含权重的K-means代码

含权重的K-means（Weighted K-means）是在传统的K-means算法基础上对每个样本点赋予一个权重，这样在计算聚类中心时会更侧重于那些具有较大权重的样本。这样做的目的是为了调整数据中各个部分的重要性，特别是在数据分布不均匀的情况下。下面是基本的含权重K-means代码概述：

import numpy as np

class WeightedKMeans:
    def __init__(self, k, max_iterations, distance_metric='euclidean'):
        self.k = k
        self.max_iterations = max_iterations
        self.distance_metric = distance_metric
        self.centroids = None
        self.labels = None

    def _distance(self, X, centroids):
        if self.distance_metric == 'euclidean':
            return np.linalg.norm(X - centroids, axis=1)
        else:  # 这里假设你已经定义了其他距离度量方法
            raise NotImplementedError

    def _reassign_clusters(self, X):
        distances = self._distance(X, self.centroids)
        assignments = distances.argmin(axis=1)
        return assignments

    def fit(self, X, weights=None):
        if weights is None:
            weights = np.ones(len(X)) / len(X)  # 如果没有提供权重，使用等权值
        self.centroids = self.init_centroids(X, self.k)
        for _ in range(self.max_iterations):
            assignments = self._reassign_clusters(X * weights)
            new_centroids = np.array([X[assignments == i].mean(axis=0) * weights[assignments == i].sum() for i in range(self.k)])
            if np.allclose(self.centroids, new_centroids):  # 如果聚类中心不再变化，算法停止
                break
            self.centroids = new_centroids
        self.labels = assignments

    def init_centroids(self, X, k):
        # 初始化方法，根据需求选择随机点、K-Means++等
        pass  # 这部分取决于具体实现

# 使用示例
wkm = WeightedKMeans(k=3, max_iterations=100)
wkm.fit(X, weights=your_weights)