demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer Set Programming with Sudoku"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classic Sudoku (First Example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Image of problem\n",
    "from IPython.display import Image\n",
    "Image(filename='./Sudoku Game Types/Classic Sudoku/sudokuExample.jpeg') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- - - - - - 2 - - \n",
      "- 8 - - - 7 - 9 - \n",
      "6 - 2 - - - 5 - - \n",
      "- 7 - - 6 - - - - \n",
      "- - - 9 - 1 - - - \n",
      "- - - - 2 - - 4 - \n",
      "- - 5 - - - 6 - 3 \n",
      "- 9 - 4 - - - 7 - \n",
      "- - 6 - - - - - - \n",
      "\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "# Set Example\n",
    "!python ./tools/sudoku_problem_viz.py \"./Sudoku Game Types/Classic Sudoku/classic-sudoku-problem.lp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clingo version 5.5.0\n",
      "Reading from ...assic Sudoku/classic-sudoku-problem.lp ...\n",
      "Solving...\n",
      "Answer: 1\n",
      "sudoku(1,7,2) sudoku(2,2,8) sudoku(2,6,7) sudoku(2,8,9) sudoku(3,1,6) sudoku(3,3,2) sudoku(3,7,5) sudoku(4,2,7) sudoku(4,5,6) sudoku(5,4,9) sudoku(5,6,1) sudoku(6,5,2) sudoku(6,8,4) sudoku(7,3,5) sudoku(7,7,6) sudoku(7,9,3) sudoku(8,2,9) sudoku(8,4,4) sudoku(8,8,7) sudoku(9,3,6) sudoku(1,1,9) sudoku(3,2,1) sudoku(2,3,3) sudoku(2,1,4) sudoku(1,2,5) sudoku(1,3,7) sudoku(4,1,1) sudoku(5,2,2) sudoku(6,2,6) sudoku(5,3,4) sudoku(6,3,9) sudoku(5,1,5) sudoku(4,3,8) sudoku(6,1,3) sudoku(7,1,8) sudoku(9,2,3) sudoku(8,3,1) sudoku(8,1,2) sudoku(7,2,4) sudoku(9,1,7) sudoku(2,5,5) sudoku(1,4,6) sudoku(3,5,4) sudoku(3,6,9) sudoku(1,5,1) sudoku(2,4,2) sudoku(1,6,3) sudoku(3,4,8) sudoku(4,4,3) sudoku(5,5,7) sudoku(6,6,8) sudoku(4,6,4) sudoku(6,4,5) sudoku(8,5,3) sudoku(7,4,7) sudoku(9,5,8) sudoku(8,6,6) sudoku(9,6,5) sudoku(7,5,9) sudoku(7,6,2) sudoku(9,4,1) sudoku(3,8,3) sudoku(2,9,6) sudoku(3,9,7) sudoku(2,7,1) sudoku(1,8,8) sudoku(1,9,4) sudoku(5,8,6) sudoku(4,7,9) sudoku(5,9,8) sudoku(6,9,1) sudoku(5,7,3) sudoku(4,8,5) sudoku(4,9,2) sudoku(6,7,7) sudoku(9,8,2) sudoku(8,9,5) sudoku(9,9,9) sudoku(7,8,1) sudoku(8,7,8) sudoku(9,7,4)\n",
      "SATISFIABLE\n",
      "\n",
      "Models       : 1\n",
      "Calls        : 1\n",
      "Time         : 0.045s (Solving: 0.00s 1st Model: 0.00s Unsat: 0.00s)\n",
      "CPU Time     : 0.047s\n"
     ]
    }
   ],
   "source": [
    "# Run Solver\n",
    "!clingo \"./Sudoku Game Types/Classic Sudoku/classic-sudoku-problem.lp\" \"./Sudoku Game Types/Classic Sudoku/classic-sudoku.lp\" -n 0\n",
    "!clingo \"./Sudoku Game Types/Classic Sudoku/classic-sudoku-problem.lp\" \"./Sudoku Game Types/Classic Sudoku/classic-sudoku.lp\" -n 0 > \"./Sudoku Game Types/Classic Sudoku/classic-solution.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution : 1\u001b[33m\n",
      "9 5 7 6 1 3 2 8 4 \n",
      "4 8 3 2 5 7 1 9 6 \n",
      "6 1 2 8 4 9 5 3 7 \n",
      "1 7 8 3 6 4 9 5 2 \n",
      "5 2 4 9 7 1 3 6 8 \n",
      "3 6 9 5 2 8 7 4 1 \n",
      "8 4 5 7 9 2 6 1 3 \n",
      "2 9 1 4 3 6 8 7 5 \n",
      "7 3 6 1 8 5 4 2 9 \n",
      "\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "# Visualize Solution\n",
    "!python ./tools/sudoku_solution_viz.py \"./Sudoku Game Types/Classic Sudoku/classic-solution.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classic Sudoku (Generated Example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For seed\n",
    "!echo %RANDOM%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Generate a problem and display example\n",
    "# Generate a full board and save it as txt file\n",
    "# DO NOT SET THIRD COMMAND LINE ARG '-n' TO ZERO\n",
    "# Change seed to see different boards built\n",
    "!clingo -n 1 --rand-freq=1 --seed=14124 \"./Sudoku Game Types/Classic Sudoku/classic-sudoku.lp\" > \"./generated/classic_board_gen.txt\"\n",
    "# Display Full Board\n",
    "!python ./tools/sudoku_solution_viz.py \"./generated/classic_board_gen.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 70% to remove an entry\n",
    "!python ./tools/sudoku_remove.py \"./generated/classic_board_gen.txt\" 0.7 \"./generated/classic_problem_gen.lp\"\n",
    "# Display Problem\n",
    "!python ./tools/sudoku_problem_viz.py \"./generated/classic_problem_gen.lp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run solver\n",
    "# May find different solution than expected\n",
    "!clingo \"./generated/classic_problem_gen.lp\" \"./Sudoku Game Types/Classic Sudoku/classic-sudoku.lp\" -n 1 > \"./generated/classic_solution_gen.txt\"\n",
    "# Visualize Solution\n",
    "!python ./tools/sudoku_solution_viz.py \"./generated/classic_solution_gen.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagonal Sudoku (Generated Example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18248\n"
     ]
    }
   ],
   "source": [
    "# For seed\n",
    "!echo %RANDOM%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution : 1\u001b[33m\n",
      "8 1 9 5 4 3 7 6 2 \n",
      "2 6 4 7 9 8 5 1 3 \n",
      "5 7 3 2 6 1 4 9 8 \n",
      "6 2 5 9 3 7 1 8 4 \n",
      "7 4 8 1 5 6 3 2 9 \n",
      "9 3 1 8 2 4 6 5 7 \n",
      "1 8 6 4 7 9 2 3 5 \n",
      "4 9 2 3 1 5 8 7 6 \n",
      "3 5 7 6 8 2 9 4 1 \n",
      "\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "# Generate a problem and display example\n",
    "# Generate a full board and save it as txt file\n",
    "# DO NOT SET THIRD COMMAND LINE ARG '-n' TO ZERO\n",
    "# Change seed to see different boards built\n",
    "!clingo -n 1 --rand-freq=1 --seed=18248 \"./Sudoku Game Types/Diagonal Sudoku/diagonal-sudoku.lp\" > \"./generated/diagonal_board_gen.txt\"\n",
    "# Display Full Board\n",
    "!python ./tools/sudoku_solution_viz.py \"./generated/diagonal_board_gen.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- 1 - - 4 3 - - 2 \n",
      "- - 4 - - - 5 - 3 \n",
      "5 - 3 - - - - - - \n",
      "- - 5 - - - 1 - - \n",
      "- - - 1 - 6 - 2 - \n",
      "- - - - 2 - - - - \n",
      "- - - 4 - - - - - \n",
      "- - - - - 5 - - - \n",
      "3 - - - - - - 4 - \n",
      "\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "# 70% to remove an entry\n",
    "!python ./tools/sudoku_remove.py \"./generated/diagonal_board_gen.txt\" 0.7 \"./generated/diagonal_problem_gen.lp\"\n",
    "# Display Problem\n",
    "!python ./tools/sudoku_problem_viz.py \"./generated/diagonal_problem_gen.lp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution : 1\u001b[33m\n",
      "8 1 6 5 4 3 9 7 2 \n",
      "2 9 4 8 6 7 5 1 3 \n",
      "5 7 3 2 1 9 6 8 4 \n",
      "4 2 5 7 3 8 1 9 6 \n",
      "7 3 9 1 5 6 4 2 8 \n",
      "6 8 1 9 2 4 3 5 7 \n",
      "9 6 7 4 8 1 2 3 5 \n",
      "1 4 2 3 7 5 8 6 9 \n",
      "3 5 8 6 9 2 7 4 1 \n",
      "\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "# Run solver\n",
    "# May find different solution than expected\n",
    "!clingo \"./generated/diagonal_problem_gen.lp\" \"./Sudoku Game Types/Diagonal Sudoku/diagonal-sudoku.lp\" -n 1 > \"./generated/diagonal_solution_gen.txt\"\n",
    "# Visualize Solution\n",
    "!python ./tools/sudoku_solution_viz.py \"./generated/diagonal_solution_gen.txt\""
   ]
  }
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