K-means均值聚类算法寻找质心，Python

谁践踏了优雅 2023-02-15 03:45 43阅读 0赞

import numpy as np
    
    # 欧氏距离计算
    def distEclud(x,y):
        return np.sqrt(np.sum((x-y)**2))  # 计算欧氏距离
    
    # 为给定数据集构建一个包含K个随机质心的集合
    def randCent(dataSet,k):
        m,n = dataSet.shape
        centroids = np.zeros((k,n))
        for i in range(k):
            index = int(np.random.uniform(0,m)) 
            centroids[i,:] = dataSet[index,:]
        return centroids
     
    # k均值聚类
    def kmeans_open(dataSet,k):
        m = np.shape(dataSet)[0]  #行的数目
        # 第一列存样本属于哪一簇
        # 第二列存样本的到簇的中心点的误差
        clusterAssment = np.mat(np.zeros((m,2)))
        clusterChange = True
     
        # 第1步 初始化centroids
        centroids = randCent(dataSet,k)
        while clusterChange:
            clusterChange = False
     
            # 遍历所有的样本（行数）
            for i in range(m):
                minDist = 100000.0
                minIndex = -1
     
                # 遍历所有的质心
                #第2步 找出最近的质心
                for j in range(k):
                    # 计算该样本到质心的欧式距离
                    distance = distEclud(centroids[j,:],dataSet[i,:])
                    if distance < minDist:
                        minDist = distance
                        minIndex = j
                # 第 3 步：更新每一行样本所属的簇
                if clusterAssment[i,0] != minIndex:
                    clusterChange = True
                    clusterAssment[i,:] = minIndex,minDist**2
            #第 4 步：更新质心
            for j in range(k):
                pointsInCluster = dataSet[np.nonzero(clusterAssment[:,0].A == j)[0]]  # 获取簇类所有的点
                centroids[j,:] = np.mean(pointsInCluster,axis=0)   # 对矩阵的行求均值
     
        return clusterAssment.A[:,0], centroids