prep_trn.m

这是个神经网络工具箱的一点MATALAB 程序

%   File Name      : prep_trn.m
%   Purpose        : prepare the training patterns
%   Author         : Hossam E. Mostafa Abdelbaki, School of Computer Science, 
%                    University of Centeral Florida (UCF). 
%   Release        : ver. 1.0.
%   Date           : October 1998.
%
%       RNNSIM is a software program available to the user without any 
%   license or royalty fees. Permission is hereby granted to use, copy, 
%   modify, and distribute this software for any purpose. The Author 
%   and UCF give no warranty, express, implied, or statuary for the 
%   software including, without limitation, waranty of merchantibility 
%   and warranty of fitness for a particular purpose. The software 
%   provided hereunder is on an "as is"  basis, and the Author and the 
%   UCF has no obligation to provide maintenance, support, updates, 
%   enhancements, or modifications. 
%
%       RNNSIM  is available for any platform (UNIX, PCWIN, MACHITOCH). 
%   It runs under MATLAB ver. 5.0 or highrer. 
%
%       User feedback, bugs, or software and manual suggestions can 
%   be sent via electronic mail to :   [e-mail]

%%%%%%%%%%%%Function: Preparing Inputs for Training%%%%%%%%%%%%%%%
%####### Setting the Excitatory and Inhibitatory Training Patterns ########
N_Train_Patterns = N_Patterns;
Applied_lambda = zeros(N_Train_Patterns,N_Input);             

Applied_LAMBDA = zeros(N_Train_Patterns,N_Input);

Applied_y = TARGET;
%yyy = size(TRAIN_INPUT);
%N_Train_Patterns = yyy(1);
for i = 1:N_Train_Patterns
   for j = 1:N_Input
      if (TRAIN_INPUT(i,j) >= 0)
         Applied_LAMBDA(i,j) = TRAIN_INPUT(i,j);
      elseif(TRAIN_INPUT(i,j) <0)
         Applied_lambda(i,j)= -TRAIN_INPUT(i,j);
      end
   end
end  
   
%##### Preparing LAMBDA #################
fill = zeros(N_Train_Patterns , N_Total - N_Input);
LAMBDA = [Applied_LAMBDA fill];

%##### Preparing lambda #################
%Applied_lambda=zeros(N_Train_Patterns,N_Input);
fill = zeros(N_Train_Patterns , N_Total - N_Input);
lambda=[Applied_lambda fill];

%##### Preparing y #################
fill = zeros(N_Train_Patterns , N_Total-N_Output);
y=[fill Applied_y];       
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%