/usr/share/octave/packages/nnet-0.1.13/train.m is in octave-nnet 0.1.13-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | ## Copyright (C) 2006 Michel D. Schmid <michaelschmid@users.sourceforge.net>
##
##
## 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; either version 2, 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.
##
## You should have received a copy of the GNU General Public License
## along with this program; see the file COPYING. If not, see
## <http://www.gnu.org/licenses/>.
## -*- texinfo -*-
## @deftypefn {Function File} {}[@var{net}] = train (@var{MLPnet},@var{mInputN},@var{mOutput},@var{[]},@var{[]},@var{VV})
## A neural feed-forward network will be trained with @code{train}
##
## @example
## [net,tr,out,E] = train(MLPnet,mInputN,mOutput,[],[],VV);
## @end example
## @noindent
##
## @example
## left side arguments:
## net: the trained network of the net structure @code{MLPnet}
## @end example
## @noindent
##
## @example
## right side arguments:
## MLPnet : the untrained network, created with @code{newff}
## mInputN: normalized input matrix
## mOutput: output matrix (normalized or not)
## [] : unused parameter
## [] : unused parameter
## VV : validize structure
## @end example
## @end deftypefn
## @seealso{newff,prestd,trastd}
## Author: Michel D. Schmid
## Comments: see in "A neural network toolbox for Octave User's Guide" [4]
## for variable naming... there have inputs or targets only one letter,
## e.g. for inputs is P written. To write a program, this is stupid, you can't
## search for 1 letter variable... that's why it is written here like Pp, or Tt
## instead only P or T.
function [net] = train(net,Pp,Tt,notUsed1,notUsed2,VV)
## check range of input arguments
error(nargchk(3,6,nargin))
## set defaults
doValidation = 0;
if nargin==6
# doValidation=1;
## check if VV is in MATLAB(TM) notation
[VV, doValidation] = checkVV(VV);
endif
## check input args
checkInputArgs(net,Pp,Tt)
## nargin ...
switch(nargin)
case 3
[Pp,Tt] = trainArgs(net,Pp,Tt);
VV = [];
case 6
[Pp,Tt] = trainArgs(net,Pp,Tt);
if isempty(VV)
VV = [];
else
if !isfield(VV,"Pp")
error("VV.Pp must be defined or VV must be [].")
endif
if !isfield(VV,"Tt")
error("VV.Tt must be defined or VV must be [].")
endif
[VV.Pp,VV.Tt] = trainArgs(net,VV.Pp,VV.Tt);
endif
otherwise
error("train: impossible code execution in switch(nargin)")
endswitch
## so now, let's start training the network
##===========================================
## first let's check if a train function is defined ...
if isempty(net.trainFcn)
error("train:net.trainFcn not defined")
endif
## calculate input matrix Im
[nRowsInputs, nColumnsInputs] = size(Pp);
Im = ones(nRowsInputs,nColumnsInputs).*Pp{1,1};
if (doValidation)
[nRowsVal, nColumnsVal] = size(VV.Pp);
VV.Im = ones(nRowsVal,nColumnsVal).*VV.Pp{1,1};
endif
## make it MATLAB(TM) compatible
nLayers = net.numLayers;
Tt{nLayers,1} = Tt{1,1};
Tt{1,1} = [];
if (!isempty(VV))
VV.Tt{nLayers,1} = VV.Tt{1,1};
VV.Tt{1,1} = [];
endif
## which training algorithm should be used
switch(net.trainFcn)
case "trainlm"
if !strcmp(net.performFcn,"mse")
error("Levenberg-Marquardt algorithm is defined with the MSE performance function, so please set MSE in NEWFF!")
endif
net = __trainlm(net,Im,Pp,Tt,VV);
otherwise
error("train algorithm argument is not valid!")
endswitch
# =======================================================
#
# additional check functions...
#
# =======================================================
function checkInputArgs(net,Pp,Tt)
## check "net", must be a net structure
if !__checknetstruct(net)
error("Structure doesn't seem to be a neural network!")
endif
## check Pp (inputs)
nInputSize = net.inputs{1}.size; #only one exists
[nRowsPp, nColumnsPp] = size(Pp);
if ( (nColumnsPp>0) )
if ( nInputSize==nRowsPp )
# seems to be everything i.o.
else
error("Number of rows must be the same, like in net.inputs.size defined!")
endif
else
error("At least one column must exist")
endif
## check Tt (targets)
[nRowsTt, nColumnsTt] = size(Tt);
if ( (nRowsTt | nColumnsTt)==0 )
error("No targets defined!")
elseif ( nColumnsTt!=nColumnsPp )
error("Inputs and targets must have the same number of data sets (columns).")
elseif ( net.layers{net.numLayers}.size!=nRowsTt )
error("Defined number of output neurons are not identically to targets data sets!")
endif
endfunction
# -------------------------------------------------------
function [Pp,Tt] = trainArgs(net,Pp,Tt);
## check number of arguments
error(nargchk(3,3,nargin));
[PpRows, PpColumns] = size(Pp);
Pp = mat2cell(Pp,PpRows,PpColumns); # mat2cell is the reason
# why octave-2.9.5 doesn't work
# octave-2.9.x with x>=6 should be
# ok
[TtRows, TtColumns] = size(Tt);
Tt = mat2cell(Tt,TtRows,TtColumns);
endfunction
# -------------------------------------------------------
function [VV, doValidation] = checkVV(VV)
## check number of arguments
error(nargchk(1,1,nargin));
if (isempty(VV))
doValidation = 0;
else
doValidation = 1;
## check if MATLAB(TM) naming convention is used
if isfield(VV,"P")
VV.Pp = VV.P;
VV.P = [];
elseif !isfield(VV,"Pp")
error("VV is defined but inside exist no VV.P or VV.Pp")
endif
if isfield(VV,"T")
VV.Tt = VV.T;
VV.T = [];
elseif !isfield(VV,"Tt")
error("VV is defined but inside exist no VV.TP or VV.Tt")
endif
endif
endfunction
# ============================================================
endfunction
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