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#!/usr/bin/env python2
from operator import add, mul
def pointwise(op, x, y):
return [op(a, b) for (a, b) in zip(x, y)]
def scalar_product(x, y):
"""Scalar product."""
return sum(pointwise(mul, x, y))
def scalar_multiplication(s, v):
"""Scalar multiplication."""
return [s*i for i in v]
# data extracted from corpus
x = {1 : [ 1 , 0 , 0 , 1 , 0 , 0 , 0 , 0 , 1 ],
2 : [ 1 , 1 , 0 , 0 , 0 , 0 , 0 , 0 , 0 ],
3 : [ 0 , 1 , 0 , 1 , 1 , 0 , 0 , 0 , 0 ],
4 : [ 0 , 0 , 0 , 0 , 0 , 0 , 1 , 0 , 0 ],
5 : [ 0 , 0 , 0 , 4 , 0 , 0 , 0 , 0 , 1 ],
6 : [ 0 , 1 , 1 , 0 , 1 , 0 , 0 , 1 , 0 ],
7 : [ 0 , 0 , 1 , 0 , 0 , 0 , 0 , 1 , 0 ],
8 : [ 0 , 0 , 0 , 0 , 0 , 2 , 0 , 0 , 0 ],
9 : [ 0 , 0 , 0 , 0 , 0 , 0 , 1 , 0 , 0 ],
10 : [ 0 , 0 , 0 , 0 , 0 , 2 , 0 , 0 , 2 ]}
y = {1:1, 2:-1, 3:-1, 4:1, 5:1, 6:1, 7:1, 8:-1, 9:1, 10:-1}
# learning rate
alpha = 0.5
# initialize parameters
w = [0,0,0,0,0,0,0,0,0]
rho = 0
turn = 1
errors = True
# perceptron algorithm
while errors:
print "\nTurn", turn
turn += 1
errors = False
for i in x.keys():
print "i =", i
trigger = y[i] * (scalar_product (w, x[i]) + rho)
if trigger <= 0:
errors = True
w = pointwise(add, w, scalar_multiplication(alpha*y[i], x[i]))
rho += alpha*y[i]
print w, rho
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