Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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136 lines
4.6 KiB
136 lines
4.6 KiB
4 years ago
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"""Routines to find the boundary of a set of nodes.
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An edge boundary is a set of edges, each of which has exactly one
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endpoint in a given set of nodes (or, in the case of directed graphs,
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the set of edges whose source node is in the set).
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A node boundary of a set *S* of nodes is the set of (out-)neighbors of
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nodes in *S* that are outside *S*.
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"""
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from itertools import chain
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__all__ = ["edge_boundary", "node_boundary"]
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def edge_boundary(G, nbunch1, nbunch2=None, data=False, keys=False, default=None):
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"""Returns the edge boundary of `nbunch1`.
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The *edge boundary* of a set *S* with respect to a set *T* is the
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set of edges (*u*, *v*) such that *u* is in *S* and *v* is in *T*.
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If *T* is not specified, it is assumed to be the set of all nodes
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not in *S*.
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Parameters
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----------
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G : NetworkX graph
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nbunch1 : iterable
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Iterable of nodes in the graph representing the set of nodes
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whose edge boundary will be returned. (This is the set *S* from
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the definition above.)
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nbunch2 : iterable
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Iterable of nodes representing the target (or "exterior") set of
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nodes. (This is the set *T* from the definition above.) If not
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specified, this is assumed to be the set of all nodes in `G`
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not in `nbunch1`.
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keys : bool
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This parameter has the same meaning as in
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:meth:`MultiGraph.edges`.
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data : bool or object
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This parameter has the same meaning as in
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:meth:`MultiGraph.edges`.
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default : object
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This parameter has the same meaning as in
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:meth:`MultiGraph.edges`.
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Returns
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-------
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iterator
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An iterator over the edges in the boundary of `nbunch1` with
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respect to `nbunch2`. If `keys`, `data`, or `default`
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are specified and `G` is a multigraph, then edges are returned
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with keys and/or data, as in :meth:`MultiGraph.edges`.
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Notes
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-----
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Any element of `nbunch` that is not in the graph `G` will be
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ignored.
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`nbunch1` and `nbunch2` are usually meant to be disjoint, but in
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the interest of speed and generality, that is not required here.
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"""
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nset1 = {v for v in G if v in nbunch1}
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# Here we create an iterator over edges incident to nodes in the set
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# `nset1`. The `Graph.edges()` method does not provide a guarantee
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# on the orientation of the edges, so our algorithm below must
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# handle the case in which exactly one orientation, either (u, v) or
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# (v, u), appears in this iterable.
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if G.is_multigraph():
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edges = G.edges(nset1, data=data, keys=keys, default=default)
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else:
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edges = G.edges(nset1, data=data, default=default)
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# If `nbunch2` is not provided, then it is assumed to be the set
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# complement of `nbunch1`. For the sake of efficiency, this is
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# implemented by using the `not in` operator, instead of by creating
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# an additional set and using the `in` operator.
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if nbunch2 is None:
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return (e for e in edges if (e[0] in nset1) ^ (e[1] in nset1))
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nset2 = set(nbunch2)
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return (
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e
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for e in edges
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if (e[0] in nset1 and e[1] in nset2) or (e[1] in nset1 and e[0] in nset2)
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)
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def node_boundary(G, nbunch1, nbunch2=None):
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"""Returns the node boundary of `nbunch1`.
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The *node boundary* of a set *S* with respect to a set *T* is the
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set of nodes *v* in *T* such that for some *u* in *S*, there is an
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edge joining *u* to *v*. If *T* is not specified, it is assumed to
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be the set of all nodes not in *S*.
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Parameters
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----------
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G : NetworkX graph
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nbunch1 : iterable
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Iterable of nodes in the graph representing the set of nodes
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whose node boundary will be returned. (This is the set *S* from
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the definition above.)
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nbunch2 : iterable
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Iterable of nodes representing the target (or "exterior") set of
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nodes. (This is the set *T* from the definition above.) If not
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specified, this is assumed to be the set of all nodes in `G`
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not in `nbunch1`.
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Returns
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-------
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set
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The node boundary of `nbunch1` with respect to `nbunch2`.
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Notes
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-----
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Any element of `nbunch` that is not in the graph `G` will be
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ignored.
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`nbunch1` and `nbunch2` are usually meant to be disjoint, but in
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the interest of speed and generality, that is not required here.
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"""
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nset1 = {n for n in nbunch1 if n in G}
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bdy = set(chain.from_iterable(G[v] for v in nset1)) - nset1
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# If `nbunch2` is not specified, it is assumed to be the set
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# complement of `nbunch1`.
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if nbunch2 is not None:
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bdy &= set(nbunch2)
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return bdy
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