kernel kmeans 实现单木分割的python代码

以下是 kernel kmeans 实现单木分割的 Python 代码：

import numpy as np

def kernel_kmeans(X, k, kernel_func, max_iter=100):
    n_samples = X.shape[]
    # Initialize cluster centers randomly
    centers = X[np.random.choice(n_samples, k, replace=False)]
    # Initialize cluster assignments
    labels = np.zeros(n_samples)
    # Initialize kernel matrix
    K = kernel_func(X, X)
    # Iterate until convergence or max iterations reached
    for i in range(max_iter):
        # Assign each sample to the closest cluster center
        for j in range(n_samples):
            distances = [np.linalg.norm(K[j] - K[c]) for c in centers]
            labels[j] = np.argmin(distances)
        # Update cluster centers
        for c in range(k):
            mask = (labels == c)
            if np.sum(mask) > :
                centers[c] = np.mean(X[mask], axis=)
        # Update kernel matrix
        K = kernel_func(X, X)
    return labels, centers

def single_linkage(X, k, kernel_func, max_iter=100):
    n_samples = X.shape[]
    # Initialize cluster assignments
    labels = np.zeros(n_samples)
    # Initialize kernel matrix
    K = kernel_func(X, X)
    # Iterate until k clusters remain or max iterations reached
    for i in range(n_samples - k):
        # Find the two closest clusters
        min_distance = np.inf
        for j in range(n_samples):
            for c in range(k):
                if labels[j] == c:
                    distance = np.min([np.linalg.norm(K[j] - K[j2]) for j2 in range(n_samples) if labels[j2] != c])
                    if distance < min_distance:
                        min_distance = distance
                        merge_idx = j
                        merge_label = c
        # Merge the two closest clusters
        labels[labels == merge_label] = k + i
        labels[merge_idx] = k + i
    # Run kernel k-means on the final clustering
    final_labels, final_centers = kernel_kmeans(X, k, kernel_func, max_iter)
    return final_labels, final_centers

希望这个代码能够帮到你！