本文整理汇总了Python中networkx.DiGraph.to_undirected方法的典型用法代码示例。如果您正苦于以下问题：Python DiGraph.to_undirected方法的具体用法？Python DiGraph.to_undirected怎么用？Python DiGraph.to_undirected使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类networkx.DiGraph的用法示例。
在下文中一共展示了DiGraph.to_undirected方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: network_properties
# 需要导入模块: from networkx import DiGraph [as 别名]
# 或者: from networkx.DiGraph import to_undirected [as 别名]
def network_properties(network : nx.DiGraph,
in_degree_threshold : float = -1,
pagerank_threshold : float = -1,
damping : float = 0.85,
spectral_offset : float = 0.5)\
-> (pd.DataFrame, sparse.spmatrix):
conn = max(nx.connected_components(network.to_undirected()), key=len)
conn = nx.subgraph(network, conn)
pr = compute_pagerank(conn, damping=damping)
names = nx.nodes(conn)
indeg = [conn.in_degree(n) for n in names]
odeg = [conn.out_degree(n) for n in names]
description = [conn.node[n].get('description', n) for n in names]
x, y, z, Adj, aff_names = node_coordinates(conn, nodelist=names,
offset=spectral_offset)
data = {'id': names,
'in_degree': indeg,
'out_degree': odeg,
'pagerank': pr,
'affinity_x': x,
'affinity_y': y,
'processing_depth': z,
'description': description}
df = pd.DataFrame(data, index=names)
df = df[df['pagerank'] > pagerank_threshold / len(names)]
df = df[df['in_degree'] > in_degree_threshold]
return df, Adj示例2: __getitem__
# 需要导入模块: from networkx import DiGraph [as 别名]
# 或者: from networkx.DiGraph import to_undirected [as 别名]
def __getitem__(self, n):
"""Get clustering status after `n` iterations
Parameters
----------
n : int
Number of iterations
Returns
-------
annotation : Annotation
Clustering status after `n` iterations
"""
# support for history[-1], history[-2]
# i = -1 ==> after last iteration
# i = -2 ==> after penultimate iteration
# ... etc ...
if n < 0:
n = len(self) + 1 + n
# dendrogram stored as directed graph
# cluster1 --> cluster2 means cluster1 was merged into cluster2
g = DiGraph()
# i = 0 ==> starting point
# i = 1 ==> after first iteration
# i = 2 ==> aftr second iterations
# ... etc ...
for i, iteration in enumerate(self.iterations):
if i+1 > n:
break
for cluster in iteration.merge:
if cluster == iteration.into:
continue
g.add_edge(cluster, iteration.into)
# any cluster is mapped to the last cluster in its topologically
# ordered connected component
mapping = {}
for clusters in connected_components(g.to_undirected()):
clusters = topological_sort(g, nbunch=clusters, reverse=True)
for cluster in clusters[1:]:
mapping[cluster] = clusters[0]
# actual mapping
return self.starting_point.translate(mapping)