/usr/share/octave/packages/nnet-0.1.13/newp.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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | ## Copyright (C) 2007 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, write to the Free
## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
## 02110-1301, USA.
## -*- 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
## @seealso{}
## 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
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