def find_matchings(graph, n=5):
best_matching = nx.max_weight_matching(graph, True)
matchings = [best_matching]
for u, v in best_matching.items():
if v <= u:
continue
smaller_graph = nx.Graph(graph)
smaller_graph.remove_edge(u, v)
matching = nx.max_weight_matching(smaller_graph, True)
if len(matching) > 0:
matchings.append(matching)
matching_costs = [(matching_cost(graph, matching), matching) for matching in matchings]
matching_costs.sort()
final_matchings = []
last_cost = None
for cost, matching in matching_costs:
if cost == last_cost:
continue
last_cost = cost
final_matchings.append((cost, matching))
return final_matchings
def find_matchings(graph, n=5):
"""
Find the n best matchings for a graph. The best matching is guaranteed to be the best, but the others are only
estimates.
"""
best_matching = nx.max_weight_matching(graph, True)
matchings = [best_matching]
for u, v in best_matching.items():
if v <= u:
continue
# Remove the matching
smaller_graph = nx.Graph(graph)
smaller_graph.remove_edge(u, v)
matching = nx.max_weight_matching(smaller_graph, True)
matchings.append(matching)
matching_costs = [(matching_cost(graph, matching), matching) for matching in matchings]
matching_costs.sort()
# HACK: The above code end up giving duplicates of the same path, even though the matching is different. To prevent
# this, we remove matchings with the same cost.
final_matchings = []
last_cost = None
for cost, matching in matching_costs:
if cost == last_cost:
continue
last_cost = cost
final_matchings.append((cost, matching))
return final_matchings
def test_s_blossom(self):
"""Create S-blossom and use it for augmentation:"""
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 8), (1, 3, 9), (2, 3, 10), (3, 4, 7)])
assert_equal(nx.max_weight_matching(G), {1: 2, 2: 1, 3: 4, 4: 3})
G.add_weighted_edges_from([(1, 6, 5), (4, 5, 6)])
assert_equal(nx.max_weight_matching(G), {1: 6, 2: 3, 3: 2, 4: 5, 5: 4, 6: 1})
def test_trivial5(self):
"""Path"""
G = nx.Graph()
G.add_edge(1, 2, weight=5)
G.add_edge(2, 3, weight=11)
G.add_edge(3, 4, weight=5)
assert_equal(nx.max_weight_matching(G), {2: 3, 3: 2})
assert_equal(nx.max_weight_matching(G, 1), {1: 2, 2: 1, 3: 4, 4: 3})
def test_negative_weights(self):
"""Negative weights"""
G = nx.Graph()
G.add_edge(1, 2, weight=2)
G.add_edge(1, 3, weight=-2)
G.add_edge(2, 3, weight=1)
G.add_edge(2, 4, weight=-1)
G.add_edge(3, 4, weight=-6)
assert_equal(nx.max_weight_matching(G), {1: 2, 2: 1})
assert_equal(nx.max_weight_matching(G, 1), {1: 3, 2: 4, 3: 1, 4: 2})
def test_s_t_blossom(self):
"""Create S-blossom, relabel as T-blossom, use for augmentation:"""
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 9), (1, 3, 8), (2, 3, 10), (1, 4, 5), (4, 5, 4), (1, 6, 3)])
assert_equal(nx.max_weight_matching(G), {1: 6, 2: 3, 3: 2, 4: 5, 5: 4, 6: 1})
G.add_edge(4, 5, weight=3)
G.add_edge(1, 6, weight=4)
assert_equal(nx.max_weight_matching(G), {1: 6, 2: 3, 3: 2, 4: 5, 5: 4, 6: 1})
G.remove_edge(1, 6)
G.add_edge(3, 6, weight=4)
assert_equal(nx.max_weight_matching(G), {1: 2, 2: 1, 3: 6, 4: 5, 5: 4, 6: 3})
def test050_linear(self):
""" Multiple edges, linear. """
g = nx.Graph()
g.add_edges_from([(0,1),(1,2),(2,3),(3,4),(4,5),(5,6)])
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
self.assertEqual( len(mate1), len(mate2) )
def test_nasty_blossom_augmenting(self):
"""Create nested blossom, relabel as T in more than one way"""
# expand outer blossom such that inner blossom ends up on an
# augmenting path:
G = nx.Graph()
G.add_weighted_edges_from(
[
(1, 2, 45),
(1, 7, 45),
(2, 3, 50),
(3, 4, 45),
(4, 5, 95),
(4, 6, 94),
(5, 6, 94),
(6, 7, 50),
(1, 8, 30),
(3, 11, 35),
(5, 9, 36),
(7, 10, 26),
(11, 12, 5),
]
)
assert_equal(
nx.max_weight_matching(G), {1: 8, 2: 3, 3: 2, 4: 6, 5: 9, 6: 4, 7: 10, 8: 1, 9: 5, 10: 7, 11: 12, 12: 11}
)
def test030_twoedges(self):
""" Two edges. """
g = nx.Graph()
g.add_edges_from([(0,1),(1,2)])
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
self.assertEqual( len(mate1), len(mate2) )
def test020_singleedge(self):
""" Single edge. """
g = nx.Graph()
g.add_edge(0,1)
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
self.assertEqual( len(mate1), len(mate2) )
def test200_icosahedralgraph(self):
""" Icosahedral graph. """
g = nx.icosahedral_graph()
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
#td.showGraph(g, mate1, "test200_icosahedralgraph")
self.assertEqual( len(mate1), len(mate2) )
def test190_octahedralgraph(self):
""" Octahedral graph. """
g = nx.octahedral_graph()
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
td.showGraph(g, mate1, "test190_octahedralgraph")
self.assertEqual( len(mate1), len(mate2) )
def weightedBipartite(matches, source, target):
"""
Weighted Bipartite Matching Filter.
Adapted from @WeiFoo's HDP codebase. See https://goo.gl/3AN2Qd.
:param matches: Dictionary. Key is a pair (source, target). Value is p-value.
Obtained from KSAnalyzer
:return: Best bipartite pair from the matches.
"""
# Tags for source and target metrics.
s_tag = "_s"
t_tag = "_t"
G = nx.Graph()
for key, val in matches.iteritems():
G.add_edge(key[0] + s_tag, key[1] + t_tag, weight=val) # add suffix to make it unique
result = nx.max_weight_matching(G)
"""
We only want matched pairs with (S->T) and (T<-S). Remove singletons;
i.e., only (S->T) or only (T->S)
"""
pairs = []
for attr_1, attr_2 in result.iteritems():
if attr_1[:-2] in source and attr_2[:-2] in target:
if (attr_1[:-2], attr_2[:-2]) not in pairs:
pairs.append((attr_1[:-2], attr_2[:-2]))
elif attr_1[:-2] in target and attr_2[:-2] in source:
if (attr_2[:-2], attr_1[:-2]) not in pairs:
pairs.append((attr_2[:-2], attr_1[:-2]))
return pairs
def add_edges_for_euler(in_g, out_g):
# optimize_dead_ends(in_g, out_g)
temp_graph = graph_of_odd_nodes(in_g, out_g)
print "DEBUG: Finished calculating shortest paths, now calculating matching..."
matching = networkx.max_weight_matching(temp_graph, maxcardinality=True)
print "DEBUG: Finished calculating matching, now adding new edges..."
short_matching = {}
for k in matching:
if k not in short_matching and matching[k] not in short_matching:
short_matching[k] = matching[k]
for source in short_matching:
add_artificial_edge(in_g, out_g, source, short_matching[source])
#assert(networkx.is_connected(out_g))
nodes = odd_nodes(out_g)
num_odd_nodes = len(nodes)
print "DEBUG: After all that we have %s odd-degree nodes." % num_odd_nodes
# reachable_nodes = set(networkx.dfs_preorder_nodes(out_g, 105437194))
# unreachable_nodes = set(out_g.nodes()) - reachable_nodes
# for n in unreachable_nodes:
# print "%s (%s) is unreachable." % (n, out_g.node[n]['pretty_name'])
# path = networkx.dijkstra_path(in_g, 105437194, n, weight='length')
# print "Here is how we would get there on the original graph: %s" % [(pn, in_g.node[pn]['pretty_name']) for pn in path]
# print "Here are the nodes we have purged from that path: %s" % [(pn, in_g.node[pn]['pretty_name']) for pn in path if pn not in out_g]
if not networkx.is_connected(out_g):
print "The graph is still not connected. Components: %s" % [[(n, in_g.node[n]['pretty_name']) for n in comp] for comp in networkx.connected_components(out_g)]
return out_g
def _pairs_from_graph(graph):
mate = nx.max_weight_matching(graph, maxcardinality=True)
# Extract pairs
matched_players = []
pairs = []
for k, v in mate:
if (k, v) not in pairs:
pairs.append((k, v))
# Sort pairs by wins
for i, pair in enumerate(pairs):
wins_zero = match_log.times_match_win(pair[0])
wins_one = match_log.times_match_win(pair[1])
if wins_zero < wins_one:
pairs[i] = (pair[1], pair[0])
# Format as matchups
matchups = Matchups()
for e in pairs:
cost = cost_map[e[0]][e[1]]
if e[0] == bye_dummy():
assert(matchups.bye_player is None)
matchups.bye_player = e[1]
elif e[1] == bye_dummy():
assert(matchups.bye_player is None)
matchups.bye_player = e[0]
else:
num_0 = tournament.player_number_of_player(e[0])
num_1 = tournament.player_number_of_player(e[1])
player_a = e[0] if num_0 < num_1 else e[1]
player_b = e[1] if player_a == e[0] else e[0]
matchups.pairs.append(Matchup(player_a, player_b, cost))
# Set bye player
return matchups
def test070_circle(self):
""" Multiple edges, circle of even length. """
g = nx.Graph()
g.add_edges_from([(0,1),(1,2),(2,3),(3,4),(4,5),(5,0)])
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
self.assertEqual( len(mate1), len(mate2) )
def test025_barbell_graph(self):
""" Very small barbell graph. """
g = nx.barbell_graph(9, 2)
mate2 = nx.max_weight_matching( g, True )
td.showGraph(g, mate2, "test025_barbell_graph_edmonds")
mate1 = mv.max_cardinality_matching( g )
self.assertEqual( len(mate1), len(mate2) )
def test180_tutte_cage_graph(self):
""" Tutte 12-cage graph. """
g = nx.LCF_graph(126, [17, 27, -13, -59, -35, 35, -11, 13, -53\
, 53, -27, 21, 57, 11, -21, -57, 59, -17], 7)
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
self.assertEqual( len(mate1), len(mate2) )
def test142_utility_graph(self):
""" Larger utility graph from LCF notation. """
g = nx.LCF_graph(60, [3, -3], 3)
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
td.showGraph(g, mate1, "test142_utility_graph")
self.assertEqual( len(mate1), len(mate2) )
def test_nested_s_blossom_relabel(self):
"""Create S-blossom, relabel as S, include in nested S-blossom:"""
G = nx.Graph()
G.add_weighted_edges_from(
[(1, 2, 10), (1, 7, 10), (2, 3, 12), (3, 4, 20), (3, 5, 20), (4, 5, 25), (5, 6, 10), (6, 7, 10), (7, 8, 8)]
)
assert_equal(nx.max_weight_matching(G), {1: 2, 2: 1, 3: 4, 4: 3, 5: 6, 6: 5, 7: 8, 8: 7})
def test090_singlebloom(self):
""" Single bloom, two edge extensions, case 1. """
g = nx.Graph()
g.add_edges_from([(0,1),(1,2),(2,3),(3,4),(4,5),(5,1),(3,6)])
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
self.assertEqual( len(mate1), len(mate2) )
def match_candidates_and_characters(characters, candidates):
matches = dict([(character, {}) for character in characters])
# generate a graph that connects candidates and characters that match
G = nx.Graph()
G.add_nodes_from(characters, bipartite=0)
G.add_nodes_from(candidates, bipartite=1)
for character in characters:
names = []
for name in [character] + characters[character]:
names.append(tuple(name.replace(',', ' ').replace('\'s ', ' \'s ').replace('s\'', 's \' ').split()))
for cand in candidates:
score = match_to_any_names(names, cand)
if score > 0:
G.add_edge(character, cand, weight=score)
# if don't find any match, try the other direction
# sparknote character name might be contained by some candidate names
if len(matches[character]) == 0 and len(candidates) > 0:
scores = [strict_fuzzy_match_reference(cand, names[0]) for cand in candidates]
index, score = max(enumerate(scores), key=operator.itemgetter(1))
if score > 0:
G.add_edge(character, candidates[index], weight=score)
max_matching = nx.max_weight_matching(G, maxcardinality=True)
return (max_matching, G)
def test160_petersengraph(self):
""" Petersen graph. """
g = nx.petersen_graph()
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
td.showGraph(g, mate1, "test160_petersengraph")
self.assertEqual( len(mate1), len(mate2) )
def test170_bullgraph(self):
""" Bull graph. """
g = nx.bull_graph()
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
#td.showGraph(g, mate1, "test170_bullgraph")
self.assertEqual( len(mate1), len(mate2) )
def maximumWeighted(match, target_lst, source_lst):
"""
using max_weighted_bipartite to select a group of matched metrics
:param match : matched metrics with p values, key is the tuple of matched metrics
:type match : dict
:param target_lst : matched target metrics
:type target_lst: list
:param source_lst : matched source metcis
:type source_lst: list
:return : matched metrics as well as corresponding values
:rtype: class o
"""
value = 0
attr_source, attr_target = [], []
G = nx.Graph()
for key, val in match.iteritems():
G.add_edge(key[0] + "source", key[1] + "target", weight=val) # add suffix to make it unique
result = nx.max_weight_matching(G)
for key, val in result.iteritems(): # in Results, (A:B) and (B:A) both exist
if key[:-6] in source_lst and val[:-6] in target_lst \
and (key[:-6], val[:-6]) in match : # get rid of (A:B) exists but (B:A) not
if key[:-6] in attr_source and val[:-6] in attr_target and\
attr_source.index(key[:-6]) == attr_target.index(val[:-6]):
continue # this is (A:B) already in attr_source and attr_target
attr_target.append(val[:-6])
attr_source.append(key[:-6])
value += match[(key[:-6], val[:-6])]
# pdb.set_trace()
return o(score=value, attr_source=attr_source, attr_target=attr_target)
def test040_threeedges(self):
""" Three edges, linear. """
g = nx.Graph()
g.add_edges_from([(0,1),(1,2),(2,3)])
mate1 = mv.max_cardinality_matching( g )
mate2 = nx.max_weight_matching( g, True )
self.assertEqual( len(mate1), len(mate2) )
def test_nested_s_blossom_relabel_expand(self):
"""Create nested S-blossom, relabel as T, expand:"""
G = nx.Graph()
G.add_weighted_edges_from(
[(1, 2, 19), (1, 3, 20), (1, 8, 8), (2, 3, 25), (2, 4, 18), (3, 5, 18), (4, 5, 13), (4, 7, 7), (5, 6, 7)]
)
assert_equal(nx.max_weight_matching(G), {1: 8, 2: 3, 3: 2, 4: 7, 5: 6, 6: 5, 7: 4, 8: 1})
def test_s_blossom_relabel_expand(self):
"""Create S-blossom, relabel as T, expand:"""
G = nx.Graph()
G.add_weighted_edges_from(
[(1, 2, 23), (1, 5, 22), (1, 6, 15), (2, 3, 25), (3, 4, 22), (4, 5, 25), (4, 8, 14), (5, 7, 13)]
)
assert_equal(nx.max_weight_matching(G), {1: 6, 2: 3, 3: 2, 4: 8, 5: 7, 6: 1, 7: 5, 8: 4})
def test_trivial4(self):
"""Small graph"""
G = nx.Graph()
G.add_edge('one', 'two', weight=10)
G.add_edge('two', 'three', weight=11)
assert_equal(nx.max_weight_matching(G),
{'three': 'two', 'two': 'three'})
def test_trivial6(self):
"""Small graph with arbitrary weight attribute"""
G = nx.Graph()
G.add_edge('one', 'two', weight=10, abcd=11)
G.add_edge('two', 'three', weight=11, abcd=10)
assert_equal(nx.max_weight_matching(G, weight='abcd'),
{'one': 'two', 'two': 'one'})
def Get_Min_Weight_Matching(G):
"Get_Min_Weight_Matching takes as input a complete weighted undirected networkx graph G, and returns a dictionary called matching, such that matching[v] == w if and only if the node v of G is matched with node w."
nodes = G.nodes()
# Get negative of distance matrix
NegDistMat = -nx.to_numpy_matrix(G, weight='length')
# Form new graph to obtain the matching
Gnew = nx.from_numpy_matrix(NegDistMat, create_using=None)
# Get min-weight matching
matching = nx.max_weight_matching(Gnew, maxcardinality=True)
# Return the matching
matching_with_correct_nodes = {
nodes[i]: nodes[matching[i]]
for i in matching.keys()
}
return matching_with_correct_nodes
def match(self):
import networkx as nx
G = nx.Graph()
G.add_nodes_from(self.V)
for e in self.E:
e.match = False
[v, w] = e.end
G.add_edge(v, w)
M = nx.max_weight_matching(G, maxcardinality=True)
print(G)
print(M)
for (v, w) in M:
e = self.get_edge(v, w)
print("\nv:", v, "\nw:", w, "\ne:", e)
if e is not None:
e.match = True
def assignment_random(cands, pos, fitness, G):
bi_edges, edge_weights, l = UW.update_weights_simple(pos, cands, fitness)
bi_G = nx.Graph()
bi_edges = [(u, v, w) for (u, v, w) in bi_edges if w != 0]
bi_G.add_weighted_edges_from(bi_edges)
final_matched = nx.max_weight_matching(bi_G)
gender = nx.get_node_attributes(G, 'att')
for (u, v) in final_matched:
if u in pos:
gender.update({u: cands[v]})
else:
gender.update({v: cands[u]})
nx.set_node_attributes(G, gender, 'att')
return G, final_matched
def get_UA_pairs(UA, AC):
"""
find the largest list of bonds in which all atom appears at most once
:param UA:
:param AC:
:return:
"""
bonds = ACParser.get_bonds(UA, AC)
if len(bonds) == 0:
return [()]
G = nx.Graph()
G.add_edges_from(bonds)
UA_pairs = [list(nx.max_weight_matching(G))]
return UA_pairs
def _get_ua_pairs(ua, ac):
bonds = []
for k, i in enumerate(ua):
for j in ua[k + 1:]:
if ac[i, j] == 1:
bonds.append(tuple(sorted([i, j])))
if len(bonds) == 0:
return [()]
G = nx.Graph()
G.add_edges_from(bonds)
ua_pairs = [list(nx.max_weight_matching(G))]
return ua_pairs
def get_max_weight_match(sim: np.ndarray) -> np.ndarray:
if nx is None:
raise ValueError("networkx must be installed to use match algorithm.")
def permute(edge):
if edge[0] < sim.shape[0]:
return edge[0], edge[1] - sim.shape[0]
else:
return edge[1], edge[0] - sim.shape[0]
G = from_biadjacency_matrix(csr_matrix(sim))
matching = nx.max_weight_matching(G, maxcardinality=True)
matching = [permute(x) for x in matching]
matching = sorted(matching, key=lambda x: x[0])
res_matrix = np.zeros_like(sim)
for edge in matching:
res_matrix[edge[0], edge[1]] = 1
return res_matrix
def matching(self, matching_graph, syndrome_key):
"""Return matches of minimum weight perfect matching (MWPM) on matching_graph.
Args:
matching_graph (nx.Graph): Graph to run MWPM.
syndrome_key (char): Which X/Z syndrome subgraph these nodes are from.
Returns:
[(node, node),]: List of matchings found from MWPM
"""
matches = nx.max_weight_matching(matching_graph, maxcardinality=True)
filtered_matches = [
(source, target) for (source, target) in matches
if not (len(source) > 3 and len(target) > 3)
] # remove 0 weighted matched edges between virtual syndrome nodes
return filtered_matches
def test_nested_s_blossom_relabel_expand(self):
"""Create nested S-blossom, relabel as T, expand:"""
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 19), (1, 3, 20), (1, 8, 8),
(2, 3, 25), (2, 4, 18), (3, 5, 18),
(4, 5, 13), (4, 7, 7), (5, 6, 7)])
assert_equal(nx.max_weight_matching(G), {
1: 8,
2: 3,
3: 2,
4: 7,
5: 6,
6: 5,
7: 4,
8: 1
})
def test_nested_s_blossom_relabel(self):
"""Create S-blossom, relabel as S, include in nested S-blossom:"""
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 10), (1, 7, 10), (2, 3, 12),
(3, 4, 20), (3, 5, 20), (4, 5, 25),
(5, 6, 10), (6, 7, 10), (7, 8, 8)])
assert_equal(nx.max_weight_matching(G), {
1: 2,
2: 1,
3: 4,
4: 3,
5: 6,
6: 5,
7: 8,
8: 7
})
def test_trivial6(self):
"""Small graph with arbitrary weight attribute"""
G = nx.Graph()
G.add_edge("one", "two", weight=10, abcd=11)
G.add_edge("two", "three", weight=11, abcd=10)
assert edges_equal(
nx.max_weight_matching(G, weight="abcd"),
matching_dict_to_set({
"one": "two",
"two": "one"
}),
)
assert edges_equal(
nx.min_weight_matching(G, weight="abcd"),
matching_dict_to_set({"three": "two"}),
)
def test_s_blossom_relabel_expand(self):
"""Create S-blossom, relabel as T, expand:"""
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 23), (1, 5, 22), (1, 6, 15),
(2, 3, 25), (3, 4, 22), (4, 5, 25),
(4, 8, 14), (5, 7, 13)])
assert_equal(nx.max_weight_matching(G), {
1: 6,
2: 3,
3: 2,
4: 8,
5: 7,
6: 1,
7: 5,
8: 4
})
def test_nested_s_blossom(self):
"""Create nested S-blossom, use for augmentation:"""
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 9), (1, 3, 9), (2, 3, 10), (2, 4, 8),
(3, 5, 8), (4, 5, 10), (5, 6, 6)])
assert_equal(
nx.max_weight_matching(G),
matching_dict_to_set({
1: 3,
2: 4,
3: 1,
4: 2,
5: 6,
6: 5
}))
def maximal_matching(assembly_points_by_sources, acyclic=True, min_cw=0.0):
assembly_points_by_sources = [
ap for ap_list in assembly_points_by_sources.values() for ap in ap_list
]
scaffold_edges = get_scaffold_edges(
assembly_points=assembly_points_by_sources)
unoriented_assembly_points = get_un_oriented_assembly_points(
assembly_points=assembly_points_by_sources)
assembly_edges_graph = MergedScaffoldAssemblyGraph()
for ap in assembly_points_by_sources:
for (u, v, weight) in ap.get_edges(sort=True, weight=True):
assembly_edges_graph.add_edge(u, v, weight=weight)
assembly_edges_graph.remove_edges_with_low_cw(cw_threshold=min_cw)
matching = networkx.max_weight_matching(G=assembly_edges_graph.graph)
cover_graph = networkx.Graph()
cover_graph.add_edges_from(scaffold_edges)
edges = matching
for u, v in edges:
cover_graph.add_edge(u,
v,
weight=assembly_edges_graph.graph[u][v]['weight'])
# sanity check
for vertex in cover_graph.nodes():
assert cover_graph.degree[vertex] <= 2
# checking for any issues with unoriented assembly points
for assembly_point in unoriented_assembly_points:
participating_edges = []
for (u, v) in assembly_point.get_edges():
if cover_graph.has_edge(u=u, v=v):
participating_edges.append((u, v))
assert len(participating_edges) <= 2 # this is just a sanity check
if len(participating_edges) == 2:
cover_graph.remove_edge(participating_edges[0][0],
participating_edges[0][1])
if acyclic:
edges_to_delete = []
for cc in networkx.connected_component_subgraphs(G=cover_graph,
copy=True):
if networkx.number_of_nodes(cc) == networkx.number_of_edges(cc):
assembly_edges = filter(lambda entry: "weight" in entry[2],
cc.edges(data=True))
edges_to_delete.append(
min(assembly_edges, key=lambda entry: entry[2]["weight"]))
for u, v, data in edges_to_delete:
cover_graph.remove_edge(u, v)
return cover_graph
def swissPairings(tournament):
"""Returns a list of pairs of players for the next round of a match.
Each player appears exactly once in the pairings. Each player is paired with
another player with and equal or nearly-equal points record, that is, a player
adjacent to him or her in the standings.
For matching resuls Blossom algorithm is used and it is provided by "networkx"
library (http://networkx.github.io/).
Args:
tournament: the id number of the tournament
Returns:
A list of tuples, each of which contains (id1, name1, id2, name2)
id1: the first player's unique id
name1: the first player's name
id2: the second player's unique id
name2: the second player's name
"""
pairs = []
players = playerTournamentStandings(tournament)
players_number = countTournamentPlayers(tournament)
if players_number % 2 != 0:
players.append((None, None, 'Bye', 0, 0, 0, 0))
# use Blossom algorighm to generrate best matches
graph = nx.complete_graph(len(players))
player_to_node = {players[key][1]: key for key, row in enumerate(players)}
# exclude already played games
for pair in playedMatches(tournament):
for opponent in pair[1]:
if graph.has_edge(player_to_node[pair[0]],
player_to_node[opponent]):
graph.remove_edge(player_to_node[pair[0]],
player_to_node[opponent])
#add weight to each pair (edge)
for n1, n2, data in graph.edges(data=True):
weight = 1 + min(players[n1][6], players[n2][6])
if players[n1][6] != players[n2][6]:
weight += 1
data['weight'] = weight
# generate pairs
for n1, n2 in nx.max_weight_matching(graph).iteritems():
if n1 < n2: continue
pairs.append(
(players[n1][1], players[n1][2], players[n2][1], players[n2][2]))
return pairs
def matching(self,weights={}):
if not weights:
for pair in self.links:
weights[pair] = random.random()
G=nx.Graph()
for pair in self.links:
G.add_edge(self.links[pair][0],self.links[pair][1],weight=weights[pair])
raw_pairs = nx.max_weight_matching(G, maxcardinality=True)
pairs = []
for pair in raw_pairs:
pairs.append(list(pair))
return pairs
def test_gnp_random_against_networkx_with_negative_weight(self):
for i in range(1024):
with self.subTest(i=i):
random.seed(i)
rx_graph = retworkx.undirected_gnp_random_graph(10,
0.75,
seed=42 + i)
for edge in rx_graph.edge_list():
rx_graph.update_edge(*edge, random.randint(-5000, 5000))
nx_graph = networkx.Graph([(x[0], x[1], {
"weight": x[2]
}) for x in rx_graph.weighted_edge_list()])
nx_matches = networkx.max_weight_matching(nx_graph)
rx_matches = retworkx.max_weight_matching(
rx_graph, weight_fn=lambda x: x, verify_optimum=True)
self.compare_rx_nx_sets(rx_graph, rx_matches, nx_matches,
42 + i, nx_graph)
def align_columns(A, B):
k = A.shape[1]
G = nx.Graph()
G.add_nodes_from(range(0, k), bipartite=0)
G.add_nodes_from(range(k, k * 2), bipartite=1)
elist = []
for i in range(0, k):
for j in range(0, k):
# elist.append((i,k+j,wasserstein_distance(L2[h,:,i],L2R3[h,:,j])))
elist.append((i, k + j, np.abs(A[:, i] - B[:, j]).sum()))
maximum = max(elist, key=lambda t: t[2])[2]
elist = list(map(lambda x: (x[0], x[1], maximum - x[2]), elist))
G.add_weighted_edges_from(elist)
matching = nx.max_weight_matching(G)
sorted_matching = sorted([sorted(pair) for pair in matching])
indexes = np.array(list(map(lambda x: x[1] - k, sorted_matching)))
return indexes
def get_maximum_weighted_matching(self):
graph = nx.Graph()
matching_graph = nx.Graph()
for idx, node in enumerate(self.nodes1):
graph.add_node(node.index, pos=(0, idx))
matching_graph.add_node(node.index, pos=(0, idx))
for idx, node in enumerate(self.nodes2):
graph.add_node(node.index, pos=(20, idx))
matching_graph.add_node(node.index, pos=(20, idx))
for edge in self.edges:
graph.add_edge(edge.node1_index,
edge.node2_index,
weight=edge.weight)
matching_edges_set = nx.max_weight_matching(graph, maxcardinality=True)
for node1, node2 in matching_edges_set:
matching_graph.add_edge(node1, node2)
return matching_graph
def find_matches(self):
searching = self.filter(state=self.model.SEARCHING)
G = nx.Graph()
for search in searching:
G.add_node(search)
for other in searching:
G.add_node(other)
can_match, score = search.matching_score(other)
if can_match:
# 1 / score because there is only max_weight_matching and no min version
G.add_edge(search, other, weight=1 / score)
matches = nx.max_weight_matching(G)
for search, other in matches:
self.suggest_match(search, other)
def test_nested_s_blossom(self):
"""Create nested S-blossom, use for augmentation:"""
G = nx.Graph()
G.add_weighted_edges_from([
(1, 2, 9),
(1, 3, 9),
(2, 3, 10),
(2, 4, 8),
(3, 5, 8),
(4, 5, 10),
(5, 6, 6),
])
dict_format = {1: 3, 2: 4, 3: 1, 4: 2, 5: 6, 6: 5}
expected = {frozenset(e) for e in matching_dict_to_set(dict_format)}
answer = {frozenset(e) for e in nx.max_weight_matching(G)}
assert answer == expected
def test_nested_s_blossom_expand(self):
"""Create nested S-blossom, augment, expand recursively:"""
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 8), (1, 3, 8), (2, 3, 10),
(2, 4, 12), (3, 5, 12), (4, 5, 14),
(4, 6, 12), (5, 7, 12), (6, 7, 14),
(7, 8, 12)])
assert_equal(nx.max_weight_matching(G), {
1: 2,
2: 1,
3: 5,
4: 6,
5: 3,
6: 4,
7: 8,
8: 7
})
def update(self, boxes):
redetected_boxes = [0] * len(boxes)
redetected_objects = [0] * len(self.tracked_objects)
if len(boxes) > 0 and len(self.tracked_objects) > 0:
# create complete bipartite graph G of boxes to tracked objects,
# where edge weight is (maxint - euclidian_distance),
# such that max-matching can be performed to find closest matches
G = nx.Graph()
for i in range(len(boxes)):
for j in range(len(self.tracked_objects)):
tracked_box = self.tracked_objects[j].box
euclid_dist = vector_euclidian_distance(
box_centre_point(boxes[i]),
box_centre_point(tracked_box))
weight = sys.maxint - euclid_dist
# set weight = 0 if box is more than one box away (discard)
box_dist = vector_euclidian_distance(
(tracked_box[0], tracked_box[1]),
(tracked_box[2], tracked_box[3]))
if euclid_dist > box_dist:
weight = 0
G.add_edge(('b', i), ('t', j), weight=weight)
# max-match using Blossom algorithm
max_match = nx.max_weight_matching(G)
for match in max_match:
if not G[('b', match[0][1])][('t',
match[1][1])]['weight'] == 0:
self.tracked_objects[match[1][1]].box = boxes[match[0][1]]
redetected_boxes[match[0][1]] = 1
redetected_objects[match[1][1]] = 1
# delete dropped objects
self.tracked_objects = [
self.tracked_objects[i] for i in range(len(redetected_objects))
if redetected_objects[i]
]
# add new detections
for i in range(len(redetected_boxes)):
if redetected_boxes[i] == 0:
self.tracked_objects.append(
TrackedObject(self.id, boxes[i], rospy.Time.now()))
self.id += 1
def experiment(length, ones_range_min, ones_range_max, reps, numStrings):
strings = []
ones = []
maxmatch_avg = []
maxmatch_std_dev = []
greedymatch_avg = []
greedymatch_std_dev = []
for numOnes in range(ones_range_min, ones_range_max + 1):
ones.append(numOnes)
freed_pages_maxmatching = []
freed_pages_greedymatching = []
for iterations in range(reps):
for i in range(numStrings):
strings.append(createRandomString(length, numOnes))
graph = makeGraph(strings)
frdpgs_maxmatching = len(nx.max_weight_matching(graph)) / 2
perc = (frdpgs_maxmatching / numStrings) * 100
freed_pages_maxmatching.append(perc)
# graph_c = nx.complement(graph)
# frdpgs_greedymatching = numStrings - color_counter(graph_c)
# perc = (frdpgs_greedymatching/numStrings)*100
# freed_pages_greedymatching.append(perc)
b, unmatched = greedyMesher(strings)
frdpgs_greedymatching = (numStrings - len(unmatched)) / 2
perc = (frdpgs_greedymatching / numStrings) * 100
freed_pages_greedymatching.append(perc)
strings = []
m = np.asarray(freed_pages_maxmatching)
m_a = np.mean(m)
maxmatch_avg.append(m_a)
m_s = np.std(m)
maxmatch_std_dev.append(m_s)
c = np.asarray(freed_pages_greedymatching)
c_a = np.mean(c)
greedymatch_avg.append(c_a)
c_s = np.std(c)
greedymatch_std_dev.append(c_s)
return ones, maxmatch_avg, maxmatch_std_dev, greedymatch_avg, greedymatch_std_dev
def get_benchmark_set(self):
# return bm_list: List[Tuple[int, int]] list of tuples [(node1, node2)]
# convert multigraph to simplified graph with weighted edges
node_degree_dict = dict(nx.degree(self.graph))
tmp_graph = nx.Graph()
tmp_graph.add_nodes_from(self.graph.nodes)
edges_list = []
for e in self.graph.edges:
edges_list.append(e[0:2])
edges_set = set(edges_list)
edges_to_add = []
# calculate weight based on degree of each node (n1.degree * n2.degree)
for e in edges_set:
c = 1 / (node_degree_dict[e[0]] * node_degree_dict[e[1]])
t = (e[0], e[1], c)
edges_to_add.append(t)
tmp_graph.add_weighted_edges_from(edges_to_add)
print("NODES:")
print(tmp_graph.nodes)
print("EDGES:")
print(tmp_graph.edges)
for e in tmp_graph.edges:
print(tmp_graph.edges[e])
node_list = self.graph.nodes
# for i in node_list:
# node_list[i]['evenlevel'] = math.inf
# node_list[i]['oddlevel'] = math.inf
# node_list[i]['blossom'] = None
# node_list[i]['predecessors'] = []
# node_list[i]['anomalies'] = []
# node_list[i]['visited'] = False
# for e in self.graph.edges:
# self.graph.edges[e]['visited'] = False
# self.graph.edges[e]['used'] = False
return nx.max_weight_matching(tmp_graph,
maxcardinality=True,
weight='weight')
def max_weight_matching(Mrkt, Agents, verbose=False, maxcardinality=True):
"""
Computes max-weight matching of graph of inputted agents
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
Implemented by NetworkX
Runtime: O(n^3) for n nodes
Arguments
---------
Mrkt: mm.Market object
The market in which the matches are made
Agents: list
list of agents initiating matches
maxcardinality: bool
if True, compute the maximum-cardinality matching
with maximum weight among all maximum-cardinality matchings.
verbose: bool
Whether algorithm prints information on action
Returns
-------
dict { agent.name : agent.name } of matches
Reference:
“Efficient Algorithms for Finding Maximum Matching in Graphs”,
Zvi Galil, ACM Computing Surveys, 1986.
"""
if verbose:
print("\nMax Weight Match Algorithm\n")
print("Agents to match ", [a.name for a in Agents], "\n")
# If Agents not whole market, get subgraph
if len(Mrkt.Graph.nodes()) != len(Agents):
to_match = deepcopy(Mrkt.Graph.subgraph(Agents))
else:
to_match = Mrkt.Graph
# Workaround of assertionerror in Nx verifyOptimum() function
# Breaks around integer weights
for u, v, d in to_match.edges(data=True):
d['weight'] = float(d['weight'])
mate = nx.max_weight_matching(to_match, maxcardinality=maxcardinality)
result = {a.name: mate[a].name for a in mate}
return result
def test_gnp_random_against_networkx_with_weight(self):
for i in range(1024):
# TODO: add back subTest usage on new testtools release
random.seed(i)
rx_graph = retworkx.undirected_gnp_random_graph(10,
.75,
seed=42 + i)
for edge in rx_graph.edge_list():
rx_graph.update_edge(*edge, random.randint(0, 5000))
nx_graph = networkx.Graph([(x[0], x[1], {
'weight': x[2]
}) for x in rx_graph.weighted_edge_list()])
nx_matches = networkx.max_weight_matching(nx_graph)
rx_matches = retworkx.max_weight_matching(rx_graph,
weight_fn=lambda x: x,
verify_optimum=True)
self.compare_rx_nx_sets(rx_graph, rx_matches, nx_matches, 42 + i,
nx_graph)
def single_chinese_postman_path(graph):
"""
Given a graph, return a list of node id's forming the shortest chinese postman path.
If we assume V' (number of nodes with odd degree) is at least some constant fraction of V (total number of nodes),
say 10%, the overall complexity is O(V^3).
"""
# Build a fully-connected graph containing only the odd edges. Complexity: O(V'*(E + V log(V)) )
odd = odd_graph(graph)
# Find the best matching of pairs of odd nodes. Complexity: O(V'^3)
matching = nx.max_weight_matching(odd, True)
# Complexity of the remainder is less approximately O(E)
eulerian_graph = build_eulerian_graph(graph, odd, matching)
nodes = eulerian_circuit(eulerian_graph)
return eulerian_graph, nodes
def getMaxWeightMatching(G):
"""Returns the maximum weighted matching of G as dict"""
# initialize matching as dict
matching = {}
# iterate over each connected component C of G (for better runtime performance)
for C in nx.connected_component_subgraphs(G) :
# TODO get maximum cardinality!!!
# compute maximum weight matching for connected component C
m = nx.max_weight_matching(C, maxcardinality=True)
# append it to the matching of the entire graph G
matching.update(m)
return matching
def _min_weight_matching(graph):
"""
Finds a perfect matching with minimum weight
"""
for v1, v2 in graph.edges_iter():
graph[v1][v2]["weight"] = -graph[v1][v2]["weight"] #want minimum weght
MIN_LOG_SIZE = 20
if len(graph) > MIN_LOG_SIZE:
logger.debug("Finding perfect matching for a component of "
"size {0}".format(len(graph)))
edges = nx.max_weight_matching(graph, maxcardinality=True)
unique_edges = set()
for v1, v2 in edges.items():
if not (v2, v1) in unique_edges:
unique_edges.add((v1, v2))
return list(unique_edges)
def getMergedRides(self, combinedRideWithDist):
mergeableTripGraph = nx.Graph()
mergeableTripGraph.add_weighted_edges_from(combinedRideWithDist)
matching_dictionary = nx.max_weight_matching(mergeableTripGraph,
maxcardinality=True)
#print(matching_dictionary)
return matching_dictionary
#g = GraphMatching::Graph::WeightedGraph[
# [1, 2, 10],
# [1, 3, 11]
#]
#m = g.maximum_weighted_matching
#print(m.edges)
##=> [[3, 1]]
#print(m.weight(g))
##=> 11
