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)
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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 )
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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()
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原文链接:https://www.py.cn/jishu/gaoji/23260.html