基于欧式距离的聚类代码

欧氏距离聚类(也称为“k均值聚类”)的代码如下：

import numpy as np
from scipy.spatial.distance import cdist

class KMeans:
    def __init__(self, k=2, max_iter=100, random_state=1):
        self.k = k
        self.max_iter = max_iter
        self.random_state = random_state

    def fit(self, X):
        rng = np.random.RandomState(self.random_state)
        i = rng.permutation(X.shape[0])[:self.k]
        self.centers = X[i]
        for _ in range(self.max_iter):
            z = cdist(X, self.centers).argmin(axis=1)
            self.centers = np.array([X[z == k].mean(axis=0)
                                     for k in range(self.k)])
        self.labels_ = z
        self.inertia_ = sum(cdist(X, self.centers, 'sqeuclidean').min(axis=1))

        return self

这是一个使用欧氏距离进行k-均值聚类的简单示例。