%single neuron delta training with soft activation function 
%      BATCH training   with several patterns
format compact; clear all;
ip= [-1,-1; 2,2; 0,0; 1,1; -0.5,0; 2,1; 0,1; 3,1; 1,1.5; 2.5,1.5] 
ip= [-1,-1; 2,2; 0,0; 1,1; -0.5,0; 2,1; 0,1; 3,1; 1,2; 3,2] 
[np,ni]=size(ip); ip(:,ni+1)=ones(np,1)  %augmenting input
dp=[-1, 1,-1, 1, -1, 1, -1, 1, -1, 1]', ww=[-1 3 -3], c=1; k=1;
figure(1); clf; hold on; xlabel('X input'); ylabel('Y input');
a=axis ; a=[-2 4 -2 4]; axis(a);j=0;
for ite=1:10000,
    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)=-2; 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 <0.01, break; end;
end;
for p=1:np, if dp(p)>0, plot(ip(p,1),ip(p,2),'ro'); 
    else plot(ip(p,1),ip(p,2),'bx'); end; end;
hold off; ite
figure(2); clf; 
semilogy(tter); xlabel('iterations'); ylabel('error');