step_five.py

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from step_one import *

if __name__ == '__main__':
    # step 1: 加载数据
    print("step 1: load data...")
    dataSet = new_data
    # fileIn = open('1.txt')
    # for line in fileIn.readlines():
    #     lineArr = line.strip().split(' ')
    #     dataSet.append([float(lineArr[0]), float(lineArr[1])])

# 设定不同k值以运算
    for k in range(3, 7):
        clf = KMeans(n_clusters=k)  # 设定k  ！！！！！！！！！！这里就是调用KMeans算法
        s = clf.fit(dataSet)  # 加载数据集合
        numSamples = len(dataSet)
        centroids = clf.labels_
        print(centroids, type(centroids))  # 显示中心点
        print(clf.inertia_)  # 显示聚类效果
        mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']
        #  画出所有样例点 属于同一分类的绘制同样的颜色
        for i in range(numSamples):
            #  markIndex = int(clusterAssment[i, 0])
            plt.plot(dataSet[i][0], dataSet[i][1], mark[clf.labels_[i]])  # mark[markIndex])
        mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']
        # 画出质点，用特殊图型
        centroids =  clf.cluster_centers_
        for i in range(k):
            plt.plot(centroids[i][0], centroids[i][1], mark[i], markersize=12)
#  print centroids[i, 0], centroids[i, 1]
        plt.show()