kernel kmeans算法python代码

以下是 kernel kmeans 算法的 Python 代码示例：

import numpy as np
from sklearn.metrics.pairwise import rbf_kernel

def kernel_kmeans(X, n_clusters, gamma=1., max_iter=100):
    n_samples, n_features = X.shape
    # Initialize cluster centers randomly
    centers = X[np.random.choice(n_samples, n_clusters, replace=False)]
    # Initialize cluster assignments
    labels = np.zeros(n_samples)
    for i in range(max_iter):
        # Compute pairwise RBF kernel matrix
        K = rbf_kernel(X, centers, gamma=gamma)
        # Assign samples to nearest cluster
        new_labels = np.argmin(K, axis=1)
        # Check convergence
        if np.all(new_labels == labels):
            break
        labels = new_labels
        # Update cluster centers
        for j in range(n_clusters):
            mask = (labels == j)
            if np.sum(mask) > :
                centers[j] = np.mean(X[mask], axis=)
    return labels, centers

其中，`X` 是输入数据矩阵，每行代表一个样本，`n_clusters` 是聚类数目，`gamma` 是 RBF 核函数的参数，`max_iter` 是最大迭代次数。函数返回聚类结果和聚类中心。