完整的最简单的谱聚类python代码_karateclub聚类

针对karate_club数据集，做了谱聚类。由于是2-way clustering，比较简单，得到了图的新的表示空间之后，没有做k-means，仅仅针对正规化后的拉普拉斯矩阵的第二特征值做了符号判断，这和Spectral Clustering Tutorial 一文中的描述一致。

引用了numpy scipy matplotlib networkx包

#coding=utf-8
#MSC means Multiple Spectral Clustering 
import numpy as np
import scipy as sp
import scipy.linalg as linalg
import networkx as nx
import matplotlib.pyplot as plt
 
def getNormLaplacian(W):
	"""input matrix W=(w_ij)
	"compute D=diag(d1,...dn)
	"and L=D-W
	"and Lbar=D^(-1/2)LD^(-1/2)
	"return Lbar
	"""
	d=[np.sum(row) for row in W]
	D=np.diag(d)
	L=D-W
	#Dn=D^(-1/2)
	Dn=np.power(np.linalg.matrix_power(D,-1),0.5)
	Lbar=np.dot(np.dot(Dn,L),Dn)
	return Lbar
 
def getKSmallestEigVec(Lbar,k):
	"""input
	"matrix Lbar and k
	"return
	"k smallest eigen values and their corresponding eigen vectors
	"