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view main/nnet/inst/prestd.m @ 11265:4d56b549b185 octave-forge
maint: update Michel D. Schmid e-mail
author | carandraug |
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date | Sat, 24 Nov 2012 22:16:49 +0000 |
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## Copyright (C) 2005 Michel D. Schmid <michael.schmid@plexso.com> ## ## 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 3 of the License, 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; 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);