使用k均值聚类算法对表4.1中的数据进行聚类。代码参考P281。

创建一个名为 testSet.txt 的文本文件，将以下内容复制粘贴进去保存即可：

0	0
1	2
3	1
8	8
9	10
10	7

表4.1

# -*- coding: utf-8 -*-
"""
Created on Thu Apr 17 16:59:58 2025

@author: 破无差
"""
import matplotlib.pyplot as plt
import numpy as np


def loadDataSet(fileName):
    dataMat = []
    fr = open(fileName)
    for line in fr.readlines():
        curLine = line.strip().split('\t')
        fitLine = list(map(float, curLine))
        dataMat.append(fitLine)
    return dataMat


def distEclud(vecA, vecB):
    return np.sqrt(np.sum(np.power(vecA - vecB, 2)))


def randCent(dataSet, k):
    n = np.shape(dataSet)[1]
    centroids = np.mat(np.zeros((k, n)))
    for j in range(n):
        minJ = np.min(dataSet[:, j])
        maxJ = np.max(dataSet[:, j])
        rangeJ = float(maxJ - minJ)
        centroids[:, j] = minJ + rangeJ * np.random.rand(k, 1)
    return centroids


def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
    m = np.shape(dataSet)[0]
    clusterAssment = np.mat(np.zeros((m, 2)))
    centroids = createCent(dataSet, k)
    clusterChanged = True
    while clusterChanged:
        clusterChanged = False
        for i in range(m):
            minDist = float('inf')
            minIndex = -1
            for j in range(k):
                distJI = distMeas(centroids[j, :], dataSet[i, :])
                if distJI < minDist:
                    minDist = distJI
                    minIndex = j
            if clusterAssment[i, 0] != minIndex:
                clusterChanged = True
            clusterAssment[i, :] = minIndex, minDist ** 2
        for cent in range(k):
            ptsInClust = dataSet[np.nonzero(clusterAssment[:, 0].A == cent)[0]]
            centroids[cent, :] = np.mean(ptsInClust, axis=0)
    return centroids, clusterAssment


def plotDataSet(filename):
    datMat = np.mat(loadDataSet(filename))
    myCentroids, clustAssing = kMeans(datMat, 4)
    clustAssing = clustAssing.tolist()
    myCentroids = myCentroids.tolist()
    xcord = [[], [], [], []]
    ycord = [[], [], [], []]
    datMat = datMat.tolist()
    m = len(clustAssing)
    for i in range(m):
        if int(clustAssing[i][0]) == 0:
            xcord[0].append(datMat[i][0])
            ycord[0].append(datMat[i][1])
        elif int(clustAssing[i][0]) == 1:
            xcord[1].append(datMat[i][0])
            ycord[1].append(datMat[i][1])
        elif int(clustAssing[i][0]) == 2:
            xcord[2].append(datMat[i][0])
            ycord[2].append(datMat[i][1])
        elif int(clustAssing[i][0]) == 3:
            xcord[3].append(datMat[i][0])
            ycord[3].append(datMat[i][1])
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(xcord[0], ycord[0], s=20, c='b', marker='*', alpha=.5)
    ax.scatter(xcord[1], ycord[1], s=20, c='r', marker='D', alpha=.5)
    ax.scatter(xcord[2], ycord[2], s=20, c='c', marker='>', alpha=.5)
    ax.scatter(xcord[3], ycord[3], s=20, c='k', marker='o', alpha=.5)
    ax.scatter(myCentroids[0][0], myCentroids[0][1], s=100, c='k', marker='+')
    ax.scatter(myCentroids[1][0], myCentroids[1][1], s=100, c='k', marker='+')
    ax.scatter(myCentroids[2][0], myCentroids[2][1], s=100, c='k', marker='+')
    ax.scatter(myCentroids[3][0], myCentroids[3][1], s=100, c='k', marker='+')
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_title('DataSet')
    plt.show()


if __name__ == '__main__':
plotDataSet('testSet.txt')

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运行结果：