perceptron.py

from random import choice
from numpy import array, dot, random

# Declare activation function
def stepFunction(value):
 if value <= 0:
     return 0
 else:
     return 1

# Define Training Data
training_data = [
  (array([0,0,1]), 0),
  (array([0,1,1]), 0),
  (array([1,0,1]), 1),
  (array([1,1,1]), 1)
]

# Define Test Data
test_data = [
  (array([0,0,0])),
  (array([0,1,0])),
  (array([1,1,1])),
  (array([0,0,1])),
  (array([1,0,0]))
]

w = random.rand(3)
errors = []
eta = 0.2
n = 1000

print("Training Perceptron for {} iterations").format(n)
print("Starting weights: {}").format(w)

for i in xrange(n):
  x, expected = choice(training_data)
  result = dot(w, x)
  error = expected - stepFunction(result)
  errors.append(error)
  w += eta * error * x
  print ".",

print ""
print "Training completed"
print("Weights after training: {}").format(w)
print("Running trained Network against Test Data")
for x in test_data:
    result = dot(x, w)
    print("{}: {} -> {}".format(x[:3], result, stepFunction(result)))