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UPDATED rat_in_maze.py (#9148)
* UPDATED rat_in_maze.py * Update reddit.py in Webprogramming b/c it was causing error in pre-commit tests while raising PR. * UPDATED rat_in_maze.py * fixed return type to only maze,otherwise raise valueError. * fixed whitespaces error,improved matrix visual. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * updated. * Try * updated * updated * Apply suggestions from code review --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com>
This commit is contained in:
Muhammad Umer Farooq
GitHub
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@ -1,91 +1,164 @@
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from __future__ import annotations
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def solve_maze(maze: list[list[int]]) -> bool:
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def solve_maze(
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maze: list[list[int]],
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source_row: int,
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source_column: int,
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destination_row: int,
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destination_column: int,
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) -> list[list[int]]:
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"""
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This method solves the "rat in maze" problem.
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In this problem we have some n by n matrix, a start point and an end point.
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We want to go from the start to the end. In this matrix zeroes represent walls
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and ones paths we can use.
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Parameters :
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maze(2D matrix) : maze
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- maze: A two dimensional matrix of zeros and ones.
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- source_row: The row index of the starting point.
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- source_column: The column index of the starting point.
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- destination_row: The row index of the destination point.
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- destination_column: The column index of the destination point.
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Returns:
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Return: True if the maze has a solution or False if it does not.
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- solution: A 2D matrix representing the solution path if it exists.
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Raises:
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- ValueError: If no solution exists or if the source or
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destination coordinates are invalid.
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Description:
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This method navigates through a maze represented as an n by n matrix,
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starting from a specified source cell and
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aiming to reach a destination cell.
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The maze consists of walls (1s) and open paths (0s).
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By providing custom row and column values, the source and destination
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cells can be adjusted.
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>>> maze = [[0, 1, 0, 1, 1],
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... [0, 0, 0, 0, 0],
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... [1, 0, 1, 0, 1],
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... [0, 0, 1, 0, 0],
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... [1, 0, 0, 1, 0]]
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>>> solve_maze(maze)
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[1, 0, 0, 0, 0]
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[1, 1, 1, 1, 0]
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[0, 0, 0, 1, 0]
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[0, 0, 0, 1, 1]
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[0, 0, 0, 0, 1]
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True
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>>> solve_maze(maze,0,0,len(maze)-1,len(maze)-1) # doctest: +NORMALIZE_WHITESPACE
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[[0, 1, 1, 1, 1],
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[0, 0, 0, 0, 1],
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[1, 1, 1, 0, 1],
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[1, 1, 1, 0, 0],
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[1, 1, 1, 1, 0]]
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Note:
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In the output maze, the zeros (0s) represent one of the possible
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paths from the source to the destination.
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>>> maze = [[0, 1, 0, 1, 1],
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... [0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 1],
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... [0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 0]]
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>>> solve_maze(maze)
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[1, 0, 0, 0, 0]
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[1, 0, 0, 0, 0]
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[1, 0, 0, 0, 0]
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[1, 0, 0, 0, 0]
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[1, 1, 1, 1, 1]
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True
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>>> solve_maze(maze,0,0,len(maze)-1,len(maze)-1) # doctest: +NORMALIZE_WHITESPACE
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[[0, 1, 1, 1, 1],
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[0, 1, 1, 1, 1],
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[0, 1, 1, 1, 1],
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[0, 1, 1, 1, 1],
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[0, 0, 0, 0, 0]]
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>>> maze = [[0, 0, 0],
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... [0, 1, 0],
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... [1, 0, 0]]
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>>> solve_maze(maze)
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[1, 1, 1]
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[0, 0, 1]
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[0, 0, 1]
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True
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>>> solve_maze(maze,0,0,len(maze)-1,len(maze)-1) # doctest: +NORMALIZE_WHITESPACE
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[[0, 0, 0],
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[1, 1, 0],
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[1, 1, 0]]
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>>> maze = [[0, 1, 0],
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>>> maze = [[1, 0, 0],
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... [0, 1, 0],
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... [1, 0, 0]]
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>>> solve_maze(maze)
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No solution exists!
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False
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>>> solve_maze(maze,0,1,len(maze)-1,len(maze)-1) # doctest: +NORMALIZE_WHITESPACE
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[[1, 0, 0],
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[1, 1, 0],
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[1, 1, 0]]
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>>> maze = [[1, 1, 0, 0, 1, 0, 0, 1],
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... [1, 0, 1, 0, 0, 1, 1, 1],
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... [0, 1, 0, 1, 0, 0, 1, 0],
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... [1, 1, 1, 0, 0, 1, 0, 1],
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... [0, 1, 0, 0, 1, 0, 1, 1],
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... [0, 0, 0, 1, 1, 1, 0, 1],
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... [0, 1, 0, 1, 0, 1, 1, 1],
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... [1, 1, 0, 0, 0, 0, 0, 1]]
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>>> solve_maze(maze,0,2,len(maze)-1,2) # doctest: +NORMALIZE_WHITESPACE
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[[1, 1, 0, 0, 1, 1, 1, 1],
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[1, 1, 1, 0, 0, 1, 1, 1],
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[1, 1, 1, 1, 0, 1, 1, 1],
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[1, 1, 1, 0, 0, 1, 1, 1],
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[1, 1, 0, 0, 1, 1, 1, 1],
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[1, 1, 0, 1, 1, 1, 1, 1],
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[1, 1, 0, 1, 1, 1, 1, 1],
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[1, 1, 0, 1, 1, 1, 1, 1]]
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>>> maze = [[1, 0, 0],
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... [0, 1, 1],
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... [1, 0, 1]]
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>>> solve_maze(maze,0,1,len(maze)-1,len(maze)-1)
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Traceback (most recent call last):
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...
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ValueError: No solution exists!
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>>> maze = [[0, 0],
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... [1, 1]]
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>>> solve_maze(maze,0,0,len(maze)-1,len(maze)-1)
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Traceback (most recent call last):
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...
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ValueError: No solution exists!
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>>> maze = [[0, 1],
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... [1, 0]]
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>>> solve_maze(maze)
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No solution exists!
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False
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>>> solve_maze(maze,2,0,len(maze)-1,len(maze)-1)
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Traceback (most recent call last):
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...
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ValueError: Invalid source or destination coordinates
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>>> maze = [[1, 0, 0],
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... [0, 1, 0],
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... [1, 0, 0]]
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>>> solve_maze(maze,0,1,len(maze),len(maze)-1)
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Traceback (most recent call last):
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...
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ValueError: Invalid source or destination coordinates
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"""
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size = len(maze)
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# Check if source and destination coordinates are Invalid.
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if not (0 <= source_row <= size - 1 and 0 <= source_column <= size - 1) or (
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not (0 <= destination_row <= size - 1 and 0 <= destination_column <= size - 1)
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):
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raise ValueError("Invalid source or destination coordinates")
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# We need to create solution object to save path.
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solutions = [[0 for _ in range(size)] for _ in range(size)]
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solved = run_maze(maze, 0, 0, solutions)
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solutions = [[1 for _ in range(size)] for _ in range(size)]
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solved = run_maze(
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maze, source_row, source_column, destination_row, destination_column, solutions
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)
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if solved:
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print("\n".join(str(row) for row in solutions))
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return solutions
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else:
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print("No solution exists!")
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return solved
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raise ValueError("No solution exists!")
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def run_maze(maze: list[list[int]], i: int, j: int, solutions: list[list[int]]) -> bool:
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def run_maze(
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maze: list[list[int]],
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i: int,
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j: int,
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destination_row: int,
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destination_column: int,
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solutions: list[list[int]],
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) -> bool:
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"""
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This method is recursive starting from (i, j) and going in one of four directions:
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up, down, left, right.
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If a path is found to destination it returns True otherwise it returns False.
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Parameters:
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maze(2D matrix) : maze
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Parameters
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maze: A two dimensional matrix of zeros and ones.
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i, j : coordinates of matrix
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solutions(2D matrix) : solutions
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solutions: A two dimensional matrix of solutions.
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Returns:
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Boolean if path is found True, Otherwise False.
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"""
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size = len(maze)
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# Final check point.
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if i == j == (size - 1):
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solutions[i][j] = 1
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if i == destination_row and j == destination_column and maze[i][j] == 0:
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solutions[i][j] = 0
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return True
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lower_flag = (not i < 0) and (not j < 0) # Check lower bounds
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@ -93,21 +166,27 @@ def run_maze(maze: list[list[int]], i: int, j: int, solutions: list[list[int]])
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if lower_flag and upper_flag:
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# check for already visited and block points.
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block_flag = (not solutions[i][j]) and (not maze[i][j])
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block_flag = (solutions[i][j]) and (not maze[i][j])
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if block_flag:
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# check visited
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solutions[i][j] = 1
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solutions[i][j] = 0
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# check for directions
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if (
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run_maze(maze, i + 1, j, solutions)
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or run_maze(maze, i, j + 1, solutions)
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or run_maze(maze, i - 1, j, solutions)
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or run_maze(maze, i, j - 1, solutions)
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run_maze(maze, i + 1, j, destination_row, destination_column, solutions)
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or run_maze(
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maze, i, j + 1, destination_row, destination_column, solutions
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)
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or run_maze(
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maze, i - 1, j, destination_row, destination_column, solutions
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)
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or run_maze(
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maze, i, j - 1, destination_row, destination_column, solutions
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)
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):
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return True
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solutions[i][j] = 0
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solutions[i][j] = 1
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return False
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return False
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@ -115,4 +194,4 @@ def run_maze(maze: list[list[int]], i: int, j: int, solutions: list[list[int]])
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
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