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diff --git a/octave_packages/nnet-0.1.13/__optimizedatasets.m b/octave_packages/nnet-0.1.13/__optimizedatasets.m
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+## 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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