def KruskalsAlgorithm(g):
"""
(Graph) -> GraphAsLists
Kruskal's algorithm to find a minimum-cost spanning tree
for the given edge-weighted, undirected graph.
"""
n = g.numberOfVertices
result = GraphAsLists(n)
for v in xrange(n):
result.addVertex(v)
queue = BinaryHeap(g.numberOfEdges)
for e in g.edges:
weight = e.weight
queue.enqueue(Association(weight, e))
partition = PartitionAsForest(n)
while not queue.isEmpty and partition.count > 1:
assoc = queue.dequeueMin()
e = assoc.value
n0 = e.v0.number
n1 = e.v1.number
s = partition.find(n0)
t = partition.find(n1)
if s != t:
partition.join(s, t)
result.addEdge(n0, n1)
return result
def KruskalsAlgorithm(g):
"""
(Graph) -> GraphAsLists
Kruskal's algorithm to find a minimum-cost spanning tree
for the given edge-weighted, undirected graph.
"""
n = g.numberOfVertices
result = GraphAsLists(n)
for v in range(n):
result.addVertex(v)
queue = BinaryHeap(g.numberOfEdges)
for e in g.edges:
weight = e.weight
queue.enqueue(Association(weight, e))
partition = PartitionAsForest(n)
while not queue.isEmpty and partition.count > 1:
assoc = queue.dequeueMin()
e = assoc.value
n0 = e.v0.number
n1 = e.v1.number
s = partition.find(n0)
t = partition.find(n1)
if s != t:
partition.join(s, t)
result.addEdge(n0, n1)
return result
def main(*argv):
"Demostration program number 6."
print Demo6.main.__doc__
BinaryHeap.main(*argv)
LeftistHeap.main(*argv)
BinomialQueue.main(*argv)
Deap.main(*argv)
return 0
def PrimsAlgorithm(g, s):
"""
(Graph, int) -> GraphAsLists
Prim's algorithm to find a minimum-cost spanning tree
for the given edge-weighted, undirected graph.
"""
n = g.numberOfVertices
table = Array(n)
for v in range(n):
table[v] = Algorithms.Entry()
table[s].distance = 0
queue = BinaryHeap(g.numberOfEdges)
queue.enqueue(Association(0, g[s]))
while not queue.isEmpty:
assoc = queue.dequeueMin()
v0 = assoc.value
if not table[v0.number].known:
table[v0.number].known = True
for e in v0.emanatingEdges:
v1 = e.mateOf(v0)
d = e.weight
if not table[v1.number].known and \
table[v1.number].distance > d:
table[v1.number].distance = d
table[v1.number].predecessor = v0.number
queue.enqueue(Association(d, v1))
result = GraphAsLists(n)
for v in range(n):
result.addVertex(v)
for v in range(n):
if v != s:
result.addEdge(v, table[v].predecessor)
return result
def DijkstrasAlgorithm(g, s):
"""
(Digraph, int) -> DigraphAsLists
Dijkstra's algorithm to solve the single-source, shortest path problem
for the given edge-weighted, directed graph.
"""
n = g.numberOfVertices
table = Array(n)
for v in range(n):
table[v] = Algorithms.Entry()
table[s].distance = 0
queue = BinaryHeap(g.numberOfEdges)
queue.enqueue(Association(0, g[s]))
while not queue.isEmpty:
assoc = queue.dequeueMin()
v0 = assoc.value
if not table[v0.number].known:
table[v0.number].known = True
for e in v0.emanatingEdges:
v1 = e.mateOf(v0)
d = table[v0.number].distance + e.weight
if table[v1.number].distance > d:
table[v1.number].distance = d
table[v1.number].predecessor = v0.number
queue.enqueue(Association(d, v1))
result = DigraphAsLists(n)
for v in range(n):
result.addVertex(v, table[v].distance)
for v in range(n):
if v != s:
result.addEdge(v, table[v].predecessor)
return result
def PrimsAlgorithm(g, s):
"""
(Graph, int) -> GraphAsLists
Prim's algorithm to find a minimum-cost spanning tree
for the given edge-weighted, undirected graph.
"""
n = g.numberOfVertices
table = Array(n)
for v in xrange(n):
table[v] = Algorithms.Entry()
table[s].distance = 0
queue = BinaryHeap(g.numberOfEdges)
queue.enqueue(Association(0, g[s]))
while not queue.isEmpty:
assoc = queue.dequeueMin()
v0 = assoc.value
if not table[v0.number].known:
table[v0.number].known = True
for e in v0.emanatingEdges:
v1 = e.mateOf(v0)
d = e.weight
if not table[v1.number].known and \
table[v1.number].distance > d:
table[v1.number].distance = d
table[v1.number].predecessor = v0.number
queue.enqueue(Association(d, v1))
result = GraphAsLists(n)
for v in xrange(n):
result.addVertex(v)
for v in xrange(n):
if v != s:
result.addEdge(v, table[v].predecessor)
return result
def DijkstrasAlgorithm(g, s):
"""
(Digraph, int) -> DigraphAsLists
Dijkstra's algorithm to solve the single-source, shortest path problem
for the given edge-weighted, directed graph.
"""
n = g.numberOfVertices
table = Array(n)
for v in xrange(n):
table[v] = Algorithms.Entry()
table[s].distance = 0
queue = BinaryHeap(g.numberOfEdges)
queue.enqueue(Association(0, g[s]))
while not queue.isEmpty:
assoc = queue.dequeueMin()
v0 = assoc.value
if not table[v0.number].known:
table[v0.number].known = True
for e in v0.emanatingEdges:
v1 = e.mateOf(v0)
d = table[v0.number].distance + e.weight
if table[v1.number].distance > d:
table[v1.number].distance = d
table[v1.number].predecessor = v0.number
queue.enqueue(Association(d, v1))
result = DigraphAsLists(n)
for v in xrange(n):
result.addVertex(v, table[v].distance)
for v in xrange(n):
if v != s:
result.addEdge(v, table[v].predecessor)
return result
