Exercises/draft/Prova_A1_2017.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "A1 de Linguagens de Programação – 28/09/2017\n",
    "--\n",
    "Professor: Renato Rocha Souza  \n",
    "--"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Nome do Aluno(a):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entre com o seu nome completo: Renato Rocha Souza\n",
      "Olá, Renato Rocha Souza!\n",
      "Bom trabalho!\n"
     ]
    }
   ],
   "source": [
    "meu_nome = input('Entre com o seu nome completo: ')\n",
    "print('Olá, {}!'.format(meu_nome))\n",
    "print('Bom trabalho!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Somente estes módulos podem ser importados para a realização das questões\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import random\n",
    "import re\n",
    "import string\n",
    "import time\n",
    "from bs4 import BeautifulSoup as bs\n",
    "from IPython.display import clear_output\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Questão 1 (1,5 pontos)  \n",
    "\n",
    "\n",
    "Mostre graficamente (usando matplotlib) que as funções:  \n",
    "$ y = 2x^2 - 10x -32$  \n",
    "e  \n",
    "$ y = 2x^3 - 12x^2 +4x +2$  \n",
    "possuem, respectivamente, 2 e 3 raízes reais e distintas.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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NJTU3kdG9iLb+1ZodSWKZiIP4sQVHSicTmpvIUkmOgXsHdVot4unzkj5hjMka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\nRrKIiIiISAv/D6D1JgIFiijVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0b8748358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-100, 100, 10000)\n",
    "y1 = 2*x**2 - 10*x - 32\n",
    "y2 = 2*x**3 - 12*x**2 + 4*x +2\n",
    "y3 = np.zeros(10000)\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(12,8))\n",
    "ax[0].plot(x,y1)\n",
    "ax[0].plot(x,y3)\n",
    "ax[0].set_xlim([-30,30])\n",
    "ax[0].set_ylim([-55,400])\n",
    "\n",
    "ax[1].plot(x,y2)\n",
    "ax[1].plot(x,y3)\n",
    "ax[1].set_xlim([-10,12])\n",
    "ax[1].set_ylim([-50,10])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Questão 2 (2.5 pontos)  \n",
    "\n",
    "Partindo do código abaixo, escreva comandos para realizar as seguintes tarefas:\n",
    "- Identificar as 10 palavras mais frequentes, independente de possuírem letras maiúsculas ou minúsculas;\n",
    "- Identificar os trechos em que há uma citação de fala de personagens (entre aspas);\n",
    "- Identificar as 5 letras mais comuns no final de palavras;\n",
    "- Salvar os resultados em um arquivo chamado \"respostas_q2.txt\" no computador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "url = 'http://www.releituras.com/guimarosa_margem.asp'\n",
    "page = requests.get(url)\n",
    "html = page.text\n",
    "sopa = bs(html, \"lxml\")\n",
    "texto = sopa.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#primeiro item\n",
    "\n",
    "texto = texto.replace('\\n',' ')\n",
    "texto = texto.replace('\\r',' ')\n",
    "texto = re.sub(' +',' ', texto)  #elimina múltiplos espaços em branco\n",
    "lista = texto.split()\n",
    "lista = [l.lower().strip(string.punctuation) for l in lista]\n",
    "\n",
    "def freqdist(iterable):\n",
    "    dic_freq = {}\n",
    "    for elem in iterable:\n",
    "        if elem in dic_freq:\n",
    "            dic_freq[elem] += 1\n",
    "        else:\n",
    "            dic_freq[elem] = 1\n",
    "    return dic_freq\n",
    "\n",
    "freqs = freqdist(lista)\n",
    "\n",
    "#https://stackoverflow.com/questions/613183/how-to-sort-a-dictionary-by-value\n",
    "ordered_freqs = sorted(freqs.items(), key=lambda x: x[1]) \n",
    "#print(ordered_freqs)\n",
    "\n",
    "#segundo item\n",
    "\n",
    "#https://stackoverflow.com/questions/2402797/regex-find-characters-between\n",
    "citacoes = re.findall(r'\"(.*?)\"',texto)\n",
    "#print(citacoes)\n",
    "\n",
    "#terceiro item\n",
    "\n",
    "ultimas = [l for l in lista if len(l) > 0]\n",
    "ultimas = [l[-1] for l in ultimas]\n",
    "freqs2 = freqdist(ultimas)\n",
    "ordered_freqs2 = sorted(freqs2.items(), key=lambda x: x[1])\n",
    "#print(ordered_freqs2)\n",
    "\n",
    "#quarto item\n",
    "\n",
    "with open('respostas_q2.txt','w') as f:\n",
    "    for key,value in ordered_freqs:\n",
    "        f.write('{},{}\\n'.format(key,value))\n",
    "    f.write('\\n')\n",
    "    for citacao in citacoes:\n",
    "        f.write('{}\\n'.format(citacao))\n",
    "    f.write('\\n')\n",
    "    for key,value in ordered_freqs2:\n",
    "        f.write('{},{}\\n'.format(key,value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Questão 3 (3 pontos)  \n",
    "\n",
    "Christian Goldbach propos que qualquer número ímpar não primo pode ser formado pela soma de um número primo e duas vezes o quadrado de outro número.\n",
    "\n",
    "$9 = 7 + 2×1^2$  \n",
    "$15 = 7 + 2×2^2$  \n",
    "$21 = 3 + 2×3^2$  \n",
    "$25 = 7 + 2×3^2$  \n",
    "$27 = 19 + 2×2^2$  \n",
    "$33 = 31 + 2×1^2$  \n",
    "\n",
    "A conjectura se mostrou falsa. \n",
    "\n",
    "Crie um programa para mostrar qual o primeiro número ímpar não primo que não pode ser escrito pela soma de um primo e duas vezes o quadrado de outro número."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5777\n",
      "CPU times: user 888 ms, sys: 0 ns, total: 888 ms\n",
      "Wall time: 888 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "max_impar = 10000\n",
    "max_quad = 100\n",
    "\n",
    "def is_prime(n):\n",
    "    if n <= 3:\n",
    "        return n >= 2\n",
    "    if n % 2 == 0 or n % 3 == 0:\n",
    "        return False\n",
    "    for i in range(5, int(n ** 0.5) + 1, 6):\n",
    "        if n % i == 0 or n % (i + 2) == 0:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "\n",
    "impares = set(range(1,max_impar,2))\n",
    "primos = set(filter(is_prime,impares))\n",
    "impares_nao_primos = impares - primos\n",
    "impares_nao_primos.discard(1)\n",
    "fatores = [2 * x**2 for x in range(1,max_quad+1)]\n",
    "\n",
    "nlins = len(primos)\n",
    "ncols = len(fatores)\n",
    "array = np.zeros((nlins,ncols))\n",
    "for i in range(nlins):\n",
    "    for j in range(ncols):\n",
    "        array[i,j] =  list(primos)[i] + fatores[j]\n",
    "\n",
    "#https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.unique.html\n",
    "#https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flatten.html\n",
    "somas = np.unique(array.flatten())\n",
    "\n",
    "#https://stackoverflow.com/questions/7088625/what-is-the-most-efficient-way-to-check-if-a-value-exists-in-a-numpy-array\n",
    "for numero in impares_nao_primos:\n",
    "    if not np.any(somas == numero):\n",
    "        print(numero)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "Questão 4 (3 pontos) \n",
    "\n",
    "Vamos ajudar uma tartaruga a sair de um labirinto?  \n",
    "\n",
    "Implemente em Python uma classe chamada “Tartaruga” com as seguintes propriedades:  \n",
    "\n",
    "- Recebe um labirinto (array do numpy) com 0s nas células desocupadas e 1s nas células ocupadas;\n",
    "- Sorteia, ao iniciar, uma posição aleatória para si neste array (representada por 2) dentre as células com 0 (não ocupadas);  \n",
    "- Possui um método \"andar\", que recebe as ordens:\n",
    "    - Norte, na qual tenta diminuir o índice da linha, mantendo o da coluna;\n",
    "    - Sul, na qual tenta aumentar o índice da linha, mantendo o da coluna;\n",
    "    - Leste, na qual tenta aumentar o índice da coluna, mantendo o da linha;\n",
    "    - Oeste, na qual tenta diminuir o índice da coluna, mantendo o da linha;\n",
    "    - Só pode andar para posições que estão ocupadas com '0'.\n",
    "- Possui um método para mostrar a sua posição no labirinto;\n",
    "- Deve guardar as posições que já visitou e a quantidade de vezes (com método para mostrar este array);\n",
    "- Possui um método \"tenta_sair\" com uma estratégia:  \n",
    "    - Anda para o norte, enquanto for possível; \n",
    "    - Quando não for possível andar para o norte, anda para o sul;\n",
    "    - Quando não for possível andar para o norte ou o sul, anda para o leste;\n",
    "    - Quando não for possível andar para o norte ou o sul ou o leste, anda para oeste;\n",
    "    - Quando passar por uma célula já visitada, deve mudar a sequência de \"tenta_sair\" na forma:  \n",
    "        NSLO -> SLON -> LONS -> ONSL -> ...  \n",
    "- Deve saber a cada passo se saiu do labirinto, mostrando uma mensagem e interrompendo o processo de tentar sair;\n",
    "- Caso não consiga sair em 1000 iterações, deve declarar que é impossível sair.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22, 22)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#exemplo de labirinto\n",
    "\n",
    "labirinto = np.array([[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],\n",
    "                      [1,0,0,0,1,0,0,0,1,1,0,1,1,0,0,0,0,0,1,0,0,0],\n",
    "                      [1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,1,0,1,1],\n",
    "                      [1,0,1,0,1,0,1,1,1,0,0,1,0,1,1,0,0,0,1,0,1,1],\n",
    "                      [1,0,0,0,1,0,0,1,1,0,0,1,1,1,1,0,0,0,1,0,1,1],\n",
    "                      [1,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,1,1],\n",
    "                      [1,0,1,0,1,0,0,1,0,0,0,1,0,1,1,1,0,0,1,0,1,1],\n",
    "                      [1,0,0,0,1,0,0,1,1,0,0,0,0,1,1,0,1,0,1,0,1,1],\n",
    "                      [1,0,1,0,1,0,0,1,1,0,0,1,1,1,1,0,0,0,1,0,1,1],\n",
    "                      [1,1,1,0,1,1,1,1,1,1,0,0,0,0,1,1,1,0,1,0,0,1],\n",
    "                      [1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,1],\n",
    "                      [1,0,1,1,1,0,1,1,1,1,1,1,0,0,0,1,1,1,1,1,0,1],\n",
    "                      [1,0,1,0,0,0,1,1,0,1,0,1,0,0,0,1,1,1,1,1,0,1],\n",
    "                      [1,0,1,0,1,1,1,1,0,1,0,1,1,1,0,0,0,0,1,1,0,1],\n",
    "                      [1,0,1,0,0,0,0,0,0,1,0,1,0,1,0,1,1,0,1,1,0,1],\n",
    "                      [1,0,1,1,0,1,1,1,1,1,0,1,0,0,0,1,1,0,1,1,0,1],\n",
    "                      [1,0,1,1,0,0,1,1,1,0,0,1,0,0,1,1,1,0,1,1,0,1],\n",
    "                      [1,1,1,1,1,0,1,1,1,0,1,1,0,0,0,1,1,1,1,1,0,1],\n",
    "                      [1,0,0,0,0,0,1,0,0,0,1,1,1,1,0,1,1,0,0,1,0,1],\n",
    "                      [1,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1],\n",
    "                      [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1],\n",
    "                      [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1]], dtype=int)\n",
    "\n",
    "labirinto.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff0b6458908>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0b6458d30>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.matshow(labirinto, cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class Tartaruga:\n",
    "    def __init__(self,labirinto):\n",
    "        self.labirinto = labirinto.copy()\n",
    "        self.dim = labirinto.shape\n",
    "        self.posicoes = labirinto.copy()\n",
    "        while True:\n",
    "            self.linha = np.random.randint(labirinto.shape[0])\n",
    "            self.coluna = np.random.randint(labirinto.shape[1])\n",
    "            if labirinto[self.linha,self.coluna] == 0:\n",
    "                self.labirinto[self.linha,self.coluna] = 2\n",
    "                break\n",
    "                \n",
    "    def mostra_posicao(self):\n",
    "        fig, ax = plt.subplots(figsize = (8,8))\n",
    "        ax.matshow(self.labirinto, cmap=plt.cm.Blues)\n",
    "    \n",
    "    def mostra_visitas(self):\n",
    "        fig, ax = plt.subplots(figsize = (8,8))\n",
    "        ax.matshow(self.posicoes, cmap=plt.cm.inferno)\n",
    "        \n",
    "    def guarda_visitas(self):\n",
    "        self.posicoes[self.linha,self.coluna] -= 1\n",
    "                \n",
    "    def saiu(self, linha, coluna):\n",
    "        if linha == 0 or coluna == 0 or linha == self.labirinto.shape[0]-1 or coluna == self.labirinto.shape[1]-1:\n",
    "            return True\n",
    "        else:\n",
    "            return False\n",
    "        \n",
    "    def parede(self, linha, coluna):\n",
    "        if self.labirinto[linha,coluna] == 1:\n",
    "            return True\n",
    "                \n",
    "    def andar(self, direcao):\n",
    "        if direcao == 'N' and not self.parede(self.linha-1,self.coluna):\n",
    "            self.labirinto[self.linha,self.coluna] = 0\n",
    "            self.linha -= 1\n",
    "            self.labirinto[self.linha,self.coluna] = 2\n",
    "            self.guarda_visitas()\n",
    "        elif direcao == 'S' and not self.parede(self.linha+1,self.coluna):\n",
    "            self.labirinto[self.linha,self.coluna] = 0\n",
    "            self.linha += 1\n",
    "            self.labirinto[self.linha,self.coluna] = 2\n",
    "            self.guarda_visitas()\n",
    "        elif direcao == 'L' and not self.parede(self.linha,self.coluna+1):\n",
    "            self.labirinto[self.linha,self.coluna] = 0\n",
    "            self.coluna += 1\n",
    "            self.labirinto[self.linha,self.coluna] = 2\n",
    "            self.guarda_visitas()\n",
    "        elif direcao == 'O' and not self.parede(self.linha,self.coluna-1):\n",
    "            self.labirinto[self.linha,self.coluna] = 0\n",
    "            self.coluna -= 1\n",
    "            self.labirinto[self.linha,self.coluna] = 2\n",
    "            self.guarda_visitas()\n",
    "\n",
    "    def tenta_sair(self,iteracoes):\n",
    "        for i in range(iteracoes):\n",
    "            direcao = random.choice(list('NSLO'))\n",
    "            self.andar(direcao)\n",
    "            if self.saiu(self.linha, self.coluna):\n",
    "                print('Tartaruga Saiu!!')\n",
    "                break\n",
    "                self.mostra_posicao()\n",
    "        if not self.saiu(self.linha, self.coluna):\n",
    "            print('Tartaruga não conseguiu sair')\n",
    "    \n",
    "# O algoritmo originalmente pedido não garantia que a tartaruga saísse...\n",
    "#\n",
    "#    def andar(self, direcao):\n",
    "#        if direcao == 'N' and not self.parede(self.linha-1,self.coluna):\n",
    "#            self.labirinto[self.linha,self.coluna] = 0\n",
    "#            self.linha -= 1\n",
    "#            self.labirinto[self.linha,self.coluna] = 2\n",
    "#            self.guarda_visitas()\n",
    "#            return True\n",
    "#        elif direcao == 'S' and not self.parede(self.linha+1,self.coluna):\n",
    "#            self.labirinto[self.linha,self.coluna] = 0\n",
    "#            self.linha += 1\n",
    "#            self.labirinto[self.linha,self.coluna] = 2\n",
    "#            self.guarda_visitas()\n",
    "#            return True\n",
    "#        elif direcao == 'L' and not self.parede(self.linha,self.coluna+1):\n",
    "#            self.labirinto[self.linha,self.coluna] = 0\n",
    "#            self.coluna += 1\n",
    "#            self.labirinto[self.linha,self.coluna] = 2\n",
    "#            self.guarda_visitas()\n",
    "#            return True\n",
    "#        elif direcao == 'O' and not self.parede(self.linha,self.coluna-1):\n",
    "#            self.labirinto[self.linha,self.coluna] = 0\n",
    "#            self.coluna -= 1\n",
    "#            self.labirinto[self.linha,self.coluna] = 2\n",
    "#            self.guarda_visitas()\n",
    "#            return True\n",
    "#        return False\n",
    "#\n",
    "#\n",
    "#        def tenta_sair(self):\n",
    "#            for i in range(100):\n",
    "#                self.mostra_posicao()\n",
    "#                sucesso = True\n",
    "#                while sucesso:\n",
    "#                    sucesso = self.andar('N')\n",
    "#                sucesso = True    \n",
    "#                while sucesso:\n",
    "#                    sucesso = self.andar('L')\n",
    "#                sucesso = True    \n",
    "#                while sucesso:\n",
    "#                    sucesso = self.andar('S')\n",
    "#                sucesso = True    \n",
    "#                while sucesso:\n",
    "#                    sucesso = self.andar('O')\n",
    "#                #time.sleep(1)\n",
    "#                clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tartaruga Saiu!!\n"
     ]
    },
    {
     "data": {
      "image/png": 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ABgILAA0EFgAaCCwANBBYAGggsADQQGABoIHAAkCDjWUPcBQ7W5vLHoEG1nU+tnf3lj3C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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0b5632e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0b647def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uPXz3y0v3mA25b++0j1DCI1gAaCCwANBAYAGggcACQAOBBYAGAgsADQQWABoILAA0EFgA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0b5a572e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "turtle = Tartaruga(labirinto)\n",
    "turtle.mostra_posicao()\n",
    "turtle.tenta_sair(10000)\n",
    "turtle.mostra_posicao()\n",
    "turtle.mostra_visitas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "Questão Bônus (0,5 pontos)  \n",
    "\n",
    "O problema de Monty Hall, também conhecido por paradoxo de Monty Hall é um problema matemático e paradoxo que surgiu a partir de um concurso televisivo dos Estados Unidos chamado \"Let’s Make a Deal\", exibido na década de 1970. O jogo consiste no seguinte: Monty Hall (o apresentador) apresentava 3 portas aos concorrentes, sabendo que atrás de uma delas está um carro (prêmio bom) e que as outras têm prêmios de pouco valor.\n",
    "\n",
    "- Na 1ª etapa o concorrente escolhe uma porta (que ainda não é aberta);  \n",
    "- Em seguida, Monty abre uma das outras duas portas que o concorrente não escolheu, sabendo de antemão que o carro não se encontra na porta que abrirá;  \n",
    "- Agora com duas portas apenas para escolher — pois uma delas já se viu, na 2ª etapa, que não tinha o prêmio — e sabendo que o carro está atrás de uma delas, o concorrente tem que se decidir se permanece com a porta que escolheu no início do jogo e abre-a ou se muda para a outra porta que ainda está fechada para então a abrir.  \n",
    "\n",
    "Demonstre graficamente que é mais provável ganhar o prêmio se trocar de porta do que se não o fizer. Quais as probabilidade de acertar, em ambos os casos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#seu código aqui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}