alternative_implementations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting array vs casting to a set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time \n",
    "\n",
    "def generate_random_arrays(n):\n",
    "    return np.random.randint(0, 10, size=(n, 9))\n",
    "\n",
    "def uniqueness_test_1(l):\n",
    "    l1 = set(l)\n",
    "    return len(l) == 9 and 0 not in l1\n",
    "\n",
    "def uniqueness_test_2(l):\n",
    "    return np.sort(l) == np.arange(1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime of set implemetation: 1.135\n",
      "Runtime of sorting implemetation: 2.266\n"
     ]
    }
   ],
   "source": [
    "data = generate_random_arrays(1000000)\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "for i in data:\n",
    "    uniqueness_test_1(i)\n",
    "\n",
    "end = time.time()\n",
    "print(f\"Runtime of set implemetation: {end - start:.3f}\")\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "for i in data:\n",
    "    uniqueness_test_2(i)\n",
    "\n",
    "end = time.time()\n",
    "print(f\"Runtime of sorting implemetation: {end - start:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.Sudoku import Sudoku\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "grid1 = \"\"\"000801000\n",
    "    000000043\n",
    "    500000000\n",
    "    000070800\n",
    "    000000100\n",
    "    020030000\n",
    "    600000075\n",
    "    003400000\n",
    "    000200600\"\"\"\n",
    "\n",
    "sudoku = Sudoku(grid1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit, prange\n",
    "\n",
    "def calculate_candidates(sudoku):\n",
    "\n",
    "    candidates = np.zeros([9, 9]) \n",
    "\n",
    "    for y in range(9):\n",
    "        for x in range(9):\n",
    "            if sudoku.get_cell(x, y) != 0:\n",
    "                candidates[y][x] = 0\n",
    "                continue\n",
    "\n",
    "            row = sudoku.get_row(y)\n",
    "            col = sudoku.get_col(x)\n",
    "            sq_index = x // 3 + 3 * (y // 3)\n",
    "            sq = sudoku.get_square(sq_index)\n",
    "            invalid_numbers = np.concatenate((row, col, sq.flatten()))\n",
    "            valid_numbers = np.setdiff1d(np.arange(1, 10), invalid_numbers)\n",
    "            candidates[y][x] = len(valid_numbers)\n",
    "\n",
    "    return candidates\n",
    "\n",
    "\n",
    "@jit(nopython=True)\n",
    "def custom_setdiff1d(ar1, ar2):\n",
    "    result = []\n",
    "    \n",
    "    ar2_set = set(ar2)\n",
    "    \n",
    "    for item in ar1:\n",
    "        if item not in ar2_set:\n",
    "            result.append(item)\n",
    "    \n",
    "    return np.array(result)\n",
    "\n",
    "@jit(nopython=True, parallel=True)\n",
    "def calculate_candidates_2(grid: np.array):\n",
    "\n",
    "    candidates = np.zeros((9, 9, 9), dtype=np.bool_)\n",
    "\n",
    "    for y in prange(9):\n",
    "        for x in prange(9):\n",
    "            if grid[y, x] != 0:\n",
    "                continue\n",
    "\n",
    "            row = grid[y, :]\n",
    "            col = grid[:, x]\n",
    "            y0 = (y // 3) * 3\n",
    "            x0 = (x // 3) * 3\n",
    "            sq = grid[y0 : y0 + 3, x0 : x0 + 3]\n",
    "            invalid_numbers = np.concatenate((row, col, sq.flatten()))\n",
    "            valid_numbers = custom_setdiff1d(np.arange(1, 10), invalid_numbers)\n",
    "            candidates[y, x, valid_numbers - 1] = 1\n",
    "\n",
    "    return candidates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 14.544921398162842\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "for _ in range(100000):\n",
    "    c = calculate_candidates_2(sudoku.grid)\n",
    "\n",
    "end_time = time.time()\n",
    "\n",
    "print(f\"Time: {end_time - start_time}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_candidates(c):\n",
    "    candidates = []\n",
    "    for row in c:\n",
    "        new_row = []\n",
    "        for elem in row:\n",
    "            new_row.append([i + 1 for i, e in enumerate(elem) if e])\n",
    "        candidates.append(new_row)\n",
    "    \n",
    "    return candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23479 34679 24679 -1 24569 -1 2579 2569 2679 \n",
      "12789 16789 126789 5679 2569 25679 2579 -1 -1 \n",
      "-1 1346789 1246789 3679 2469 234679 279 12689 126789 \n",
      "1349 134569 14569 1569 -1 24569 -1 23569 2469 \n",
      "34789 3456789 456789 569 245689 245689 -1 23569 24679 \n",
      "14789 -1 1456789 1569 -1 45689 4579 569 4679 \n",
      "-1 1489 12489 139 189 389 2349 -1 -1 \n",
      "12789 15789 -1 -1 15689 56789 29 1289 1289 \n",
      "14789 145789 145789 -1 1589 35789 -1 1389 1489 \n"
     ]
    }
   ],
   "source": [
    "grid_of_candidates = decode_candidates(c)\n",
    "for row in grid_of_candidates:\n",
    "    for cell in row:\n",
    "        if cell:\n",
    "            print(\"\".join([str(i) for i in cell]), end=\" \")\n",
    "        else:\n",
    "            print(\"-1\", end=\" \")\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy array vs python list for storing candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def grid_of_candidates_list():\n",
    "    grid_of_candidates = [[[] for _ in range(9)] for _ in range(9)]\n",
    "    for y in range(9):\n",
    "        for x in range(9):\n",
    "            grid_of_candidates[y][x] = [1, 2, 3, 4]\n",
    "\n",
    "    return grid_of_candidates\n",
    "\n",
    "\n",
    "def grid_of_candidates_array():\n",
    "    grid_of_candidates = np.empty((9, 9), dtype=object)\n",
    "    for y in range(9):\n",
    "        for x in range(9):\n",
    "            grid_of_candidates[y, x] = [1, 2, 3, 4]\n",
    "\n",
    "    return grid_of_candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List implementation: 1.666\n",
      "Array implementation: 2.149\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "n = 100000\n",
    "implementations = [(\"List\", grid_of_candidates_list), (\"Array\", grid_of_candidates_array)]\n",
    "\n",
    "for i in implementations:\n",
    "    start = time.time()\n",
    "    for _ in range(n):\n",
    "        grid = i[1]()\n",
    "        for row in grid:\n",
    "            for elem in row:\n",
    "                elem\n",
    "\n",
    "    end = time.time()\n",
    "    print(f\"{i[0]} implementation: {end - start:.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116, 39, 2.94)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from src.Sudoku import Sudoku\n",
    "from src.SudokuAnalyzer import SudokuAnalyzer\n",
    "\n",
    "grid = \"370009405094070300086500000718000050900600037003010009069180003031950084857030090\"\n",
    "\n",
    "sudoku = Sudoku(grid)\n",
    "analyzer = SudokuAnalyzer(sudoku)\n",
    "analyzer.get_sudoku_description()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_data_points(i):\n",
    "    with open(f'solver_logs/m{i}.txt', 'r') as file:\n",
    "        lines = file.readlines()\n",
    "\n",
    "    Y = [(81 - 39) - int(line.split()[3]) for line in lines]\n",
    "    X = list(range(1, len(Y) + 1))\n",
    "\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.scatter(X, Y, s=1)\n",
    "    plt.title(f'Plot for method {i}')\n",
    "    plt.xlabel('Step number')\n",
    "    plt.ylabel('Steps away from the solution')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "for i in range(4):\n",
    "    plot_data_points(i)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear search vs binary search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Binary search runtime: 0.0338\n",
      "Linear search runtime: 43.2358\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time \n",
    "from src.SudokuDB import SudokuDB\n",
    "\n",
    "n = 30\n",
    "\n",
    "\n",
    "db = SudokuDB()\n",
    "test_data = np.random.uniform(low=1, high=10, size=n)\n",
    "implementations = [(\"Binary search\", db.get_sudoku_by_diff_2), (\"Linear search\", db.get_sudoku_by_diff)]\n",
    "answers = []\n",
    "for i in implementations:\n",
    "    results = []\n",
    "    start = time.time()\n",
    "\n",
    "    for d in test_data:\n",
    "        results.append(i[1](d)) \n",
    "\n",
    "    end = time.time()\n",
    "\n",
    "    answers.append(results)\n",
    "    print(f\"{i[0]} runtime: {end - start:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}