感覺器--代碼實作

import numpy as np
from functools import reduce


class Perceptron(object):
    def __init__(self, input_num, activator):
        self.activator = activator
        self.weights = [ for _ in range(input_num)]
        self.bias = 

    def __str__(self):
        return 'weights\t:%s\nbias\t:%f\n' % (self.weights, self.bias)


    def predict(self, input_vec):
        return self.activator(
            reduce(lambda a, b: a + b, [x_w[] * x_w[] for x_w in zip(input_vec, self.weights)], ) + self.bias)

    def train(self, input_vecs, labels, iteration, rate):
        for i in range(iteration):
            self._one_iteration(input_vecs, labels, rate)

    def _one_iteration(self, input_vecs, labels, rate):
        samples = list(zip(input_vecs, labels))

        for (input_vec, label) in samples:
            output = self.predict(input_vec)
            self._update_weights(input_vec, output, label, rate)

    def _update_weights(self, input_vec, output, label, rate):
        delta = label - output
        self.weights = [x_w1[] + rate * delta * x_w1[] for x_w1 in zip(input_vec, self.weights)]
        self.bias += rate * delta


def f(x):
    return  if x >  else 


def get_training_dataset():
    input_vecs = [[,], [,], [,], [,]]
    labels = [, , , ]
    return input_vecs, labels    


def train_and_perceptron():

    p = Perceptron(, f)
    input_vecs, labels = get_training_dataset()
    p.train(input_vecs, labels, , )
    return p


if __name__ == '__main__': 
    and_perception = train_and_perceptron()
    print (and_perception)
    print ('1 and 1 = %d' % and_perception.predict([, ]))
    print ('0 and 0 = %d' % and_perception.predict([, ]))
    print ('1 and 0 = %d' % and_perception.predict([, ]))
    print ('0 and 1 = %d' % and_perception.predict([, ]))