tp2-CSP-Sudoku.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TP AI CSP: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sans Heuristique :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e167093f-8fec-4c6c-9b3e-a570427aa73a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SudokuCSP:\n",
    "    def __init__(self, puzzle):\n",
    "        self.grid = puzzle\n",
    "        self.choices_explored = 0\n",
    "        self.start_time = time.time() \n",
    "        sns.set(style=\"whitegrid\")\n",
    "\n",
    "    def is_valid(self, row, col, num):\n",
    "        for i in range(9):\n",
    "            if self.grid[row][i] == num or self.grid[i][col] == num:\n",
    "                return False\n",
    "\n",
    "        start_row, start_col = 3 * (row // 3), 3 * (col // 3)\n",
    "        for i in range(3):\n",
    "            for j in range(3):\n",
    "                if self.grid[start_row + i][start_col + j] == num:\n",
    "                    return False\n",
    "\n",
    "        return True\n",
    "\n",
    "    def find_empty_location(self):\n",
    "        for i in range(9):\n",
    "            for j in range(9):\n",
    "                if self.grid[i][j] == 0:\n",
    "                    return i, j\n",
    "        return None\n",
    "\n",
    "    def solve(self):\n",
    "        empty_location = self.find_empty_location()\n",
    "\n",
    "        if not empty_location:\n",
    "            elapsed_time = time.time() - self.start_time\n",
    "            print(f\"Sudoku solved in {elapsed_time:.6f} seconds.\")\n",
    "            print(f\"Number of choices explored: {self.choices_explored}\")\n",
    "            return True\n",
    "\n",
    "        row, col = empty_location\n",
    "\n",
    "        for num in range(1, 10):\n",
    "            if self.is_valid(row, col, num):\n",
    "                self.grid[row][col] = num\n",
    "                self.choices_explored += 1\n",
    "                if self.solve():\n",
    "                    return True\n",
    "                self.grid[row][col] = 0\n",
    "\n",
    "        # If no number leads to a solution, backtrack\n",
    "        return False\n",
    "\n",
    "    def display(self):\n",
    "        fig, ax = plt.subplots(figsize=(6, 6))\n",
    "        ax = sns.heatmap(self.grid, cmap=\"YlOrRd\", annot=True, cbar=False, linewidths=1, linecolor='black', fmt='g', ax=ax)\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        # Draw lines for the 3x3 subgrids\n",
    "        for i in range(0, 10, 3):\n",
    "            ax.axhline(i, color='white', lw=5)\n",
    "            ax.axvline(i, color='white', lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2999f080-a15c-4b54-9132-b96e97c94ef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sudoku solved in 0.124706 seconds.\n",
      "Number of choices explored: 4208\n",
      "Sudoku solved:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example usage:\n",
    "puzzle = [\n",
    "    [5, 3, 0, 0, 7, 0, 0, 0, 0],\n",
    "    [6, 0, 0, 1, 9, 5, 0, 0, 0],\n",
    "    [0, 9, 8, 0, 0, 0, 0, 6, 0],\n",
    "    [8, 0, 0, 0, 6, 0, 0, 0, 3],\n",
    "    [4, 0, 0, 8, 0, 3, 0, 0, 1],\n",
    "    [7, 0, 0, 0, 2, 0, 0, 0, 6],\n",
    "    [0, 6, 0, 0, 0, 0, 2, 8, 0],\n",
    "    [0, 0, 0, 4, 1, 9, 0, 0, 5],\n",
    "    [0, 0, 0, 0, 8, 0, 0, 7, 9]\n",
    "]\n",
    "\n",
    "sudoku_csp = SudokuCSP(puzzle)\n",
    "if sudoku_csp.solve():\n",
    "    print(\"Sudoku solved:\")\n",
    "    sudoku_csp.display()\n",
    "else:\n",
    "    print(\"No solution exists.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avec Heuristique : "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MRV Heuristic :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SudokuCSP:\n",
    "    def __init__(self, puzzle):\n",
    "        self.grid = puzzle\n",
    "        self.choices_explored = 0\n",
    "        self.start_time = time.time() \n",
    "        sns.set(style=\"whitegrid\")\n",
    "\n",
    "    def is_valid(self, row, col, num):\n",
    "        for i in range(9):\n",
    "            if self.grid[row][i] == num or self.grid[i][col] == num:\n",
    "                return False\n",
    "\n",
    "        start_row, start_col = 3 * (row // 3), 3 * (col // 3)\n",
    "        for i in range(3):\n",
    "            for j in range(3):\n",
    "                if self.grid[start_row + i][start_col + j] == num:\n",
    "                    return False\n",
    "\n",
    "        return True\n",
    "    \n",
    "    def count_valid_choices(self, row, col):\n",
    "        choices = 0\n",
    "        for num in range(1, 10):\n",
    "            if self.is_valid(row, col, num):\n",
    "                choices += 1\n",
    "\n",
    "        return choices\n",
    "    \n",
    "    def find_empty_location(self):\n",
    "        min_choices = float('inf')\n",
    "        empty_location = None\n",
    "\n",
    "        for i in range(9):\n",
    "            for j in range(9):\n",
    "                if self.grid[i][j] == 0:\n",
    "                    choices = self.count_valid_choices(i, j)\n",
    "\n",
    "                    if choices < min_choices:\n",
    "                        min_choices = choices\n",
    "                        empty_location = (i, j)\n",
    "\n",
    "        return empty_location\n",
    "    \n",
    "    def solve(self):\n",
    "        empty_location = self.find_empty_location()\n",
    "\n",
    "        if not empty_location:\n",
    "            elapsed_time = time.time() - self.start_time\n",
    "            print(f\"Sudoku solved in {elapsed_time:.6f} seconds.\")\n",
    "            print(f\"Number of choices explored: {self.choices_explored}\")\n",
    "            return True\n",
    "\n",
    "        row, col = empty_location\n",
    "\n",
    "        for num in range(1, 10):\n",
    "            if self.is_valid(row, col, num):\n",
    "                self.grid[row][col] = num\n",
    "                self.choices_explored += 1\n",
    "                \n",
    "                if self.solve():\n",
    "                    return True\n",
    "\n",
    "                self.grid[row][col] = 0\n",
    "        return False\n",
    "\n",
    "    def display(self):\n",
    "        fig, ax = plt.subplots(figsize=(6, 6))\n",
    "        ax = sns.heatmap(self.grid, cmap=\"Pastel1\", annot=True, cbar=False, linewidths=1, linecolor='black', fmt='g', ax=ax)\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        # Draw lines for the 3x3 subgrids\n",
    "        for i in range(0, 10, 3):\n",
    "            ax.axhline(i, color='white', lw=5)\n",
    "            ax.axvline(i, color='white', lw=5)\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sudoku solved in 0.018765 seconds.\n",
      "Number of choices explored: 51\n",
      "Sudoku solved:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example usage:\n",
    "puzzle = [\n",
    "    [5, 3, 0, 0, 7, 0, 0, 0, 0],\n",
    "    [6, 0, 0, 1, 9, 5, 0, 0, 0],\n",
    "    [0, 9, 8, 0, 0, 0, 0, 6, 0],\n",
    "    [8, 0, 0, 0, 6, 0, 0, 0, 3],\n",
    "    [4, 0, 0, 8, 0, 3, 0, 0, 1],\n",
    "    [7, 0, 0, 0, 2, 0, 0, 0, 6],\n",
    "    [0, 6, 0, 0, 0, 0, 2, 8, 0],\n",
    "    [0, 0, 0, 4, 1, 9, 0, 0, 5],\n",
    "    [0, 0, 0, 0, 8, 0, 0, 7, 9]\n",
    "]\n",
    "\n",
    "sudoku_csp = SudokuCSP(puzzle)\n",
    "if sudoku_csp.solve():\n",
    "    print(\"Sudoku solved:\")\n",
    "    sudoku_csp.display()\n",
    "else:\n",
    "    print(\"No solution exists.\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}