Perceptron Learning Rule

//perceotron.sce
x = [1 -2 0 -1; 0 1.5 -0.5 -1; -1 1 0.5 -1]'
w = [1 -1 0 0.5]'
k = 0.1
d = [-1 -1 1]
[r c] = size(x)
while(1)
    e = 0
    for i=1:c
        net = w'*x(: , i)
        if net> 0
            o = 1
        else
            o = -1
        end
        r = d(i)-o
        e = e + 0.5*(d(i)-o)^2 //square error minimization
        w=w+k*r.*x(:, i)
    end
    if e == 0
        break
    end
end
save('val.sod', 'w', 'x');

//perceptronrecall.sce
load('val.sod', 'w', 'x')
[r c] = size(x)
for i=1:c
    net = w'*x(: , i)
    if net> 0
            o = 1
    else
            o = -1
    end
end