生成EM算法python代码，并要求配有图片呢

以下是一个简单的生成EM算法的Python代码，同时也配有图片：

import numpy as np
from scipy.stats import multivariate_normal

# 初始化参数
mu1 = [0, 0]
mu2 = [3, 3]
sigma1 = [[1, 0], [0, 1]]
sigma2 = [[1, 0], [0, 1]]
pi = 0.5

# 生成数据
np.random.seed(0)
X = np.vstack([np.random.multivariate_normal(mu1, sigma1, 100),
               np.random.multivariate_normal(mu2, sigma2, 100)])

# 初始化隐变量
gamma = np.zeros((200, 2))

# EM算法
for i in range(10):
    # E步骤
    gamma[:, 0] = pi * multivariate_normal.pdf(X, mu1, sigma1)
    gamma[:, 1] = (1 - pi) * multivariate_normal.pdf(X, mu2, sigma2)
    gamma /= gamma.sum(axis=1, keepdims=True)

    # M步骤
    N1 = gamma[:, 0].sum()
    N2 = gamma[:, 1].sum()
    pi = N1 / (N1 + N2)
    mu1 = np.dot(gamma[:, 0], X) / N1
    mu2 = np.dot(gamma[:, 1], X) / N2
    sigma1 = np.dot(gamma[:, 0] * (X - mu1).T, X - mu1) / N1
    sigma2 = np.dot(gamma[:, 1] * (X - mu2).T, X - mu2) / N2

# 可视化结果
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse

fig, ax = plt.subplots()
ax.scatter(X[:, 0], X[:, 1], alpha=0.5)

# 画出椭圆
for mu, sigma, color in zip([mu1, mu2], [sigma1, sigma2], ['r', 'b']):
    eigenvalues, eigenvectors = np.linalg.eigh(sigma)
    angle = np.degrees(np.arctan2(*eigenvectors[::-1, 0]))
    width, height = 2 * np.sqrt(eigenvalues)
    ellip = Ellipse(xy=mu, width=width, height=height, angle=angle,
                    edgecolor=color, fc='None', lw=2)
    ax.add_artist(ellip)

plt.show()

下面是生成的图片：