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[mypy] Fix type annotations in data_structures/binary_tree/lowest_common_ancestor.py #5757

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Merged
merged 2 commits into from
Nov 3, 2021
Merged

[mypy] Fix type annotations in data_structures/binary_tree/lowest_common_ancestor.py #5757

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9316580
Fix type annotations in lowest_common_ancestor.py
dylanbuchi Nov 3, 2021
c022c32
Refactor line 53 in lowest_common_ancestor.py
dylanbuchi Nov 3, 2021
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14 changes: 7 additions & 7 deletions data_structures/binary_tree/lowest_common_ancestor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@

from __future__ import annotations

import queue
from queue import Queue


def swap(a: int, b: int) -> tuple[int, int]:
Expand Down Expand Up @@ -37,7 +37,7 @@ def create_sparse(max_node: int, parent: list[list[int]]) -> list[list[int]]:
# returns lca of node u,v
def lowest_common_ancestor(
u: int, v: int, level: list[int], parent: list[list[int]]
) -> list[list[int]]:
) -> int:
# u must be deeper in the tree than v
if level[u] < level[v]:
u, v = swap(u, v)
Expand All @@ -50,7 +50,7 @@ def lowest_common_ancestor(
return u
# moving both nodes upwards till lca in found
for i in range(18, -1, -1):
if parent[i][u] != 0 and parent[i][u] != parent[i][v]:
if parent[i][u] not in [0, parent[i][v]]:
u, v = parent[i][u], parent[i][v]
# returning longest common ancestor of u,v
return parent[0][u]
Expand All @@ -61,16 +61,16 @@ def breadth_first_search(
level: list[int],
parent: list[list[int]],
max_node: int,
graph: dict[int, int],
root=1,
graph: dict[int, list[int]],
root: int = 1,
) -> tuple[list[int], list[list[int]]]:
"""
sets every nodes direct parent
parent of root node is set to 0
calculates depth of each node from root node
"""
level[root] = 0
q = queue.Queue(maxsize=max_node)
q: Queue[int] = Queue(maxsize=max_node)
q.put(root)
while q.qsize() != 0:
u = q.get()
Expand All @@ -88,7 +88,7 @@ def main() -> None:
parent = [[0 for _ in range(max_node + 10)] for _ in range(20)]
# initializing with -1 which means every node is unvisited
level = [-1 for _ in range(max_node + 10)]
graph = {
graph: dict[int, list[int]] = {
1: [2, 3, 4],
2: [5],
3: [6, 7],
Expand Down