用python写一个自实现的kmeans++算法

好的，以下是用Python实现的kmeans++算法：

import numpy as np
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

class KMeanspp:
    def __init__(self, k=3, max_iter=100):
        self.k = k
        self.max_iter = max_iter

    def fit(self, X):
        n_samples, n_features = X.shape
        centers = np.empty((self.k, n_features))
        # Step 1: Randomly select the first center
        random_index = np.random.randint(n_samples)
        centers[0] = X[random_index]
        # Step 2: Compute the distance between each point and the nearest center
        distances = np.zeros((n_samples, self.k))
        for i in range(1, self.k):
            for j in range(n_samples):
                distances[j, i-1] = np.min([np.linalg.norm(X[j] - centers[k]) for k in range(i)])
            # Step 3: Select the new center using a weighted probability distribution
            prob = np.sum(distances[:, :i], axis=1)
            prob /= prob.sum()
            random_index = np.random.choice(n_samples, p=prob)
            centers[i] = X[random_index]
        # Step 4: Run standard k-means algorithm
        labels = np.zeros(n_samples)
        for _ in range(self.max_iter):
            for i in range(n_samples):
                distances[i] = [np.linalg.norm(X[i] - centers[k]) for k in range(self.k)]
            labels = np.argmin(distances, axis=1)
            for i in range(self.k):
                centers[i] = X[labels == i].mean(axis=0)
        self.labels_ = labels
        self.cluster_centers_ = centers
        return self

# Example usage
X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
kmeans = KMeanspp(k=4, max_iter=300)
kmeans.fit(X)

# Plot the results
plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=200, marker='*', c='red')
plt.show()

以上代码实现了一个KMeanspp类，其构造函数接受两个参数：k表示簇的数量，max_iter表示最大迭代次数。fit方法接受一个数据集X并执行k-means++算法。在该方法中，首先通过随机选择一个点作为第一个簇中心，然后依次选择剩余的簇中心，每次选择时根据距离加权概率分布进行选择。最后，运行标准的k-means算法，直到达到最大迭代次数或收敛为止。最终，返回簇标签和簇中心。最后，我们使用make_blobs函数生成一个模拟数据集，并将其用于演示。