def preceptron_learning(): line = Line() line.random_line() sample = line.generate_sample(100) # print sample p = Preceptron([.01,0]) p.learn(sample) sample = line.generate_sample(100000) incorrect = p.fng(sample) #print p.weight #print p.count return p.count, incorrect
w = [99, 99]
error = [0, 0]
c = 0
while(scalar([W[0]-w[0], W[1]-w[1]]) > 0.01):
c += 1
for sample in samples:
x = sample[0]
X = [1, x]
y = sample[1]
e = [(float(y)/(1+math.pow(math.e,y*(W[0]*X[0]+W[1]*X[1])))) * i for i in X]
error[0] += e[0]
error[1] += e[1]
delta_error = [-(1.0/N)*i for i in error]
w = W
W[0] = W[0] - lr*delta_error[0]
W[1] = W[1] - lr*delta_error[1]
print "C",c
return W
if __name__ == "__main__":
l = Line()
n = 100000
runs = 1
eout = 0
for i in xrange(0,runs):
l.random_line()
weight = learn()
print weight
eout += Eout(weight, n)
print eout, float(eout)/(n*runs)
