kmean python_机器学习笔记关于python实现Kmean算法

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这次是一个关于Kmean的类聚算法，

简单来说就是到中心点的距离的加权和

看起来很厉害

写出来一点不厉害

一、随机取点

importnumpy as npimportcv2from matplotlib importpyplot as plt

X= np.random.randint(25,50,(25,2))

Y= np.random.randint(60,85,(25,2))

Z=np.vstack((X,Y))#convert to np.float32

Z =np.float32(Z)

plt.hist(Z,100,[0,100]),plt.show()

二、kmean部分

调用cv2库里的kmean

对A、B两类进行标记

#define criteria and apply kmeans()

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

ret,label,center=cv2.kmeans(Z,2,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)#Now separate the data, Note the flatten()

A = Z[label.ravel()==0]

B= Z[label.ravel()==1]

三、类聚结果

画图画图画图

#Plot the data

plt.scatter(A[:,0],A[:,1])

plt.scatter(B[:,0],B[:,1],c = ‘r‘)

plt.scatter(center[:,0],center[:,1],s = 80,c = ‘y‘, marker = ‘s‘)

plt.xlabel(‘Height‘),plt.ylabel(‘Weight‘)

plt.show()

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最后

代码汇总

import numpy as np

import cv2

from matplotlib import pyplot as plt

X = np.random.randint(25,50,(25,2))

Y = np.random.randint(60,85,(25,2))

Z = np.vstack((X,Y))

# convert to np.float32

Z = np.float32(Z)

plt.hist(Z,100,[0,100]),plt.show()

# define criteria and apply kmeans()

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

ret,label,center=cv2.kmeans(Z,2,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)

# Now separate the data, Note the flatten()

A = Z[label.ravel()==0]

B = Z[label.ravel()==1]

# Plot the data

plt.scatter(A[:,0],A[:,1])

plt.scatter(B[:,0],B[:,1],c = ‘r‘)

plt.scatter(center[:,0],center[:,1],s = 80,c = ‘y‘, marker = ‘s‘)

plt.xlabel(‘Height‘),plt.ylabel(‘Weight‘)

plt.show()

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