function [crit,WW]=BP(InpPat, TarPat, Wini, alfa, n_iter,NP)
%
%[crit,WW]=BP(InpPat, TarPat, Wini, alfa, n_iter,NP)
%
%This function computes the 2-norm of the error vector, considering a BP learning rule
%
%Input Arguments:	InpPat - Matrix of input data
%						TarPat - Matrix of output desired data
%						Wini - vector of initial weights
%						alfa - learning rate
%						n_iter - how many iterations
%						NP - toplogy of the MLP - Two hidden layers network, with just 1 output
%							The weights are arranged as:
%								ni*NP(1)
%								NP(1)`*NP(2)
%								NP(2)*1
%								Bias for the 1st hidden layer
%								Bias for the 2nd hidden layer
%								Bias for the output neuron
%
%Output arguments:crit - vector of the 2-morm of the errors
%						WW - matrix of the evolution of the weights


W=Wini
for iter=1:n_iter
  [Y,G,E]=OneLay2(InpPat,TarPat,W,NP);
  crit(iter)=norm(TarPat-Y);
  WW(iter,:)=W;
  W=W-alfa*E';
end