def is_biconnected(graph):
"""Test if the undirected graph is biconnected."""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
return False
algorithm = TarjanCutNode(graph)
algorithm.run()
return len(algorithm.cut_nodes) == 0
def is_biconnected(graph):
"""Test if the undirected graph is biconnected."""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
return False
algorithm = TarjanCutNode(graph)
algorithm.run()
return len(algorithm.cut_nodes) == 0
def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
raise ValueError("the graph is not connected")
self.graph = graph
self.color = dict((node, None) for node in self.graph.iternodes())
self._color_list = [False] * self.graph.v()
def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
raise ValueError("the graph is not connected")
self.graph = graph
self.color = dict((node, None) for node in self.graph.iternodes())
self._color_list = [False] * self.graph.v()
def __init__(self, graph):
"""The algorithm initialization.
Parameters
----------
graph : undirected weighted graph or multigraph
"""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
raise ValueError("the graph is not connected")
self.graph = graph
self.mst = self.graph.__class__(self.graph.v(), directed=False)
self._in_queue = dict((node, True) for node in self.graph.iternodes())
self._pq = PriorityQueue()
def __init__(self, graph):
"""The algorithm initialization.
Parameters
----------
graph : undirected weighted graph or multigraph
"""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
raise ValueError("the graph is not connected")
self.graph = graph
self.mst = self.graph.__class__(self.graph.v(), directed=False)
self._in_queue = dict((node, True) for node in self.graph.iternodes())
self._pq = PriorityQueue()
def __init__(self, graph):
"""The algorithm initialization.
Parameters
----------
graph : undirected weighted graph or multigraph
"""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
raise ValueError("the graph is not connected")
self.graph = graph
self.mst = self.graph.__class__(self.graph.v(), directed=False)
for node in self.graph.iternodes(): # isolated nodes are possible
self.mst.add_node(node)
self._in_mst = dict((node, False) for node in self.graph.iternodes())
def __init__(self, graph):
"""The algorithm initialization.
Parameters
----------
graph : undirected weighted graph or multigraph
"""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
raise ValueError("the graph is not connected")
self.graph = graph
self.mst = self.graph.__class__(self.graph.v(), directed=False)
for node in self.graph.iternodes(): # isolated nodes are possible
self.mst.add_node(node)
self._in_mst = dict((node, False) for node in self.graph.iternodes())
def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
raise ValueError("the graph is not connected")
self.graph = graph
self.color = dict((node, None) for node in self.graph.iternodes())
self.order = list()
self.parent = dict()
self.m = 0 # graph.e() is slow
for edge in self.graph.iteredges():
self.m += 1
if edge.source == edge.target:
raise ValueError("a loop detected")
if 2 * self.m == self.graph.v() * (self.graph.v() - 1):
raise ValueError("complete graph detected")
def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError("the graph is directed")
if not is_connected(graph):
raise ValueError("the graph is not connected")
self.graph = graph
self.color = dict((node, None) for node in self.graph.iternodes())
self.order = list()
self.parent = dict()
self.m = 0 # graph.e() is slow
for edge in self.graph.iteredges():
self.m += 1
if edge.source == edge.target:
raise ValueError("a loop detected")
if 2 * self.m == self.graph.v() * (self.graph.v() - 1):
raise ValueError("complete graph detected")
self._color_list = [False] * self.graph.v()
from graphtheory.spanningtrees.kruskal import KruskalMST from graphtheory.spanningtrees.kruskal import KruskalMSTSorted V = 50 graph_factory = GraphFactory(Graph) G = graph_factory.make_random(V, False, 0.5) #G = graph_factory.make_complete(V, False) #G = graph_factory.make_cyclic(V, False) E = G.e() #G.show() print "Calculate parameters ..." print "Nodes:", G.v(), V print "Edges:", G.e(), E print "Directed:", G.is_directed() print "Connected:", is_connected(G) print "Biconnected:", is_biconnected(G) print "Acyclic:", is_acyclic(G) print "Bipartite:", is_bipartite(G) Delta = max(G.degree(node) for node in G.iternodes()) print "Delta:", Delta print "Testing BoruvkaMST ..." t1 = timeit.Timer(lambda: BoruvkaMST(G).run()) print V, E, t1.timeit(1) # single run print "Testing PrimMST ..." t1 = timeit.Timer(lambda: PrimMST(G).run()) print V, E, t1.timeit(1) # single run print "Testing PrimMSTWithEdges ..."
from graphtheory.spanningtrees.kruskal import KruskalMST from graphtheory.spanningtrees.kruskal import KruskalMSTSorted V = 10 graph_factory = GraphFactory(Graph) G = graph_factory.make_random(V, False, 0.5) #G = graph_factory.make_complete(V, False) #G = graph_factory.make_cyclic(V, False) E = G.e() #G.show() print "Calculate parameters ..." print "Nodes:", G.v(), V print "Edges:", G.e(), E print "Directed:", G.is_directed() print "Connected:", is_connected(G) print "Biconnected:", is_biconnected(G) print "Acyclic:", is_acyclic(G) print "Bipartite:", is_bipartite(G) Delta = max(G.degree(node) for node in G.iternodes()) print "Delta:", Delta print "Testing BoruvkaMST ..." t1 = timeit.Timer(lambda: BoruvkaMST(G).run()) print V, E, t1.timeit(1) # single run print "Testing PrimMST ..." t1 = timeit.Timer(lambda: PrimMST(G).run()) print V, E, t1.timeit(1) # single run print "Testing PrimMatrixMST ..."
from graphtheory.spanningtrees.kruskal import KruskalMST
from graphtheory.spanningtrees.kruskal import KruskalMSTSorted
V = 50
graph_factory = GraphFactory(Graph)
G = graph_factory.make_random(V, False, 0.5)
#G = graph_factory.make_complete(V, False)
#G = graph_factory.make_cyclic(V, False)
E = G.e()
#G.show()
print ( "Calculate parameters ..." )
print ( "Nodes: {} {}".format(G.v(), V) )
print ( "Edges: {} {}".format(G.e(), E) )
print ( "Directed: {}".format(G.is_directed()) )
print ( "Connected: {}".format(is_connected(G)) )
print ( "Biconnected: {}".format(is_biconnected(G)) )
print ( "Acyclic: {}".format(is_acyclic(G)) )
print ( "Bipartite: {}".format(is_bipartite(G)) )
Delta = max(G.degree(node) for node in G.iternodes())
print ( "Delta: {}".format(Delta) )
print ("Testing BoruvkaMST ..." )
t1 = timeit.Timer(lambda: BoruvkaMST(G).run())
print ( "{} {} {}".format(V, E, t1.timeit(1)) ) # single run
print ("Testing PrimMST ..." )
t1 = timeit.Timer(lambda: PrimMST(G).run())
print ( "{} {} {}".format(V, E, t1.timeit(1)) ) # single run
print ("Testing PrimMSTWithEdges ..." )
def test_is_connected(self):
self.assertFalse(is_connected(self.G))
self.G.add_edge(Edge(5, 6))
self.assertTrue(is_connected(self.G))
self.assertRaises(ValueError, is_connected, Graph(1, True))
def test_is_connected(self):
self.assertFalse(is_connected(self.G))
self.G.add_edge(Edge(5, 6))
self.assertTrue(is_connected(self.G))
self.assertRaises(ValueError, is_connected, Graph(1, True))
