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breadth_first_traversal.py
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breadth_first_traversal.py
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"""
Breadth-first-traversal is an algorithm for traversing a tree or
graph data structure. Starting at the tree root (or some arbitrary node of a
graph, sometimes referred to as a 'search key'[1]) and explores the neighbor
nodes at that level first, before moving to the next level.
"""
from collections import deque
def breadth_first_traversal(graph, source):
""" Performs a breadth-first traversal on a graph
Args:
graph (list of list of int): Adjacency matrix representation of graph
source (int): Index of source vertex to begin search from
Returns:
list of dicts describing each vertex in the searched graph
-> [{distance: _, predecessor: _ }]
"""
vertex_info = []
for i in range(len(graph)):
vertex_info.append({"distance": None, "predecessor": None})
vertex_info[source]["distance"] = 0
search_queue = deque()
search_queue.append(source)
while search_queue:
u = search_queue.popleft()
for v in graph[u]:
if vertex_info[v]["distance"] is None:
vertex_info[v]["distance"] = vertex_info[u]["distance"] + 1
vertex_info[v]["predecessor"] = u
search_queue.append(v)
return vertex_info
def main():
graph_adj_list = [
[1],
[0, 4, 5],
[3, 4, 5],
[2, 6],
[1, 2],
[1, 2, 6],
[3, 5],
[]
]
vertex_info = breadth_first_traversal(graph_adj_list, 3)
for i in range(len(graph_adj_list)):
print("vertex %s : distance = %s, predecessor = %s" %
(i, vertex_info[i]["distance"], vertex_info[i]["predecessor"]))
assert(vertex_info[0] == {
"distance": 4,
"predecessor": 1
})
assert(vertex_info[1] == {
"distance": 3,
"predecessor": 4
})
assert(vertex_info[2] == {
"distance": 1,
"predecessor": 3
})
assert(vertex_info[3] == {
"distance": 0,
"predecessor": None
})
assert(vertex_info[4] == {
"distance": 2,
"predecessor": 2
})
assert(vertex_info[5] == {
"distance": 2,
"predecessor": 2
})
assert(vertex_info[6] == {
"distance": 1,
"predecessor": 3
})
assert(vertex_info[7] == {
"distance": None,
"predecessor": None
})
if __name__ == '__main__':
main()