本文實(shí)例為大家分享了Python實(shí)現(xiàn)k-means算法的具體代碼，供大家參考，具體內(nèi)容如下

創(chuàng)新互聯(lián)建站專注于企業(yè)網(wǎng)絡(luò)營(yíng)銷推廣、網(wǎng)站重做改版、梨樹(shù)網(wǎng)站定制設(shè)計(jì)、自適應(yīng)品牌網(wǎng)站建設(shè)、HTML5、電子商務(wù)商城網(wǎng)站建設(shè)、集團(tuán)公司官網(wǎng)建設(shè)、成都外貿(mào)網(wǎng)站建設(shè)公司、高端網(wǎng)站制作、響應(yīng)式網(wǎng)頁(yè)設(shè)計(jì)等建站業(yè)務(wù)，價(jià)格優(yōu)惠性價(jià)比高，為梨樹(shù)等各大城市提供網(wǎng)站開(kāi)發(fā)制作服務(wù)。

這也是周志華《機(jī)器學(xué)習(xí)》的習(xí)題9.4。

數(shù)據(jù)集是西瓜數(shù)據(jù)集4.0，如下

編號(hào),密度,含糖率
1,0.697,0.46
2,0.774,0.376
3,0.634,0.264
4,0.608,0.318
5,0.556,0.215
6,0.403,0.237
7,0.481,0.149
8,0.437,0.211
9,0.666,0.091
10,0.243,0.267
11,0.245,0.057
12,0.343,0.099
13,0.639,0.161
14,0.657,0.198
15,0.36,0.37
16,0.593,0.042
17,0.719,0.103
18,0.359,0.188
19,0.339,0.241
20,0.282,0.257
21,0.784,0.232
22,0.714,0.346
23,0.483,0.312
24,0.478,0.437
25,0.525,0.369
26,0.751,0.489
27,0.532,0.472
28,0.473,0.376
29,0.725,0.445
30,0.446,0.459

算法很簡(jiǎn)單，就不解釋了，代碼也不復(fù)雜，直接放上來(lái)：

# -*- 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_vectors):
    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()

新聞標(biāo)題：Python實(shí)現(xiàn)k-means算法-創(chuàng)新互聯(lián)
當(dāng)前鏈接：http://www.chinadenli.net/article2/doscic.html

成都網(wǎng)站建設(shè)公司_創(chuàng)新互聯(lián)，為您提供網(wǎng)站內(nèi)鏈、網(wǎng)站策劃、網(wǎng)站排名、品牌網(wǎng)站制作、網(wǎng)站設(shè)計(jì)、靜態(tài)網(wǎng)站

廣告

聲明：本網(wǎng)站發(fā)布的內(nèi)容（圖片、視頻和文字）以用戶投稿、用戶轉(zhuǎn)載內(nèi)容為主，如果涉及侵權(quán)請(qǐng)盡快告知，我們將會(huì)在第一時(shí)間刪除。文章觀點(diǎn)不代表本網(wǎng)站立場(chǎng)，如需處理請(qǐng)聯(lián)系客服。電話：028-86922220；郵箱：631063699@qq.com。內(nèi)容未經(jīng)允許不得轉(zhuǎn)載，或轉(zhuǎn)載時(shí)需注明來(lái)源： 創(chuàng)新互聯(lián)