X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Fnnet-0.1.13%2F__calcperf.m;fp=octave_packages%2Fnnet-0.1.13%2F__calcperf.m;h=a768e54279d80f9667d4189c2fe1b74807ea5439;hp=0000000000000000000000000000000000000000;hb=c880e8788dfc484bf23ce13fa2787f2c6bca4863;hpb=1705066eceaaea976f010f669ce8e972f3734b05

diff --git a/octave_packages/nnet-0.1.13/__calcperf.m b/octave_packages/nnet-0.1.13/__calcperf.m
new file mode 100644
index 0000000..a768e54
--- /dev/null
+++ b/octave_packages/nnet-0.1.13/__calcperf.m
@@ -0,0 +1,110 @@
+## Copyright (C) 2006 Michel D. Schmid    <email: 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{perf}, @var{Ee}, @var{Aa}, @var{Nn}] = __calcperf (@var{net},@var{xx},@var{Im},@var{Tt})
+## @code{__calcperf} calculates the performance of a multi-layer neural network.
+## PLEASE DON'T USE IT ELSEWHERE, it proparly won't work.
+## @end deftypefn
+
+## Author: Michel D. Schmid
+
+
+function [perf,Ee,Aa,Nn] = __calcperf(net,xx,Im,Tt)
+
+  ## comment:
+  ## perf, net performance.. from input to output through the hidden layers
+  ## Aa, output values of the hidden and last layer (output layer)
+  ## is used for NEWFF network types
+
+  ## calculate bias terms
+  ## must have the same number of columns like the input matrix Im
+  [nRows, nColumns] = size(Im);
+  Btemp = cell(net.numLayers,1); # Btemp: bias matrix
+  ones1xQ = ones(1,nColumns);
+  for i= 1:net.numLayers
+    Btemp{i} = net.b{i}(:,ones1xQ);
+  endfor
+
+  ## shortcuts
+  IWtemp = cell(net.numLayers,net.numInputs,1);# IW: input weights ...
+  LWtemp = cell(net.numLayers,net.numLayers,1);# LW: layer weights ...
+  Aa = cell(net.numLayers,1);# Outputs hidden and output layer
+  Nn = cell(net.numLayers,1);# outputs before the transfer function
+  IW = net.IW; # input weights
+  LW = net.LW; # layer weights
+
+  ## calculate the whole network till outputs are reached...
+  for iLayers = 1:net.numLayers
+
+    ## calculate first input weights to weighted inputs..
+    ## this can be done with matrix calculation...
+    ## called "dotprod"
+    ## to do this, there must be a special matrix ...
+    ## e.g.  IW = [1 2 3 4 5; 6 7 8 9 10] * [ 1 2 3; 4 5 6; 7 8 9; 10 11 12; 1 2 3];
+    if (iLayers==1)
+      IWtemp{iLayers,1} = IW{iLayers,1} * Im;
+      onlyTempVar = [IWtemp(iLayers,1) Btemp(iLayers)];
+    else
+      IWtemp{iLayers,1} = [];
+    endif
+
+    ## now calculate layer weights to weighted layer outputs
+    if (iLayers>1)
+      Ad = Aa{iLayers-1,1};
+      LWtemp{iLayers,1} = LW{iLayers,iLayers-1} * Ad;
+      onlyTempVar = [LWtemp(iLayers,1) Btemp(iLayers)];
+    else
+      LWtemp{iLayers,1} = [];
+    endif
+
+    Nn{iLayers,1} = onlyTempVar{1};
+    for k=2:length(onlyTempVar)
+      Nn{iLayers,1} = Nn{iLayers,1} + onlyTempVar{k};
+    endfor
+
+    ## now calculate with the transfer functions the layer output
+    switch net.layers{iLayers}.transferFcn
+    case "purelin"
+      Aa{iLayers,1} = purelin(Nn{iLayers,1});
+    case "tansig"
+      Aa{iLayers,1} = tansig(Nn{iLayers,1});
+    case "logsig"
+      Aa{iLayers,1} = logsig(Nn{iLayers,1});
+    otherwise
+      error(["Transfer function: " net.layers{iLayers}.transferFcn " doesn't exist!"])
+    endswitch
+
+  endfor  # iLayers = 1:net.numLayers
+
+  ## now calc network error
+  Ee = cell(net.numLayers,1);
+
+  for i=net.numLayers
+    Ee{i,1} = Tt{i,1} - Aa{i,1};# Tt: target
+    # Ee will be the error vector cell array
+  endfor
+
+  ## now calc network performance
+  switch(net.performFcn)
+  case "mse"
+    perf = __mse(Ee);
+  otherwise
+    error("for performance functions, only mse is currently valid!")
+  endswitch
+
+endfunction