[2]Реализация первых трёх этапов
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MylnikovAS/task_2/docs/data/steps_123.py
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MylnikovAS/task_2/docs/data/steps_123.py
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import time
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import csv
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from collections import deque
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import heapq
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from abc import ABC, abstractmethod
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class Cell:
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def __init__(self, x: int, y: int):
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self.x = x
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self.y = y
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self.isWall = False
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self.isStart = False
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self.isExit = False
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self.weight = 1
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def isPassable(self) -> bool:
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return not self.isWall
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def __lt__(self, other):
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return (self.x, self.y) < (other.x, other.y)
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class Maze:
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def __init__(self, width: int, height: int):
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self.width = width
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self.height = height
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self.cells = [[Cell(x, y) for y in range(height)] for x in range(width)]
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self.start = None
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self.exit = None
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def getCell(self, x: int, y: int) -> Cell:
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if 0 <= x < self.width and 0 <= y < self.height:
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return self.cells[x][y]
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return None
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def getNeighbors(self, cell: Cell):
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neighbors = []
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directions = [(0, -1), (0, 1), (-1, 0), (1, 0)]
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for dx, dy in directions:
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nx, ny = cell.x + dx, cell.y + dy
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neighbor = self.getCell(nx, ny)
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if neighbor and neighbor.isPassable():
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neighbors.append(neighbor)
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return neighbors
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class MazeBuilder(ABC):
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@abstractmethod
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def buildFromStringList(self, lines: list) -> Maze:
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pass
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class TextMazeBuilder(MazeBuilder):
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def buildFromStringList(self, lines: list) -> Maze:
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height = len(lines)
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width = len(lines[0]) if height > 0 else 0
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maze = Maze(width, height)
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for y, line in enumerate(lines):
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for x, char in enumerate(line):
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cell = maze.getCell(x, y)
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if char == '#':
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cell.isWall = True
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elif char == 'S':
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cell.isStart = True
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maze.start = cell
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elif char == 'E':
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cell.isExit = True
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maze.exit = cell
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elif char == 'W':
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cell.weight = 3
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elif char == 'D':
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cell.weight = 2
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return maze
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class PathFindingStrategy(ABC):
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def __init__(self):
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self.visited_count = 0
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@abstractmethod
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def findPath(self, maze: Maze, start: Cell, exit: Cell) -> list:
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pass
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def _reconstruct_path(self, came_from: dict, start: Cell, exit: Cell) -> list:
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if exit not in came_from:
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return []
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path = []
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current = exit
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while current != start:
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path.append(current)
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current = came_from[current]
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path.append(start)
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path.reverse()
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return path
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class BFSStrategy(PathFindingStrategy):
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def findPath(self, maze: Maze, start: Cell, exit: Cell) -> list:
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self.visited_count = 0
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queue = deque([start])
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came_from = {start: None}
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while queue:
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current = queue.popleft()
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self.visited_count += 1
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if current == exit:
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break
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for neighbor in maze.getNeighbors(current):
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if neighbor not in came_from:
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queue.append(neighbor)
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came_from[neighbor] = current
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return self._reconstruct_path(came_from, start, exit)
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class DFSStrategy(PathFindingStrategy):
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def findPath(self, maze: Maze, start: Cell, exit: Cell) -> list:
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self.visited_count = 0
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stack = [start]
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came_from = {start: None}
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while stack:
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current = stack.pop()
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self.visited_count += 1
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if current == exit:
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break
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for neighbor in maze.getNeighbors(current):
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if neighbor not in came_from:
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stack.append(neighbor)
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came_from[neighbor] = current
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return self._reconstruct_path(came_from, start, exit)
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class AStarStrategy(PathFindingStrategy):
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def findPath(self, maze: Maze, start: Cell, exit: Cell) -> list:
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self.visited_count = 0
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def heuristic(a, b):
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return abs(a.x - b.x) + abs(a.y - b.y)
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pq = []
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heapq.heappush(pq, (0, start))
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came_from = {start: None}
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g_score = {start: 0}
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while pq:
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_, current = heapq.heappop(pq)
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self.visited_count += 1
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if current == exit:
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break
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for neighbor in maze.getNeighbors(current):
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tentative_g_score = g_score[current] + neighbor.weight
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if neighbor not in g_score or tentative_g_score < g_score[neighbor]:
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came_from[neighbor] = current
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g_score[neighbor] = tentative_g_score
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f_score = tentative_g_score + heuristic(neighbor, exit)
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heapq.heappush(pq, (f_score, neighbor))
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return self._reconstruct_path(came_from, start, exit)
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class DijkstraStrategy(PathFindingStrategy):
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def findPath(self, maze: Maze, start: Cell, exit: Cell) -> list:
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self.visited_count = 0
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pq = []
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heapq.heappush(pq, (0, start))
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came_from = {start: None}
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g_score = {start: 0}
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while pq:
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current_g, current = heapq.heappop(pq)
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self.visited_count += 1
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if current == exit:
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break
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for neighbor in maze.getNeighbors(current):
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tentative_g_score = g_score[current] + neighbor.weight
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if neighbor not in g_score or tentative_g_score < g_score[neighbor]:
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came_from[neighbor] = current
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g_score[neighbor] = tentative_g_score
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heapq.heappush(pq, (tentative_g_score, neighbor))
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return self._reconstruct_path(came_from, start, exit)
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