python中K-means算法基础知识点_Python

python中K-means算法基础知识点

2021-08-29 10:10十一 Python

在本篇文章里小编给大家整理的是一篇关于python中K-means算法基础知识点内容，有兴趣的朋友们可以学习参考下。

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

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									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：

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									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：

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									# -*- 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()