Puzzles/Dominos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dominos\n",
    "\n",
    "- A domino consists of two half-Dominoes. Each half-domino shows a number between 0 and N.\n",
    "- Each domino exists exactly once: 0-0, 0-1, etc. up to N-N. Note that there is no difference between X-Y and X-Y (e.g. 1-2 and 2-1).\n",
    "- Divide the grid along the grid lines into dominoes so that each domino occurs exactly once.\n",
    "\n",
    "----\n",
    "\n",
    "- 谜题“多米诺”由两张半多米诺骨牌组成。每个半多米诺骨牌显示一个介于 0 和 N 之间的数字。\n",
    "- 每张多米诺骨牌只存在一次：0-0、0-1 等，直到 N-N。请注意，X-Y 和 X-Y 之间没有区别（例如 1-2 和 2-1）\n",
    "- 沿网格线将网格划分为多米诺骨牌，使每个多米诺骨牌只出现一次。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 8\n",
      "7 8\n",
      "[['5', '5', '4', '0', '2', '4', '4', '5'], ['6', '2', '4', '6', '3', '1', '0', '1'], ['1', '1', '2', '0', '3', '2', '2', '6'], ['3', '5', '0', '0', '3', '2', '1', '6'], ['1', '2', '5', '2', '6', '5', '1', '3'], ['5', '4', '6', '0', '1', '4', '0', '0'], ['3', '6', '4', '3', '3', '5', '6', '4']]\n"
     ]
    }
   ],
   "source": [
    "def readGrid(path):\n",
    "    with open(f\"../assets/data/Dominos/{path}.txt\") as f:\n",
    "        num = f.readline()\n",
    "        m, n = num.split(\" \")[0], num.split(\" \")[1]\n",
    "        grid = f.readlines()\n",
    "        res = [g.strip().split(\" \") for g in grid]\n",
    "        return int(m), int(n), res\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    m, n, grid = readGrid(\"problems/5_7x8\")\n",
    "    print(m, n)\n",
    "    print(len(grid), len(grid[0]))\n",
    "    print(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0, 0) (0, 0, 6) (0, 1, 0) (0, 1, 6) (0, 2, 1) (0, 2, 5) (0, 3, 1) (0, 5, 5) (0, 6, 0) (0, 6, 2) (0, 6, 4) (0, 6, 6) (1, 0, 2) (1, 0, 3) (1, 0, 4) (1, 0, 5) (1, 2, 0) (1, 2, 3) (1, 2, 4) (1, 2, 7) (1, 3, 5) (1, 3, 6) (1, 4, 0) (1, 4, 1) (1, 4, 2) (1, 4, 3) (1, 4, 4) (1, 4, 7) "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from ortools.sat.python import cp_model as cp\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "\n",
    "def dominos_solver(X, Y, grid):\n",
    "    model = cp.CpModel()\n",
    "    solver = cp.CpSolver()\n",
    "    x = {}\n",
    "    y = {}\n",
    "    domino_nums = {}\n",
    "    digit_fill = {}\n",
    "    for i in range(X):\n",
    "        for j in range(Y):\n",
    "            digit_fill[i, j] = []\n",
    "            # 记录哪些方格会占据这个格子\n",
    "            \n",
    "    # 处理第一个形状\n",
    "    for i in range(X):\n",
    "        for j in range(Y - 1):\n",
    "            x[0, i, j] = model.NewBoolVar(f\"x_0_{i}_{j}\")\n",
    "            a_, b_ = int(grid[i][j]), int(grid[i][j + 1])\n",
    "            if a_ > b_:\n",
    "                a_, b_ = b_, a_\n",
    "            if f\"{a_}_{b_}\" not in domino_nums:\n",
    "                domino_nums[f\"{a_}_{b_}\"] = [x[0, i, j]]\n",
    "            else:\n",
    "                domino_nums[f\"{a_}_{b_}\"].append(x[0, i, j])\n",
    "            digit_fill[i, j].append(x[0, i, j])\n",
    "            digit_fill[i, j + 1].append(x[0, i, j])\n",
    "    \n",
    "    # 处理第二个形状\n",
    "    for i in range(X - 1):\n",
    "        for j in range(Y):\n",
    "            x[1, i, j] = model.NewBoolVar(f\"x_1_{i}_{j}\")\n",
    "            a_, b_ = int(grid[i][j]), int(grid[i + 1][j])\n",
    "            if a_ > b_:\n",
    "                a_, b_ = b_, a_\n",
    "            if f\"{a_}_{b_}\" not in domino_nums:\n",
    "                domino_nums[f\"{a_}_{b_}\"] = [x[1, i, j]]\n",
    "            else:\n",
    "                domino_nums[f\"{a_}_{b_}\"].append(x[1, i, j])\n",
    "            digit_fill[i, j].append(x[1, i, j])\n",
    "            digit_fill[i + 1, j].append(x[1, i, j])\n",
    "    \n",
    "    for i in range(X):\n",
    "        for j in range(Y):\n",
    "            model.Add(sum(digit_fill[i, j]) == 1)\n",
    "    \n",
    "    for k, v in domino_nums.items():\n",
    "        model.Add(sum(v) <= 1)\n",
    "    \n",
    "    status = solver.Solve(model)\n",
    "    result = []\n",
    "    if status == cp.OPTIMAL:\n",
    "        for k, v in x.items():\n",
    "            if solver.Value(v) > 1e-5:\n",
    "                result.append(k)\n",
    "    return result\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    X, Y, grid = readGrid(\"problems/5_7x8\")\n",
    "    result = dominos_solver(X, Y, grid)\n",
    "    for r in result:\n",
    "        print(r, end = \" \")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def coordinatesParser(m, n, idx):\n",
    "    \"\"\"_summary_\n",
    "        进行坐标转换 \n",
    "    Args:\n",
    "        m (_type_): _description_\n",
    "        n (_type_): _description_\n",
    "        idx (_type_): _description_\n",
    "\n",
    "    Returns:\n",
    "        _type_: _description_\n",
    "    \"\"\"\n",
    "    ori_x , ori_y = idx // n, idx % n \n",
    "    return (0.5 + ori_y, 0.5 + ( m - ori_x - 1) )\n",
    "\n",
    "def coordinatesParserNodes(m, n, idx):\n",
    "    ori_x, ori_y = idx // (n + 1), idx % (n + 1)\n",
    "    return (ori_y, m - ori_x )\n",
    "\n",
    "def generate_random_color():\n",
    "    # 生成三个随机的十六进制数字，每个数字的范围是0-255\n",
    "    r = random.randint(0, 255)\n",
    "    g = random.randint(0, 255)\n",
    "    b = random.randint(0, 255)\n",
    "    \n",
    "    # 将RGB值转换为十六进制字符串\n",
    "    hex_color = f'#{r:02x}{g:02x}{b:02x}'\n",
    "    return hex_color\n",
    "\n",
    "\n",
    "# Visualize 2 b done\n",
    "def Vis(m, n, grid, result):\n",
    "    grid_color = dict()\n",
    "    for v in result:\n",
    "        grid_color[f\"{v[0]}_{v[1]}_{v[2]}\"] = generate_random_color()\n",
    "\n",
    "    plt.figure(figsize=(n,m))\n",
    "\n",
    "    ax = plt.gca()\n",
    "    x_major_locator = MultipleLocator(1)\n",
    "    y_major_locator = MultipleLocator(1)\n",
    "    ax.xaxis.set_major_locator(x_major_locator)\n",
    "    # 把x/y轴的主刻度设置为1的倍数\n",
    "    ax.yaxis.set_major_locator(y_major_locator)\n",
    "\n",
    "    plt.xlim(0,n)\n",
    "    plt.ylim(0,m)\n",
    "\n",
    "\n",
    "    # 绘制无法被访问的方块\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            if grid[i][j] != -1:    \n",
    "                new_coor = coordinatesParser(m, n, i * n + j)\n",
    "                ax.text(new_coor[0], new_coor[1], str(grid[i][j]), \n",
    "                        horizontalalignment = \"center\", \n",
    "                        verticalalignment = 'center',\n",
    "                        fontsize = 24)\n",
    "    for v in result:\n",
    "        if v[0] == 0:\n",
    "            i, j = v[1], v[2]\n",
    "            ax.add_patch(Rectangle((j, X - i - 1), 1, 1, color = grid_color[f\"{v[0]}_{v[1]}_{v[2]}\"], alpha = 0.5))\n",
    "            ax.add_patch(Rectangle((j + 1, X - i - 1), 1, 1, color = grid_color[f\"{v[0]}_{v[1]}_{v[2]}\"], alpha = 0.5))\n",
    "        else:\n",
    "            i, j = v[1], v[2]\n",
    "            ax.add_patch(Rectangle((j, X - i - 1), 1, 1, color = grid_color[f\"{v[0]}_{v[1]}_{v[2]}\"], alpha = 0.5))\n",
    "            ax.add_patch(Rectangle((j, X - i - 2), 1, 1, color = grid_color[f\"{v[0]}_{v[1]}_{v[2]}\"], alpha = 0.5))\n",
    "\n",
    "    plt.grid(linestyle = \"-\")\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    Vis(X, Y, grid, result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py310_arm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}