请你用python编写K均值聚类的算法

好的，以下是Python实现的K均值聚类算法：

import numpy as np

class KMeans:
    def __init__(self, k=2, max_iter=100, random_state=None):
        self.k = k
        self.max_iter = max_iter
        self.random_state = random_state
        
    def fit(self, X):
        np.random.seed(self.random_state)
        self.centroids = X[np.random.choice(X.shape[0], self.k, replace=False)]
        
        for i in range(self.max_iter):
            clusters = [[] for _ in range(self.k)]
            
            # Assign each data point to the closest centroid
            for x in X:
                distances = [np.linalg.norm(x - c) for c in self.centroids]
                closest_idx = np.argmin(distances)
                clusters[closest_idx].append(x)
                
            # Update centroids as the mean of the data points in each cluster
            for j in range(self.k):
                if clusters[j]:
                    self.centroids[j] = np.mean(clusters[j], axis=0)
                    
        self.labels_ = np.zeros(X.shape[0])
        for i, cluster in enumerate(clusters):
            for x in cluster:
                self.labels_[np.where(X == x)[0][0]] = i
                
        return self
    
    def predict(self, X):
        labels = np.zeros(X.shape[0])
        for i, x in enumerate(X):
            distances = [np.linalg.norm(x - c) for c in self.centroids]
            closest_idx = np.argmin(distances)
            labels[i] = closest_idx
        return labels

其中，KMeans类的构造函数中可以指定聚类数k、最大迭代次数max_iter和随机数种子random_state，fit方法接收数据X并进行聚类，predict方法可用于对新数据进行预测。