mirror of
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ae0fc85401
* Fix ruff errors
Renamed neural_network/input_data.py to neural_network/input_data.py_tf
because it should be left out of the directory for the following
reasons:
1. Its sole purpose is to be used by neural_network/gan.py_tf, which is
itself left out of the directory because of issues with TensorFlow.
2. It was taken directly from TensorFlow's codebase and is actually
already deprecated. If/when neural_network/gan.py_tf is eventually
re-added back to the directory, its implementation should be changed
to not use neural_network/input_data.py anyway.
* updating DIRECTORY.md
* Change input_data.py_tf file extension
Change input_data.py_tf file extension because algorithms-keeper bot is being picky about it
---------
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
140 lines
3.3 KiB
Python
140 lines
3.3 KiB
Python
"""
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Bi-directional Dijkstra's algorithm.
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A bi-directional approach is an efficient and
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less time consuming optimization for Dijkstra's
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searching algorithm
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Reference: shorturl.at/exHM7
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"""
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# Author: Swayam Singh (https://github.com/practice404)
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from queue import PriorityQueue
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from typing import Any
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import numpy as np
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def pass_and_relaxation(
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graph: dict,
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v: str,
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visited_forward: set,
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visited_backward: set,
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cst_fwd: dict,
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cst_bwd: dict,
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queue: PriorityQueue,
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parent: dict,
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shortest_distance: float,
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) -> float:
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for nxt, d in graph[v]:
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if nxt in visited_forward:
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continue
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old_cost_f = cst_fwd.get(nxt, np.inf)
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new_cost_f = cst_fwd[v] + d
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if new_cost_f < old_cost_f:
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queue.put((new_cost_f, nxt))
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cst_fwd[nxt] = new_cost_f
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parent[nxt] = v
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if nxt in visited_backward:
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if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
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shortest_distance = cst_fwd[v] + d + cst_bwd[nxt]
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return shortest_distance
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def bidirectional_dij(
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source: str, destination: str, graph_forward: dict, graph_backward: dict
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) -> int:
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"""
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Bi-directional Dijkstra's algorithm.
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Returns:
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shortest_path_distance (int): length of the shortest path.
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Warnings:
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If the destination is not reachable, function returns -1
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>>> bidirectional_dij("E", "F", graph_fwd, graph_bwd)
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3
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"""
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shortest_path_distance = -1
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visited_forward = set()
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visited_backward = set()
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cst_fwd = {source: 0}
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cst_bwd = {destination: 0}
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parent_forward = {source: None}
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parent_backward = {destination: None}
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queue_forward: PriorityQueue[Any] = PriorityQueue()
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queue_backward: PriorityQueue[Any] = PriorityQueue()
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shortest_distance = np.inf
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queue_forward.put((0, source))
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queue_backward.put((0, destination))
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if source == destination:
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return 0
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while not queue_forward.empty() and not queue_backward.empty():
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_, v_fwd = queue_forward.get()
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visited_forward.add(v_fwd)
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_, v_bwd = queue_backward.get()
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visited_backward.add(v_bwd)
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shortest_distance = pass_and_relaxation(
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graph_forward,
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v_fwd,
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visited_forward,
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visited_backward,
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cst_fwd,
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cst_bwd,
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queue_forward,
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parent_forward,
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shortest_distance,
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)
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shortest_distance = pass_and_relaxation(
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graph_backward,
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v_bwd,
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visited_backward,
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visited_forward,
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cst_bwd,
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cst_fwd,
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queue_backward,
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parent_backward,
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shortest_distance,
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)
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if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
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break
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if shortest_distance != np.inf:
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shortest_path_distance = shortest_distance
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return shortest_path_distance
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graph_fwd = {
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"B": [["C", 1]],
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"C": [["D", 1]],
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"D": [["F", 1]],
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"E": [["B", 1], ["G", 2]],
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"F": [],
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"G": [["F", 1]],
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}
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graph_bwd = {
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"B": [["E", 1]],
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"C": [["B", 1]],
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"D": [["C", 1]],
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"F": [["D", 1], ["G", 1]],
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"E": [[None, np.inf]],
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"G": [["E", 2]],
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}
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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