sam_consensus_v3: env/lib/python3.9/site-packages/networkx/algorithms/matching.py comparison

comparison env/lib/python3.9/site-packages/networkx/algorithms/matching.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

author	shellac
date	Mon, 22 Mar 2021 18:12:50 +0000
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+:4f3585e2f14b
+"""Functions for computing and verifying matchings in a graph."""
+from collections import Counter
+from itertools import combinations
+from itertools import repeat
+__all__ = [
+"is_matching",
+"is_maximal_matching",
+"is_perfect_matching",
+"max_weight_matching",
+"maximal_matching",
+]
+def maximal_matching(G):
+r"""Find a maximal matching in the graph.
+A matching is a subset of edges in which no node occurs more than once.
+A maximal matching cannot add more edges and still be a matching.
+Parameters
+----------
+G : NetworkX graph
+Undirected graph
+Returns
+-------
+matching : set
+A maximal matching of the graph.
+Notes
+-----
+The algorithm greedily selects a maximal matching M of the graph G
+(i.e. no superset of M exists). It runs in $O(|E|)$ time.
+"""
+matching = set()
+nodes = set()
+for u, v in G.edges():
+# If the edge isn't covered, add it to the matching
+# then remove neighborhood of u and v from consideration.
+if u not in nodes and v not in nodes and u != v:
+matching.add((u, v))
+nodes.add(u)
+nodes.add(v)
+return matching
+def matching_dict_to_set(matching):
+"""Converts a dictionary representing a matching (as returned by
+:func:`max_weight_matching`) to a set representing a matching (as
+returned by :func:`maximal_matching`).
+In the definition of maximal matching adopted by NetworkX,
+self-loops are not allowed, so the provided dictionary is expected
+to never have any mapping from a key to itself. However, the
+dictionary is expected to have mirrored key/value pairs, for
+example, key ``u`` with value ``v`` and key ``v`` with value ``u``.
+"""
+# Need to compensate for the fact that both pairs (u, v) and (v, u)
+# appear in `matching.items()`, so we use a set of sets. This way,
+# only the (frozen)set `{u, v}` appears as an element in the
+# returned set.
+return {(u, v) for (u, v) in set(map(frozenset, matching.items()))}
+def is_matching(G, matching):
+"""Decides whether the given set or dictionary represents a valid
+matching in ``G``.
+A *matching* in a graph is a set of edges in which no two distinct
+edges share a common endpoint.
+Parameters
+----------
+G : NetworkX graph
+matching : dict or set
+A dictionary or set representing a matching. If a dictionary, it
+must have ``matching[u] == v`` and ``matching[v] == u`` for each
+edge ``(u, v)`` in the matching. If a set, it must have elements
+of the form ``(u, v)``, where ``(u, v)`` is an edge in the
+matching.
+Returns
+-------
+bool
+Whether the given set or dictionary represents a valid matching
+in the graph.
+"""
+if isinstance(matching, dict):
+matching = matching_dict_to_set(matching)
+# TODO This is parallelizable.
+return all(len(set(e1) & set(e2)) == 0 for e1, e2 in combinations(matching, 2))
+def is_maximal_matching(G, matching):
+"""Decides whether the given set or dictionary represents a valid
+maximal matching in ``G``.
+A *maximal matching* in a graph is a matching in which adding any
+edge would cause the set to no longer be a valid matching.
+Parameters
+----------
+G : NetworkX graph
+matching : dict or set
+A dictionary or set representing a matching. If a dictionary, it
+must have ``matching[u] == v`` and ``matching[v] == u`` for each
+edge ``(u, v)`` in the matching. If a set, it must have elements
+of the form ``(u, v)``, where ``(u, v)`` is an edge in the
+matching.
+Returns
+-------
+bool
+Whether the given set or dictionary represents a valid maximal
+matching in the graph.
+"""
+if isinstance(matching, dict):
+matching = matching_dict_to_set(matching)
+# If the given set is not a matching, then it is not a maximal matching.
+if not is_matching(G, matching):
+return False
+# A matching is maximal if adding any unmatched edge to it causes
+# the resulting set to *not* be a valid matching.
+#
+# HACK Since the `matching_dict_to_set` function returns a set of
+# sets, we need to convert the list of edges to a set of sets in
+# order for the set difference function to work. Ideally, we would
+# just be able to do `set(G.edges()) - matching`.
+all_edges = set(map(frozenset, G.edges()))
+matched_edges = set(map(frozenset, matching))
+unmatched_edges = all_edges - matched_edges
+# TODO This is parallelizable.
+return all(not is_matching(G, matching | {e}) for e in unmatched_edges)
+def is_perfect_matching(G, matching):
+"""Decides whether the given set represents a valid perfect matching in
+``G``.
+A *perfect matching* in a graph is a matching in which exactly one edge
+is incident upon each vertex.
+Parameters
+----------
+G : NetworkX graph
+matching : dict or set
+A dictionary or set representing a matching. If a dictionary, it
+must have ``matching[u] == v`` and ``matching[v] == u`` for each
+edge ``(u, v)`` in the matching. If a set, it must have elements
+of the form ``(u, v)``, where ``(u, v)`` is an edge in the
+matching.
+Returns
+-------
+bool
+Whether the given set or dictionary represents a valid perfect
+matching in the graph.
+"""
+if isinstance(matching, dict):
+matching = matching_dict_to_set(matching)
+if not is_matching(G, matching):
+return False
+counts = Counter(sum(matching, ()))
+return all(counts[v] == 1 for v in G)
+def max_weight_matching(G, maxcardinality=False, weight="weight"):
+"""Compute a maximum-weighted matching of G.
+A matching is a subset of edges in which no node occurs more than once.
+The weight of a matching is the sum of the weights of its edges.
+A maximal matching cannot add more edges and still be a matching.
+The cardinality of a matching is the number of matched edges.
+Parameters
+----------
+G : NetworkX graph
+Undirected graph
+maxcardinality: bool, optional (default=False)
+If maxcardinality is True, compute the maximum-cardinality matching
+with maximum weight among all maximum-cardinality matchings.
+weight: string, optional (default='weight')
+Edge data key corresponding to the edge weight.
+If key not found, uses 1 as weight.
+Returns
+-------
+matching : set
+A maximal matching of the graph.
+Notes
+-----
+If G has edges with weight attributes the edge data are used as
+weight values else the weights are assumed to be 1.
+This function takes time O(number_of_nodes ** 3).
+If all edge weights are integers, the algorithm uses only integer
+computations.  If floating point weights are used, the algorithm
+could return a slightly suboptimal matching due to numeric
+precision errors.
+This method is based on the "blossom" method for finding augmenting
+paths and the "primal-dual" method for finding a matching of maximum
+weight, both methods invented by Jack Edmonds [1]_.
+Bipartite graphs can also be matched using the functions present in
+:mod:`networkx.algorithms.bipartite.matching`.
+References
+----------
+.. [1] "Efficient Algorithms for Finding Maximum Matching in Graphs",
+Zvi Galil, ACM Computing Surveys, 1986.
+"""
+#
+# The algorithm is taken from "Efficient Algorithms for Finding Maximum
+# Matching in Graphs" by Zvi Galil, ACM Computing Surveys, 1986.
+# It is based on the "blossom" method for finding augmenting paths and
+# the "primal-dual" method for finding a matching of maximum weight, both
+# methods invented by Jack Edmonds.
+#
+# A C program for maximum weight matching by Ed Rothberg was used
+# extensively to validate this new code.
+#
+# Many terms used in the code comments are explained in the paper
+# by Galil. You will probably need the paper to make sense of this code.
+#
+class NoNode:
+"""Dummy value which is different from any node."""
+pass
+class Blossom:
+"""Representation of a non-trivial blossom or sub-blossom."""
+__slots__ = ["childs", "edges", "mybestedges"]
+# b.childs is an ordered list of b's sub-blossoms, starting with
+# the base and going round the blossom.
+# b.edges is the list of b's connecting edges, such that
+# b.edges[i] = (v, w) where v is a vertex in b.childs[i]
+# and w is a vertex in b.childs[wrap(i+1)].
+# If b is a top-level S-blossom,
+# b.mybestedges is a list of least-slack edges to neighbouring
+# S-blossoms, or None if no such list has been computed yet.
+# This is used for efficient computation of delta3.
+# Generate the blossom's leaf vertices.
+def leaves(self):
+for t in self.childs:
+if isinstance(t, Blossom):
+yield from t.leaves()
+else:
+yield t
+# Get a list of vertices.
+gnodes = list(G)
+if not gnodes:
+return set()  # don't bother with empty graphs
+# Find the maximum edge weight.
+maxweight = 0
+allinteger = True
+for i, j, d in G.edges(data=True):
+wt = d.get(weight, 1)
+if i != j and wt > maxweight:
+maxweight = wt
+allinteger = allinteger and (str(type(wt)).split("'")[1] in ("int", "long"))
+# If v is a matched vertex, mate[v] is its partner vertex.
+# If v is a single vertex, v does not occur as a key in mate.
+# Initially all vertices are single; updated during augmentation.
+mate = {}
+# If b is a top-level blossom,
+# label.get(b) is None if b is unlabeled (free),
+#                 1 if b is an S-blossom,
+#                 2 if b is a T-blossom.
+# The label of a vertex is found by looking at the label of its top-level
+# containing blossom.
+# If v is a vertex inside a T-blossom, label[v] is 2 iff v is reachable
+# from an S-vertex outside the blossom.
+# Labels are assigned during a stage and reset after each augmentation.
+label = {}
+# If b is a labeled top-level blossom,
+# labeledge[b] = (v, w) is the edge through which b obtained its label
+# such that w is a vertex in b, or None if b's base vertex is single.
+# If w is a vertex inside a T-blossom and label[w] == 2,
+# labeledge[w] = (v, w) is an edge through which w is reachable from
+# outside the blossom.
+labeledge = {}
+# If v is a vertex, inblossom[v] is the top-level blossom to which v
+# belongs.
+# If v is a top-level vertex, inblossom[v] == v since v is itself
+# a (trivial) top-level blossom.
+# Initially all vertices are top-level trivial blossoms.
+inblossom = dict(zip(gnodes, gnodes))
+# If b is a sub-blossom,
+# blossomparent[b] is its immediate parent (sub-)blossom.
+# If b is a top-level blossom, blossomparent[b] is None.
+blossomparent = dict(zip(gnodes, repeat(None)))
+# If b is a (sub-)blossom,
+# blossombase[b] is its base VERTEX (i.e. recursive sub-blossom).
+blossombase = dict(zip(gnodes, gnodes))
+# If w is a free vertex (or an unreached vertex inside a T-blossom),
+# bestedge[w] = (v, w) is the least-slack edge from an S-vertex,
+# or None if there is no such edge.
+# If b is a (possibly trivial) top-level S-blossom,
+# bestedge[b] = (v, w) is the least-slack edge to a different S-blossom
+# (v inside b), or None if there is no such edge.
+# This is used for efficient computation of delta2 and delta3.
+bestedge = {}
+# If v is a vertex,
+# dualvar[v] = 2 * u(v) where u(v) is the v's variable in the dual
+# optimization problem (if all edge weights are integers, multiplication
+# by two ensures that all values remain integers throughout the algorithm).
+# Initially, u(v) = maxweight / 2.
+dualvar = dict(zip(gnodes, repeat(maxweight)))
+# If b is a non-trivial blossom,
+# blossomdual[b] = z(b) where z(b) is b's variable in the dual
+# optimization problem.
+blossomdual = {}
+# If (v, w) in allowedge or (w, v) in allowedg, then the edge
+# (v, w) is known to have zero slack in the optimization problem;
+# otherwise the edge may or may not have zero slack.
+allowedge = {}
+# Queue of newly discovered S-vertices.
+queue = []
+# Return 2 * slack of edge (v, w) (does not work inside blossoms).
+def slack(v, w):
+return dualvar[v] + dualvar[w] - 2 * G[v][w].get(weight, 1)
+# Assign label t to the top-level blossom containing vertex w,
+# coming through an edge from vertex v.
+def assignLabel(w, t, v):
+b = inblossom[w]
+assert label.get(w) is None and label.get(b) is None
+label[w] = label[b] = t
+if v is not None:
+labeledge[w] = labeledge[b] = (v, w)
+else:
+labeledge[w] = labeledge[b] = None
+bestedge[w] = bestedge[b] = None
+if t == 1:
+# b became an S-vertex/blossom; add it(s vertices) to the queue.
+if isinstance(b, Blossom):
+queue.extend(b.leaves())
+else:
+queue.append(b)
+elif t == 2:
+# b became a T-vertex/blossom; assign label S to its mate.
+# (If b is a non-trivial blossom, its base is the only vertex
+# with an external mate.)
+base = blossombase[b]
+assignLabel(mate[base], 1, base)
+# Trace back from vertices v and w to discover either a new blossom
+# or an augmenting path. Return the base vertex of the new blossom,
+# or NoNode if an augmenting path was found.
+def scanBlossom(v, w):
+# Trace back from v and w, placing breadcrumbs as we go.
+path = []
+base = NoNode
+while v is not NoNode:
+# Look for a breadcrumb in v's blossom or put a new breadcrumb.
+b = inblossom[v]
+if label[b] & 4:
+base = blossombase[b]
+break
+assert label[b] == 1
+path.append(b)
+label[b] = 5
+# Trace one step back.
+if labeledge[b] is None:
+# The base of blossom b is single; stop tracing this path.
+assert blossombase[b] not in mate
+v = NoNode
+else:
+assert labeledge[b][0] == mate[blossombase[b]]
+v = labeledge[b][0]
+b = inblossom[v]
+assert label[b] == 2
+# b is a T-blossom; trace one more step back.
+v = labeledge[b][0]
+# Swap v and w so that we alternate between both paths.
+if w is not NoNode:
+v, w = w, v
+# Remove breadcrumbs.
+for b in path:
+label[b] = 1
+# Return base vertex, if we found one.
+return base
+# Construct a new blossom with given base, through S-vertices v and w.
+# Label the new blossom as S; set its dual variable to zero;
+# relabel its T-vertices to S and add them to the queue.
+def addBlossom(base, v, w):
+bb = inblossom[base]
+bv = inblossom[v]
+bw = inblossom[w]
+# Create blossom.
+b = Blossom()
+blossombase[b] = base
+blossomparent[b] = None
+blossomparent[bb] = b
+# Make list of sub-blossoms and their interconnecting edge endpoints.
+b.childs = path = []
+b.edges = edgs = [(v, w)]
+# Trace back from v to base.
+while bv != bb:
+# Add bv to the new blossom.
+blossomparent[bv] = b
+path.append(bv)
+edgs.append(labeledge[bv])
+assert label[bv] == 2 or (
+label[bv] == 1 and labeledge[bv][0] == mate[blossombase[bv]]
+)
+# Trace one step back.
+v = labeledge[bv][0]
+bv = inblossom[v]
+# Add base sub-blossom; reverse lists.
+path.append(bb)
+path.reverse()
+edgs.reverse()
+# Trace back from w to base.
+while bw != bb:
+# Add bw to the new blossom.
+blossomparent[bw] = b
+path.append(bw)
+edgs.append((labeledge[bw][1], labeledge[bw][0]))
+assert label[bw] == 2 or (
+label[bw] == 1 and labeledge[bw][0] == mate[blossombase[bw]]
+)
+# Trace one step back.
+w = labeledge[bw][0]
+bw = inblossom[w]
+# Set label to S.
+assert label[bb] == 1
+label[b] = 1
+labeledge[b] = labeledge[bb]
+# Set dual variable to zero.
+blossomdual[b] = 0
+# Relabel vertices.
+for v in b.leaves():
+if label[inblossom[v]] == 2:
+# This T-vertex now turns into an S-vertex because it becomes
+# part of an S-blossom; add it to the queue.
+queue.append(v)
+inblossom[v] = b
+# Compute b.mybestedges.
+bestedgeto = {}
+for bv in path:
+if isinstance(bv, Blossom):
+if bv.mybestedges is not None:
+# Walk this subblossom's least-slack edges.
+nblist = bv.mybestedges
+# The sub-blossom won't need this data again.
+bv.mybestedges = None
+else:
+# This subblossom does not have a list of least-slack
+# edges; get the information from the vertices.
+nblist = [
+(v, w) for v in bv.leaves() for w in G.neighbors(v) if v != w
+]
+else:
+nblist = [(bv, w) for w in G.neighbors(bv) if bv != w]
+for k in nblist:
+(i, j) = k
+if inblossom[j] == b:
+i, j = j, i
+bj = inblossom[j]
+if (
+bj != b
+and label.get(bj) == 1
+and ((bj not in bestedgeto) or slack(i, j) < slack(*bestedgeto[bj]))
+):
+bestedgeto[bj] = k
+# Forget about least-slack edge of the subblossom.
+bestedge[bv] = None
+b.mybestedges = list(bestedgeto.values())
+# Select bestedge[b].
+mybestedge = None
+bestedge[b] = None
+for k in b.mybestedges:
+kslack = slack(*k)
+if mybestedge is None or kslack < mybestslack:
+mybestedge = k
+mybestslack = kslack
+bestedge[b] = mybestedge
+# Expand the given top-level blossom.
+def expandBlossom(b, endstage):
+# Convert sub-blossoms into top-level blossoms.
+for s in b.childs:
+blossomparent[s] = None
+if isinstance(s, Blossom):
+if endstage and blossomdual[s] == 0:
+# Recursively expand this sub-blossom.
+expandBlossom(s, endstage)
+else:
+for v in s.leaves():
+inblossom[v] = s
+else:
+inblossom[s] = s
+# If we expand a T-blossom during a stage, its sub-blossoms must be
+# relabeled.
+if (not endstage) and label.get(b) == 2:
+# Start at the sub-blossom through which the expanding
+# blossom obtained its label, and relabel sub-blossoms untili
+# we reach the base.
+# Figure out through which sub-blossom the expanding blossom
+# obtained its label initially.
+entrychild = inblossom[labeledge[b][1]]
+# Decide in which direction we will go round the blossom.
+j = b.childs.index(entrychild)
+if j & 1:
+# Start index is odd; go forward and wrap.
+j -= len(b.childs)
+jstep = 1
+else:
+# Start index is even; go backward.
+jstep = -1
+# Move along the blossom until we get to the base.
+v, w = labeledge[b]
+while j != 0:
+# Relabel the T-sub-blossom.
+if jstep == 1:
+p, q = b.edges[j]
+else:
+q, p = b.edges[j - 1]
+label[w] = None
+label[q] = None
+assignLabel(w, 2, v)
+# Step to the next S-sub-blossom and note its forward edge.
+allowedge[(p, q)] = allowedge[(q, p)] = True
+j += jstep
+if jstep == 1:
+v, w = b.edges[j]
+else:
+w, v = b.edges[j - 1]
+# Step to the next T-sub-blossom.
+allowedge[(v, w)] = allowedge[(w, v)] = True
+j += jstep
+# Relabel the base T-sub-blossom WITHOUT stepping through to
+# its mate (so don't call assignLabel).
+bw = b.childs[j]
+label[w] = label[bw] = 2
+labeledge[w] = labeledge[bw] = (v, w)
+bestedge[bw] = None
+# Continue along the blossom until we get back to entrychild.
+j += jstep
+while b.childs[j] != entrychild:
+# Examine the vertices of the sub-blossom to see whether
+# it is reachable from a neighbouring S-vertex outside the
+# expanding blossom.
+bv = b.childs[j]
+if label.get(bv) == 1:
+# This sub-blossom just got label S through one of its
+# neighbours; leave it be.
+j += jstep
+continue
+if isinstance(bv, Blossom):
+for v in bv.leaves():
+if label.get(v):
+break
+else:
+v = bv
+# If the sub-blossom contains a reachable vertex, assign
+# label T to the sub-blossom.
+if label.get(v):
+assert label[v] == 2
+assert inblossom[v] == bv
+label[v] = None
+label[mate[blossombase[bv]]] = None
+assignLabel(v, 2, labeledge[v][0])
+j += jstep
+# Remove the expanded blossom entirely.
+label.pop(b, None)
+labeledge.pop(b, None)
+bestedge.pop(b, None)
+del blossomparent[b]
+del blossombase[b]
+del blossomdual[b]
+# Swap matched/unmatched edges over an alternating path through blossom b
+# between vertex v and the base vertex. Keep blossom bookkeeping
+# consistent.
+def augmentBlossom(b, v):
+# Bubble up through the blossom tree from vertex v to an immediate
+# sub-blossom of b.
+t = v
+while blossomparent[t] != b:
+t = blossomparent[t]
+# Recursively deal with the first sub-blossom.
+if isinstance(t, Blossom):
+augmentBlossom(t, v)
+# Decide in which direction we will go round the blossom.
+i = j = b.childs.index(t)
+if i & 1:
+# Start index is odd; go forward and wrap.
+j -= len(b.childs)
+jstep = 1
+else:
+# Start index is even; go backward.
+jstep = -1
+# Move along the blossom until we get to the base.
+while j != 0:
+# Step to the next sub-blossom and augment it recursively.
+j += jstep
+t = b.childs[j]
+if jstep == 1:
+w, x = b.edges[j]
+else:
+x, w = b.edges[j - 1]
+if isinstance(t, Blossom):
+augmentBlossom(t, w)
+# Step to the next sub-blossom and augment it recursively.
+j += jstep
+t = b.childs[j]
+if isinstance(t, Blossom):
+augmentBlossom(t, x)
+# Match the edge connecting those sub-blossoms.
+mate[w] = x
+mate[x] = w
+# Rotate the list of sub-blossoms to put the new base at the front.
+b.childs = b.childs[i:] + b.childs[:i]
+b.edges = b.edges[i:] + b.edges[:i]
+blossombase[b] = blossombase[b.childs[0]]
+assert blossombase[b] == v
+# Swap matched/unmatched edges over an alternating path between two
+# single vertices. The augmenting path runs through S-vertices v and w.
+def augmentMatching(v, w):
+for (s, j) in ((v, w), (w, v)):
+# Match vertex s to vertex j. Then trace back from s
+# until we find a single vertex, swapping matched and unmatched
+# edges as we go.
+while 1:
+bs = inblossom[s]
+assert label[bs] == 1
+assert (labeledge[bs] is None and blossombase[bs] not in mate) or (
+labeledge[bs][0] == mate[blossombase[bs]]
+)
+# Augment through the S-blossom from s to base.
+if isinstance(bs, Blossom):
+augmentBlossom(bs, s)
+# Update mate[s]
+mate[s] = j
+# Trace one step back.
+if labeledge[bs] is None:
+# Reached single vertex; stop.
+break
+t = labeledge[bs][0]
+bt = inblossom[t]
+assert label[bt] == 2
+# Trace one more step back.
+s, j = labeledge[bt]
+# Augment through the T-blossom from j to base.
+assert blossombase[bt] == t
+if isinstance(bt, Blossom):
+augmentBlossom(bt, j)
+# Update mate[j]
+mate[j] = s
+# Verify that the optimum solution has been reached.
+def verifyOptimum():
+if maxcardinality:
+# Vertices may have negative dual;
+# find a constant non-negative number to add to all vertex duals.
+vdualoffset = max(0, -min(dualvar.values()))
+else:
+vdualoffset = 0
+# 0. all dual variables are non-negative
+assert min(dualvar.values()) + vdualoffset >= 0
+assert len(blossomdual) == 0 or min(blossomdual.values()) >= 0
+# 0. all edges have non-negative slack and
+# 1. all matched edges have zero slack;
+for i, j, d in G.edges(data=True):
+wt = d.get(weight, 1)
+if i == j:
+continue  # ignore self-loops
+s = dualvar[i] + dualvar[j] - 2 * wt
+iblossoms = [i]
+jblossoms = [j]
+while blossomparent[iblossoms[-1]] is not None:
+iblossoms.append(blossomparent[iblossoms[-1]])
+while blossomparent[jblossoms[-1]] is not None:
+jblossoms.append(blossomparent[jblossoms[-1]])
+iblossoms.reverse()
+jblossoms.reverse()
+for (bi, bj) in zip(iblossoms, jblossoms):
+if bi != bj:
+break
+s += 2 * blossomdual[bi]
+assert s >= 0
+if mate.get(i) == j or mate.get(j) == i:
+assert mate[i] == j and mate[j] == i
+assert s == 0
+# 2. all single vertices have zero dual value;
+for v in gnodes:
+assert (v in mate) or dualvar[v] + vdualoffset == 0
+# 3. all blossoms with positive dual value are full.
+for b in blossomdual:
+if blossomdual[b] > 0:
+assert len(b.edges) % 2 == 1
+for (i, j) in b.edges[1::2]:
+assert mate[i] == j and mate[j] == i
+# Ok.
+# Main loop: continue until no further improvement is possible.
+while 1:
+# Each iteration of this loop is a "stage".
+# A stage finds an augmenting path and uses that to improve
+# the matching.
+# Remove labels from top-level blossoms/vertices.
+label.clear()
+labeledge.clear()
+# Forget all about least-slack edges.
+bestedge.clear()
+for b in blossomdual:
+b.mybestedges = None
+# Loss of labeling means that we can not be sure that currently
+# allowable edges remain allowable throughout this stage.
+allowedge.clear()
+# Make queue empty.
+queue[:] = []
+# Label single blossoms/vertices with S and put them in the queue.
+for v in gnodes:
+if (v not in mate) and label.get(inblossom[v]) is None:
+assignLabel(v, 1, None)
+# Loop until we succeed in augmenting the matching.
+augmented = 0
+while 1:
+# Each iteration of this loop is a "substage".
+# A substage tries to find an augmenting path;
+# if found, the path is used to improve the matching and
+# the stage ends. If there is no augmenting path, the
+# primal-dual method is used to pump some slack out of
+# the dual variables.
+# Continue labeling until all vertices which are reachable
+# through an alternating path have got a label.
+while queue and not augmented:
+# Take an S vertex from the queue.
+v = queue.pop()
+assert label[inblossom[v]] == 1
+# Scan its neighbours:
+for w in G.neighbors(v):
+if w == v:
+continue  # ignore self-loops
+# w is a neighbour to v
+bv = inblossom[v]
+bw = inblossom[w]
+if bv == bw:
+# this edge is internal to a blossom; ignore it
+continue
+if (v, w) not in allowedge:
+kslack = slack(v, w)
+if kslack <= 0:
+# edge k has zero slack => it is allowable
+allowedge[(v, w)] = allowedge[(w, v)] = True
+if (v, w) in allowedge:
+if label.get(bw) is None:
+# (C1) w is a free vertex;
+# label w with T and label its mate with S (R12).
+assignLabel(w, 2, v)
+elif label.get(bw) == 1:
+# (C2) w is an S-vertex (not in the same blossom);
+# follow back-links to discover either an
+# augmenting path or a new blossom.
+base = scanBlossom(v, w)
+if base is not NoNode:
+# Found a new blossom; add it to the blossom
+# bookkeeping and turn it into an S-blossom.
+addBlossom(base, v, w)
+else:
+# Found an augmenting path; augment the
+# matching and end this stage.
+augmentMatching(v, w)
+augmented = 1
+break
+elif label.get(w) is None:
+# w is inside a T-blossom, but w itself has not
+# yet been reached from outside the blossom;
+# mark it as reached (we need this to relabel
+# during T-blossom expansion).
+assert label[bw] == 2
+label[w] = 2
+labeledge[w] = (v, w)
+elif label.get(bw) == 1:
+# keep track of the least-slack non-allowable edge to
+# a different S-blossom.
+if bestedge.get(bv) is None or kslack < slack(*bestedge[bv]):
+bestedge[bv] = (v, w)
+elif label.get(w) is None:
+# w is a free vertex (or an unreached vertex inside
+# a T-blossom) but we can not reach it yet;
+# keep track of the least-slack edge that reaches w.
+if bestedge.get(w) is None or kslack < slack(*bestedge[w]):
+bestedge[w] = (v, w)
+if augmented:
+break
+# There is no augmenting path under these constraints;
+# compute delta and reduce slack in the optimization problem.
+# (Note that our vertex dual variables, edge slacks and delta's
+# are pre-multiplied by two.)
+deltatype = -1
+delta = deltaedge = deltablossom = None
+# Compute delta1: the minimum value of any vertex dual.
+if not maxcardinality:
+deltatype = 1
+delta = min(dualvar.values())
+# Compute delta2: the minimum slack on any edge between
+# an S-vertex and a free vertex.
+for v in G.nodes():
+if label.get(inblossom[v]) is None and bestedge.get(v) is not None:
+d = slack(*bestedge[v])
+if deltatype == -1 or d < delta:
+delta = d
+deltatype = 2
+deltaedge = bestedge[v]
+# Compute delta3: half the minimum slack on any edge between
+# a pair of S-blossoms.
+for b in blossomparent:
+if (
+blossomparent[b] is None
+and label.get(b) == 1
+and bestedge.get(b) is not None
+):
+kslack = slack(*bestedge[b])
+if allinteger:
+assert (kslack % 2) == 0
+d = kslack // 2
+else:
+d = kslack / 2.0
+if deltatype == -1 or d < delta:
+delta = d
+deltatype = 3
+deltaedge = bestedge[b]
+# Compute delta4: minimum z variable of any T-blossom.
+for b in blossomdual:
+if (
+blossomparent[b] is None
+and label.get(b) == 2
+and (deltatype == -1 or blossomdual[b] < delta)
+):
+delta = blossomdual[b]
+deltatype = 4
+deltablossom = b
+if deltatype == -1:
+# No further improvement possible; max-cardinality optimum
+# reached. Do a final delta update to make the optimum
+# verifyable.
+assert maxcardinality
+deltatype = 1
+delta = max(0, min(dualvar.values()))
+# Update dual variables according to delta.
+for v in gnodes:
+if label.get(inblossom[v]) == 1:
+# S-vertex: 2*u = 2*u - 2*delta
+dualvar[v] -= delta
+elif label.get(inblossom[v]) == 2:
+# T-vertex: 2*u = 2*u + 2*delta
+dualvar[v] += delta
+for b in blossomdual:
+if blossomparent[b] is None:
+if label.get(b) == 1:
+# top-level S-blossom: z = z + 2*delta
+blossomdual[b] += delta
+elif label.get(b) == 2:
+# top-level T-blossom: z = z - 2*delta
+blossomdual[b] -= delta
+# Take action at the point where minimum delta occurred.
+if deltatype == 1:
+# No further improvement possible; optimum reached.
+break
+elif deltatype == 2:
+# Use the least-slack edge to continue the search.
+(v, w) = deltaedge
+assert label[inblossom[v]] == 1
+allowedge[(v, w)] = allowedge[(w, v)] = True
+queue.append(v)
+elif deltatype == 3:
+# Use the least-slack edge to continue the search.
+(v, w) = deltaedge
+allowedge[(v, w)] = allowedge[(w, v)] = True
+assert label[inblossom[v]] == 1
+queue.append(v)
+elif deltatype == 4:
+# Expand the least-z blossom.
+expandBlossom(deltablossom, False)
+# End of a this substage.
+# Paranoia check that the matching is symmetric.
+for v in mate:
+assert mate[mate[v]] == v
+# Stop when no more augmenting path can be found.
+if not augmented:
+break
+# End of a stage; expand all S-blossoms which have zero dual.
+for b in list(blossomdual.keys()):
+if b not in blossomdual:
+continue  # already expanded
+if blossomparent[b] is None and label.get(b) == 1 and blossomdual[b] == 0:
+expandBlossom(b, True)
+# Verify that we reached the optimum solution (only for integer weights).
+if allinteger:
+verifyOptimum()
+return matching_dict_to_set(mate)

Mercurial > repos > shellac > sam_consensus_v3

comparison env/lib/python3.9/site-packages/networkx/algorithms/matching.py @ 0:4f3585e2f14b draft default tip