Python 基于连通图的Numpy聚类
对连通图进行聚类的最佳方法是什么 例1: 结果:Python 基于连通图的Numpy聚类,python,graph,numpy,Python,Graph,Numpy,对连通图进行聚类的最佳方法是什么 例1: 结果: ==> [[0,1,2,3],[4,5]] ==> [[0,1,3],[2,4,5]] ==> [[0,1],[2,3],[4,5]] 例2 结果: ==> [[0,1,2,3],[4,5]] ==> [[0,1,3],[2,4,5]] ==> [[0,1],[2,3],[4,5]] 例3 结果: ==> [[0,1,2,3],[4,5]] ==> [[0,1,3],[2,4,5]]
==> [[0,1,2,3],[4,5]]
==> [[0,1,3],[2,4,5]]
==> [[0,1],[2,3],[4,5]]
例2
结果:
==> [[0,1,2,3],[4,5]]
==> [[0,1,3],[2,4,5]]
==> [[0,1],[2,3],[4,5]]
例3
结果:
==> [[0,1,2,3],[4,5]]
==> [[0,1,3],[2,4,5]]
==> [[0,1],[2,3],[4,5]]
谢谢在一些示例中,比如说
ex2
,您给出了一个有向图或一个有向图,这样a!=A.T
。在这种情况下,可以通过考虑找到更合理的定义。在这种情况下,拆分为[0,1,3],[4,5],[2]
。可以帮助您找到这些:
import numpy as np
import networkx as nx
A = np.array([[0,1,0,1,0,0],
[1,1,0,1,0,0],
[0,1,0,1,0,0],
[1,0,0,0,0,0],
[0,0,1,0,1,1],
[0,0,0,0,1,1]])
G = nx.from_numpy_matrix(A, create_using=nx.DiGraph())
for subg in nx.strongly_connected_component_subgraphs(G):
print subg.nodes()
看一看,但是你能解释为什么ex2的结果是[[0,1,3],[2,4,5]?对于列,0,1,3是连接的,2,4,5也是连接的