def _breadth_first_search(self, start, end):
"""
Solves a maze using breadth-first search.
:param start: tuple with start coordinates
:param end: tuple with end coordinates
:return: None
"""
visited = self.maze.copy(
) # List of visited cells, value of visited cell is 0
queue = collections.deque() # List of cells [cell, ...]
cell = utils.stack_empty(
) # Tuple of current cell with according stack ((x, y), stack)
x, y = start
cell = utils.stack_push(cell, (x, y))
queue.append(cell)
visited[x, y, 0] = 0 # Mark as visited
while queue:
self._enqueue(queue, visited)
if queue[0][0] == end: # Stop if end has been found
cell = utils.stack_push(queue[0], end) # Push end into cell
return utils.draw_path(self.solution, utils.stack_deque(cell))
raise utils.MazeError("No solution found.")
def _depth_first_search(self, start, end):
"""Solves a maze using depth-first search."""
visited = self.maze.copy() # List of visited cells, value of visited cell is 0
stack = collections.deque() # List of visited cells [(x, y), ...]
x, y = start
visited[x, y, 0] = 0 # Mark as visited
while x and y:
while x and y:
stack.append((x, y))
if (x, y) == end: # Stop if end has been found
return utils.draw_path(self.solution, stack)
x, y = self._solve_walk(x, y, visited)
x, y = self._solve_backtrack(stack, visited)
raise utils.MazeError("No solution found.")