/usr/share/octave/packages/nnet-0.1.13/__optimizedatasets.m is in octave-nnet 0.1.13-2.
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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 | ## 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{retmatrix} = __optimizedatasets (@var{matrix},@var{nTrainSets},@var{nTargets},@var{bRand})
## @code{__optimizedatasets} reranges the data sets depending on the input arguments.
## @code{matrix} is the data set matrix containing inputs and outputs (targets) in row order.
## This means for example: the first three rows are inputs and the fourth row is an output row.
## The second argument is used in the optimizing algorithm. All cols with min and max values must
## be in the range of the train data sets. The third argument defines how much rows are equal to the
## neural network targets. These rows must be at the end of the data set!
## The fourth arguemnt is optional and defines if the data sets have to be randomised before
## optimizing.
## Default value for bRand is 1, means randomise the columns.
## @end deftypefn
## Author: mds
function retmatrix = __optimizedatasets(matrix,nTrainSets,nTargets,bRand)
## check number of inputs
error(nargchk(3,4,nargin));
# set default values
bRandomise = 1;
if (nargin==4)
bRandomise = bRand;
endif
# if needed, randomise the cols
if (bRandomise)
matrix = __randomisecols(matrix);
endif
# analyze matrix, which row contains what kind of data?
# a.) binary values? Means the row contains only 0 and 1
# b.) unique values?
# c.) Min values are several times contained in the row
# d.) Max values are several times contained in the row
matrix1 = matrix(1:end-nTargets,:);
analyzeMatrix = __analyzerows(matrix1);
# now sort "matrix" with help of analyzeMatrix
# following conditions must be kept:
# a.) rows containing unique values aren't sorted!
# b.) sort first rows which contains min AND max values only once
# c.) sort secondly rows which contains min OR max values only once
# d.) at last, sort binary data if still needed!
retmatrix = __rerangecolumns(matrix,analyzeMatrix,nTrainSets);
endfunction
%!shared retmatrix, matrix
%! disp("testing __optimizedatasets")
%! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \
%! 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \
%! -1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \
%! 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1];
%! ## The last row is equal to the neural network targets
%! retmatrix = __optimizedatasets(matrix,9,1);
%! ## the above statement can't be tested with assert!
%! ## it contains random values! So pass a "success" message
%!assert(1==1);
%! matrix = [1 2 3 2 1 2 3 0 5 4 3 2 2 2 2 2 2; \
%! 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0; \
%! -1 3 2 4 9 1 1 1 1 1 9 1 1 1 9 9 0; \
%! 2 3 2 3 2 2 2 2 3 3 3 3 1 1 1 1 1];
%! ## The last row is equal to the neural network targets
%! retmatrix = __optimizedatasets(matrix,9,1,0);
%!assert(retmatrix(1,1)==5);
%!assert(retmatrix(2,1)==0);
%!assert(retmatrix(3,1)==1);
%!assert(retmatrix(4,1)==3);
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