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[Octave-cvsupdate] SF.net SVN: octave:[5267] trunk/octave-forge/main/nnet/doc/latex/users/ examples/1
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From: <mic...@us...> - 2008-08-25 05:55:09
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Revision: 5267
http://octave.svn.sourceforge.net/octave/?rev=5267&view=rev
Author: michaelschmid
Date: 2008-08-25 05:55:17 +0000 (Mon, 25 Aug 2008)
Log Message:
-----------
renamed example template to MLP9_1_1.m_template
Added Paths:
-----------
trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m_template
Removed Paths:
-------------
trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m
Deleted: trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m
===================================================================
--- trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m 2008-08-24 18:41:15 UTC (rev 5266)
+++ trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m 2008-08-25 05:55:17 UTC (rev 5267)
@@ -1,98 +0,0 @@
-## Copyright (C) 2006 Michel D. Schmid <mic...@us...>
-##
-##
-## 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/>.
-
-## Author: Michel D. Schmid
-
-
-## load data
-mData = load("mData.txt","mData");
-mData = mData.mData;
-[nRows, nColumns] = size(mData);
- # this file contains 13 columns.
- # The first 12 columns are the inputs
- # the last column is the output,
- # remove column 4, 8 and 12!
- # 89 rows.
-
-
-mOutput = mData(:,end);
-mInput = mData(:,1:end-1);
-mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
-
-## now prepare data
-mInput = mInput';
-mOutput = mOutput';
-
-# now split the data matrix in 3 pieces, train data, test data and validate data
-# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
-# in this neural network we have 12 weights, for each weight at least 3 train sets..
-# (that's a rule of thumb like 1/2, 1/3 and 1/6)
-# 1/2 of 89 = 44.5; let's take 44 for training
-nTrainSets = floor(nRows/2);
-# now the rest of the sets are again 100%
-# ==> 2/3 for test sets and 1/3 for validate sets
-nTestSets = (nRows-nTrainSets)/3*2;
-nValiSets = nRows-nTrainSets-nTestSets;
-
-mValiInput = mInput(:,1:nValiSets);
-mValliOutput = mOutput(:,1:nValiSets);
-mInput(:,1:nValiSets) = [];
-mOutput(:,1:nValiSets) = [];
-mTestInput = mInput(:,1:nTestSets);
-mTestOutput = mOutput(:,1:nTestSets);
-mInput(:,1:nTestSets) = [];
-mOutput(:,1:nTestSets) = [];
-mTrainInput = mInput(:,1:nTrainSets);
-mTrainOutput = mOutput(:,1:nTrainSets);
-
-[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
-
-## comments: there is no reason to standardize the outputs because we have only
-# one output ...
-
-# define the max and min inputs for each row
-mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
-
-## define network
-nHiddenNeurons = 1;
-nOutputNeurons = 1;
-
-MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],\
- {"tansig","purelin"},"trainlm","","mse");
-## for test purpose, define weights by hand
-MLPnet.IW{1,1}(:) = 1.5;
-MLPnet.LW{2,1}(:) = 0.5;
-MLPnet.b{1,1}(:) = 1.5;
-MLPnet.b{2,1}(:) = 0.5;
-
-saveMLPStruct(MLPnet,"MLP3test.txt");
-
-## define validation data new, for matlab compatibility
-VV.P = mValiInput;
-VV.T = mValliOutput;
-
-## standardize also the validate data
-VV.P = trastd(VV.P,cMeanInput,cStdInput);
-
-[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
-
-# make preparations for net test and test MLPnet
-# standardise input & output test data
-[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
-
-[simOut] = sim(net,mTestInputN);
-simOut
Copied: trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m_template (from rev 5251, trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m)
===================================================================
--- trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m_template (rev 0)
+++ trunk/octave-forge/main/nnet/doc/latex/users/examples/1/MLP9_1_1.m_template 2008-08-25 05:55:17 UTC (rev 5267)
@@ -0,0 +1,98 @@
+## Copyright (C) 2006 Michel D. Schmid <mic...@us...>
+##
+##
+## 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/>.
+
+## Author: Michel D. Schmid
+
+
+## load data
+mData = load("mData.txt","mData");
+mData = mData.mData;
+[nRows, nColumns] = size(mData);
+ # this file contains 13 columns.
+ # The first 12 columns are the inputs
+ # the last column is the output,
+ # remove column 4, 8 and 12!
+ # 89 rows.
+
+
+mOutput = mData(:,end);
+mInput = mData(:,1:end-1);
+mInput(:,[4 8 12]) = []; # delete column 4, 8 and 12
+
+## now prepare data
+mInput = mInput';
+mOutput = mOutput';
+
+# now split the data matrix in 3 pieces, train data, test data and validate data
+# the proportion should be about 1/2 train, 1/3 test and 1/6 validate data
+# in this neural network we have 12 weights, for each weight at least 3 train sets..
+# (that's a rule of thumb like 1/2, 1/3 and 1/6)
+# 1/2 of 89 = 44.5; let's take 44 for training
+nTrainSets = floor(nRows/2);
+# now the rest of the sets are again 100%
+# ==> 2/3 for test sets and 1/3 for validate sets
+nTestSets = (nRows-nTrainSets)/3*2;
+nValiSets = nRows-nTrainSets-nTestSets;
+
+mValiInput = mInput(:,1:nValiSets);
+mValliOutput = mOutput(:,1:nValiSets);
+mInput(:,1:nValiSets) = [];
+mOutput(:,1:nValiSets) = [];
+mTestInput = mInput(:,1:nTestSets);
+mTestOutput = mOutput(:,1:nTestSets);
+mInput(:,1:nTestSets) = [];
+mOutput(:,1:nTestSets) = [];
+mTrainInput = mInput(:,1:nTrainSets);
+mTrainOutput = mOutput(:,1:nTrainSets);
+
+[mTrainInputN,cMeanInput,cStdInput] = prestd(mTrainInput);# standardize inputs
+
+## comments: there is no reason to standardize the outputs because we have only
+# one output ...
+
+# define the max and min inputs for each row
+mMinMaxElements = min_max(mTrainInputN); # input matrix with (R x 2)...
+
+## define network
+nHiddenNeurons = 1;
+nOutputNeurons = 1;
+
+MLPnet = newff(mMinMaxElements,[nHiddenNeurons nOutputNeurons],\
+ {"tansig","purelin"},"trainlm","","mse");
+## for test purpose, define weights by hand
+MLPnet.IW{1,1}(:) = 1.5;
+MLPnet.LW{2,1}(:) = 0.5;
+MLPnet.b{1,1}(:) = 1.5;
+MLPnet.b{2,1}(:) = 0.5;
+
+saveMLPStruct(MLPnet,"MLP3test.txt");
+
+## define validation data new, for matlab compatibility
+VV.P = mValiInput;
+VV.T = mValliOutput;
+
+## standardize also the validate data
+VV.P = trastd(VV.P,cMeanInput,cStdInput);
+
+[net] = train(MLPnet,mTrainInputN,mTrainOutput,[],[],VV);
+
+# make preparations for net test and test MLPnet
+# standardise input & output test data
+[mTestInputN] = trastd(mTestInput,cMeanInput,cStdInput);
+
+[simOut] = sim(net,mTestInputN);
+simOut
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