Манхэттенская эвристика

MininaVD/docs2/data2/strategiesA_star_strategy.ipynb

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+{
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+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8e6c2ad-712c-44a0-8ebc-ed0d67234c05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import heapq\n",
+    "from typing import List, Dict, Optional, Tuple\n",
+    "from strategiesPathfinding_strategy import PathFindingStrategy\n",
+    "from modelsMaze import Maze\n",
+    "from modelsCell import Cell\n",
+    "\n",
+    "class AStarStrategy(PathFindingStrategy):\n",
+    "    \"\"\"Алгоритм A* с манхэттенской эвристикой.\"\"\"\n",
+    "    \n",
+    "    @property\n",
+    "    def name(self) -> str:\n",
+    "        return \"A*\"\n",
+    "    \n",
+    "    def _heuristic(self, a: Cell, b: Cell) -> int:\n",
+    "        \"\"\"Манхэттенское расстояние.\"\"\"\n",
+    "        return abs(a.x - b.x) + abs(a.y - b.y)\n",
+    "    \n",
+    "    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:\n",
+    "        if start == exit_cell:\n",
+    "            return [start]\n",
+    "        \n",
+    "        # Приоритетная очередь: (f_score, counter, cell)\n",
+    "        open_set = [(0, 0, start)]\n",
+    "        counter = 1\n",
+    "        \n",
+    "        came_from: Dict[Cell, Optional[Cell]] = {}\n",
+    "        \n",
+    "        g_score: Dict[Cell, float] = {start: 0}\n",
+    "        f_score: Dict[Cell, float] = {start: self._heuristic(start, exit_cell)}\n",
+    "        \n",
+    "        visited_count = 0\n",
+    "        \n",
+    "        while open_set:\n",
+    "            current_f, _, current = heapq.heappop(open_set)\n",
+    "            visited_count += 1\n",
+    "            \n",
+    "            if current == exit_cell:\n",
+    "                self._last_visited_count = visited_count\n",
+    "                return self._reconstruct_path(came_from, start, current)\n",
+    "            \n",
+    "            for neighbor in maze.get_neighbors(current):\n",
+    "                tentative_g_score = g_score.get(current, float('inf')) + 1\n",
+    "                \n",
+    "                if tentative_g_score < g_score.get(neighbor, float('inf')):\n",
+    "                    came_from[neighbor] = current\n",
+    "                    g_score[neighbor] = tentative_g_score\n",
+    "                    f_score[neighbor] = tentative_g_score + self._heuristic(neighbor, exit_cell)\n",
+    "                    heapq.heappush(open_set, (f_score[neighbor], counter, neighbor))\n",
+    "                    counter += 1\n",
+    "        \n",
+    "        self._last_visited_count = visited_count\n",
+    "        return []\n",
+    "    \n",
+    "    @property\n",
+    "    def last_visited_count(self) -> int:\n",
+    "        return getattr(self, '_last_visited_count', 0)"
+   ]
+  }
+ ],
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