Hi,
I am not sure if I am sending in the fix at the right place, but I
discovered a bug (really a corner case that has not been handled) in the
nnet package.
I am attaching both the error and the patch I think should fix it.
Please let me know if I it looks fixed.
Regards,
py
octave:7> type test
test is the user-defined function defined from: /tmp/test.m
Pr = [0.1 0.8; 0.1 0.75; 0.01 0.8];
net = newff(Pr, [4 1], {"tansig","purelin"}, "trainlm", "learngdm", "mse");
[net, tr, out, E] = train(net, 1, 1, [], [], []);
octave:8> test
error: VV is defined but inside exist no VV.P or VV.Pp
error: called from:
error: /media/share/octave/trunk/octave-forge/main/nnet/inst/train.m at line
199, column 7
error: /media/share/octave/trunk/octave-forge/main/nnet/inst/train.m at line
67, column 7
error: /tmp/test.m at line 3, column 20
--- train.m 2010-10-22 19:39:54.036975142 -0400
+++ new_train.m 2010-10-22 19:38:05.404763661 -0400
@@ -1,214 +1,212 @@
-## Copyright (C) 2006 Michel D. Schmid <[email protected]>
-##
-##
-## 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
+## Copyright (C) 2006 Michel D. Schmid <[email protected]>
+##
+##
+## 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
-##
-## left side arguments:
-## @example
-## net: the trained network of the net structure @code{MLPnet}
-## tr :
-## out:
-## E : Error
-## @end example
-## @noindent
-##
-## right side arguments:
-## @example
-## 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] = 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] = checkVV(VV)
-
- ## check number of arguments
- error(nargchk(1,1,nargin));
-
- ## 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
-
-
- endfunction
-
-# ============================================================
-
-endfunction
\ No newline at end of file
+## <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
+##
+## left side arguments:
+## @example
+## net: the trained network of the net structure @code{MLPnet}
+## tr :
+## out:
+## E : Error
+## @end example
+## @noindent
+##
+## right side arguments:
+## @example
+## 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;
+
+ ## 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
+ doValidation=1;
+ ## check if VV is in MATLAB(TM) notation
+ [VV] = checkVV(VV);
+ [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 (doValidation)
+ 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] = checkVV(VV)
+
+ ## check number of arguments
+ error(nargchk(1,1,nargin));
+
+ ## 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
+
+
+ endfunction
+
+# ============================================================
+
+endfunction
------------------------------------------------------------------------------
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