%single neuron delta training with soft activation function 
%                 BATCH training
format compact; clear all;
ip=[1 2; 2 1], dp=[-1,1]', ww=[1 3 -3], c=2; k=0.5;
figure(1); clf; hold on; xlabel('X input'); ylabel('Y input');
plot(ip(1,1),ip(1,2),'ro'); plot(ip(2,1),ip(2,2),'bx');
[np,ni]=size(ip); ip(:,ni+1)=ones(np,1)  %augmenting input
a=axis ; a=[0 4 0 4]; axis(a);j=0;
for ite=1:125,
    if ite>1, plot(x,y,'g'); end;
    net=ip*ww';  op=tanh(0.5.*k.*net);
    fp=k.*(1-op.*op);   er=dp-op; 
    dw=(c*er.*fp)'*ip;    ww=ww+dw;
    x(1)=-1; y(1)=-(ww(1)*x(1)+ww(3))./ww(2);
    x(2)=4; y(2)=-(ww(1)*x(2)+ww(3))./ww(2);
    plot(x,y,'r');
    %       pause;  
    ter=er'*er,     tter(ite)=ter;
    if ter <0.001, break; end;
end;
hold off; ite
figure(2); clf; 
semilogy(tter); xlabel('iterations'); ylabel('error');