def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError("the graph is directed")
self.graph = graph
self.color = dict()
self.m = 0 # graph.e() is slow
for edge in self.graph.iteredges():
if edge.source == edge.target:
raise ValueError("a loop detected")
else:
self.color[edge] = None # edge.source < edge.target
self.m += 1
if len(self.color) < self.m:
raise ValueError("edges are not unique")
algorithm = Bipartite(self.graph) # O(V+E) time
algorithm.run()
# Dictionaries for node indices.
self.D1 = dict()
self.D2 = dict()
idx1 = 0
idx2 = 0
for node in self.graph.iternodes(): # O(V) time
if algorithm.color[node] == 1:
self.D1[node] = idx1
idx1 += 1
else:
self.D2[node] = idx2
idx2 += 1
if self.m != len(self.D1) * len(self.D2):
raise ValueError("the graph is not complete bipartite")
def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError("the graph is directed")
self.graph = graph
self.color = dict()
self.m = 0 # graph.e() is slow
for edge in self.graph.iteredges():
if edge.source == edge.target:
raise ValueError("a loop detected")
else:
self.color[edge] = None # edge.source < edge.target
self.m += 1
if len(self.color) < self.m:
raise ValueError("edges are not unique")
# Tast czy graf jest dwudzielny.
# Wlasciwie potem nie jest jawnie potrzebny podzial wierzcholkow.
algorithm = Bipartite(self.graph) # O(V+E) time
algorithm.run()
# dict with missing colors for nodes.
self.missing = None
def __init__(self, graph):
"""The algorithm initialization.
Parameters
----------
graph : undirected graph
"""
if graph.is_directed():
raise ValueError("the graph is directed")
self.graph = graph
self.mate = dict((node, None) for node in self.graph.iternodes())
self.distance = dict()
self.cardinality = 0
algorithm = Bipartite(self.graph)
algorithm.run()
self.v1 = set()
self.v2 = set()
for node in self.graph.iternodes():
if algorithm.color[node] == 1:
self.v1.add(node)
else:
self.v2.add(node)
self.Q = Queue() # for nodes from self.v1