function [x]=LM_mat(InpPat,TarPat, Wini,n_iter,NP)
%
%[crit,WW]=LM_mat(InpPat,TarPat, Wini,n_iter,NP)
%
%This function implements a quasi-Newton type of learning rule, using the function fminunc available in the optimization toolbox
%
%Input Arguments:	InpPat - Matrix of input data
%						TarPat - Matrix of output desired data
%						Wini - vector of initial weights
%						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:x - final weights

%options = optimset('GradObj','on','Display','iter','MaxIter',n_iter);

%x = fminunc('funls',Wini,options,InpPat,TarPat,NP);
options=foptions;
options(1)=1;
options(14)=400;
x=leastsq('funlm',Wini,options,'jac',InpPat,TarPat,NP);