EM算法和GMM（下）

GMM调参

# !/usr/bin/python
# -*- coding:utf-8 -*-

import numpy as np
from sklearn.mixture import GaussianMixture
import matplotlib as mpl
import matplotlib.colors
import matplotlib.pyplot as plt

mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False


def expand(a, b, rate=0.05): d = (b - a) * rate return a-d, b+d


def accuracy_rate(y1, y2): acc = np.mean(y1 == y2) return acc if acc > 0.5 else 1-acc


if __name__ == '__main__': np.random.seed(0) cov1 = np.diag((1, 2)) print(cov1) N1 = 500 N2 = 300 N = N1 + N2 x1 = np.random.multivariate_normal(mean=(1, 2), cov=cov1, size=N1) m = np.array(((1, 1), (1, 3))) x1 = x1.dot(m) x2 = np.random.multivariate_normal(mean=(-1, 10), cov=cov1, size=N2) x = np.vstack((x1, x2)) y &
  

 

  
1
  
2
  
3
  
4
  
5
  
6
  
7
  
8
  
9
  
10
  
11
  
12
  
13
  
14
  
15
  
16
  
17
  
18
  
19
  
20
  
21
  
22
  
23
  
24
  
25
  
26
  
27
  
28
  
29
  
30
  
31
  
32
  
33
  
34
  
35
 

文章来源: maoli.blog.csdn.net，作者：刘润森！，版权归原作者所有，如需转载，请联系作者。

原文链接：maoli.blog.csdn.net/article/details/89216765