给出一个二分kmeans的python代码

当然，我可以给您提供一个二分kmeans的Python代码。请参考以下代码示例：

import random
import numpy as np
from sklearn.cluster import KMeans

class BinaryKMeans:
    def __init__(self, k, max_iters=100):
        self.k = k
        self.max_iters = max_iters
        # 初始化质心为None
        self.centroids = None

    # 计算欧几里得距离
    def euclidean_distance(self, a, b):
        return np.linalg.norm(a - b)

    def find_nearest_centroid(self, x, centroids):
        nearest_centroid = None
        min_distance = float('inf')

        for centroid in centroids:
            distance = self.euclidean_distance(x, centroid)
            if distance < min_distance:
                min_distance = distance
                nearest_centroid = centroid

        return nearest_centroid

    def get_variance(self, cluster):
        centroid = np.mean(cluster, axis=0)
        var = np.sum((cluster - centroid) ** 2)
        return var

    def binary_kmeans(self, X):
        n = X.shape[0]

        # 初始化簇
        clusters = []
        clusters.append(X)

        # 初始化簇的标识
        cluster_labels = np.zeros(n)

        for i in range(self.max_iters):
            k = len(clusters)

            if k >= self.k:
                break

            variances = []
            new_clusters = []

            # 对每个簇进行二分
            for j in range(k):
                cluster = clusters[j]

                # 进行KMeans聚类
                kmeans = KMeans(n_clusters=2, max_iter=50, random_state=0).fit(cluster)

                # 获取KMeans聚类后的簇
                cluster1 = cluster[kmeans.labels_ == 0]
                cluster2 = cluster[kmeans.labels_ == 1]

                # 计算两个簇的方差
                var1 = self.get_variance(cluster1)
                var2 = self.get_variance(cluster2)

                variances.append(var1 + var2)
                new_clusters.append(cluster1)
                new_clusters.append(cluster2)

                # 更新簇的标识
                indices = np.where(cluster_labels == j)[0]

                labels1 = kmeans.labels_[indices][kmeans.labels_[indices] == 0]
                labels2 = kmeans.labels_[indices][kmeans.labels_[indices] == 1]

                cluster_labels[indices[labels1]] = len(clusters)
                cluster_labels[indices[labels2]] = len(clusters) + 1

            # 更新质心
            clusters = new_clusters

        # 获取最终的质心
        self.centroids = np.zeros((self.k, X.shape[1]))

        for i in range(self.k):
            cluster = X[cluster_labels == i]
            centroid = np.mean(cluster, axis=0)
            self.centroids[i] = centroid

        return cluster_labels

if __name__ == '__main__':
    # 随机生成100个二维数据点
    X = np.random.rand(100, 2)

    # 调用二分kmeans算法
    binary_kmeans = BinaryKMeans(k=5)
    labels = binary_kmeans.binary_kmeans(X)

    print(labels)

希望能对您有所帮助，如有问题请随时提出。