Add a useful package (from Source forge) for octave

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

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+## Copyright (C) 2006 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{netoutput} =} sim (@var{net}, @var{mInput})
+## @code{sim} is usuable to simulate a before defined neural network.
+## @code{net} is created with newff(@dots{}) and @var{mInput} should be the
+## corresponding input data set!
+## @end deftypefn
+
+## Author: Michel D. Schmid
+
+
+## Comments: see in "A neural network toolbox for Octave User's Guide" [4]
+##  for variable naming... there have inputs or targets only one letter,
+## e.g. for inputs is written P. To write a program, this is stupid, you can't
+##  search for 1 letter variable... that's why it is written here like Pp, or Tt
+## instead only P or T.
+
+function [netoutput] = sim(net,mInput)
+
+  ## check range of input arguments
+  error(nargchk(2,2,nargin))
+
+  ## check input args
+  ## check "net", must be a net structure
+  if !__checknetstruct(net)
+    error("Structure doesn't seem to be a neural network")
+  endif
+  ## check "mInput", must have defined size
+  [nRows, nColumns] = size(mInput);
+  if (nRows != net.inputs{1}.size)
+    error(["Simulation input data must have: " num2str(net.inputs{1}.size) " rows."])
+  endif
+
+  ## first get weights...
+  IW = net.IW{1};
+  b1 = net.b{1};
+  b1 = repmat(b1,1,size(mInput,2));
+  nLoops = net.numLayers;
+  for i=1:nLoops
+
+    trf = net.layers{i}.transferFcn;
+    ## calculate the outputs for each layer from input to output
+
+    if i==1
+      Nn{i,1} = IW*mInput + b1;
+    else
+      LWx = net.LW{i,i-1};
+      bx = net.b{i};
+      bx = repmat(bx,1,size(Aa{i-1,1},2));
+      Nn{i,1} = LWx*Aa{i-1,1} + bx;
+    endif
+
+    switch(trf)
+      case "tansig"
+        Aa{i,1} = tansig(Nn{i,1});
+      case "purelin"
+        Aa{i,1} = purelin(Nn{i,1});
+      case "logsig"
+        Aa{i,1} = logsig(Nn{i,1});
+      otherwise
+        error(["sim:Unknown transfer fucntion: " trf "!"]);
+    endswitch
+  endfor
+
+  netoutput = Aa{i,1};
+
+endfunction
+