AI - Lab 03 - Local Search - Hill Climbing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial Intelligence — Lab — Exercise 03 — Question 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 08 September 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Centre for a Set of Points\n",
    "<ul>\n",
    "<li>Points A, B, C, D, E</li>\n",
    "<li>Find a centre point to these points.</li>\n",
    "<li>Total distance of centre from all the points is minimum.</li>\n",
    "<li>Implement and test a hill-climbing method to find the centre for the set of points.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Point:\n",
    "    \"\"\"Class to work with a Point (x, y) and its related functions. \"\"\"\n",
    "    \n",
    "    def __init__(self, name, x, y):\n",
    "        \"\"\"Define a Coordinate (X, Y) with a name. \"\"\"\n",
    "        \n",
    "        self.name = name\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        \n",
    "    def __str__(self):\n",
    "        \"\"\"Returns the Point's Name and Coordinates in a formatted manner. \"\"\"\n",
    "        \n",
    "        return self.name + \"(\" + str(self.x) + \", \" + str(self.y) + \")\"\n",
    "        \n",
    "    def manhattan_distance(self, p):\n",
    "        \"\"\"Find the Manhattan distance between the point and another point P. \"\"\"\n",
    "        \n",
    "        md = abs(self.x - p.x) + abs(self.y - p.y)\n",
    "        return md\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Hill_Climb:\n",
    "    \"\"\"Class to work with the Hill-Climbing algorithm and its related functionalities.\"\"\"\n",
    "    \n",
    "    def __init__(self, points):\n",
    "        \"\"\"Define the points for hill climbing algorithm. \"\"\"\n",
    "        \n",
    "        self.points = points\n",
    "        self.centre = Point('O', 0, 0)\n",
    "        #Approximate centroid point, can also use this as the initial centre.\n",
    "        #self.centre.x = sum(point.x for point in points)//len(points)\n",
    "        #self.centre.y = sum(point.y for point in points)//len(points)\n",
    "        \n",
    "\n",
    "    def manhattan_sum(self, centre):\n",
    "        \"\"\"Find the Manhattan distance sum between a given point and all the other points. \"\"\"\n",
    "        \n",
    "        ms = sum(centre.manhattan_distance(point) for point in self.points)\n",
    "        return ms\n",
    "\n",
    "    def find_centre_point(self):\n",
    "        \"\"\"Finds the most appropriate centre point for the given set of points. \"\"\"\n",
    "        \n",
    "        current_ms = self.manhattan_sum(self.centre)\n",
    "        t = Point('O', 0, 0)\n",
    "        \n",
    "        print(\"Initial Centre\\t\\t:\\t\", self.centre)\n",
    "        print(\"Initial Manhattan Sum\\t:\\t\", current_ms)\n",
    "        \n",
    "        self.centres = [Point('O', self.centre.x, self.centre.y)]\n",
    "        \n",
    "        while True:\n",
    "            new_ms = self.manhattan_sum(self.centre)\n",
    "            \n",
    "            u = Point('U', self.centre.x, self.centre.y + 1)\n",
    "            d = Point('D', self.centre.x, self.centre.y - 1)\n",
    "            l = Point('L', self.centre.x - 1, self.centre.y)\n",
    "            r = Point('R', self.centre.x + 1, self.centre.y)\n",
    "            \n",
    "            neighbors = [u, l, d, r]\n",
    "            \n",
    "            for neighbor in neighbors:\n",
    "                if self.manhattan_sum(neighbor) < new_ms:\n",
    "                    new_ms = self.manhattan_sum(neighbor)\n",
    "                    t.x = neighbor.x\n",
    "                    t.y = neighbor.y\n",
    "            \n",
    "            if current_ms > new_ms:\n",
    "                self.centres.append(Point('O', t.x, t.y))\n",
    "                self.centre.x = t.x\n",
    "                self.centre.y = t.y\n",
    "                current_ms = new_ms\n",
    "\n",
    "            else:\n",
    "                break\n",
    "            \n",
    "        \n",
    "        print(\"\\nFinal Centre\\t\\t:\\t\", self.centre)\n",
    "        print(\"Final Manhattan Sum\\t:\\t\", current_ms)\n",
    "\n",
    "        return self.centres\n",
    "    \n",
    "    def plot_graph(self):\n",
    "        \"\"\"Plots the graph of the given points, the intermediate centres and the final centre. \"\"\"\n",
    "\n",
    "        fc = self.centres[-1]\n",
    "        \n",
    "        plt.plot([p.x for p in self.centres], [p.y for p in self.centres], color = \"green\", label = \"Intermediate Centres\")\n",
    "        plt.scatter([p.x for p in points], [p.y for p in points], color = \"blue\", label = \"Given Points\")\n",
    "        plt.scatter([fc.x], [fc.y], color = \"red\", label = \"Final Centre\")\n",
    "        plt.title(\"Plot of the Points & Centres\")\n",
    "        plt.legend()\n",
    "        \n",
    "        for p in self.points:\n",
    "            plt.annotate(p.name, (p.x, p.y), ha = \"center\", xytext = (p.x + 0.25 , p.y))\n",
    "            \n",
    "        plt.annotate(fc.name, (fc.x, fc.y), ha = \"center\", xytext = (fc.x + 0.25 , fc.y))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\tHill Climbing Algorithm\n",
      "\n",
      "\n",
      "Given Points\t\t:\t A(0, 6) B(1, 2) C(3, 1) D(7, 0) E(9, 3) \n",
      "\n",
      "Initial Centre\t\t:\t O(0, 0)\n",
      "Initial Manhattan Sum\t:\t 32\n",
      "\n",
      "Final Centre\t\t:\t O(3, 2)\n",
      "Final Manhattan Sum\t:\t 23\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    \"\"\"Driver program to execute Hill Climbing algorithm for centre points.\"\"\"\n",
    "    \n",
    "    print(\"\\n\\t\\tHill Climbing Algorithm\\n\")\n",
    "    \n",
    "    a = Point('A', 0, 6)\n",
    "    b = Point('B', 1, 2)\n",
    "    c = Point('C', 3, 1)\n",
    "    d = Point('D', 7, 0)\n",
    "    e = Point('E', 9, 3)\n",
    "\n",
    "    points = [a, b, c, d, e]\n",
    "    \n",
    "    print(\"\\nGiven Points\\t\\t:\\t\", end = \" \")\n",
    "    \n",
    "    for p in points:\n",
    "        print(p, end = \" \")\n",
    "        \n",
    "    print(\"\\n\")\n",
    "\n",
    "    h = Hill_Climb(points)\n",
    "    h.find_centre_point()\n",
    "    h.plot_graph()"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}