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maint: update Michel D. Schmid e-mail
author | carandraug |
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date | Sat, 24 Nov 2012 22:16:49 +0000 |
parents | 73c74436563f |
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## Copyright (C) 2007 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{net}} = newp (@var{Pr},@var{ss},@var{transFunc},@var{learnFunc}) ## @code{newp} create a perceptron ## ## @example ## PLEASE DON'T USE THIS FUNCTIONS, IT'S STILL NOT FINISHED! ## ========================================================= ## @end example ## @example ## Pr - R x 2 matrix of min and max values for R input elements ## ss - a scalar value with the number of neurons ## transFunc - a string with the transfer function ## default = "hardlim" ## learnFunc - a string with the learning function ## default = "learnp" ## @end example ## ## ## @end deftypefn ## Author: Michel D. Schmid function net = newp(Pr,ss,transFunc,learnFunc) ## initial descriptipn ## newp(Pr,ss,transFunc,learnFunc) ## * Pr is a nx2 matrix with min and max values of standardized inputs ## Pr means: p-range ## * ss is a scalar value which describes the number of neurons ## of output neurons ## * transFunc is the transfer function, standard is "hardlim" ## * learnFunc is the learning function, standard is "learnp" ## check range of input arguments error(nargchk(1,4,nargin)) ## set defaults if (nargin <2) ss = 1; # means one neuron endif if (nargin <3) transFunc = "hardlim"; endif if (nargin <4) learnFunc = "learnp"; endif ## check input args checkInputArgs(Pr,ss); # ## get number of layers (without input layer) # nLayers = length(ss); ## Standard architecture of neural network net = __newnetwork(1,1,1,"newp"); ## description: ## first argument: number of inputs, nothing else allowed till now ## it's not the same like the number of neurons in this input ## second argument: number of layers, including output layer ## third argument: number of outputs, nothing else allowed till now ## it's not the same like the number of neurons in this output ## fourth argument: network type ## set inputs with limit of only ONE input net.inputs{1}.range = Pr; [nRows, nColumns] = size(Pr); net.inputs{1}.size = nRows; ## set size of IW net.IW{1,1} = zeros(1,nRows); ## set number of bias, one per layer net.b{iBiases,1} = 0; ## define everything with "layers" net.numLayers = ss(end); net.layers = cell(1,1); net.layers{1}.size = ss(end); net.layers{1}.transFcn = transFunc; ## next row of code is only for MATLAB(TM) compatibility ## I never used this the last 4 years ... net.targets{i}.userdata = "Put your custom informations here!"; ## performance function net.performFnc = "mae"; ## learning net.biases{1}.learnFcn = learnFunc; net.inputWeights{1,1}.learnFcn = learnFunc; ## adaption net.adaptFcn = "trains"; ## Training net.trainFcn = "trainc"; ## Initialization net = __init(net); # ====================================================== # # additional check functions... # # ====================================================== function checkInputArgs(Pr,ss) ## check if Pr has correct format if !isreal(Pr) | (size(Pr,2)!=2) error("Input ranges must be a two column matrix!") endif if any(Pr(:,1) > Pr(:,2)) # check if numbers in the second column are larger as in the first one error("Input ranges has values in the second column larger as in the same row of the first column.") endif ## check if ss has correct format, must be a scalar value if ( (size(ss,1)!=1) || (size(ss,2)!=1)) error("Layer sizes is not a scalar value.") endif endfunction # ======================================================== endfunction