Add a useful package (from Source forge) for octave

[CreaPhase.git] / octave_packages / nnet-0.1.13 / newp.m

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+## 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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