04-brute-force.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plánování hrubou silou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from itertools import product\n",
    "from search import parse_state, is_goal, actions, move\n",
    "from search import show_plan, visualize_search, log_search_step\n",
    "import solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generuj a testuj\n",
    "\n",
    "Protipolém k hladovým algoritmům je *hrubá síla*,\n",
    "technika návrhu algoritmů založená na zkoušení všech možností.\n",
    "Častým případem hrubé síly je hledání jednoho řešení,\n",
    "které splňuje nějaké požadavky.\n",
    "V našem případě budeme generovat všechny možné plány a testovat,\n",
    "zda nás plán bezpečně dostane do cíle.\n",
    "Takové algoritmu říkáme *generuj a testuj*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generování plánů\n",
    "\n",
    "Všechny možné plány délky *n* jsou řetězce *n* písmen, kde každé písmeno je buď 'l', 'f', nebo 'r'.\n",
    "Jde tedy o *n-členné variace s opakováním ze 3 prvků*.\n",
    "Ty v Pythonu vygenerujeme pomocí funkce `product`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ll', 'lf', 'lr', 'fl', 'ff', 'fr', 'rl', 'rf', 'rr']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def all_plans_of_length(n):\n",
    "    return [''.join(plan) for plan in product('lfr', repeat=n)]\n",
    "        \n",
    "all_plans_of_length(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Úkol 4.1: Počet plánů</b><br/>\n",
    "Kolik je všech plánů délky n?\n",
    "\n",
    "Hypotézu si můžete ověřit zavoláním\n",
    "<i>len(all_plans_of_length(n))</i>\n",
    "pro různé hodnoty n.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testování plánu\n",
    "\n",
    "Dále budeme potřebovat testovat, zda je plán \"správný\",\n",
    "tj. zda nás dovede do cíle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_correct(initial_state, plan):\n",
    "    state = initial_state\n",
    "    for action in plan:\n",
    "        state = move(state, action)\n",
    "    return is_goal(state)\n",
    "\n",
    "state = parse_state(\n",
    "'|A|A| | | |'\n",
    "'| |A|A| | |'\n",
    "'| | |A|A| |'\n",
    "'| | | | | |'\n",
    "'| | |S| | |')\n",
    "is_correct(state, 'rrff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementace\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Úkol 4.2: Generuj a testuj</b><br/>\n",
    "Implementujte funkci <i>generate_and_test(initial_state)</i>,\n",
    "která vrací plán pro cestu z <i>initial_state</i> do cílového stavu\n",
    "nalezený hrubou silou.\n",
    "\n",
    "<p>\n",
    "Tipy:\n",
    "<ul>\n",
    "<li>Pro vygenerování všech plánů využijte funkci <i>all_plans_of_length</i>.</li>\n",
    "<li>Požadovaná délka plánu je výška mapy minus 1:\n",
    "    <code>initial_state.height - 1</code>.\n",
    "</li>\n",
    "<li>Pro testování plánu využijte funkci <i>is_correct</i>.</li>\n",
    "<li>Vraťte první vygenerovaný plán, který splní podmínku.</li>\n",
    "</ul>\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f873f0e3240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_and_test(initial_state):\n",
    "    pass  # TODO: implementujte tuto funkci\n",
    "\n",
    "plan = solutions.generate_and_test(state)  # Vzorove reseni.\n",
    "#plan = generate_and_test(state)  # Vase reseni.\n",
    "show_plan(state, plan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shrnutí\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<ul>\n",
    "<li>\n",
    "Problém plánování lze řešit hrubou silou\n",
    "zkoušením všech možných plánů (\"generuj a testuj\").\n",
    "</li>\n",
    "<li>\n",
    "Zásadní nevýhodou hrubé síly je její časová náročnost.\n",
    "Počet plánů roste exponenciálně vzhledem k délce plánu.\n",
    "</li>\n",
    "</ul>\n",
    "</div>\n",
    "\n",
    "Pojďme vymyslet efektivnější řešení: [05 - Prohledávání do hloubky](./05-depth-first-search.ipynb)."
   ]
  }
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          "text/plain": "'| | | | | |\\n| | | | | |\\n|A|A|A|A| |\\n| | | | | |\\n| | |S| | |'"
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          "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
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          "\u001b[0;32m<ipython-input-18-8d2676849e30>\u001b[0m in \u001b[0;36mshow_plan_step\u001b[0;34m(step)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mshow_plan_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
          "\u001b[0;31mTypeError\u001b[0m: 'itertools.accumulate' object is not subscriptable"
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       "outputs": [
        {
         "data": {
          "image/png": 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          "text/plain": "<matplotlib.figure.Figure at 0x7f79c579d080>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "c498d2d898084bf183a39fa2041a55c1": {
      "model_module": "@jupyter-widgets/base",
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      "model_name": "LayoutModel",
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     },
     "d69c017bc8514c5087260020f4c721f9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
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     },
     "d849d1d42f8a47b6bb239d06764f8e7f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d891c1fc1b434fcb96ab17e8ed417343": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_669b0b45dba54805b5aea4ce8603696e",
        "IPY_MODEL_518213d9c1d2402a9911687a20219837"
       ],
       "layout": "IPY_MODEL_7908f9d9f0b0467f884101ae8c41c6fe"
      }
     },
     "eaf3df9729424f62bd153ed332053b4f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "eb64d4b5266b4f308a71321a0f84c51d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_a45e349f196b494cae977e6da5c9b838",
        "IPY_MODEL_54cc9d28c9fc4333bbaeb32c74262ea4"
       ],
       "layout": "IPY_MODEL_81580ee1350f44778e7bbf0d6c4789ce"
      }
     },
     "ebd5518599a945f7a54739b1d7b3ee39": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f08e779637d342469a88eab227c921ab": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_4714af76ab8f4325a720f661b4fb1688",
       "outputs": [
        {
         "data": {
          "image/png": 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          "text/plain": "<matplotlib.figure.Figure at 0x7f79c58c3cc0>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f5c27d07517d41f5a1fe52da5439231f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_6ca8dfea53214a97839304e3034221e5",
       "outputs": [
        {
         "data": {
          "image/png": 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