Davids perceptron.pl

#!/usr/bin/env python

__author__ = "David Stainton"
__license__ = "Apache License"


import sys

class perceptron(object):

    def __init__(self, values):
        self.weight = []
        self.threshold = .5
        self.learning_rate = .1
        for value in values:
            self.weight.append(value)

    def is_success_train_input(self, values, expected):
        sum = dot_product(values, self.weight)
        if sum == self.threshold:
            output = 0
        if sum < self.threshold:
            output = 0
        if sum > self.threshold:
            output = 1
        if output != expected:
            for elk in range(0,len(values)):
                if values[elk] == 1:
                    change = (expected - output) * self.learning_rate
                    #print "%s %s" % (elk, change)
                    self.weight[elk] += change
            return False

        return True


    def display(self):
        print(self.weight)

             
def dot_product(a, b):
    sum = 0
    i = 0
    while i < len(a):
        sum += a[i] * b[i]
        i += 1
    return sum

def main():

    # initialize with weight values...
    p = perceptron([0.0,0.0])
    
    # single values corresponding to expected results for each training input
    expect = [1,0,1,1]

    # training set
    input = [[1,1], [0,0], [0,1], [1,0]]

    # try to expose our perceptron to the training set 10 times
    repeat = 10

    for c in range(0,repeat):
        results = []

        for elk in range(len(expect)):
            results.append(p.is_success_train_input(input[elk], expect[elk]))
            p.display()

        if False not in results:
            return

    p.display()



if __name__ == "__main__":
    main()