1 # -*-coding:utf-8-*- 2 ‘@author: xijun.gong‘ 3 import numpy as np 4 import random 5 import math 6  7  8 class Perceptron: 9     def __init__(self, learnRate, maxIter, bit_len):10         """11         :param bit_len12         :param learnRate:13         :param maxIter:  最大迭代次数14         """15         self.learmRate = learnRate;16         self.weight = None;17         self.maxIter = maxIter;18         # produce map19         self.bit_len = bit_len;20         self.nummap = None;21         self.initMap()22         pass23 24     def initMap(self):25         maxNum = (1 << self.bit_len);  # 该位数下的最大值26         self.nummap = np.zeros((maxNum, self.bit_len), dtype=np.int);  # include zero27         for _id in xrange(maxNum):28             for index in xrange(self.bit_len):29                 self.nummap[_id][index] = 1 & (_id >> index);30         pass31 32     def initWeight(self):33         """34         :return:35         """36         self.weight = np.ones(self.bit_len) / self.bit_len;37 38     def fit(self, fds, labels):39         """40         :param fds: 训练样本集合41         :param labels:42         :return:43         """44         feature_nums = fds.shape[1]  # 样本中的特征参数数量45         self.initWeight()46         for iter in xrange(self.maxIter):47             print ‘train as iter is {} ‘.format(iter)48             acc_cnt = 049             for _ind, sample in enumerate(fds):50                 a = self.nummap[int(sample[0])];51                 b = self.nummap[int(sample[1])];52                 label_y = sum(self.weight * (a + b))53                 # 计算var_w 表示倒三角w54                 print ‘the reality:{} , predict {}‘.format(labels[_ind], label_y);55                 if math.fabs(labels[_ind] - label_y) <= 0.000001:56                     acc_cnt += 1;57                     continue;58                 var_w = self.learmRate * (labels[_ind] - label_y) * (a + b)59                 self.weight += var_w;60             print ‘accuary is {}‘.format(acc_cnt / (len(fds) * 1.0))61             if acc_cnt == len(fds):62                 np.save(‘weight.npy‘, {‘weight‘: self.weight});63                 return;64         pass65 66     def load(self, path=‘weight.npy‘):67         return np.load(path)[‘weight‘]68 69     def predict(self, fd):70         a = self.nummap[fd[0]];71         b = self.nummap[fd[1]];72         return sum(self.weight * (a + b))73 74     def predict_prod(self):75         pass76 77 78 if __name__ == ‘__main__‘:79     import time80 81     perceptron = Perceptron(learnRate=0.01, maxIter=2000, bit_len=5);82     xa = np.arange(31);83     xb = np.zeros(31);84     labels = np.zeros(31)85     for i in xrange(31):86         xb[i] = random.randint(0, (int(time.time() + 1)) % 31)87         labels[i] = xb[i] + xa[i]88     perceptron.fit(np.array([xa, xb]).T, labels)89     print ‘predict is {}‘.format(perceptron.predict([24, 13]))

train as iter is 277 the reality:0.0 , predict 0.0the reality:16.0 , predict 16.0000005749the reality:16.0 , predict 15.9999994995the reality:3.0 , predict 3.00000059084the reality:18.0 , predict 17.999999818the reality:15.0 , predict 15.0000000195the reality:20.0 , predict 19.9999998534the reality:22.0 , predict 22.0000009642the reality:10.0 , predict 9.99999911021the reality:22.0 , predict 21.9999996143the reality:23.0 , predict 22.9999990943the reality:17.0 , predict 17.0000000549the reality:25.0 , predict 24.9999994128the reality:18.0 , predict 18.0000008934the reality:20.0 , predict 19.9999998534the reality:15.0 , predict 15.0000000195the reality:27.0 , predict 26.999999038the reality:31.0 , predict 30.9999993919the reality:25.0 , predict 25.0000003525the reality:21.0 , predict 20.9999999986the reality:35.0 , predict 34.9999997457the reality:29.0 , predict 28.9999993564the reality:39.0 , predict 38.9999996894the reality:26.0 , predict 26.0000009079the reality:31.0 , predict 30.9999993919the reality:25.0 , predict 24.9999990026the reality:33.0 , predict 32.9999994273the reality:32.0 , predict 31.9999999473the reality:32.0 , predict 31.9999991549the reality:34.0 , predict 34.0000002657the reality:33.0 , predict 32.9999994273accuary is 1.0predict is 36.9999984312