/usr/include/mlpbase.h is in libalglib-dev 2.6.0-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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Copyright (c) 2007-2008, 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 _mlpbase_h
#define _mlpbase_h
#include "ap.h"
#include "ialglib.h"
struct multilayerperceptron
{
ap::integer_1d_array structinfo;
ap::real_1d_array weights;
ap::real_1d_array columnmeans;
ap::real_1d_array columnsigmas;
ap::real_1d_array neurons;
ap::real_1d_array dfdnet;
ap::real_1d_array derror;
ap::real_1d_array x;
ap::real_1d_array y;
ap::real_2d_array chunks;
ap::real_1d_array nwbuf;
};
/*************************************************************************
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(int nin, int 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(int nin, int nhid, int 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(int nin,
int nhid1,
int nhid2,
int 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(int nin,
int nout,
double b,
double d,
multilayerperceptron& network);
/*************************************************************************
Same as MLPCreateB0 but with non-linear hidden layer.
-- ALGLIB --
Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreateb1(int nin,
int nhid,
int nout,
double b,
double d,
multilayerperceptron& network);
/*************************************************************************
Same as MLPCreateB0 but with two non-linear hidden layers.
-- ALGLIB --
Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreateb2(int nin,
int nhid1,
int nhid2,
int nout,
double b,
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(int nin,
int nout,
double a,
double b,
multilayerperceptron& network);
/*************************************************************************
Same as MLPCreateR0, but with non-linear hidden layer.
-- ALGLIB --
Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreater1(int nin,
int nhid,
int nout,
double a,
double b,
multilayerperceptron& network);
/*************************************************************************
Same as MLPCreateR0, but with two non-linear hidden layers.
-- ALGLIB --
Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpcreater2(int nin,
int nhid1,
int nhid2,
int nout,
double a,
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(int nin, int nout, multilayerperceptron& network);
/*************************************************************************
Same as MLPCreateC0, but with one non-linear hidden layer.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcreatec1(int nin, int nhid, int nout, multilayerperceptron& network);
/*************************************************************************
Same as MLPCreateC0, but with two non-linear hidden layers.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcreatec2(int nin,
int nhid1,
int nhid2,
int nout,
multilayerperceptron& network);
/*************************************************************************
Copying of neural network
INPUT PARAMETERS:
Network1 - original
OUTPUT PARAMETERS:
Network2 - copy
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpcopy(const multilayerperceptron& network1,
multilayerperceptron& network2);
/*************************************************************************
Serialization of MultiLayerPerceptron strucure
INPUT PARAMETERS:
Network - original
OUTPUT PARAMETERS:
RA - array of real numbers which stores network,
array[0..RLen-1]
RLen - RA lenght
-- ALGLIB --
Copyright 29.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpserialize(const multilayerperceptron& network,
ap::real_1d_array& ra,
int& rlen);
/*************************************************************************
Unserialization of MultiLayerPerceptron strucure
INPUT PARAMETERS:
RA - real array which stores network
OUTPUT PARAMETERS:
Network - restored network
-- ALGLIB --
Copyright 29.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpunserialize(const ap::real_1d_array& ra,
multilayerperceptron& network);
/*************************************************************************
Randomization of neural network weights
-- ALGLIB --
Copyright 06.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlprandomize(multilayerperceptron& network);
/*************************************************************************
Randomization of neural network weights and standartisator
-- ALGLIB --
Copyright 10.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlprandomizefull(multilayerperceptron& network);
/*************************************************************************
Internal subroutine.
-- ALGLIB --
Copyright 30.03.2008 by Bochkanov Sergey
*************************************************************************/
void mlpinitpreprocessor(multilayerperceptron& network,
const ap::real_2d_array& xy,
int ssize);
/*************************************************************************
Returns information about initialized network: number of inputs, outputs,
weights.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpproperties(const multilayerperceptron& network,
int& nin,
int& nout,
int& wcount);
/*************************************************************************
Tells whether network is SOFTMAX-normalized (i.e. classifier) or not.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
bool mlpissoftmax(const multilayerperceptron& network);
/*************************************************************************
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.
Subroutine does not allocate memory for this vector, it is
responsibility of a caller to allocate it. Array must be
at least [0..NOut-1].
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpprocess(multilayerperceptron& network,
const ap::real_1d_array& x,
ap::real_1d_array& y);
/*************************************************************************
Error function for neural network, internal subroutine.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
double mlperror(multilayerperceptron& network,
const ap::real_2d_array& xy,
int ssize);
/*************************************************************************
Natural error function for neural network, internal subroutine.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
double mlperrorn(multilayerperceptron& network,
const ap::real_2d_array& xy,
int ssize);
/*************************************************************************
Classification error
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
int mlpclserror(multilayerperceptron& network,
const ap::real_2d_array& xy,
int ssize);
/*************************************************************************
Relative classification error on the test set
INPUT PARAMETERS:
Network - network
XY - test set
NPoints - test set size
RESULT:
percent of incorrectly classified cases. Works both for
classifier networks and general purpose networks used as
classifiers.
-- ALGLIB --
Copyright 25.12.2008 by Bochkanov Sergey
*************************************************************************/
double mlprelclserror(multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints);
/*************************************************************************
Average cross-entropy (in bits per element) on the test set
INPUT PARAMETERS:
Network - neural network
XY - test set
NPoints - test set size
RESULT:
CrossEntropy/(NPoints*LN(2)).
Zero if network solves regression task.
-- ALGLIB --
Copyright 08.01.2009 by Bochkanov Sergey
*************************************************************************/
double mlpavgce(multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints);
/*************************************************************************
RMS error on the test set
INPUT PARAMETERS:
Network - neural network
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 04.11.2007 by Bochkanov Sergey
*************************************************************************/
double mlprmserror(multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints);
/*************************************************************************
Average error on the test set
INPUT PARAMETERS:
Network - neural network
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 11.03.2008 by Bochkanov Sergey
*************************************************************************/
double mlpavgerror(multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints);
/*************************************************************************
Average relative error on the test set
INPUT PARAMETERS:
Network - neural network
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 11.03.2008 by Bochkanov Sergey
*************************************************************************/
double mlpavgrelerror(multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints);
/*************************************************************************
Gradient calculation. Internal subroutine.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpgrad(multilayerperceptron& network,
const ap::real_1d_array& x,
const ap::real_1d_array& desiredy,
double& e,
ap::real_1d_array& grad);
/*************************************************************************
Gradient calculation (natural error function). Internal subroutine.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpgradn(multilayerperceptron& network,
const ap::real_1d_array& x,
const ap::real_1d_array& desiredy,
double& e,
ap::real_1d_array& grad);
/*************************************************************************
Batch gradient calculation. Internal subroutine.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpgradbatch(multilayerperceptron& network,
const ap::real_2d_array& xy,
int ssize,
double& e,
ap::real_1d_array& grad);
/*************************************************************************
Batch gradient calculation (natural error function). Internal subroutine.
-- ALGLIB --
Copyright 04.11.2007 by Bochkanov Sergey
*************************************************************************/
void mlpgradnbatch(multilayerperceptron& network,
const ap::real_2d_array& xy,
int ssize,
double& e,
ap::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(multilayerperceptron& network,
const ap::real_2d_array& xy,
int ssize,
double& e,
ap::real_1d_array& grad,
ap::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(multilayerperceptron& network,
const ap::real_2d_array& xy,
int ssize,
double& e,
ap::real_1d_array& grad,
ap::real_2d_array& h);
/*************************************************************************
Internal subroutine, shouldn't be called by user.
*************************************************************************/
void mlpinternalprocessvector(const ap::integer_1d_array& structinfo,
const ap::real_1d_array& weights,
const ap::real_1d_array& columnmeans,
const ap::real_1d_array& columnsigmas,
ap::real_1d_array& neurons,
ap::real_1d_array& dfdnet,
const ap::real_1d_array& x,
ap::real_1d_array& y);
#endif
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