Perceptron

# type your code here
import random

data = [
    [[0,0,1], 0],
    [[0,1,1], 1],
    [[1,0,1], 1],
    [[1,1,1], 1],
]

def get_x(data,index):
    return data[index][0]

def get_expected(data,index):
    return data[index][1]

def step(x):
    if x > 0:
        return 1
    else:
        return 0

def scalar_product(a,b):
    R = 0
    for i in xrange(0,len(a)):
        R += a[i]*b[i]
    return R

def add_vectors(a,b):
    R = []
    for i in xrange(0,len(a)):
        R = R + [a[i]+b[i]]
    return R

def scale_vector(scale,a):
    R = []
    for i in xrange(0,len(a)):
        R = R + [a[i]*scale]
    return R

w = [random.random(),random.random(),random.random()]

errors = []
learn_rate = 0.02
n = 1000

for i in xrange(n):
    x, expected = random.choice(data)
    result = scalar_product(w, x)
    error = expected - step(result)
    errors.append(error)
    w = add_vectors(w,scale_vector(learn_rate * error, x))


print "%5s %10s %10s"%("x_n","w_n","output")
for x,_ in data:
    result = scalar_product(x, w)
    print "%5s %10f %10d "%(x[:2], result, step(result))