谱聚类python实践

import numpy as np

from sklearn.cluster import SpectralClustering

import matplotlib.pyplot as plt

import sklearn.datasets as ds

import matplotlib

from sklearn.metrics import calinski_harabaz_score

from sklearn.neighbors import KNeighborsClassifier

matplotlib.rcParams['font.sans-serif'] = [u'SimHei']

matplotlib.rcParams['axes.unicode_minus'] = False

colors =['black','lightcoral','orange','tan','lightgreen','cornflowerblue','lime','cyan','purple','yellow','fuchsia','darkblue','plum','palegreen','pink']

data,y = ds.make_blobs(300, n_features=2, centers=3, cluster_std=[1,0.5,1],random_state=3)

plt.subplot(211)

plt.title(u"原始图形")

for i in range(3):

plt.scatter(data[y==i][:,0],data[y==i][:,1],color=colors[i+4])

n_cluster = [2,3,4,5,6]

gamma = [0.0001,0.001,0.01,0.1,10]

for i in n_cluster:

for j in gamma:

model = SpectralClustering(n_clusters=i,gamma=j)

model.fit(data)

score = calinski_harabaz_score(data,model.labels_)

print "簇数：",i,"sigmma:",j,"ch指数:",score

model = SpectralClustering(n_clusters=3,gamma=0.01)

model.fit(data)

pre_y = model.labels_

plt.subplot(212)

plt.title(u"聚类结果")

for i in range(3):

plt.scatter(data[pre_y==i][:,0],data[pre_y==i][:,1],color=colors[i])

plt.show()

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