when is breadth-first search is optimal code example
Example 1: breadth first traversal python program
class Graph:
def __init__(self):
self.vertices = {}
def add_vertex(self, key):
"""Add a vertex with the given key to the graph."""
vertex = Vertex(key)
self.vertices[key] = vertex
def get_vertex(self, key):
"""Return vertex object with the corresponding key."""
return self.vertices[key]
def __contains__(self, key):
return key in self.vertices
def add_edge(self, src_key, dest_key, weight=1):
"""Add edge from src_key to dest_key with given weight."""
self.vertices[src_key].add_neighbour(self.vertices[dest_key], weight)
def does_edge_exist(self, src_key, dest_key):
"""Return True if there is an edge from src_key to dest_key."""
return self.vertices[src_key].does_it_point_to(self.vertices[dest_key])
def __iter__(self):
return iter(self.vertices.values())
class Vertex:
def __init__(self, key):
self.key = key
self.points_to = {}
def get_key(self):
"""Return key corresponding to this vertex object."""
return self.key
def add_neighbour(self, dest, weight):
"""Make this vertex point to dest with given edge weight."""
self.points_to[dest] = weight
def get_neighbours(self):
"""Return all vertices pointed to by this vertex."""
return self.points_to.keys()
def get_weight(self, dest):
"""Get weight of edge from this vertex to dest."""
return self.points_to[dest]
def does_it_point_to(self, dest):
"""Return True if this vertex points to dest."""
return dest in self.points_to
class Queue:
def __init__(self):
self.items = []
def is_empty(self):
return self.items == []
def enqueue(self, data):
self.items.append(data)
def dequeue(self):
return self.items.pop(0)
def display_bfs(vertex):
"""Display BFS Traversal starting at vertex."""
visited = set()
q = Queue()
q.enqueue(vertex)
visited.add(vertex)
while not q.is_empty():
current = q.dequeue()
print(current.get_key(), end=' ')
for dest in current.get_neighbours():
if dest not in visited:
visited.add(dest)
q.enqueue(dest)
g = Graph()
print('Menu')
print('add vertex <key>')
print('add edge <src> <dest>')
print('bfs <vertex key>')
print('display')
print('quit')
while True:
do = input('What would you like to do? ').split()
operation = do[0]
if operation == 'add':
suboperation = do[1]
if suboperation == 'vertex':
key = int(do[2])
if key not in g:
g.add_vertex(key)
else:
print('Vertex already exists.')
elif suboperation == 'edge':
src = int(do[2])
dest = int(do[3])
if src not in g:
print('Vertex {} does not exist.'.format(src))
elif dest not in g:
print('Vertex {} does not exist.'.format(dest))
else:
if not g.does_edge_exist(src, dest):
g.add_edge(src, dest)
else:
print('Edge already exists.')
elif operation == 'bfs':
key = int(do[1])
print('Breadth-first Traversal: ', end='')
vertex = g.get_vertex(key)
display_bfs(vertex)
print()
elif operation == 'display':
print('Vertices: ', end='')
for v in g:
print(v.get_key(), end=' ')
print()
print('Edges: ')
for v in g:
for dest in v.get_neighbours():
w = v.get_weight(dest)
print('(src={}, dest={}, weight={}) '.format(v.get_key(),
dest.get_key(), w))
print()
elif operation == 'quit':
break
Example 2: breadth first search
procedure BFS(G, start_v) is
2 let Q be a queue
3 label start_v as discovered
4 Q.enqueue(start_v)
5 while Q is not empty do
6 v := Q.dequeue()
7 if v is the goal then
8 return v
9 for all edges from v to w in G.adjacentEdges(v) do
10 if w is not labeled as discovered then
11 label w as discovered
12 w.parent := v
13 Q.enqueue(w)