random_walk_ex2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'drunk' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-4147ef46bcd7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mrandom_drunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtakeStep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mstepChoices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstepChoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'drunk' is not defined"
     ]
    }
   ],
   "source": [
    "class random_drunk(drunk):\n",
    "    def takeStep(self):\n",
    "        stepChoices = [(0,1), (0,-1), (1,0), (-1,0)]\n",
    "        return random.choice(stepChoices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'drunk' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-e2fd64acd394>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mbiased_drunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtakeStep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mstepChoices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstepChoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'drunk' is not defined"
     ]
    }
   ],
   "source": [
    "# biased random walk to if step forward you go 1.1 of a step size and if backward you go 0.9 of a step size so it's\n",
    "# biased to moving forward\n",
    "class biased_drunk(drunk):\n",
    "    def takeStep(self):\n",
    "        stepChoices = [(0.0,1.1), (0.0, -0.9), (1.0, 0.0), (-1.0, 0.0)]\n",
    "        return random.choice(stepChoices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class field(object):\n",
    "    def __init__(self):\n",
    "        self.drunks{}\n",
    "        \n",
    "    def addDrunk(self,drunk,loc):\n",
    "        if drunk in self.drunks:\n",
    "            raise ValueError('Duplicate Drunk')\n",
    "        else:\n",
    "            self.drunks[drunk] = loc\n",
    "            \n",
    "    def getLoc(self,drunk):\n",
    "        if drunk not in self.drunks:\n",
    "            raise ValueError('Drunk not in field')\n",
    "    return self.drunks[drunk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moveDrunk(self, drunk):\n",
    "    if drunk not in self.drunks:\n",
    "        raise ValueError('Drunk not in field')\n",
    "    xDist, yDist = drunk.takeStep()\n",
    "    self.drunks[drunk] = self.drunks[drunk].move(xDist, yDist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UsualDrunk random walk of 0 steps\n",
      " Mean = 0.0\n",
      " Max = 0.0 Min = 0.0\n",
      "UsualDrunk random walk of 100 steps\n",
      " Mean = 8.634\n",
      " Max = 21.6 Min = 1.4\n",
      "UsualDrunk random walk of 1000 steps\n",
      " Mean = 27.147\n",
      " Max = 66.3 Min = 4.2\n",
      "UsualDrunk random walk of 10000 steps\n",
      " Mean = 90.204\n",
      " Max = 210.6 Min = 7.2\n",
      "UsualDrunk random walk of 1000 steps\n",
      " Mean = 26.828\n",
      " Max = 66.3 Min = 4.2\n",
      "UsualDrunk random walk of 10000 steps\n",
      " Mean = 90.073\n",
      " Max = 210.6 Min = 7.2\n",
      "MasochistDrunk random walk of 1000 steps\n",
      " Mean = 58.425\n",
      " Max = 133.3 Min = 6.7\n",
      "MasochistDrunk random walk of 10000 steps\n",
      " Mean = 515.575\n",
      " Max = 694.6 Min = 377.7\n",
      "Starting simulation of UsualDrunk\n",
      "Starting simulation of 10 steps\n",
      "Starting simulation of 100 steps\n",
      "Starting simulation of 1000 steps\n",
      "Starting simulation of 10000 steps\n",
      "Starting simulation of 100000 steps\n",
      "Starting simulation of MasochistDrunk\n",
      "Starting simulation of 10 steps\n",
      "Starting simulation of 100 steps\n",
      "Starting simulation of 1000 steps\n",
      "Starting simulation of 10000 steps\n",
      "Starting simulation of 100000 steps\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-80b7207ba03e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    179\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0mnumSteps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0msimAll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mUsualDrunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMasochistDrunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumSteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m pylab.plot(numSteps, pylab.array(numSteps)**0.5, 'k-.',\n",
      "\u001b[0;32m<ipython-input-8-80b7207ba03e>\u001b[0m in \u001b[0;36msimAll\u001b[0;34m(drunkKinds, walkLengths, numTrials)\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0mcurStyle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstyleChoice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnextStyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Starting simulation of'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdClass\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m         \u001b[0mmeans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimDrunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumTrials\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdClass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwalkLengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m         pylab.plot(walkLengths, means, curStyle,\n\u001b[1;32m    172\u001b[0m                    label = dClass.__name__)\n",
      "\u001b[0;32m<ipython-input-8-80b7207ba03e>\u001b[0m in \u001b[0;36msimDrunk\u001b[0;34m(numTrials, dClass, walkLengths)\u001b[0m\n\u001b[1;32m    158\u001b[0m         print('Starting simulation of',\n\u001b[1;32m    159\u001b[0m               numSteps, 'steps')\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimWalks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumSteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumTrials\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdClass\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0mmean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mmeanDistances\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-80b7207ba03e>\u001b[0m in \u001b[0;36msimWalks\u001b[0;34m(numSteps, numTrials, dClass)\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddDrunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHomer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m         distances.append(round(walk(f, Homer,\n\u001b[0;32m--> 110\u001b[0;31m                                     numSteps), 1))\n\u001b[0m\u001b[1;32m    111\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdistances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-80b7207ba03e>\u001b[0m in \u001b[0;36mwalk\u001b[0;34m(f, d, numSteps)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetLoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumSteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoveDrunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistFrom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetLoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-80b7207ba03e>\u001b[0m in \u001b[0;36mmoveDrunk\u001b[0;34m(self, drunk)\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdrunk\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrunks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Drunk not in field'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0mxDist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myDist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdrunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtakeStep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m         \u001b[0;31m#use move method of Location to get new location\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrunks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdrunk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrunks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdrunk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxDist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myDist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-80b7207ba03e>\u001b[0m in \u001b[0;36mtakeStep\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     84\u001b[0m         stepChoices = [(0.0,1.1), (0.0,-0.9),\n\u001b[1;32m     85\u001b[0m                        (1.0, 0.0), (-1.0, 0.0)]\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstepChoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwalk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumSteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/software_download/anaconda3/lib/python3.6/random.py\u001b[0m in \u001b[0;36mchoice\u001b[0;34m(self, seq)\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;31m## -------------------- sequence methods  -------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m         \u001b[0;34m\"\"\"Choose a random element from a non-empty sequence.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb658761908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random, pylab\n",
    "\n",
    "#set line width\n",
    "pylab.rcParams['lines.linewidth'] = 4\n",
    "#set font size for titles \n",
    "pylab.rcParams['axes.titlesize'] = 20\n",
    "#set font size for labels on axes\n",
    "pylab.rcParams['axes.labelsize'] = 20\n",
    "#set size of numbers on x-axis\n",
    "pylab.rcParams['xtick.labelsize'] = 16\n",
    "#set size of numbers on y-axis\n",
    "pylab.rcParams['ytick.labelsize'] = 16\n",
    "#set size of ticks on x-axis\n",
    "pylab.rcParams['xtick.major.size'] = 7\n",
    "#set size of ticks on y-axis\n",
    "pylab.rcParams['ytick.major.size'] = 7\n",
    "#set size of markers, e.g., circles representing points\n",
    "#set numpoints for legend\n",
    "pylab.rcParams['legend.numpoints'] = 1\n",
    "\n",
    "class Location(object):\n",
    "    def __init__(self, x, y):\n",
    "        \"\"\"x and y are numbers\"\"\"\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "\n",
    "    def move(self, deltaX, deltaY):\n",
    "        \"\"\"deltaX and deltaY are numbers\"\"\"\n",
    "        return Location(self.x + deltaX, self.y + deltaY)\n",
    "\n",
    "    def getX(self):\n",
    "        return self.x\n",
    "\n",
    "    def getY(self):\n",
    "        return self.y\n",
    "\n",
    "    def distFrom(self, other):\n",
    "        xDist = self.x - other.getX()\n",
    "        yDist = self.y - other.getY()\n",
    "        return (xDist**2 + yDist**2)**0.5\n",
    "\n",
    "    def __str__(self):\n",
    "        return '<' + str(self.x) + ', ' + str(self.y) + '>'\n",
    "\n",
    "class Field(object):\n",
    "    def __init__(self):\n",
    "        self.drunks = {}\n",
    "        \n",
    "    def addDrunk(self, drunk, loc):\n",
    "        if drunk in self.drunks:\n",
    "            raise ValueError('Duplicate drunk')\n",
    "        else:\n",
    "            self.drunks[drunk] = loc\n",
    "            \n",
    "    def moveDrunk(self, drunk):\n",
    "        if drunk not in self.drunks:\n",
    "            raise ValueError('Drunk not in field')\n",
    "        xDist, yDist = drunk.takeStep()\n",
    "        #use move method of Location to get new location\n",
    "        self.drunks[drunk] =\\\n",
    "            self.drunks[drunk].move(xDist, yDist)\n",
    "        \n",
    "    def getLoc(self, drunk):\n",
    "        if drunk not in self.drunks:\n",
    "            raise ValueError('Drunk not in field')\n",
    "        return self.drunks[drunk]\n",
    "\n",
    "class Drunk(object):\n",
    "    def __init__(self, name = None):\n",
    "        \"\"\"Assumes name is a str\"\"\"\n",
    "        self.name = name\n",
    "\n",
    "    def __str__(self):\n",
    "        if self != None:\n",
    "            return self.name\n",
    "        return 'Anonymous'\n",
    "\n",
    "class UsualDrunk(Drunk):\n",
    "    def takeStep(self):\n",
    "        stepChoices = [(0,1), (0,-1), (1, 0), (-1, 0)]\n",
    "        return random.choice(stepChoices)\n",
    "\n",
    "class MasochistDrunk(Drunk):\n",
    "    def takeStep(self):\n",
    "        stepChoices = [(0.0,1.1), (0.0,-0.9),\n",
    "                       (1.0, 0.0), (-1.0, 0.0)]\n",
    "        return random.choice(stepChoices)\n",
    "\n",
    "def walk(f, d, numSteps):\n",
    "    \"\"\"Assumes: f a Field, d a Drunk in f, and numSteps an int >= 0.\n",
    "       Moves d numSteps times, and returns the distance between\n",
    "       the final location and the location at the start of the \n",
    "       walk.\"\"\"\n",
    "    start = f.getLoc(d)\n",
    "    for s in range(numSteps):\n",
    "        f.moveDrunk(d)\n",
    "    return start.distFrom(f.getLoc(d))\n",
    "    \n",
    "def simWalks(numSteps, numTrials, dClass):\n",
    "    \"\"\"Assumes numSteps an int >= 0, numTrials an int > 0,\n",
    "         dClass a subclass of Drunk\n",
    "       Simulates numTrials walks of numSteps steps each.\n",
    "       Returns a list of the final distances for each trial\"\"\"\n",
    "    Homer = dClass('Homer')\n",
    "    origin = Location(0, 0)\n",
    "    distances = []\n",
    "    for t in range(numTrials):\n",
    "        f = Field()\n",
    "        f.addDrunk(Homer, origin)\n",
    "        distances.append(round(walk(f, Homer,\n",
    "                                    numSteps), 1))\n",
    "    return distances\n",
    "\n",
    "def drunkTest(walkLengths, numTrials, dClass):\n",
    "    \"\"\"Assumes walkLengths a sequence of ints >= 0\n",
    "         numTrials an int > 0, dClass a subclass of Drunk\n",
    "       For each number of steps in walkLengths, runs simWalks with\n",
    "         numTrials walks and prints results\"\"\"\n",
    "    for numSteps in walkLengths:\n",
    "        distances = simWalks(numSteps, numTrials, dClass)\n",
    "        print(dClass.__name__, 'random walk of', numSteps, 'steps')\n",
    "        print(' Mean =', round(sum(distances)/len(distances), 4))\n",
    "        print(' Max =', max(distances), 'Min =', min(distances))\n",
    "        \n",
    "random.seed(0)\n",
    "drunkTest((0, 100, 1000, 10000), 100, UsualDrunk)\n",
    "#\n",
    "def simAll(drunkKinds, walkLengths, numTrials):\n",
    "    for dClass in drunkKinds:\n",
    "        drunkTest(walkLengths, numTrials, dClass)\n",
    "        \n",
    "#random.seed(0)\n",
    "#simAll((UsualDrunk, MasochistDrunk),\n",
    "#       (1000, 10000), 100)\n",
    "#        \n",
    "#xVals = [1, 2, 3, 4]\n",
    "#yVals1 = [1, 2, 3, 4]\n",
    "#pylab.plot(xVals, yVals1, 'b-', label = 'first')\n",
    "#yVals2 = [1, 7, 3, 5]\n",
    "#pylab.plot(xVals, yVals2, 'r--', label = 'second')\n",
    "#pylab.legend()\n",
    "\n",
    "class styleIterator(object):\n",
    "    def __init__(self, styles):\n",
    "        self.index = 0\n",
    "        self.styles = styles\n",
    "\n",
    "    def nextStyle(self):\n",
    "        result = self.styles[self.index]\n",
    "        if self.index == len(self.styles) - 1:\n",
    "            self.index = 0\n",
    "        else:\n",
    "            self.index += 1\n",
    "        return result\n",
    "    \n",
    "def simDrunk(numTrials, dClass, walkLengths):\n",
    "    meanDistances = []\n",
    "    for numSteps in walkLengths:\n",
    "        print('Starting simulation of',\n",
    "              numSteps, 'steps')\n",
    "        trials = simWalks(numSteps, numTrials, dClass)\n",
    "        mean = sum(trials)/len(trials)\n",
    "        meanDistances.append(mean)\n",
    "    return meanDistances\n",
    "\n",
    "def simAll(drunkKinds, walkLengths, numTrials):\n",
    "    styleChoice = styleIterator(('m-', 'b--', 'g-.'))\n",
    "    for dClass in drunkKinds:\n",
    "        curStyle = styleChoice.nextStyle()\n",
    "        print('Starting simulation of', dClass.__name__)\n",
    "        means = simDrunk(numTrials, dClass, walkLengths)\n",
    "        pylab.plot(walkLengths, means, curStyle,\n",
    "                   label = dClass.__name__)\n",
    "    pylab.title('Mean Distance from Origin ('\n",
    "                + str(numTrials) + ' trials)')\n",
    "    pylab.xlabel('Number of Steps')\n",
    "    pylab.ylabel('Distance from Origin')\n",
    "    pylab.legend(loc = 'best')\n",
    "\n",
    "#random.seed(0)\n",
    "#numSteps = (10,100,1000,10000,100000)\n",
    "#simAll((UsualDrunk, MasochistDrunk), numSteps, 100)\n",
    "#\n",
    "#pylab.plot(numSteps, pylab.array(numSteps)**0.5, 'k-.',\n",
    "#           label = 'Square root of steps')\n",
    "#pylab.plot(numSteps, pylab.array(numSteps)*0.05, 'g-.',\n",
    "#           label = 'numSteps*0.05')\n",
    "#pylab.legend(loc = 'best')\n",
    "\n",
    "def getFinalLocs(numSteps, numTrials, dClass):\n",
    "    locs = []\n",
    "    d = dClass()\n",
    "    for t in range(numTrials):\n",
    "        f = OddField()\n",
    "        f.addDrunk(d, Location(0, 0))\n",
    "        for s in range(numSteps):\n",
    "            f.moveDrunk(d)\n",
    "        locs.append(f.getLoc(d))\n",
    "    return locs\n",
    "\n",
    "def plotLocs(drunkKinds, numSteps, numTrials):\n",
    "    styleChoice = styleIterator(('k+', 'r^', 'mo'))\n",
    "    for dClass in drunkKinds:\n",
    "        locs = getFinalLocs(numSteps, numTrials, dClass)\n",
    "        xVals, yVals = [], []\n",
    "        for loc in locs:\n",
    "            xVals.append(loc.getX())\n",
    "            yVals.append(loc.getY())\n",
    "        xVals = pylab.array(xVals)\n",
    "        yVals = pylab.array(yVals)\n",
    "        meanX = sum(abs(xVals))/len(xVals)\n",
    "        meanY = sum(abs(yVals))/len(yVals)\n",
    "        curStyle = styleChoice.nextStyle()\n",
    "        pylab.plot(xVals, yVals, curStyle,\n",
    "                      label = dClass.__name__ +\\\n",
    "                      ' mean abs dist = <'\n",
    "                      + str(meanX) + ', ' + str(meanY) + '>')\n",
    "    pylab.title('Location at End of Walks ('\n",
    "                + str(numSteps) + ' steps)')\n",
    "    pylab.ylim(-1000, 1000)\n",
    "    pylab.xlim(-1000, 1000)\n",
    "    pylab.xlabel('Steps East/West of Origin')\n",
    "    pylab.ylabel('Steps North/South of Origin')\n",
    "    pylab.legend(loc = 'lower center')\n",
    "\n",
    "#random.seed(0)\n",
    "#plotLocs((UsualDrunk, MasochistDrunk), 10000, 1000)\n",
    "\n",
    "class OddField(Field):\n",
    "    def __init__(self, numHoles = 1000,\n",
    "                 xRange = 100, yRange = 100):\n",
    "        Field.__init__(self)\n",
    "        self.wormholes = {}\n",
    "        for w in range(numHoles):\n",
    "            x = random.randint(-xRange, xRange)\n",
    "            y = random.randint(-yRange, yRange)\n",
    "            newX = random.randint(-xRange, xRange)\n",
    "            newY = random.randint(-yRange, yRange)\n",
    "            newLoc = Location(newX, newY)\n",
    "            self.wormholes[(x, y)] = newLoc\n",
    "\n",
    "    def moveDrunk(self, drunk):\n",
    "        Field.moveDrunk(self, drunk)\n",
    "        x = self.drunks[drunk].getX()\n",
    "        y = self.drunks[drunk].getY()\n",
    "        if (x, y) in self.wormholes:\n",
    "            self.drunks[drunk] = self.wormholes[(x, y)]\n",
    "            \n",
    "#TraceWalk using oddField          \n",
    "def traceWalk(fieldKinds, numSteps):\n",
    "    styleChoice = styleIterator(('b+', 'r^', 'ko'))\n",
    "    for fClass in fieldKinds:\n",
    "        d = UsualDrunk()\n",
    "        f = fClass()\n",
    "        f.addDrunk(d, Location(0, 0))\n",
    "        locs = []\n",
    "        for s in range(numSteps):\n",
    "            f.moveDrunk(d)\n",
    "            locs.append(f.getLoc(d))\n",
    "        xVals, yVals = [], []\n",
    "        for loc in locs:\n",
    "            xVals.append(loc.getX())\n",
    "            yVals.append(loc.getY())\n",
    "        curStyle = styleChoice.nextStyle()\n",
    "        pylab.plot(xVals, yVals, curStyle,\n",
    "                   label = fClass.__name__)\n",
    "    pylab.title('Spots Visited on Walk ('\n",
    "                + str(numSteps) + ' steps)')\n",
    "    pylab.xlabel('Steps East/West of Origin')\n",
    "    pylab.ylabel('Steps North/South of Origin')\n",
    "    pylab.legend(loc = 'best')\n",
    "\n",
    "#random.seed(0)\n",
    "#traceWalk((Field, OddField), 500)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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