Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/generators/stochastic.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/generators/stochastic.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,50 @@ +"""Functions for generating stochastic graphs from a given weighted directed +graph. + +""" + +from networkx.classes import DiGraph +from networkx.classes import MultiDiGraph +from networkx.utils import not_implemented_for + +__all__ = ["stochastic_graph"] + + +@not_implemented_for("undirected") +def stochastic_graph(G, copy=True, weight="weight"): + """Returns a right-stochastic representation of directed graph `G`. + + A right-stochastic graph is a weighted digraph in which for each + node, the sum of the weights of all the out-edges of that node is + 1. If the graph is already weighted (for example, via a 'weight' + edge attribute), the reweighting takes that into account. + + Parameters + ---------- + G : directed graph + A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`. + + copy : boolean, optional + If this is True, then this function returns a new graph with + the stochastic reweighting. Otherwise, the original graph is + modified in-place (and also returned, for convenience). + + weight : edge attribute key (optional, default='weight') + Edge attribute key used for reading the existing weight and + setting the new weight. If no attribute with this key is found + for an edge, then the edge weight is assumed to be 1. If an edge + has a weight, it must be a a positive number. + + """ + if copy: + G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G) + # There is a tradeoff here: the dictionary of node degrees may + # require a lot of memory, whereas making a call to `G.out_degree` + # inside the loop may be costly in computation time. + degree = dict(G.out_degree(weight=weight)) + for u, v, d in G.edges(data=True): + if degree[u] == 0: + d[weight] = 0 + else: + d[weight] = d.get(weight, 1) / degree[u] + return G