Wikipedia に載ってた形式ニューロン(Threshold Logic Unit) をそのまま Python で書いた後に、ちょいちょい推敲してみる。

test.py

#!/usr/bin/env python
from tlu import TLU

if __name__ == '__main__':
    # training_sets from
    # http://en.wikipedia.org/wiki/Perceptron#Example
    threshold = 0.5
    learning_rate = 0.1
    weights = [0, 0, 0]
    training_set = [
        (1, (1, 0, 0)),
        (1, (1, 0, 1)),
        (1, (1, 1, 0)),
        (0, (1, 1, 1)),
    ]

    tlu = TLU(threshold, weights)

    while not tlu.train(training_set, learning_rate):
        pass

    print(tlu.dump())

tlu.py

# coding: utf-8
from itertools import izip


class TLU(object):

    def __init__(self, threshold, weights):
        self.threshold = threshold
        self.weights = tuple(weights)

    def fire(self, inputs):
        t = sum(value * w for value, w in izip(inputs, self.weights))
        return self.threshold < t

    def train1(self, desired, inputs, rate):
        result = self.fire(inputs)
        diff = desired - result

        if diff == 0:
            return True

        diff_r = diff * rate
        dw = (diff_r * v for v in inputs)
        weights2 = (w + d for w, d in izip(self.weights, dw))
        self.weights = tuple(weights2)

        return False

    def train(self, trainingset, rate):
        is_perfect = True
        for desired, inputs in trainingset:
            is_perfect &= self.train1(desired, inputs, rate)
        return is_perfect

    def dump(self):
        return (self.threshold, self.weights)