python-patterns/patterns/other/graph_search.py at master · dolfly/python-patterns · GitHub

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class GraphSearch:

"""Graph search emulation in python, from source

http://www.python.org/doc/essays/graphs/

dfs stands for Depth First Search

bfs stands for Breadth First Search"""

def __init__(self, graph: dict[str, list[str]]) -> None:

self.graph = graph

def find_path_dfs(

self, start: str, end: str, path: list[str] | None = None

) -> list[str] | None:

path = path or []

path.append(start)

if start == end:

return path

for node in self.graph.get(start, []):

if node not in path:

newpath = self.find_path_dfs(node, end, path[:])

if newpath:

return newpath

return None

def find_all_paths_dfs(

self, start: str, end: str, path: list[str] | None = None

) -> list[list[str]]:

path = path or []

path.append(start)

if start == end:

return [path]

paths: list[list[str]] = []

for node in self.graph.get(start, []):

if node not in path:

newpaths = self.find_all_paths_dfs(node, end, path[:])

paths.extend(newpaths)

return paths

def find_shortest_path_dfs(

self, start: str, end: str, path: list[str] | None = None

) -> list[str] | None:

path = path or []

path.append(start)

if start == end:

return path

shortest = None

for node in self.graph.get(start, []):

if node not in path:

newpath = self.find_shortest_path_dfs(node, end, path[:])

if newpath:

if not shortest or len(newpath) < len(shortest):

shortest = newpath

return shortest

def find_shortest_path_bfs(self, start: str, end: str) -> list[str] | None:

"""

Finds the shortest path between two nodes in a graph using breadth-first search.

:param start: The node to start from.

:type start: str or int

:param end: The node to find the shortest path to.

:type end: str or int

:returns queue_path_to_end, dist_to[end]: A list of nodes

representing the shortest path from `start` to `end`, and a dictionary

mapping each node in the graph (except for `start`) with its distance from it

(in terms of hops). If no such path exists, returns an empty list and an empty

dictionary instead.

"""

queue: list[str] = [start]

dist_to: dict[str, int] = {start: 0}

edge_to: dict[str, str] = {}

if start == end:

return queue

while len(queue):

value = queue.pop(0)

for node in self.graph[value]:

if node not in dist_to.keys():

edge_to[node] = value

dist_to[node] = dist_to[value] + 1

queue.append(node)

if end in edge_to.keys():

path: list[str] = []

node = end

while dist_to[node] != 0:

path.insert(0, node)

node = edge_to[node]

path.insert(0, start)

return path

return None

def main():

"""

# example of graph usage

>>> graph = {

... 'A': ['B', 'C'],

... 'B': ['C', 'D'],

... 'C': ['D', 'G'],

... 'D': ['C'],

... 'E': ['F'],

... 'F': ['C'],

... 'G': ['E'],

... 'H': ['C']

... }

# initialization of new graph search object

>>> graph_search = GraphSearch(graph)

>>> print(graph_search.find_path_dfs('A', 'D'))

['A', 'B', 'C', 'D']

# start the search somewhere in the middle

>>> print(graph_search.find_path_dfs('G', 'F'))

['G', 'E', 'F']

# unreachable node

>>> print(graph_search.find_path_dfs('C', 'H'))

None

# non existing node

>>> print(graph_search.find_path_dfs('C', 'X'))

None

>>> print(graph_search.find_all_paths_dfs('A', 'D'))

[['A', 'B', 'C', 'D'], ['A', 'B', 'D'], ['A', 'C', 'D']]

>>> print(graph_search.find_shortest_path_dfs('A', 'D'))

['A', 'B', 'D']

>>> print(graph_search.find_shortest_path_dfs('A', 'F'))

['A', 'C', 'G', 'E', 'F']

>>> print(graph_search.find_shortest_path_bfs('A', 'D'))

['A', 'B', 'D']

>>> print(graph_search.find_shortest_path_bfs('A', 'F'))

['A', 'C', 'G', 'E', 'F']

# start the search somewhere in the middle

>>> print(graph_search.find_shortest_path_bfs('G', 'F'))

['G', 'E', 'F']

# unreachable node

>>> print(graph_search.find_shortest_path_bfs('A', 'H'))

None

# non existing node

>>> print(graph_search.find_shortest_path_bfs('A', 'X'))

None

"""

if __name__ == "__main__":

import doctest

doctest.testmod()

python-patterns/patterns/other/graph_search.py at master · dolfly/python-patterns · GitHub

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