%single neuron perceptron traning with soft activation function 
format compact;  clear all;
ip=[1 2; 2 1]; dp=[-1,1]'; ww=[1 3 -3]; c=3; k=0.3;
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=1:20,
    for p=1:np,
        j=j+1; if j>1, plot(x,y,'g'); end;
        net(p)=ip(p,:)*ww' ;
        op(p)=tanh(k*0.5*net(p)); %hiperbolic function
        er(p)=dp(p)-op(p);       ww=ww+c*er(p)*ip(p,:);
        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;  
    end
    ter=sqrt(er*er'),     tter(ite)=ter;
    if ter <0.0001, break; end;
end;
hold off; ite
figure(2); clf; 
semilogy(tter); xlabel('iterations'); ylabel('error');