Perceptron.7zPerceptron.7zPerceptron.7zPerceptron.7zPerceptron.7

# -*- coding: utf-8 -*- """ Created on Mon Jan 29 09:20:04 2018 @author: liudiwei """ class Perceptron(object): def __init__(self, input_num, activation_func): """init parameter activation_func: this is a function of activation input_num: number of sample """ self.activation_func = activation_func self.weights = [0.0 for _ in range(input_num)] self.bias = 0.0 def train(self, input_vecs, labels, iteration, rate): """training model input_vec: input vetcor, a 2-D list labels: class label list iteration: rate: learning rate """ for i in range(iteration): self._one_iteration(input_vecs, labels, rate) def _one_iteration(self, input_vecs, labels, rate): """training model on input_vecs dataset""" samples = zip(input_vecs, labels) for (input_vec, class_label) in samples: output_val = self.predict(input_vec) self._update_weights(input_vec, output_val, class_label, rate) def _update_weights(self, input_vec, output_val, class_label, rate): """update weights for each iteration""" delta = class_label - output_val self.weights = map(lambda (x, w): w + rate * delta * x, zip(input_vec, self.weights)) self.bias += rate * delta def __to_string__(self): return 'weights\t: %s\nbias\t: %f\n' % (self.weights, self.bias) def predict(self, input_vec): """input input_vec and return a prediction value""" return self.activation_func( reduce(lambda a, b: a + b, map(lambda (x, w): x * w, zip(input_vec, self.weights)), 0.0) + self.bias)