{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File Name</th>\n",
       "      <th>Line 1</th>\n",
       "      <th>Line 2</th>\n",
       "      <th>Line 3</th>\n",
       "      <th>Line 4</th>\n",
       "      <th>Line 5</th>\n",
       "      <th>Line 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN144.tsp</td>\n",
       "      <td>NAME : CHN144</td>\n",
       "      <td>COMMENT : China 144-city problem</td>\n",
       "      <td>TYPE : TSP</td>\n",
       "      <td>DIMENSION : 144</td>\n",
       "      <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
       "      <td>NODE_COORD_SECTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eil101.tsp</td>\n",
       "      <td>NAME : eil101</td>\n",
       "      <td>COMMENT : 101-city problem (Christofides/Eilon)</td>\n",
       "      <td>TYPE : TSP</td>\n",
       "      <td>DIMENSION : 101</td>\n",
       "      <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
       "      <td>NODE_COORD_SECTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>eil76.tsp</td>\n",
       "      <td>NAME: eil76</td>\n",
       "      <td>TYPE: TSP</td>\n",
       "      <td>COMMENT: 76-city problem (Christofides/Eilon)</td>\n",
       "      <td>DIMENSION: 76</td>\n",
       "      <td>EDGE_WEIGHT_TYPE: EUC_2D</td>\n",
       "      <td>NODE_COORD_SECTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GR96.tsp</td>\n",
       "      <td>NAME: gr96</td>\n",
       "      <td>TYPE: TSP</td>\n",
       "      <td>COMMENT: Africa-Subproblem of 666-city TSP (Gr...</td>\n",
       "      <td>DIMENSION: 96</td>\n",
       "      <td>EDGE_WEIGHT_TYPE: GEO</td>\n",
       "      <td>DISPLAY_DATA_TYPE: COORD_DISPLAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PBK411.tsp</td>\n",
       "      <td>NAME : pbk411</td>\n",
       "      <td>COMMENT : Bonn VLSI data set with 411 points</td>\n",
       "      <td>TYPE : TSP</td>\n",
       "      <td>DIMENSION : 411</td>\n",
       "      <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
       "      <td>NODE_COORD_SECTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PR76.tsp</td>\n",
       "      <td>NAME : pr76</td>\n",
       "      <td>COMMENT : 76-city problem (Padberg/Rinaldi)</td>\n",
       "      <td>TYPE : TSP</td>\n",
       "      <td>DIMENSION : 76</td>\n",
       "      <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
       "      <td>NODE_COORD_SECTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RBU737.tsp</td>\n",
       "      <td>NAME : rbu737</td>\n",
       "      <td>COMMENT : Bonn VLSI data set with 737 points</td>\n",
       "      <td>TYPE : TSP</td>\n",
       "      <td>DIMENSION : 737</td>\n",
       "      <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
       "      <td>NODE_COORD_SECTION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ulysses16.tsp</td>\n",
       "      <td>NAME: ulysses16.tsp</td>\n",
       "      <td>TYPE: TSP</td>\n",
       "      <td>COMMENT: Odyssey of Ulysses (Groetschel/Padberg)</td>\n",
       "      <td>DIMENSION: 16</td>\n",
       "      <td>EDGE_WEIGHT_TYPE: GEO</td>\n",
       "      <td>DISPLAY_DATA_TYPE: COORD_DISPLAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ulysses8.tsp</td>\n",
       "      <td>NAME: ulysses16.tsp</td>\n",
       "      <td>TYPE: TSP</td>\n",
       "      <td>COMMENT: Odyssey of Ulysses (Groetschel/Padberg)</td>\n",
       "      <td>DIMENSION: 8</td>\n",
       "      <td>EDGE_WEIGHT_TYPE: GEO</td>\n",
       "      <td>DISPLAY_DATA_TYPE: COORD_DISPLAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XIT1083.tsp</td>\n",
       "      <td>NAME : xit1083</td>\n",
       "      <td>COMMENT : Bonn VLSI data set with 1083 points</td>\n",
       "      <td>TYPE : TSP</td>\n",
       "      <td>DIMENSION : 1083</td>\n",
       "      <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
       "      <td>NODE_COORD_SECTION</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       File Name               Line 1  \\\n",
       "0     CHN144.tsp        NAME : CHN144   \n",
       "1     eil101.tsp        NAME : eil101   \n",
       "2      eil76.tsp          NAME: eil76   \n",
       "3       GR96.tsp           NAME: gr96   \n",
       "4     PBK411.tsp        NAME : pbk411   \n",
       "5       PR76.tsp          NAME : pr76   \n",
       "6     RBU737.tsp        NAME : rbu737   \n",
       "7  ulysses16.tsp  NAME: ulysses16.tsp   \n",
       "8   ulysses8.tsp  NAME: ulysses16.tsp   \n",
       "9    XIT1083.tsp       NAME : xit1083   \n",
       "\n",
       "                                            Line 2  \\\n",
       "0                 COMMENT : China 144-city problem   \n",
       "1  COMMENT : 101-city problem (Christofides/Eilon)   \n",
       "2                                        TYPE: TSP   \n",
       "3                                        TYPE: TSP   \n",
       "4     COMMENT : Bonn VLSI data set with 411 points   \n",
       "5      COMMENT : 76-city problem (Padberg/Rinaldi)   \n",
       "6     COMMENT : Bonn VLSI data set with 737 points   \n",
       "7                                        TYPE: TSP   \n",
       "8                                        TYPE: TSP   \n",
       "9    COMMENT : Bonn VLSI data set with 1083 points   \n",
       "\n",
       "                                              Line 3            Line 4  \\\n",
       "0                                         TYPE : TSP   DIMENSION : 144   \n",
       "1                                         TYPE : TSP   DIMENSION : 101   \n",
       "2      COMMENT: 76-city problem (Christofides/Eilon)     DIMENSION: 76   \n",
       "3  COMMENT: Africa-Subproblem of 666-city TSP (Gr...     DIMENSION: 96   \n",
       "4                                         TYPE : TSP   DIMENSION : 411   \n",
       "5                                         TYPE : TSP    DIMENSION : 76   \n",
       "6                                         TYPE : TSP   DIMENSION : 737   \n",
       "7   COMMENT: Odyssey of Ulysses (Groetschel/Padberg)     DIMENSION: 16   \n",
       "8   COMMENT: Odyssey of Ulysses (Groetschel/Padberg)      DIMENSION: 8   \n",
       "9                                         TYPE : TSP  DIMENSION : 1083   \n",
       "\n",
       "                      Line 5                            Line 6  \n",
       "0  EDGE_WEIGHT_TYPE : EUC_2D                NODE_COORD_SECTION  \n",
       "1  EDGE_WEIGHT_TYPE : EUC_2D                NODE_COORD_SECTION  \n",
       "2   EDGE_WEIGHT_TYPE: EUC_2D                NODE_COORD_SECTION  \n",
       "3      EDGE_WEIGHT_TYPE: GEO  DISPLAY_DATA_TYPE: COORD_DISPLAY  \n",
       "4  EDGE_WEIGHT_TYPE : EUC_2D                NODE_COORD_SECTION  \n",
       "5  EDGE_WEIGHT_TYPE : EUC_2D                NODE_COORD_SECTION  \n",
       "6  EDGE_WEIGHT_TYPE : EUC_2D                NODE_COORD_SECTION  \n",
       "7      EDGE_WEIGHT_TYPE: GEO  DISPLAY_DATA_TYPE: COORD_DISPLAY  \n",
       "8      EDGE_WEIGHT_TYPE: GEO  DISPLAY_DATA_TYPE: COORD_DISPLAY  \n",
       "9  EDGE_WEIGHT_TYPE : EUC_2D                NODE_COORD_SECTION  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# 定义文件夹路径\n",
    "folder_path = './data'\n",
    "\n",
    "# 初始化一个空的DataFrame来存储信息\n",
    "columns = ['File Name', 'Line 1', 'Line 2', 'Line 3', 'Line 4', 'Line 5', 'Line 6']\n",
    "df = pd.DataFrame(columns=columns)\n",
    "\n",
    "# 遍历文件夹中的所有文件\n",
    "for file_name in os.listdir(folder_path):\n",
    "    if file_name.endswith('.tsp'):\n",
    "        file_path = os.path.join(folder_path, file_name)\n",
    "        with open(file_path, 'r') as file:\n",
    "            lines = file.readlines()\n",
    "            # 取前六行作为基本信息\n",
    "            basic_info = lines[:6]\n",
    "            # 如果行数不足六行，用空字符串填充\n",
    "            while len(basic_info) < 6:\n",
    "                basic_info.append('')\n",
    "            # 将信息添加到DataFrame中\n",
    "            new_row = pd.DataFrame({\n",
    "                'File Name': [file_name],\n",
    "                'Line 1': [basic_info[0].strip()],\n",
    "                'Line 2': [basic_info[1].strip()],\n",
    "                'Line 3': [basic_info[2].strip()],\n",
    "                'Line 4': [basic_info[3].strip()],\n",
    "                'Line 5': [basic_info[4].strip()],\n",
    "                'Line 6': [basic_info[5].strip()]\n",
    "            })\n",
    "            df = pd.concat([df, new_row], ignore_index=True)\n",
    "\n",
    "# 显示表格\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.path import Path\n",
    "import matplotlib.patches as patches\n",
    "import os\n",
    "\n",
    "# 确保plot文件夹存在\n",
    "if not os.path.exists('./plot'):\n",
    "    os.makedirs('./plot')\n",
    "\n",
    "# 设置中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "# 生成10个随机分布的节点\n",
    "np.random.seed(2025)\n",
    "n_points = 10\n",
    "points = np.random.rand(n_points, 2) * 100\n",
    "\n",
    "# 计算两点间距离的函数\n",
    "def calculate_distance(route):\n",
    "    total = 0\n",
    "    for i in range(len(route)):\n",
    "        j = (i + 1) % len(route)\n",
    "        city1 = points[route[i]]\n",
    "        city2 = points[route[j]]\n",
    "        total += np.sqrt(np.sum((city1 - city2) ** 2))\n",
    "    return total\n",
    "\n",
    "# 生成随机解\n",
    "random_solution = list(range(n_points))\n",
    "np.random.shuffle(random_solution)\n",
    "random_distance = calculate_distance(random_solution)\n",
    "\n",
    "# 使用启发式算法求解TSP问题\n",
    "def heuristic_solution():\n",
    "    n = n_points\n",
    "    # 初始解:从城市0开始\n",
    "    current_solution = [0]\n",
    "    unvisited = set(range(1, n))\n",
    "    \n",
    "    # 不断选择最近的未访问城市\n",
    "    while unvisited:\n",
    "        current = current_solution[-1]\n",
    "        # 找到距离当前城市最近的未访问城市\n",
    "        next_city = min(unvisited, \n",
    "                       key=lambda x: np.sqrt(np.sum((points[current] - points[x]) ** 2)))\n",
    "        current_solution.append(next_city)\n",
    "        unvisited.remove(next_city)\n",
    "    \n",
    "    # 2-opt局部搜索优化\n",
    "    improved = True\n",
    "    while improved:\n",
    "        improved = False\n",
    "        for i in range(n-2):\n",
    "            for j in range(i+2, n):\n",
    "                # 计算当前路径长度\n",
    "                old_distance = (\n",
    "                    np.sqrt(np.sum((points[current_solution[i]] - points[current_solution[i+1]]) ** 2)) +\n",
    "                    np.sqrt(np.sum((points[current_solution[j]] - points[current_solution[(j+1)%n]]) ** 2))\n",
    "                )\n",
    "                # 计算交换后的路径长度\n",
    "                new_distance = (\n",
    "                    np.sqrt(np.sum((points[current_solution[i]] - points[current_solution[j]]) ** 2)) +\n",
    "                    np.sqrt(np.sum((points[current_solution[i+1]] - points[current_solution[(j+1)%n]]) ** 2))\n",
    "                )\n",
    "                \n",
    "                if new_distance < old_distance:\n",
    "                    # 如果交换后更优,则进行2-opt交换\n",
    "                    current_solution[i+1:j+1] = reversed(current_solution[i+1:j+1])\n",
    "                    improved = True\n",
    "                    break\n",
    "            if improved:\n",
    "                break\n",
    "    \n",
    "    # 添加回到起点\n",
    "    current_solution.append(0)\n",
    "    return current_solution\n",
    "\n",
    "better_solution = heuristic_solution()\n",
    "better_distance = calculate_distance(better_solution)\n",
    "\n",
    "# 创建并保存随机解图\n",
    "plt.figure(figsize=(8, 8))\n",
    "ax1 = plt.gca()\n",
    "for i, point in enumerate(points):\n",
    "    ax1.scatter(point[0], point[1], c='blue', s=100)\n",
    "    ax1.annotate(f'{i}', (point[0], point[1]), xytext=(5, 5), textcoords='offset points',fontsize = 20)\n",
    "\n",
    "for i in range(len(random_solution)):\n",
    "    j = (i + 1) % len(random_solution)\n",
    "    city1 = points[random_solution[i]]\n",
    "    city2 = points[random_solution[j]]\n",
    "    ax1.plot([city1[0], city2[0]], [city1[1], city2[1]], 'r-')\n",
    "\n",
    "ax1.set_title(f'总距离: {random_distance:.2f}',fontsize = 26)\n",
    "ax1.grid(True)\n",
    "ax1.margins(0.13)\n",
    "plt.savefig('./plot/random_solution.png',dpi=300)\n",
    "plt.close()\n",
    "\n",
    "# 创建并保存贪心解图\n",
    "plt.figure(figsize=(8, 8))\n",
    "ax2 = plt.gca()\n",
    "for i, point in enumerate(points):\n",
    "    ax2.scatter(point[0], point[1], c='blue', s=100)\n",
    "    ax2.annotate(f'{i}', (point[0], point[1]), xytext=(5, 5), textcoords='offset points',fontsize = 20)\n",
    "\n",
    "for i in range(len(better_solution)):\n",
    "    j = (i + 1) % len(better_solution)\n",
    "    city1 = points[better_solution[i]]\n",
    "    city2 = points[better_solution[j]]\n",
    "    ax2.plot([city1[0], city2[0]], [city1[1], city2[1]], 'g-')\n",
    "\n",
    "ax2.set_title(f'总距离: {better_distance:.2f}',fontsize = 26)\n",
    "ax2.grid(True)\n",
    "ax2.margins(0.13)\n",
    "plt.savefig('./plot/better_solution.png')\n",
    "plt.close()\n",
    "\n",
    "# 显示两个图\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 8))\n",
    "\n",
    "# 绘制随机解\n",
    "for i, point in enumerate(points):\n",
    "    ax1.scatter(point[0], point[1], c='blue', s=100)\n",
    "    ax1.annotate(f'{i}', (point[0], point[1]), xytext=(5, 5), textcoords='offset points',fontsize = 20)\n",
    "\n",
    "for i in range(len(random_solution)):\n",
    "    j = (i + 1) % len(random_solution)\n",
    "    city1 = points[random_solution[i]]\n",
    "    city2 = points[random_solution[j]]\n",
    "    ax1.plot([city1[0], city2[0]], [city1[1], city2[1]], 'r-')\n",
    "\n",
    "ax1.set_title(f'总距离: {random_distance:.2f}',fontsize = 26)\n",
    "ax1.grid(True)\n",
    "ax1.margins(0.13)\n",
    "\n",
    "# 绘制贪心解\n",
    "for i, point in enumerate(points):\n",
    "    ax2.scatter(point[0], point[1], c='blue', s=100)\n",
    "    ax2.annotate(f'{i}', (point[0], point[1]), xytext=(5, 5), textcoords='offset points',fontsize = 20)\n",
    "\n",
    "for i in range(len(better_solution)):\n",
    "    j = (i + 1) % len(better_solution)\n",
    "    city1 = points[better_solution[i]]\n",
    "    city2 = points[better_solution[j]]\n",
    "    ax2.plot([city1[0], city2[0]], [city1[1], city2[1]], 'g-')\n",
    "\n",
    "ax2.set_title(f'总距离: {better_distance:.2f}',fontsize = 26)\n",
    "ax2.grid(True)\n",
    "ax2.margins(0.13)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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