请手写k-means聚类

好的，下面是手写k-means聚类的Python代码：

import numpy as np

class KMeans:
    def __init__(self, n_clusters=8, max_iter=300):
        self.n_clusters = n_clusters
        self.max_iter = max_iter
        
    def fit(self, X):
        n_samples, n_features = X.shape
        
        # Initialize centroids randomly
        self.centroids = X[np.random.choice(n_samples, self.n_clusters, replace=False)]
        
        # Iterate until convergence or max iterations reached
        for i in range(self.max_iter):
            # Assign each sample to closest centroid
            distances = np.sqrt(((X - self.centroids[:, np.newaxis])**2).sum(axis=2))
            self.labels = np.argmin(distances, axis=0)
            
            # Update centroids based on new assignments
            for k in range(self.n_clusters):
                self.centroids[k] = X[self.labels == k].mean(axis=0)
                
            # Check for convergence
            if i > 0 and np.all(self.labels == prev_labels):
                break
                
            prev_labels = self.labels
                
    def predict(self, X):
        distances = np.sqrt(((X - self.centroids[:, np.newaxis])**2).sum(axis=2))
        return np.argmin(distances, axis=0)

这个实现的k-means算法使用了随机初始化的质心，并且在每次迭代中计算每个样本点到每个质心的距离，然后将每个样本点分配到最近的质心。然后，根据新的样本点分配更新质心位置。最终，算法会收敛到一些质心，这些质心用于预测新的样本点所属的聚类。