/usr/share/octave/packages/nnet-0.1.13/prestd.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 90 91 92 93 94 95 96 97 98 99 100 | ## 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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