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self.mus[i]) 113 | pos2 = x - self.mus[i] 114 | sigma = np.dot(pos1.T, pos2) / weight_all 115 | self.sigmas[i] = sigma 116 | def __calc_new_alpha(self, x, posterior): 117 | k = self.k 118 | m = len(x) 119 | n = self.n 120 | for i in range(0, k): 121 | weight_all = np.sum(posterior[:,i]) 122 | alpha = 1./m * weight_all 123 | self.alphas[i] = alpha 124 | 125 | '''迭代 126 | ''' 127 | def __iterate(self, x): 128 | iters = self.iters 129 | for i in range(0, iters): 130 | #后验概率 m x k 131 | posterior = self.__cacl_posterior(x) 132 | self.__calc_new_mu(x, posterior) 133 | self.__calc_new_sigma(x, posterior) 134 | self.__calc_new_alpha(x, posterior) 135 | #计算似然性 136 | ll = self.__calc_log_like(x) 137 | logger.info('Iteration %d: Log-likelihood:%f', i+1, ll) 138 | 139 | def LoadMu(self, mus = {}): 140 | k = self.k 141 | for i in range(0, k): 142 | self.mus[i] = mus[i] 143 | 144 | '''训练模型 145 | ''' 146 | def Train(self, x): 147 | #x必须为二维矩阵 148 | self.__init_mu(x) 149 | #迭代 150 | self.__iterate(x) 151 | 152 | '''预测 153 | ''' 154 | def Predict(self, x): 155 | posterior = self.__cacl_posterior(x) 156 | logger.debug('posterior:\n%s', posterior) 157 | pred = myfunc.HArgmax(posterior, len(posterior)) 158 | return pred 159 | 160 | if __name__ == '__main__': 161 | logger.debug('start...') 162 | 163 | params = {} 164 | params['cluster_num'] = 3 165 | #2元高斯混合模型 166 | params['attr_num'] = 2 167 | #迭代次数 168 | params['iters'] = 100 169 | 170 | 171 | #加载数据 172 | train_file = './data/xiguashuju.txt' 173 | train_data = np.genfromtxt(train_file, delimiter = ',', dtype = np.float) 174 | logger.debug('训练样本:%s', train_data) 175 | 176 | #训练样本x 177 | train_x = train_data 178 | 179 | #创建模型 180 | gmm = MyGMM(params) 181 | 182 | #自己加载初始mu点 183 | # mu0 = [0.403, 0.237] 184 | # mu1 = [0.714, 0.346] 185 | # mu2 = [0.532, 0.472] 186 | #gmm.LoadMu({0:mu0, 1:mu1, 2:mu2}) 187 | #开始训练模型 188 | gmm.Train(train_x) 189 | #预测样本聚类 190 | pred_y = gmm.Predict(train_x) 191 | logger.debug('pred:\n%s', pred_y) 192 | 193 | #显示 194 | import matplotlib.pyplot as plt 195 | markers = ['o', '*', '.', '+'] 196 | colors = ['red', 'yellow', 'blue', 'black'] 197 | for j in range(0, len(train_x)): 198 | plt.plot(train_x[j][0], train_x[j][1], color = colors[pred_y[j]], marker = markers[pred_y[j]]) 199 | plt.show() 200 | -------------------------------------------------------------------------------- /xigua.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | #coding:utf8 3 | import numpy as np 4 | import os 5 | import sys 6 | from mltoolkits import * 7 | import logging 8 | import mygmm 9 | 10 | logger = logging.getLogger(sys.argv[0]) 11 | logger.setLevel(logging.DEBUG) 12 | 13 | if __name__ == '__main__': 14 | logger.debug('start...') 15 | 16 | params = {} 17 | params['cluster_num'] = 3 18 | #2元高斯混合模型 19 | params['attr_num'] = 2 20 | #迭代次数 21 | params['iters'] = 200 22 | 23 | 24 | #加载数据 25 | train_file = './data/xiguashuju.txt' 26 | train_data = np.genfromtxt(train_file, delimiter = ',', dtype = np.float) 27 | logger.debug('训练样本:%s', train_data) 28 | 29 | #训练样本x 30 | train_x = train_data 31 | 32 | #创建模型 33 | gmm = mygmm.MyGMM(params) 34 | 35 | #自己加载初始mu点 36 | # mu0 = [0.403, 0.237] 37 | # mu1 = [0.714, 0.346] 38 | # mu2 = [0.532, 0.472] 39 | #gmm.LoadMu({0:mu0, 1:mu1, 2:mu2}) 40 | #开始训练模型 41 | gmm.Train(train_x) 42 | #预测样本聚类 43 | pred_y = gmm.Predict(train_x) 44 | logger.debug('pred:\n%s', pred_y) 45 | 46 | #显示 47 | import matplotlib.pyplot as plt 48 | markers = ['o', '*', '.', '+'] 49 | colors = ['red', 'yellow', 'blue', 'black'] 50 | for j in range(0, len(train_x)): 51 | plt.plot(train_x[j][0], train_x[j][1], color = colors[pred_y[j]], marker = markers[pred_y[j]]) 52 | plt.show() 53 | 54 | 55 | --------------------------------------------------------------------------------