%single neuron LMS traning with soft activation function 
format compact;  clear all;
ip=[1 2; 2 1]
dp=[-1,1]'
ww=[1 3 -3]
c=0.1; k=1;
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)  %agumenting input
a=axis ;
a=[0 4 0 4];
axis(a); j=0;
for ite=1:1000,
     if ite>1, plot(x,y,'g'); end;
    net=ip*ww'
    op=tanh(0.5.*k.*net);
    fp=k.*(1-op.*op);
    er=dp-net;
    dw=(c*er)'*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');