python中K-means算法基础知识点

能够学习和掌握编程，最好的学习方式，就是去掌握基本的使用技巧，再多的概念意义，总归都是为了使用服务的，K-means算法又叫K-均值算法，是非监督学习中的聚类算法。主要有三个元素，其中N是元素个数，x表示元素，c(j)表示第j簇的质心，下面就使用方式给大家简单介绍实例使用。

K-Means算法进行聚类分析

km = KMeans(n_clusters = 3)
km.fit(X)
centers = km.cluster_centers_
print(centers)

三个簇的中心点坐标为：

[[5.006 3.428 ]

[6.81276596 3.07446809]

[5.77358491 2.69245283]]

比较一下K-Means聚类结果和实际样本之间的差别：

predicted_labels = km.labels_
fig, axes = plt.subplots(1, 2, figsize=(16,8))
axes[0].scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, 
        edgecolor='k', s=150)
axes[1].scatter(X[:, 0], X[:, 1], c=predicted_labels, cmap=plt.cm.Set1,
        edgecolor='k', s=150)
axes[0].set_xlabel('Sepal length', fontsize=16)
axes[0].set_ylabel('Sepal width', fontsize=16)
axes[1].set_xlabel('Sepal length', fontsize=16)
axes[1].set_ylabel('Sepal width', fontsize=16)
axes[0].tick_params(direction='in', length=10, width=5, colors='k', labelsize=20)
axes[1].tick_params(direction='in', length=10, width=5, colors='k', labelsize=20)
axes[0].set_title('Actual', fontsize=18)
axes[1].set_title('Predicted', fontsize=18)

k-means算法实例扩展内容：

# -*- 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()

到此这篇关于python中K-means算法基础知识点的文章就介绍到这了,更多相关python中K-means算法是什么内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！