生成EM算法python代码

以下是一个简单的生成EM算法的Python代码示例：

import numpy as np

def em_algorithm(data, num_clusters, num_iterations):
    # 初始化参数
    num_samples, num_features = data.shape
    means = np.random.rand(num_clusters, num_features)
    covariances = [np.eye(num_features)] * num_clusters
    weights = np.ones(num_clusters) / num_clusters

    # EM算法主循环
    for i in range(num_iterations):
        # E步骤：计算每个样本属于每个簇的概率
        probabilities = np.zeros((num_samples, num_clusters))
        for k in range(num_clusters):
            probabilities[:, k] = weights[k] * multivariate_normal.pdf(data, means[k], covariances[k])
        probabilities /= probabilities.sum(axis=1, keepdims=True)

        # M步骤：更新参数
        weights = probabilities.mean(axis=0)
        means = np.dot(probabilities.T, data) / probabilities.sum(axis=0, keepdims=True).T
        for k in range(num_clusters):
            diff = data - means[k]
            covariances[k] = np.dot(probabilities[:, k] * diff.T, diff) / probabilities[:, k].sum()

    return means, covariances, weights

这个代码使用了多元高斯分布来建模每个簇，使用EM算法来估计模型参数。