error function for training.

NOTE: it is expected that trainer object settings are NOT  changed  during
      step-by-step training, i.e. no  one  changes  stopping  criteria  or
      training set during training. It is possible and there is no defense
      against  such  actions,  but  algorithm  behavior  in  such cases is
      undefined and can be unpredictable.

  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
void mlpstarttraining(mlptrainer &s, multilayerperceptron &network, const bool randomstart, const xparams _xparams = alglib::xdefault);


/*************************************************************************
IMPORTANT: this is an "expert" version of the MLPTrain() function.  We  do
           not recommend you to use it unless you are pretty sure that you
           need ability to monitor training progress.

  ! 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.

This function performs step-by-step training of the neural  network.  Here
"step-by-step" means that training starts  with  MLPStartTraining()  call,
and then user subsequently calls MLPContinueTraining() to perform one more
iteration of the training.

This  function  performs  one  more  iteration of the training and returns
either True (training continues) or False (training stopped). In case True
was returned, Network weights are updated according to the  current  state
of the optimization progress. In case False was  returned,  no  additional
updates is performed (previous update of  the  network weights moved us to
the final point, and no additional updates is needed).

EXAMPLE:
    >
    > [initialize network and trainer object]
    >
    > MLPStartTraining(Trainer, Network, True)
    > while MLPContinueTraining(Trainer, Network) do
    >     [visualize training progress]
    >

INPUT PARAMETERS:
    S           -   trainer object
    Network     -   neural  network  structure,  which  is  used to  store
                    current state of the training process.

OUTPUT PARAMETERS:
    Network     -   weights of the neural network  are  rewritten  by  the
                    current approximation.

NOTE: this method uses sum-of-squares error function for training.

NOTE: it is expected that trainer object settings are NOT  changed  during
      step-by-step training, i.e. no  one  changes  stopping  criteria  or
      training set during training. It is possible and there is no defense
      against  such  actions,  but  algorithm  behavior  in  such cases is
      undefined and can be unpredictable.

NOTE: It  is  expected that Network is the same one which  was  passed  to
      MLPStartTraining() function.  However,  THIS  function  checks  only
      following:
      * that number of network inputs is consistent with trainer object
        settings
      * that number of network outputs/classes is consistent with  trainer
        object settings
      * that number of network weights is the same as number of weights in
        the network passed to MLPStartTraining() function
      Exception is thrown when these conditions are violated.

      It is also expected that you do not change state of the  network  on
      your own - the only party who has right to change network during its
      training is a trainer object. Any attempt to interfere with  trainer
      may lead to unpredictable results.


  -- ALGLIB --
     Copyright 23.07.2012 by Bochkanov Sergey
*************************************************************************/
bool mlpcontinuetraining(mlptrainer &s, multilayerperceptron &network, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Training neural networks ensemble using  bootstrap  aggregating (bagging).
Modified Levenberg-Marquardt algorithm is used as base training method.

INPUT PARAMETERS:
    Ensemble    -   model with initialized geometry
    XY          -   training set
    NPoints     -   training set size
    Decay       -   weight decay coefficient, >=0.001
    Restarts    -   restarts, >0.

OUTPUT PARAMETERS:
    Ensemble    -   trained model
    Info        -   return code:
                    * -2, if there is a point with class number
                          outside of [0..NClasses-1].
                    * -1, if incorrect parameters was passed
                          (NPoints<0, Restarts<1).
                    *  2, if task has been solved.
    Rep         -   training report.
    OOBErrors   -   out-of-bag generalization error estimate

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpebagginglm(mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep, mlpcvreport &ooberrors, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Training neural networks ensemble using  bootstrap  aggregating (bagging).
L-BFGS algorithm is used as base training method.

INPUT PARAMETERS:
    Ensemble    -   model with initialized geometry
    XY          -   training set
    NPoints     -   training set size
    Decay       -   weight decay coefficient, >=0.001
    Restarts    -   restarts, >0.
    WStep       -   stopping criterion, same as in MLPTrainLBFGS
    MaxIts      -   stopping criterion, same as in MLPTrainLBFGS

OUTPUT PARAMETERS:
    Ensemble    -   trained model
    Info        -   return code:
                    * -8, if both WStep=0 and MaxIts=0
                    * -2, if there is a point with class number
                          outside of [0..NClasses-1].
                    * -1, if incorrect parameters was passed
                          (NPoints<0, Restarts<1).
                    *  2, if task has been solved.
    Rep         -   training report.
    OOBErrors   -   out-of-bag generalization error estimate

  -- ALGLIB --
     Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlpebagginglbfgs(mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, ae_int_t &info, mlpreport &rep, mlpcvreport &ooberrors, const xparams _xparams = alglib::xdefault);


/*************************************************************************
Training neural networks ensemble using early stopping.

INPUT PARAMETERS:
    Ensemble    -   model with initialized geometry
    XY          -   training set
    NPoints     -   training set size
    Decay       -   weight decay coefficient, >=0.001
    Restarts    -   restarts, >0.

OUTPUT PARAMETERS:
    Ensemble    -   trained model
    Info        -   return code:
                    * -2, if there is a point with class number
                          outside of [0..NClasses-1].
                    * -1, if incorrect parameters was passed
                          (NPoints<0, Restarts<1).
                    *  6, if task has been solved.
    Rep         -   training report.
    OOBErrors   -   out-of-bag generalization error estimate

  -- ALGLIB --
     Copyright 10.03.2009 by Bochkanov Sergey
*************************************************************************/
void mlpetraines(mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep, const xparams _xparams = alglib::xdefault);


/*************************************************************************
This function trains neural network ensemble passed to this function using
current dataset and early stopping training algorithm. Each early stopping
round performs NRestarts  random  restarts  (thus,  EnsembleSize*NRestarts
training rounds is performed in total).

  ! 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:
    S           -   trainer object;
    Ensemble    -   neural network ensemble. It must have same  number  of
                    inputs and outputs/classes  as  was  specified  during
                    creation of the trainer object.
    NRestarts   -   number of restarts, >=0:
                    * NRestarts>0 means that specified  number  of  random
                      restarts are performed during each ES round;
                    * NRestarts=0 is silently replaced by 1.

OUTPUT PARAMETERS:
    Ensemble    -   trained ensemble;
    Rep         -   it contains all type of errors.

NOTE: this training method uses BOTH early stopping and weight decay!  So,
      you should select weight decay before starting training just as  you
      select it before training "conventional" networks.

NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(),
      or  single-point  dataset  was  passed,  ensemble  is filled by zero
      values.

NOTE: this method uses sum-of-squares error function for training.

  -- ALGLIB --
     Copyright 22.08.2012 by Bochkanov Sergey
*************************************************************************/
void mlptrainensemblees(mlptrainer &s, mlpensemble &ensemble, const ae_int_t nrestarts, mlpreport &rep, const xparams _xparams = alglib::xdefault);
#endif

#if defined(AE_COMPILE_DATACOMP) || !defined(AE_PARTIAL_BUILD)
/*************************************************************************
k-means++ clusterization.
Backward compatibility function, we recommend to use CLUSTERING subpackage
as better replacement.

  -- ALGLIB --
     Copyright 21.03.2009 by Bochkanov Sergey
*************************************************************************/
void kmeansgenerate(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t k, const ae_int_t restarts, ae_int_t &info, real_2d_array &c, integer_1d_array &xyc, const xparams _xparams = alglib::xdefault);
#endif
}

/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS)
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
#if defined(AE_COMPILE_PCA) || !defined(AE_PARTIAL_BUILD)
void pcabuildbasis(/* Real    */ const ae_matrix* x,
     ae_int_t npoints,
     ae_int_t nvars,
     /* Real    */ ae_vector* s2,
     /* Real    */ ae_matrix* v,
     ae_state *_state);
void pcatruncatedsubspace(/* Real    */ const ae_matrix* x,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nneeded,
     double eps,
     ae_int_t maxits,
     /* Real    */ ae_vector* s2,
     /* Real    */ ae_matrix* v,
     ae_state *_state);
void pcatruncatedsubspacesparse(const sparsematrix* x,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nneeded,
     double eps,
     ae_int_t maxits,
     /* Real    */ ae_vector* s2,
     /* Real    */ ae_matrix* v,
     ae_state *_state);
#endif
#if defined(AE_COMPILE_BDSS) || !defined(AE_PARTIAL_BUILD)
void dserrallocate(ae_int_t nclasses,
     /* Real    */ ae_vector* buf,
     ae_state *_state);
void dserraccumulate(/* Real    */ ae_vector* buf,
     /* Real    */ const ae_vector* y,
     /* Real    */ const ae_vector* desiredy,
     ae_state *_state);
void dserrfinish(/* Real    */ ae_vector* buf, ae_state *_state);
void dsnormalize(/* Real    */ ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t* info,
     /* Real    */ ae_vector* means,
     /* Real    */ ae_vector* sigmas,
     ae_state *_state);
void dsnormalizec(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t* info,
     /* Real    */ ae_vector* means,
     /* Real    */ ae_vector* sigmas,
     ae_state *_state);
double dsgetmeanmindistance(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_state *_state);
void dstie(/* Real    */ ae_vector* a,
     ae_int_t n,
     /* Integer */ ae_vector* ties,
     ae_int_t* tiecount,
     /* Integer */ ae_vector* p1,
     /* Integer */ ae_vector* p2,
     ae_state *_state);
void dstiefasti(/* Real    */ ae_vector* a,
     /* Integer */ ae_vector* b,
     ae_int_t n,
     /* Integer */ ae_vector* ties,
     ae_int_t* tiecount,
     /* Real    */ ae_vector* bufr,
     /* Integer */ ae_vector* bufi,
     ae_state *_state);
void dsoptimalsplit2(/* Real    */ const ae_vector* _a,
     /* Integer */ const ae_vector* _c,
     ae_int_t n,
     ae_int_t* info,
     double* threshold,
     double* pal,
     double* pbl,
     double* par,
     double* pbr,
     double* cve,
     ae_state *_state);
void dsoptimalsplit2fast(/* Real    */ ae_vector* a,
     /* Integer */ ae_vector* c,
     /* Integer */ ae_vector* tiesbuf,
     /* Integer */ ae_vector* cntbuf,
     /* Real    */ ae_vector* bufr,
     /* Integer */ ae_vector* bufi,
     ae_int_t n,
     ae_int_t nc,
     double alpha,
     ae_int_t* info,
     double* threshold,
     double* rms,
     double* cvrms,
     ae_state *_state);
void dssplitk(/* Real    */ const ae_vector* _a,
     /* Integer */ const ae_vector* _c,
     ae_int_t n,
     ae_int_t nc,
     ae_int_t kmax,
     ae_int_t* info,
     /* Real    */ ae_vector* thresholds,
     ae_int_t* ni,
     double* cve,
     ae_state *_state);
void dsoptimalsplitk(/* Real    */ const ae_vector* _a,
     /* Integer */ const ae_vector* _c,
     ae_int_t n,
     ae_int_t nc,
     ae_int_t kmax,
     ae_int_t* info,
     /* Real    */ ae_vector* thresholds,
     ae_int_t* ni,
     double* cve,
     ae_state *_state);
void _cvreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _cvreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _cvreport_clear(void* _p);
void _cvreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_MLPBASE) || !defined(AE_PARTIAL_BUILD)
ae_int_t mlpgradsplitcost(ae_state *_state);
ae_int_t mlpgradsplitsize(ae_state *_state);
void mlpcreate0(ae_int_t nin,
     ae_int_t nout,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreate1(ae_int_t nin,
     ae_int_t nhid,
     ae_int_t nout,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreate2(ae_int_t nin,
     ae_int_t nhid1,
     ae_int_t nhid2,
     ae_int_t nout,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreateb0(ae_int_t nin,
     ae_int_t nout,
     double b,
     double d,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreateb1(ae_int_t nin,
     ae_int_t nhid,
     ae_int_t nout,
     double b,
     double d,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreateb2(ae_int_t nin,
     ae_int_t nhid1,
     ae_int_t nhid2,
     ae_int_t nout,
     double b,
     double d,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreater0(ae_int_t nin,
     ae_int_t nout,
     double a,
     double b,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreater1(ae_int_t nin,
     ae_int_t nhid,
     ae_int_t nout,
     double a,
     double b,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreater2(ae_int_t nin,
     ae_int_t nhid1,
     ae_int_t nhid2,
     ae_int_t nout,
     double a,
     double b,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreatec0(ae_int_t nin,
     ae_int_t nout,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreatec1(ae_int_t nin,
     ae_int_t nhid,
     ae_int_t nout,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcreatec2(ae_int_t nin,
     ae_int_t nhid1,
     ae_int_t nhid2,
     ae_int_t nout,
     multilayerperceptron* network,
     ae_state *_state);
void mlpcopy(const multilayerperceptron* network1,
     multilayerperceptron* network2,
     ae_state *_state);
void mlpcopyshared(const multilayerperceptron* network1,
     multilayerperceptron* network2,
     ae_state *_state);
ae_bool mlpsamearchitecture(const multilayerperceptron* network1,
     const multilayerperceptron* network2,
     ae_state *_state);
void mlpcopytunableparameters(const multilayerperceptron* network1,
     multilayerperceptron* network2,
     ae_state *_state);
void mlpexporttunableparameters(const multilayerperceptron* network,
     /* Real    */ ae_vector* p,
     ae_int_t* pcount,
     ae_state *_state);
void mlpimporttunableparameters(multilayerperceptron* network,
     /* Real    */ ae_vector* p,
     ae_state *_state);
void mlpserializeold(const multilayerperceptron* network,
     /* Real    */ ae_vector* ra,
     ae_int_t* rlen,
     ae_state *_state);
void mlpunserializeold(/* Real    */ const ae_vector* ra,
     multilayerperceptron* network,
     ae_state *_state);
void mlprandomize(multilayerperceptron* network, ae_state *_state);
void mlprandomizefull(multilayerperceptron* network, ae_state *_state);
void mlpinitpreprocessor(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t ssize,
     ae_state *_state);
void mlpinitpreprocessorsparse(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t ssize,
     ae_state *_state);
void mlpinitpreprocessorsubset(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t setsize,
     /* Integer */ const ae_vector* idx,
     ae_int_t subsetsize,
     ae_state *_state);
void mlpinitpreprocessorsparsesubset(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t setsize,
     /* Integer */ const ae_vector* idx,
     ae_int_t subsetsize,
     ae_state *_state);
void mlpproperties(const multilayerperceptron* network,
     ae_int_t* nin,
     ae_int_t* nout,
     ae_int_t* wcount,
     ae_state *_state);
ae_int_t mlpntotal(const multilayerperceptron* network, ae_state *_state);
ae_int_t mlpgetinputscount(const multilayerperceptron* network,
     ae_state *_state);
ae_int_t mlpgetoutputscount(const multilayerperceptron* network,
     ae_state *_state);
ae_int_t mlpgetweightscount(const multilayerperceptron* network,
     ae_state *_state);
ae_bool mlpissoftmax(const multilayerperceptron* network,
     ae_state *_state);
ae_int_t mlpgetlayerscount(const multilayerperceptron* network,
     ae_state *_state);
ae_int_t mlpgetlayersize(const multilayerperceptron* network,
     ae_int_t k,
     ae_state *_state);
void mlpgetinputscaling(const multilayerperceptron* network,
     ae_int_t i,
     double* mean,
     double* sigma,
     ae_state *_state);
void mlpgetoutputscaling(const multilayerperceptron* network,
     ae_int_t i,
     double* mean,
     double* sigma,
     ae_state *_state);
void mlpgetneuroninfo(multilayerperceptron* network,
     ae_int_t k,
     ae_int_t i,
     ae_int_t* fkind,
     double* threshold,
     ae_state *_state);
double mlpgetweight(multilayerperceptron* network,
     ae_int_t k0,
     ae_int_t i0,
     ae_int_t k1,
     ae_int_t i1,
     ae_state *_state);
void mlpsetinputscaling(multilayerperceptron* network,
     ae_int_t i,
     double mean,
     double sigma,
     ae_state *_state);
void mlpsetoutputscaling(multilayerperceptron* network,
     ae_int_t i,
     double mean,
     double sigma,
     ae_state *_state);
void mlpsetneuroninfo(multilayerperceptron* network,
     ae_int_t k,
     ae_int_t i,
     ae_int_t fkind,
     double threshold,
     ae_state *_state);
void mlpsetweight(multilayerperceptron* network,
     ae_int_t k0,
     ae_int_t i0,
     ae_int_t k1,
     ae_int_t i1,
     double w,
     ae_state *_state);
void mlpactivationfunction(double net,
     ae_int_t k,
     double* f,
     double* df,
     double* d2f,
     ae_state *_state);
void mlpprocess(multilayerperceptron* network,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
void mlpprocessi(multilayerperceptron* network,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
double mlperror(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlperrorsparse(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlperrorn(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t ssize,
     ae_state *_state);
ae_int_t mlpclserror(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlprelclserror(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlprelclserrorsparse(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpavgce(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpavgcesparse(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlprmserror(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlprmserrorsparse(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpavgerror(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpavgerrorsparse(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpavgrelerror(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpavgrelerrorsparse(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t npoints,
     ae_state *_state);
void mlpgrad(multilayerperceptron* network,
     /* Real    */ const ae_vector* x,
     /* Real    */ const ae_vector* desiredy,
     double* e,
     /* Real    */ ae_vector* grad,
     ae_state *_state);
void mlpgradn(multilayerperceptron* network,
     /* Real    */ const ae_vector* x,
     /* Real    */ const ae_vector* desiredy,
     double* e,
     /* Real    */ ae_vector* grad,
     ae_state *_state);
void mlpgradbatch(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t ssize,
     double* e,
     /* Real    */ ae_vector* grad,
     ae_state *_state);
void mlpgradbatchsparse(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t ssize,
     double* e,
     /* Real    */ ae_vector* grad,
     ae_state *_state);
void mlpgradbatchsubset(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t setsize,
     /* Integer */ const ae_vector* idx,
     ae_int_t subsetsize,
     double* e,
     /* Real    */ ae_vector* grad,
     ae_state *_state);
void mlpgradbatchsparsesubset(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t setsize,
     /* Integer */ const ae_vector* idx,
     ae_int_t subsetsize,
     double* e,
     /* Real    */ ae_vector* grad,
     ae_state *_state);
void mlpgradbatchx(const multilayerperceptron* network,
     /* Real    */ const ae_matrix* densexy,
     const sparsematrix* sparsexy,
     ae_int_t datasetsize,
     ae_int_t datasettype,
     /* Integer */ const ae_vector* idx,
     ae_int_t subset0,
     ae_int_t subset1,
     ae_int_t subsettype,
     ae_shared_pool* buf,
     ae_shared_pool* gradbuf,
     ae_state *_state);
ae_bool _trypexec_mlpgradbatchx(const multilayerperceptron* network,
    /* Real    */ const ae_matrix* densexy,
    const sparsematrix* sparsexy,
    ae_int_t datasetsize,
    ae_int_t datasettype,
    /* Integer */ const ae_vector* idx,
    ae_int_t subset0,
    ae_int_t subset1,
    ae_int_t subsettype,
    ae_shared_pool* buf,
    ae_shared_pool* gradbuf, ae_state *_state);
void mlpgradnbatch(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t ssize,
     double* e,
     /* Real    */ ae_vector* grad,
     ae_state *_state);
void mlphessiannbatch(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t ssize,
     double* e,
     /* Real    */ ae_vector* grad,
     /* Real    */ ae_matrix* h,
     ae_state *_state);
void mlphessianbatch(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t ssize,
     double* e,
     /* Real    */ ae_vector* grad,
     /* Real    */ ae_matrix* h,
     ae_state *_state);
void mlpinternalprocessvector(/* Integer */ const ae_vector* structinfo,
     /* Real    */ const ae_vector* weights,
     /* Real    */ const ae_vector* columnmeans,
     /* Real    */ const ae_vector* columnsigmas,
     /* Real    */ ae_vector* neurons,
     /* Real    */ ae_vector* dfdnet,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
void mlpalloc(ae_serializer* s,
     const multilayerperceptron* network,
     ae_state *_state);
void mlpserialize(ae_serializer* s,
     const multilayerperceptron* network,
     ae_state *_state);
void mlpunserialize(ae_serializer* s,
     multilayerperceptron* network,
     ae_state *_state);
void mlpallerrorssubset(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t setsize,
     /* Integer */ const ae_vector* subset,
     ae_int_t subsetsize,
     modelerrors* rep,
     ae_state *_state);
void mlpallerrorssparsesubset(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t setsize,
     /* Integer */ const ae_vector* subset,
     ae_int_t subsetsize,
     modelerrors* rep,
     ae_state *_state);
double mlperrorsubset(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t setsize,
     /* Integer */ const ae_vector* subset,
     ae_int_t subsetsize,
     ae_state *_state);
double mlperrorsparsesubset(multilayerperceptron* network,
     const sparsematrix* xy,
     ae_int_t setsize,
     /* Integer */ const ae_vector* subset,
     ae_int_t subsetsize,
     ae_state *_state);
void mlpallerrorsx(const multilayerperceptron* network,
     /* Real    */ const ae_matrix* densexy,
     const sparsematrix* sparsexy,
     ae_int_t datasetsize,
     ae_int_t datasettype,
     /* Integer */ const ae_vector* idx,
     ae_int_t subset0,
     ae_int_t subset1,
     ae_int_t subsettype,
     ae_shared_pool* buf,
     modelerrors* rep,
     ae_state *_state);
ae_bool _trypexec_mlpallerrorsx(const multilayerperceptron* network,
    /* Real    */ const ae_matrix* densexy,
    const sparsematrix* sparsexy,
    ae_int_t datasetsize,
    ae_int_t datasettype,
    /* Integer */ const ae_vector* idx,
    ae_int_t subset0,
    ae_int_t subset1,
    ae_int_t subsettype,
    ae_shared_pool* buf,
    modelerrors* rep, ae_state *_state);
void _modelerrors_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _modelerrors_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _modelerrors_clear(void* _p);
void _modelerrors_destroy(void* _p);
void _smlpgrad_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _smlpgrad_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _smlpgrad_clear(void* _p);
void _smlpgrad_destroy(void* _p);
void _multilayerperceptron_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _multilayerperceptron_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _multilayerperceptron_clear(void* _p);
void _multilayerperceptron_destroy(void* _p);
#endif
#if defined(AE_COMPILE_MLPE) || !defined(AE_PARTIAL_BUILD)
void mlpecreate0(ae_int_t nin,
     ae_int_t nout,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreate1(ae_int_t nin,
     ae_int_t nhid,
     ae_int_t nout,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreate2(ae_int_t nin,
     ae_int_t nhid1,
     ae_int_t nhid2,
     ae_int_t nout,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreateb0(ae_int_t nin,
     ae_int_t nout,
     double b,
     double d,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreateb1(ae_int_t nin,
     ae_int_t nhid,
     ae_int_t nout,
     double b,
     double d,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreateb2(ae_int_t nin,
     ae_int_t nhid1,
     ae_int_t nhid2,
     ae_int_t nout,
     double b,
     double d,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreater0(ae_int_t nin,
     ae_int_t nout,
     double a,
     double b,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreater1(ae_int_t nin,
     ae_int_t nhid,
     ae_int_t nout,
     double a,
     double b,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreater2(ae_int_t nin,
     ae_int_t nhid1,
     ae_int_t nhid2,
     ae_int_t nout,
     double a,
     double b,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreatec0(ae_int_t nin,
     ae_int_t nout,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreatec1(ae_int_t nin,
     ae_int_t nhid,
     ae_int_t nout,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreatec2(ae_int_t nin,
     ae_int_t nhid1,
     ae_int_t nhid2,
     ae_int_t nout,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecreatefromnetwork(const multilayerperceptron* network,
     ae_int_t ensemblesize,
     mlpensemble* ensemble,
     ae_state *_state);
void mlpecopy(const mlpensemble* ensemble1,
     mlpensemble* ensemble2,
     ae_state *_state);
void mlperandomize(mlpensemble* ensemble, ae_state *_state);
void mlpeproperties(const mlpensemble* ensemble,
     ae_int_t* nin,
     ae_int_t* nout,
     ae_state *_state);
ae_bool mlpeissoftmax(const mlpensemble* ensemble, ae_state *_state);
void mlpeprocess(mlpensemble* ensemble,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
void mlpeprocessi(mlpensemble* ensemble,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
void mlpeallerrorsx(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* densexy,
     const sparsematrix* sparsexy,
     ae_int_t datasetsize,
     ae_int_t datasettype,
     /* Integer */ const ae_vector* idx,
     ae_int_t subset0,
     ae_int_t subset1,
     ae_int_t subsettype,
     ae_shared_pool* buf,
     modelerrors* rep,
     ae_state *_state);
void mlpeallerrorssparse(mlpensemble* ensemble,
     const sparsematrix* xy,
     ae_int_t npoints,
     double* relcls,
     double* avgce,
     double* rms,
     double* avg,
     double* avgrel,
     ae_state *_state);
double mlperelclserror(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpeavgce(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpermserror(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpeavgerror(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mlpeavgrelerror(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
void mlpealloc(ae_serializer* s,
     const mlpensemble* ensemble,
     ae_state *_state);
void mlpeserialize(ae_serializer* s,
     const mlpensemble* ensemble,
     ae_state *_state);
void mlpeunserialize(ae_serializer* s,
     mlpensemble* ensemble,
     ae_state *_state);
void _mlpensemble_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mlpensemble_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpensemble_clear(void* _p);
void _mlpensemble_destroy(void* _p);
#endif
#if defined(AE_COMPILE_CLUSTERING) || !defined(AE_PARTIAL_BUILD)
void clusterizercreate(clusterizerstate* s, ae_state *_state);
void clusterizersetpoints(clusterizerstate* s,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nfeatures,
     ae_int_t disttype,
     ae_state *_state);
void clusterizersetdistances(clusterizerstate* s,
     /* Real    */ const ae_matrix* d,
     ae_int_t npoints,
     ae_bool isupper,
     ae_state *_state);
void clusterizersetahcalgo(clusterizerstate* s,
     ae_int_t algo,
     ae_state *_state);
void clusterizersetkmeanslimits(clusterizerstate* s,
     ae_int_t restarts,
     ae_int_t maxits,
     ae_state *_state);
void clusterizersetkmeansinit(clusterizerstate* s,
     ae_int_t initalgo,
     ae_state *_state);
void clusterizersetseed(clusterizerstate* s,
     ae_int_t seed,
     ae_state *_state);
void clusterizerrunahc(clusterizerstate* s,
     ahcreport* rep,
     ae_state *_state);
void clusterizerrunkmeans(clusterizerstate* s,
     ae_int_t k,
     kmeansreport* rep,
     ae_state *_state);
void clusterizergetdistances(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nfeatures,
     ae_int_t disttype,
     /* Real    */ ae_matrix* d,
     ae_state *_state);
void clusterizergetdistancesbuf(apbuffers* buf,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nfeatures,
     ae_int_t disttype,
     /* Real    */ ae_matrix* d,
     ae_state *_state);
void clusterizergetkclusters(const ahcreport* rep,
     ae_int_t k,
     /* Integer */ ae_vector* cidx,
     /* Integer */ ae_vector* cz,
     ae_state *_state);
void clusterizerseparatedbydist(const ahcreport* rep,
     double r,
     ae_int_t* k,
     /* Integer */ ae_vector* cidx,
     /* Integer */ ae_vector* cz,
     ae_state *_state);
void clusterizerseparatedbycorr(const ahcreport* rep,
     double r,
     ae_int_t* k,
     /* Integer */ ae_vector* cidx,
     /* Integer */ ae_vector* cz,
     ae_state *_state);
void kmeansinitbuf(kmeansbuffers* buf, ae_state *_state);
void kmeansgenerateinternal(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t k,
     ae_int_t initalgo,
     ae_int_t seed,
     ae_int_t maxits,
     ae_int_t restarts,
     ae_bool kmeansdbgnoits,
     ae_int_t* info,
     ae_int_t* iterationscount,
     /* Real    */ ae_matrix* ccol,
     ae_bool needccol,
     /* Real    */ ae_matrix* crow,
     ae_bool needcrow,
     /* Integer */ ae_vector* xyc,
     double* energy,
     kmeansbuffers* buf,
     ae_state *_state);
void kmeansupdatedistances(/* Real    */ const ae_matrix* xy,
     ae_int_t idx0,
     ae_int_t idx1,
     ae_int_t nvars,
     /* Real    */ const ae_matrix* ct,
     ae_int_t cidx0,
     ae_int_t cidx1,
     /* Integer */ ae_vector* xyc,
     /* Real    */ ae_vector* xydist2,
     ae_shared_pool* bufferpool,
     ae_state *_state);
ae_bool _trypexec_kmeansupdatedistances(/* Real    */ const ae_matrix* xy,
    ae_int_t idx0,
    ae_int_t idx1,
    ae_int_t nvars,
    /* Real    */ const ae_matrix* ct,
    ae_int_t cidx0,
    ae_int_t cidx1,
    /* Integer */ ae_vector* xyc,
    /* Real    */ ae_vector* xydist2,
    ae_shared_pool* bufferpool, ae_state *_state);
void _kmeansbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _kmeansbuffers_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _kmeansbuffers_clear(void* _p);
void _kmeansbuffers_destroy(void* _p);
void _clusterizerstate_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _clusterizerstate_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _clusterizerstate_clear(void* _p);
void _clusterizerstate_destroy(void* _p);
void _ahcreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _ahcreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _ahcreport_clear(void* _p);
void _ahcreport_destroy(void* _p);
void _kmeansreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _kmeansreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _kmeansreport_clear(void* _p);
void _kmeansreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_DFOREST) || !defined(AE_PARTIAL_BUILD)
void dfcreatebuffer(const decisionforest* model,
     decisionforestbuffer* buf,
     ae_state *_state);
void dfbuildercreate(decisionforestbuilder* s, ae_state *_state);
void dfbuildersetdataset(decisionforestbuilder* s,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nclasses,
     ae_state *_state);
void dfbuildersetrndvars(decisionforestbuilder* s,
     ae_int_t rndvars,
     ae_state *_state);
void dfbuildersetrndvarsratio(decisionforestbuilder* s,
     double f,
     ae_state *_state);
void dfbuildersetrndvarsauto(decisionforestbuilder* s, ae_state *_state);
void dfbuildersetsubsampleratio(decisionforestbuilder* s,
     double f,
     ae_state *_state);
void dfbuildersetseed(decisionforestbuilder* s,
     ae_int_t seedval,
     ae_state *_state);
void dfbuildersetrdfalgo(decisionforestbuilder* s,
     ae_int_t algotype,
     ae_state *_state);
void dfbuildersetrdfsplitstrength(decisionforestbuilder* s,
     ae_int_t splitstrength,
     ae_state *_state);
void dfbuildersetimportancetrngini(decisionforestbuilder* s,
     ae_state *_state);
void dfbuildersetimportanceoobgini(decisionforestbuilder* s,
     ae_state *_state);
void dfbuildersetimportancepermutation(decisionforestbuilder* s,
     ae_state *_state);
void dfbuildersetimportancenone(decisionforestbuilder* s,
     ae_state *_state);
double dfbuildergetprogress(const decisionforestbuilder* s,
     ae_state *_state);
double dfbuilderpeekprogress(const decisionforestbuilder* s,
     ae_state *_state);
void dfbuilderbuildrandomforest(decisionforestbuilder* s,
     ae_int_t ntrees,
     decisionforest* df,
     dfreport* rep,
     ae_state *_state);
double dfbinarycompression(decisionforest* df, ae_state *_state);
double dfbinarycompression8(decisionforest* df, ae_state *_state);
void dfprocess(const decisionforest* df,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
void dfprocessi(const decisionforest* df,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
double dfprocess0(decisionforest* model,
     /* Real    */ const ae_vector* x,
     ae_state *_state);
ae_int_t dfclassify(decisionforest* model,
     /* Real    */ const ae_vector* x,
     ae_state *_state);
void dftsprocess(const decisionforest* df,
     decisionforestbuffer* buf,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
double dfrelclserror(const decisionforest* df,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double dfavgce(const decisionforest* df,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double dfrmserror(const decisionforest* df,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double dfavgerror(const decisionforest* df,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double dfavgrelerror(const decisionforest* df,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
void dfcopy(const decisionforest* df1,
     decisionforest* df2,
     ae_state *_state);
void dfalloc(ae_serializer* s,
     const decisionforest* forest,
     ae_state *_state);
void dfserialize(ae_serializer* s,
     const decisionforest* forest,
     ae_state *_state);
void dfunserialize(ae_serializer* s,
     decisionforest* forest,
     ae_state *_state);
void dfbuildrandomdecisionforest(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nclasses,
     ae_int_t ntrees,
     double r,
     ae_int_t* info,
     decisionforest* df,
     dfreport* rep,
     ae_state *_state);
void dfbuildrandomdecisionforestx1(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nclasses,
     ae_int_t ntrees,
     ae_int_t nrndvars,
     double r,
     ae_int_t* info,
     decisionforest* df,
     dfreport* rep,
     ae_state *_state);
void dfbuildinternal(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nclasses,
     ae_int_t ntrees,
     ae_int_t samplesize,
     ae_int_t nfeatures,
     ae_int_t flags,
     ae_int_t* info,
     decisionforest* df,
     dfreport* rep,
     ae_state *_state);
void _decisionforestbuilder_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _decisionforestbuilder_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _decisionforestbuilder_clear(void* _p);
void _decisionforestbuilder_destroy(void* _p);
void _dfworkbuf_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _dfworkbuf_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _dfworkbuf_clear(void* _p);
void _dfworkbuf_destroy(void* _p);
void _dfvotebuf_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _dfvotebuf_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _dfvotebuf_clear(void* _p);
void _dfvotebuf_destroy(void* _p);
void _dfpermimpbuf_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _dfpermimpbuf_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _dfpermimpbuf_clear(void* _p);
void _dfpermimpbuf_destroy(void* _p);
void _dftreebuf_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _dftreebuf_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _dftreebuf_clear(void* _p);
void _dftreebuf_destroy(void* _p);
void _decisionforestbuffer_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _decisionforestbuffer_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _decisionforestbuffer_clear(void* _p);
void _decisionforestbuffer_destroy(void* _p);
void _decisionforest_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _decisionforest_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _decisionforest_clear(void* _p);
void _decisionforest_destroy(void* _p);
void _dfreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _dfreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _dfreport_clear(void* _p);
void _dfreport_destroy(void* _p);
void _dfinternalbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _dfinternalbuffers_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _dfinternalbuffers_clear(void* _p);
void _dfinternalbuffers_destroy(void* _p);
#endif
#if defined(AE_COMPILE_LINREG) || !defined(AE_PARTIAL_BUILD)
void lrbuild(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     linearmodel* lm,
     lrreport* rep,
     ae_state *_state);
void lrbuilds(/* Real    */ const ae_matrix* xy,
     /* Real    */ const ae_vector* s,
     ae_int_t npoints,
     ae_int_t nvars,
     linearmodel* lm,
     lrreport* rep,
     ae_state *_state);
void lrbuildzs(/* Real    */ const ae_matrix* xy,
     /* Real    */ const ae_vector* s,
     ae_int_t npoints,
     ae_int_t nvars,
     linearmodel* lm,
     lrreport* rep,
     ae_state *_state);
void lrbuildz(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     linearmodel* lm,
     lrreport* rep,
     ae_state *_state);
void lrunpack(const linearmodel* lm,
     /* Real    */ ae_vector* v,
     ae_int_t* nvars,
     ae_state *_state);
void lrpack(/* Real    */ const ae_vector* v,
     ae_int_t nvars,
     linearmodel* lm,
     ae_state *_state);
double lrprocess(const linearmodel* lm,
     /* Real    */ const ae_vector* x,
     ae_state *_state);
double lrrmserror(const linearmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double lravgerror(const linearmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double lravgrelerror(const linearmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
void lrcopy(const linearmodel* lm1, linearmodel* lm2, ae_state *_state);
void lrlines(/* Real    */ const ae_matrix* xy,
     /* Real    */ const ae_vector* s,
     ae_int_t n,
     double* a,
     double* b,
     double* vara,
     double* varb,
     double* covab,
     double* corrab,
     double* p,
     ae_state *_state);
void lrline(/* Real    */ const ae_matrix* xy,
     ae_int_t n,
     double* a,
     double* b,
     ae_state *_state);
void _linearmodel_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _linearmodel_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _linearmodel_clear(void* _p);
void _linearmodel_destroy(void* _p);
void _lrreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _lrreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _lrreport_clear(void* _p);
void _lrreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_FILTERS) || !defined(AE_PARTIAL_BUILD)
void filtersma(/* Real    */ ae_vector* x,
     ae_int_t n,
     ae_int_t k,
     ae_state *_state);
void filterema(/* Real    */ ae_vector* x,
     ae_int_t n,
     double alpha,
     ae_state *_state);
void filterlrma(/* Real    */ ae_vector* x,
     ae_int_t n,
     ae_int_t k,
     ae_state *_state);
#endif
#if defined(AE_COMPILE_SSA) || !defined(AE_PARTIAL_BUILD)
void ssacreate(ssamodel* s, ae_state *_state);
void ssasetwindow(ssamodel* s, ae_int_t windowwidth, ae_state *_state);
void ssasetseed(ssamodel* s, ae_int_t seed, ae_state *_state);
void ssasetpoweruplength(ssamodel* s, ae_int_t pwlen, ae_state *_state);
void ssasetmemorylimit(ssamodel* s, ae_int_t memlimit, ae_state *_state);
void ssaaddsequence(ssamodel* s,
     /* Real    */ const ae_vector* x,
     ae_int_t n,
     ae_state *_state);
void ssaappendpointandupdate(ssamodel* s,
     double x,
     double updateits,
     ae_state *_state);
void ssaappendsequenceandupdate(ssamodel* s,
     /* Real    */ const ae_vector* x,
     ae_int_t nticks,
     double updateits,
     ae_state *_state);
void ssasetalgoprecomputed(ssamodel* s,
     /* Real    */ const ae_matrix* a,
     ae_int_t windowwidth,
     ae_int_t nbasis,
     ae_state *_state);
void ssasetalgotopkdirect(ssamodel* s, ae_int_t topk, ae_state *_state);
void ssasetalgotopkrealtime(ssamodel* s, ae_int_t topk, ae_state *_state);
void ssacleardata(ssamodel* s, ae_state *_state);
void ssagetbasis(ssamodel* s,
     /* Real    */ ae_matrix* a,
     /* Real    */ ae_vector* sv,
     ae_int_t* windowwidth,
     ae_int_t* nbasis,
     ae_state *_state);
void ssagetlrr(ssamodel* s,
     /* Real    */ ae_vector* a,
     ae_int_t* windowwidth,
     ae_state *_state);
void ssaanalyzelastwindow(ssamodel* s,
     /* Real    */ ae_vector* trend,
     /* Real    */ ae_vector* noise,
     ae_int_t* nticks,
     ae_state *_state);
void ssaanalyzelast(ssamodel* s,
     ae_int_t nticks,
     /* Real    */ ae_vector* trend,
     /* Real    */ ae_vector* noise,
     ae_state *_state);
void ssaanalyzesequence(ssamodel* s,
     /* Real    */ const ae_vector* data,
     ae_int_t nticks,
     /* Real    */ ae_vector* trend,
     /* Real    */ ae_vector* noise,
     ae_state *_state);
void ssaforecastlast(ssamodel* s,
     ae_int_t nticks,
     /* Real    */ ae_vector* trend,
     ae_state *_state);
void ssaforecastsequence(ssamodel* s,
     /* Real    */ const ae_vector* data,
     ae_int_t datalen,
     ae_int_t forecastlen,
     ae_bool applysmoothing,
     /* Real    */ ae_vector* trend,
     ae_state *_state);
void ssaforecastavglast(ssamodel* s,
     ae_int_t m,
     ae_int_t nticks,
     /* Real    */ ae_vector* trend,
     ae_state *_state);
void ssaforecastavgsequence(ssamodel* s,
     /* Real    */ const ae_vector* data,
     ae_int_t datalen,
     ae_int_t m,
     ae_int_t forecastlen,
     ae_bool applysmoothing,
     /* Real    */ ae_vector* trend,
     ae_state *_state);
void _ssamodel_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _ssamodel_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _ssamodel_clear(void* _p);
void _ssamodel_destroy(void* _p);
#endif
#if defined(AE_COMPILE_LDA) || !defined(AE_PARTIAL_BUILD)
void fisherlda(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nclasses,
     /* Real    */ ae_vector* w,
     ae_state *_state);
void fisherldan(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nclasses,
     /* Real    */ ae_matrix* w,
     ae_state *_state);
#endif
#if defined(AE_COMPILE_MCPD) || !defined(AE_PARTIAL_BUILD)
void mcpdcreate(ae_int_t n, mcpdstate* s, ae_state *_state);
void mcpdcreateentry(ae_int_t n,
     ae_int_t entrystate,
     mcpdstate* s,
     ae_state *_state);
void mcpdcreateexit(ae_int_t n,
     ae_int_t exitstate,
     mcpdstate* s,
     ae_state *_state);
void mcpdcreateentryexit(ae_int_t n,
     ae_int_t entrystate,
     ae_int_t exitstate,
     mcpdstate* s,
     ae_state *_state);
void mcpdaddtrack(mcpdstate* s,
     /* Real    */ const ae_matrix* xy,
     ae_int_t k,
     ae_state *_state);
void mcpdsetec(mcpdstate* s,
     /* Real    */ const ae_matrix* ec,
     ae_state *_state);
void mcpdaddec(mcpdstate* s,
     ae_int_t i,
     ae_int_t j,
     double c,
     ae_state *_state);
void mcpdsetbc(mcpdstate* s,
     /* Real    */ const ae_matrix* bndl,
     /* Real    */ const ae_matrix* bndu,
     ae_state *_state);
void mcpdaddbc(mcpdstate* s,
     ae_int_t i,
     ae_int_t j,
     double bndl,
     double bndu,
     ae_state *_state);
void mcpdsetlc(mcpdstate* s,
     /* Real    */ const ae_matrix* c,
     /* Integer */ const ae_vector* ct,
     ae_int_t k,
     ae_state *_state);
void mcpdsettikhonovregularizer(mcpdstate* s, double v, ae_state *_state);
void mcpdsetprior(mcpdstate* s,
     /* Real    */ const ae_matrix* _pp,
     ae_state *_state);
void mcpdsetpredictionweights(mcpdstate* s,
     /* Real    */ const ae_vector* pw,
     ae_state *_state);
void mcpdsolve(mcpdstate* s, ae_state *_state);
void mcpdresults(const mcpdstate* s,
     /* Real    */ ae_matrix* p,
     mcpdreport* rep,
     ae_state *_state);
void _mcpdstate_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mcpdstate_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mcpdstate_clear(void* _p);
void _mcpdstate_destroy(void* _p);
void _mcpdreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mcpdreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mcpdreport_clear(void* _p);
void _mcpdreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_LOGIT) || !defined(AE_PARTIAL_BUILD)
void mnltrainh(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nclasses,
     ae_int_t* info,
     logitmodel* lm,
     mnlreport* rep,
     ae_state *_state);
void mnlprocess(logitmodel* lm,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
void mnlprocessi(logitmodel* lm,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
void mnlunpack(const logitmodel* lm,
     /* Real    */ ae_matrix* a,
     ae_int_t* nvars,
     ae_int_t* nclasses,
     ae_state *_state);
void mnlpack(/* Real    */ const ae_matrix* a,
     ae_int_t nvars,
     ae_int_t nclasses,
     logitmodel* lm,
     ae_state *_state);
void mnlcopy(const logitmodel* lm1, logitmodel* lm2, ae_state *_state);
double mnlavgce(logitmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mnlrelclserror(logitmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mnlrmserror(logitmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mnlavgerror(logitmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double mnlavgrelerror(logitmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t ssize,
     ae_state *_state);
ae_int_t mnlclserror(logitmodel* lm,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
void _logitmodel_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _logitmodel_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _logitmodel_clear(void* _p);
void _logitmodel_destroy(void* _p);
void _logitmcstate_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _logitmcstate_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _logitmcstate_clear(void* _p);
void _logitmcstate_destroy(void* _p);
void _mnlreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mnlreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mnlreport_clear(void* _p);
void _mnlreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_KNN) || !defined(AE_PARTIAL_BUILD)
void knncreatebuffer(const knnmodel* model,
     knnbuffer* buf,
     ae_state *_state);
void knnbuildercreate(knnbuilder* s, ae_state *_state);
void knnbuildersetdatasetreg(knnbuilder* s,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nout,
     ae_state *_state);
void knnbuildersetdatasetcls(knnbuilder* s,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t nclasses,
     ae_state *_state);
void knnbuildersetnorm(knnbuilder* s, ae_int_t nrmtype, ae_state *_state);
void knnbuilderbuildknnmodel(knnbuilder* s,
     ae_int_t k,
     double eps,
     knnmodel* model,
     knnreport* rep,
     ae_state *_state);
void knnrewritekeps(knnmodel* model,
     ae_int_t k,
     double eps,
     ae_state *_state);
void knnprocess(knnmodel* model,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
double knnprocess0(knnmodel* model,
     /* Real    */ const ae_vector* x,
     ae_state *_state);
ae_int_t knnclassify(knnmodel* model,
     /* Real    */ const ae_vector* x,
     ae_state *_state);
void knnprocessi(knnmodel* model,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
void knntsprocess(const knnmodel* model,
     knnbuffer* buf,
     /* Real    */ const ae_vector* x,
     /* Real    */ ae_vector* y,
     ae_state *_state);
double knnrelclserror(const knnmodel* model,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double knnavgce(const knnmodel* model,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double knnrmserror(const knnmodel* model,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double knnavgerror(const knnmodel* model,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
double knnavgrelerror(const knnmodel* model,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
void knnallerrors(const knnmodel* model,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     knnreport* rep,
     ae_state *_state);
void knnalloc(ae_serializer* s, const knnmodel* model, ae_state *_state);
void knnserialize(ae_serializer* s,
     const knnmodel* model,
     ae_state *_state);
void knnunserialize(ae_serializer* s, knnmodel* model, ae_state *_state);
void _knnbuffer_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _knnbuffer_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _knnbuffer_clear(void* _p);
void _knnbuffer_destroy(void* _p);
void _knnbuilder_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _knnbuilder_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _knnbuilder_clear(void* _p);
void _knnbuilder_destroy(void* _p);
void _knnmodel_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _knnmodel_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _knnmodel_clear(void* _p);
void _knnmodel_destroy(void* _p);
void _knnreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _knnreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _knnreport_clear(void* _p);
void _knnreport_destroy(void* _p);
#endif
#if defined(AE_COMPILE_MLPTRAIN) || !defined(AE_PARTIAL_BUILD)
void mlptrainlm(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     double decay,
     ae_int_t restarts,
     ae_int_t* info,
     mlpreport* rep,
     ae_state *_state);
void mlptrainlbfgs(multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     double decay,
     ae_int_t restarts,
     double wstep,
     ae_int_t maxits,
     ae_int_t* info,
     mlpreport* rep,
     ae_state *_state);
void mlptraines(multilayerperceptron* network,
     /* Real    */ const ae_matrix* trnxy,
     ae_int_t trnsize,
     /* Real    */ const ae_matrix* valxy,
     ae_int_t valsize,
     double decay,
     ae_int_t restarts,
     ae_int_t* info,
     mlpreport* rep,
     ae_state *_state);
void mlpkfoldcvlbfgs(const multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     double decay,
     ae_int_t restarts,
     double wstep,
     ae_int_t maxits,
     ae_int_t foldscount,
     ae_int_t* info,
     mlpreport* rep,
     mlpcvreport* cvrep,
     ae_state *_state);
void mlpkfoldcvlm(const multilayerperceptron* network,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     double decay,
     ae_int_t restarts,
     ae_int_t foldscount,
     ae_int_t* info,
     mlpreport* rep,
     mlpcvreport* cvrep,
     ae_state *_state);
void mlpkfoldcv(mlptrainer* s,
     const multilayerperceptron* network,
     ae_int_t nrestarts,
     ae_int_t foldscount,
     mlpreport* rep,
     ae_state *_state);
void mlpcreatetrainer(ae_int_t nin,
     ae_int_t nout,
     mlptrainer* s,
     ae_state *_state);
void mlpcreatetrainercls(ae_int_t nin,
     ae_int_t nclasses,
     mlptrainer* s,
     ae_state *_state);
void mlpsetdataset(mlptrainer* s,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_state *_state);
void mlpsetsparsedataset(mlptrainer* s,
     const sparsematrix* xy,
     ae_int_t npoints,
     ae_state *_state);
void mlpsetdecay(mlptrainer* s, double decay, ae_state *_state);
void mlpsetcond(mlptrainer* s,
     double wstep,
     ae_int_t maxits,
     ae_state *_state);
void mlpsetalgobatch(mlptrainer* s, ae_state *_state);
void mlptrainnetwork(mlptrainer* s,
     multilayerperceptron* network,
     ae_int_t nrestarts,
     mlpreport* rep,
     ae_state *_state);
void mlpstarttraining(mlptrainer* s,
     multilayerperceptron* network,
     ae_bool randomstart,
     ae_state *_state);
ae_bool mlpcontinuetraining(mlptrainer* s,
     multilayerperceptron* network,
     ae_state *_state);
void mlpebagginglm(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     double decay,
     ae_int_t restarts,
     ae_int_t* info,
     mlpreport* rep,
     mlpcvreport* ooberrors,
     ae_state *_state);
void mlpebagginglbfgs(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     double decay,
     ae_int_t restarts,
     double wstep,
     ae_int_t maxits,
     ae_int_t* info,
     mlpreport* rep,
     mlpcvreport* ooberrors,
     ae_state *_state);
void mlpetraines(mlpensemble* ensemble,
     /* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     double decay,
     ae_int_t restarts,
     ae_int_t* info,
     mlpreport* rep,
     ae_state *_state);
void mlptrainensemblees(mlptrainer* s,
     mlpensemble* ensemble,
     ae_int_t nrestarts,
     mlpreport* rep,
     ae_state *_state);
void _mlpreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mlpreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpreport_clear(void* _p);
void _mlpreport_destroy(void* _p);
void _mlpcvreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mlpcvreport_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpcvreport_clear(void* _p);
void _mlpcvreport_destroy(void* _p);
void _smlptrnsession_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _smlptrnsession_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _smlptrnsession_clear(void* _p);
void _smlptrnsession_destroy(void* _p);
void _mlpetrnsession_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mlpetrnsession_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpetrnsession_clear(void* _p);
void _mlpetrnsession_destroy(void* _p);
void _mlptrainer_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mlptrainer_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mlptrainer_clear(void* _p);
void _mlptrainer_destroy(void* _p);
void _mlpparallelizationcv_init(void* _p, ae_state *_state, ae_bool make_automatic);
void _mlpparallelizationcv_init_copy(void* _dst, const void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpparallelizationcv_clear(void* _p);
void _mlpparallelizationcv_destroy(void* _p);
#endif
#if defined(AE_COMPILE_DATACOMP) || !defined(AE_PARTIAL_BUILD)
void kmeansgenerate(/* Real    */ const ae_matrix* xy,
     ae_int_t npoints,
     ae_int_t nvars,
     ae_int_t k,
     ae_int_t restarts,
     ae_int_t* info,
     /* Real    */ ae_matrix* c,
     /* Integer */ ae_vector* xyc,
     ae_state *_state);
#endif

}
#endif

