Python kmean

# -*- coding: utf-8 -*-

from sklearn.cluster import KMeans

from sklearn.externals import joblib

import numpy

feature =numpy.random.rand(40,20)

#调用kmeans类

clf = KMeans(n_clusters=9)

s = clf.fit(feature)

print s

#9个中心

print clf.cluster_centers_

#每个样本所属的簇

print clf.labels_

#用来评估簇的个数是否合适，距离越小说明簇分的越好，选取临界点的簇个数

print clf.inertia_

#进行预测

print clf.predict(feature)

#保存模型

joblib.dump(clf , 'c:/km.pkl')

#载入保存的模型

clf = joblib.load('c:/km.pkl')

'''

#用来评估簇的个数是否合适，距离越小说明簇分的越好，选取临界点的簇个数

for i in range(5,30,1):
    clf = KMeans(n_clusters=i)
    s = clf.fit(feature)
    print i , clf.inertia_
'''