--- /dev/null
+## Copyright (C) 2008 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{mTrain}, @var{mTest}, @var{mVali}] = subset (@var{mData},@var{nTargets},@var{iOpti},@var{fTest},@var{fVali})
+## @code{subset} splits the main data matrix which contains inputs and targets into 2 or 3 subsets
+## depending on the parameters.
+##
+## The first parameter @var{mData} must be in row order. This means if the network
+## contains three inputs, the matrix must be have 3 rows and x columns to define the
+## data for the inputs. And some more rows for the outputs (targets), e.g. a neural network
+## with three inputs and two outputs must have 5 rows with x columns!
+## The second parameter @var{nTargets} defines the number or rows which contains the target values!
+## The third argument @code{iOpti} is optional and can have three status:
+## 0: no optimization
+## 1: will randomise the column order and order the columns containing min and max values to be in the train set
+## 2: will NOT randomise the column order, but order the columns containing min and max values to be in the train set
+## default value is @code{1}
+## The fourth argument @code{fTest} is also optional and defines how
+## much data sets will be in the test set. Default value is @code{1/3}
+## The fifth parameter @code{fTrain} is also optional and defines how
+## much data sets will be in the train set. Default value is @code{1/6}
+## So we have 50% of all data sets which are for training with the default values.
+##
+## @example
+## [mTrain, mTest] = subset(mData,1)
+## returns three subsets of the complete matrix
+## with randomized and optimized columns!
+## @end example
+## @example
+## [mTrain, mTest] = subset(mData,1,)
+## returns two subsets
+## @end example
+##
+## @end deftypefn
+
+## Author: Michel D. Schmid
+
+
+function [mTrain, mTest, mVali] = subset(mData,nTargets,iOpti,fTest,fVali)
+
+ ## check range of input arguments
+ error(nargchk(2,5,nargin))
+
+ ## check the input arguments ...!
+ if (nTargets==0)
+ error("No TARGETS defined! This doesn't make any sense for feed-forward neural networks! Please define at least one row of targets")
+ endif
+
+ ## set default values
+ if (nargin==2)
+ iOpti = 1;
+ fTest = 1/3;
+ fVali = 1/6;
+ elseif (nargin==3)
+ fTest = 1/3;
+ fVali = 1/6;
+ elseif (nargin==4)
+ ## if fTest is set and nothing is set
+ ## for fVali I assume that fVali is not used!
+ fVali = 0;
+ endif
+
+ ## calculate the number of train, test and validation sets
+ fTrain = 1-fTest-fVali;
+ nTrainSets = floor(size(mData,2)*fTrain);
+ diffRestSets = size(mData,2)-nTrainSets;
+ nTestSets = floor(size(mData,2)*fTest);
+ nValiSets = size(mData,2)-nTrainSets-nTestSets;
+
+
+ ## now let's see if matrix must be optimized!
+ bOptiAgain = 1;
+ while (bOptiAgain)
+ if (iOpti == 1)
+ # check that only one optimizing run is enough!!
+ # maybe it's necessary to do it twice ..!
+ # check that all min and max values are in the train set ...!
+ mData = __optimizedatasets(mData,nTrainSets,nTargets,iOpti);
+ mTrain = mData(:,1:nTrainSets);
+ iRuns = size(mTrain,1);
+ i = 1;
+ j = 1;
+ while (i < iRuns)
+ if ( max(mTrain(i,:)) == max(mData(i,:)) )
+ j += 1;
+ endif
+ i +=1;
+ endwhile
+ if (i==j)
+ bOptiAgain = 0;
+ endif
+ elseif (iOpti == 2)
+ # check that only one optimizing run is enough!!
+ # maybe it's necessary to do it twice ..!
+ # check that all min and max values are in the train set ...!
+ mData = __optimizedatasets(mData,nTrainSets,nTargets,iOpti);
+ mTrain = mData(:,1:nTrainSets);
+ iRuns = size(mTrain,1);
+ j = 1;
+ i = 1;
+ while (i < iRuns)
+ if (max(mTrain(i,:))==max(mData(i,:)))
+ j += 1;
+ endif
+ i += 1;
+ endwhile
+ if (i==j)
+ bOptiAgain = 0;
+ endif
+ else
+ ## in this case, iOpti must be 0 ==> nothing todo
+ bOptiAgain = 0;
+ endif
+ endwhile #END OF while(bOptiAgain)
+
+ ## now split up
+ if (nargout==1)
+ mTrain = mData;
+ elseif (nargout==2);
+ mTrain = mData(:,1:nTrainSets);
+ mTest = mData(:,nTrainSets+1:nTrainSets+nTestSets);
+ elseif (nargout==3)
+ mTrain = mData(:,1:nTrainSets);
+ mTest = mData(:,nTrainSets+1:nTrainSets+nTestSets);
+ mVali = mData(:,nTrainSets+nTestSets+1:end);
+ endif
+
+endfunction
+
+%!shared matrix, nTargets, mTrain, mTest, mVali
+%! disp("testing subset")
+%! matrix = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20; \
+%! 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \
+%! -2 2 2 2 2 0 0 0 0 0 10 12 13 12 13 44 33 32 98 11; \
+%! 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0; \
+%! 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4; \
+%! 1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99];
+%! nTargets = 1; # the last row is equivalent to the target values.
+%! [mTrain, mTest, mVali] = subset(matrix,nTargets); ############################
+%!assert(size(mTrain,2)==10);# 50% of 20
+%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
+%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
+%! # It's not possible to test the column order with this call!
+%! # randomizing is used! But all max and min values should be
+%! # in the training set
+%!assert(max(mTrain(1,:))==max(matrix(1,:)));
+%!assert(min(mTrain(1,:))==min(matrix(1,:)));
+%!assert(max(mTrain(2,:))==max(matrix(2,:)));
+%!assert(min(mTrain(2,:))==min(matrix(2,:)));
+%!assert(max(mTrain(3,:))==max(matrix(3,:)));
+%!assert(min(mTrain(3,:))==min(matrix(3,:)));
+%!assert(max(mTrain(4,:))==max(matrix(4,:)));
+%!assert(min(mTrain(4,:))==min(matrix(4,:)));
+%!
+%!
+%! [mTrain, mTest, mVali] = subset(matrix,nTargets,0); ############################
+%!assert(size(mTrain,2)==10);# 50% of 20
+%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
+%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
+%!assert(mTrain==matrix(:,1:10));
+%!assert(mTest==matrix(:,11:16));
+%!assert(mVali==matrix(:,17:20));
+%!
+%!
+%! [mTrain, mTest, mVali] = subset(matrix,nTargets,2); ############################
+%!assert(size(mTrain,2)==10);# 50% of 20
+%!assert(size(mTest,2)==6);# 1/3 of 20 = 6 (floor)
+%!assert(size(mVali,2)==4);# 1/6 of 20 = 4 (floor)
+%!assert(max(mTrain(1,:))==max(matrix(1,:)));
+%!assert(min(mTrain(1,:))==min(matrix(1,:)));
+%!assert(max(mTrain(2,:))==max(matrix(2,:)));
+%!assert(min(mTrain(2,:))==min(matrix(2,:)));
+%!assert(max(mTrain(3,:))==max(matrix(3,:)));
+%!assert(min(mTrain(3,:))==min(matrix(3,:)));
+%!assert(max(mTrain(4,:))==max(matrix(4,:)));
+%!assert(min(mTrain(4,:))==min(matrix(4,:)));
+%!
+%!
+%! ## next test ... optimize twice
+%! matrix = [1 2 3 4 5 6 7 20 8 10 11 12 13 14 15 16 17 18 18 9; \
+%! 0 2 4 1 3 5 3 4 1 -1 -2 -9 -1 10 12 20 11 11 11 11; \
+%! -2 2 2 2 2 0 0 0 0 0 10 12 13 12 13 44 33 32 98 11; \
+%! 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0; \
+%! 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4; \
+%! 1 2 3 4 5 6 7 8 9 10 11 12 13 33 44 55 66 77 88 99];
+%! [mTrain, mTest, mVali] = subset(matrix,nTargets,2); ############################
+%!assert(max(mTrain(1,:))==max(matrix(1,:)));
+%!assert(min(mTrain(1,:))==min(matrix(1,:)));
+%!assert(max(mTrain(2,:))==max(matrix(2,:)));
+%!assert(min(mTrain(2,:))==min(matrix(2,:)));
+%!assert(max(mTrain(3,:))==max(matrix(3,:)));
+%!assert(min(mTrain(3,:))==min(matrix(3,:)));
+%!assert(max(mTrain(4,:))==max(matrix(4,:)));
+%!assert(min(mTrain(4,:))==min(matrix(4,:)));
+
+## \todo, a lot of tests to be sure, everything is working OK!!
+## all combinations of arguments must be testet!
git://git.creatis.insa-lyon.fr / CreaPhase.git / blobdiff