--- /dev/null
+## 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
\ No newline at end of file
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