X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Fnnet-0.1.13%2Fprestd.m;fp=octave_packages%2Fnnet-0.1.13%2Fprestd.m;h=325bd87909064bd942ee969bcd4402e9c350c745;hp=0000000000000000000000000000000000000000;hb=f5f7a74bd8a4900f0b797da6783be80e11a68d86;hpb=1705066eceaaea976f010f669ce8e972f3734b05

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+## Copyright (C) 2005 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{pn},@var{meanp},@var{stdp},@var{tn},@var{meant},@var{stdt}] =prestd(@var{p},@var{t})
+## @code{prestd} preprocesses the data so that the mean is 0 and the standard deviation is 1.
+## @end deftypefn
+
+## @seealso{trastd}
+
+## Author: Michel D. Schmid
+
+function [pn,meanp,stdp,tn,meant,stdt] = prestd(Pp,Tt)
+
+  ## inital description
+  ## prestd(p,t)
+  ##  * p are the general descriptions for the inputs of
+  ##    neural networks
+  ##  * t is written for "targets" and these are the outputs
+  ##    of a neural network
+  
+  ## some more detailed description:
+  ## for more informations about this
+  ## formula programmed in this file, see:
+  ## 1. http://en.wikipedia.org/wiki/Standard_score
+  ## 2. http://www.statsoft.com/textbook/stathome.html
+  ##    choose "statistical glossary", choose "standardization"
+
+  ## check range of input arguments
+  error(nargchk(1,2,nargin))
+
+  ## do first inputs
+  meanp = mean(Pp')';
+  stdp = std(Pp')';
+  [nRows,nColumns]=size(Pp);
+  rowOnes = ones(1,nColumns);
+
+  ## now set all standard deviations which are zero to 1
+  [nRowsII, nColumnsII] = size(stdp);
+  rowZeros = zeros(nRowsII,1); # returning a row containing only zeros
+  findZeros = find(stdp==0); # returning a vector containing index where zeros are
+  rowZeros(findZeros)=1; #
+  nequal = !rowZeros;
+  if (sum(rowZeros) != 0)
+    warning("Some standard deviations are zero. Those inputs won't be transformed.");
+    meanpZero = meanp.*nequal;
+    stdpZero = stdp.*nequal + 1*rowZeros;
+  else
+    meanpZero = meanp;
+    stdpZero = stdp;
+  endif
+
+  ## calculate the standardized inputs
+  pn = (Pp-meanpZero*rowOnes)./(stdpZero*rowOnes);
+
+  ## do also targets
+  if ( nargin==2 )
+    meant = mean(Tt')';
+    stdt = std(Tt')';
+
+    ## now set all standard deviations which are zero to 1
+    [nRowsIII, nColumnsIII] = size(stdt);
+    rowZeros = zeros(nRowsIII,1);
+    findZeros = find(stdt==0);
+    rowZeros(findZeros)=1;
+    nequal = !rowZeros;
+    if (sum(rowZeros) != 0)
+      warning("Some standard deviations are zero. Those targets won't be transformed.");
+      meantZero = meant.*nequal;
+      stdtZero = stdt.*nequal + 1*rowZeros;
+    else
+      meantZero = meant;
+      stdtZero = stdt;
+    endif
+
+    ## calculate the standardized targets
+    tn = (Tt-meantZero*rowOnes)./(stdtZero*rowOnes);
+  endif
+endfunction
+
+
+%!shared Pp, Tt, pn
+%!  Pp = [1 2 3 4; -1 3 2 -1];
+%!  Tt = [3 4 5 6];
+%!  [pn,meanp,stdp] = prestd(Pp);
+%!assert(pn,[-1.16190 -0.38730 0.38730 1.16190; -0.84887 1.09141 0.60634 -0.84887],0.00001);
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