• K-means聚类算法

• 测试：

# -*- coding: utf-8 -*-
"""
Created on Thu Aug 31 10:59:20 2017

@author: Administrator
"""

‘‘‘
现有1999年全国31个省份城镇居民家庭平均每人全年消费性支出的八个主
要变量数据，这八个变量分别是：食品、 衣着、 家庭设备用品及服务、 医疗
保健、 交通和通讯、 娱乐教育文化服务、 居住以及杂项商品和服务。 利用已
有数据，对31个省份进行聚类。
‘‘‘

import numpy as np
from sklearn.cluster import KMeans

def loadData(filePath):
    fr = open(filePath,‘r+‘)
    lines = fr.readlines()
    retData = []
    retCityName = []
    for line in lines:
        items = line.strip().split(",")
        retCityName.append(items[0])
        retData.append([float(items[i]) for i in range(1,len(items))])
    return retData,retCityName

if __name__ == ‘__main__‘:

    fpath=‘F:\RANJIEWEN\MachineLearning\Python机器学习实战_mooc\data\聚类\\‘
    data,cityName = loadData(fpath+‘city.txt‘)
    km = KMeans(n_clusters=4)
    label = km.fit_predict(data)
    expenses = np.sum(km.cluster_centers_,axis=1)
    #print(expenses)
    CityCluster = [[],[],[],[]]
    for i in range(len(cityName)):
        CityCluster[label[i]].append(cityName[i])
    for i in range(len(CityCluster)):
        print("Expenses:%.2f" % expenses[i])
        print(CityCluster[i])

• DBSCAN密度聚类

• 测试

# -*- coding: utf-8 -*-
"""
Created on Thu Aug 31 11:14:37 2017

@author: Administrator
"""

‘‘‘
现有大学校园网的日志数据，290条大学生的校园网使用情况数据，数据包
括用户ID，设备的MAC地址，IP地址，开始上网时间，停止上网时间，上
网时长，校园网套餐等。 利用已有数据，分析学生上网的模式。
实验目的：
通过DBSCAN聚类，分析学生上网时间和上网时长的模式。

‘‘‘

import numpy as np
import sklearn.cluster as skc
from sklearn import metrics
import matplotlib.pyplot as plt

mac2id=dict()
onlinetimes=[]

fpath=‘F:\RANJIEWEN\MachineLearning\Python机器学习实战_mooc\data\聚类\\‘
f=open(fpath+‘TestData.txt‘,encoding=‘utf-8‘)
for line in f:
    mac=line.split(‘,‘)[2]
    onlinetime=int(line.split(‘,‘)[6])
    starttime=int(line.split(‘,‘)[4].split(‘ ‘)[1].split(‘:‘)[0])
    if mac not in mac2id:
        mac2id[mac]=len(onlinetimes)
        onlinetimes.append((starttime,onlinetime))
    else:
        onlinetimes[mac2id[mac]]=[(starttime,onlinetime)]
real_X=np.array(onlinetimes).reshape((-1,2))

X=real_X[:,0:1]

## 聚类数据变换技巧
# X=np.log(1+real_X[:,1:])

db=skc.DBSCAN(eps=0.01,min_samples=20).fit(X)
labels = db.labels_

print(‘Labels:‘)
print(labels)
raito=len(labels[labels[:] == -1]) / len(labels)
print(‘Noise raito:‘,format(raito, ‘.2%‘))

n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

print(‘Estimated number of clusters: %d‘ % n_clusters_)
print("Silhouette Coefficient: %0.3f"% metrics.silhouette_score(X, labels))

for i in range(n_clusters_):
    print(‘Cluster ‘,i,‘:‘)
    print(list(X[labels == i].flatten()))

plt.hist(X,24)

• 基于聚类的图像分割

• 测试

# -*- coding: utf-8 -*-
"""
Created on Thu Aug 31 15:03:11 2017

@author: Administrator
"""

‘‘‘
目标：利用K-means聚类算法对图像像素点颜色进行聚类实现简单的图像分割
输出：同一聚类中的点使用相同颜色标记，不同聚类颜色不同
‘‘‘

import numpy as np
import PIL.Image as image
from sklearn.cluster import KMeans

def loadData(filePath):
    f = open(filePath,‘rb‘)
    data = []
    img = image.open(f)
    m,n = img.size
    for i in range(m):
        for j in range(n):
            x,y,z = img.getpixel((i,j))
            data.append([x/256.0,y/256.0,z/256.0])
    f.close()
    return np.mat(data),m,n

imPath=‘F:\RANJIEWEN\MachineLearning\Python机器学习实战_mooc\data\基于聚类的整图分割\\‘
imgData,row,col = loadData(imPath+‘bull.jpg‘)
label = KMeans(n_clusters=4).fit_predict(imgData)

label = label.reshape([row,col])
pic_new = image.new("L", (row, col))
for i in range(row):
    for j in range(col):
        pic_new.putpixel((i,j), int(256/(label[i][j]+1)))
pic_new.save("result-bull-4.jpg", "JPEG")