143 lines
5.2 KiB
Python
143 lines
5.2 KiB
Python
""" Functions related to graph covers."""
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from functools import partial
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from itertools import chain
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import networkx as nx
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from networkx.utils import arbitrary_element, not_implemented_for
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__all__ = ["min_edge_cover", "is_edge_cover"]
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@not_implemented_for("directed")
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@not_implemented_for("multigraph")
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@nx._dispatch
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def min_edge_cover(G, matching_algorithm=None):
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"""Returns the min cardinality edge cover of the graph as a set of edges.
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A smallest edge cover can be found in polynomial time by finding
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a maximum matching and extending it greedily so that all nodes
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are covered. This function follows that process. A maximum matching
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algorithm can be specified for the first step of the algorithm.
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The resulting set may return a set with one 2-tuple for each edge,
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(the usual case) or with both 2-tuples `(u, v)` and `(v, u)` for
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each edge. The latter is only done when a bipartite matching algorithm
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is specified as `matching_algorithm`.
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Parameters
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----------
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G : NetworkX graph
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An undirected graph.
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matching_algorithm : function
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A function that returns a maximum cardinality matching for `G`.
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The function must take one input, the graph `G`, and return
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either a set of edges (with only one direction for the pair of nodes)
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or a dictionary mapping each node to its mate. If not specified,
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:func:`~networkx.algorithms.matching.max_weight_matching` is used.
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Common bipartite matching functions include
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:func:`~networkx.algorithms.bipartite.matching.hopcroft_karp_matching`
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or
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:func:`~networkx.algorithms.bipartite.matching.eppstein_matching`.
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Returns
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-------
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min_cover : set
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A set of the edges in a minimum edge cover in the form of tuples.
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It contains only one of the equivalent 2-tuples `(u, v)` and `(v, u)`
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for each edge. If a bipartite method is used to compute the matching,
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the returned set contains both the 2-tuples `(u, v)` and `(v, u)`
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for each edge of a minimum edge cover.
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Examples
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--------
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>>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
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>>> sorted(nx.min_edge_cover(G))
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[(2, 1), (3, 0)]
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Notes
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-----
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An edge cover of a graph is a set of edges such that every node of
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the graph is incident to at least one edge of the set.
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The minimum edge cover is an edge covering of smallest cardinality.
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Due to its implementation, the worst-case running time of this algorithm
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is bounded by the worst-case running time of the function
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``matching_algorithm``.
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Minimum edge cover for `G` can also be found using the `min_edge_covering`
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function in :mod:`networkx.algorithms.bipartite.covering` which is
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simply this function with a default matching algorithm of
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:func:`~networkx.algorithms.bipartite.matching.hopcraft_karp_matching`
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"""
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if len(G) == 0:
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return set()
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if nx.number_of_isolates(G) > 0:
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# ``min_cover`` does not exist as there is an isolated node
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raise nx.NetworkXException(
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"Graph has a node with no edge incident on it, " "so no edge cover exists."
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)
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if matching_algorithm is None:
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matching_algorithm = partial(nx.max_weight_matching, maxcardinality=True)
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maximum_matching = matching_algorithm(G)
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# ``min_cover`` is superset of ``maximum_matching``
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try:
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# bipartite matching algs return dict so convert if needed
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min_cover = set(maximum_matching.items())
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bipartite_cover = True
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except AttributeError:
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min_cover = maximum_matching
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bipartite_cover = False
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# iterate for uncovered nodes
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uncovered_nodes = set(G) - {v for u, v in min_cover} - {u for u, v in min_cover}
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for v in uncovered_nodes:
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# Since `v` is uncovered, each edge incident to `v` will join it
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# with a covered node (otherwise, if there were an edge joining
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# uncovered nodes `u` and `v`, the maximum matching algorithm
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# would have found it), so we can choose an arbitrary edge
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# incident to `v`. (This applies only in a simple graph, not a
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# multigraph.)
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u = arbitrary_element(G[v])
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min_cover.add((u, v))
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if bipartite_cover:
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min_cover.add((v, u))
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return min_cover
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@not_implemented_for("directed")
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@nx._dispatch
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def is_edge_cover(G, cover):
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"""Decides whether a set of edges is a valid edge cover of the graph.
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Given a set of edges, whether it is an edge covering can
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be decided if we just check whether all nodes of the graph
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has an edge from the set, incident on it.
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Parameters
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----------
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G : NetworkX graph
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An undirected bipartite graph.
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cover : set
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Set of edges to be checked.
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Returns
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-------
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bool
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Whether the set of edges is a valid edge cover of the graph.
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Examples
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--------
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>>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
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>>> cover = {(2, 1), (3, 0)}
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>>> nx.is_edge_cover(G, cover)
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True
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Notes
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-----
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An edge cover of a graph is a set of edges such that every node of
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the graph is incident to at least one edge of the set.
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"""
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return set(G) <= set(chain.from_iterable(cover))
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