GMM調(diào)參

# !/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 pltmpl.rcParams['font.sans-serif'] = ['SimHei'] mpl.rcParams['axes.unicode_minus'] = Falsedef expand(a, b, rate=0.05):d = (b - a) * ratereturn a-d, b+ddef accuracy_rate(y1, y2):acc = np.mean(y1 == y2)return acc if acc > 0.5 else 1-accif __name__ == '__main__':np.random.seed(0)cov1 = np.diag((1, 2))print(cov1)N1 = 500N2 = 300N = N1 + N2x1 = 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 &

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