感知机此处不介绍，这里只是简单的做了一个使用感知机思路，训练一个y=a+b计算模型.
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_len 12 :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 map 19 self.bit_len = bit_len; 20 self.nummap = None; 21 self.initMap() 22 pass 23 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 zero 27 for _id in xrange(maxNum): 28 for index in xrange(self.bit_len): 29 self.nummap[_id][index] = 1 & (_id >> index); 30 pass 31 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 = 0 49 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w 54 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 pass 65 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 pass 76 77 78 if __name__ == '__main__': 79 import time 80 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]))
运行结果：
train as iter is 277 the reality:0.0 , predict 0.0 the reality:16.0 , predict 16.0000005749 the reality:16.0 , predict 15.9999994995 the reality:3.0 , predict 3.00000059084 the reality:18.0 , predict 17.999999818 the reality:15.0 , predict 15.0000000195 the reality:20.0 , predict 19.9999998534 the reality:22.0 , predict 22.0000009642 the reality:10.0 , predict 9.99999911021 the reality:22.0 , predict 21.9999996143 the reality:23.0 , predict 22.9999990943 the reality:17.0 , predict 17.0000000549 the reality:25.0 , predict 24.9999994128 the reality:18.0 , predict 18.0000008934 the reality:20.0 , predict 19.9999998534 the reality:15.0 , predict 15.0000000195 the reality:27.0 , predict 26.999999038 the reality:31.0 , predict 30.9999993919 the reality:25.0 , predict 25.0000003525 the reality:21.0 , predict 20.9999999986 the reality:35.0 , predict 34.9999997457 the reality:29.0 , predict 28.9999993564 the reality:39.0 , predict 38.9999996894 the reality:26.0 , predict 26.0000009079 the reality:31.0 , predict 30.9999993919 the reality:25.0 , predict 24.9999990026 the reality:33.0 , predict 32.9999994273 the reality:32.0 , predict 31.9999999473 the reality:32.0 , predict 31.9999991549 the reality:34.0 , predict 34.0000002657 the reality:33.0 , predict 32.9999994273 accuary is 1.0 predict is 36.9999984312