## 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 8 910] * [ 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