%% Example 2:  Pattern Classification, Batch Training.

%% Set up the data (Y is actually the target matrix).
N=100;  %Number of points  
X1=0.8*randn(2,N)+repmat([2;2],1,N);
X2=1.5*randn(2,N)+repmat([1;-2],1,N);
X=[X1 X2];
X(3,:)=ones(1,2*N);

Y=[repmat([1;0],1,N) repmat([0;1],1,N)];
%% Solve for the weights and biases.
C=Y/X;

%% Plotting routines:
 plot(X1(1,:),X1(2,:),'o',X2(1,:),X2(2,:),'x');
 hold on
 n1=min(X(1,:));n2=max(X(1,:));
 t=linspace(n1,n2);
 L1=(-C(1,1)*t+(-C(1,3)+0.5))./C(1,2);
 L2=(-C(2,1)*t+(-C(2,3)+0.5))./C(2,2);
 plot(t,L1,t,L2);