def pi_N(N, pi, mean_1, cov_1, mean_2, cov_2, steps):
gmm = GMM(N, pi, mean_1, cov_1, mean_2, cov_2)
pi_steps = np.zeros(shape=steps)
# 均值
pi_mu = 0
# 方差
pi_sigma = 0
# 计算steps次估计值和均值
for i in range(steps):
D = gmm.dataset()
pi_learn, _, _, _, _ = gmm.EM(D, N)
pi_mu += pi_learn
pi_steps[i] = pi_learn
pi_mu /= steps
# 计算steps次方差
for i in range(steps):
pi_sigma += (pi_steps[i] - pi_mu)**2
pi_sigma /= steps
return pi_mu, pi_sigma
"""
@author:hanmy
@file:ParaEsti.py
@time:2019/04/24
"""
import numpy as np
import matplotlib.pyplot as plt
from gmm import GMM
if __name__ == "__main__":
N = 10000
pi = 0.8
mean_1 = np.array([0.0, 0.0])
cov_1 = np.mat([[1.0, 0.0], [0.0, 1.0]])
mean_2 = np.array([3.0, 3.0])
cov_2 = np.mat([[1.0, 0.5], [0.5, 1.0]])
gmm = GMM(N, pi, mean_1, cov_1, mean_2, cov_2)
plt.figure()
plt.gca().set_aspect('equal') # 令x轴和y轴的同一区间的刻度相同
D = gmm.dataset()
plt.scatter(D[:, 0], D[:, 1])
plt.show()
pi_learn, mean_1_learn, cov_1_learn, mean_2_learn, cov_2_learn = gmm.EM(D, N)
print("权重值：", pi_learn)
print("第一个分模型的均值：\n", mean_1_learn)
print("第一个分模型的协方差矩阵：\n", cov_1_learn)
print("第二个分模型的均值：\n", mean_2_learn)
print("第二个分模型的协方差矩阵：\n", cov_2_learn)
