fill_chess-Github.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyomo.environ import *\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random \n",
    "import operator\n",
    "import pandas as pd\n",
    "from skimage.transform import rescale, resize, downscale_local_mean\n",
    "\n",
    "from matplotlib.image import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {}\n",
    "M =6\n",
    "n=0\n",
    "for i in range(1,M+1):\n",
    "    for j in range(1,M+1):\n",
    "        n+=1\n",
    "        dic[n,'x'] =i\n",
    "        dic[n,'y'] =j  \n",
    "N = n "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(i,j):\n",
    "    return sqrt( (dic[i,'x']-dic[j,'x'])**2 +  (dic[i,'y']-dic[j,'y'])**2  )\n",
    "connect = {}\n",
    "for i in range(1,1+N):\n",
    "    for j in range(1,1+N):\n",
    "        if dist(i,j) ==1:\n",
    "            connect[i,j] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_bound = {}\n",
    "for i in range(1,1+N):\n",
    "    upper_bound[i] = 1 + sum(1 for j in range(1,1+N) if (i,j) in connect)\n",
    "\n",
    "Nmax = max(upper_bound.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 4\n",
      "6 3\n"
     ]
    }
   ],
   "source": [
    "for j in range(1,9):\n",
    "    if (5,j) in connect: # and 5<=upper_bound[j]:\n",
    "        print(j, upper_bound[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AbstractModel()\n",
    "model.i = RangeSet(N)\n",
    "model.j = Set(initialize= model.i)\n",
    "model.n = RangeSet(Nmax)\n",
    "def rule_w(model,i,j):\n",
    "    return connect.get((i,j),0)\n",
    "model.w = Param(model.i,model.j,initialize= rule_w)\n",
    "model.y = Var(model.i, model.n, domain = Binary)\n",
    "\n",
    "def rule_c2(model,i):\n",
    "    return sum(model.y[i,n] for n in model.n if n <= upper_bound[i] ) ==1\n",
    "model.C2 = Constraint(model.i, rule=rule_c2)\n",
    "\n",
    "def rule_c3(model,i,n):\n",
    "    if n <= upper_bound[i] and sum(1 for j in model.j if (i,j) in connect and n<=upper_bound[j] )>0 and sum(1 for m in model.n if n<m <= upper_bound[i]):\n",
    "        return sum(model.y[j,n] for j in model.j if (i,j) in connect and n<=upper_bound[j] ) >= sum(model.y[i,m] for m in model.n if n<m <= upper_bound[i])\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C3 = Constraint(model.i, model.n, rule=rule_c3)\n",
    "\n",
    "def rule_OF(model):\n",
    "    return sum( n*model.y[i,n] for n in model.n for i in model.i if n <=upper_bound[i] )\n",
    "\n",
    "model.obj1 = Objective(rule=rule_OF, sense=maximize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = SolverFactory('cbc')\n",
    "opt.options = {'sec': 300, 'threads': 6, 'ratio': 0.05}\n",
    "\n",
    "instance = model.create_instance()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is feasible and optimal\n",
      "93.0\n"
     ]
    }
   ],
   "source": [
    "results = opt.solve(instance) # solves and updates instance\n",
    "from pyomo.opt import SolverStatus, TerminationCondition\n",
    "if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):\n",
    "     print (\"this is feasible and optimal\")\n",
    "elif results.solver.termination_condition == TerminationCondition.infeasible:\n",
    "     print (\"do something about it? or exit?\")\n",
    "else:\n",
    "     print ('ERROR ' + str(results.solver))\n",
    "print(value(instance.obj1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "KOLOR = ['r', 'k', 'dodgerblue', 'g', 'gold', 'pink','dodgerblue','orange','brown','olive']\n",
    "\n",
    "for i in instance.i:\n",
    "    m = int(value(sum(n*instance.y[i,n] for n in instance.n if n <=upper_bound[i] ) ))\n",
    "    plt.scatter(dic[i,'x'], dic[i,'y'], s= 2000 , marker ='s', c=KOLOR[m-1])\n",
    "    plt.text(dic[i,'x']-0.08, dic[i,'y']-0.02, s= str(m) , c='w', fontsize=20, fontweight='bold')\n",
    "\n",
    "plt.axis('off')\n",
    "plt.savefig('neighbors '+ str(Nmax)+  ' .png', format='png', dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}