Python实现k-means算法

 # -*- coding: utf-8 -*- """Excercise 9.4""" import numpy as np import pandas as pd import matplotlib.pyplot as plt import sys import random data = pd.read_csv(filepath_or_buffer = '../dataset/watermelon4.0.csv', sep = ',')[["密度","含糖率"]].values ########################################## K-means ####################################### k = int(sys.argv[1]) #Randomly choose k samples from data as mean vectors mean_vectors = random.sample(data,k) def dist(p1,p2): return np.sqrt(sum((p1-p2)*(p1-p2))) while True: print mean_vectors clusters = map ((lambda x:[x]), mean_vectors) for sample in data: distances = map((lambda m: dist(sample,m)), mean_vectors) min_index = distances.index(min(distances)) clusters[min_index].append(sample) new_mean_vectors = [] for c,v in zip(clusters,mean_vectors): new_mean_vector = sum(c)/len(c) #If the difference betweenthe new mean vector and the old mean vector is less than 0.0001 #then do not updata the mean vector if all(np.divide((new_mean_vector-v),v) <np.array([0.0001,0.0001]) ): new_mean_vectors.append(v) else: new_mean_vectors.append(new_mean_vector) if np.array_equal(mean_vectors,new_mean_vector<div style="color:transparent">来源gaodai.ma#com搞#代!码网</div>s): break else: mean_vectors = new_mean_vectors #Show the clustering result total_colors = ['r','y','g','b','c','m','k'] colors = random.sample(total_colors,k) for cluster,color in zip(clusters,colors): density = map(lambda arr:arr[0],cluster) sugar_content = map(lambda arr:arr[1],cluster) plt.scatter(density,sugar_content,c = color) plt.show()