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/*************************************************************************
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 "linalg.h"
#include "statistics.h"
#include "alglibmisc.h"
#include "specialfunctions.h"
#include "solvers.h"
#include "optimization.h"
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES)
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
typedef struct
{
double relclserror;
double avgce;
double rmserror;
double avgerror;
double avgrelerror;
} cvreport;
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;
} clusterizerstate;
typedef struct
{
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 k;
ae_matrix c;
ae_vector cidx;
} kmeansreport;
typedef struct
{
ae_int_t nvars;
ae_int_t nclasses;
ae_int_t ntrees;
ae_int_t bufsize;
ae_vector trees;
} decisionforest;
typedef struct
{
double relclserror;
double avgce;
double rmserror;
double avgerror;
double avgrelerror;
double oobrelclserror;
double oobavgce;
double oobrmserror;
double oobavgerror;
double oobavgrelerror;
} 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;
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;
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;
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;
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;
typedef struct
{
ae_int_t ensemblesize;
ae_vector weights;
ae_vector columnmeans;
ae_vector columnsigmas;
multilayerperceptron network;
ae_vector y;
} mlpensemble;
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;
}
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS C++ INTERFACE
//
/////////////////////////////////////////////////////////////////////////
namespace alglib
{
/*************************************************************************
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(const _clusterizerstate_owner &rhs);
_clusterizerstate_owner& operator=(const _clusterizerstate_owner &rhs);
virtual ~_clusterizerstate_owner();
alglib_impl::clusterizerstate* c_ptr();
alglib_impl::clusterizerstate* c_ptr() const;
protected:
alglib_impl::clusterizerstate *p_struct;
};
class clusterizerstate : public _clusterizerstate_owner
{
public:
clusterizerstate();
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:
* 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,...]).
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(const _ahcreport_owner &rhs);
_ahcreport_owner& operator=(const _ahcreport_owner &rhs);
virtual ~_ahcreport_owner();
alglib_impl::ahcreport* c_ptr();
alglib_impl::ahcreport* c_ptr() const;
protected:
alglib_impl::ahcreport *p_struct;
};
class ahcreport : public _ahcreport_owner
{
public:
ahcreport();
ahcreport(const ahcreport &rhs);
ahcreport& operator=(const ahcreport &rhs);
virtual ~ahcreport();
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
-- ALGLIB --
Copyright 27.11.2012 by Bochkanov Sergey
*************************************************************************/
class _kmeansreport_owner
{
public:
_kmeansreport_owner();
_kmeansreport_owner(const _kmeansreport_owner &rhs);
_kmeansreport_owner& operator=(const _kmeansreport_owner &rhs);
virtual ~_kmeansreport_owner();
alglib_impl::kmeansreport* c_ptr();
alglib_impl::kmeansreport* c_ptr() const;
protected:
alglib_impl::kmeansreport *p_struct;
};
class kmeansreport : public _kmeansreport_owner
{
public:
kmeansreport();
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 &k;
real_2d_array c;
integer_1d_array cidx;
};
/*************************************************************************
*************************************************************************/
class _decisionforest_owner
{
public:
_decisionforest_owner();
_decisionforest_owner(const _decisionforest_owner &rhs);
_decisionforest_owner& operator=(const _decisionforest_owner &rhs);
virtual ~_decisionforest_owner();
alglib_impl::decisionforest* c_ptr();
alglib_impl::decisionforest* c_ptr() const;
protected:
alglib_impl::decisionforest *p_struct;
};
class decisionforest : public _decisionforest_owner
{
public:
decisionforest();
decisionforest(const decisionforest &rhs);
decisionforest& operator=(const decisionforest &rhs);
virtual ~decisionforest();
};
/*************************************************************************
*************************************************************************/
class _dfreport_owner
{
public:
_dfreport_owner();
_dfreport_owner(const _dfreport_owner &rhs);
_dfreport_owner& operator=(const _dfreport_owner &rhs);
virtual ~_dfreport_owner();
alglib_impl::dfreport* c_ptr();
alglib_impl::dfreport* c_ptr() const;
protected:
alglib_impl::dfreport *p_struct;
};
class dfreport : public _dfreport_owner
{
public:
dfreport();
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;
};
/*************************************************************************
*************************************************************************/
class _linearmodel_owner
{
public:
_linearmodel_owner();
_linearmodel_owner(const _linearmodel_owner &rhs);
_linearmodel_owner& operator=(const _linearmodel_owner &rhs);
virtual ~_linearmodel_owner();
alglib_impl::linearmodel* c_ptr();
alglib_impl::linearmodel* c_ptr() const;
protected:
alglib_impl::linearmodel *p_struct;
};
class linearmodel : public _linearmodel_owner
{
public:
linearmodel();
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(const _lrreport_owner &rhs);
_lrreport_owner& operator=(const _lrreport_owner &rhs);
virtual ~_lrreport_owner();
alglib_impl::lrreport* c_ptr();
alglib_impl::lrreport* c_ptr() const;
protected:
alglib_impl::lrreport *p_struct;
};
class lrreport : public _lrreport_owner
{
public:
lrreport();
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;
};
/*************************************************************************
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(const _modelerrors_owner &rhs);
_modelerrors_owner& operator=(const _modelerrors_owner &rhs);
virtual ~_modelerrors_owner();
alglib_impl::modelerrors* c_ptr();
alglib_impl::modelerrors* c_ptr() const;
protected:
alglib_impl::modelerrors *p_struct;
};
class modelerrors : public _modelerrors_owner
{
public:
modelerrors();
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(const _multilayerperceptron_owner &rhs);
_multilayerperceptron_owner& operator=(const _multilayerperceptron_owner &rhs);
virtual ~_multilayerperceptron_owner();
alglib_impl::multilayerperceptron* c_ptr();
alglib_impl::multilayerperceptron* c_ptr() const;
protected:
alglib_impl::multilayerperceptron *p_struct;
};
class multilayerperceptron : public _multilayerperceptron_owner
{
public:
multilayerperceptron();
multilayerperceptron(const multilayerperceptron &rhs);
multilayerperceptron& operator=(const multilayerperceptron &rhs);
virtual ~multilayerperceptron();
};
/*************************************************************************
*************************************************************************/
class _logitmodel_owner
{
public:
_logitmodel_owner();
_logitmodel_owner(const _logitmodel_owner &rhs);
_logitmodel_owner& operator=(const _logitmodel_owner &rhs);
virtual ~_logitmodel_owner();
alglib_impl::logitmodel* c_ptr();
alglib_impl::logitmodel* c_ptr() const;
protected:
alglib_impl::logitmodel *p_struct;
};
class logitmodel : public _logitmodel_owner
{
public:
logitmodel();
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(const _mnlreport_owner &rhs);
_mnlreport_owner& operator=(const _mnlreport_owner &rhs);
virtual ~_mnlreport_owner();
alglib_impl::mnlreport* c_ptr();
alglib_impl::mnlreport* c_ptr() const;
protected:
alglib_impl::mnlreport *p_struct;
};
class mnlreport : public _mnlreport_owner
{
public:
mnlreport();
mnlreport(const mnlreport &rhs);
mnlreport& operator=(const mnlreport &rhs);
virtual ~mnlreport();
ae_int_t &ngrad;
ae_int_t &nhess;
};
/*************************************************************************
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(const _mcpdstate_owner &rhs);
_mcpdstate_owner& operator=(const _mcpdstate_owner &rhs);
virtual ~_mcpdstate_owner();
alglib_impl::mcpdstate* c_ptr();
alglib_impl::mcpdstate* c_ptr() const;
protected:
alglib_impl::mcpdstate *p_struct;
};
class mcpdstate : public _mcpdstate_owner
{
public:
mcpdstate();
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(const _mcpdreport_owner &rhs);
_mcpdreport_owner& operator=(const _mcpdreport_owner &rhs);
virtual ~_mcpdreport_owner();
alglib_impl::mcpdreport* c_ptr();
alglib_impl::mcpdreport* c_ptr() const;
protected:
alglib_impl::mcpdreport *p_struct;
};
class mcpdreport : public _mcpdreport_owner
{
public:
mcpdreport();
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;
};
/*************************************************************************
Neural networks ensemble
*************************************************************************/
class _mlpensemble_owner
{
public:
_mlpensemble_owner();
_mlpensemble_owner(const _mlpensemble_owner &rhs);
_mlpensemble_owner& operator=(const _mlpensemble_owner &rhs);
virtual ~_mlpensemble_owner();
alglib_impl::mlpensemble* c_ptr();
alglib_impl::mlpensemble* c_ptr() const;
protected:
alglib_impl::mlpensemble *p_struct;
};
class mlpensemble : public _mlpensemble_owner
{
public:
mlpensemble();
mlpensemble(const mlpensemble &rhs);
mlpensemble& operator=(const mlpensemble &rhs);
virtual ~mlpensemble();
};
/*************************************************************************
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(const _mlpreport_owner &rhs);
_mlpreport_owner& operator=(const _mlpreport_owner &rhs);
virtual ~_mlpreport_owner();
alglib_impl::mlpreport* c_ptr();
alglib_impl::mlpreport* c_ptr() const;
protected:
alglib_impl::mlpreport *p_struct;
};
class mlpreport : public _mlpreport_owner
{
public:
mlpreport();
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(const _mlpcvreport_owner &rhs);
_mlpcvreport_owner& operator=(const _mlpcvreport_owner &rhs);
virtual ~_mlpcvreport_owner();
alglib_impl::mlpcvreport* c_ptr();
alglib_impl::mlpcvreport* c_ptr() const;
protected:
alglib_impl::mlpcvreport *p_struct;
};
class mlpcvreport : public _mlpcvreport_owner
{
public:
mlpcvreport();
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(const _mlptrainer_owner &rhs);
_mlptrainer_owner& operator=(const _mlptrainer_owner &rhs);
virtual ~_mlptrainer_owner();
alglib_impl::mlptrainer* c_ptr();
alglib_impl::mlptrainer* c_ptr() const;
protected:
alglib_impl::mlptrainer *p_struct;
};
class mlptrainer : public _mlptrainer_owner
{
public:
mlptrainer();
mlptrainer(const mlptrainer &rhs);
mlptrainer& operator=(const mlptrainer &rhs);
virtual ~mlptrainer();
};
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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)
* 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(const clusterizerstate &s, const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype);
void clusterizersetpoints(const clusterizerstate &s, const real_2d_array &xy, const ae_int_t disttype);
/*************************************************************************
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(const clusterizerstate &s, const real_2d_array &d, const ae_int_t npoints, const bool isupper);
void clusterizersetdistances(const clusterizerstate &s, const real_2d_array &d, const bool isupper);
/*************************************************************************
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
-- ALGLIB --
Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizersetahcalgo(const clusterizerstate &s, const ae_int_t algo);
/*************************************************************************
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(const clusterizerstate &s, const ae_int_t restarts, const ae_int_t maxits);
/*************************************************************************
This function performs agglomerative hierarchical clustering
FOR USERS OF SMP EDITION:
! This function can utilize multicore capabilities of your system. In
! order to do this you have to call version with "smp_" prefix, which
! indicates that multicore code will be used.
!
! This note is given for users of SMP edition; if you use GPL edition,
! or commercial edition of ALGLIB without SMP support, you still will
! be able to call smp-version of this function, but all computations
! will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
!
! You should remember that starting/stopping worker thread always have
! non-zero cost. Multicore version is pretty efficient on large
! problems which need more than 1.000.000 operations to be solved,
! gives moderate speed-up in mid-range (from 100.000 to 1.000.000 CPU
! cycles), but gives no speed-up for small problems (less than 100.000
! operations).
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.
-- ALGLIB --
Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizerrunahc(const clusterizerstate &s, ahcreport &rep);
void smp_clusterizerrunahc(const clusterizerstate &s, ahcreport &rep);
/*************************************************************************
This function performs clustering by k-means++ algorithm.
You may change algorithm properties like number of restarts or iterations
limit by calling ClusterizerSetKMeansLimits() functions.
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).
-- ALGLIB --
Copyright 10.07.2012 by Bochkanov Sergey
*************************************************************************/
void clusterizerrunkmeans(const clusterizerstate &s, const ae_int_t k, kmeansreport &rep);
/*************************************************************************
This function returns distance matrix for dataset
FOR USERS OF SMP EDITION:
! This function can utilize multicore capabilities of your system. In
! order to do this you have to call version with "smp_" prefix, which
! indicates that multicore code will be used.
!
! This note is given for users of SMP edition; if you use GPL edition,
! or commercial edition of ALGLIB without SMP support, you still will
! be able to call smp-version of this function, but all computations
! will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
!
! You should remember that starting/stopping worker thread always have
! non-zero cost. Multicore version is pretty efficient on large
! problems which need more than 1.000.000 operations to be solved,
! gives moderate speed-up in mid-range (from 100.000 to 1.000.000 CPU
! cycles), but gives no speed-up for small problems (less than 100.000
! operations).
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)
* 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)
NOTES: 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.
-- 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);
void smp_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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
This function serializes data structure to string.
Important properties of s_out:
* it contains alphanumeric characters, dots, underscores, minus signs
* these symbols are grouped into words, which are separated by spaces
and Windows-style (CR+LF) newlines
* although serializer uses spaces and CR+LF as separators, you can
replace any separator character by arbitrary combination of spaces,
tabs, Windows or Unix newlines. It allows flexible reformatting of
the string in case you want to include it into text or XML file.
But you should not insert separators into the middle of the "words"
nor you should change case of letters.
* s_out can be freely moved between 32-bit and 64-bit systems, little
and big endian machines, and so on. You can serialize structure on
32-bit machine and unserialize it on 64-bit one (or vice versa), or
serialize it on SPARC and unserialize on x86. You can also
serialize it in C++ version of ALGLIB and unserialize in C# one,
and vice versa.
*************************************************************************/
void dfserialize(decisionforest &obj, std::string &s_out);
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void dfunserialize(std::string &s_in, decisionforest &obj);
/*************************************************************************
This subroutine builds random decision forest.
INPUT PARAMETERS:
XY - training set
NPoints - training set size, NPoints>=1
NVars - number of independent variables, NVars>=1
NClasses - task type:
* NClasses=1 - regression task with one
dependent variable
* NClasses>1 - classification task with
NClasses classes.
NTrees - number of trees in a forest, NTrees>=1.
recommended values: 50-100.
R - percent of a training set used to build
individual trees. 0<R<=1.
recommended values: 0.1 <= R <= 0.66.
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<1, NVars<1, NClasses<1, NTrees<1, R<=0
or R>1).
* 1, if task has been solved
DF - model built
Rep - training report, contains error on a training set
and out-of-bag estimates of generalization error.
-- 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);
/*************************************************************************
This subroutine builds random decision forest.
This function gives ability to tune number of variables used when choosing
best split.
INPUT PARAMETERS:
XY - training set
NPoints - training set size, NPoints>=1
NVars - number of independent variables, NVars>=1
NClasses - task type:
* NClasses=1 - regression task with one
dependent variable
* NClasses>1 - classification task with
NClasses classes.
NTrees - number of trees in a forest, NTrees>=1.
recommended values: 50-100.
NRndVars - number of variables used when choosing best split
R - percent of a training set used to build
individual trees. 0<R<=1.
recommended values: 0.1 <= R <= 0.66.
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<1, NVars<1, NClasses<1, NTrees<1, R<=0
or R>1).
* 1, if task has been solved
DF - model built
Rep - training report, contains error on a training set
and out-of-bag estimates of generalization error.
-- 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);
/*************************************************************************
Procesing
INPUT PARAMETERS:
DF - decision forest model
X - input vector, array[0..NVars-1].
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);
/*************************************************************************
'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.
-- ALGLIB --
Copyright 28.02.2010 by Bochkanov Sergey
*************************************************************************/
void dfprocessi(const decisionforest &df, const real_1d_array &x, real_1d_array &y);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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
NVars - number of independent variables
OUTPUT PARAMETERS:
Info - return code:
* -255, in case of unknown internal error
* -4, if internal SVD subroutine haven't converged
* -1, if incorrect parameters was passed (NPoints<NVars+2, NVars<1).
* 1, if subroutine successfully finished
LM - linear model in the ALGLIB format. Use subroutines of
this unit to work with the model.
AR - additional results
-- 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, ae_int_t &info, linearmodel &lm, lrreport &ar);
/*************************************************************************
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[0..NPoints-1], S[i]>0.
NPoints - training set size, NPoints>NVars+1
NVars - number of independent variables
OUTPUT PARAMETERS:
Info - return code:
* -255, in case of unknown internal error
* -4, if internal SVD subroutine haven't converged
* -1, if incorrect parameters was passed (NPoints<NVars+2, NVars<1).
* -2, if S[I]<=0
* 1, if subroutine successfully finished
LM - linear model in the ALGLIB format. Use subroutines of
this unit to work with the model.
AR - additional results
-- 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, ae_int_t &info, linearmodel &lm, lrreport &ar);
/*************************************************************************
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, ae_int_t &info, linearmodel &lm, lrreport &ar);
/*************************************************************************
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, ae_int_t &info, linearmodel &lm, lrreport &ar);
/*************************************************************************
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);
/*************************************************************************
"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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
void filtersma(real_1d_array &x, const ae_int_t k);
/*************************************************************************
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);
void filterema(real_1d_array &x, const double alpha);
/*************************************************************************
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 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 filterlrma(real_1d_array &x, const ae_int_t n, const ae_int_t k);
void filterlrma(real_1d_array &x, const ae_int_t k);
/*************************************************************************
Multiclass Fisher LDA
Subroutine finds coefficients of linear combination which optimally separates
training set on classes.
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>=0
NVars - number of independent variables, NVars>=1
NClasses - number of classes, NClasses>=2
OUTPUT PARAMETERS:
Info - return code:
* -4, if internal EVD subroutine hasn't converged
* -2, if there is a point with class number
outside of [0..NClasses-1].
* -1, if incorrect parameters was passed (NPoints<0,
NVars<1, NClasses<2)
* 1, if task has been solved
* 2, if there was a multicollinearity in training set,
but task has been solved.
W - linear combination coefficients, array[0..NVars-1]
-- 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, ae_int_t &info, real_1d_array &w);
/*************************************************************************
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[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>=0
NVars - number of independent variables, NVars>=1
NClasses - number of classes, NClasses>=2
OUTPUT PARAMETERS:
Info - return code:
* -4, if internal EVD subroutine hasn't converged
* -2, if there is a point with class number
outside of [0..NClasses-1].
* -1, if incorrect parameters was passed (NPoints<0,
NVars<1, NClasses<2)
* 1, if task has been solved
* 2, if there was a multicollinearity in training set,
but task has been solved.
W - basis, array[0..NVars-1,0..NVars-1]
columns of matrix stores basis vectors, sorted by
quality of training set separation (in descending order)
-- 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, ae_int_t &info, real_2d_array &w);
/*************************************************************************
This function serializes data structure to string.
Important properties of s_out:
* it contains alphanumeric characters, dots, underscores, minus signs
* these symbols are grouped into words, which are separated by spaces
and Windows-style (CR+LF) newlines
* although serializer uses spaces and CR+LF as separators, you can
replace any separator character by arbitrary combination of spaces,
tabs, Windows or Unix newlines. It allows flexible reformatting of
the string in case you want to include it into text or XML file.
But you should not insert separators into the middle of the "words"
nor you should change case of letters.
* s_out can be freely moved between 32-bit and 64-bit systems, little
and big endian machines, and so on. You can serialize structure on
32-bit machine and unserialize it on 64-bit one (or vice versa), or
serialize it on SPARC and unserialize on x86. You can also
serialize it in C++ version of ALGLIB and unserialize in C# one,
and vice versa.
*************************************************************************/
void mlpserialize(multilayerperceptron &obj, std::string &s_out);
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void mlpunserialize(std::string &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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
Randomization of neural network weights
-- ALGLIB --
Copyright 06.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlprandomize(const multilayerperceptron &network);
/*************************************************************************
Randomization of neural network weights and standartisator
-- ALGLIB --
Copyright 10.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlprandomizefull(const multilayerperceptron &network);
/*************************************************************************
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);
/*************************************************************************
Returns number of inputs.
-- ALGLIB --
Copyright 19.10.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpgetinputscount(const multilayerperceptron &network);
/*************************************************************************
Returns number of outputs.
-- ALGLIB --
Copyright 19.10.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpgetoutputscount(const multilayerperceptron &network);
/*************************************************************************
Returns number of weights.
-- ALGLIB --
Copyright 19.10.2011 by Bochkanov Sergey
*************************************************************************/
ae_int_t mlpgetweightscount(const multilayerperceptron &network);
/*************************************************************************
Tells whether network is SOFTMAX-normalized (i.e. classifier) or not.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
bool mlpissoftmax(const multilayerperceptron &network);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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(const multilayerperceptron &network, const ae_int_t k, const ae_int_t i, ae_int_t &fkind, double &threshold);
/*************************************************************************
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(const multilayerperceptron &network, const ae_int_t k0, const ae_int_t i0, const ae_int_t k1, const ae_int_t i1);
/*************************************************************************
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(const multilayerperceptron &network, const ae_int_t i, const double mean, const double sigma);
/*************************************************************************
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(const multilayerperceptron &network, const ae_int_t i, const double mean, const double sigma);
/*************************************************************************
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(const multilayerperceptron &network, const ae_int_t k, const ae_int_t i, const ae_int_t fkind, const double threshold);
/*************************************************************************
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(const 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);
/*************************************************************************
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);
/*************************************************************************
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(const multilayerperceptron &network, const real_1d_array &x, real_1d_array &y);
/*************************************************************************
'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(const multilayerperceptron &network, const real_1d_array &x, real_1d_array &y);
/*************************************************************************
Error of the neural network on dataset.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x, depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
double smp_mlperror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
Error of the neural network on dataset given by sparse matrix.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x, depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
double smp_mlperrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
/*************************************************************************
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize);
/*************************************************************************
Classification error of the neural network on dataset.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
ae_int_t smp_mlpclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
Relative classification error on the test set.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
double smp_mlprelclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
Relative classification error on the test set given by sparse matrix.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
double smp_mlprelclserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
/*************************************************************************
Average cross-entropy (in bits per element) on the test set.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
double smp_mlpavgce(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
Average cross-entropy (in bits per element) on the test set given by
sparse matrix.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
double smp_mlpavgcesparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
/*************************************************************************
RMS error on the test set given.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
double smp_mlprmserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
RMS error on the test set given by sparse matrix.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
double smp_mlprmserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
/*************************************************************************
Average absolute error on the test set.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
double smp_mlpavgerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
Average absolute error on the test set given by sparse matrix.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
double smp_mlpavgerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
/*************************************************************************
Average relative error on the test set.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
double smp_mlpavgrelerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
Average relative error on the test set given by sparse matrix.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
double smp_mlpavgrelerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints);
/*************************************************************************
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(const multilayerperceptron &network, const real_1d_array &x, const real_1d_array &desiredy, double &e, real_1d_array &grad);
/*************************************************************************
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(const multilayerperceptron &network, const real_1d_array &x, const real_1d_array &desiredy, double &e, real_1d_array &grad);
/*************************************************************************
Batch gradient calculation for a set of inputs/outputs
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad);
void smp_mlpgradbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad);
/*************************************************************************
Batch gradient calculation for a set of inputs/outputs given by sparse
matrices
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t ssize, double &e, real_1d_array &grad);
void smp_mlpgradbatchsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t ssize, double &e, real_1d_array &grad);
/*************************************************************************
Batch gradient calculation for a subset of dataset
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const 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);
void smp_mlpgradbatchsubset(const 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);
/*************************************************************************
Batch gradient calculation for a set of inputs/outputs for a subset of
dataset given by set of indexes.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const 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);
void smp_mlpgradbatchsparsesubset(const 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);
/*************************************************************************
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad);
/*************************************************************************
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, real_2d_array &h);
/*************************************************************************
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, real_2d_array &h);
/*************************************************************************
Calculation of all types of errors.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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.
OUTPUT PARAMETERS:
Rep - it contains all type of errors.
NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatch function.
-- ALGLIB --
Copyright 04.09.2012 by Bochkanov Sergey
*************************************************************************/
void mlpallerrorssubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep);
void smp_mlpallerrorssubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep);
/*************************************************************************
Calculation of all types of errors on sparse dataset.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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.
OUTPUT PARAMETERS:
Rep - it contains all type of errors.
NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatch function.
-- ALGLIB --
Copyright 04.09.2012 by Bochkanov Sergey
*************************************************************************/
void mlpallerrorssparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep);
void smp_mlpallerrorssparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep);
/*************************************************************************
Error of the neural network on dataset.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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.
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(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize);
double smp_mlperrorsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize);
/*************************************************************************
Error of the neural network on sparse dataset.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support
!
! First improvement gives close-to-linear speedup on multicore systems.
! Second improvement gives constant speedup (2-3x depending on your CPU)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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.
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(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize);
double smp_mlperrorsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize);
/*************************************************************************
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);
/*************************************************************************
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(const logitmodel &lm, const real_1d_array &x, real_1d_array &y);
/*************************************************************************
'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(const logitmodel &lm, const real_1d_array &x, real_1d_array &y);
/*************************************************************************
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);
/*************************************************************************
"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);
/*************************************************************************
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(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const logitmodel &lm, const real_2d_array &xy, const ae_int_t ssize);
/*************************************************************************
Classification error on test set = MNLRelClsError*NPoints
-- ALGLIB --
Copyright 10.09.2008 by Bochkanov Sergey
*************************************************************************/
ae_int_t mnlclserror(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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(const mcpdstate &s, const real_2d_array &xy, const ae_int_t k);
void mcpdaddtrack(const mcpdstate &s, const real_2d_array &xy);
/*************************************************************************
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(const mcpdstate &s, const real_2d_array &ec);
/*************************************************************************
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(const mcpdstate &s, const ae_int_t i, const ae_int_t j, const double c);
/*************************************************************************
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(const mcpdstate &s, const real_2d_array &bndl, const real_2d_array &bndu);
/*************************************************************************
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(const mcpdstate &s, const ae_int_t i, const ae_int_t j, const double bndl, const double bndu);
/*************************************************************************
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(const mcpdstate &s, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k);
void mcpdsetlc(const mcpdstate &s, const real_2d_array &c, const integer_1d_array &ct);
/*************************************************************************
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(const mcpdstate &s, const double v);
/*************************************************************************
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(const mcpdstate &s, const real_2d_array &pp);
/*************************************************************************
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(const mcpdstate &s, const real_1d_array &pw);
/*************************************************************************
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(const mcpdstate &s);
/*************************************************************************
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);
/*************************************************************************
This function serializes data structure to string.
Important properties of s_out:
* it contains alphanumeric characters, dots, underscores, minus signs
* these symbols are grouped into words, which are separated by spaces
and Windows-style (CR+LF) newlines
* although serializer uses spaces and CR+LF as separators, you can
replace any separator character by arbitrary combination of spaces,
tabs, Windows or Unix newlines. It allows flexible reformatting of
the string in case you want to include it into text or XML file.
But you should not insert separators into the middle of the "words"
nor you should change case of letters.
* s_out can be freely moved between 32-bit and 64-bit systems, little
and big endian machines, and so on. You can serialize structure on
32-bit machine and unserialize it on 64-bit one (or vice versa), or
serialize it on SPARC and unserialize on x86. You can also
serialize it in C++ version of ALGLIB and unserialize in C# one,
and vice versa.
*************************************************************************/
void mlpeserialize(mlpensemble &obj, std::string &s_out);
/*************************************************************************
This function unserializes data structure from string.
*************************************************************************/
void mlpeunserialize(std::string &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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
Randomization of MLP ensemble
-- ALGLIB --
Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
void mlperandomize(const mlpensemble &ensemble);
/*************************************************************************
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);
/*************************************************************************
Return normalization type (whether ensemble is SOFTMAX-normalized or not).
-- ALGLIB --
Copyright 17.02.2009 by Bochkanov Sergey
*************************************************************************/
bool mlpeissoftmax(const mlpensemble &ensemble);
/*************************************************************************
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(const mlpensemble &ensemble, const real_1d_array &x, real_1d_array &y);
/*************************************************************************
'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(const mlpensemble &ensemble, const real_1d_array &x, real_1d_array &y);
/*************************************************************************
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(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const 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);
/*************************************************************************
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(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, ae_int_t &info, mlpreport &rep);
/*************************************************************************
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(const 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);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
This function estimates generalization error using cross-validation on the
current dataset with current training settings.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support (C++ computational core)
!
! Second improvement gives constant speedup (2-3X). First improvement
! gives close-to-linear speedup on multicore systems. Following
! operations can be executed in parallel:
! * FoldsCount cross-validation rounds (always)
! * NRestarts training sessions performed within each of
! cross-validation rounds (if NRestarts>1)
! * gradient calculation over large dataset (if dataset is large enough)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, const ae_int_t foldscount, mlpreport &rep);
void smp_mlpkfoldcv(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, const ae_int_t foldscount, mlpreport &rep);
/*************************************************************************
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);
/*************************************************************************
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);
/*************************************************************************
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(const mlptrainer &s, const real_2d_array &xy, const ae_int_t npoints);
/*************************************************************************
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(const mlptrainer &s, const sparsematrix &xy, const ae_int_t npoints);
/*************************************************************************
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(const mlptrainer &s, const double decay);
/*************************************************************************
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(const mlptrainer &s, const double wstep, const ae_int_t maxits);
/*************************************************************************
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(const mlptrainer &s);
/*************************************************************************
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.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support (C++ computational core)
!
! Second improvement gives constant speedup (2-3X). First improvement
! gives close-to-linear speedup on multicore systems. Following
! operations can be executed in parallel:
! * NRestarts training sessions performed within each of
! cross-validation rounds (if NRestarts>1)
! * gradient calculation over large dataset (if dataset is large enough)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, mlpreport &rep);
void smp_mlptrainnetwork(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, mlpreport &rep);
/*************************************************************************
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(const mlptrainer &s, const multilayerperceptron &network, const bool randomstart);
/*************************************************************************
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.
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support (C++ computational core)
!
! Second improvement gives constant speedup (2-3X). First improvement
! gives close-to-linear speedup on multicore systems. Following
! operations can be executed in parallel:
! * gradient calculation over large dataset (if dataset is large enough)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const mlptrainer &s, const multilayerperceptron &network);
bool smp_mlpcontinuetraining(const mlptrainer &s, const multilayerperceptron &network);
/*************************************************************************
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(const 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);
/*************************************************************************
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(const 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);
/*************************************************************************
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(const 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);
/*************************************************************************
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).
FOR USERS OF COMMERCIAL EDITION:
! Commercial version of ALGLIB includes two important improvements of
! this function:
! * multicore support (C++ and C# computational cores)
! * SSE support (C++ computational core)
!
! Second improvement gives constant speedup (2-3X). First improvement
! gives close-to-linear speedup on multicore systems. Following
! operations can be executed in parallel:
! * EnsembleSize training sessions performed for each of ensemble
! members (always parallelized)
! * NRestarts training sessions performed within each of training
! sessions (if NRestarts>1)
! * gradient calculation over large dataset (if dataset is large enough)
!
! In order to use multicore features you have to:
! * use commercial version of ALGLIB
! * call this function with "smp_" prefix, which indicates that
! multicore code will be used (for multicore support)
!
! In order to use SSE features you have to:
! * use commercial version of ALGLIB on Intel processors
! * use C++ computational core
!
! This note is given for users of commercial edition; if you use GPL
! edition, you still will be able to call smp-version of this function,
! but all computations will be done serially.
!
! We recommend you to carefully read ALGLIB Reference Manual, section
! called 'SMP support', before using parallel version of this function.
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(const mlptrainer &s, const mlpensemble &ensemble, const ae_int_t nrestarts, mlpreport &rep);
void smp_mlptrainensemblees(const mlptrainer &s, const mlpensemble &ensemble, const ae_int_t nrestarts, mlpreport &rep);
/*************************************************************************
Principal components analysis
Subroutine builds orthogonal basis where first axis corresponds to
direction with maximum variance, second axis maximizes variance in subspace
orthogonal to first axis and so on.
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
<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>:
Info - return code:
* -4, if SVD subroutine haven't converged
* -1, if wrong parameters has been passed (NPoints<0,
NVars<1)
* 1, if task is solved
S2 - array[0..NVars-1]. variance values corresponding
to basis vectors.
V - array[0..NVars-1,0..NVars-1]
matrix, whose columns store basis vectors.
-- 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, ae_int_t &info, real_1d_array &s2, real_2d_array &v);
}
/////////////////////////////////////////////////////////////////////////
//
// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS)
//
/////////////////////////////////////////////////////////////////////////
namespace alglib_impl
{
void dserrallocate(ae_int_t nclasses,
/* Real */ ae_vector* buf,
ae_state *_state);
void dserraccumulate(/* Real */ ae_vector* buf,
/* Real */ ae_vector* y,
/* Real */ 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 */ 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 */ 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 */ ae_vector* a,
/* Integer */ 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 */ ae_vector* a,
/* Integer */ 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 */ ae_vector* a,
/* Integer */ 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);
ae_bool _cvreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _cvreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _cvreport_clear(void* _p);
void _cvreport_destroy(void* _p);
void clusterizercreate(clusterizerstate* s, ae_state *_state);
void clusterizersetpoints(clusterizerstate* s,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nfeatures,
ae_int_t disttype,
ae_state *_state);
void clusterizersetdistances(clusterizerstate* s,
/* Real */ 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 clusterizerrunahc(clusterizerstate* s,
ahcreport* rep,
ae_state *_state);
void _pexec_clusterizerrunahc(clusterizerstate* s,
ahcreport* rep, ae_state *_state);
void clusterizerrunkmeans(clusterizerstate* s,
ae_int_t k,
kmeansreport* rep,
ae_state *_state);
void clusterizergetdistances(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nfeatures,
ae_int_t disttype,
/* Real */ ae_matrix* d,
ae_state *_state);
void _pexec_clusterizergetdistances(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nfeatures,
ae_int_t disttype,
/* Real */ ae_matrix* d, ae_state *_state);
void clusterizergetkclusters(ahcreport* rep,
ae_int_t k,
/* Integer */ ae_vector* cidx,
/* Integer */ ae_vector* cz,
ae_state *_state);
void clusterizerseparatedbydist(ahcreport* rep,
double r,
ae_int_t* k,
/* Integer */ ae_vector* cidx,
/* Integer */ ae_vector* cz,
ae_state *_state);
void clusterizerseparatedbycorr(ahcreport* rep,
double r,
ae_int_t* k,
/* Integer */ ae_vector* cidx,
/* Integer */ ae_vector* cz,
ae_state *_state);
void kmeansgenerateinternal(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nvars,
ae_int_t k,
ae_int_t maxits,
ae_int_t restarts,
ae_int_t* info,
/* Real */ ae_matrix* ccol,
ae_bool needccol,
/* Real */ ae_matrix* crow,
ae_bool needcrow,
/* Integer */ ae_vector* xyc,
ae_state *_state);
ae_bool _clusterizerstate_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _clusterizerstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _clusterizerstate_clear(void* _p);
void _clusterizerstate_destroy(void* _p);
ae_bool _ahcreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _ahcreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _ahcreport_clear(void* _p);
void _ahcreport_destroy(void* _p);
ae_bool _kmeansreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _kmeansreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _kmeansreport_clear(void* _p);
void _kmeansreport_destroy(void* _p);
void kmeansgenerate(/* Real */ 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);
void dfbuildrandomdecisionforest(/* Real */ 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 */ 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 */ 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 dfprocess(decisionforest* df,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void dfprocessi(decisionforest* df,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
double dfrelclserror(decisionforest* df,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double dfavgce(decisionforest* df,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double dfrmserror(decisionforest* df,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double dfavgerror(decisionforest* df,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double dfavgrelerror(decisionforest* df,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
void dfcopy(decisionforest* df1, decisionforest* df2, ae_state *_state);
void dfalloc(ae_serializer* s, decisionforest* forest, ae_state *_state);
void dfserialize(ae_serializer* s,
decisionforest* forest,
ae_state *_state);
void dfunserialize(ae_serializer* s,
decisionforest* forest,
ae_state *_state);
ae_bool _decisionforest_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _decisionforest_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _decisionforest_clear(void* _p);
void _decisionforest_destroy(void* _p);
ae_bool _dfreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _dfreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _dfreport_clear(void* _p);
void _dfreport_destroy(void* _p);
ae_bool _dfinternalbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _dfinternalbuffers_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _dfinternalbuffers_clear(void* _p);
void _dfinternalbuffers_destroy(void* _p);
void lrbuild(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nvars,
ae_int_t* info,
linearmodel* lm,
lrreport* ar,
ae_state *_state);
void lrbuilds(/* Real */ ae_matrix* xy,
/* Real */ ae_vector* s,
ae_int_t npoints,
ae_int_t nvars,
ae_int_t* info,
linearmodel* lm,
lrreport* ar,
ae_state *_state);
void lrbuildzs(/* Real */ ae_matrix* xy,
/* Real */ ae_vector* s,
ae_int_t npoints,
ae_int_t nvars,
ae_int_t* info,
linearmodel* lm,
lrreport* ar,
ae_state *_state);
void lrbuildz(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nvars,
ae_int_t* info,
linearmodel* lm,
lrreport* ar,
ae_state *_state);
void lrunpack(linearmodel* lm,
/* Real */ ae_vector* v,
ae_int_t* nvars,
ae_state *_state);
void lrpack(/* Real */ ae_vector* v,
ae_int_t nvars,
linearmodel* lm,
ae_state *_state);
double lrprocess(linearmodel* lm,
/* Real */ ae_vector* x,
ae_state *_state);
double lrrmserror(linearmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double lravgerror(linearmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double lravgrelerror(linearmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
void lrcopy(linearmodel* lm1, linearmodel* lm2, ae_state *_state);
void lrlines(/* Real */ ae_matrix* xy,
/* Real */ ae_vector* s,
ae_int_t n,
ae_int_t* info,
double* a,
double* b,
double* vara,
double* varb,
double* covab,
double* corrab,
double* p,
ae_state *_state);
void lrline(/* Real */ ae_matrix* xy,
ae_int_t n,
ae_int_t* info,
double* a,
double* b,
ae_state *_state);
ae_bool _linearmodel_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _linearmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _linearmodel_clear(void* _p);
void _linearmodel_destroy(void* _p);
ae_bool _lrreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _lrreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _lrreport_clear(void* _p);
void _lrreport_destroy(void* _p);
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);
void fisherlda(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nvars,
ae_int_t nclasses,
ae_int_t* info,
/* Real */ ae_vector* w,
ae_state *_state);
void fisherldan(/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_int_t nvars,
ae_int_t nclasses,
ae_int_t* info,
/* Real */ ae_matrix* w,
ae_state *_state);
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(multilayerperceptron* network1,
multilayerperceptron* network2,
ae_state *_state);
void mlpcopyshared(multilayerperceptron* network1,
multilayerperceptron* network2,
ae_state *_state);
ae_bool mlpsamearchitecture(multilayerperceptron* network1,
multilayerperceptron* network2,
ae_state *_state);
void mlpcopytunableparameters(multilayerperceptron* network1,
multilayerperceptron* network2,
ae_state *_state);
void mlpexporttunableparameters(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(multilayerperceptron* network,
/* Real */ ae_vector* ra,
ae_int_t* rlen,
ae_state *_state);
void mlpunserializeold(/* Real */ 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 */ ae_matrix* xy,
ae_int_t ssize,
ae_state *_state);
void mlpinitpreprocessorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t ssize,
ae_state *_state);
void mlpinitpreprocessorsubset(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* idx,
ae_int_t subsetsize,
ae_state *_state);
void mlpinitpreprocessorsparsesubset(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* idx,
ae_int_t subsetsize,
ae_state *_state);
void mlpproperties(multilayerperceptron* network,
ae_int_t* nin,
ae_int_t* nout,
ae_int_t* wcount,
ae_state *_state);
ae_int_t mlpntotal(multilayerperceptron* network, ae_state *_state);
ae_int_t mlpgetinputscount(multilayerperceptron* network,
ae_state *_state);
ae_int_t mlpgetoutputscount(multilayerperceptron* network,
ae_state *_state);
ae_int_t mlpgetweightscount(multilayerperceptron* network,
ae_state *_state);
ae_bool mlpissoftmax(multilayerperceptron* network, ae_state *_state);
ae_int_t mlpgetlayerscount(multilayerperceptron* network,
ae_state *_state);
ae_int_t mlpgetlayersize(multilayerperceptron* network,
ae_int_t k,
ae_state *_state);
void mlpgetinputscaling(multilayerperceptron* network,
ae_int_t i,
double* mean,
double* sigma,
ae_state *_state);
void mlpgetoutputscaling(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 */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void mlpprocessi(multilayerperceptron* network,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
double mlperror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlperror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints, ae_state *_state);
double mlperrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlperrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints, ae_state *_state);
double mlperrorn(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t ssize,
ae_state *_state);
ae_int_t mlpclserror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
ae_int_t _pexec_mlpclserror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints, ae_state *_state);
double mlprelclserror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlprelclserror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints, ae_state *_state);
double mlprelclserrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlprelclserrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints, ae_state *_state);
double mlpavgce(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlpavgce(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints, ae_state *_state);
double mlpavgcesparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlpavgcesparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints, ae_state *_state);
double mlprmserror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlprmserror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints, ae_state *_state);
double mlprmserrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlprmserrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints, ae_state *_state);
double mlpavgerror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlpavgerror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints, ae_state *_state);
double mlpavgerrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlpavgerrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints, ae_state *_state);
double mlpavgrelerror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlpavgrelerror(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t npoints, ae_state *_state);
double mlpavgrelerrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints,
ae_state *_state);
double _pexec_mlpavgrelerrorsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t npoints, ae_state *_state);
void mlpgrad(multilayerperceptron* network,
/* Real */ ae_vector* x,
/* Real */ ae_vector* desiredy,
double* e,
/* Real */ ae_vector* grad,
ae_state *_state);
void mlpgradn(multilayerperceptron* network,
/* Real */ ae_vector* x,
/* Real */ ae_vector* desiredy,
double* e,
/* Real */ ae_vector* grad,
ae_state *_state);
void mlpgradbatch(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t ssize,
double* e,
/* Real */ ae_vector* grad,
ae_state *_state);
void _pexec_mlpgradbatch(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t ssize,
double* e,
/* Real */ ae_vector* grad, ae_state *_state);
void mlpgradbatchsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t ssize,
double* e,
/* Real */ ae_vector* grad,
ae_state *_state);
void _pexec_mlpgradbatchsparse(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t ssize,
double* e,
/* Real */ ae_vector* grad, ae_state *_state);
void mlpgradbatchsubset(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* idx,
ae_int_t subsetsize,
double* e,
/* Real */ ae_vector* grad,
ae_state *_state);
void _pexec_mlpgradbatchsubset(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* idx,
ae_int_t subsetsize,
double* e,
/* Real */ ae_vector* grad, ae_state *_state);
void mlpgradbatchsparsesubset(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* idx,
ae_int_t subsetsize,
double* e,
/* Real */ ae_vector* grad,
ae_state *_state);
void _pexec_mlpgradbatchsparsesubset(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* idx,
ae_int_t subsetsize,
double* e,
/* Real */ ae_vector* grad, ae_state *_state);
void mlpgradbatchx(multilayerperceptron* network,
/* Real */ ae_matrix* densexy,
sparsematrix* sparsexy,
ae_int_t datasetsize,
ae_int_t datasettype,
/* Integer */ 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 */ ae_matrix* xy,
ae_int_t ssize,
double* e,
/* Real */ ae_vector* grad,
ae_state *_state);
void mlphessiannbatch(multilayerperceptron* network,
/* Real */ 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 */ ae_matrix* xy,
ae_int_t ssize,
double* e,
/* Real */ ae_vector* grad,
/* Real */ ae_matrix* h,
ae_state *_state);
void mlpinternalprocessvector(/* Integer */ ae_vector* structinfo,
/* Real */ ae_vector* weights,
/* Real */ ae_vector* columnmeans,
/* Real */ ae_vector* columnsigmas,
/* Real */ ae_vector* neurons,
/* Real */ ae_vector* dfdnet,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void mlpalloc(ae_serializer* s,
multilayerperceptron* network,
ae_state *_state);
void mlpserialize(ae_serializer* s,
multilayerperceptron* network,
ae_state *_state);
void mlpunserialize(ae_serializer* s,
multilayerperceptron* network,
ae_state *_state);
void mlpallerrorssubset(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* subset,
ae_int_t subsetsize,
modelerrors* rep,
ae_state *_state);
void _pexec_mlpallerrorssubset(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* subset,
ae_int_t subsetsize,
modelerrors* rep, ae_state *_state);
void mlpallerrorssparsesubset(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* subset,
ae_int_t subsetsize,
modelerrors* rep,
ae_state *_state);
void _pexec_mlpallerrorssparsesubset(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* subset,
ae_int_t subsetsize,
modelerrors* rep, ae_state *_state);
double mlperrorsubset(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* subset,
ae_int_t subsetsize,
ae_state *_state);
double _pexec_mlperrorsubset(multilayerperceptron* network,
/* Real */ ae_matrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* subset,
ae_int_t subsetsize, ae_state *_state);
double mlperrorsparsesubset(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* subset,
ae_int_t subsetsize,
ae_state *_state);
double _pexec_mlperrorsparsesubset(multilayerperceptron* network,
sparsematrix* xy,
ae_int_t setsize,
/* Integer */ ae_vector* subset,
ae_int_t subsetsize, ae_state *_state);
void mlpallerrorsx(multilayerperceptron* network,
/* Real */ ae_matrix* densexy,
sparsematrix* sparsexy,
ae_int_t datasetsize,
ae_int_t datasettype,
/* Integer */ 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 _modelerrors_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _modelerrors_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _modelerrors_clear(void* _p);
void _modelerrors_destroy(void* _p);
ae_bool _smlpgrad_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _smlpgrad_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _smlpgrad_clear(void* _p);
void _smlpgrad_destroy(void* _p);
ae_bool _multilayerperceptron_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _multilayerperceptron_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _multilayerperceptron_clear(void* _p);
void _multilayerperceptron_destroy(void* _p);
void mnltrainh(/* Real */ 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 */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void mnlprocessi(logitmodel* lm,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void mnlunpack(logitmodel* lm,
/* Real */ ae_matrix* a,
ae_int_t* nvars,
ae_int_t* nclasses,
ae_state *_state);
void mnlpack(/* Real */ ae_matrix* a,
ae_int_t nvars,
ae_int_t nclasses,
logitmodel* lm,
ae_state *_state);
void mnlcopy(logitmodel* lm1, logitmodel* lm2, ae_state *_state);
double mnlavgce(logitmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double mnlrelclserror(logitmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double mnlrmserror(logitmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double mnlavgerror(logitmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double mnlavgrelerror(logitmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t ssize,
ae_state *_state);
ae_int_t mnlclserror(logitmodel* lm,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
ae_bool _logitmodel_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _logitmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _logitmodel_clear(void* _p);
void _logitmodel_destroy(void* _p);
ae_bool _logitmcstate_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _logitmcstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _logitmcstate_clear(void* _p);
void _logitmcstate_destroy(void* _p);
ae_bool _mnlreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mnlreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mnlreport_clear(void* _p);
void _mnlreport_destroy(void* _p);
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 */ ae_matrix* xy,
ae_int_t k,
ae_state *_state);
void mcpdsetec(mcpdstate* s,
/* Real */ 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 */ ae_matrix* bndl,
/* Real */ 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 */ ae_matrix* c,
/* Integer */ ae_vector* ct,
ae_int_t k,
ae_state *_state);
void mcpdsettikhonovregularizer(mcpdstate* s, double v, ae_state *_state);
void mcpdsetprior(mcpdstate* s,
/* Real */ ae_matrix* pp,
ae_state *_state);
void mcpdsetpredictionweights(mcpdstate* s,
/* Real */ ae_vector* pw,
ae_state *_state);
void mcpdsolve(mcpdstate* s, ae_state *_state);
void mcpdresults(mcpdstate* s,
/* Real */ ae_matrix* p,
mcpdreport* rep,
ae_state *_state);
ae_bool _mcpdstate_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mcpdstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mcpdstate_clear(void* _p);
void _mcpdstate_destroy(void* _p);
ae_bool _mcpdreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mcpdreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mcpdreport_clear(void* _p);
void _mcpdreport_destroy(void* _p);
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(multilayerperceptron* network,
ae_int_t ensemblesize,
mlpensemble* ensemble,
ae_state *_state);
void mlpecopy(mlpensemble* ensemble1,
mlpensemble* ensemble2,
ae_state *_state);
void mlperandomize(mlpensemble* ensemble, ae_state *_state);
void mlpeproperties(mlpensemble* ensemble,
ae_int_t* nin,
ae_int_t* nout,
ae_state *_state);
ae_bool mlpeissoftmax(mlpensemble* ensemble, ae_state *_state);
void mlpeprocess(mlpensemble* ensemble,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void mlpeprocessi(mlpensemble* ensemble,
/* Real */ ae_vector* x,
/* Real */ ae_vector* y,
ae_state *_state);
void mlpeallerrorsx(mlpensemble* ensemble,
/* Real */ ae_matrix* densexy,
sparsematrix* sparsexy,
ae_int_t datasetsize,
ae_int_t datasettype,
/* Integer */ 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,
sparsematrix* xy,
ae_int_t npoints,
double* relcls,
double* avgce,
double* rms,
double* avg,
double* avgrel,
ae_state *_state);
double mlperelclserror(mlpensemble* ensemble,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double mlpeavgce(mlpensemble* ensemble,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double mlpermserror(mlpensemble* ensemble,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double mlpeavgerror(mlpensemble* ensemble,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
double mlpeavgrelerror(mlpensemble* ensemble,
/* Real */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
void mlpealloc(ae_serializer* s, mlpensemble* ensemble, ae_state *_state);
void mlpeserialize(ae_serializer* s,
mlpensemble* ensemble,
ae_state *_state);
void mlpeunserialize(ae_serializer* s,
mlpensemble* ensemble,
ae_state *_state);
ae_bool _mlpensemble_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mlpensemble_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpensemble_clear(void* _p);
void _mlpensemble_destroy(void* _p);
void mlptrainlm(multilayerperceptron* network,
/* Real */ 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 */ 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 */ ae_matrix* trnxy,
ae_int_t trnsize,
/* Real */ ae_matrix* valxy,
ae_int_t valsize,
double decay,
ae_int_t restarts,
ae_int_t* info,
mlpreport* rep,
ae_state *_state);
void mlpkfoldcvlbfgs(multilayerperceptron* network,
/* Real */ 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(multilayerperceptron* network,
/* Real */ 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,
multilayerperceptron* network,
ae_int_t nrestarts,
ae_int_t foldscount,
mlpreport* rep,
ae_state *_state);
void _pexec_mlpkfoldcv(mlptrainer* s,
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 */ ae_matrix* xy,
ae_int_t npoints,
ae_state *_state);
void mlpsetsparsedataset(mlptrainer* s,
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 _pexec_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);
ae_bool _pexec_mlpcontinuetraining(mlptrainer* s,
multilayerperceptron* network, ae_state *_state);
void mlpebagginglm(mlpensemble* ensemble,
/* Real */ 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 */ 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 */ 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 _pexec_mlptrainensemblees(mlptrainer* s,
mlpensemble* ensemble,
ae_int_t nrestarts,
mlpreport* rep, ae_state *_state);
ae_bool _mlpreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mlpreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpreport_clear(void* _p);
void _mlpreport_destroy(void* _p);
ae_bool _mlpcvreport_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mlpcvreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpcvreport_clear(void* _p);
void _mlpcvreport_destroy(void* _p);
ae_bool _smlptrnsession_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _smlptrnsession_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _smlptrnsession_clear(void* _p);
void _smlptrnsession_destroy(void* _p);
ae_bool _mlpetrnsession_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mlpetrnsession_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpetrnsession_clear(void* _p);
void _mlpetrnsession_destroy(void* _p);
ae_bool _mlptrainer_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mlptrainer_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mlptrainer_clear(void* _p);
void _mlptrainer_destroy(void* _p);
ae_bool _mlpparallelizationcv_init(void* _p, ae_state *_state, ae_bool make_automatic);
ae_bool _mlpparallelizationcv_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic);
void _mlpparallelizationcv_clear(void* _p);
void _mlpparallelizationcv_destroy(void* _p);
void pcabuildbasis(/* Real */ ae_matrix* x,
ae_int_t npoints,
ae_int_t nvars,
ae_int_t* info,
/* Real */ ae_vector* s2,
/* Real */ ae_matrix* v,
ae_state *_state);
}
#endif
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