你写一个使用余弦相似度作为距离度量的Kmeans算法

好的，我可以为您提供一个使用余弦相似度作为距离度量的Kmeans算法的Python代码，示例如下：

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

class CosineKmeans:
    def __init__(self, n_clusters=8, max_iter=300, random_state=None):
        self.n_clusters = n_clusters
        self.max_iter = max_iter
        self.random_state = random_state
    
    def fit(self, X):
        rng = np.random.RandomState(self.random_state)
        n_samples, n_features = X.shape
        
        # Initialize centroids
        centroids_indices = rng.permutation(n_samples)[:self.n_clusters]
        self.centroids = X[centroids_indices]
        
        for i in range(self.max_iter):
            # Calculate cosine similarities between each sample and each centroid
            similarities = cosine_similarity(X, self.centroids)
            
            # Assign each sample to the closest centroid
            labels = np.argmax(similarities, axis=1)
            
            # Update centroids
            for j in range(self.n_clusters):
                mask = labels == j
                if np.sum(mask) > 0:
                    self.centroids[j] = np.mean(X[mask], axis=0)
    
    def predict(self, X):
        similarities = cosine_similarity(X, self.centroids)
        labels = np.argmax(similarities, axis=1)
        return labels

在这个示例中，我们使用了sklearn中的cosine_similarity函数来计算余弦相似度，fit方法用于训练模型，predict方法用于预测样本所属的簇。您可以根据自己的需求修改代码并进行测试。