Intro - Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# README:<br>\n",
    "This notebook was made by PhD. Junior A. Koch and PhD. Luana B. Peres as a Machine Learning introduction course for Bioinformatics PhD candidates at Universidade Federal de Santa Catarina on spring of 2018.<br>\n",
    "Esse notebook foi criado por PhD. Junior A. Koch e PhD. Luana B. Peres como curso introdutório de Machine Learning para doutorandos em Bioinformática na Universidade Federal de Santa Catarina durante a primavera de 2018."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conteúdo\n",
    "-  Instalação - Anaconda\n",
    "    -  Jupyter notebook\n",
    "    -  Mais opções\n",
    "-  Introdução aos conceitos de machine learning\n",
    "    -  O que é e para que serve?\n",
    "    -  Tipos de machine learning\n",
    "    -  Overfitting e underfitting\n",
    "    -  Métricas\n",
    "        -  Métricas para classificação\n",
    "        -  Métricas para regressão\n",
    "-  Classificação\n",
    "    -  Temos que classificar\n",
    "    -  Wisconsin Breast Cancer Dataset\n",
    "    -  Extra: HIV cleavage\n",
    "-  Regressão\n",
    "    -  Como funciona?\n",
    "    -  Extra: Diabetes\n",
    "\n",
    "-  Extras: Gene expression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instalação - Anaconda\n",
    "\n",
    "O Anaconda é um conjunto de bibliotecas científicas de Python em um software apenas (e grátis), embora não seja necessária a sua instalação ela poupa o trabalho de instalar todas as bibliotecas individualmente.  \n",
    "A instalação é simples e pode ser obtida através do link: https://conda.io/docs/user-guide/install/index.html\n",
    "\n",
    "Utilizaremos aqui Python 3.6, portanto é necessário que você escolha esta versão para o seu sistema operacional.\n",
    "\n",
    "## Jupyter notebook\n",
    "\n",
    "Python também pode ser utilizado em um navegador convencional através de um notebook (isto que você está vendo é um notebook). Suas utilidades são muitas e você pode encontrar diversos tutoriais nos links abaixo:\n",
    "-   http://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Notebook%20Basics.ipynb\n",
    "-   http://webserver2.tecgraf.puc-rio.br/~mgattass/fcg/T1PythonInstall.pdf\n",
    "-   https://www.dataquest.io/blog/jupyter-notebook-tutorial/\n",
    "-   https://medium.com/ibm-data-science-experience/markdown-for-jupyter-notebooks-cheatsheet-386c05aeebed\n",
    "\n",
    "\n",
    "## Mais opções\n",
    "\n",
    "É possível utilizar a ferramento do Google chamada Colab (https://colab.sandbox.google.com/), onde é possível importar seu próprio notebook ou criar um do zero. Com esta ferramenta nada precisa ser instalado em seu computador ou servidor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução aos conceitos de machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## O que é e para que serve?"
   ]
  },
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   "metadata": {},
   "source": [
    "\"Machine Learning é uma area de estudo que dá a computadores a habilidade de aprender sem serem explicitamente programados.\" - Arthur Samuel, 1959\n",
    "\n",
    "O objetivo fundamental de algoritmos de Machine Learning é aprender a partir de dados e generalizar além do training set para interpretar com sucesso dados nunca antes vistos.\n",
    "\n",
    "Filtro de spam, detecção de fraude de cartão de crédito, sistema de recomendação de filmes da Netflix e sugestão de amizades do Facebook são exemplos de programas de Machine Learning.\n",
    "\n",
    "<img src=\"images/ML approach.png\" alt=\"ML approach\" style=\"width: 600px;\"/>\n",
    "<center><font size=\"1\">Fonte: Géron, A., 2017. Hands-On Machine Learning with Scikit-Learn and TensorFlow. 1st ed. United States of America: O'Reilly Media, Inc.</font></center>\n",
    "\n",
    "### Inteligência artificial e Machine learning\n",
    "\n",
    "<img src=\"images/AI_ven_diagram.png\" alt=\"AI Ven Diagram\" style=\"width: 400px;\"/>\n",
    "<center><font size=\"1\">Fonte: https://blog.algorithmia.com/page/32/</font></center>"
   ]
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   "source": [
    "## Tipos de machine learning\n",
    "Os sistemas de Machine Learning podem ser divididos em diferentes categorias de acordo com o tipo e a quantidade de supervisão que eles recebem durante o treinamento.\n",
    "\n",
    "***Supervised learning***: O modelo é treinado com uma série de exemplos (*training data*) contendo os dados de entrada (input) e os respectivos resultados esperados (aqui chamados *labels*). Portanto, o objetivo da aprendizagem supervisionada é aprender uma função que melhor aproxime a relação entre input e resultado, e que então seja capaz de predizer um valor correspondente para qualquer novo e desconhecido input.\n",
    "\n",
    "<img src=\"images/Supervised_Learning.png\" alt=\"Supervised Learning\" style=\"width: 500px;\"/>\n",
    "<center><font size=\"0.5\">Fonte: Géron, A., 2017. Hands-On Machine Learning with Scikit-Learn and TensorFlow. 1st ed. United States of America: O'Reilly Media, Inc.</font></center>\n",
    "\n",
    "Alguns dos mais importantes algoritmos de *supervised learning* são:\n",
    "    + k-Nearest Neighbors (Classificação);\n",
    "    + Linear Regression (Regressão);\n",
    "    + Logistic Regression (Classificação);\n",
    "    + Support Vector Machines (SVMs) (Classificação e Regressão);\n",
    "    + Decision Trees and Random Forests (Classificação e Regressão);\n",
    "    + Neural Networks (Classificação e Regressão).\n",
    "\n",
    "***Unsupervised learning:*** Os modelos são treinados com *unlabeled data*, ou seja, os dados alimentados ao modelo não contêm os resultados rotulados, ou seja, o sistema tenta aprender sem um professor. Portanto seu objetivo é inferir a estrutura natural presente dentro de um conjunto de dados.\n",
    "\n",
    "<img src=\"images/Unsupervised_Learning.png\" alt=\"Unsupervised Learning\" style=\"width: 500px;\"/>\n",
    "<center><font size=\"0.5\">Fonte: Géron, A., 2017. Hands-On Machine Learning with Scikit-Learn and TensorFlow. 1st ed. United States of America: O'Reilly Media, Inc.</font></center>\n",
    "\n",
    "Alguns dos mais importantes algoritmos de *unsupervised learning* são:\n",
    "    + k-Means (Clustering);\n",
    "    + Hierarchical Cluster Analysis, HCA (Clustering);\n",
    "    + Expectation Maximization (Clustering);\n",
    "    + Apriori (Assiciation);\n",
    "    + Eclat (Association);\n",
    "\n",
    "***Classificação e Regressão***  \n",
    "As aplicações mais comuns de ML envolvem um problema de classificação ou de regressão:\n",
    "-  Classificação - são problemas nos quais o resultado previsto pertence a alguma classe ou categoria, por exemplo: identificar se uma célula está doente ou não (1 e 0); identificar se os objetos em uma imagens são pessoas, cachorro, bicicleta ou nenhum destes (0, 1, 2, 3).\n",
    "-  Regressão - são problemas nos quais o resultado previsto é um número real, por exemplo: prever a temperatura nas próximas horas; a quantidade de hemácias; a expectativa de vida de rato de laboratório.\n",
    "\n",
    "***Semisupervised learning:*** Alguns algoritmos conseguem lidar com dados parcialmente *labeled*. Nesse caso, os modelos são geralmente treinados com muitos dados *unlabeled* a alguns poucos *labeled*. A maioria dos algoritmos de *semisupervised learning* são uma combinação de algoritmos supervisionados e não-supervisionados.\n",
    "\n",
    "***Reinforcement learning:*** O sistema de aprendizagem, chamado de agente neste contexto, pode observar o ambiente, selecionar e executar ações e obter recompensas em retorno (ou penalidades na forma de recompensas negativas). Deve então aprender por si mesmo qual é a melhor estratégia, chamada política, para obter a maior recompensa ao longo do tempo. Uma política define qual ação o agente deve escolher quando estiver em uma determinada situação.\n",
    "\n",
    "Na prática, os sistemas mais utilizados são *Supervised Learning* e *Unsupervised Learning*.\n",
    "Em resumo:\n",
    "\n",
    "<img src=\"images/Tipos_ML.png\" alt=\"Tipos de Machine Learning\" style=\"width: 1200px;\"/>"
   ]
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   "source": [
    "## Overfitting e underfitting"
   ]
  },
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   "source": [
    "Se o modelo tem uma boa performance no training data mas generaliza mal de acordo com as métricas da validação cruzada, então o modelo está overfitting. Se o modelo tem uma má performance em ambos, então ele está underfitting.\n",
    "\n",
    "O exemplo abaixo demonstra o problema de *underfitting* e *overfitting* de um modelo de regressão (Fonte: [Underfitting vs. Overfitting](http://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html))\n",
    "\n",
    "<img src=\"images/plot_underfitting_overfitting.png\" alt=\"Overfitting-Underfitting\" style=\"width: 1200px;\"/>\n",
    "\n",
    "***Underfitting*** ocorre quando o modelo é simples demais para aprender a estrutura subjacente dos dados. Esse é o caso do primeiro caso no exemplo acima: o modelo linear (polinômio de 1º grau) não se ajusta suficientemente bem aos dados, que possuem caracterítica polinomial. As predições desse modelo estão fadadas a serem inexatas, e não irá generalizer bem nem mesmo ao dados de trainamento.\n",
    "\n",
    "***Overfitting***, por outro lado, ocorre quando o modelo é muito complexo e ajusta-se demasiadamente bem ao *training data*, inclusive aos ruídos e outliers. Esse é o caso do terceiro caso no exemplo acima: o modelo (polinômio de 15º grau) memorizou essensialmente todo o *training data* e tampouco aprendeu a relação entre input e resultado. Nesse caso o modelo apresentará boa performance no *training data* mas generalizará mal para novos dados. O *overfitting* é mais provável com modelos não paramétricos e não lineares que têm mais flexibilidade ao aprender uma função.\n",
    "\n",
    "No caso do meio no exemplo acima, o modelo (polinômio de 4º grau) é balancedo e aprendeu a relação real entre input e output sem memorizar o *training data*, aproximando-se quase perfeitamente à função verdadeira."
   ]
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   "source": [
    "## Métricas\n",
    "\n",
    "Para sabermos a qualidade de um modelo precisamos de alguma medida que nos diga o quão bem ele se ajusta aos pontos do *training set* e do *test set*, chamamos isso de métricas. Vejamos algumas métricas para *supervised training*:\n",
    "\n",
    "### Métricas para classificação\n",
    "\n",
    "***Accuracy (acurácia)***\n",
    "É a porcentagem de acertos obtida pelo modelo. De modo geral a acurácia é uma medida mais qualitativa e pode ser usada para identificar se está ocorrendo *overfitting* ou *underfitting*, como pode ser visto na imagem abaixo:\n",
    "\n",
    "<img src=\"images/overfitting_underfitting_cartoon.png\" alt=\"Overfitting-Underfitting\" style=\"width: 500px;\"/>\n",
    "<center><font size=\"1\">Fonte: https://github.com/amueller/introduction_to_ml_with_python</font></center>\n",
    "\n",
    "***Confusion matrix (matriz de confusão)***\n",
    "É uma matriz que fornece os dados de acertos e erros do modelo de modo mais detalhado que a acurácia. Para um problema com duas classes diferentes teremos uma matriz do tipo:\n",
    "\n",
    "<img src=\"images/confusion_matrix.png\" alt=\"Confusion Matrix\" style=\"width: 350px;\"/>\n",
    "\n",
    "Definimos no modelo uma das classes sendo a *positiva* e outra como a *negativa* então a matrix mostra:\n",
    "-  $TP$ é o número de vezes que o modelo previu a classe positiva e acertou;\n",
    "-  $TN$ é o número de vezes que o modelo previu a classe negativa e acertou;\n",
    "-  $FP$ é o número de vezes que o modelo previu a classe positiva e errou;\n",
    "-  $FN$ é o número de vezes que o modelo previu a classe pnegativa e errou;\n",
    "\n",
    "Essa análise é especialmente importante quando queremos que $FP$ ou $FN$ sejam pequenos, um modelo ideal tem esses valores iguais a zero.\n",
    "\n",
    "***Precision e Recall***  \n",
    "Estes são quantidades usadas para avaliar modelo justamente com base em $FP$ e $FN$. São dados por: \n",
    "\n",
    "<center><font size=\"1\">$Precision = \\frac{TP}{TP+FP},\\ \\ \\ \\ \\ \\ Recall = \\frac{TP}{TP+FN}$.</font></center>  \n",
    "\n",
    "Dependendo do problema em questão podemos querer que Precision ou Recall sejam altos (o máximo é 1), geralmente eles competem entre si.\n",
    "\n",
    "<img src=\"images/precision_recall_conf_matrix.png\" alt=\"Precison Recall\" style=\"width: 350px;\"/>\n",
    "\n",
    "***F1***  \n",
    "Esta é uma métrica que leva em consideração tanto Precision quanto Recall, é uma média ponderada entre os dois com igual peso e é dada por:  \n",
    "\n",
    "<center><font size=\"1\">$F1 = \\frac{2}{\\frac{1}{Precision}+\\frac{1}{Recall}}$.</font></center>  \n",
    "\n",
    "\n",
    "***Threshold***  \n",
    "Um gráfico típico da disputa entre Precision e Recall é como o da figura abaixo:\n",
    "\n",
    "<img src=\"images/precision_recall_plot.png\" alt=\"Precison Recall threshold\" style=\"width: 400px;\"/>\n",
    "<center><font size=\"1\">Fonte: https://github.com/ageron/handson-ml</font></center>\n",
    "\n",
    "Como há uma competição entre Precision e Recall podemos definir um limite (threshold) baseado no quanto estamos dispostos a errar.\n",
    "\n",
    "***Curva de ROC e AUC***  \n",
    "\n",
    "A curva de ROC (Receiver Operating Characteristic) é uma maneira gráfica de analisar Precision (False Positive Rate) e Recall (True Positive Rate):\n",
    "<img src=\"images/roc_curve.png\" alt=\"ROC Curve\" style=\"width: 400px;\"/>\n",
    "<center><font size=\"1\">Fonte: https://github.com/ageron/handson-ml</font></center>\n",
    "\n",
    "A linha tracejada representa a curva caso a classificação seja feita de forma aleatória (50% de cada classe). A linha azul representa o que o modelo conseguiu, onde *True positive rate* é o mesmo que Recall e *False positive rate* é o mesmo que Precision. A área sob é curva  (AUC) é a quantificação deste gráfico, onde num caso ideal teríamos $AUC=1$.\n",
    "\n",
    "### Métricas para regressão\n",
    "\n",
    "Como o resultado de uma regressão é um número podemos usar como métricas as quantidades responsáveis por indicar estatisticamente o quão perto ou não a previsão está do valor de comparação, do *test set*.\n",
    "\n",
    "***Mean absolute error (MAE)***  \n",
    "\n",
    "O MAE costuma ser indicado quando comparamos diferentes modelos, desejamos um MAE menor possível.\n",
    "\n",
    "$MAE = \\frac{1}{n}\\sum_{i=1}^{n}|\\hat{y}_{i}-y_{i}|$\n",
    "\n",
    "***Mean squared error (MSE)***  \n",
    "\n",
    "O MSE é a métrica mais usada, principalmente quando escolhemos um modelos e estamos querendo melhorá-lo.\n",
    "\n",
    "$MSE = \\frac{1}{n}\\sum_{i=1}^{n}(\\hat{y}_{i}-y_{i})^{2}$\n",
    "\n",
    "***Root mean squared error (RMSE)***  \n",
    "\n",
    "É a raiz quadrada do MSE e portanto tem o mesmo objetivo, porém o RMSE será um número da ordem de grandeza dos valores de referência.\n",
    "\n",
    "$RMSE = \\sqrt(\\frac{1}{n}\\sum_{i=1}^{n}(\\hat{y}_{i}-y_{i})^{2})$\n",
    "\n",
    "***Standard deviation (STD)***\n",
    "\n",
    "$STD = \\sqrt(\\frac{1}{n}\\sum_{i=1}^{n}(\\hat{y}_{i}-y_{medio})^{2})$\n",
    "\n",
    "O desvio padrão é um indicativo de confiância em uma medida, por exemplo: se o valor médio de nossas previsões é *100* e o de referência é *102* só podemos saber se este *100* é um bom resultado se o desvio padrão é pequeno. Neste caso um *STD = 3* indicaria um bom resultado, enquanto um *STD=20* indicaria um resultado ruim.\n",
    "\n",
    "Obs: $\\hat{y}_{i}$ são os valores previstos e $y_{i}$ são os valores do *test set*."
   ]
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   "source": [
    "## Estrutura de criação e manutenção de um sistema de Machine Learning\n",
    "\n",
    "De maneira geral a utilização de Machine Learning se dá com uma estrutura bem definida como mostrado a seguir. Procure sempre olhar para o seu sistema e identificar se está no caminho certo.\n",
    "\n",
    "<img src=\"images/ML_steps.png\" alt=\"Estrutura de um sistem de ML\" style=\"width: 1200px;\"/>\n",
    "\n",
    "1.  ***O problema***  \n",
    "  Antes de iniciar nossa abordagem devemos entender que problema queremos resolver. Assim determinamos se será um aprendizado supervisionado ou não, se usaremos classificação ou regressão, que métricas são as mais indicadas e quais seus valores aceitáveis, como nossa solução será usada para resolver o problema e etc.  \n",
    "  \n",
    "2.  ***Consiga os dados***  \n",
    "  É importante saber como obter os dados. Nós vamos obtê-los através de algum experimento? Que variáveis são importantes para o modelo? É necessário autorização de alguém? São dados livres ou sigilosos?\n",
    "\n",
    "3.  ***Explore os dados***  \n",
    "  Com os dados em mãos devemos explorá-los através de gráficos e tabelas. Assim podemos identificar sua qualidade, se há dados faltando, se são suficientes, se precisam de alguma transformação, se podemos criar novas variáveis com as que temos, etc.\n",
    "\n",
    "4.  ***Prepare os dados para os algoritmos de Machine Learning***  \n",
    "  Após identificar o formato dos dados tomamos as atitudes necessárias para deixá-los de um modo que os algoritmos possam entender. Podemos talvez preencher dados faltantes com a média das outras amostras, variáveis com alta correlação devem ser evitadas, variáveis do tipo *string* devem ser representadas de forma numérica, modificar as escalas das variáveis, etc.\n",
    "\n",
    "5.  ***Escolha alguns modelos e os treine***  \n",
    "  Com os dados prontos devemos utilizar os algoritmos que achamos mais adequados e testar cada um de forma rápida analisando as métricas definidas. Então verificamos se há underfitting ou overfitting, se todas as veriáveis são mesmo necessárias, etc.\n",
    "\n",
    "6.  ***Melhore o modelo***  \n",
    "  Agora podemos escolher alguns dos modelos mais promissores e ajustar seus parâmetros de modo mais detalhado para encontrar a melhor solução possível para cada um.\n",
    "\n",
    "7.  ***Apresente sua solução***  \n",
    "  Como sempre é feito em ciência, documentamos tudo o que foi feito e criamos maneiras de mostrar nosso resultado graficamente, é importante deixar claro como nossa solução resolve o problema. Não devemos descartar os resultados \"menos positivos\" é comum que estes tenham muito a nos ensinar sobre o problema.\n",
    "\n",
    "8.  ***Faça a manutenção do sistema***  \n",
    "  Depois de tudo isso estabelecemos como nosso código e modelo será usado por outras pessoas. Normalmente é necessário um *pipeline* que faça todas as transformações nos dados que entrarão no sistema. O modelo deve ser acompanhado de perto verificando periodicamente se ele ainda é adequado ao problema. Muitas vezes os dados podem ter alguma modificação ou viés que muda a qualidade do nosso sistema."
   ]
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   "metadata": {},
   "source": [
    "# Classificação<a id='Classificação'></a>\n",
    "\n",
    "## Temos que classificar\n",
    "\n",
    "Para entendermos como funciona a construção de modelo de machine learning no caso de classificação vamos usar um exemplo bem simples. Neste momento não se preocupe com o significado das linhas de código, apenas procure compreender os passos utilizados no processo.\n",
    "\n",
    "***O problema***\n",
    "\n",
    "Um pesquisador foi a campo coletar dados sobre dois pokemons, Pichu e Pikachu. Este pesquisador determinou que as características mais relevantes para classificar entre um dos dois são o ***comprimento da cauda*** e a ***largura das orelhas***.\n",
    "\n",
    "|<h1><center>Pichu</center></h1>        | <h1><center>Pikachu</center></h1>          |\n",
    "|:-------------------------------------:|:------------------------------------------:|\n",
    "|<img src=\"images/pichu.png\" alt=\"Pichu\" style=\"width: 200px;\"/>|<img src=\"images/pikachu.png\" alt=\"Pikachu\" style=\"width: 200px;\"/>|\n",
    "\n",
    "Nossa tarefa é utilizar Machine Learning para que apenas com os dados de ***comprimento da cauda*** e a ***largura das orelhas*** possamos identificar se o pokemon é um Pichu ou um Pikachu.\n",
    "\n",
    "***Consiga os dados***  \n",
    "Os dados que o pesquisador coletou estão na tabela abaixo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# pokemon dataset\n",
    "pokemon = pd.read_csv(r\".\\datasets\\outros\\pokemon.csv\")\n",
    "pokemon = pokemon.drop('Unnamed: 0',axis=1)\n",
    "pokemon_test = pd.read_csv(r\".\\datasets\\outros\\pokemon_test.csv\")\n",
    "pokemon_test = pokemon_test.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
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       "      <th>Pokemon</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>24.3</td>\n",
       "      <td>12.9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>22.2</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>21.2</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pokemon  Comprimento Cauda (cm)  Largura Orelha (cm)\n",
       "0        0                    17.3                 17.2\n",
       "1        1                    24.3                 12.9\n",
       "2        0                    15.5                 12.6\n",
       "3        1                    22.2                 12.0\n",
       "4        1                    21.2                 13.3"
      ]
     },
     "execution_count": 713,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Explore os dados***\n",
    "\n",
    "Um dataset sempre deve trazer um arquivo contendo informações básicas, neste problema poderíamos obter a informação de que o valor $1$ na primeira coluna significa que os dados são de um Pikachu e consequentemente um valor $0$ são de um Pichu. O arquivo ainda poderia informar em que região os dados foram obtidos, em que período do ano, quem foi o pesquisador resposável pela coleta de dados, etc.  \n",
    "Vejamos alguns gráficos utilizando estes dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+wAQ3XBiiRDtBa+uJBWWaD00Dl93KCwdOUpvXufgBUZAtxZGUIkgzFnLKLUTzWtc5imDUDf/1zCBXbCpm08rE+QbCnA4hnQ5QUbz8ZHaJpLq6evbLeHbBpfkYAIqFEBYpZRC9KFM0x51FoosuLYWFiv30j3ppnDy3D73cMUBVqcbaNdVntYeLFsVCZLGia64I8fyb4xw4PkmpK493bik9xwcxn0wLcd7EGEMTfpqb18d03FykY2GrpRZcSu9hm2LJaJrGL141YzbDJ95Xl5Rrnl5UlgGRQ9u2bePQoUPs3bu3bK6CS/MhpfQDzwEfMZr+CL0QU87h8YXoG/HRVLV8s9BsbFYz79xSyp23NPLx99YuyxEdyeo6OycHvbg9meHLShZKEWQpj7wwRPugmZ3X11FVYkvKNe02MxYzTM2k/0NWXV3Njh072L1794PMX3BpPj6FHmX0FnoZ17sSLW860jvsRUNftZsphJXWiV7lMI5EmYaykIPHp/jhYz2srw1x/aXxzSm0ECaTiUKHlckMUAQALS0ttLS0XNfc3NwRbossuBTRdtusz53A1YmWL93pGfZiNkF1aXIGGvGgsUo3x7X3eti0KvHm0kxBzQiyjCNdbv5h9wnqK/K55dJQwh3Es3EVWDJiRqBYPr3DPqpKbcsKG002JS4rRU4LJ1QI6VkoRZBFvPjWOP/3R+0UFVj5fx9fjT0F/lqXQymCXCAQ1Ogf81FXnjmzAdBnratrHBzvUYogEqUIsoAZb5D7ftvHl3/SQX15Pv98+3lJ8wvMptBhwT0TJKTqEmQ1A6M+QiGoLU+vyKdoWF3roLPfQ1CtMD6N8hFkMG5PkIPHp3ijfYpgUONDV1Tyx9fVzFm7NVm4HBY0Q7a51jUosoPwyt/assyaEQCsqrXjC2icGvbSmEGO7kSintQMZHI6wCtyksNdbkIhOK/ewcevq+GitUWpFu30y39yRimCbKZ/1EeJ04oj35JqUWJmjbEyub1nRikCA/WkZhDBkMZrRyd5WU4AsLHJyebzXJS48qivSI8pusuhvxiUnyC7GRjzZ5x/IExDZT4WM3So0pWnUYogQ5jxBvn1S8P0Dvs4r97BFZuK03LEXWgoAlWyMnuZ9gSZmglSlUFho5HkWc3UVeTTNaAUQZj0e5MozsHtCfLzZweZnAnwnovLECsWLtadSmx5ZmxWE1Mz6b+6WLE0Bsb0spKpCkiIB01VdrWoLAIVNZTm+PwhHn1hiClPkP/RUpnWSiCMy2HJmEVlitgZGPMDUFmS3vmkFqKxyk7viA+fP/0z5SYDpQjSGE3T+N3rowxN+Llhazl1aeIHWIzCAqvyEWQxA6M+SgutKY1OWy6N1fmENOge8i6+cw6Qub9kDnCka5pjp2bYdn5R3JJuJQOnXV9LoMhOBsZ8GW0WAk5HC3UphzGgfARpy7QnyLNvjFFfYeOidYktKBNvXA4z094QwZCGZQmVpBTpy7Q3iNsTSohZKBjU6B+NfoTu88e+ICx8jTwLmE3wVpeb85vmN7fOV+cj28j+O8xQXjg0TiCocc3mUsxJzhe0XJx2PXJoWi0qyzqGxnX/QCLqTXj8IY60R+/AXb8i9kplkdcoclp5q3OalTXz1/+Yq85HNpL9d5iBDIz5ONw1zUVrXZQWRvfAzTWashZURDXCWsrIaiGcRgipWl2cfQyHFUFR5jqKw5QVWhme8KdajLRAPaVpyEuHJ8jPM3GJiH6l8Fyjqa7O4agqOC1lZLUQLrtaVJatDE34cdrNGbmieDalhXl09HmUCRPlLE47TvTO0NHnYcvaQmx5mfnzuCJmBIrsYnjcT3kWzAYASl1WQlpmVNRLNJn5pslifrV/mPw8ExeuztyiGXabGbMJpmZUjHY2EQxpDE/6074edbSUFuoGkbEppQiUIkgj+ka8vHZsik2rXBk7GwA957vTYVEzgixjbCpAKAQVxZkdOhqmxKUrtNFJpQgy922ThTz64jAm4ILVzlSLsmxcai1B1hF2FJcXZYdr0W4z47CZGZ1SDmOlCNIEjy/Eb18d5mJRiMuR+Q+a02FhSs0IsorhST8mE5S6ssM0BLp5aFSZhpQiSBdeODSO2xPims0lqRYlLjjtyjSUbYxM+ClxWrFkUI3ixShx5TGmTEPRhY8KIT4K3AXkAfdIKb87a/tm4F6gCHgW+KSUMiCEqDXa64Bp4FYpZUf8xM8e9r46Qm2ZjfUrCnjtmDvV4iwbl92CP6Dh84cy2t+hOMPIZCBrIobClBZaeaszhMcXwm7L3X666J0LIeqBrwBXAJuB24UQG2btthu4Q0q5DjABnzDafwI8KqXcYvz9tXgJnk30jng52D7Fuy8uw5Rhq4jnw6kK1GQV/kCI8akAZYWZb7aMpNSl38/oZG77CaJRgdcCT0kpR6SUbuAh4JbwRiFEE+CQUu43mu4DPiyEqAAuBH5gtP8H+qxCMYunXx/DZIJrt5SmWpS44bTrXUuZh7KDvlEfGlCWZTOCEkMRjOX4WoJoFEEd0BvxuRdoiGL7GqAL+IYQ4hV0BeJblrRZyjNvjLJxpZPKDM/oGEk435BSBNnBqSH90c22GUGR04oJGM9xh3E0v6oZiExGYwJCUWy3AluAL0gp7xRC7ATuB66OVri2trZod10yra2tCb/GQvSPQ9eAlfdvmaa1tRVrQQVdncMxn6ehtIGuzu5z2rs6O5d87HKO8wcBLJzqG6ZAGzpne31hOd3t57bHwnJ/u3379rFr164ngsEgxOb7Wgn82GgfA/5YSrn4F53B9Ax5MZFdEUMAFrOJIqcl5yOHolEE3cCVEZ9rgJ5Z22vn2N4HTEopf2W0PwB8OxbhNm3aRH5+4oqxtLa20tzcnLDzR8OP9/ZiNg3wB9e/g9LCPPpHvVHlB5qNy+WgsanprLauzs5z2qI9dqnXDKNpGnltPdjsRTQ2nRsJVVPjoro09muGWe5v19XVxZ49e9i5c+ctP/jBD44BLwghnpZSvhWx225gp5RyvxDi39F9X98H/gF4UEr5fSHEp9F9aH+4ZGEygFPDXopd2RUxFKbYZc35GUE0pqEngXcJISqFEAXAzcDj4Y3GSMgjhGgxmj4G/EZKeRzoFkJcb7S/H0jt8DsNea5tnAtWR59lNFMwmUwUpHEI6f79+9m4cSPbt28fj8X3ZfxtQZ8NADiBrC9+2zvsO52SIdsodeUx5g6gafHNwptJLKoIpJSngM8DTwMHgAeklC8LIR4TQlxs7HYrcLcQ4gjg4szI/0PAZ4UQbcBngD+J9w1kMt2DHroHvVy+sTjVoiQEp92ctopgcHCQkpKzZirR+r4A/g64UwhxCvhrsjwaLhDU6B/1UZZlg5UwxU4r/oDGtDd3c2NFpeKllA+gm3Yi226I+PsgcOkcx0li8AnkGvsPTwCw9fzo001nEgV2C0Nj6RmWFwqd89BH6/sC3dd1u5TyESHEzcAvhBAXSCmjHlImw/+1FObyuwxNQjBkJeQdp6tzLKrzLMXvFOsx8bqGfxrAgjzWTcWsYoCL+bJS7WOMF9k518sQ9h+eYE2dI+Prv86Hy26h05OeNWGrq6tnv4yj8n0JISqB9VLKRwCklA8LIXYBFcBgtNdPtP9rKcznd9l/eBzo4LyV1dSURSfzUvxOsR4Tr2sUuwO8eKyPfFcFjU1n5/layJeVDj7G2Xi93iUNMnJ3KV2CmZwO0D/qnfffsVNu3up0s2llwVnt8a4WlkoK7Bb8QX11cbqxbds2Dh06xN69e8ti8X0BQ0b7lQDG9kkpZdRKINPoHtSr3GVbxFCYwgILZjOM5XDyOTUjSBDT3iCtb89fC1WenEbTIN9mOWu/eFcLSyWRi8rSLc1EdXU1O3bs4Ac/+MGD6Gafe8O+L+DvpZSvovu+fiiEKAJeA74tpdSEEB8C/lUI4QAm0ZVI1nJy0Eux00J+lqZgMJtMFBdYc7ougVIEKaKz34PdZqaqJDtHWaDPCEBXBOkYFdXS0kJLS8t1zc3NHeG2KH1fLwNbkyJkGtA96KE2SpNQplLisjLuTs/AhmSQnSo+zdE0jZMDHhqr8rMmt9BchGsXT3vSzzSkiA5N0+ga8FJbnp1+rDDFTivjORxCqhRBChga9zPtDdFYZU+1KAmlQKWZyHjG3UGmZoLUlmW3IihxWQkENdw5OmhRiiAFdA3okTSN1dmtCPLzTFjMShFkMt1Del+tyXJFUGwknxvP0eRzShGkgJODXsqLrKcTs2UrJpNJFajJcE4N6RFDWa8InLldyF4pgiQTDGn0Dvuor8ju2UCYArslp1dsZjrdg16sFhMVWZZ+ejaFBRbMJqUIFEmif9RHIKjRUJndURhh1Iwgszk15KWu3IbZnL1BDaCHkBYZDuNcRCmCJHPKWJxTX5EbiqAg38y0UgQZy6khb8701RJX7q4lUIogyXQPeakozsuZ+qhOuwWvXyMQzM2wvEwmGNLoGfbljiLI4RDS3HgbpQnBoEbvsJeGHHmw4EwIqZoVZB4DY7llxiw2Qkhz0aelFEESGRjzEQxBbXluPFgABUaaiWmvUgSZRq6ZMUtyOHJIKYIk0jui132ty/JVmpGcqV2ce6OsTKfbCB3NlRns6bUEShEoEknPsJdip/W0uSQXUEXsM5dTQ16cdvPpGPtsp9BhhJDmYOSQUgRJQtP09QO5NBsAcOSbMaF8BJmIHjqa3fmwIjGbTRQV5GYIqVIESWJsKoDHF8op/wDo8dmOfLMyDWUguRQ6GiZXC9krRZAkeod1/0C2J++aC311sZoRZBI+f4jBcX/uKQKnNScL2StFkCT6Rn3k55koLcwNe2skTrtFmYYyjN4RH5qWOxFDYUpcFvwBjZkcCyFViiBJ9I34qC615Yy9NZKCfLNyFmcY4WRzdTlmyjydfC7H/ARKESQBXyDEyISf6hw0C4ExI/CGcm66ncn0DOfWGoIwYUWQa34CpQiSwMCoDw2oKc1NRVBgt6BpMOPLrel2JnNqSA91djlyJ9QZoMhpxWTKvboEShEkgf5R3VFcnaOKILKIvSIz0COGcq+/WswmCgssObe6WCmCJNA34qPYacGRn1ujqzAFqnZxxpGLoaNhSnIwHbVSBElgYMyfs7MBUKuLM40Zb5CRyUDOOYrDFBvpqHPJp6UUQYJxe/Ti31VKEagQ0gyhx1jzksszAl9Aw5NDPi2lCBLMwJj+UFWV5K4isFpM2PJManVxhhCOGMrlGQHkVhZSpQgSzMCoH4DKkuyu+boYzny1qCxTOLOGIDcHLyU5uJZAKYIEMzDmo7TQis2a2191gapdnDH0DHspK7TmbHBDkdOKidxaS5B7+Q6SzOCYj4ZKe6rFSDlOu5k+I4w2Xdi3bx+7du16IhgMAtwjpfxu5HYhxGbgXqAIeBb4pJQyIISoNdrrgGngVillR1KFTyC5HDEERgip06JmBIr4MDUTxO0JUV2a22YhMBLPedJndXF/fz979uxh586dtwCbgduFEBtm7bYbuENKuQ4wAZ8w2n8CPCql3GL8/bVkyZ0MTg3lTp3i+Shx5lYWUqUIEsig4SiuLM5NW2skTruFQFDDH0gPRbB//342btzI9u3bx6WUbuAh4JbwdiFEE+CQUu43mu4DPiyEqAAuBH5gtP8HcFfyJE8sbk+QcXfuho6GKcmxEFJlGkogg+O6o7gixx3FoCeeA/1FY8tL/fhjcHCQkpKSyKZe4NKIz3VGW+T2BmAN0AV8QwhxJdAH3BHr9dva2mI9JCn87rkDgBXPeDetrSdPt1sLKujqHI76PA2lDXR1dsd07ViPSeQ1Qj4TvoCZYx29nDoxMu9+ra2tMV0/XVGKIIEMjPkocSlHMZy9qKy0MPWKMRQ6J5TVBEQ2mgFtju1WYAvwBSnlnUKIncD9wNWxXH/Tpk3k56fXqLu1tZWiytVAF1deej4raxynt/WPemmcdEV9LpfLQWNTU0zXj/WYRF5Ds3to6x5Cyyuh+R2r5tyntbWV5ubmmK6faLxe75IGGeoNlUCGxvxUqdkAELmoLD3WElRXVzM2NhbZVAP0RHzuBmrn2N4HTEopf2W0P8DZM4mM5pSxhiDXKunNJhxC2jeSXgEOiUIpggQxNRNkciao/AMGBWlQurCQAAAgAElEQVSWZmLbtm0cOnSIvXv3lgkhCoCbgcfD26WUnYBHCNFiNH0M+I2U8jjQLYS43mh/P5Ad9gGgZ8hHZXEe+WlgvkslhQV6Ift0i3RLFLn9ayeQzn4PoBaShcnPM2Exp48iqK6uZseOHezevftB4ADwgJTyZSHEY0KIi43dbgXuFkIcAVzAt432DwGfFUK0AZ8B/iTZ8ieKU8O5HToaxmw2Uey0ns4cnO1E5SMQQnwUPTIijxjirSO2bwH2SylzpoedUQRqRgBgMplwptmispaWFlpaWq5rbm7uCLdJKW+I+Psgc5h9pJSSGH0CmcKpIS9XXVCy+I45QInLSv+IP9ViJIVFZwRCiHrgK8AVxB5vjTHt/lcgp96Inf0eCh0W7DY16QrjNNYSKNKTaa9u0qzPcf9AmBKXlf4xH6FQ9oeQRvOWuhZ4Sko5Eku8dcTx3wDuiY+4mUPXgFeFjc7C6bAwlUYzAsXZDE/p/9flYEGauShxWfEHNIYmsn9WEI0imC+eetHtQogPAAVSyoeWKWdG4fGF6BvVnW6KM+gzAqUI0pWhKRMADcpHAOiKAKB70JtiSRJPND6C+eKpF9wuhKhB9ytcu1ThkrHoJhELQrpHQNOs4Bujq3Ns8QMiWMoimYWO6+rsTPo158M/oy/SaW/vpL6wnO72oZivGUm2LOZJF4YnTZhNuVtSdTYlLn0gd2rIy0VrC1MsTWKJRhF0A1dGfI423vpGoBx4VggBgBDiAHCllHIyGuESvegmUQtC+l8eBro5/7x6ipyxrdlbyiKZ+Y7r6uyM6lzxvOZCuE1u3uoZpayqjpqaUqpLY79mmOX+dktdeJMNTE4HmPaeOzMb8eRTXgwjk+eaQnz+7LeTz8ZpN2PPM6sZgcGTwBeFEJWAGz3e+vbwRillpxDCI4RokVLu40y89b3okUQACCE0KeXm+IqfnrT3zODIN1NYkJtpfOfjzOpi5TBOJdPeIK1vT53T3tnvo7jQPue29Ssc57RlOyaTiZoyG91DnlSLknAW9RFIKU8BnweeJvZ465ykvXeGxqp8TCZTqkVJK1Tt4vRF0zSmvGfMIQqdmjKbmhGEkVI+gL6UPrJt0XjrWfvnxFsxGNI40efhyncUp1qUtEMpgvRl2hsiGDKddpAqdGrLbOw/PIHHF8rqUPDsvbMU0Tvsw+ML0VilIi9mc3p18YxSBOlGuD5vqVIEZ1FTpjvOw3WcsxWlCOLMib4ZAFaoqmTnEF5dPO1VPoJ0I6wIipUiOItao25ztpuHlCKIM+29M5jNalHOfDjtFqbUjCDtGJvyYzZpKsBhFuFQWqUIFDHR3uthRaVd1SCYB6cjvfINKXTGpgI488GsAhzOIj/PTFVJHicHsztySL2t4kx77wyrapRZaD5cdgvumWDOlADMFMKKQHEuKyrtakagiJ7J6QBD435W1+ZezHW0OB0W/EGNGZ/yE6QLIU1jzB3Ala+U81ysqMrn5KAnq5PPKUUQR9p79emjUgTz4zJCSEcnA4vsqUgWk9NBQiFwqonsnKyosuP1a6drkGcjShHEkfZePWJoda16oubD6TAUwZRSBOlCOGJIzQjmJhwK3jWQvX4CpQjiSHvvDKUua1oUZ09XzswIsnd0lWmMTem/hUuNX+ak0QgFV4pAERXtvTPKLLQIakaQfoxOBrBZTeSrJQRzUuS0Uuy0cnIgex3GShHECX8gRNeAl1XKLLQgVosJe56ZMeUjSBtGpwKUFFpRkaPzE3YYZytKEcSJ7kEvgaCmZgRR4HRY1IwgjRibClCqks0tSGOVna5+b9aGPStFECfOOIqVIlgMp8N82kGpSC3+QIipmaDKMbQIjVX5THmCWRvtphRBnGjv9ZBnNakyf1Hgsluy9oHKNMIKWWUdXZiV1foArzNLHcZKEcSJ9t4ZVlbbsViUoXUxnA4L49MBgsHsnGZnEmETXUmhUgQL0VStD/A6+pQiUMyDpmkqYigGXHYLmgYjUyqENNWcnhHEWFI11yhx5VHstNLZrxSBYh6GJvxMTAdZU6cUQTS4jBDSoSxeqZkpjE76KXRYyFNJEhdlZbWdjixVBGoYEAfae1RqiVhwOfRul2pFsG/fPnbt2vVEMBgEuEdK+d3I7UKIzeh1t4uAZ4FPSikDEdu3APullBnrGBqdClCqzEJR0VRt54nWEUIhDbM5u0zAahgQB9p7ZzCZUGsIoqTQmBEMjPlSJkN/fz979uxh586dtwCbgduFEBtm7bYbuENKuQ4wAZ8IbxBCFAD/CmRs4QlN0xidDKiV8FHSVGNnxhfKypxDShHEgeM9M9SV2SjIV0U9osGWpy8qGxxL3QO1f/9+Nm7cyPbt28ellG7gIeCW8HYhRBPgkFLuN5ruAz4ccYpvAPckS95EMDUTJBDU1IwgSlZW6wO9bPQTKEUQB473zrBa+QeixmQyUVZkTalpaHBwkJKSksimXqAh4nOd0XbOdiHEB4ACKeVDiZYzkYyqOsUx0WQognA52mxC9YBl4vYE6Rvxcd3FZakWJaMoK8pLqWkoFDqnHoIJiGw0A9rs7UKIGuAu4NrlXL+trW05hy8La0EFXZ3DtA+YADPT4/10Tevbujo75zymobSBrs7uqK8R6/7peo36wnK624dOfy4psNB6qIfzCvVztLa2xnT9dEUpgmWiVhQvjbJCK2+2u1N2/erq6tkv4xqgJ+JzN1A7x/YbgXLgWSEEAEKIA8CVUsrJaK+/adMm8vNT42PuH/XSOOmifXQUW940a9c0YjKZ6OrspLGpac5jXC7HvNvisX+6XqOmxkV16Zn91x86wakhL83N62ltbaW5uTmm6ycar9e7pEGGMg0tk2OndEWwtl4pglgoL8xjdCqAL5CaSmXbtm3j0KFD7N27t8xw/N4MPB7eLqXsBDxCiBaj6WPAb6SU90op10gpN0spNxv7bo5FCaQLo1MBylx5mFS2uahZVevg1JAXrz+7KuwpRbBMjp2aobxI1SCIlbKi1IaQVldXs2PHDnbv3v0gcAB4QEr5shDiMSHExcZutwJ3CyGOAC7g2ykRNkGMTvrViuIYWV1rJ6Rl3wpj1QuWybGeadbUFaRajIyjvEhXnEPjfurKU2MiaWlpoaWl5brm5uaOcJuU8oaIvw8Cly50DillRg6nvb4Qbk+IMjWAiYnVNfrM/0TfDJVZNIzOoltJPh5fkO5BrzILLYHwC2gwhQ7jXGbEqBBXpmYEMVFTZsNhM5/2DWYLShEsg/ZeDyENzlOhozETfgENZOHinExgxMj+WlakZgSxYDabWFljp703u0xDShEsg7CjeI2aEcSMLc9MkdOiZgQpYmTCj9ViorBALYKMlTV1Dtp7ZghlUfJcpQiWwbGeaYqdVirUqGpJVJfY6B9ViiAVjEz6KXVZMauIoZg5r97BjC/E8FSqJYkfShEsg6PdM6xrcKjwuyVSU2ajf1SZhlLByGRAmYWWyNp6PTikZzR7nnulCJbIjDfIyQEP6xpUxNBSqS61MTDmI5RNc+wMYMYb1MtTKkfxkmisspNnNSlFoIBjho1QKYKlU11qwx/QVCH7JNM7opvjylXo6JKwWkysrnHQM5ZqSeKHUgRL5Gi3npxFhY4unepSPYOz8hMkl1NDXgA1I1gGa+od9IyasmY2qxTBEjnaPUNlcZ5aUbwMagxF0DeiFEEy6R70YrWYKFZZR5fM2noH3oDp9Owq01GKYIkc7Z5WZqFlUhWeEagQ0qTSPeilrFBFDC2H8LMftgxkOkoRLIGxqQB9Iz7WrVCKYDnk55kpLbTSnyWjqkyhe8irIoaWSVOVnTyLhjyZHYpAzQ2XwJGTevrk8xuVIlguai1Bchl3Bxh3B9m0SimCaAgGNfpHvXNuqy+38uaJqXO2F+RbKCzIrFdrZkmbJhzpmsZiPhNPrFg6NWW2rBlVZQLhMotqEWR0ePwhjrTPnVfIip/2/iAvH5nEYjljZmte68pORSCE+Ch6VaY84B4p5Xdnbd8M3AsUAc8Cn5RSBoxc7nejF/geBv7EyPOe0RzucrO61oHdpixry6W61MZzb44RDGpnPUyKxBAus6hMQ8un1KkRGoDBcT81ZbZUi7MsFn2TCSHqga8AVwCbgduFEBtm7bYbuENKuQ69pN8njPafAjuNAh4/JQvyuQeDGvLkDOuVWSgu1JbbCIaUwzhZdPR5cNrNOO1qELNcSp36/9lg2oymN1wLPCWlHJFSuoGHgFvCG4UQTYBDSrnfaLoP+LAQIh+4S0r5htH+BtAYN8lTREe/B68/xPmNzlSLkhXUG7UIeofntsMq4ktHn4eGynyVFiUO2PPAaTfTN5L5fTca01Ad0BvxuZezi3XMtb1BSulFnykghDADXwT+OxbhklHgO9bi0y8dNwEWAhPtLHRouEB4rCylIPdCx81XjDyR14yGcFHwSQ+Alf2vH4PJ2BbnZEvh8GQRDGmc6PNw1QXFqRYlKzCZoLYsPyvWEkSjCMxA5BNqAkLRbhdC2ID7jWv9YyzCJbrA91KKT+892klFsZtrr7xgwVFVuEB4rCylIPd8xy1UjDxR14yWcFFwTdP49t42LAUVNDfXR338cguHL7XIdybTO6zX2m2sSk1FuGykttzGsZ4ZpmYCuByZ5SCOJBrJu4ErIz7XAD2zttfOtV0I4QJ+ie4ovklKmVGpJienA0x7g6c/a5rGG8enWN9YwMAiNm2fPzuWnicak8lEXUX+6bQHisRx3Kiq1VhlZ2hC5XeKB7WGabNn2Me6huxWBE8CXxRCVAJu4Gbg9vBGKWWnEMIjhGiRUu4DPgb8xti8GziGHkUUIsOY9gZpfftM0vGxqQBj7gD2fPNZ7XOxfoXKQRQtdeU2jvdkV+m/dKS9x4PVYqK+Il8pgjhRUZyH1WKid9iX0ZkGFnUWSylPAZ8HngYOAA9IKV8WQjwmhLjY2O1W4G4hxBHABXxbCLEFuAloAV4TQhwQQjyWkLtIEj2GQ7M+RcXWs5W68nz6Rn0EgmoWlUjae2dorMrHqsJ044bFbKKmzJbxwQ5RzWWklA8AD8xquyHi74Oc7UAGeB3dX5A19Ax5sdvMKmtjnKmvyCcUgoFRH3UVSskmiuO9M1y8rjDVYmQdteU2Xj0yic8fwpaXmWG5mSl1CtA0je4hL3XlNhV6F2fqjBnWqQwfVaUzI5N+RicDrKpRJst4U1+Rj0Zm91+lCKJk3B1kcjrIiip7qkXJOhoqdUXQPZi5D1K683a37oNZ26AUQbypLcvHbIZTGdx/lSKIku5BPUfLikpluog3xU4rxU4rXQOeVIuStbx9ahqzCc6rU4og3lgtJmrLbBkd+aaM3VFycsCLy2GhRBXzSAhN1fmnE6Ili3379rFr164ngsEgZHkOraPd0zRW2bHbLIy7VcRQvKmvyOeVI5N4fRkXHAmoGUFUhDSN7kEvK9TS/ITRWGWns9+DpiUncqi/v589e/awc+fOW8jyHFqapvH2qRllFkogDZX2jPYTKEUQBYNjfjz+kPIPJJCmKjvT3hBDE8lZc7h//342btzI9u3bx7M9h9bguJ+xqQDrVNr0hFFdasNqMXFyIDMVgbJzREFHX3hFpvIPJIrGal3JdvV7qSxOfErfwcFBSkpKIpuSlkMr2bxtlFNcm8ELntIdq8VEXYUtY/1cShFEQUefh5oyG458S6pFyVqawopgwENzEmLdQ6FzbLlJy6EFyUmoGOaZN81YTCZGew/TOrBwQsT5khTGmmBwKQkJM/Eakd9XUZ6Jrikzh4/10G0eiUmuVKMUwSK4PUEGxvxs21CUalGymnDkULIcxtXV1bNfxknNoZXohIqR/OzVY5zXoLHt0rXA/AkRF0pSGGuCwaUkJMy0a8z+vlylftq6++l3F3D11atikiteLDWZovIRLEL4xbSyRvkHEs3KajsdSVIE27Zt49ChQ+zdu7dMCFGAnkPr8fB2IwrIY0QIwdw5tD5imIrSFn8gxNHuaTaoQkoJp7TQisthoe2EO9WixIxSBIvQ3juDy2FRNV6TwJo6Bx19MwSTkHOourqaHTt2sHv37gfJ4hxax3tm8AU0NqxUhZQSjclkorHKzqEON/5AZoWRKtPQAsx4g3T1e9i0yqXCRpPAmjoHXr9G16AnKakQWlpaaGlpua65ubkj3JZtObTe6tQdxRtURb2ksKrGzludbto63Gw5L3PyOqkZwQK80e4mGNJfUIrEs7Ze/56PnVIpqePFW11uakptqlh9klhhZHd96fBEqkWJCaUIFuCVo5MU5JupLU98OKNCX53psJmVIogTmqbxVqeb85uUfyBZ5FnNbGgq4KXDE0lbHBkPlCKYh2lvkDfap1hT58CszEJJwWw2sbrOwbGe6VSLkhWcHPQyOhnggtWxl0xVLJ3Na1z0jfqSnjJlOShFMA/72sbx+TXWrVCjqWRyXp2D4z0egqHMGU2lKweP61X0LlyjFEEyuWhtISaT/g7JFJQimIenXh+lsiSP2jJlFkoma+sdeP0hTmboCs104sDxKapLbdSWqRXxyaTEZWVjk5PnlCLIbAbHfRxsn+LyDUUqWijJnN+kR7cc6sy8WOx0IhjSeKN9Ss0GUsQV7yims9+TMSknlCKYgydbR9E0uHxjcapFyTlqy2yUFVozclFOOtHeO8PUTJALlX8gJVyxqQSTCZ57cyzVokSFUgSzCIY0fvPyMJvXuKguVWahZGMymdi00klbhzujoi7SjVflJACbz1OKIBWUF+WxaaWTpw+MZUQ/VopgFq/ICQbH/bxvW3mqRclZNq1yMTTuZ2AsOSmps5GXDk+wrsFBWaFaP5Aq3nVRKaeGvMiT6R8FpxTBLH69f5iyQivbzldmoVSxyUiH0HZiKsWSZCYjk35k9zRb16s+nEqu2FSCzWrid6+PplqURVGKIIKOvhlePTrJ+7ZWYLUoJ3GqaKq243JYONiuFMFSeOWIvqp16/kqY24qcdotXL6xmN8fHMPnT+/cQ0oRRPDwc4Pk55m5UZmFUorZbKJ5bSGvyElCaj1BzOw/PEFFcR6ra1XG3FTznovLmJoJpr3TWCkCg8ExH78/OMZ7Li6lyKly8aWarecXMTYV4G2VbiImJmcCvHp0kis2FavQ5zRg8xoXDZX5/PqluQsBpQtKERj87OkBAG6+sirFkigALl5XiNkELx3JrORdqeb5N8cJBDWu2VyaalEU6FFwN1xazuGu6bROnaIUAdA74uW3rw7z3kvKVMhomlBYYGVDk5OXDmfO6sx04OkDo9RX5J/O5KpIPdc2l2K3mfn5c4OpFmVelCIAfry3D4vZxEeurk61KIoItm0oor3XQ/dgZqzOTDX9oz7aOtxcs7lEmYXSiEKHlRsuLeeZN8boG0nPgnY5rwgOdbj5/cExbr6ykopiFXOdTlxzYSlmMzz5WvqH36UDv9o/hMkE724uS7Uoill88IpKzCYTDz2bnrOCnFYEwRB8/9FTlBflseNq5RtIN8qK8rh4bSFPvjaqspEugscX4vFXRrh8QzFVJcq8mW5UFOfx7uZSfvvqCL1pOCvIaUXw/FETx3tm+LMb67DbLKkWRzEH1zaXMTzh57W3J1MtSlrz1OujTM0EuenyilSLopiHW99Vg8UM9/+2L9WinEPOKoLOfg9PvWWmZVMxV2xSKzDTla3nF1FelMdDzw6kWpS0xecP8eDT/YiGAjaqIvVpS3lRHh+6opJn3hjjUEd6JVXMSUUw4w3ylQc6sOfBX3ygXjnW0hib1cwtV1XyRrubtg610ngufrV/mKFxPx9/b63qy2nOh7dXUVWSx7d+fjKtVhvnnCIIhTTuefgkpwa97NgaolQl5Up73ntJOcVOKz95oi8jMjkmk5EJPw8+3c9Fa12q9kAG4Mi38JcfXMHJQS8/eTJ9TEQ5pQg0TePff9PLs2+O8/H31rKmSr1UMgG7zcwfXlvNG+1uFUEUgaZpfPu/u/H5Q/z5++tTLY4iSprXFXLD1nIeenaQfYfSY51MzigCTdP40eO9/Pz5QT5wWQU3X1mZapEUMXDDpeVsaCrgh7/uYXDMl2px0oJHXhjipcMT3HZdLQ2VKq9QJvFnN9axrsHBv+zp4nAaVOPLCUXg8YX4+p4uHnp2kBu3lfNnN9YpW2qGYTab+KubVxAMafz9/Sfw5Hipgufbxvi3X/dw2YYiPqAihTIOm9XMFz62irJCK393X3vKncdZrwgOd7r5zHeP8vuDY/zRu2v41AfqMZuVEshEGirtfP7WlZwc8PAfz1oYmcg9baBpGo++OMQ/PdCJaCjgbz/ShEX154ykrCiPf9q5hmKnlc/de5xf7R9KWbbdqNJsCiE+CtwF5AH3SCm/O2v7ZuBeoAh4FviklDIghGgEdgNVgARulVImJfTj2Klp9jwzwHNvjlNRnMc/3LaK5nUqP3umc9HaQu66dSX/9MAJ7vjOUXZeX8fVF5YsSbnv27ePXbt2PREMBiED+vXJAQ//9useXj06yaXri/jc/2zEbsv6sVxWU1Vi455PreVrP+viu4+c4pmDY3zs3TW8Y5UzqVaLRXuREKIe+ApwBbAZuF0IsWHWbruBO6SU6wAT8Amj/XvA96SU64FXgb+Ll+CzGXcHaD06yf17e/nUtySf/s7bvHp0ko9cXcUP/kooJZBFbNtQzO3XBKkoyuPre7q4/ZtH+NnT/RztnsYfiC4kr7+/nz179rBz585bSNN+rWka3YNeHn9lmLt+1M7td0vaOtz82Y11/P3HVuLIV4sgs4HCAitfvm0V/+vmBrqHvHz2h8f51LeO8tPf9dHWMcWMN5hwGaKZEVwLPCWlHAEQQjwE3AJ82fjcBDiklPuN/e8DviSEuBe4CvgfEe3PAJ+N4poWAJ/vXKdg/6iPx14aZtobZNobZMIdZGQygNujf1lmM5xXV8CNl1azbUMRTrsFCOD1Bua8kNc7/3LvgN+HmaWZH4IBy5KOjedxNqsW1bmSLSvo3+0CX/2i1JTAV/+0kZePTPDU66M88nwvjzzfi9ViorzISmlhHuWFeXzkmiqKCs7t5vv27WPjxo1s37596s4773QnqV/DAn37+bYx3jg+hdsbZGwqyOCYD19ANxVUFOdx27vLeeeWUooKrAT8Pubu0YszX79eqL/E+lsvpW9k2jXm+76W2revfoeLy89fzb62cV44NM6vXujjVy/o20pdep8uLLBQkG9mXUPBnKnGI/pVTKOEaBRBHdAb8bkXuHSR7Q1ABTAhpQzMao+GWoCjR4/OufGSRc8yCUxy4tipRS/U1ta24PbKJQ66JgaWdmw8j6tsBFj8O0i2rAB9J/V/y+HwW4coBG66ELgwcksA0DOWdrWPzH3s4cOUlJSA3teOk5x+Hb7enH27BLhqzXyHBQEPXe39MVxqfub63RbqL7H+1kvpG5l2jfm+r+X27UrrXH06CERql1Ha2hZ8tsP9OiqiUQRmINKDYQJCUWyf3c6s4xbiFeBK9Ics8fMiRc7x1FNPfdrhcJTv2LHjFaMpGf0aVN9WJBYLuhJ4ZbEdI4lGEXSjd9wwNUDPrO21c2wfAIqFEBYpZdDYJ/K4eWlubvYCz0ezr0KxFAYHB98ArjT6GiShX4Pq24qkEPVMIEw0IQdPAu8SQlQKIQqAm4HHwxullJ2ARwjRYjR9DPiNlNIPPAd8xGj/I+A3sQqoUCQI1a8VCoNFFYGU8hTweeBp4ADwgJTyZSHEY0KIi43dbgXuFkIcAVzAt432T6FHY7yFPqu4K943oFAsBdWvFYozmFQSL4VCocht1GoUhUKhyHGUIlAoFIocRykChUKhyHGUIlAoFIocJ6qkc9mCEKIIeAG4UUrZIYS4Fvgm4AD+U0qZsdEfQogvADuMj7+WUv5tttyfEOLL6OkfNODfpZTfzJZ7SzXZ/EzEk2x+viCHZgRCiK3oC3nWGZ8dwI+Am4DzgUuEENenTsKlY3TI9wBb0BOoNQsh/oAsuD8hxHbgncAFwMXAp4UQF5IF95ZqsvmZiCfZ/HyFyRlFgJ458i84swr0UuBtKeUJI2/MbuDDqRJumfQCfy2l9BkLng6jP9wZf39SymeAa4x7qEKfxZaQBfeWBmTzMxFPsvb5CpMzpiEp5U4AIUS4ab6kYhmHlPJQ+G8hxFr0Key/kj335xdCfAn4G+C/yKLfLpVk8zMRT7L9+YLcmhHMZrFkehmHEGIj8ATwv4F2suj+pJRfACqBFeijsay5tzQi656JeJLNz1cuK4L5koplJEZOnN8Bn5NS3k+W3J8QYr1RKQwp5TTwc+BqsuDe0pCs6DOJIFufrzA5Yxqag5cAIYQ4DzgBfBTd+ZNxCCFWAP8NfERK+ZTRnC33txq9IMwV6COwm4AfAF/PgntLN7Klz8SVLH++gBxWBFJKjxDiNuBhwA48BjyUUqGWzt+g38M3I+y9u4DbyPD7k1I+JoS4FHgdPX//w1LKnwkhBsnwe0s3suyZiCdZ+3yFUUnnFAqFIsfJZR+BQqFQKFCKQKFQKHIepQgUCoUix1GKQKFQKHIcpQgUCoUix8nY8FEhhAX4DHr8rhWwAY8Cfy+l9CZZlg8A10op/zLB1ykGfiGlfOcSjr0RPQyuBMgD2oC/kVKejJNsFcCglNK0hGN/AnxNStm2TBlWAN8CbpFSZuwqz6UihFgJtEkpXamWJRaEEJXAPwLXAG70FboPAN+UUgZjPNcXgQop5R1CiA70vvDqMuX7GvA7KeXeZZ6nENgDfEhKObOcc8WbTJ4RfB+4DHiXlHIzcAkggHuTLYiU8peJVgIGpeiJwWJCCPFR4J+BncZ3tQm9YPvTQoj8+IoYs2w7gPHlKgEAQ6kdQC8ur8gAhBAlwD7gKLBeSnkherbZS4CfpFI2ACHENuD85SoBACnlJPAg8A/LFizOZOSMwBj53ArUSiknAKSUbiHEJ4EWY59i4LvoaU8CMA0AAAiCSURBVGM14DfA/5VSBoQQHvQ84tcCLuCL6JkD34G+TPz9xvkCwFeB6wGncfzPjUU3f2q0jQP3o488bhRC/B5oBbahZ8v8N/Tl59uN/XdIKd805PuWcc089OXr/ztCvq+ip76tBf5ZSvl94D8AhxDiANAMXA58HSgAfMBdUsrH5/jKvgLcLqU8ZnxXmhDiq0AXkC+EsKIr1rVAOTAJfFRKKY37+Y6U8iHjez39WQjxIePc08ArEb+Pc77zzSHbl4jI2iiE+D/AHwMB4G30RTsfBG5GH7g0oS/v/yFwB3reoW9KKb9hnOJe4BUhxL9JKX1zXC8nEUKsQ38eCtH71AH0lbIeIYQXeAS4EP25qgK+hr6A7wD6c3IFemqPW6SUNxrnvI0z/f4+oAxYA/wK+Pf5rjdLtD8HXpdSfj3cIKUcFUL8EdAphLgE/bn5FvpswYWuJN4D3IVuCZhGn92+OMet/5kQYpdxTz+RUn5eCGEG7kZ/RgvR8wTtlFLum+P4LwLfifge/wT4a+O7GULvq2uAf0J/noQh51eBvzQ+Pyyl/CvjFHuArwkhvi6l7J/jeikhU2cEzcChsBIII6Xsk1I+bHz8NjCM/qK9GL2T/42xLR/ok1Jeiv4Svxf4X8AGoBg9jQGABZiWUjajZxz8kTGNBdgIXC2lvGYO+VZKKVuAP0Qfif9eSnkx8DjwaWOfu4FW49xbgArgzgj5hqSUl6MXZLlbCGEHPg7MGKP6EvSVjJ+RUl6A3iF3CyFWRQoihCgHVqKPuiK/K01K+VPjO7weGJNSXialXIf+Ur9jjvuKPG81+pL6m4176IzYHNX5hBCbAEd4NmCY2G4DLpNSbkJfuh8+7krgk+h1CVYA/xN4F3AD8P+MhxspZQ/6796ykPw5yCeA+6WU24DzgFXA+4xtNuBRKaVA/85/Avyh0c+eBuqjvEaBlHKjlPKzi1wvksuBZ2c3GgrjeXQFBPos9g+Mvt6Ibkq6QUq5Bbgd+LkxAJmNx3j2LgX+2jAfbkXPtHqZlHID+jvgc7MPNGYrVwJ7jc8XoivI9xpy/BL4vLH7JcBXje9sAvg/xv1eBPyFEKIu4r5eQe+3aUOmKoIQi8t+PfrIVTN8BruMtjBhhXEceFNKecqwK59AH9mE+Q6AlPIN4E3gKqP9jdmKKIKfR5wbdAUQ/hw+943oo5UD6DOIS9GVVphHjP9fQ1cMszv5VuCYlPIlQ75D6C/7q2ftF7aVz/t9GaP9+4QQnxZCfMs4x2J25ivQv7e3jM8/WML51gPHIj5fC/yXlHLUOM+dUsqvGNtekVKejPiN9hp/H0df4l8QcZ4T6CMxxRk+CwwKIf4WfbZWx9m/yXPG/1cBb0kpDwIYCdbm6+ezeT6G60WSN097PmcyfJ6UUoYHG+9Gn2X8znh+forez8+b4xwPGPfRB/QDVcbM4S705+9f0Adbc8l2HtAbMbN8F/DbsF9NSnmPlPKTxrYTUsrXjb+PA09LvX7BEPr3F/lOSbv+mZGmIfSET+cLIQoNuxsAQoh6dFPMLZybUtfM2R0u0qHsX+BagVnnCDuvphY45ixntdSLWczGAnxYSnnYkL1klrwzxrGakd9kthPWMmv/sHxnPVTGNPso+jT4ychtQog96Kady9FHVd9Bf3BG0EdwGNeIvLYt4u/I9tPfkxDizxc4XyQaZyuoQOQ9Gd9JifFxdgDAQr+ZnzO/k0LnQfTnfQ/wa/RRdeTvF+7PAc7ta+HBxEJ9IfIc0VwvTHjwck9koxAibAL6MrqSjzy3Bd15+5GI/Vegm3U/OOv8kf1EA0xCiPehm5q+gT7gOoI+e5/NYv3TgW6qhAzvnxk5IzCm/z9FN9UUwenaq98Dhg2P/G+BO4QQJsMhejt6LvFY+SPj/Behj2CficMtYMj3VxHy/ZJFzDHoHdEihDABLwLrjYRs4VzpVwG/n+O4LwHfMjIlIoSwCCHuQvefHAGuA+6TUv47IIH3oz9sAIPopjWEEBvQTTOgT+c3GtNl0E06YRY6XyQS3b4a5kngQ+HfFN0+e+fsg6JglXFfijNcB3xZSvmfxuetzP2b7APWCSEuABBC3IyujDX0vrBJCGEXQuShD7iWe73voQ/qPmdEAiKE+P/tnT1oFGEQhp/KxkrBHwQLq1HExlItVIwGC7ExYioFUbBIZ6MWFpZ2EhJbwZ/OQiI2FjkwYJBDs0lgBGuLgIhIBBstZo4cx+W8SzaQ+L0PbLHs7eze7rffzLwz7O4g5JqGu8922ectcNbMDubvzwNzxLeD+2GIkMImgA/AxVXO7QuwJ2VZCJnsjJm1Xj99k5B+B2XTjc8t6QiSW8AiMJPp4ftcv57bx4gCUZWLE9HvoBw3syahh19uyRY1MEbIPRUxiCv+Pai+ArPAAvFgXgIemVlFRN7X3P1z507u/ozQVJ/ntVog6iGnUzZ7SKTJc4RE0GQlzX5APHTzRHTWSJtLROvu07w+7RF/L3vt5zUP/DKzQ7n+miiIv8v/tJcVDbYvsnaxm46aSEFsN7OfHcsR4A7wMq/rYyKg6XZPvgFXgCd5X88RAcgyoZVPE5NYg5hEV6Pf4/0guv/2A4s5zlq2R7sZTjnyBvDCzD4RXTgX3L1Xlt7OJHAyz61JTPgHWnWmtuN8J8bvqVyviI/SvMnjDhN1q74xs21Edv5qkP02Gr19tAdm9gfYlTqf2ACytfWEu9fS8mnRR77k7uN12CuNzMbuAffdfTkz4Slgn7sXN1mY2THgrrt3K3Svxd5V4LC7367DXl1s5YxA/AdktrIzo9Z1kTrxUSLiE2sgI/TfRAvuRyKaHynRCQC4+wzgZja8XltZ9xglJM9NhTICIYQoHGUEQghROHIEQghROHIEQghROHIEQghROHIEQghROHIEQghROH8Bt6zOsDePK7QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "plt.figure(figsize=(15,10))\n",
    "grid = sns.distplot(pokemon['Comprimento Cauda (cm)'], hist_kws={'edgecolor':'white'}, ax=ax1)\n",
    "grid = sns.distplot(pokemon['Largura Orelha (cm)'], hist_kws={'edgecolor':'white'}, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pokemon.plot(figsize=(10,5),kind=\"scatter\", x=\"Comprimento Cauda (cm)\", y=\"Largura Orelha (cm)\", c='Pokemon',colormap='winter');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Prepare os dados para Machine Learning***  \n",
    "\n",
    "Neste caso, os dados estão bons o suficiente e não precisamos de nenhuma transformação. Digamos que uma das medidas, por exemplo, tivesse sido feita em metros. É uma boa prática deixar os dados na mesma escala, então deixaríamos ambas medidas em metros ou centímetros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Escolha alguns modelos e os treine***\n",
    "\n",
    "Se encontrarmos um jeito de automaticamente separar as duas categorias com um reta, por exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pokemon.plot(figsize=(10,5),kind=\"scatter\", x=\"Comprimento Cauda (cm)\", y=\"Largura Orelha (cm)\", alpha=0.9,c='Pokemon',colormap='winter');\n",
    "plt.plot([13.5,27],[10,18],'k');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sabemos que os dados que corresponderem a pontos superiores a reta são de Pichu (0) e pontos inferiores a reta são de Pikachu (1).  \n",
    "Claro que neste caso erraríamos ao analisar dois pontos, como você pode observar no gráfico, mas é um preço pequeno para poder automatizar este processo.  \n",
    "\n",
    "A representação matemática da reta acima, $y = ax+b$, pode ser encontrada facilmente quando há apenas duas variáveis no nosso problema, entretanto problemas reais podem possuir milhares de variáveis. Para nós humanos isso se torna inviável.\n",
    "\n",
    "Vamos então usar machine learning para descobrir qual é esta reta e quão perto ela chega de classificar corretamente estes pokemons. Para fins de comparação usaremos os algoritmos LogisticRegression e KNeighborsClassifer. Neste momento não vamos detalhar muito o que está sendo feito, pois este exemplo é mais ilustrativo, nas seções adiante nos aprofundaremos mais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "poke_target = pokemon['Pokemon'].copy()\n",
    "poke_data = pokemon.drop('Pokemon',axis=1)\n",
    "poke_target_test = pokemon_test['Pokemon'].copy()\n",
    "poke_data_test = pokemon_test.drop('Pokemon',axis=1)\n",
    "\n",
    "# k-nearest neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# logistic regression\n",
    "log = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# treina o classificador com os dados\n",
    "knn.fit(poke_data, poke_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ainda não vamos entrar em detalhes do funcionamento específico deste algoritmo, apenas vamos dizer que ele verifica, para um determinado sample, os 3 samples mais próximos dele e analisa a classificação destes pontos.<br>\n",
    "\n",
    "Com o treino feito vamos usar o que o algoritmo aprendeu e tentar prever o resultado de dados ainda desconhecidos, em 'pokemon_test'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previsto para os 10 primeiros samples: [0 0 0 1 0 1 1 0 1 0]\n",
      "Classe real dos 10 primeiros samples:  [0 0 0 1 0 0 1 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "poke_predict = knn.predict(poke_data_test)\n",
    "print('Previsto para os 10 primeiros samples:',poke_predict[:10])\n",
    "print('Classe real dos 10 primeiros samples: ',poke_target_test.values[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.98\n"
     ]
    }
   ],
   "source": [
    "# Verificando a acurácia média\n",
    "poke_score = knn.score(poke_data_test,poke_target_test)\n",
    "print(poke_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este valor quer dizer que 98% das previsões estavam corretas, mas a acurácia não é uma métrica definitiva. Vamos verificar a matriz de confusão (confusion matrix)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# confusion_matrix(classe real , classe prevista)\n",
    "cm = confusion_matrix(poke_target_test,poke_predict)\n",
    "\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "    \n",
    "plot_confusion_matrix(cm, ['Pichu','Pikachu'],\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A confusion matrix é uma maneira melhor de compreender os resultados da previsão. No nosso caso a classe positiva é 1, ou Pikachu e a classe negativa é 0, Pichu. Então temos\n",
    "-  TN - prevemos 24 samples pertencendo a classe Pikachu e acertamos;\n",
    "-  FP - prevemos 0 samples pertencendo a classe Pichu e erramos;\n",
    "-  FN - prevemos 1 sample pertencendo a classe Pikachu e erramos;\n",
    "-  TP - prevemos 25 samples pertencendo a classe Pichu e acertamos.\n",
    "\n",
    "Ou seja, o único erro foi proveniente de uma amostra na qual o algoritmo identificou como sendo um Pikachu, mas na verdade era um Pichu. Um caso ideal é composto apenas por valores na diagonal principal da matriz, ou seja, $FP$ e $FN$ nulos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Melhore o modelo***\n",
    "\n",
    "Para treinar o algoritmo acima utilizamos um parâmetro chamado 'n_neighbors' (número de primeiros vizinhos) igual a três. Cada algoritmo possui seus próprios parâmetros e a compreenssão dos mesmos é necessária para entender o que está ocorrendo e para encontrar o melhor modelo possível. Vamos agora verificar a chamada fronteira de decisão encontrada pelo KNeighborsClassifier com 1 e 3 vizinhos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x178efd5438>"
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mglearn\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 4))\n",
    "for n_neighbors, ax in zip([1, 3], axes):\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(poke_data.values, poke_target.values)\n",
    "    mglearn.plots.plot_2d_separator(clf, poke_data_test.values, fill=True, eps=0.5, ax=ax, alpha=.4)\n",
    "    #mglearn.discrete_scatter(poke_data.values[:, 0], poke_data.values[:, 1], poke_target.values, ax=ax)\n",
    "    mglearn.discrete_scatter(poke_data_test.values[:, 0], poke_data_test.values[:, 1], poke_target_test.values, ax=ax)\n",
    "    ax.set_title(\"{} neighbor(s)\".format(n_neighbors))\n",
    "    ax.set_xlabel(\"Comprimento Cauda\")\n",
    "    ax.set_ylabel(\"Largura Orelha\")\n",
    "axes[0].legend(loc=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pelos gráficos, parece que se n_neighbors=1 o algoritmo está mais próximo de identificar corretamente todos os samples. Note que este algoritmo faz algo mais poderoso do que simplesmente traçar uma reta para dividir os datapoints, ele divide o gráfico em regiões diferentes onde habitam os pontos de cada classe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificando com outros algoritmo\n",
    "log.fit(poke_data, poke_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96\n"
     ]
    }
   ],
   "source": [
    "poke_score_log = log.score(poke_data_test,poke_target_test)\n",
    "print(poke_score_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previsto para os 10 primeiros samples: [0 0 0 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0]\n",
      "Classe real dos 10 primeiros samples:  [0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "poke_predict_log = log.predict(poke_data_test)\n",
    "print('Previsto para os 10 primeiros samples:',poke_predict_log[:20])\n",
    "print('Classe real dos 10 primeiros samples: ',poke_target_test.values[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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i5owOXn/s8qzDqJn+I0/JOoSaeuiasb3mOw3aYG1uu+iwuhw7okJzvi5nTZZUiW7bsChERKpQ+bbPJqpE3f2FRgYiIlJJgeQLORnc9akHzolIjlR6jlIzVaIiIs2mTwH6JEzS1KeZJiApZWb9gGHA00C/+KH2IiIN1Yx9otU8HuSjwPPAXcBGwN/N7GP1DkxEpKvivfNJr4bHVMVnLgFGAa+5+3zCjE4/rGtUIiLdqfR8pSZNomu6+6zigrvfjfpSRSQDfYjoEyW8Msii1STDpWbWn3jgvZlZfUMSEeleM947X00S/Q7wO2CQmd0E7A2Mq2tUIiLdqDRTU1PO4uTu08zsGcJkzC3Aee4+u+6RiYh0VenZ8s3YJ2pmA4CFwC3AL4B/xutERBqqGa/OV9Ocf5X/nIjkRWBo7cMRESmvpRDRklBuNtUsTkXuvrJaNbNWYAygi0si0nB5fcbSSu6+xN1/SugfFRFpuCjhvyxU88jk0v7PCNgZ6F+3iEREyihQoRJtWCTv60mfaDH0l4FT6xaRiEgZLYWIls6EPtFmHOIEjHT3jrpHIiJSQSGqMJ9ok/aJ3lj3KEREqtGE985XU4k+aWZjgIeAt4or3X1h3aISEelGIYooJGTKprxjCTgA6PrUqU7C3UsiIg3TUoCWhEmZW1Yhh5rZusAfgf3cfZ6ZjQIuA/oBt7j7N5P2T3raZ5u7v+fua/Q8LBGR2itQoRLtYXvezD4CXA0Mj5f7AdcBewB/B+4ys8+5+6/LHSOpEn0Y2KlHEYmI1Fk1LfapU6cOHTNmTNfVi9x9UZd1xwMnA5Pj5V2AOe4+F8DMbiS0xFcpiWYzclVEpIyKV+fjn9OnT3+wm80TgHNLV7j7cQAlM3wOIdzWXlTxFvekJLqGme1ImWTq7o8nHVhEpNYqXliKt40aNWr3m2++eX6XzV2r0O4P8e9zhUTAiqQdkpLo5sDtdJ9EO+PtIiINU3ECknjb6NGj50+YMGHeKpxiPjC4ZHkQsCBph6QkOsvdd1yFIERE6iOqez/jI4QHeAwD5hImXLouaYcsbjUVEVklhSpeabj7u8DRhFb4LOAZ4LakfZIq0d+njEdEpKaiKHm2plWdycndNy15fx+wfbX7lk2i7n7aKkUjIlInLVHEiir6RBtJjz4WkdyodHt8FuMylURFJFcSB9sn3BJaL0qiIpIbYbampD5RGp5IlURFJDdaoojOhCTaQqQkKiJSjvpERURSiKKoQnNeV+dFRMqKSB5Qr0pURCSBKlERkRQKESQ87LNpH5ksItIUCkR01nBm+1pQEhWR3Fj5VM9y2xsXykpKoiKSGxH1mYAkDSVREcmNAsmlaCGpw7ROlERFJDcqNuczaM8riYpIbiiJioik0Ix9ono8yGrg0UceYdy4cVmHIbE+fQpce/6XmH7t6Tw4+Uz23WO7ldsO/+zOPHDDGRlG19wKUZiEpNyroEpUau17l17MTTdOzqSZI907Yp9dWPjG2xz7rZ8xYL21+NNN43l5wVw+PHwjvnzgrpkM08mLqMIMJFn8PVcl2sttvvkW3HzrHVmHISXuuPdxJkyatnJ52fIVtLS0cP6pB3DWpbdnGFnzi6r4r9HqVoma2abAs4Qn5nUCrYTnN48FfgIcB+wN7OnuR6c4z57Aue6+Z6qAe6mDDj6EF+bNyzoMKfH24iUArL1mG7+45FjOmzSN7599IEcf8yMWv7s04+iaW6XbPnvjhaUF7r5DccHMvgdc4u77xMt1Pr1Icxr6wfW5+bJx/GTK73nub6/Q1tbGxG98gTVa+7DV5oO45MxDVJV2I6owo2ivqkTLuB+4wMzmAXuWbjCzHwAfBL4IHAycAfQD2oBj3P2PZrYD8GNgTWAhcGS8+0AzuxvYAnDgMGAw8EDxUahmdi6Au59bp+8mUpUNB6zDryadwn9dNIUHHn0WgFmzZvGZ469n48EDmHzhWCXQMlbHSnQlM+sLHAo8TGjGl247F9gIOILQ9P8KsJ+7v2pmxwBfB/YHfg6Md/dpZnYicBpwF7AxsB/wAvAnYBQwM23Mc2Y/nfYQTWHBggUAzJzRkXEktfPQNWOzDmGVDR06lAEDBjDl0ve/w5w5c3jomrG0tray2WYDc/396qnSVHhZZNF6J9EhZvZE/L4NeBQ4h39Pop8DBgIj3X0ZgJkdBOxvob2/J7DczD4ADHb3aQDufmX82T2BGe4+N16eDXygFsFvufW2tLa21eJQmRqxfTtDhgxhxPbtWYdSM/1HnpJ1CDX10DVj2e2467MOoyYGbbA2t110WF2O3YwjFxraJ1rUpS90HvAN4Aoz+xihqf4ocCPwe+BJ4BRgKSWPoDKzNYAh8eKykuN1Ev5fF38W9Y2PISJ51mSZtBmGOM1292uBt4GTgeGEBPj/CH2oBwMt7v4GMN/MilXsUcB5CcddBAwws4Fm1gZ8tl5fQEQaoxBFFV8Nj6nhZyzvRODbwGvAE8AzhH7NV4BN4s98Efh23EVwOHBWuYPFSfdi4DFgOqG6FZEci6p4NVrdmvPuPg/YtMy24vqfxi/c/VlC3yiEC0ylTos/8xSwW5dtL1Jypb90zKm7nw+c38PQRaRZNeEzk3Xbp4jkRqVxollkUSVREcmNSlPhZTAns5KoiORI8xWiSqIikh+VJxmJ3h8H2SBKoiKSG5Wa80QoiYqIlJPVMKYkSqIikhur473zIiI1U01zvifM7H5gQ96/JfwEd3+kJ8dQEhWR3Khlc97MIsJt5psUJz9aFUqiIpIvVWTRqVOnDh0zZkzX1YvcfVHJcnEmpHvMbAPgane/vKfhNNO98yIiiaIoeRKSYlN/+vTpDwJzu7xO73K4/sB9wEHAp4GvmNlePY1JlaiI5Ea1Y+1HjRq1+8033zy/y+bSKhR3f5gwSTwAZnYtsA9wb09iUhIVkfyoMouOHj16/oQJE+YlHcrMdgPa3P2+kr17POewkqiI5EalO5Z6+KC69YHz4sng+wJfJjyaqEfUJyoiuVGIKr+qFT9q6C7gL0AHcF3cxO8RVaIikh81noDE3b8FfCtFREqiIpIfNW7O14SSqIjkRqU7lnr1c+dFRGpBE5CIiKyiShOQJE5OUidKoiKSG2rOi4ik0IRPB1ESFZGcabJOUSVREcmN4kQj5XSqT1REpDw150VEUtCFJRGRVJqvFlUSFZHciCpMMrJClaiISHlqzouIpKAJSERE0mi+LlElURHJjybMoUqiIpIflQbbJ22rFyVREcmPJixFlURFJDeaMIcqiYpIvmQxjCmJkqiI5EZ4omdSn2gDgymes/GnFBHpPVSJikhu6I4lEZEUdMeSiEgKhQoTkGTRJ6okKiL50YRjnJRERSQ31JwXEUmjwoUlVaIiIgmasDWvJFpGC8DSJUuyjqOmlix5L+sQambQBmtnHULN9ZbvNLD/msW3LbU+9vJly1iaUIouX7as1qesKOrs7Gz4SZtdR0fHbsCDWcchknO7t7e3P1SLA3V0dAwAngP6V/Hx14Fh7e3tC2tx7kpUiXbvMWB34EVgecaxiORNCzCY8O+oJtrb2xd2dHQMA9at4uNvNiqBgipREZFUdO+8iEgKSqIiIikoiYqIpKAkKiKSgpKoiEgKSqIiIikoiYqIpKAkKiKSgpKoiEgKSqKrKTOLSn+KyKpREl1NuXvxft9NQck0L8xspJltkXUc8j4l0dWYmW0JdJjZwSVJVZqUmW0MfAUYa2abZhyOxJREVyNdq013nwOcClxkZgdmE5VUy93/BtwJrAMcbWabZRySoFmcVktmthfwKjDD3VeY2eeBi4CT3P3X2UYnXZnZYcBn3f3YePmzwP7Ay8AN7j4vw/BWe6pEVwNm1vXP+Sjgv4ERZtbH3acQKpxbzWzvhgcoK5lZ1M2f15PAjmY2EcDd/w+YCRwIfMHM+jY4TCmhJLoacPcVAGa2g5l9wN2/BDxPSKTbxB/7C3Al8NdsopTYWiV/XqPN7ABCq+FQYCczuyL+3ALCpMfXuPvSbEIVUHO+VzOzTwAD3f12MzsVOBN4HJjt7l83s0uA4cBC4CPAXu7+j+wiXr2Z2TBgEqGl8Engf4A3gT8D1wKLgLsJSXUIsJ+7z8omWinS40F6t8HA5WY2nFBxforweIXxZnaRu59lZvsBBlyiBJq5d4AngJuAFcB2wFrAd4EjCYl0J+DjgMcXmiRjqkR7ITMrlDQJDwCuAP7k7oeaWSshoY4H3nL34zMMVfiPP68PACcBXwP2cPcZZrYJcBawBjDR3Z/MLlrpSn2ivYyZRSX/IIcQmn+HAiPN7EB3X0K4UPE9oGBmg7KLVrr8eX0C+BBwP+EX3wVmtpW7v0D483oD+GdmwUq3VIn2UmZ2NrAvMIjQ37kP8B3gbHe/Lb4C3Nfde8/D6HPMzE4BTgTuA6YCM+LlXQl/Zk/FIyka/2B1SaQk2kuYWT93Xxy/3xu4EPgYsI67vxKv3wu4HTjK3X+ZWbDyb8xsK+AWwi+9l9x9mZltBHwY2CX++QVgme4saz5qzvcCZmbAVWbWEq9qBea4+7vAQjPrZ2b3Ea7qHkEYYygZ6WaegiXAP919fpxA+wKfJlShE4ET3X2pEmhzUhLNOTMbAbwHTAaGx5NTvAL0MbMdgZa4Qn0C2Mjd73L357KLePUW94F2xu93MLNh7v5XoK+Z/RQgHve5MbCGu7/u7i9nF7FUouZ8jpnZ5wiVytOEcYMjgXuAE4CTgQ8CDrxFuEf+M+7+fDbRSikz+yrwVcJFvlnAj4AphKr0YcJtnV9099mZBSlV0TjRnIr7N78LjAGeAw4G5hCa8tcBxwC7AVsAIwgDs5VAm0A8V8HngVGEX3ajCX9uowgD7VuBMe7umQUpVVMSzSEz+zShavmIuz8br5sJjHL3z5vZj4GrCX1pP9dV3WyVjgONfYjQglhB+Df4DeDbwFUat5s/6hPNp7b455Yl6w4G+gK4+wmE8YRXmVkfYHljw5MiM2stGQf6eTPbBngGWEwYdvYYYVjTPGCbeM5QyRH1ieZUfLvmDwn3w29NGM50SOm4TzMb5O4vZRTiai8eunQqcAHwD+DXwFfd/VkzWwPoAE4n9IOeDxyqi0j5o+Z8Trn7tHiozNXAv9x9SwiVD2E84Qol0OzESXJdIALOINzoMBdYD8Dd3zWzyYR5XAvAWCXQfFIlmnNmtg9wOXC6u0/NOh5ZOW73LMIoiV2AwwldLYcCdwD9CRf/+hIuBr7l7guyiVbSUp9ozrn73YQm48/iq76SoZJxu1OAzQh90zcTmuxrEfpDnwSOJvzye0UJNN9UifYS8SMjntNA+uyUGbf7G8LD5dYnjAtdApzh7ovNbE13fyereKU2lERFaiAet3sRoQlfHLf7ScJ1hw8R5gMdQJjm7nXg68CKLkOfJIeUREVSisft3sa/j9v9KHCaux9hZlcSJr4+nvCkzhfdXVPa9RLqExVJr9K43RMJz0SaCDytBNq7qBIVqQGN2119KYmK1IiZ7U/CuN1Mg5O6URIVqSGN2139KImK1FjctL8RGOfuU7KOR+pLF5ZEaszdpxEe5/F41rFI/akSFRFJQZWoiEgKSqIiIikoiYqIpKAkKiKSgpKoiEgKmtleumVmmwLPA0+VrI6AH7r7dSmPPQ24zd1/amZPAHu6+6Iyn10PuNPdP9XDcxwKnOLue3ZZvydwubtvW2H/TmCgu7/ag3P+lHBv/KU9iVXyTUlUkix29x2KC2a2EfC0mf3Z3Z+sxQlKj19Gf8Ls8CJNSUlUqubu/zCzOcBwM9sJOJYwW/sb7v5JMzuWMF9mAXiNUAk+Y2ZDgBsIExW/AGxYPGZpxWdmXwe+DCwjPDbjaOB6oF9csbYDwwkTfWwAtAATi5WxmZ1HmLfztXj/RGY2HLiCMD3dYOAJ4HB3fzf+yHfNbGT8fb4ZD6Kn3Pfs0f9M6TXUJypVM7NdgWHAI/GqEYSm+CfNbA9CAtzd3XcELgbujD93BfAndx9BeJTJVt0cezQhae4aN7XnAqcAY3m/Io4I83ae4+7twB7AmWb2UTM7ADgE2IEwg9J6VXyl44Eb3P2j8ffaDNi3ZPtf3X0n4IvADWY2sML3lNWQKlFJUqwAIfxdeRU40t3/Hp6ED30ZAAABvklEQVTFxpPu/ma8fV9CIvpjvA2gv5kNAEYRpojD3Z8zs992c65RwK3u/nr8ua/Byr7ZouHAFsB1JefoB+wIbAPc4e7/ive7jpCwk4wH9jKzs+NjDwHWLtl+VRzL02Y2C9gV2C3he8pqSElUkiyu0Gf5Vsn7FmCyu48HMLMCISm9DnQSqsiiZd0ca1n8OeL91yc8l6hUC6HroLSf9oPAG8AlVZyjq5sI/wamAHcBG3c5xvKS9wVgKcnfU1ZDas5LrfwGOMLMBsfLXwHui9//HzAOwMw2Jjx7qKvpwMFmtm68fC7wNUIybDGzCHBgsZl9MT7WhwgPhWsHfg0cZmbrx4ntqCpi/gxwnrvfEi9/hJAki46Oz7MT73djJH1PWQ2pEpWacPd7zOwi4F4zWwG8CRzs7p1mdjJwvZnNBuYTLuB03f9uM9sG+EPcTJ5J6LN8B3g0Xt4dOAD4YdwE7wt8y93/AGBm2wF/JlSFM4CBFcL+BnCnmb1NqGZ/R0iWRZub2V8IFfIX3H0hkPQ9e/K/THoJzeIkIpKCmvMiIikoiYqIpKAkKiKSgpKoiEgKSqIiIikoiYqIpKAkKiKSwv8H7RBiZTtSIlYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion_matrix(classe real , classe prevista)\n",
    "cm2 = confusion_matrix(poke_target_test,poke_predict_log)\n",
    "plot_confusion_matrix(cm2, ['Pichu','Pikachu',],\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para finalizar vamos verificar a decision boundary para LogisticRegression dois valores para o seu parâmetro C (veremos mais tarde o seu significado)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x178ece3048>"
      ]
     },
     "execution_count": 727,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 4))\n",
    "for C, ax in zip([0.01,1.], axes):\n",
    "    clf = LogisticRegression(C=C).fit(poke_data.values, poke_target.values)\n",
    "    mglearn.plots.plot_2d_separator(clf, poke_data_test.values, fill=True, eps=0.5, ax=ax, alpha=.4)\n",
    "    mglearn.discrete_scatter(poke_data_test.values[:, 0], poke_data_test.values[:, 1], poke_target_test.values, ax=ax)\n",
    "    ax.set_title(\"C = {}\".format(C))\n",
    "    ax.set_xlabel(\"Comprimento Cauda\")\n",
    "    ax.set_ylabel(\"Largura Orelha\")\n",
    "axes[0].legend(loc=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O resultado com este algoritmo foi diferente, ele usa uma reta, mas isso não significa que o KNeighborsClassifier sempre será melhor que LogisticRegression. Cada problema é único e devemos testar vários algoritmos afim de determinar qual especifica um modelo melhor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Apresente sua solução***\n",
    "\n",
    "Considerando que $FP$ ou $FN$ iguais a um são erros aceitáveis podemos escolher o algoritmos KNeighborsClassifier como o melhor para o problema. O que faríamos agora é utilizar novos dados no algoritmo já treinado usando o método *predict* para obter  classificação. Vejamos, digamos que um estagiário que não consegue classificar os dois pokemons foi a campo e voltou com as medidas:  \n",
    "\n",
    "|Comprimento da cauda (cm)|Largura da orelha (cm)|\n",
    "|:---:|:---:|\n",
    "|21.3|14.1|\n",
    "|18.4|17.6|\n",
    "\n",
    "Podemos usá-las da seguinte maneira:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {},
   "outputs": [],
   "source": [
    "#poke_new_data = np.array([[medidas do sample 1],[medidas do sample 2]])\n",
    "poke_new_data = np.array([[21.3,14.1],[18.4,17.6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previsão: [1 0]\n"
     ]
    }
   ],
   "source": [
    "poke_new_predict = knn.predict(poke_new_data)\n",
    "print('Previsão:',poke_new_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x178e867128>"
      ]
     },
     "execution_count": 730,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 4))\n",
    "for n_neighbors, ax in zip([1,3], axes):\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(poke_data.values, poke_target.values)\n",
    "    mglearn.plots.plot_2d_separator(clf, poke_data.values, fill=True, eps=0.5, ax=ax, alpha=.4)\n",
    "    mglearn.discrete_scatter(poke_new_data[:, 0], poke_new_data[:, 1], poke_new_predict, ax=ax)\n",
    "    ax.set_title(\"{} neighbor(s)\".format(n_neighbors))\n",
    "    ax.set_xlabel(\"Comprimento Cauda\")\n",
    "    ax.set_ylabel(\"Largura Orelha\")\n",
    "axes[0].legend(loc=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Faça a manutenção do sistema***\n",
    "\n",
    "Este exemplo foi bem simples, poderíamos apenas indicar o algoritmo treinado e como utilizá-lo.\n",
    "\n",
    "Agora é hora de nos aprofundarmos em problemas mais complexos onde precisaremos de mais ferramentas para fazer boas previsões bem como outros algoritmos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Wisconsin Breast Cancer Dataset\n",
    "Este é um conjunto de dados reais que contem medidas clínicas relacionadas ao cancer de mama. Há duas classes possíveis como resultado da análise: benigno e maligno. Nossa tarefa é utilizar machine learning para prever se um cancer é maligno ou não baseado nas medidas do tecido. Se esta tarefa for bem sucedida não será necessário gastos extras como o tempo de especialistas ou exames clínicos adicionais.\n",
    "\n",
    "Vamos utilizar uma versão um pouco modificada da original, http://mlr.cs.umass.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)\n",
    "\n",
    "Citation Request:\n",
    "   This breast cancer databases was obtained from the University of Wisconsin    Hospitals, Madison from Dr. William H. Wolberg.  If you publish results when using this database, then please include this information in your acknowledgements.  Also, please cite one or more of:\n",
    "   1. O. L. Mangasarian and W. H. Wolberg: \"Cancer diagnosis via linear \n",
    "      programming\", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.\n",
    "\n",
    "   2. William H. Wolberg and O.L. Mangasarian: \"Multisurface method of \n",
    "      pattern separation for medical diagnosis applied to breast cytology\", \n",
    "      Proceedings of the National Academy of Sciences, U.S.A., Volume 87, \n",
    "      December 1990, pp 9193-9196.\n",
    "\n",
    "   3. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: \"Pattern recognition \n",
    "      via linear programming: Theory and application to medical diagnosis\", \n",
    "      in: \"Large-scale numerical optimization\", Thomas F. Coleman and Yuying\n",
    "      Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30.\n",
    "\n",
    "   4. K. P. Bennett & O. L. Mangasarian: \"Robust linear programming \n",
    "      discrimination of two linearly inseparable sets\", Optimization Methods\n",
    "      and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np    # biblioteca científica\n",
    "import pandas as pd   # biblioteca para visualização e manipulação de dados\n",
    "# importamos o dataset\n",
    "cancer = pd.read_csv(r\".\\datasets\\Wisconsin\\data.csv\")\n",
    "cancer = cancer.drop('Unnamed: 32', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "            ...             radius_worst  texture_worst  perimeter_worst  \\\n",
       "0           ...                    25.38          17.33           184.60   \n",
       "1           ...                    24.99          23.41           158.80   \n",
       "2           ...                    23.57          25.53           152.50   \n",
       "3           ...                    14.91          26.50            98.87   \n",
       "4           ...                    22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 732,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificando o que há no dataset\n",
    "cancer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 32)"
      ]
     },
     "execution_count": 733,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# o dataset contém as medidas clínicas, neste caso temos 30 medidas (features) para cada uma das 569 amostras (samples)\n",
    "cancer.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos detalhar as informações contidas neste dataset:\n",
    "-  'id' é simplesmente uma identificação de cada amostra, não precisamos desta coluna;\n",
    "-  'diagnosis' traz a informação sobre o tumor ser benigno (B) ou maligno (M). Esta coluna é chamada de 'target' e é esta que pretendemos prever com o algoritmo;\n",
    "-  as demais colunas contêm as medidas clínicas sobre o tumor, estude-as por uns instantes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando o método 'info()' no dataset podemos identificar o nome dos features, a quantidade de dados que cada um possui e que tipo de dado é este.<br>\n",
    "Vemos que todos os features possuem 569 entradas, isso é bom. Muitas vezes encontramos datasets com dados incompletos e precisamos utilizar estratégicas 'espertas' do ponto de vista estatístico para resolver este problema. Algumas vezes adicionamos algum valor nos faltantes (como a média) ou podemos descartar o sample com problema. Vemos que 'id' possui os dados como 'int64', isto se refere a números inteiros, enquanto os demais features são 'float64', números reais (com casas decimais). Já 'diagnosis' tem dados do tipo 'object', ou seja, texto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 734,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificando o nome de cada coluna\n",
    "cancer.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "metadata": {},
   "outputs": [],
   "source": [
    "# descartando 'id'\n",
    "cancer = cancer.drop('id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "1         0.1812           ...                    24.99          23.41   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "3         0.2597           ...                    14.91          26.50   \n",
       "4         0.1809           ...                    22.54          16.67   \n",
       "\n",
       "   perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0           184.60      2019.0            0.1622             0.6656   \n",
       "1           158.80      1956.0            0.1238             0.1866   \n",
       "2           152.50      1709.0            0.1444             0.4245   \n",
       "3            98.87       567.7            0.2098             0.8663   \n",
       "4           152.20      1575.0            0.1374             0.2050   \n",
       "\n",
       "   concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "1           0.2416                0.1860          0.2750   \n",
       "2           0.4504                0.2430          0.3613   \n",
       "3           0.6869                0.2575          0.6638   \n",
       "4           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 736,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      "diagnosis                  569 non-null object\n",
      "radius_mean                569 non-null float64\n",
      "texture_mean               569 non-null float64\n",
      "perimeter_mean             569 non-null float64\n",
      "area_mean                  569 non-null float64\n",
      "smoothness_mean            569 non-null float64\n",
      "compactness_mean           569 non-null float64\n",
      "concavity_mean             569 non-null float64\n",
      "concave points_mean        569 non-null float64\n",
      "symmetry_mean              569 non-null float64\n",
      "fractal_dimension_mean     569 non-null float64\n",
      "radius_se                  569 non-null float64\n",
      "texture_se                 569 non-null float64\n",
      "perimeter_se               569 non-null float64\n",
      "area_se                    569 non-null float64\n",
      "smoothness_se              569 non-null float64\n",
      "compactness_se             569 non-null float64\n",
      "concavity_se               569 non-null float64\n",
      "concave points_se          569 non-null float64\n",
      "symmetry_se                569 non-null float64\n",
      "fractal_dimension_se       569 non-null float64\n",
      "radius_worst               569 non-null float64\n",
      "texture_worst              569 non-null float64\n",
      "perimeter_worst            569 non-null float64\n",
      "area_worst                 569 non-null float64\n",
      "smoothness_worst           569 non-null float64\n",
      "compactness_worst          569 non-null float64\n",
      "concavity_worst            569 non-null float64\n",
      "concave points_worst       569 non-null float64\n",
      "symmetry_worst             569 non-null float64\n",
      "fractal_dimension_worst    569 non-null float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 137.9+ KB\n"
     ]
    }
   ],
   "source": [
    "cancer.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O método 'describe()' nos mostra algumas informações sobre as colunas numéricas:\n",
    "-  'count' - quantas entradas o feature possui;\n",
    "-  'mean' - a média das medidas;\n",
    "-  'std' - o desvio padrão;\n",
    "-  'min' - o valor mínimo encontrado;\n",
    "-  'max' - o valor máximo;\n",
    "-  '25%' - o valor de separa os 25% menores valores;\n",
    "-  '50%' - o valor de separa as medidas em dois, mais conhecido como mediana;\n",
    "-  '75%' - o valor de separa os 75% menores valores;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>radius_mean</th>\n",
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       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  fractal_dimension_mean           ...             \\\n",
       "count     569.000000              569.000000           ...              \n",
       "mean        0.181162                0.062798           ...              \n",
       "std         0.027414                0.007060           ...              \n",
       "min         0.106000                0.049960           ...              \n",
       "25%         0.161900                0.057700           ...              \n",
       "50%         0.179200                0.061540           ...              \n",
       "75%         0.195700                0.066120           ...              \n",
       "max         0.304000                0.097440           ...              \n",
       "\n",
       "       radius_worst  texture_worst  perimeter_worst   area_worst  \\\n",
       "count    569.000000     569.000000       569.000000   569.000000   \n",
       "mean      16.269190      25.677223       107.261213   880.583128   \n",
       "std        4.833242       6.146258        33.602542   569.356993   \n",
       "min        7.930000      12.020000        50.410000   185.200000   \n",
       "25%       13.010000      21.080000        84.110000   515.300000   \n",
       "50%       14.970000      25.410000        97.660000   686.500000   \n",
       "75%       18.790000      29.720000       125.400000  1084.000000   \n",
       "max       36.040000      49.540000       251.200000  4254.000000   \n",
       "\n",
       "       smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count        569.000000         569.000000       569.000000   \n",
       "mean           0.132369           0.254265         0.272188   \n",
       "std            0.022832           0.157336         0.208624   \n",
       "min            0.071170           0.027290         0.000000   \n",
       "25%            0.116600           0.147200         0.114500   \n",
       "50%            0.131300           0.211900         0.226700   \n",
       "75%            0.146000           0.339100         0.382900   \n",
       "max            0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 738,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis                         M\n",
       "radius_mean                   17.99\n",
       "texture_mean                  10.38\n",
       "perimeter_mean                122.8\n",
       "area_mean                      1001\n",
       "smoothness_mean              0.1184\n",
       "compactness_mean             0.2776\n",
       "concavity_mean               0.3001\n",
       "concave points_mean          0.1471\n",
       "symmetry_mean                0.2419\n",
       "fractal_dimension_mean      0.07871\n",
       "radius_se                     1.095\n",
       "texture_se                   0.9053\n",
       "perimeter_se                  8.589\n",
       "area_se                       153.4\n",
       "smoothness_se              0.006399\n",
       "compactness_se              0.04904\n",
       "concavity_se                0.05373\n",
       "concave points_se           0.01587\n",
       "symmetry_se                 0.03003\n",
       "fractal_dimension_se       0.006193\n",
       "radius_worst                  25.38\n",
       "texture_worst                 17.33\n",
       "perimeter_worst               184.6\n",
       "area_worst                     2019\n",
       "smoothness_worst             0.1622\n",
       "compactness_worst            0.6656\n",
       "concavity_worst              0.7119\n",
       "concave points_worst         0.2654\n",
       "symmetry_worst               0.4601\n",
       "fractal_dimension_worst      0.1189\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 739,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando os dados da primeira amostra\n",
    "cancer.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.8</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.6</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.9</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.8</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.5</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38           122.8     1001.0   \n",
       "1         M        20.57         17.77           132.9     1326.0   \n",
       "2         M        19.69         21.25           130.0     1203.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "\n",
       "   symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "1         0.1812           ...                    24.99          23.41   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "\n",
       "   perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0            184.6      2019.0            0.1622             0.6656   \n",
       "1            158.8      1956.0            0.1238             0.1866   \n",
       "2            152.5      1709.0            0.1444             0.4245   \n",
       "\n",
       "   concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "1           0.2416                0.1860          0.2750   \n",
       "2           0.4504                0.2430          0.3613   \n",
       "\n",
       "   fractal_dimension_worst  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 740,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando os dados das três primeiras amostras\n",
    "cancer.iloc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      M\n",
       "1      M\n",
       "2      M\n",
       "3      M\n",
       "4      M\n",
       "5      M\n",
       "6      M\n",
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       "17     M\n",
       "18     M\n",
       "19     B\n",
       "20     B\n",
       "21     B\n",
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       "      ..\n",
       "539    B\n",
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       "556    B\n",
       "557    B\n",
       "558    B\n",
       "559    B\n",
       "560    B\n",
       "561    B\n",
       "562    M\n",
       "563    M\n",
       "564    M\n",
       "565    M\n",
       "566    M\n",
       "567    M\n",
       "568    B\n",
       "Name: diagnosis, Length: 569, dtype: object"
      ]
     },
     "execution_count": 741,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando os dados da coluna 'diagnosis'.\n",
    "cancer['diagnosis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 742,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando a contagem da coluna 'diagnosis'.\n",
    "cancer['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos separar o dataset em dois (descartando 'id'):\n",
    "    X são os dados dos features usados para o treinamento;\n",
    "    y é o dado que queremos prever, o target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = cancer.diagnosis.copy()\n",
    "X = cancer.drop(['diagnosis'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    M\n",
       "1    M\n",
       "2    M\n",
       "3    M\n",
       "4    M\n",
       "Name: diagnosis, dtype: object"
      ]
     },
     "execution_count": 744,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   fractal_dimension_mean           ...             radius_worst  \\\n",
       "0                 0.07871           ...                    25.38   \n",
       "1                 0.05667           ...                    24.99   \n",
       "2                 0.05999           ...                    23.57   \n",
       "3                 0.09744           ...                    14.91   \n",
       "4                 0.05883           ...                    22.54   \n",
       "\n",
       "   texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0          17.33           184.60      2019.0            0.1622   \n",
       "1          23.41           158.80      1956.0            0.1238   \n",
       "2          25.53           152.50      1709.0            0.1444   \n",
       "3          26.50            98.87       567.7            0.2098   \n",
       "4          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 745,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bibliotecas para gráficos\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Benign:  357\n",
      "Number of Malignant :  212\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": [
    "# histograma de 'diagnosis'\n",
    "ax = sns.countplot(y,label=\"Count\")\n",
    "B, M = cancer['diagnosis'].value_counts()\n",
    "print('Number of Benign: ',B)\n",
    "print('Number of Malignant : ',M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tOoBWq7XzFkoHbj+vNyr97vc4mqR9XlhYWLbzcyV2GEmS1BznAX9AKaF/BHBRRDwcuJxSYeR9JJIkLe+7wN9m5vcAIuJyyu1n97Wssydwdw2xSWPJDiNJkhqgmsVl18z8m2rgzuuA3wH+k1JW/9vABat5zUGWUS93ha3f1296mbfx9W8cYpQ0MT4FfDAiHgT8F3AU8FHgdRGxH3AncBxlEGxJXbDDSJKkZngZZdpfMvOfKIN3AhAR7wF+i1V2GA27jLqf1296mbfx9W8QMfZaQq/6tI7PsnSsFmmYMvPGiHg78CVgZ+CzlMrd24HLgF2AqyidSJK6YIeRJEk1i4i1wOHAidXjxwP7Z+Zl1SprgE31RCdJ3XNwbdUpMy9i+wqiawE/jFIP7DCSJKl+TwC+lZmLM6KtAd4VEddRBu48FfhgXcFJkiRp+lgjKklS/R4DfGfxQWZ+A3grcD3wTeCmzPzrmmKTNEU2btq87M+SpOljhZEkSTXLzI8AH1my7C+Av6gnIknTylvKJEmLrDCSJEmSpoiVQ5KkbthhJEmSJE2RxSqixUoiSfXxNlA1mbekSZIkSZJUA28DVZNZYSRJkiRJkqRt2GEkSZIkSZKkbdhhJEmSJEmSpG3YYSRJkiRJkqRt2GEkSZIkSZKkbfQ9S1pEvAN4SGaeGBHrgAuB3YEvAKdl5r39bqNuGzdtZu3OO2z3syRJkiRJ0iTqq+cjIp4JnNCy6BLg9MzcH1gDnNLP6zfF4lSHR73+ZjuLJEmSJEnSxOu59yMi9gDeDLylevxIYNfMvKFa5WLgmH4DrMPGTZvrDkGSpNp5PpQkSZpe/dySdj5wJrBv9XhvYEPL7zcA+/Tx+rVZrChadPVbD6oxGkmS6tF6PvRcKE0fh2WQpOnWU4dRRJwM3JWZ10bEidXiHYAtLautAVZ9aXL9+vW9hNS1ubm5vl9jfn5+AJE0b1vjwvdEkiRp+Ow0lqTp1muF0bHAXhFxE7AH8EBKZ9FeLevsCdy92heenZ1lZmamx7BGYxCdTt2Yn58f2bbGxbi8JwsLC0Pv/JQkSZLUTFblaRL09AnOzGdl5mxmrgP+EPhEZp4E/DQiDqtWewlw9YDilCRJkiRpLLROnCSNq0F3eR4PvDMibqdUHb17wK8vSZIkSZKkIetn0GsAMvNiyoxoZObNwKH9vqak8WX5rSRJWokDaktS8/XdYSRJrZxlUJIkrcQBtSWp+ezKlyRJkiRJ0jasMJIkSZIkTYSIeAfwkMw8MSLWARcCuwNfAE7LzHtrDVAaI1YYSZKkvmzctHnZnyVJGqWIeCZwQsuiS4DTM3N/YA1wSi2BSWPKCiNJkmoWEZ8DHgZsqha9DPh54CxgZ+BdmfnemsJbVusgtY5FIkmqW0TsAbwZeAtwUEQ8Etg1M2+oVrkYOAc4r54IpfFjh5EkSTWKiDXA/sAjF8vkI+LhwIeBOWAB+HJEfC4zv1lfpNuyk0iS1DDnA2cC+1aP9wY2tPx+A7DPqIOSxpkdRpIk1Suq/z8TET8LXAD8F3BdZv4HQER8FHgh8KZ6QpQ0DkY5Pf3GTZuZm5sb+Xal5UTEycBdmXltRJxYLd4B2NKy2hpg1fdNr1+/frtl8/PzKz5vMT9Wq5vXrkuTYxuWadznVnYYSZJUrwcD1wK/R7n97PPApWx/VfTQkUcmaay0Vv7BcKv/rDJUwxwL7BURNwF7AA+kdBbt1bLOnsDdq33h2dlZZmZm7n88Pz/fc2fQSlo7Yhc2bWam6ohtQqfsMPe7qSZpnxcWFpbt/FyJHUaSJNUoM/8e+PvFxxHxfuBc4I9bVuvpqmi/V8WGfXW06VftjK9/4xCjpPGXmc9a/LmqMDoiM0+KiPURcVhmXg+8BLi6rhi7sbQj1k5Z1W3iO4ya0BsrSVI7EfEUYCYzr60WrQG+zQCuitZ1Vayb7Tb9qp3x9W8QMfZ6RVSDZ5taY+p44IKI2B34GvDumuORxsrEdxiNsjRXkqQePAh4U0T8MuWWtBOA3wQuiYiHAj8CfgM4tb4QJU0729QaF5l5MWVGNDLzZrylW+qZlwkkSapRZn4KuBL4OjAPXFSVzp8JfA64CfhQZn6lviglSZI0bSa+wkiaFlWp7ZeB52XmtyPiSMo4KLsCl2bmWdV664ALgd2BLwCnLU7lLakemfkG4A1Lln0I+FA9EUlS97q9Xc3b2iRpvHjEliZARDwZ+BKwf/V4V+Ai4GjgQOCQiDiqWv0S4PTM3J8yVsopo49YkqTxEhFnR8St1b+3V8uOjIhvRMQdEfHHK73GpFq8Xa31lrV+1pMkNYMdRtJkOAX4XbYOinsocEdm3llVD10CHBMRjwR2zcwbqvUuBo4ZdbCSJI2Tqmr32cDBwDpgLiJeTPuLM5Ikjb2J7DDauGnVMw8PZDuj2q60VGaenJlfbFm0N7Ch5fEGYJ8OyyVJUnsbgFdl5sbM3ATcRqnq3e7iTJ1BSpI0SBM5hlHrLA7DnMHB2SLUYDsAW1oerwE2d1i+Ku2mOO5mCuX5+fnVbq7RJm1/ujGN+zxN+h1jpPX5jleiSZGZty7+HBGPBV4EvAcvwkiSJthEdhgNk41fjYnvAHu1PN6Tcrtau+WrMjs7y8zMzDbLuuksArpaZ1x0u8+TZJL2eWFhoW3n5zTr96LLqC7aSHWIiMdRZjV8DXAv1diBlVVfhBn0MaiJx+fWiwyd4qv7YkTd2x+USdkPSc1gh9Eq2RDWmLgRiIjYD7gTOI4yVfc/R8RPI+KwatrulwBX1xmoJEnjICIOAy4DXpGZH46Iw+nzIsxyF2AmTbedWHV2dk3KxZC69sMLMNLkslRGmkCZ+VPgRErD9pvA7cBHq18fD7wzIm4HHgi8u44YJUkaFxGxL3AFcFxmfrhafP/FmYjYkXJxZiQXYVrHzVxo8Biaju8pSePNCiNpgmTmo1p+vhbYrgwuM2+mzKImSZK682pgF+DciFhc9j62XpzZBbiKrRdnhmppxXtTq98d71OSxpsdRpIkSVIHmXkGcEabX9sLIkmaSN6SJkmSJEmSpG3YYSRJkiRJ0hhqHSvMccM0aN6SJkmSJEnSGHIWbw2TFUaSJEmSJI0JK4k0KnYYSZIkSZLUUEs7iBarilpnIVxuPTuW1C9vSZMkSZIkqaFabzuD9reedbue1K2+Oowi4mzgRdXDKzPztRFxJHAusCtwaWae1WeMkiRJkiRJGqGeb0mrOoaeDRwMrAPmIuLFwEXA0cCBwCERcdQgApUkSZIkSdJo9DOG0QbgVZm5MTM3AbcB+wN3ZOadmXkvcAlwzADilDQBnPZTkiRJksZDz7ekZeatiz9HxGMpt6a9h9KRtGgDsE/P0UmaKE77KUmSJEnjoe9BryPiccCVwGuAeylVRovWAKsqI1i/fn2/ITE3N9f3a/Rqfn6+0a83CXxPJEmSJtPGTZtZu/PWmyAWNm1mpnrc+rul6y19LEnqX7+DXh8GXAa8IjM/HBGHA3u1rLIncPdqXnN2dpaZmZlVx9KUk8QgO6vm5+dr7fxqonF5TxYWFgbS+SlJkjRNlpvlabnqZGeDklanU4er1E7PHUYRsS9wBXBsZl5XLb6x/Cr2A+4EjqMMgj103uoiSZIkSdPLWbzb8/uyetFPhdGrgV2AcyNicdn7gBMpVUe7AFcBH+1jG5IkTbw2DdwPAE8BflQtPyczL68lQEmSGm7JLN5bgGuqWbz/BDgcuAu4MiKOysyr64tUGh/9DHp9BnBGm1/bZSlJUhfaNHCfDzwJeFpmbuj0fEmSBLTM4g0QEdvM4l0tW5zF2w4jqQt9D3qtrbwvVJLUg+UauI+o/l0UEQ8HLqdUGK1qIoluec6SJI27Yc3ivdy4pN1MwtOUcVcXY10aTy8TCU3j5EPTuM+t7DAaIO8LlSStVpsG7lOBI4DfAf4T+BTw28AFq3ntbhs5c3NzQzt/dYqh6Y0w4+vfOMQoabIMehbvpZMyjcskPIvaxbrafRi3/R6ESdrnXidlssNIkqQGaG3gZmYCz2/53XuA32KVHUZNaOS0i6HpjTDj698gYnTWUUmrMYxZvFfDil1NGjuMJEmq2TIN3McD+2fmZdUqa4BNtQUoSVLDNWEWb+840aSxw2hIHM9IktSNNg3cNcC7IuI64B7gVOCDNYXYs6XnP8+HkqQhchZvacDsMBoSe5clSV1q18B9K3A9sDNwWWb+dT3h9a71XAieDyVJw+Ms3tLg2WEkSVKNVmjg/sUoY5EkSZIWWRcuSZJGYuOmrRPTHHDgbI2RSJIkaSVWGEmSpJHwdm1JkqTxYYXRCLReUW39eenjhQ7rSZOmU15IkiRJkuplhdEIdLqiuvR3XnnVtLDSQJIkSRoeZydVv/z0jLluq5es4JAkjSPPZZIW9XIM8BiiabZ4gbZ1xlJpNawwGnOrqV6SJGnceC6TtKiX44HHEEnqnRVGE6TTVROvrkiSxoXnKUmShsfvhuqWFUYTpPUKCmx7FcWrK5KkceE5S5Kk4el0nm0d98gxkDTWf/1p6Q1dup8LU7LfkiT1wiunkiT1pnXcIzuLNNYVRtNyBXK5yqFp2G9JknoxLe0DSVu1VkIsbNrMTBdfdBefMzc3ZyWFtIzWvDjgwNmao1EdxrrDaJJt3LSZubm5usOQJGmsWVovTYelHcXddBrbuSx1Zo7IVlNDOQWiJEntLb3VrN2tZ5bWS5KGxdueNemsMJJUu6VX/a0CkCZfL7ePtOo00YOk8dK0836nykSrFtVqEs5Ffo7ViR1Gkmq39GT78Tc9/v6fPYlJk6nd7SOLjyVNj6bd9tIpnqbFKvVrEjq9NDx+CxsxyxallXkLiSRJ27IN2T/fQ2n4nKl0slhhNGL24EqSJGm1rGzpn+1wafg8Vk0WL91LGkvdDngraXuTmC/d7NPSdRbaXAVdOlPpJL5fkiRJKxmrCiPHMhmMTgONthvIr9v33oEANWjtPlNeJZR6N6750+m80s0VzeX2e/Fx69hp4/r+SOpdu+NLL23g1Tyvm9drba/bvlZduv0OqckyVh1GNuAGo9uBRnspJ7QEUYPmZ0rSomEeDzzWSNOt3TGg2+8fg/6e0q697vFJdRn0ZBXdFhpYkFAv321JY6PTbSEOsCdJmiSey5rPdonUu24nuXEynHqNVYWRpOnW7TS3rbeWeCVCkjSOrKxvvm7bJf7tNOl6vXVzFPH4XaA/Q+kwiojjgLOAnYF3ZeZ7h7EdjZduxkdqekKPU6wrmeQ8tZGmSTHJeTpOHNNPnZinUvOZp8PVru299PzXzXq9nDO73U63r+F5e6uBvwsR8XDgzcBTgHXAqRHxC4PejsZPu3LCcSozHKdYO5mmPHU2NY2racrTpms99rdqN8tar89pp5fjmMe+0TBPtVqtubgwxDwdxDFgUm6rM0/r03ouXHo+bLdeL9+xut1Ot68xzt/zBm0YFUZHAtdl5n8ARMRHgRcCbxrCtjRg3cwQUVePa+s0x4PoeZ7ynuOpydOlJf2tt6u1zvDgDCRqoKnJ03HSbtDP1mNLr89pdxzq9takbp/TlHN63TEMiHmqVRn0wMHdbKfX156gim3zdIwM+pzQ6Xyz0nffubm5xg/IPewYhtFhtDewoeXxBuDQLp63I8DGjRs7rvSgB2y5/+eFhYX7H3fz8zCfMynxbNm8iRf/0W0AXPzaA+//3dLlCwu01fra3f6u03NadRvDSs/v5zW6ibXlc7zj6rcwEkPJ08X3pMl5sfSzvNzPF7wy2LK55WRy72bW7lQeb7p3MztXPz/m5/e/f59bly993O7542qlPB21pe9pt+/3pOcpdP5bNTlPxzGe1mMLbHsOXc1z2p3nlsbT+n+r5c7hy63b7/m0W50+g93EMOl5art3euJp1ek5K6230jm49fedXrtbtnuXz9NO73OTP4fjEM9K59N22m1npe+x/X73HdX5tJNhnk/XbNmyZeW1ViEizgR2ycw3VI9PAeYy87ROz5ufn38K8MWBBiPV76lzc3NfqjuIpcxTaRvmqdR85qnUfOap1HyrytNhVBh9B3hqy+M9gbu7eN5Xq+dtAO4bQlzSKO0I7EX5XDeReSqZp9I4ME+l5jNPpebrKU+HUWH0cODljXKHAAAgAElEQVRLlDK/HwFfBk7NzK8MdEOSemaeSs1nnkrNZ55KzWeeSr0b+EAamfkvwJnA54CbgA+ZjFKzmKdS85mnUvOZp1LzmadS7wZeYSRJkiRJkqTxNt5T9UiSJEmSJGng7DCSJEmSJEnSNuwwkiRJkiRJ0jbsMJIkSZIkSdI2dqo7gGkUEbtTpnN8XmZ+OyKOBM4FdgUuzcyzqvXWARcCuwNfAE7LzHsj4hHAJcDDgASOz8x7atiVgYiIs4EXVQ+vzMzXTvt7Mm4i4jjgLGBn4F2Z+d6aQxqIQXw2awh7ICLiHcBDMvNE82681J2PTT7HjcP5JiLeBLwQ2AK8PzPPbVqM1bZ7PkZExIOAvwIeA3wPeFFmfneQ8Wmwuj2uRMT/Aa7LzItHGF7XVtqPiDgaOAdYA9wJnJSZPxh5oCvoYj+eT9mPHYGvUqaQ3zjyQMdQF+/tRLaJ+tjvE4C3Af9arXplZp45ush71+txbdz/1qtlhdGIRcSTgS8B+1ePdwUuAo4GDgQOiYijqtUvAU7PzP0pJ65TquV/AfxFZh4A/APwhtHtwWBVjeBnAwcD64C5iHgxU/yejJuIeDjwZuAplL/hqRHxC/VG1b8BfjbHTkQ8EzihZZF5Nybqzscmn+PG4XwTEYcDzwCeADwJ+L2IOKhJMVZx9nuM+GPgi5l5IHAB8GeDjE+D1c1xJSL2johPUjo7G2ml/ag6u88DnpuZBwHfAN5YQ6gddbEfuwF/DjwrMx8H7AKcWEOoY6fLc+jEtYn63O8nAa/MzHXVv3HpLOrnuDa2f+te2GE0eqcAvwvcXT0+FLgjM++sqhEuAY6JiEcCu2bmDdV6F1fLdwaeBny0dfmIYh+GDcCrMnNjZm4CbqN80Zjm92TcHEnpdf+PzPwR5e/Q2AbjKvT92awj6H5FxB6UE+hbqsfm3XipOx+bfI5r/PkmM/8OeHoVy8MoleAPalKMAzpGPJdSYQTw18BR1fpqpm6OK8cDHwc+MurgVmGl/dgZ+N3M/Jfq8TeAR4w4xm503I9q2aMy818j4gGUY0njqqQaquN7O8Ftop72u/r5EOCEiLglIi6JiAePMO5+9HRcm4C/9arZYTRimXlyZn6xZdHelEbsog3APh2WPwT4YcutLovLx1Jm3rp48ImIx1JuFdjMFL8nY6jd32WsDeizOY7OB85ka+PSvBsvtX4Wm3yOG5fzTWZuiohzgG8C13aIpa4YB3GMuP851e9/CDx0gDFqsFY8rmTmn2bmhSONavU67kdmfj8zL4f7qyNfB1wx0gi7083fY1NViXgXJRc/M7rwxtpK7+2ktol63e/Fn/+IUhl7F6W6bRz0elwb97/1qtlhVL8dKOMULFpDacB2u5xq+ViLiMcBnwVeA/wTvifjpN3fZSL0+dkcKxFxMnBXZl7bsti8Gy9N+yw27hw3DuebzDyb0oGyL6UKqhExDvAYsWbJ8ro/p+qsaceVXnW1HxHxM8CVwM2Z+cERxbYaXe1HZl6dmT8LfIpyq51WttJ7O6ltol73m8x8fmZen5lbgLcDRzEeej2ujfvfetXsMKrfd4C9Wh7vSSnlb7f834CfiYgdq+V7sbX0fyxFxGGUq6ivq07MU/+ejJl2f5exN4DP5rg5Fnh2RNwEvAn4NeBkzLtx0rTPYqOO500/30TEAdXAomTmj4GPAUc0KMZBHSP+pVqPiNgJ+G/A9wcUowavaceVXq24HxGxF/BFyu1oJ48utFXpuB8RsUdEPLvl939Fqf7Qylb6jEzqd5Ge9jsifiYi/mfL8jXAuEz40utxbdz/1qtmh1H9bgQiIvarPnjHAVdn5j8DP60atwAvqZZvopzIjq2W/xZw9aiDHpSI2JdS7ntcZn64WjzV78kY+lvgmRHx0Ope+d8Arqk5pr4N4rM58qD7lJnPyszZzFwH/CHwicw8CfNunDQtHxtzPB+T881jgAsiYiYi1lIGuj6/KTEO8BhxVfWY6vdfrNZXMzXtuNKrjvtR5dcngY9k5iuqiokmWunvsQa4JMpMTlDGV/nSiGMcVx3f2wn+LtLTfgP3AK+NMuEFwOnA5aMLuy89Hdcm4G+9anYY1Swzf0qZueAyyngFt7N1EK3jgXdGxO3AA4F3V8t/hzKS+zeBp1KmAxxXr6bM3nBuRNxUXbU8kel+T8ZKNTjkmcDngJuAD2XmV+qNaiAG9dmcBObdmGhaPjbsHNf4801mXkW5FebrwDzw5apzqzExtrHaON4A/GJE3Fqt87tDjk99aHdciYirIuJJ9UbXvS7249eAJwIvXDxGRETjxmVaaT8y8/vAqcCnIuJmIIDfry/i8dHlZ70px92B6XW/M/M+yniA50XEbcAc8NrR78Hq9XlcG9u/dS/WbNnS1M5zSZIkSZIk1cEKI0mSJEmSJG3DDqMJUQ06dl3dcYxSRDw3It5UdxzSKFSl8Q9aYR1zQmow81hqhknJxYh4dERcVnccUrci4tci4t3Vz43PMcFOdQeggXkwcGjdQYzYIcAedQchjUI1yOxKzAmpwcxjqRkmKBcfSRmjSBoLmfkJ4BPVw3HIsannGEZdioiXAq8C7gP+HTgBOAp4ebXsX4HTM/NbEXEx8GPg8cDPUZLi+8CvUqbsOzkzr6vW+wmwDngY8Bng5Zm5qdrey4C1lER6W2aeV8Xy+mr79wJ3UAbDvAJ4GnALZcCxHwFvA55Nme7v7S3P/23KYF07VHGdnpm3R8RTgHOBHYEtwFsz87J2yzu8V+8C/isz3xBletK7gWdk5uci4jeBX83MYyPiDcCLq/34VhXHdyPi88B/AAcA51Gm3z0L2Fy9168BFoCPVzH978w8s+MfUBNluXzMzLsi4lTa5+QPKTm5L2W63N/KzHuqmR3eDewGbAReXeXnsjkYEV8G/tdiDkTEnwBk5u+3y60lsR8B/Anwz5TP+E+AEzPztoj4GeC9lGPCFsqsC3+QmfdGxBbgocDzgOdT8uGxlGPNCcDutOQE8B7g/wAPqTZ9ZWa+YYX39fOUgXZ/kXJM+t+UY9bh1fvzosy8pYrzz6r3c2fKNOWvqeJs976duFzcmXlbp5g0uczjoeXxC1hyzszML3TK206vp8lnLg4+FyPi14FXZeZTq8cJfDgzz46IfYCvAPtQBto+u9rH/wJeWQ28+0bgl4C9gZuBPwbeTxm4fw1wIWUGxQQeDnwhM5/TLh5Nrx6+ww41t4HbgBcCf8S2OXYoZYbCC6r1zwJ+NjP/Z4d9+zy2W4fOW9K6EBEHUU5G/z0zn0DpALqWMgr80zPzIOBDwBURsaZ62hOBZ1A6cV4F3JOZv0z5sL6u5eWfDDwL+IXq38si4oHAKcCvZObBlGn73l7F8muUDqJfysxZ4E7KFIYnAT/JzHXViPUzwL9X23whZVT7XSLicMqB4qnVa7+drdMfngOcm5lzwEur+Dstb+djlAMRwH8HvlvtI5QT42URcVK1ziHVe7oeuLjlNX6Qmb+Qme8B/hT4ncx8EmVmlSMy80bgfcCldhZNlzb5eGZEPIPOOTlH+TweCDwKOCYidqZ0tr6pyqdTgD+LiN1pk4PABZR8W5yC9zeBC1fIraWeBLyniv8DwF9Wy99NaRQ/vlrnIMrMTksdDvxeFfONwOuWyYlTgH/KzCdSZnB4bHXCXMmjMvOwar/eDny+yr1rgN+r1nknMF8dEw6mNKBf2enY1S7uLuLRBDKPgeHl8XbnzGr5snm7wmtpwpmLwHBy8dPAEyLiQRHxKEoHVGtb+Apg/2obv1G9x38IfLx6v6BUDx2cmb9JuVj6ySp/f4Xy/WILcDLwj3YWaTk9focdam4vxrZMjr23ei0iYgfgt6vfr8R265DZYdSdZwKfzsy7ADLzXZSkuTQzv1ctu5jSw/+o6jmfzMxNmfldSrXPNdXyf2Tb0ruLM/OezFygXLl4TmbeQ7ni8dyI+CPKlH8PrNY/EvibzPxBtd1XZuab28T98er/r1E6kHYDngvsB3w5ypTCbwceHBF7AB8B3hsRf0U5WPxB9fx2y9v5ErBPRPwc5YDzx8CzImItJfGuonQWfSAzf1Q958+AZ1brAHyx5fU+DFweZWrTB7NtImv6bJePmXka5bPWKSevycyFzNxEqcTbg9KIvC8zr6yeM5+Zj8/MH9I+By8Ffiki9gSeA3wrM++gc24tdXNmLn7GLwIOjoifpeTFn2fmluqY8D62dr62ms/M71Q/f43ly3mvAX4jIq6iXDl5XWb+57Lv6LY+Vv3/jy2vs/h4cTvPo3Ru30S5snMo8PgVjl3dxq3pYB4PL4/bnTOXzdsVXkuTz1wcQi5m5k+Av6V0Eh1FqQZ6dNXJdDRwGeUC7LWZ+U/Vc64D/o3S1ga4IbdWAF4OvDYiPga8gHJHwuZ225cqvXyHHXZut/NJ4OeqTq7nAHdmZnaxj7Zbh8wOo+7cS+nFByAidgV+vnVZZQ2lzA3KLVOtNnV47UU7APdVpao3Ua4sfIlSWt4ulsUrF8v5CUBmLq6/hlL295dZKpHWUSqhnkSp6DmfckD4LCVRvxERu7Rb3mabVCewT1GugDyZ0sO8F3AM8OUqORdvb2vd952qGAHuaXm9M4GnAP9Aqa76Qrttaypsl48RcQDbf6Zg25z8ScvyLdXv7l36nIiY7ZSDmflj4G+A4yhXThavlrTNrTb70BojlLLgHdg+L3Zme8vtyzYy86vAoynluY8CvhIRc0vXW8Y2x66qwbDUjsAxLfv6ZOD0FY5dXcWtqWEeDymPO5wzl83bTq+lqWAuDu+cejmlLfxsSsXR3wG/DswCn2f597g1xta28Kcot8V8hFIhcUv1vkqd9PIddti5vawsd8icT7mb5aV0V10EtluHzg6j7nwOODLKeDxQriz8CvA/IuKhANUtVt8H/u8qX/vYiJipOmBOoPSuPgn4HqUy5zOUns/Fcr6/BV7QUq76RkpJ+b3Aji3lhO18Gnhxy76cRilNpLrX9OCqp/lU4EHAnu2Wr7Cdj1HKHW/JzI3AdcBbKVdUoPT+vjQidqsev5xy//U2SR8RO0XEt4EHZOb7KPeyPyEiZqp9Xu7Er8m2XD6+nfKZWm1OJrAlIp5VPeeJlM/qL9M+B6F0gp4AHMbWz3Tb3FrGuoh4QvXzqZSO1P+/eo3TI2JN9Rk/ldJR2637cyIi3ga8ITOvAM4AbqU0Ugfh08D/bInzE5Qvnp2OXVIr87i9nvN4hXNmu7zVdDMX2+v3nPpJSoXHOsqYRZ+hjNtydfXl+FrgORHxmGobz6CMG3Pj0heKiA8Bx2bmhyl5/UPKF3/bwupkUN9hB5nbrZZ+fi+kjBs0R/tbUHthu7UPdhh1ITNvodw7fE1E3Ewp0/15yv2Q10XErZRkeF4P5aE/ptx+dUv1/wcoH9jvUJLzNuARlA/zfpl5VbXO9RFxC6Xj5kxgA+VkdGtVhttuXz5DuZf1sxHxDUqv7wuqKqTXAm+KiK9Trnyck5nf7rC8k7+lDNS3eGL+NGUA8E9Wj99frfOViLiNcuXo+GXivRd4BfChiPgapaf6pVXH0nWUE+17VohFE6RNPp6WmZ9llTlZfY5eAJxdlam+r3r8KdrkYPW8ecrVy49m5k+rZZ1ya6nvAm+ucvjXgZdUy19OGbTvlupfAu1uOV1Oa068i9KIXk+pNLiTcqvKILyccovrLZQBEW+hfMFoe+wa0HY1IczjjnrO4xXOme3yVlPMXOyor3NqdcvabcDXqw6iT1M6hC6rfv9NSufPx6rXfRtlYpjlbnX7I+D46m90I+XL9BeAbwI/jYivdHHRWFNmUN9hB5nbS2zzXS4z/42SX3/dplKoV7Zb++AsaTWKMhL9+sx8R92xSBqNKDO6/HmWAfQkjSHzWGoGc1GaHBHxEOCrwNMWx11S/XaqOwCNp4h4DctUBFX+NDP/apTxSFpZRDydclVpOZ/LDlOXSmoG87heEfEO4CGZeWJErKPcQrE7pdrjtJZBijXhzEVpcCLiFOAtwNmLnUXmWDNYYSRJkiStICKeSbkN6cqqw2g9cHJm3hAR7wf+ITPPqzdKSZIGxzGMJEmSpA6iTOf+ZsoVcCLikcCumXlDtcrFlNlgJUmaGI25JW1+fn4GOIQyePN9NYcj9WtHYC/gq3NzcwsrrTwuzFNNGPNUar6m5On5lElG9q0e703JsUUbgK6nOTdPNWGakqcDZZ5qwvSUp43pMKIk4xfrDkIasKcCX6o7iAEyTzWJzFOp+WrL04g4GbgrM6+NiBOrxTsAreM6rAFWM1OueapJ5PlUar5V5WmTOow2AOy///6sXbsWgPXr1zM729xJD5ocX5Njg8mPb+PGjXzrW9+Cba8+ToLt8nSppv9twRgHoenxwcoxmqfN/vsNg/s9fhqSp8cCe1XTSe8BPJDSWbRXyzp7Anev4jWnPk8nff9g8vdxcf8akqfDMNZ52tTYjGv1BhFbr3napA6j+wDWrl3LzMzM/Qtbf26iJsfX5NhgauKbtPLVZfN0qab/bcEYB6Hp8UHXMZqnU8T9Hlu15WlmPmvx56rC6IjMPCki1kfEYZl5PfAS4OpVvKx5yuTvH0z+Pi7ZP8+nDdPU2Ixr9QYY26ry1EGvJUmSpNU7HnhnRNxOqTp6d83xSJI0UE2qMJIkSZIaKzMvpsyIRmbeDBxaZzySJA2TFUaVjZs2L/uzpNEzH6XJsDR/zWdp8nkOl7ZnXmhcWWFUWbvzDhz1+psBuPqtB9UcjTTdzEdpMrTmMpjP0jTwHC5tz7zQuLLCSJIkSZIkSduY6g6jduWAltBLkiRJkqRpNtW3pLUrDbSEXpIkSZIkTbOprjCSJEmSJEnS9uwwkiRJkiRJ0jbsMJIkSZIkaYAcB1eToO8xjCLiHcBDMvPEiFgHXAjsDnwBOC0z7+13G5IkaXps3LSZtTvvsN3PkiSNi3bj5UrjpK8WWEQ8EzihZdElwOmZuT+wBjiln9eXJEnTZ7GRfdTrb7azSJI0UZyRW+Ok5wqjiNgDeDPwFuCgiHgksGtm3lCtcjFwDnBev0FKkiRJkjTunJFb46Sfy3bnA2cCP6ge7w1saPn9BmCfPl5fkiRJkiRJNeipwigiTgbuysxrI+LEavEOwJaW1dYAq66vW79+/TaP5+fnewmxK3Nzc12v2y6OYcbXrybHBsYnSdPGsYkkSZLGR6+3pB0L7BURNwF7AA+kdBbt1bLOnsDdq33h2dlZZmZmgPKFfTWdOsO0XBxNim+pJscGkx/fwsLCdp2fkjTtHABUkiRpfPR0aS8zn5WZs5m5DvhD4BOZeRLw04g4rFrtJcDVA4pTkiRJkiRJIzLoWvDjgXdGxO2UqqN3D/j1JUmSJEmSNGQ9z5K2KDMvpsyIRmbeDBza72tKkiRJkiSpPo42Kal2Gzetenx8SZIkSdIQ9V1hJEn9ah0IFxwMV5oGzpgmSZLUbHYYSVMgIt4BPCQzT4yIdcCFwO7AF4DTMvPeWgPskl8wpcnhjGkaNxHxJuCFlJmB35+Z50bEkcC5wK7ApZl5Vp0xSpqcdq/UBH7bkiZcRDwTOKFl0SXA6Zm5P7AGOKWWwHqw+AXzqNffbGeR1CCtt5V6i6kmUUQcDjwDeALwJOD3IuIg4CLgaOBA4JCIOKq+KCVNUrtXagK/cUkTLCL2AN4MvKV6/Ehg18y8oVrlYuCYeqKTNCnszNWky8y/A55eVSY8jFKl/yDgjsy8s1p+CZ5TpdrY7pUGz1vSpMl2PnAmsG/1eG9gQ8vvNwD7jDooSduzhF5qtszcFBHnAK8G/oYBnFPXr1/f8ffz8/OrjLJ+c3Nz2zzutA/juH+rNen72LD9s90rDdhEdhh1GufEcU80LSLiZOCuzLw2Ik6sFu9AGXth0Rpg1fePDLqBu7Rx2apTzvbTSGlYA2dZTY+x6fHBeMQI25TQX1ktugQ4OTNviIj3U0roz6srPklFZp4dEX8CfBLYnz7PqbOzs8zMzCz7u/n5+Y7nx3HRbh8mZf86mfR9XNy/hYWFFduGw9bEdu9q/vbDaq80tR1kXKtXV2wT2WHUaSBNB9nUFDkW2CsibgL2AB5IOWnu1bLOnsDdq33hUTZwO+Vsr9sZhwZc02NsenywcoxNaODCdiX0B7UpoT8HO4yk2kTEAcAumXlTZv44Ij5GGQD7vpbVejqnShqIsW73DqNN1dS2mnGt3iBi67Xda6mNNKEy81mZOZuZ64A/BD6RmScBP42Iw6rVXgJcXVuQkmBrCf0PqseW0EvN8xjggoiYiYi1lIGuzwciIvaLiB2B4/CcKtWirnbvAQfObvPYiR80aSaywkhSR8dTGr27A18D3l1zPNLUamIJfS9ar3r1eut3azyrGQOln+1Mk2nd70HJzKsi4lDg65Sqossy88MR8T3gMmAX4CrgozWGKWl7Q2337vaAmfsr4cE7WDR57DCSpkBmXky5rYXMvBk4tM54JN1vrEvol9Prrd+d4hlUrE0uNx+mcd7vptw6CpCZbwTeuGTZtYDfEKUGsd0rDY63pEmSVBNvHZUkSVJT2WEkSVLzHA+8MyJup1QdeeuoJEmSRspb0rrQOh5Dr2MzSJLUiSX0kiRJahI7jLrQ63gMkiRJkiRJ48hSGUmSJEmSJG3DDqNV2rhp68zGBxw4W2MkkiRJ0mi0toGXeyxJmjzekrZK3p4mSdJgx/RzfECp+VrbwGA7WJKmwcR3GNkIlSRp8AZ5AcUvopIkSc0z8R1GNkIlSZKk1fPCqyRNN88AkiRJkrazeOG19eKrpMFqHQ/MscHUNBNfYSRJkiRJUhM5Rq6azAojSZIkSZL6ZIWQJs3EdBiZnJIkSZKkungbpybNxHQYmZzSeLGTV5IkSZKaa2I6jCSNFzt5JUmSJKm57DCSJEmSJKlmzpimprHDSJIkSRLQ25dUv9hKg9Fagb92Z7+qq35+CiVJkiQBvd0y7m3mkjSZdqo7AEmSJKnpIuJs4EXVwysz87URcSRwLrArcGlmnlVbgJIkDZgVRpIkaSw4toPqUnUMPRs4GFgHzEXEi4GLgKOBA4FDIuKo+qIcX+a2JDWTHUaSJGksOLaDarQBeFVmbszMTcBtwP7AHZl5Z2beC1wCHFNnkOPK3JakZvKWNEmS1FgbN232C6Rql5m3Lv4cEY+l3Jr2HkpH0qINwD6red3169d3/P38/PxqXm4g5ubmul53Mb5Oz+m0D+2eX8d+D8sk7ctyJn3/pGlnh5EkSWqsxcoDgKvfelDN0WjaRcTjgCuB1wD3UqqMFq0BVnU/1ezsLDMzM8v+bn5+flWdN3XoJr5263Tav6bvd7fG4W/Yj8X9W1hYWLHzU9J48pKdJEmStIKIOAy4FnhdZn4Q+A6wV8sqewJ31xGbJEnDYIWRJEmS1EFE7AtcARybmddVi28sv4r9gDuB4yiDYE+Fbm4XXbpO3beYNi0eSWo6O4z64ElHqo/5J0kaoVcDuwDnRsTisvcBJwKXVb+7CvhoHcHVoZvbRVvX6bTeqDQtHklqur46jCLibMqgfwBXZuZrq2lHzwV2BS7NzLP6jLGxPOlI9TH/JEmjkplnAGe0+bUnIEnSROr5cnzVMfRs4GBgHTAXES+mlOIeDRwIHBIRRw0iUEnjb+OmVY0FKkmShmyU5+bWbR1w4OzItitJ6k0/FUYbgFdl5kaAiLiNMlPEHZl5Z7XsEuAY4Op+Ax0HrbfEeHuMtD1nO5IkqVlGWbFrO0CSxkvPHUaZeevizxHxWMqtae+hdCQt2gDs03N0Y8aToDRe7OSVJEmSpOX1Peh1RDwOuBJ4DXAvpcpo0RpgVXWu69ev3+bx/Px8V8+bm5tbzWaGrvXL549+vMDtt5X9OuDAWXZ7wMz967X+btC6fe/qYnyqm528kiRJkrS8fge9PowyM8QrMvPDEXE4sFfLKnsCd6/mNWdnZ5mZKR0q8/PzjesI6tbSL6Kt+7G07HcY+9j0927S41tYWNiu81PTy0omSZIkrYbtRzVBzx1GEbEvcAVwbGZeVy2+sfwq9gPuBI6jDII9FCaOtLJpn82wCaxkkiSNwtK2cZPbyk2OrRO/xDfbJLV7bT+qCfqpMHo1sAtwbkQsLnsfcCKl6mgX4Crgo31soyOTSOpsyWyGW4BrqtkM/wQ4HLgLuDIijsrMgQxOv3TWExtT0somqYE7KuP0xVgalVEOYN2vcW3Hj2vc06COdm/TtJ4L5+bm2nZwes5Ut/oZ9PoM4Iw2v/boKTXDyGcz3O0BM2PTWJWawAZub8bpi7EkaSSmfhbvTudGOzvVi74HvVZ/7OnVMA1rNsNO4zMtHftplJ/r1QxUvtw4VcMa6LzX7TR94PWmxwfjESM2cCVJ6lsT2r3D1NqmaZ1IqXUSpeXiWXzeqNq93Whq+6ypcUF9sdlhVDN7ejUKg57NsHVw+pWM8jPe7Um73aDmozrpd7OdSR8YfhRWirEpg9MPq4ErSdI0qrPdO0xL2zTtJlha6XkrLR+2prYhmxoXDCa2Xtu9dhg1lJVHGpRhzGYoafAG3cBdqVHQ75WqJjaqutmnJl89HKZp3W9J02Ua271+V9Qw2WHUUFYeaRCaMJuhpJUNo4Hb6Ypok6+i9WOlfZrU/V7JOO93UyoBJ8E0XYycpn3VVtPa7vV7o4bJDiNpstU+m+EksOGpYZrWBu6gmadSZ9P0pXKa9lXbsN07Is5UOj3sMJImmLMZDoYNTw2ZDdwBME8labrZ7h0dZyqdHnYYNYg9s5I0fWzgDp7VRpoEBxw4u83jafksD7pyoa73beOmzfffCjotfzsNj58h1cUOowbx6qgkSf3zfKpJsNsDZqbyCv6gKxfqOh54HNIgWdGjuthNKWkibNy0edmfJQ2GeSWpDsM8v7d7bdsUklRYYTQCgy6ltSRR2p5X8qTh8uqmpDoM8/ze7rVtU0hSYYfRCPR70rGRLkmSVPWw0goAACAASURBVL+I2B34MvC8zPx2RBwJnAvsClyamWfVGqAkSQNkmYokYfm5JKmziHgy8CVg/+rxrsBFwNHAgcAhEXFUfRFOl6Xn6oUhnbttE2ic9fv5tX0sK4wkCcvPpeV4C7S0jVOA3wX+snp8KHBHZt4JEBGXAMcAV9cT3nRZrgJ/GOdxK/01zgZ5p4uf/elkh5GkidP6JXdh02ZmlowBVifHJNM4saEobZWZJwNExOKivYENLatsAPYZcViSJA2NHUaSJs7SL7n/j717j790rvf//5gZMx+DjUQZ507zIh8ZfRwqRKE2iYTsSIbt9CtftVN2hS3JIQoVYTs0akhlohxGamg7NdQHw4SXEjaZ3UF7V8Rnhvn8/ni/18z1WfNZ57Wu03reb7e5zWdd61rXel3Xul7X4f1+X+93nmoGVVMpIlIaE4HRxOsJQEu1EgsXLqz53tDQ0ArThoeH6y6v3meS7xW5smK89cly2ZXPbLrZIKuuMjDuPGMqsha/wsCUSQC88I8RHn2k9j5QBI32SREpNhUYiYh0gVoOiYj0nWeAaYnX6wLPtrKAwcFBBgbGL2QYTzuFJON9psitB3tRUNTJspOfqVUhVKsia+4ZW/Z0fXpteHiYoaEhRkZG6hZ+ikhxqcBIRKQL1HJIimTTzQbHvFYBp0hb7gHMzN4IPAEcSOgEW6QtyWOxjssikgcqMBIREekzq64yoAJOkQ65+0tmNhOYA6wM3ARck2lQUmhFbvklIuWkAiMRERERkSa5+yaJv+cBurMXEZFSUjtHEZGcSI7gVv3IkIiISKuyHhlURPKv+jih44YkqYWRiEhOqCm6iIh0k84rItKI+uGUetTCSET6yuIlS3M1IolqcUREREQkTfWuP3VtKkkqMBKRvlKpRUnWpGQpb/GIiIj0I90kSz+pd/2pa1NJUoFRwSVPbjrRiXRHs7mk2hmR/FPfYCLSDN0ki9TX7Wtb3ccWg/owKjg9my7Sfc0+y10v/5SbIvmgXBQREelct8+nOj8Xg1oYFZBKYEWyo/wTKSfVdEpR9dP+2k/r2g4dx0Sk29TCqIBUGiuSHeWfSDkpt6Wo+mnf7ad1bYe2j4h0m1oYiYiIiIjknFqP5E8Wv0P1d2pfkLxJ7pMjVfvnSM5GK5bG1MJIRERERCTn1Hokf7L4TZrtZ1EkK9V5Ub2/6jhWLIVqYaQS9O6r3qYjqr0SyYV6Ncnt1DKrZlqk/jlPREREslGvVVKta1hdz6ajUC2MVKLefeNtU5X6imSv2yOwqWZaROc8EcmXxUuWMmVyZ/X37Swj+ZluxCDSqUatkmrNJ71XqAIjEZF+pIs5SZP2txUlt8nIkqUM6EZLRLqgG5XhqkQSkV5SgVGJJC9ch4aGxlzUtnOBW6v2ofrzumAW6S21rpQ06UZiRbVqPrV9REREpMxUYFQijZrad6v2QTevIvnX7YLc6hYWyREuVGgsosoUSZf2r/6iR9ck77LYP9LaP/v9/K4Coz7Ubzu5SD/qdsFus8+Wi/QrVaZImtQSsL904/fWo2vSS1mPGNjL7+z387tKDfpQZadP7vgiUkz1RpXodHkafUJEREREsqTr0WypwEhSk0z2TTcb7NqydBCRfpYsAB7oQmFwcnlqiSgiIlJMuj6WslBjh2z15JE0MzsQOBGYDJzn7hf04nukWLrZbFBNZDunPO1fvXzmu9vL7vf+E5SnxdXv+24/UZ5KHvX7YzTVlKf9q9nzcbJBQ5EGier19UbXozaz9YHTgB2AGcCRZvbmbn+PiLRPedrfetmKqNvL7ucWT8rTYuvnfbefKE+lV9JqIdQPrfaVp/2t2fPxqqsMtHzerrXs5PReXwf0+nqjF5HvCtzq7n9x9xeAa4D9evA9kkPN9qdSa77qz3R64qoXTzvLLtFJVXkqDTW7j9ear5f5N1InFyuvh4aGlKdNKvh26rpO9/1689X7TDfPjcl5hoaG6uZMFnQ+Fakvrcdw+qRwW3lactXnkVr3oe3O16lmr2FbXVYaevFI2nrAosTrRcC2TXxuEsDixYvHTBwZGRnzes1VRse8V3ndzN+9/Ew/xJNU/ZmkD5/6CACzjt9s2d+V15XPjS5dMu58432mavErxFD9/dXaWXa95SaXV+vzif14Ut3gstPVPK3WT3lRhniq1crTWsuuNV+tPK+8bpC6DeOpl4vK09bytJnfup/yIrk96m2TWtut0Xz19v1Oz43jLWu85TWbf72kPNX5VPHkL55qja63R0ZGlKcl+N2LHs945+1G16bNzjfe+T2pVo7Uu85q53ycXH69a5Ra2s3TCaOjo43naoGZnQCs7O4nxddHAEPufnS9zw0PD+8A3NHVYESyt+PQ0NCdWQdRTXkqMobyVCT/lKci+ac8Fcm/lvK0Fy2MngF2TLxeF3i2ic/9Mn5uEfBKD+ISSdMkYBphv84j5amI8lSkCJSnIvmnPBXJv7bytBctjNYH7iQ083sBuBs40t3v7eoXiUjblKci+ac8Fck/5alI/ilPRdrX9Z7N3P33wAnAbcADwFVKRpF8UZ6K5J/yVCT/lKci+ac8FWlf11sYiYiIiIiIiIhIsZV27EQREREREREREWmPCoxERERERERERGQMFRiJiIiIiIiIiMgYKjASEREREREREZExVGAkIiIiIiIiIiJjrJTVF5vZgcCJwGTgPHe/oOr9GcClwOrA7cDR7v6ymW0EzAZeAzhwkLs/n3JsewOnABOAJ4BD3f1/zewQ4EzgD3HWG939hG7G1mR8JwOHAf8bJ13i7hfU2qZpxhdjmJWYfR3gf919MMXttzpwN7Cnuz9Z9V5m+10R5DlvuxBjLvI3Md+3gVvdfVZ8nZttWCfGVLZhMzHWOU6XPpeLkKe90MF6bw+cC0wBngMOc/enUg2+A+2ud+L9rYD57j6QXtSS1OlvmHftnlOKpN1zUuqBtqmJ9duHsH6TgF8CR7r74tQD7aG85mme8yuveZHX/bmF3/J9wPnu/rpexwQZtTAys/WB04AdgBnAkWb25qrZZgPHuPt0wk50RJz+TeCb7r4p8CvgpDRji4UNFwLvc/ctgQeBL8S3twY+5e4z4r9e3Gw2s+22Bv4lEUdlZ6u1TVOLz90fqMQFvINQqHV0Iu5eb7/tgDuB6TVmyWS/K4I8522XYsxF/prZemZ2PbBf1cdzsw3rxNjzbdhMjA2O06XO5SLkaS90uN5XAofH89KVwNfTibpzHa43ZrYK8A1CYZlkoNPfMO86PKcUQofnpNxrYv1WBc4HdnP3zYGVgZkZhNozec3TPOdXXvMir/tzk/sYZvZa4CuEfSwVWT2StiuhhPMv7v4CcA2JndjMNgamuvv8OGkWsL+ZTQbeGedfNj3N2Aglfh9399/H1w8CG8W/twEOMbOHzGy2mb2qy7E1Ex+Em7bPm9mDZna+ma1ca5tmFF/F54D/cvc74+s0tt8RwMeBZ6vfyHi/K4I8521HMca/85K/BwE/Ar6fiDs327BWjFEa27CZGMc9TvdJLhchT3uh3fUeAE509wfj9OQ5vQg6OeYBfBU4L6VYZXyd/oZ518k5pSg6uXcogrrrF6dt4u5/iIXQr2H5Uw5lkdc8zXN+5TUv8ro/N3sPfSmh9VNqsiowWg9YlHi9CNigiffXBv6WaN5X/bmex+buz7n7tQBmNhX4LHBdYt5TgbcATxNKJ7utbnxmthpwP/AZ4K3AmoRa4kbbPJX4EnGuARzJ2B2+59vP3Q939ztqvJ3lflcEec7bTmOs/J1p/gK4+9nufmnV5/K0DWvFWJm319uwYYx1jtP9kMtFyNNeaGu93X3E3WcDmNlEQu3mdRRH28c8M9sLWMXdr0Gy1Ml5qwg6OacURSf3DkXQzG+4xMx2J5z71wZuSS+8VOQ1T/OcX3nNi7zuzw3jMrNjgfuA+aQoqz6MJgKjidcTgKVNvF89narPpREbsKzA41pggbtfAeDu+yTePwt4vMuxNYzPQ38TeyTi+CpwOXBTvc+lFV/CR4Dr3P2PlQkpbb96stzviiDPedsohobv5yF/W/gcTX6uHe3GmGYOt3Wcjs19y57LRcjTXmg79wHMbApwBeG66PTehdl1ba23ma1L6Cdh155HKI10tO8WQNHjb0bb9w4F0dT6uftc4NVmdjrhUaMD0wkvFXnN0zznV17zIq/7c6PrlEFgX2AXUq40yKqF0TPAtMTrdRn7iFCt9/8IrGFmk+L0aYzzaFGPY8PMpgF3EJrOHR6nrWFm/5aYbQLQi47O6sZnZhuZ2WFVcSxp9Lm04kv4AHB15UWK26+eLPe7Ishz3nYUY17yt448bcNxpZzDbR2n6Y9cLkKe9kK7611pmXszobBob3df0ttQu6rd9d4TeDVwu5k9AGBmD5jZP/U2XBlH2/tuQRQ9/ma0e04qikb3HmuZ2XsS719JaGlcJnnN0zznV17zIq/7c6PttX98/1eEhiDrmVmtp2a6KqsCo58Bu5jZOvHZwH0JF2sAeBid5CULI5cAHAzMjRdxdwAHxOkfBeamGVu8mL4e+L67f9LdKyWBzwPHW+hUGeAYQmlpt9WND3gROMvMXmdmEwj99Vxba5tmEB8xriHgF4nJaW2/mjLe74ogz3nbUYzkJ3/HladtWEeaOdzWcbpPcrkIedoL7eY+hI5Kfwsc4O4jKcbcDe3+3pe6+xt8+SAUxL//nvYKSEf7bhG0e04pknbvHYqi0W84AZhtYaRNCDe2d1Iuec3TPOdXXvMir/tzo33sZHefHs/ZewDPuvuOKcSVTYFR7NzqBOA24AHgKne/18xuMrOt42wHAeea2aPAaiwfteRjhF7DHwZ2JDSpTjO2vQh9A+0Xa+MeMLNL3f0V4EPAhWb2CKFA5PhuxtZMfO7+J+AoQgI6Yaf/avx4rW2aWnxxtnWAxe7+UuJzqWy/8eRhvyuCPOdtpzHmJX8bfDxP23C8z6WWw+0ep+PHS53LRcjTXmh3vS0MKb83sD1wX9xXbspgFdrS4e8tOVD237DD814hdHhOyr0m7j2eI/RLeoOZLQAM+PfsIu6+vOZpnvMrr3mR1/05z7/lhNHRohVyi4iIiIiIiIhIL2X1SJrkhJntZWZfj3+/z8y+mHVMIv2maHlo4ZHXOVnHISIiIiIivaMCoz7n7j9292Pjy22AtbKMR6QfFTAPNyY00ZU+Z2YXmtkTZnZaF5Z1i5mt3WCenc1sYYvLXdvMRuPfywpns5D194vUolxuTdbfL+WkPGxN1t/fKTPbxswuyjqORlbKOoB+Y2EEs+OAV4A/A4cAuwPHxml/AI5x98fMbBbwN2ALYENCD/IfdffnLXQs+3VgVWAx8Gl3vzUu/yhgCuGm80x3v9DM7ga+6u5zYhxfjiE9AuwHnAocDUwys78C2xI6Ibskzn8i8Gp3T46CVL1uHyT0ebE0rstn3P12C8Mlfi2ux2RgXnwv7VHQRIDy5qGZfQA4rtIJnpk5cLW7n2xmGwD3Eobi3As4mVBp8HfgU/E56S8AbwfWAxYAXwIuA1Ym9Id2KXBx/H99M/uJu7+35R9AyuQoYCN3f6YLy9qtC8uoy91/DPy419+T1+8XqUO5XKDvl9JSHhbo+7tgc8J1ea6pwChFZrYl8GXgre7+tJl9klB4MhF4u7v/ycxmAteZ2ebxY0PAuwmFMPcA+5vZbOA64HB3v9HMhoBZFnrmPwLYw92fM7O3AT8FLgQuAQ4F5ljolf4jwM6Ejj5x93tiCefa7n5CvPH8PHCJmU0E/hX45wareDZwkLvPtzAc4c7A7cC5wLC7z4zfPQv4FHBWe1tSpH0lz8OfAFeY2ZrAmsDqhAuGkwmFRNcB04GLgHe4++/M7N3Aj8ys0mJoY2DQ3V82s8uA6939TDNbFzgvfvZw4HwVFvU3C8O5TgDmmtmbgWsIQ89+HlgS/58CvAa4wt1Pip8br8C28hjmbWa2B7Blrc83GdsHgdOAfwC/TEyfCezn7nua2c+BYeBt8Tv+kzCM7U6EQuAPuftD9So9zOwl4EzgPYThbs+KhcPrAt8GKrW7N7r7SVXfvwHhuLBJ3I5XuPvZZrZJ/I6bgO2AVwHHu3vdUQfN7EngKsKx6lWEc+z2hOPXEmAvd3/WzNYHzgc2iutztbufHpfxeUIn4FPjNvi0u18bC5M3ieu4MfB74CPuvqheTFIMyuXc5fL/R6g8Wgy8BBzl7g/Xy10pPuVhfvIw3hsMufvBZjYZeA74hLt/y8x2IFT+bmdmR1K7snkt4A3ADYTBqM4BJgGjwBmEStwvAmuY2bfc/dAmf47U6ZG0dO0C/MTdnwZw9/MIN3Df8zC6Ge4+C1ifsLMD3OzuIx6GPH6IsPNtAbzi7jfGzwy7+xbu/jdgT+B9ZnYqoaf11eJyvge8PSbce4HH3P03dWK9HnhtvLl+L/CEu3uD9bsauNZCD/eVi1ViTEeZ2QOEA8m2cR1EslDaPHT3FwnDcu5GaDF1MfC6eHLeG5hDuJmc5+6/i5+5Ffgj4aYSYH6i9d+1wPFm9kPgg8Cx7r60TrzSR3z5cK7vAp4GFrr7ZoR8Og44xN23Jlw8fi42Y68U2P6zu7+FUDN4QuJC6V3AM7U+30xcZvZa4HJgX3cfAp6qM/sm7r49ofD2LODn8TtvBv5fnKdS6TEEbEW4YP1UfG8A+LO7v4PQSvBcM1uZUGj8O3d/K2HEuTfFPEy6ErjN3bcgFOx8xMz+Jb73esJxalvgs4TC2mas7O5vA/6DcLH+NXffkvD7zIzzfAe4PK7PtsCuZvYhM9sY2BXYOf42J7D8poO4Hvu7+6bAC4QbWikB5TKQk1yOlUnnEbbrNoQ83iG+PW7u1lueFIfyEMhJHgI/BN4bK2t3IJzzKi229iJU/L6bMBrwu+J59ipCZfOEON8q7r65u/87cApwTlznw4B3x/uQ/wDuyHNhEajAKG0vE0oVATCzqYSSx+qh6iYQSlwBXkxMH43vvVz9GTMbjCWrDxBq/+4kMSSyu/8D+AFwIKGFQ91hCz0MkX0xYac+jNCqoC53P4GQVL8iXJjeHt+aRLjInOHuMwilu8c0Wp5Ij5Q6DwmFPHsQamd+AvwX8AFgEPg5y2s3kiYm1vX5xPffALwJ+D7hpP5QXD+R8dwB4O6jwPuBITM7mVCrNoFQw7hCga27jyl4aPD5ZuwAPOTuD8fXF9eZ94fx/8fj/zcnXlf6EmtU6fGj+P99hIvdVeNy9jWzmwiPGHzW3f9a+YCZrUq4oL0grvNfCa1vd4+zLCHUhlaW22y/ZpXO6B8H/sfdFyTXJ37vTsCpcX3mE1orzHD3p4CPAgeZ2ZmEAqHVEsv+eSwQB7i/hZikeJTLGeVyPO//ALjbzM4H/g+4rF7u1lueFJryMLs8/G9Cod0QoWX/GcC7YmHQXoRz7T9Tv7L5zsQivw9cYGZXxmV+vt73540KjNJ1G6E2YFp8fRThxu5fzGwdADM7lNDs7bd1luPAqJntFj/zVuBW4B3Anwj9jtxCSMhKbQWEx2EOISTUeCMcvczym0YIN7P7EHbsRs1nV4rN4Vdx94uAjwFvMbMBwk3rv5nZhPj6x6jASLJT2jyMridcQMwgNHe9hdA30tx4ITqPUGvy+hjXuwl9M91TvSAzuwo4wN2vJuT03wiFa9UxikAsbIwXbvcDbyVcmH2GcLG2QkGrmU01s02TC2nw+WYl563XX95I8kVsRVitUaXHi/GzlfWa4O6/BF5HaB2wCXCvhcdWKyay4vokC24XJ1rzjY4zbzPrU2tdJhAeSa2sz9uA0+Mx7BeER1lvIdRaJ793vIJzKSflcvzuLHLZ3T9CuMH/LaE1xHepk7uNlieFpTyM353ROTVZAXsNoWXVAcCL7v4441fAJiubkxWwFxMKxX5KeGLgwdhyqhBUYJQid3+IkKQ3m9kCQsnkGwhN8241s18TbiT39DqPfbj7COHxkJNj6exF8fUNhGaHTuhEdyPCjesb4+eGCc9YXuPuL42z6FsJN5LfiPP/kdBa6Ls1Ej4Z08vAJ4GrzOw+Qu3IYTHWYwmlww8ROgx+CPVfJBkpcx7G+f8av/f+WED0E0KB0Jz4/sOEwp8fWhgZ40zg/cmamoRTCa0NFhAKlK4ltBx8GHjJzO5NNL0VqXgTodDhRHe/ntBP1wDh4mq8AtvK+eAVwoVWvc8343Zg89hUH5Y/itWulis9Ygudk9z9OuATwK8JrfwAcPe/E1oIfDzOvwahdc9PO4y1rthCaD6x+b+F/s7uIjyy+k7gV+5+DstbJja7zaWclMsp53J8zOhp4DkPj8yfCGzTIHel3JSH2ZxTf0h4ImCiuz9LqEg5i+WVvTfTZGWzhUFvtoqtkI4k9DO6LgWpgFWn1ylz99nA7KrJF8R/1fPOrPXa3X/F8meak/aqen1U1TLeWvV6FqHJHu5+N8ub0RGfbd2KkJgNxSS+bpzpfwYOamYZImkocx7GZbw38ffvqapJcfcfEAp1qz/3harXjxA75K7yF0IHiiLjeZBQcPqomY0QKgkeBt7o7j8xs0qBLcAiwuOWEPbJ/yL0WzDu56mqvRyPh47rDwSuNLPFcZmdOJbQQedDhAu7n9G40uM8Qgf0CwkxLyD08/fhxDwHEZqoH0roiPQqwnFg4w7jbeRA4Hwzeyh+73fd/UoL/VTsa2aPECoUbyA8xvZPPY5H8ku5nHIuu/ufzexLwDwze5FwQ3lEfHvc3G31O6RwlIcZnFM9dDQ/SmiZD6Gg6ySWV8D+1Mwqlc0TCZXDe7r7Uls2jswyxwNfi7k9Cpzi7k+a2UqEiucfuvsH24kzDRNGR6tbUomAmR1BaOZ6srt/M057F6EVxnhu8xpDfYtIe5SHIiIiIiKSFRUYiYiISKHEGtVaLVfPLmOtu5kdRHicdjxXuvvZacZTZma2OnA3obb4STPbldBR7FRCJ6cnxvlmEPqZW53w2MbRvnyUSWmCcnkFymVJnfJwBcrDBBUYiYiIiIgAZrYdYXCCTYHpwB8IfdLtRBg150bgPHefGx+PONzd55vZZYQ+oC7MKHQREZGuU6fXIiIiIiLBEYSOU5+Nr7cFfuPuT8TWQ7OB/c1sY2Cqu8+P880C9k87WBERkV7KTafXw8PDA8A2hM66Xsk4HJFOTQKmAb8cGhpq2KFcUShPpWSUpyL5l2qeuvvhAIlOS9cj5FLFImCDOtObojyVktH5VCT/2srT3BQYEZLxjqyDEOmyHYE7sw6ii5SnUkbKU5H8yypPJxJGtamYACytM71ZylMpI51PRfKvpTzNU4HRIoDp06czZcqUcWdYuHAhg4ODqQbVjiLEqRi7Z7w4Fy9ezGOPPQZjax/LoDR52mvaDvnfBv2cp3mS9/0kC9omy+UgT58h1MhWrEt4XK3W9GaNydN++M3Lvo5lXz+ovY45yNNe6Ys8LeN6lXGdoLP1ajdP81Rg9ArAlClTGBgYqDlTvffypAhxKsbuqRNn2ZqvlipPe03boTDboC/zNE+KEmeatE1WkFWe3gOYmb0ReAI4ELjc3Z8ys5fMbHt3vws4GJjbwnJXyNN++M3Lvo5lXz9ouI6lP5+W9Tcu43qVcZ2gK+vVUp6q02sRERERkXG4+0vATGAO8DDwKHBNfPsg4FwzexRYDfh6FjGKiIj0Sp5aGIlIj5jZV4C13X2mmc0ALgVWB24Hjo4jv4iIiAjg7psk/p4HbDnOPAsIo6iJiIiUUmlaGC1esnTcv0X6nZntAhySmDQbOMbdpxM66Twik8DaoDwXKafqfFZ+i9Sn86FI+SnPJQ9K08JoyuSJ7P65BQDMPWOFSiCRvmRmawGnAacDW5rZxsBUd58fZ5kFnAJcmE2ErVGei5RTMrdB+S3SiM6HIuWnPJc8KE2BkYiM62LgBGDD+Ho9xvaMvwjYoNWFLly4sO77w8PDrS6yKUNDQ6l8T7fkPb40aBtI0uIlS5kyeeIKf4uIiIhI/qjASKSkzOxw4Gl3n2dmM+PkicBoYrYJQMttXAcHB2v20D88PLxCwU6vpPU97UhzO+RV3rfByMhIw8JP6S7VloqIiIgUhwqMRMrrAGCamT0ArEUYwWUUmJaYZ13g2QxiExERERGRJlS3ylUrXUmLCoxESsrdd6v8HVsY7ezuh5rZQjPb3t3vAg4G5mYVo4iIiIiI1Ke+/iQrKpYU6T8HAeea2aOEVkdfzzgeERERERERyRm1MBLpA+4+izAiGu6+ANg2y3hERESkNXoERaQc9HiZFIkKjEREREREck6dxouUgx4vkyJRUaaIiIiIiIhIxhYvaXnwYpGeUoGRiHTVppsNjnndzokv+RmdOEVERESkH1RaHyVbIIlkqalH0sxsdeBuYE93f9LMdgXOAaYC33P3E+N8M4BLgdWB24Gj3f3lnkQuIrm06ioDHTezVbN7ERERERGRbDVsYWRm2wF3AtPj66nA5cDewGbANma2e5x9NnCMu08HJgBH9CJoERGRsjGzr5jZrPj3DDP7lZk9ZmaXmpn6HBQRERGRVDXzSNoRwMeBZ+PrbYHfuPsTsfXQbGB/M9sYmOru8+N8s4D9uxyviIhI6ZjZLsAhiUmqgBERERGRTDUsMHL3w939jsSk9YBFideLgA3qTBcREZEazGwt4DTg9PhaFTAiIiIikrl2mrhPBEYTrycAS+tMb8nChQvrvj88PDzu9KGhoabmS0vW398Mxdg9RYkzTxYvWcqUyRNX+FukD10MnABsGF93pQKm0fk0C62cq/v9uNrv6y8i0i90HSx51k6B0TPAtMTrdQmPq9Wa3pLBwUEGBgbGfW94eHiFi81amp2vF1qJMyuKsXvGi3NkZCSXN2t5oo6tRcDMDgeedvd5ZjYzTu5KBUy982leDA0NNV0R1E+Kcv5Lg86nIlJ2uiaWPGunwOgewMzsjcATwIHA5e7+lJm9ZGbbu/tdwMHA3C7GZImKJQAAIABJREFUKiIiUjYHANPM7AFgLWA1QmFRxxUwIiIiIiKdaLnAyN1firWgc4CVgZuAa+LbBwGXmNnqwH3A17sUp4iISOm4+26Vv+O5dWd3P9TMFqoCRiQ/YmvAYxKTXgd8B1gV2AF4IU4/xd2vTTk8ERGRnmi6wMjdN0n8PQ9Yob2cuy8gjKImIiIi7VMFjEiOuPulwKUAZrY5cB3wBeA24J3uvqj2p3tLfQKKiEivtPNIWm7opChSDsplEXD3WYQR0QpdAaObV+kDFwKfB/4BbARcbmbrA9cSWhi13OdYJ9T/iYiI9EqhC4x0ghQph2Qug/JZpMh0bpYyM7Ndganu/gMzez1wK/Ax4K/ADcC/ApdkGKKIiEjXFLrASEREREQkRUcB5wC4+++AfSpvmNk3gI/SQoFRcgS4WiMGVjQzcl51q74X/jHCo4/kZ5S5RutYdGVfP+iPdRSR5VRgJCIiIiLSgJlNAXYCZsbXWwDT3X1OnGUCsKSVZQ4ODjIwMMDw8HBTBUKNjNditxvL7YZurWNelX39oPY6joyMjCn8FJHyUIGRiIiIiEhjbwEec/fKiGgTgPPM7FbgeeBI4IqsghORwMy+Aqzt7jPNbAahw/rVgduBo9395UwDFCkQ9UQpIiIiItLY64FnKi/c/UHgDOAu4GHgAXf/bkaxiQhgZrsAhyQmzQaOcffphELeIzIJTKSg1MJIREREciXZD8vIkqUMaNQ1yQF3/z7w/app3wS+mU1EIpJkZmsBpwGnA1ua2caETurnx1lmAacQRjoUkSaowEhERERypXqkNY26JiIiTbgYOAHYML5eD1iUeH8RsEHaQYkUmQqMREREREREpLDM7HDgaXefZ2Yz4+SJwGhitgnA0laX3cpohs3oRufoyRa33RgNsYyj35VxnSD99SplgVEygdR8XUREREREpNQOAKaZ2QPAWsBqhMKiaYl51gWebXXB3R7NsBuqW+J2Elee1qtbyrhO0Nl6tTuaYSlLUioJtPvnFqiwSKTgFi9puSJIRERERPqIu+/m7oPuPgP4D+DH7n4o8JKZbR9nOxiYm1mQIgWk0hQRybVkAbCIiIiISAsOAs41s0cJrY6+nnE8IoVSykfSREREREREpP+4+yzCiGi4+wJg2yzjESkytTASERGRjujRUREREZHyUYGRiIiIdESPjoqIiIiUjwqMRESqVLeWUOsJERERERHpN+rDSESkSnKoUgjDlYqIiIiIiPQTtTASERGR1FVa7g0NDWUciYiIiIiMRwVGItJTyce5Rnr4aFfye+o9QtbsfCLSW+r3SERERCTf9EiaiPRU8vGuuWdsOebvXn5Pp/OJiIiIiIj0M7UwEhERERERERGRMVRgJCIiIiIiItIj6gZBikoFRiIiIlII6oNMRESKSP32SVGpwEhESkc3lSLllLzgnjJZlzAiIiIivaROr0WkdNSxtYiIiIiISGdUYCQiIiIi0oCZ3Qa8BlgSJx0FvAE4EZgMnOfuF2QUnoiISNepwEhERERatnjJUj0WJn3DzCYA04GN3f3lOG194GpgCBgB7jaz29z94ewiFRER6R4VGImIiEjL9Oin9BmL/99iZq8GLgH+Dtzq7n8BMLNrgP2AL2YTooiISHepalBEREQKT53dS4+9CpgH7APsAhwNbAQsSsyzCNgg/dBqU16IiEgn1MJIpOTM7GTgQ/Hlje5+vJntCpwDTAW+5+4nZhagiEgXqMWT9JK7/wL4ReW1mV1GOI9+KTHbBKClUpmFCxcu+3t4eLjuvENDQ60sGlgxLxp9R69l/f29Vvb1g/5YRxFZTgVGIiUWC4beA2wFjAI3m9mHgS8DOwFPAzea2e7uPje7SLOR7INF/bGIFItyVtJkZjsAA+4+L06aADwJTEvMti7wbCvLHRwcZGBggOHh4bYKhFqVxnfUktY6ZqXs6we113FkZGRM4aeIlEehCow23Www6xBEimYRcJy7LwYws0cInXb+xt2fiNNmA/sDfVdgpBYJIsWVzF9QDkvPrQl80czeQRgR7RDgI8BsM1sHeAHYFzgyuxDrUyWJSDnVy23lvXSqUAVGq64y0PLFYb2kESk7d/915W8zexPh0bRvkPM+F0RERPLE3W8ws+2A+4FJwAXufpeZnQDcBkwBLnX3e7OMsx5VkoiUU73cVt5LpwpVYNQO1UCKgJltDtwIfAZ4mdDKqKKjPheqpdkcO/kcfb3vbXW+8eZp55l9PeevbSAi5eHuJwEnVU27Crgqm4hERER6q/QFRiL9zsy2B+YAn3T3q81sJ7rU50LWmi2c6sZ8rRaE9UNfBo3kfRuozwURERERkdo6KjAys9uA1wBL4qSjgDcAJxKe7z7P3S/oKEIRaZuZbQhcBxzg7rfGyfeEt+yNwBPAgcDlGYXYc80+hqrHVUVERERERJZru8DIzCYQHmvZ2N1fjtPWB64GhoAR4G4zu83dH+5GsN2gjr+kz3waWBk4x8wq0y4CZhJaHa0M3ARck0VwaWj2sVQ94y1ZMrOTCX2MAdzo7sfHUQ7PAaYC33P3EzMLUERERET6TictjCp3n7eY2auBS4C/A7e6+18AzOwaYD/gix1F2UW6KZR+4u6fAD5R420lgEgOxIKh9wBbAaPAzWb2YeDLwE7A08CNZra7u/fdaIbtUOWQiIiISOc6uYJ6FTAP2AfYBTga2AiNviQiKVi8pKV+ukXybBFwnLsvdvclwCOEFry/cfcnYive2cD+WQZZJJXKod0/t0CFRSIi0jd0fSzd1nYLI3f/BfCLymszu4zQdP5LidlyP/pSr0bwKcLIQIqxe4oSZ5loBEQpC3f/deVvM3sT4dG0b9BhBUyvO/Ru9pzc7CiFvVTEY3QRYxYRkWzp+li6rZM+jHYABtx9Xpw0AXiSgo2+1KtCqDyPDASKsZvGi1OjL4lIq8xsc+BG4DPAy4RWRhUtV8AUbTTDssfQiqKc/9Kg86mIiEh2OmmnvSZwtpmtbGb/BBwCfATYxczWMbNVgH2Bm7sQp4iISGmZ2faEx7w/6+5XAM/QYQWMiIiIiEgnOnkk7QYz2w64H5gEXODud5nZCcBtwBTgUne/tzuhioiIlI+ZbQhcBxzg7rfGyfeEt+yNwBPAgcDlGYUoIiJSCBp1VKS7OhklDXc/CTipatpVwFWdLFdERKSPfBpYGTjHrDIAKRcBM4E58b2bgGuyCE5ERKQINOqoSPd1VGAkIiIinXH3TwCfqPF2oXur1JD2IiKSomWjjgKY2ZhRR+O0yqijKjASaYIKjERERKQnNFqLiIikpVejjor0MxUYiYiIiIiISCl0e9TR5EiNw8PDbcWUl5Evx4t/080Gx7x+4R8jPPpI8UenbPe3yru010sFRiIiIiIiIlJ4cdTROcAn3f1qM9uJDkcdHRwcZGBggOHh4dwU/LSrVvzVrYGLvp5l+K3G08l6jYyMjCn8bJYKjEREGkj2w6I+WUSKqzp/lc8iIuWRt1FHdY6RMlCBkYhIA8l+WNQHi0hxqU8lEZFSy9Woo7p+lDJQgZGIiIg0VNSa0qLGLSIirSnzqKMiWVGBkYiIiDRU1NY5quEVERFRFwvSnr4uMFLSiIiI9C9dB0grzOxkwjDdADe6+/Fm9i1gB+CFOP0Ud782kwBFROpQBYq0o68LjJQ0IiIi/UvXAdIsM9sVeA+wFTAK3Gxm+wBbA+9090VZxici0gpVkkiz+rrASERERESkCYuA49x9MYCZPQJsFP9dbmbrA9cSWhgtzS5MEZHGVGEizVKBkYiIiIhIHe7+68rfZvYmwqNpOwI7Ax8D/grcAPwrcEk3vlMtAESKpwx5q8e1JUkFRiIiLdBJVESkf5nZ5sCNwGfc3YF9Eu99A/goLRQYLVy4cNnfw8PDY94bGhrqaUfz1d+Xhiy+M01lXz/oj3XsRBla7pRhHaR7VGAkItICnURFRPqTmW0PzAE+6e5Xm9kWwHR3nxNnmQAsaWWZg4ODDAwMMDw8zNDQUJcjrq/yfdWVH72qDMliHdNU9vWD2us4MjIypvBTRMpDBUYiIm1KXlSPLFnKQOICe9PNBlP5XrVyEhHpPTPbELgOOMDdb42TJwDnmdmtwPPAkcAVGYXYkuS5I1kRAqoMERGR5VRgJCLSpurWRmldcKuVk0j3qSBWGvg0sDJwjplVpl0EnAHcBUwG5rj7d7MJrzU6j4iISDNUYCQiIiJ9TzfQUo+7fwL4RI23v5lmLCIiImlR9ZmISA8sXrJ8VOWRxN/J6SJ5p/11RYuVzyIiItIn1MJIRKQHaj2uppYLUiRqdbMibRMRERHpF2phJCKSIbVWEMk/5aaIiIj0IxUYRbppE5EsVFor7P65BepkVySnknkqIiIi0i/0SFqkJuYiIiIiIiIiIoGqs0VEUtRsC0a1ehTJP+WpiIiIlJlaGImIpCjZmhFqt2hUq0eR/FOeioiISJmphZGIiIgso5Yyvd0GapUkIiJFoXOWqIWRiIiILKNWM823BOx02f26fUVEpBh0zhK1MBIREREREUAtCkSkdTpulJcKjEREcqKdE6xO0CIi0k2VFgXJVnagc4yI1JY8bkyZrCKGMtGvKSKSE7Uu0pv9jE7Q0qzqGz/dCIrIeHSOEZFO6Hqj+NSHkYiISJ/pZR89IlJOi5csHVNoVP1aRKSarjeKTwVG49AJUURE+onOc92X3KYjS5YyoO0rBacbP5EV6fy5Im2TclGB0Th0QhQRkX6iUVC6r3qbavuKiJSP7htXpGuKclHRXxPUqayIZKnZ4047xyo9Wy4iIiIijSSvEUfavF7UfXXxqIVRE5KlpD/64hbLpqu5nYikodnaq3ZqdFQzJpJPyWsMXW+IiEjWarWcrbxuZxmSf7r6aFG9UYxUSioiedHtlkOqERJJl0ankrzTeUFEOtFpy3gdd9KhFkYdqK6Zr7Q+GhoaUm2giGSq2y2HVCMkUl83zvuVZQwNDTX9XbrekKzUOi9o/xSRZnTaMl7Xo+noSYGRmR0InAhMBs5z9wt68T15ox1YiqRf81SkSJSnxdGNQtpmryN0vZEvytOxBUO1unKoV6Far5BJBVDSDcrTfGv2GCDp6/qWN7P1gdOAHYAZwJFm9uZuf4+ItE952n+60VHheMuqXl43H32rft1sM+SyNFdWnpZfu53TNzNfOznS7qOszSyv+rjTzeNGlpSnQa0uG5LTq99L/u71unzQ45nSKeVp/jV7DEiqd47ZdLPB3gRK/q4zex1PL1oY7Qrc6u5/ATCza4D9gC82+NwkgMWLF9edac1VRpf9PTIysux1M3/38jPVr59/4UUmrxRLSV9eypRx/q5+veTlpcs+k/y7+jPJ9zoxMjLS8TJ6Lbkdu7Xenaj1G1Vvy8R+PCnF8FqhPM3BZ9KMZ3TpEj586iMAzDp+szF/Vy+7lsp8yWWNt7zkIto5zlQvO7mIWt9Taxn15lOeKi+yXvZ4udTOfEmd5ki9/Kun0fKSx4nq18rTYLzjZdnyIrkvX/IpY3Tp8uu6dvb3Xql1vddIM/GlcY3fLdXbAcZfR+VpufI063jaOTeOd47p5fGi2XNtL4y3Xr08n04YHR1tPFcLzOxzwKrufmJ8fTiwrbsfWe9zw8PDOwB3dDUYkeztODQ0dGfWQVRTnoqMoTwVyT/lqUj+KU9F8q+lPO1FC6OJQLIUagLQTNuoXwI7AouAV3oQl0iaJgHTCPt1HilPRZSnIkWgPBXJP+WpSP61lae9KDB6hpBYFesCzzb60NDQ0AiQuxJpkQ48nnUAdShPRQLlqUj+KU9F8k95KpJ/LedpLwqMfgZ8wczWAV4A9gXqNvcTkdQpT0XyT3kqkn/KU5H8U56KtKnrvaq5+++BE4DbgAeAq9z93m5/j4i0T3kqkn/KU5H8U56K5J/yVKR9Xe/0WkREREREREREii0/4zaKiIiIiIiIiEguqMBIRERERERERETGUIGRiIiIiIiIiIiMoQIjEREREREREREZY6WsAxiPmR0InAhMBs5z9wuq3p8BXAqsDtwOHO3uL+cpxsR83wZudfdZKYaX/P5G23Jv4BRgAvAEcKi7/2/OYtwnxjgJ+CVwpLsvzlOMifneB5zv7q9LM74sFCFPe62JbXAycBhQyalLau07RWZmqwN3A3u6+5NV75V+P5Da2j1OmNm0OH094B/AQdX7VhF1sD02Ab4dp/8fcIi7P5Vm7NK+Dn73jYDZwGsAJ+TB86kG34ROrwfM7FTgFXf/QmpBt6iD33B74FxgCvAccFhec7eDddwROI+wjk8Qjk+p3kt0QwfrfwhwJvCHOOuN7n5CepHX1u79at6PPR2sV25/K2j/vr3Xv1fuWhiZ2frAacAOwAzgSDN7c9Vss4Fj3H06YYMdkbcYzWw9M7se2C/N2KpiqBtnvMm7EHifu28JPAh8IWcxrgqcD+zm7psDKwMz8xRjYr7XAl8h7JOlVoQ87bUmt8HWwL+4+4z4r4yFRdsBdwLTa8xS6v1AauvwOPEd4Hp33yr+/eV0ou6dDrfHqcB33X0GMCcuRwqgw9/9m8A33X1T4FfASelE3bxO1s/M1jCzy4DjUgy5ZR3+hlcCh8fcvRL4ejpRt6bDdfwWcLC7bwE8DHwmnai7p8P13xr4VOJaLxcFEB3er+b22NPheuXyt4KO79t7+nvlrsAI2JVQEvgXd38BuIbEj21mGwNT3X1+nDQL2D9PMUYHAT8Cvp9ybEmN4pwMfNzdfx9fPwhslKcY47RN3P0PZrYKoeQ07VqLZn5vCLUOp6QaWXaKkKe91sx+sTXweTN70MzON7OVU4+y944APg48W/1Gn+wHUltbxwkzWxvYErg4Tv8Wocat6Do5bk4i1GoDrAq8mErE0g3t5sFk4J1x/mXT0wq6BZ3s13sDvwG+ml64bWn3NxwATnT3B+P0LK6zm9XJ77iZuz8c99n1Sf86vRs6Wf9tgEPM7CEzm21mr0ox7nraul8twLGnk/vwvP5W0OZ9exq/Vx4LjNYDFiVeLwI2aOH9NDSMwd3PdvdLU41qRXXjdPfn3P1aADObCnwWuC7VCJvblkvMbHfgaWBt4Jb0wgOaiNHMjgXuA+bTH4qQp71Wdx3NbDXgfkJN21uBNclRDU23uPvh7n5Hjbf7YT+Q2to9TrwB+G/gq2b2S8JFUKqPIfdIJ8fNk4BPmdnvCa0xCt/iqo+0+7uvDfwt8ehWXo+fbe/X7v5tdz8TeKXXQXaorXV09xF3nw1gZhMJrQHSvs5uVie/4xIz2wJ4BngXcHVvQ+2JTo7PiwitQN9CuFc5v3dhtqTd+9W8H3s6uQ/P628F7d+39/z3ymOB0URgNPF6ArC0hffTkIcYmtFUnGa2BnAjsMDdr0gptoqmYnT3ue7+auAGQnO8NNWN0cwGgX0JB6B+UYQ87bW66+juz7v7Hu7+aDyIfxXYI+UYs9YP+4HU1u5xYiVgK0JN2zaEWsK0z0290Mlx8wpC/33rA0cD15pZ6R9/Lol2f/fq6ZDP42c/XA90tI5mNoXwONpKwOm9C7MjHa2juz/k7q8lXAt/r4dx9krb6+/u+7j7Xe4+CpwF7N7jWJvVbu7l/djT9jElx78VtH/f3vPfK48FRs8A0xKv12Xsow6N3k9DHmJoRsM4Y8eidxCatR2eXmjL1I3RzNYys/ck3r+SUCqcpkbbcf/4/q+Am4D1zKxWi4uyKEKe9lqjfXcjMzss8f4EYElKseVFP+wHUlu7x4n/Af7u7jfE6VcB2/YwzrS0tT3MbB1gU3f/EYC7z4nvrd3bcKVL2s2DPwJrmNmkOH0a+Tx+9sP1QNvrGFsb30woLNrb3fN6HdDu8WllM/tAYvps0r9O74Z2138NM/u3xPQJQF4G9mg39/J+7GlrvXL+W0H79+09/73yWGD0M2AXM1sn9lmzL+FAC4CHkQVeiqMOABwMzM1TjDlSN864Y10PfN/dPxlLW3MVIyGZZ8fe3yEUztyZpxjd/WR3nx47NNwDeNbdd0w5xrQVIU97rdG++yJwlpm9LrYE+DhwbQZxZqZP9gOpra3jhLs/DjwTH0UGeD8wnGLcvdLucfPPcfqOAPH9v7v7n1KNXtrVbh4sIdwYHBCnf5R8Hj/74Xqgk3WcDfwWOMDdR1KMuVXtruMS4AIzG4rTP0T61+nd0O76Pw8cb2EAEIBjyM+1Xlv3qwU49rR7H57n3wravG9P4/fKXYFR7MjpBOA24AHgKne/18xuMrOt42wHAeea2aPAaqQ84kCTMWauiTj3IvStsp+ZPRD/pdrvUqMY3f054EjgBjNbABjw73mKMc1Y8qIIedprTey7fwKOIhzcnVD4mfeOPbuin/YDqa3D48QHgX83s4XAJ4DDKLh2t0e8KPwg8BUze5DQjH7f9NdA2tFhHnyMMFLOw8CO5LDz9364Hmh3Hc1sK0LH3tsD98Xr7JsyWIWGOjg+vUK4Uf1PM3uA0ElvFk8sdKTD9f8QcKGZPQIMAcenvwYr6vD+JbfHnnbXK8+/FXR8397T32vC6GgWjUpERERERERERCSvctfCqJ/FEsQ3d2lZrzOzOd1Yloi0Rrkskj3lYXHjFhERkXxYKesAZDl37+YIShsTHt8SkZQpl0WypzwEihu39DEzuwG4xt1nxcecdnb3/8s6LhFpjXK5HPRIWpeY2c7Al4GngE0JHd7OBB6P03cCJgH3A8e6+9/M7EngHsJoAp8HziU8+7sacAbw34QLvReAM4Fj4+s57v5v8XvfT3hOcQrwD+DTwL2EPlPWB2539/ea2TtiHKsCrwCnuPsNZjYT+Nc4/a/u/q466/hzQsejbwNeA/wnoQf3neLnP+TuD1kY7u9rwBbAZGAe8Bl3f9nCqFFHxXjXAs509wtjHPsQhgF8U1yXQ9z9kaZ+AJEu6ZNc/mD8rqVxGZ9x99vr5W4721KkXWXPQwujCh1XGSDBzBy42t1PNrMN4nduQOiz4GRCi/C/A5+KfRp8AXg7sB6wAPgScBmwMqG/tEuBi6vjbvoHEMlQ8iYz61hEpH3K5XJQC6Pu2hr4tLvfYWZHA98BfkwYsm/I3UfN7HTCherH4mcWuvsBAGZ2bmJZ2wAfc/f7zWwu8DlgZ2B1wjCOZxMuSE8nlNY+Z2abE3pYfyOhw7nz44Xtq4BvAe919yfNbD3gnth5JsDmwCbu/rcm1nETd98+9jA/H9jL3Y+Lsf8/QgfV5wLD7j4z9ug+C/iUmX0TOALYI8b7NuCnwIVx2TsBg+7+jJl9A/gscEgTMYl0W9lz+WzgIHefb2bvifHcTo3cJXSyK5K2MufhT4ArzGxNYM0Yx26EwqG9gOuA6cBFwDvc/Xdm9m7gR2ZWaTG0MeGc+bKZXQZc7+5nmtm6wHnxs8vibm6Ti3RPLPj9GqGQdjXgLkKnrf9EKNg83N3vijl0BaEA9ClCpWRlGaPAOsCewH7uvmecPrPy2sx2AM4hFCKPAme4e91HMc3spfiZXWNsXyCMxLsFYUjq97v7C2a2WVyHV8flf93dLzeziYRz5tvGWZ9ZwN/isjYkDIH9UXd/vvWtKJK9nOfyKYRGB4uB54CZ7r6oVu52vjX6j/ow6q4F7n5H/PtyYCvCsIt7A/fHpngfAJJ9KtzB+J5w9/vj348Dt7n7Ynf/M+EktBbh4nIaMC8u+0pCi4E3Vi3r7XG+6+J8NxGS8C3x/QebLCwC+GEiJlg+3N/jMSYIB4Kj4ncNA9sCW8QT5Z7A+8zsVEJP8Ksllj3s7s/Ev+9LLE8kbWXP5auBa+PoCq9ieYHQuLnbxPJEeqG0eejuLxIKo3YDdie0BnpdbOW3NzAHeDcwz91/Fz9zK/BHwsguAPMTrf+uJQwX/EPCiGrHuvvSejGIpGQQ+DChdfnawNvd/c2Em8rPxnkuIOzPmxNa/m3a4necApzj7kOEERXf3cRnBoD/cfdtYyyXAp8kHE/WAPY2s5WAa4DPxmXvBHw6VnhuR7gpHm99IOTpPwObAZsQCqNEiix3uWxmGxLydht33xq4BdiuQe5Ki9TCqLuSj21MiP9PBI5x97kAZrYaocl4Ra3ahpGq10vGmWcS4WLygMqEmDjPEobUS873iLtvl5hvPeBPhCEiW6nxGBOXu9eKa//K42SxBnU0NrP/BeFRtjsJibxn4nMvJv4eZfk2FElbqXPZ3U8ws8sJN6szgeMIhUPj5m4zyxTpgVLnIaGQZw9CC6OzCBfWHyBclP+ccONanX8TCY+Lkvye+Djcmwg5vQtwspkNIZK9p939KeApMzuRUCnxBkILv7/HeXYlPP6Ju//WzG5t8Tu+D1wQHyn9GeGR1GZUWi48DjzkYVhrzOwJQiHydOANwOXLG/YxFdgqdqdQa30Abnb3kbi8h1AlqBRfHnP594THsu+LrYfnuvs8CwNejJu7hCdkpAVqYdRdM8ysUsN4JHA38D3gGDObEpuvXkLoS6Eb5gHvMbNNAcxsD0Kz16mEC+3KReV84E1m9s443wzgN4R+DXrhJ8C/mdkEMxsgPEJwDOHxgj8R+lq4hVhYFB99EcmT0uayma1koa+XVdz9IsKjPG+JuVord0WyUNo8jK4nFO7MIPRZdAtwKuGC95UYz3vN7PXxe95NeLzlnuoFmdlVwAHufjUhp/9GuFhOxi2ShecBzOx9wI1x2o8Ij0xWCoKrKwnH6zevep4plT/c/WJCa9ifAu8FHjSzlWksWZBcqxD5r+4+o/KP8AjatxqsD6gSVMond7kcW9LuRKj8fA4418zOok7uNrWmMoYKjLrrf4DTYk3CBwhN508FniR0zPkwIUGO68aXufvDhIvoq81sQfyuveKjXw8DL5nZvcCfgX2Bs+N83wEOdvcnuxHHOI4l9AXxEOFi+yFC7ektwDOETjgfATYiFCBVN/cXyVppczk+wvJJ4Cozuw/4AXBYrAmtlbsiWShtHsbv+ysio4oXAAAgAElEQVThXHh/LCD6CaFAaE4ino8BPzSzhYS+mt4fP1ftVOCgGM89hNZLtyfjNjPdsEqWdiP0s3Uh8CtCTlcqDG8m5B5mthEwXmfxfwIGzWxlM5tM6NCe+Jm7Ca1+ZsXlrEkYlKVTDrxoZh+J37MhsJDwuFm99REps9zkspltScjJR9z9DEK/YttQP3elRRolrUssdAZ2vrsPZh2LiLRPuSySPeWhSPEl8zi23PsuoTuMlQiViPsSKg9fTaj5fwOhYnEl4AoPQ3FXOsr9P0JfZu8EFgG3AW9JdJT7NUJF+Cgw293PaRDbKLCOu//ZzPYjPOq6c3zvGuDn7n5+vCH9GuGRssnA19z9ogbrczmhA/6vxOXNSr4WKZqc5/LJhL6Vnie07DvWwwAX4+ZulzZJX1GBUZeU4eLWzN5FKJkdz20ehx0WKTPlskj2lIciIiIi2VOBkYiIiIiIlIaZfYbQCf14znb3K9OMR0Tao1zOngqMRERERERERERkDHV6LSIiIiIiIiIiY6yUdQAVw8PDA4RezRcBr2QcjkinJgHTgF8ODQ2NNJq5KJSnUjLKU5H8y1WemtlXgLXdfaaZzQAuBVYnjEp3dByJsiHlqZRMrvK0W5SnUjJt5WluCowIyXhH1kGIdNmOwJ1ZB9FFylMpI+WpSP5lnqdmtgtwCHBjnDQbONzd55vZZcARwIVNLk55KmWUWp6a2erA3cCe7v6kme0KnANMBb7n7ifG+dou2EV5KuXUUp7mqcBoEcD06dOZMmVKyx9euHAhg4OFHUylKWVfxzKt3+LFi3nssccg7tclsixPH3vssdz+Xnnel/IaW17jgt7F1g95Wut8muffuyLvMeY9PihHjHnJUzNbCzgNOB3Y0sw2Bqa6+/w4yyzgFJovMOrourcb8rp/5DUuUGy1pJ2nZrYdcAkwPb6eShiqfSfgaeBGM9vd3efSWcFuIa57uy3P+3m39du6Ri3laZ4KjF4BmDJlCgMDA20toN3PFUnZ17GE61e25qvL8hTy/XspttblNS7oeWylzdN62y3Pv3dF3mPMe3xQqhizztOLgROADePr9Rh70b0I2KCF5b0CVG6yM5O4gciVvMYFiq2BtPL0CODjwHfi622B37j7EwBmNhvY38weprOC3cJc93ab1rXUWsrTPBUYiYiIiIjkipkdDjzt7vPMbGacPBFIDjU8AVja6rIHBwczu1kZHh5maGgok++uJ69xgWKrZWRkJNXCKnc/HMDMKpNqFeB2WrAr0vdUYCQiIiIiUtsBwDQzewBYC1iNUFg0LTHPusCzGcQmIrULcLtSsFspDBseHm4/woLRukqFCoxatHjJUqZMnrjC3yJZS6nzv5YpZ0T6V3XO6xggReTuu1X+ji2Mdnb3Q81soZlt7+53AQcDc7OKMQ3V+TuyZCkDOr9LPjzD+AW4taa3ZHBwkIULF7bcgquo18B5bknXbf22ru3ouMDo/2fvzuMlqctD/38GhAElqEQNm0sM8oAcZcwRzA0oLmCCS4iJyA9QGRNEf2qiicDVgBsGUZOLCyHRCIje0UACrixuQKK4xqOgo/CEJOhFGaNRb4woZwZn7h9VPfSc6T6n96rq/rxfr3nN6e7q6qeq66lv9bee+taobi/aFDvvtANHv/JGAK4+5+CKo5EKExz8r2/mjDS72vMf3Ado6pwIvKs8YfMV4O0VxzNWnfLZ9l018UUgImI/4FbgBOCizPx2RNxZVceux8CaBkN1c7bdXrRlHfCSzNyfouTv+cPMX1LPWoP/tc6abB38r+y0bQ3+1+muLsdOOlhJkpooMy/OzLXl3zdm5qGZeUBmnpCZixWHJ82kzLwTWAtcDnwTuBm4rHz5ROAtEXEzxeWkU92xK43awBVGY7i96FRpagmimsnB/yRJ0qh5DKs6y8yHtP19DbBdGU9m3khxIlXSAIa5JG3UtxedKpYgqmK1Gfxv6XXBkxpYrs4D2NU1trrGBfWOTb3zZIrULB7PStJsG6jDaJy3Fx3mloyT+EHR64/fcf1InvYfTdO+fBNUy8H/YPvcGIc6D2BX19jqGheML7ZJ3wZY/viUJElqkkErjMZ2e9G5uTlWr17dd0BV/djp9TNHEVudf9CNwjQtXw1+iNZy8D9JkiRJUjMMVAuemUdl5lxmrgFeDXwkM58H3BkRh5WTTf0P0Y2bNi/7eFTvkfrl4H+SJEmSpGEMM4ZRJzN/e9FxvEfqlYP/SZIkSZJGYegOo8y8mOKOaP4QlSRJkiRJmgLenkSSJEmSJEnbsMNIkiRJ0sDax+Ts9nenx9K0cRvXtBn1GEaSJEmSZkj7GJ3t43M6dqdmTbdckJrKCiNJkiRJI2GFhSRNDzuMJEmSJI1Eq8KivbJIktRMdhhJkiRJkiRpG3YYSZIkSZIkaRsOei2pEhs3bWbnnXbY7m9pVkXEXwL3y8y1EbEGuADYHfg08MLMvKvSACVJkjRT/IUmqRLtYxzYWaRZFxFPAk5qe2od8JLM3B9YBTy/ksAkSZI0s/yVJmlivHOKtL2I2AM4G3hD+fjBwK6Z+YVykouBY6uJTpIkSbPKDiNJE+OdU6SO3gmcAfy4fLw3sKHt9Q3AvpMOSpIkSbPNMYwkSapIRJwM3JaZ10TE2vLpHYAtbZOtAvouz1u/fv2yry8sLPQ7y77Nz88PFcMkYhxG3eMDY5QkSYOzw2iEHLhXktSn44C9IuIGYA9gN4rOor3aptkTuL3fGc/NzbF69eqOry0sLKzYmTMJy8VQlxi7qXt8MB0xLi4urtj5KUmSxsPejRHychtJUj8y86jMnMvMNcCrgY9k5vOAOyPisHKy5wBXVxakJEmSZpIdRpIk1c+JwFsi4maKqqO3VxyPJEmSZoyXpEmSVAOZeTHFHdHIzBuBQ6uMR5IkSbPNCiNJkiRJkiRtww4jSZI0cRs3be74tyRJ08Y2T03lJWmSJGniWjeKALj6nIMrjkaSpPGxzVNTWWEkSZIkqTLtFRcHHDhXYSSSpHZWGE3Axk2b2Xkn++YkSZKkpay+kKR6shdjAlqNYKshlCRJd1s6noPjO0iSJFXPCqMeWCEkSdL4tFcXgBUGkiRJdWAvSA+sEJIkSZIkSbPEDiNJkiRJkiRtww4jSZIkSZIkbcMOI0mSJEmSJG3DDqMaab8rjHeIkSTNqo2bNjM/P7/1b0nToT2fF81tSao975JWI+13ifEOMZKkWWV7KE2npbltnktSvVlhJEmSRsaKIEmSpOlgh5Gkyi39gekPTqm5WhUErcoBSc1iGyyNl8e9ahIvSZNUufYSdbA0XZKkqnhJqDReHveqSawwkiRJkiRJ0jbsMJIkSZIkSdI27DCqqfZrWb2uVZIkSZIkTZJjGNWU149LkiRJkqSqWGEkSZIkSZKkbVhhJEmSJK0gIl4DPKt8eGVmnh4RRwLnArsCl2bmmZUFKEnSiNlhJEmS+rZx02Z23mmH7f6WplHZMfRk4FHAFuBjEXE88CbgCOA24MqIODozr64uUmm2RcR1wAOATeVTLwB+DTgT2Al4a2aeX1F4UuN4dCdJkvrWGmvv6FfeaGeRZsEG4OWZuTEzNwE3AfsDt2TmrZl5F7AOOLbKIKVZFhGrKPLy4Mxck5lrgO8AZwOHA2uAUyLi4RWGuR1vdqQ6s8JImnKeaZEkaTiZ+Y3W3xHxMIpL086j6Ehq2QDsO+HQJN0tyv8/ERG/DLwL+G/g2sz8EUBEXAY8EzirmhC3582OVGdDdRh5LbdUb21nWh5cnv0kIvYBLgHmgUXgcxFxXWZ+s7pIJUmqv4g4CLgSOA24i6KNbVkF9FUesH79+tEFN4CFhYXtnpufn68gkm11iqsujK3W7gtcA/wRxUnRfwQuZfuO3UP7mWkrT3tZv6PInzp8j3WIYVJmaVkHMXCHkddyS43QyDMtktTiWEmqi4g4DLgceFlmXhIRRwB7tU2yJ3B7P/Ocm5tj9erVI4yydwsLC7XoHOqkrnHVeZ1VGdvi4mLlnZ8Amfl54POtxxFxIUUhw5+3TdZ3x+7c3Bzr16+f2Pqtehur83Y+arO2rIMYpsJo67XcABGxzbXc5XOta7ntMJKqMZYzLZI0KZbqqw4i4oHAh4DjMvPa8ukvFi/FfsCtwAnARRWFKM28iDgcWJ2Z15RPrQK+xZAdu9IsG7jDaFzXcg/TOz2ucrJx9zq24l7uc1rTTHvJ3LQv36SN60zLJEpzh90W6rwt1TW2usYF9Y5N0kw4FdgFODeiVbzLO4C1FFVHuwBXAZdVEZwkAO4DnBURv0lxovQk4NnAuoi4P3AH8PvAKdWFKDXL0INej/pa7kFLc5tcTtZL3PPz841exl5M0/LVpTR3XGdaJlGaO8y867wt1TW2usYF44utLnkqqf4y86XAS7u8bOmbVAOZeUVEPAb4KrAjcH5mfjYizgCuA3YGLsjML1UZp9Qkww56PfJruSWNlGdaJEnSduOBSdMoM18FvGrJc+8H3l9NRFKzDTxyZNu13Cdk5iXl01uv5Y6IHSmu5Xb8ohFrb+Rt8LWczLyCogLwq8ACcFFmfhZonWm5AXi/Z1okSZpurfHAjn7ljQ4eL0nqyTAVRl7LXREHAFU/mnimxbsiSc0yqTxt/5zFTZtZ3faZ7iskSZJGa5hBr6fqWu6qfqB6gCttz05RzZqIeA3FzSMArszM0yPiSIpB6ncFLs3MMysLcAWTytmln9P6e9yfK0mSNIvsqShVVabb/rmSpNlTdgw9GXgUsAaYj4jjKW7PfQxwIHBIRBxdXZSSJEmaNXYYSZJUrQ3AyzNzY2ZuAm6iuOPoLZl5a2beBawDjq0ySEmahKXjcy46dqckVWaou6Q1kWOjSJLqJDO/0fo7Ih5GcWnaeRQdSS0bgH37me/69euXfX1hYaGf2XHAgXPc656r+3rPpPW7TE37vEEYo5qm/dJT2PbyUy89laTJmrkOI8dGkSTVUUQcRHFXw9OAuyiqjFpWAX2dWp+bm2P16s4dPAsLC8zPz/cdY93HDBpkmQY16DqcpGmIcXFxccXOT0mSNB6W10iSVLGIOAy4BnhFZr4H+A6wV9skewK3VxHbNFl6OYuXt0iSJHU3cxVGkiTVSUQ8EPgQcFxmXls+/cXipdgPuBU4gWIQbA2h06UukiRJ6swOI0kj5dhgUt9OBXYBzo2I1nPvANYCl5evXQVcVkVwkqab7bYkqZuZ7jCygZRGzzP4Un8y86XAS7u8bAJJGivbbWlw/p7UtJvpDiMHwJYkSZIkDcIOV007u0M7cBDM7bWvE9ePJElSc23ctLn2d9DrpA7Ho3WIQZImZaYrjLqxp3h7VmNJkiRNh6Ye19Uh7jrEIEmTYoWRJEmSJEmStmGH0RRZWhZrmawkSZIkSRqEl6RNES+lkyRJkiRJo2CF0YwY5wB9Dv4nSZKkuuh2bLpcNX7734se20oSYIXRzBjnAH0O/idJkqS66HZsulw1/tL3eGyrqm3ctJmdd9phu7+lSbLDSJIkSZKkGvGkvOrAbsqGs0xW085LHiX1otdLUCbxmVJduF1KkoZhhVHD2fOsade+jX/4rEdsfd7SXEnterkExUuyNWvcRiVJw/DXlrbh2VLVWevA9+hX3mhnkaSJGaRttD2VJElN5y8ubcMf5JIkbWuQttH2VKq/YTtz7RjWqLkdqW48gplB3W4bOsj7JUnTa1b397O63NKsae/YHfb9dgxrFIbdJqVRcwyjGdTttqGtx/28v9f3SJKaZ1bHP7GdU1PN8vh+3ZZ9lteJJA3LDiNJkjRV/IGoWTXLnZ1VDHwvTcrSds12TpNih1EDtHYI8/PzVYciSVLt+QNRkjRNZrkzWNWyw6gB3EFIkiRJd5tUhUX753jyVtKsmcoOo/Yd+yyX6w277LO87iRJklRfkzqh6olbSbNsKnsDvGNBYZR3fpAkaZqN8vbakiRJ02AqK4wkTb+lFXCLmzazunx8wIFzVYUlqaGGHffIKgRJ0qR4RY0mxQ4jSY3U6ceZg9xK6scgB9mD3IjCA3tJ0ih5cwdNikcskiRpJg1y6fWw77GzSKPm5ZD11P69+B1pUpZua257GpYVRhopz6JKUvO4764HvwcNwkqDevJ7URW8PFqjZoeRRsrGUZKax313Pfg9SJL65QkGjZNblqSpYxm4NDhzRpL6N4p9p8cvGoR3ttY4WWGksVnuLlbdesKXPm+PuQbRfpb+w2c9YpvX3Kak5VnlIkn9G8WlQO5/NU5e9qxB2GGksRnkLlZed6tRc5uSVCeDHLB7MkWSNKxuHZJL25QDDpybeGyqr0Z3GLVv3N02bA+qJPXLH2eSxmWQCgI7vgVWB0jqXy/7CtsYLafRHUa9HHSZAJL65X5D0iQsdyA/6g6Bbp0NdkI0h5crSerXuPYbnlydHY3uMJIkSWqq5TqnR32Q321+dkI0kz/OJI1LLycSPLk6O2xpVIlx3gWiqfNWffm9S6qTbvuk9r8XN21mfn5+onFpcrwrkqRhLHc8223/4jHwbBpLhVFEnACcCewEvDUzzx/H56i5xnlGs6nznrRZzdP2MyW93LkPehsk0LO9GodZzVMtr9dqIc/+ToZ5KtWfebqtXn/TDPLbx+Pj6TLyby8i9gHOBg4H1gCnRMTDRzHv5Xo17fGcTkvPlnZ6fpB5LTe/WagmGWee1l37WZPVQ56hbZ+XjaFGbZbzVGqKqo571SyDHM92e8/ikvcsV2XYbbpePqefWOvO9nSyPD6eLuOoMDoSuDYzfwQQEZcBzwTOGnbGk7zWX/XQ7WzpIN9xp+1nhsdzGFueNtWw3/vSMyjt1Uvtf3urUvVhbHnqdqhe9Do49iBnkFvvmZ+fb/oZ6EqOe9UsgxzPLveeXn8P9bL9rPSeKTkm9rhXGtA4Ooz2Bja0Pd4AHNrD+3YE2Lhx47IT3eeeW7b+vbi4uPVxL3+P8z3GM9x0nabp9T2ddJtuuXn38v6VXmtp2453XDbQ6owkT7utg6Zuh72856d3/Jyd7nH3D5vW81s2b+L419+09fmLTz9w6+P2v9/1p7HNett01+at8+v296SslFej1O+y9hNbr/OelTzt5B47wvGvv2Hr44tPP3Cq8nTW4mk37HuWat+Ptb+8dH83yO6j27zbzXKewnS3p8az8nFmr5+z0md2+6zlPrOXWFtmJU9n8bi3l2PgTuum23u6HZdVeQw8yePfpZYu6yDLPsC66ytPV23ZsmXlqfoQEWcAu2Tmq8rHzwfmM/OFy71vYWHhcOAzIw1Gqt5j5+fnr686iKXMU2kb5qlUf+apVH/mqVR/feXpOCqMvgM8tu3xnsDtPbzvn8v3bQB+MYa4pEnaEdiLYruuI/NUMk+lJjBPpfozT6X6GyhPx1FhtA9wPUWZ3x3A54BTMvNLI/0gSQMzT6X6M0+l+jNPpfozT6XBjfziwMz8LnAGcB1wA/B+k1GqF/NUqj/zVKo/81SqP/NUGtzIK4wkSZIkSZLUbI29h6kkSZIkSZLGww4jSZIkSZIkbcMOI0mSJEmSJG3DDiNJkiRJkiRt4x5VBzCoiLgOeACwqXzqBcCvAWcCOwFvzczzKwpvYBGxO8WtHp+Wmd+KiCOBc4FdgUsz88xyujXABcDuwKeBF2bmXRWF3bMOy/du4HCKW1wCvC4zP9htuVUPEXECNcy1TvuFzPxihSH1nNM1iKtjLlYQ12uAZ5UPr8zM0+uyzpqqzu1l3du8urdZ/eRLheuwU4y1Wo+zwvao77hq2x71k1dVxDdN6nrMOy5L87HicMamUw5VGc+4RcRZwDOBLcCFmXlur+9tZIVRRKwC9gcOzsw1mbkG+A5wNsWOcg1wSkQ8vMIw+xYRjwGup1g2ImJX4CLgGOBA4JCIOLqcfB3wkszcH1gFPH/yEfdn6fKVHg08rvU9lgeMyy23KhYR+1DDXOu0X6hBZ1E/OV1ZXKXtcrGCuI4Engw8imLbmo+I46nBOmuqOreXdW/z6t5mDZAvVazDTjE+gxqtx1lhe9R3XLVtj/rJq0nHNm3qesw7Ll3yceosk0NTKSKOAJ4IPJJiP/FHERG9vr+RHUZAawE/ERE3RsRLgCOBazPzR5l5B3AZRS9akzwfeDFwe/n4UOCWzLy1PAu4Djg2Ih4M7JqZXyinuxg4dtLBDmCb5YuIewIPAi6KiK9FxOsiYge6LHdVQWs7dc21TvuFqvWU01XHtUwuTtoG4OWZuTEzNwE3URy01GGdNVWd28u6t3l1b7N6zpcK12GnGB9EvdbjrLA96k+d26N+8krDqUt7OSlL9xPTqlsOTaXM/CfgCeV+6wEUV5ndsfy77tbUS9LuC1wD/BFFeeA/ApdSfPktGygaw8bIzJMB2jr89mb7Zdp3medrrcPy7QlcC7wI+C/gCuAPgZ/SwOWbIZ22vzrk2nb7hYjIzPxkVQH1kdMT1UcuvmvCcX2j9XdEPIyiVPg8arDOGqy27WXd27y6t1l95ktV67BTjI8FHk9N1uOssD3qO67atkd95tVE19sUqusx71h0yMep1CWHDqsuovHLzE0R8TrgVOAfgO/2+t5Gdhhl5ueBz7ceR8SFFNcT/3nbZKuAzRMObdR2oLjOsKW1TN2eb5TM/Hdga/lfRJwHPJei977xyzfFarn9ddkvPAWorMOog7quu265WMmBZkQcBFwJnAbcxbal0bVYZ03RsPay1m1eXdusHvOl0nXYHmNmJjVcjzOoFnm1lO1R73rMKzuMhlPLPNFoLMmhW6qOZ9wy8zUR8SbgoxTVZH/by/saWaoYEYdHxJPanloFfAvYq+25PWl+Od136LxM3Z5vlIh4RET8fttTqygGZZ2K5Ztitfx+uuwXNnWbviJ1XXfdcrGKWA6jqIh5RWa+h5qus6ZoWHtZ6zavjm1WH/lSmxjruB5nVC3Xt+3RYLHVab1Nmdp85xqtDvk9tSLigChufEFm/gz4AMV4Rj1pZIURcB/grIj4TYoS+5OAZwPrIuL+FNfk/T5wSnUhjsQXgYiI/YBbgROAizLz2xFxZ0QclpmfBZ4DXF1loANaBbw1Iq6lKEU/BXgPXZa7sii11KeA19Yw1zrtF15YbUjbqeu23S0XJyoiHgh8CDguM68tn67rOmuKJrWXdW/zatVm9ZMvVa3DLjHWaj3OsLqub9ujwWKrxXqbQnU95tUQuuTQNHso8LqIOJyiYu4Y+th3NbLCKDOvoCgf+yqwQHEw9FngDOA64Abg/Zn5peqiHF5m3gmsBS4HvgncTFGyDXAi8JaIuBnYDXh7FTEOIzO/BpwDfJZi+W7IzL9bYblVscz8LjXMtS77hc8v/67Jquu23S0XKwjlVGAX4NyIuCEibqBYX2up2Tpriia1l3Vv82rYZvWbL1Wsw04x/ib1Wo8zqa7r2/Zo4Ng65lUFsU2Vuh7zamjb5VBE1O0k88hk5lVseyz4ucy8pNf3r9qyZcvKU0mSJEmSJGlmNLLCSJIkSZIkSeNjh1FDRMQVEbG2/PuGiLhPxSENJCKeGhFnVR2HNEoR8eqIOKb8++KIOLXqmCT1xzyW6sFcvJvHzZo2EfGrEXF51XGod00d9HqmZeaaqmMYwiHAHlUHIY3YEynGDJDUXOaxVA/m4t08bta0eTAQVQeh3jmG0YRFxOOBt1GMtL8bxeB0vw78EsUdDk7OzM9GxN4UdzfYG/g28ADgrzLz4ojYAtwfeBrwzMx8Wjnvta3H5Sjo5wI7UoyGfk5mdu3NjYi3Av+dma+KiL0obhn5xMy8LiKeDTw9M4+LiFcBxwN3Af8CvCQzvxcR/wj8CDgA+Bvgu8CZwGbgF8BpwCLw4TKmv83MM4Zbm9L2ImI34N3Awyi2vwXg74Czgf9D0UjdAbwR+OPy8eWZ+Sfl+08pn/8F8B8U2/i/RMS9gfOBNRQ5dTXwZ8ALgDcBPwD+lOLOA7tT3Hr1V4D1wAmZeUdE3Fl+7pMpbtP65sz8m/Jz/xB4EUXl5w/Lz725Wy73m+PlZ9xZvudIiv3Pa4FjgUdQ5PzTyzgPpNhP/XI5/7dn5kURsQPwFuA32H6fdTHwk3JeDwS+Bjw3M3+6XExSJ+bxsuvmdcAzgI3lZ6zNzA3d8raf9S4tZS52XS8eN6vWuuQuwPdb21K5rf4+RdtxDivkdPk7dsXpynk/nWKb3hn4GcVA018CEtgH+DRFvn8GuAl4CMVv34dn5onlPA4HzsvMRy2znK8Ffq2c517lcl5HcVfYXwVObw0AHxFnlMu7A/At4EWZeXtE/AbwZmB1OY9PZuYfRsRDgGuAq4DHAPct5/fBldb/NPGStGrMUTQeLwDuB/yPzHw4RZK8opzmfOALmXkQRRIe0OdnvA44NzPngT+gOFuznA8AR5d//zbwPeCo8vHvAJdHxPPKaQ7JzEdSNNoXt83jx5n58Mw8D/gLiiR8NPAq4PGZ+UXgHcClNnoao2cAv1RW4h1SPvfQ8u83ls//BHgl8FSKDtsXR8TeEfFE4HTgCZl5MPB+4EMRsYrijkI/pOgQeTRwMHBqZp4PfBk4ra0B2YeiU2Z/YF/g98rnVwP/mZm/CTyT4o5Fu0TEERQN22PLRvHNQGte3XK53xxvff73MvNQiv3NBcDLgIcD9waOiYh7UNz15RXlvI8ATi0b08dQdGJ32mcBzFPsPw6kaPiP7SEmqRPzuIPyVsAvo2iHHw18AnjMCnkrDcNc7MzjZtVdp9x9J/C8ss0AOIViG4MecrrX6SLiYcAbgKeUOXgKRc7sApwM/Ftm/lY5v32B12fm/sC7gKdFRKuqrj2+5RxeLu+vA0+h6HR6HPASitwmIp5Lsb85tIz9KorjYICXAq/OzMdQHBP/TkTMl689FPh4eez8CuCtPcQzVewwqsZtmfntLG75fSbwgoj4S4rGbrdymiMpG5XM/Ffg2j4/4++B8yPifRQ/4v5shemvB/aNiP1RmUcAACAASURBVF+haPj+HDgqInamOPC8iqLRe3dm3lG+523Ak8ppoOghbrkE+GBEXEDRG/vmPuOXBnU9cFB59q61Y/9X4NbM/Go5zb8B12Xmxsz8T4oGbw+Kbf/SzPwBQGZeTHGg+hCK7f+vMnNLZi5SNGCtg8WlPpSZP8vMX1AcID6g7bUPl/9/heJg914UDe1+wOfK2+O+Gbhv2WB2y+V+c7yldcb034CvZ+Z3M3MzcGu5DvanOFNzURnLPwG7Ao9aYZ8F8LHMXMzMTcDXsYxegzOPO/sucCPwlTIHb8jMD7FM3q4wP2kl5mL39eJxs+psu9zNzC9THO89taxK3ZvixAP0ltO9TncURaXONWUOvo+iymm/DnHeBXweIDO/D1wBPCci7gv8VvnelXwqM/8rM39OUe33sbb4WnE/jaJC/stlTH/E3ZfGnQTcJyL+DPhrivazdXy7iSKfodjPzNyxrR1G1fgpFAPZAVeWz32YorFcVT7e0vY3FMm01NJpWg0QmflOil7UT1Ik29ciYpduAZU/GK+g6JV9DEUP714UFQKfy+KyklaZbssOFONgtWL4adv8zqDo7f0ysJai7FAau8y8laJBOoeijP1TFJdPLS6ZdFOHty/dxqHYvnei2N6Xbv87dQmjfd5L8/TnZZytea0qP/d/Z+aa8qzHr1Occf1xt1zuN8fbtK+Hbuvgv1qxlPH8BvDuFfZZW5ety3JLPTOPOyvb6iMo2tUfUlRUvJll8rbbvKRemIudedysuuuUu+VlYudTVNH9AcWljq1ttJec7nW6HYFrOrRJ6zvNLzPbf+e24juB4hK3XoY26DWmN7XF82jgsPK1T1Pk8s3AWRQnZ1p5urHMd5jRY1s7jKp1FPDRLK63/jLwuxQbMxQ9o6cARMSDgCd0eP8PgLmy/HYnirP9lO/5HEVFwMXlfO5Dcf33cj5AUTr89czcSFHVdA53VyR8DPiDiLhX+fiPgU+XZ4a2ioh7RMS3gHtm5jsoriF/ZESspuj46nZAIA0tIv5/ih9Jn8jM/wl8nOJgsRcfA/6/iLh/Oa/nUfwo+9dyPi+JiFXltnwKxYElDL9dfxw4vhwHAeCFFNdMd83lAXO8Fwn8vLyuvXUJzHqKM67L7bOkkTGPO4uIgyny8abMPIdiTLFDWD5vpYGZi8vyuFm1tUzuXkZRffpMYFzj3F0DPDkiDihjeQrF2Ja7ssI2nZmfo6hGOpXeLkfr1ceBkyNi9/LxWcD/juLO44cA/zMzP0Bxidx+eHy7lR1G1XoH8PiI+DpFidu/Ab8axcCyLwYeHhE3ARcCN3R4/ycoys5vpugZ/XLba6cDZ0XEV4F/BF6Xmd9aIZ5PUZQmthrsj1MMMPjR8vGF5TRfKuP6deDEpTMpe4lfBrw/Ir4C/APwB2UDeS3wWxFx3gqxSIN6L8VO/psRsUAxNs/XenljZn6S4gfYtRHxDYoS1aeVZxb+mKIM/uvlv6QY9BPgI8A5EXHSIAFn5icoBvn8ZER8jeKsyu+VZ3265fIgOd5LLBspBhk9uYzlE8CrMvOzLL/PkkbJPO78GTdSXDrz5Yj4MsVZ2D9dIW+lYZiL3XncrDrrlLtvL9uLyygq4f5zHB+cmd+k6Hi9JCJuBF4P/E5ZLfRN4M6I+BLdq3XeDdyemT3ta3p0AUVV4BfK/dEjKW4a8X8pOnq/EhHrKS7f+yydL5+bSd4lTZIkSZKkKVdWvH0aeHFmfqHqeJaKYkDuDwLrMvPSquNRcR2tZkREnEaHMxulv8jMXgYVk1RT5rjUfOZxvZWDjd8vM9dGxBqKs9a7U/wAe+GSsTjUYOaipk1E/Bbwd8Bf17Sz6OEU1T0fpKi0IyJ+iW0HiG/335n52AmFN7OsMJIkSZJWEBFPorib1ZVlh9F64OTM/EJEXAh8uRzjTZKkqeC4E5IkSdIyyluynw28oXz8YGDXtrP0F1PcIUuSpKlRm0vSFhYWVlOMUL4B+EXF4UjD2pHi9qr/PD8/v/RWj41lnmrKmKdS/dUlT98JnAE8sHy8N0WOtWyguLtOT8xTTZm65OlImaeaMgPlaW06jCiSsdv1iVJTPRa4vuogRsg81TQyT6X6qyxPI+Jk4LbMvCYi1pZP7wC0j+uwiuJW0L0yTzWNbE+l+usrT+vUYbQBYP/992fnnXceyQzXr1/P3NzcSOZVd7OyrE1Zzo0bN/Iv//IvsO3Zx2mwXZ7W8Tsxpt7MekyzlKfToo7b7Ci5fNurSZ4eB+wVETcAewC7UXQW7dU2zZ7A7X3Ms9Z5Oq3boss1HjXJ03HYJk+rXs+9akKcxjg6vcY5aJ7WqcPoFwA777wzq1evHtlMRzmvupuVZW3YctaifHWEd3XpmKd1/E6MqTfGBNQkT0doLO1pXUzjMrVz+bqqLE8z86jW32WF0eMz83kRsT4iDsvMzwLPAa7uY7a1z9O6xjUsl2uspr49rcl6XlET4jTG0ekzzr7y1EGvpSlX3tXlpLan1gEvycz9KUron19JYJIkNduJwFsi4maKqqO3VxyPJEkjVacKI0kjtuSuLgd3uavL6wBvAyxJ0goy82KKtpPMvBE4tMp4JG1vhJX10syzw6gHGzdtZueddtjub6kBRnpXl5b169dv83hhYWHA8Man6pgOOHCOe92zKA+942fFjQiqjqkTY9Io2E5KGpWl+5Be9ynuhwTbVNZfWT61Djg5M78QERdSVNZ7onQZ5pLa2WHUg5132oGjX3kjAFefc3DF0Ui9GdNdXQCYm5vbeq3swsIC8/PzQ0Y7WnWJael+ow4xtavLemo3yZgWFxe36/zUYGwnJY1K+/4Eet+nuB+SlfWjYS6pnR1G0vQax11dJEmSpDoaS2W9NMvsMJKm1Jju6iJJkiTVyjgr69urkZty2fwwcS6t9B7XMjdhXTYhRhhvnHYYSbPnROBdEbE78BW8q4skSZKabWyV9a2hGOp4KX8no45zHMvchHXZhBih9zgHHYrBDiNpBnhXF0mSJE0rK+v7M8jA1g6GPZvsMJIkSZIkTSMr6zsYZGBrB8OeTXYYSZIkSZKmgpX10uhYRyZJY7Zx0+at1xZv3NT3WIuSJEmSNHFWGEnSmFnCK0mSJKlprDCSJEmSJEnSNuwwkqQlll421u0ysvbnvdRMkiRJ0jTxkjRJWqL9EjLofhmZl5pJkiRJmlZWGEmSJEmSJGkbdhhJkqSR8VJNSd24f5CkZvGSNEmSNDJeqimpG/cPktQsVhhJEr2f6fSMqCRJktSZlYTTxQojSaL3s56eHdU4RMTTgdcA9wI+kZkvjYgjgXOBXYFLM/PMKmOUpDrZuGkzO+/kuW+pbjxWni52GEmSVKGIeCjwDuAxwH8A10bE0cA7gSOA24ArI+LozLy6ukglqT56vaOpJGlwdhhJklStZ1BUEH0HICKOAx4G3JKZt5bPrQOOBewwkioSEWcBzwS2ABdm5rlWAkqSppkdRpIkVWs/YGNEfAR4EHAF8A1gQ9s0G4B9K4hNEhARRwBPBB4J7AR8MyKuAS7CSkBJ0pSyw0iSpGrdA3gc8Hjgp8BHgJ9TVDG0rAL6Gjly/fr1IwpvZfPz811fW1hYGOlnjXp+dePy1VNm/lNEPCEz74qIfSjy9j5YCShJXS0da8yxx5pn6A6jiPhL4H6ZuTYi1gAXALsDnwZemJl3DfsZkiRNse8Bn8rMHwBExAcpfnT+om2aPYHb+5np3Nwcq1evHlmQg1quM6lfCwsLI51f3bh821tcXJxo5+dyMnNTRLwOOBX4B2BvhqwErMuydTKOzr2l33+nz+i0jfQ63UrvWe75ppvW5VKzOdZY8w3VYRQRTwJOAq4sn1oHnJyZX4iIC4HnA38zXIiSJE21K4D3RMR9gP8GjgYuA14REfsBtwInUFz6IqlCmfmaiHgT8FFgf4asBKxLx+5Sk+q87PUzBomlW8fTNHbKVr1cderYVTWsHJpeA3+rEbEHcDbwhvLxg4FdM/ML5SQXU5whlaTa2Lhpc8e/66DOsWl8MvOLwJuB64FvAt+mONmyFri8fO5mik4kSRWIiAPKSnoy82fAByguI92rbbK+KwElaRq0Konaq4k0HYapMHoncAbwwPLx0GW5kjRu7aWxdSuLrXNsGq/MvIjtK4iuARq9ITh2gabIQ4HXRcThFFVFx1AcC/+FlYCDad8fuG+QpHoaqMMoIk4GbsvMayJibfn0DgxZlgujv5Z7FNfztpd4tjdod/xskZtvqk/55axcuzwry6nZ4EGyppljF2haZOZVEXEo8FWK8cUuz8xLIuIHFJWAuwBXYSVgzzxJIjWXx6+zY9AKo+OAvSLiBmAPYDeKzqKhy3JHeS33OK7nXdq41eU66KqvXZ6Upiyn13KrVx4wS1IzZOZrgdcuea7xlYCS1C9PCM2OgboFM/OozJzLzDXAq4GPZObzgDsj4rBysufgbUUlSZIkLaPXcfsc308anPmjQYy6juxE4C0RcTNF1dHbRzx/SZIkSVOk1wFzHVhXGpz5o0EMM+g1AJl5McUd0cjMG4FDh52nJEmSJEmSquNIVZK0Akt4pe2ZF5IkSdPNDiNJWoElvNL2zAtJkqTpZoeRJEmSJEmStmGH0Qi1l+dbqi9Jmia2a5IkSbNl6EGvdbdWeT7A1eccXHE0kiSNTnsbB7ZzkiRJ084KI0mSNBFW4kqSVC+2x1qOFUaSNAIbN21m551W7oPvdTppGlmJK0lSvVhBrOXYYVRq/xHnDz9J/eq1sbVRliRJ0iQM+3vV37uyw6g0yFlPf/hJkiRJ4+MPVmlww1b2Whks976SJEmSaqn1g7X9JK0kaTLsMBoTB/aUJEmS6svjdUlanpekjYnle5IkSVJ9ebwuScuzwqgDzzBIkiRJkqRZZoVRBw5mLc2GOg+kOcidGyVJmpRe2qm6tV8HHDhXdQiS1Ch2GEmaWXUuRa9zbBqPiPhL4H6ZuTYi1gAXALsDnwZemJl3VRqgJLXppZ2q20nYe91zda3ikaS6q0+XvyRJMyoingSc1PbUOuAlmbk/sAp4fiWBSdoqIl4TEd8o/725fO7IiPhaRNwSEX9edYySJI2SHUaSJFUoIvYAzgbeUD5+MLBrZn6hnORi4NhqopMERccQ8GTgUcAaYD4ijgcuAo4BDgQOiYijq4uyHuowFqh3P5Ok0bDDSJKkar0TOAP4cfl4b2BD2+sbgH0nHZSkbWwAXp6ZGzNzE3ATsD9wS2beWl4yug47d7dehtZ+6VeVMdRpDCVJahrHMJKmXES8BnhW+fDKzDy9PFN6LrArcGlmnllZgNIMi4iTgdsy85qIWFs+vQOwpW2yVUDfp8jXr18/fIBt5ufnRzo/gIWFhYm8p0lcvnrKzG+0/o6Ih1G0q+cxZOfuqPN0lFb6rpbuE1rTj2NfMaxeY2vq9gnNjl1SfdlhJE2xJSX0W4CPlSX0bwKOAG4DroyIozPz6uoilWbWccBeEXEDsAewG0Wu7tU2zZ7A7f3OeG5ujtWrV48kyHHp94flwsJCLX+MjorLt73FxcVadapExEHAlcBpwF0UVUYtfXfu1jVPB/mu6rzt9hpbnZdhOVXvO+qUp54olUZr5mo0vaZZM8YSeqnGMvOozJzLzDXAq4GPZObzgDsj4rBysucAduhKFStz8hrgFZn5HuA7jKBzV9JoONZY8fu2qR2fLUt/o/ubvVozV2Hkrao1SyZVQl/HMuhuMTW1ER3XOm7SdzdjTgTeFRG7A18B3l5xPNJMi4gHAh8CjsvMa8unv1i8FPsBtwInUPwwnTkbN22u7VhBdY5NI7f1RClARGxzorR8rnWidCpPxLT/1oVm/t6dhmWYJjPXYSTNonGW0FddBt1JHWMa1rjGj6nbeppkTHUqoQfIzIsp7ohGZt4IHFplPJK2cSqwC3BuRLSeewewFri8fO0q4LIqgqtanU/I1jk2jdYkTpTW/aRW3Y7rlmp14M7Pz3PHzxa5+abtj8M6LUNV673u33fLOOO0w0iacmUJ/eXAyzLzkog4AkvoJUnqWWa+FHhpl5fthZBqZFwnSut4oq1plnbg1nl8saZ8373GOeiJUuszJ8Bxk1SVthL6EzLzkvLprSX0EbEjRQn9VJblSpIkaXY41pg0WlYYTYClsKqQJfSSJEmaeo41Jo2eHUbSFLOEfvq1D+bpwJ6SJGmGeaJUGjE7jCSpwaxgVFPZ2SlJGiVPlEqjZ4eRJEmaODs7JUmS6s3TeZIkSZK2441bJGm2DVVhFBGvAZ5VPrwyM0+PiCOBc4FdgUsz88whY5wqS8vuLcOXJElSHVkJKNWfvyc1TgN3GJUdQ08GHgVsAT4WEccDbwKOAG4DroyIozPTW3aX2htesPGVJEmSJA3Gjl2N0zBdkRuAl2fmxszcBNwE7A/ckpm3ZuZdwDrg2BHEKUmSJGkEBrnUzEvSJGn2DFxhlJnfaP0dEQ+juDTtPIqOpJYNwL4DRydJkiRppAapSLBKXtIoebfUZhj6LmkRcRBwJXAacBdFlVHLKqCv0xHr168fNqRtLCwsbPN4fn6+4+tLn5+kpTFWPZ+6m5XllLqxUZUkSVKTeSldMww76PVhwOXAyzLzkog4AtirbZI9gdv7mefc3ByrV68eJqytFhYWVuwIqrKjaJQx9LKs06Apy7m4uDjyzk+pxQZWkjQuGzdtbsSxliRp/IYZ9PqBwIeA4zLz2vLpLxYvxX7ArcAJwEVDRylJkiRp7DwpIUlqGabC6FRgF+DciGg99w5gLUXV0S7AVcBlQ3yGJEmSJEmSJmyYQa9fCry0y8uejpAkqeEmNV7W0s9xnC5JdeH+SRo/86q+hh70WpLqoL2hOeDAuYqjkabDpC5N8e5LkurK/ZM0fl4KW192GFXM2wlKo2FDI0mSJEmjY+9ExVo/co9+5Y12FkmSRHECpdPfkiRJmhwrjBquVZU0Pz9vhZLUwSzlhRWLzRURrwGeVT68MjNPj4gjgXOBXYFLM/PMygKcMCsGVVcRsTvwOeBpmfmtWc5TSdL089dEw1mhJC2vPUfaxyCYRu4Pmqn8wflk4FHAGmA+Io4HLgKOAQ4EDomIo6uLUlJEPAa4Hti/fLwr5qkkjZVVx9Wa6V8UbnCSmsB91dTbALw8Mzdm5ibgJoofpLdk5q2ZeRewDji2yiCr0r79O6C9KvZ84MXA7eXjQzFPJWmsPCFarZm+JM2Sd0lN4B1apltmfqP1d0Q8jOLStPMoOpJaNgD79jPf9evXDx3b/Pz80PMY1tK2emFhoeKIxsvlq6/MPBkgIlpP7c2QeSpJg3DoAU3KTHcY1Y3jj0jS7IqIg4ArgdOAuygveymtAvoqNZubm2P16tWjC7Am6tCJNS4LCwsu3xKLi4sj6fwckx2ALW2P+87TqpZt6ffQ3pE3zdvgSjp1aHZaH3Xs+KxjTBofCx80KXYY1YiJL42GHa5qmog4DLgceFlmXhIRRwB7tU2yJ3dfBiOpHr7DkHlal47dWe4katfreqjb+qq6s7nmHbuShmCHkaSpY+ermiQiHgh8CDguM68tn/5i8VLsB9wKnEAxuO5MsxJXNWOeShqb9nZucdNmVtvmqQJ2GEmSVK1TgV2Ac9vGRnkHsJai6mgX4CrgsiqCqxM7g1UnmXlnRKzFPJU0BkvbPMezVBWmvsPIM5CD80yuJI1fZr4UeGmXlz0ilGomMx/S9vc1mKeSNBHD/j5d+h5/465s6juMmnp3oUGSYdQJ4JlcSZIkqbNej9c9CSuNxrC/T5vaN1Clqe8waqpBksEE0LTzgEuSJNVFr8frnoSV1FRT+Wtr46bijqZ1u4NBHbXWVbfHUp20DriOfuWNdhZJkiRJ0hhNZYWRvfi9sypJktTO6j1Js6jXO1K5j5Q0S9zbNVCvVUDt03X7e9B5S2qu9ipMc15LtVfySdKsaN/3rV5mP+g+UtIsmcoKo2k37PXSy73f6ixp+pnnkiRJklYyNRVGniVf3jjXT6/VS5KqY25KkjqxfZAkdTM1FUaeMV/eONeP616qP/NUktSJ41lKkrqZmgojjZdVRJKkOvEun5Kazv2YpLqbmgojjZfVCZKkOrEqQlLTuR+TRmPp3Qu9m+Ho2GGkvpmAkiRJkqQ6sPN1fOwwUt9MSEmSpPprP8m3uGkzqz3hNzZWOEiaRnYYNUCTGpz2WJsUtySp2bq1P7ZLmmVLhxRweIHx8YSqNFmTatNnvTPYDqMGaFID5FhHkqQqdGt/bJckSZo+k2rfm/RbfBxmp2tMteJd16TxMq8kafa47282j4/l9z4a7etxccTrdNby1AojVcIzvtJ4zfrZEPVn2sqruy3PrJeVa/q57282j4/lNjAa47wcd9a+o0Z3GHmgN7sck0KSRmfaDn56uTxt6WvLsc2RJEmzqNFHPK0Dv/aDP82G9u/eA/fp00up5yyUgE7C0vW42GXdz1r5rdTONkdSv4ZtK213p4vfZ7Ns3LSZ+fn5vt/T6e+mG0uFUUScAJwJ7AS8NTPPH8fnqN56Lf3v9cztIIk7SDyzos552ku1g2X3o9FpPTpwcH3UOU+nQbdbjs96+6D+mKfqZNh203Z3tKrOU7/PZhnk+5rW73jkR0MRsQ9wNnA4sAY4JSIePurPUf21n5Fd7qxsr2duh60o6zWeWTDOPF3ao95LD/ty00xTD33T9Lruuw0s2O3vfuY9iGk5w2N7On7t7cLqIduYQfZ9ar46tKdua80y6u+r/YRqr8dTvW5L3aqOm6aqPB1kPWuyxjk4drfPGeT4ern3HHDgXN/z7sc4KoyOBK7NzB8BRMRlwDOBs4adsWf8mq2X72+QKqDl3jPqbWaKxrEYW54OUvmz3Humtbe+CXpd98sNLNjp75XmN6wp2mbGlqda3qj3Y5pqlbenU7TPmwmj/r4GbatXmqY13ZRsW5XkaT/j6U3Jem6ccQ6OvdznjPI997rn6rFuP+PoMNob2ND2eANwaA/v2xFg48aNy050/Otv2vr3xacfyH3uuQWAxcXFFf/udbpB3jPOeU9LPFs2b9r6/XX77tqnaU23uLj14UDv6fSZrXkMon1+3WbRth3vONCHjN9I83TpuhxkPc9qXjQ1nqUGiaebQXOzWzzdzFqettt012Z2use2Hd5N3A6riOend/x867pbuh67bePt03Va9+1a71vuPd1e6+dzqtLL8rWb5TyF3tvTqvOi6vfMQjzLGeV0y8XTzSzl6XLHQJ1en7bt0HiWz4tej5V7NcrcHjRPV23ZsmXlqfoQEWcAu2Tmq8rHzwfmM/OFy71vYWHhcOAzIw1Gqt5j5+fnr686iKXMU2kb5qlUf+apVH/mqVR/feXpOCqMvgM8tu3xnsDtPbzvn8v3bQB+MYa4pEnaEdiLYruuI/NUMk+lJjBPpfozT6X6GyhPx1FhtA9wPUWZ3x3A54BTMvNLI/0gSQMzT6X6M0+l+jNPpfozT6XBjfzi9sz8LnAGcB1wA/B+k1GqF/NUqj/zVKo/81SqP/NUGtzIK4wkSZIkSZLUbPW7fYYkSZIkSZIqZYeRJEmSJEmStmGHkSRJkiRJkrZhh5EkSZIkSZK2YYeRJEmSJEmStnGPqgMYpYjYHfgc8LTM/FZEHAmcC+wKXJqZZ1Ya4IhExGuAZ5UPr8zM06dxWSPiLOCZwBbgwsw8dxqXs24i4gTgTGAn4K2Zef6S19cAFwC7A58GXpiZd0XEScAbgf8oJ70yM8+IiAcB64AHAAmcmJk/HXdMwB7AJ9omuzdw/8zcLSKOAD4A3Fa+9tXMfN4oY2qb7r3AtZl5cfm44/qIiPsA7wMeCvwAeFZmfm9CMR0GvAXYGfgh8AeZ+e2K19PYtifV06D7nrbXXw/8IjNfO7Gg+zDEvrVjfk40+BUMsWyPBd5KsWy3Aidl5o8nGvwMGnX7NZGgezDq9mb8Efemh/w6BngdsIoij56XmT+u+/dVN03Ii6Zs403YZoeIcWLrsocYn1HGuCPwz8Apmblx1OtxaiqMIuIxwPXA/uXjXYGLgGOAA4FDIuLo6iIcjbLD5MnAo4A1wHxEHM+ULWv5Q/WJwCOBRwN/FBEHM2XLWTcRsQ9wNnA4xfZ1SkQ8fMlk64CXZOb+FDvR55fPPxr408xcU/5r7Tz/GvjrzDwA+DLwqknElJnfb8UC/DrwLeCUtlj/si3WfjtBVowpIvaOiI9SdHq267Y+/hz4TGYeCLwLeNsEY3ofcHK5rt4HvL18vsr1NJbtSfU0zL4nIu4dERcCL59gyH0Zct/aLT9rYchlezfwnMx8BPBN4LTJRD27xtR+VW5M7U3lVlqu8mT53wBPzcyDga8Bry1fru33VTdNyIumbONN2GaHjHEi67KHGO8F/BVwVGYeBOwCrC1fHul6nJoOI4qDjxcDt5ePDwVuycxbyzOQ64BjqwpuhDYAL8/MjZm5CbiJopNsqpY1M/8JeEK5PA+gqIa7D1O2nDV0JMUZiR9l5h3AZbQ1OhHxYGDXzPxC+dTF3P0dHAKcFBFfj4h1EXHfiNgJeFw5n6XTTyKmlucBP8vM97fF+uSI+FpEfCQiHjjKmEonAh8G/r4t1uXWx1MpfgwC/B1wdDn9uGNaDZyZmV8rn/oa8KDy70rWU9tnj2N7Uj0Nk+fHALcA/2ty4fZtoOVbIT/rYpjv7sDM/GaZ2/sAVheN3zjarzoYaXsz/nB7ttJy7QS8ODO/Wz7+GvCgBnxfddOEvGjKNt6EbXagGMu/J7Uul42xfO4hmfkfEXFPit/LPx7HepyaDqPMPDkzP9P21N4UnSstG4B9JxvV6GXmN1oHXRHxMIpL0zYzncu6KSJeR3HW8Rqm9DutmZXW8XKvbwBeT1EVdhtFr/f9gJ/k3ZeNDPKdDRMTEbEjcAbwirZp/i9wXmY+ErgKuGTEMZGZf5GZFyx533LrY+s8y9d/Atx/3DFl5mJmrgOIhVjOmgAAIABJREFUiB0ozqB8qHy5qvXUmnYc25PqaeA8z8z3ZuYbgV+MO8ghDLR8K+RnXQzz3W2KiEcA3wGeQP/7GPVvHO1XHYy6vamLZZcrM3+YmR+ErVdXvIJiH1H376tumpAXTdnGm7DNDhpja9pJrMtevu9N5dU2t1Gsv08whvU4NR1GHexAMfZNyyqKjpWpEBEHAZ+kKN/+d6Z0WTPzNRQ/mh9IUUk1lctZIyvlTdfXM/MZmfnZzNwCvBk4usP00P93NnBMpd+mqEz7euuJzHxhZn6g/PsdwEERce8RxtTr+2h736olz/e7fQ+1z4uInSkqnO4BvAEqXU/j3J5UT8Pmed0NtXyd8rNGhlq2zPx6Zv4KxQH4pWOMU4VxtF91MOr2pi56Wq6ybb4SuDEz39PhfXR6n7ZqQl40ZRtvwjY7aIyTXJc9xZiZV2fmLwNXUFxGN/L1OM0dRt8B9mp7vCd3X67WaFEMgHkN8Ipy4526ZY2IA6IYJJPM/BnFwLuPZ8qWs4ZW2pY6vl6OIfInbc+vAu4Cvg/cu6zyoXxvv9/ZQDG1Pf5d2s5aR8QOEXFGW0wtd9G7QXNuufXx3XI+RMQ9gF+iGOB23DEREbsBH6P4MXpMecaisvU05u1J9TRsntfdwMvXKT/HG2rfBm03domI3217fh3FGVuN1zjarzoYdXtTFysuV0TsBXyG4rKZk8un6/591U0T8qIp23gTttmBYpzwulw2xojYIyKe3Pb6+yja0JGvx2nuMPoiEBGxX7nCTgCurjimoZVjiHwIOCEzWz+Cp3FZHwq8KyJWl2dWjwHeyfQtZ918CnhSRNy/vB729yl+qACQxZ157iw7LQGeQ/Ed/BQ4PYrB5wFeAnyw/GHzGeC48vnn0v93NmhMLf+jjKE1/WbgGeV8iIjnAl8srwUeSUzdrLA+riofU77+mT5/GA4UU2kd8K/AcZm5WMZa2XpivNuT6mnYPK+7YZZvu/ysmUGXbRNwfkTMl88/i+LmJRqvcbRfdTDS9mZMMQ5i2eUqj4c/Cvx9Zr6srHpowvdVN03Ii6Zs403YZgeKkcmuy5W+71XAuijuiAbFOEXXj2M9Tm2HUWbeSTFS+OUUY+DczN2DPzXZqRSjoJ8bETdExA0Uy7mWKVrWzLyKogTwq8AC8Lmyg2wtU7ScdVMO7nYGcB1wA/D+zPxSRFwVEY8uJzsReEtE3AzsBrw9M39BcbD/NxFxEzAPnF5O/yKKkf2/CTyW4vaQY4+pbRYPpeilb3cS8LKI+AbFgNgn04ceY+qm2/p4FfAbZUwvohjEf+wxRcSjKDpkDwO+Uu5XripfrmQ9jXN7Uj2NIM9rbdDlWyE/a2HIduM44G/LY5ln0uc+Rv0bU/tVuTG1N5XrYbl+h+JOsM9s/S6IiNYYNrX9vuqmCXnRlG28CdvsoDFOcl2uFGNm/pDi7s9XRMSNQAD/s3z7SNfjqi1bll7iJkmSJEmSpFk2tRVG6k1E/GpEXF51HNIsa2oeRsQnIuJ+VcchSZIkafTsMNKDKUrYJFWnqXl4VNUBaLYM00kZEe9qGy9HUoXMZal65uHdohjQ+tqq46ije1QdwKyI4i4n7wYeRnFru4Xype9n5hnlNM+mGNDqbcA5wP+h+BF5B/BG4I/Lx5dn5p9ExON7ma6c99Mprl/cGfgZxVhIXwIuAPaJiI8DL6AYJOsm4CHAe4CHZ+aJ5TwOB87LzEcts5wHABdSjLO0CrggM/+6fO2Mcvl2AL4FvCgzvWODJmYW8jAifhn4NvArmXlHRLwTOCAzjyhfv4Xi2uwdgL8Cfpni9pv/KzPfWy7P28rl2I3i2ucLlqyzF1DkOcB1EfGUzLytl+9AGtIwnZRHUdw8QVL1zGWpeubh3e4LHFp1EHXkGEYTEhHPAU7MzN8uR15/B0WSfQR4UGbeFRGfBs4GFilGRj8kM78aEVcD96a4rfzuFLfGewiwf4/T3YvytvSZ+cOIOKh8337AIcBfZeZcRDwEuBV4XGZ+JiIeANwC/Gpm/igi3gt8NjO77hwi4kLglsx8Y0TsCbyV4m5mzwaeAjy7XNZTgN/NzKcMt2al3s1QHl4LnJuZV0RElvHsBzyIYqD4RwIJnJaZH4iIvSk6ro4FVgPXAA/NzG93WWdvysx/jYgtwP0z8z8H+T6kfkTEuylufLAeeBpF+/IgYCfgksx8Q0Q8AfgH4GBgA8W2fC3FSYzTKHLrucCbKHLusnLe/9h6HBGLwIfLeZxI0Xn6NorO1R0pBmy+aIVYL6boFH4E8CsU+5gfAk+nuDXuyZl5bRR3AX0TcEQ5768Cf5yZP4mIpwF/RtHB/ADgPZn5qrJT92zg34G5cvlfkJmf7XulShVoWC4fDpxbTr8FOCczL18ud4ddP9IkNCUPI2IN8NHMfGD5+OPA9zLzpIhYTXGc/VCKY9u/AO4JbATOzMyPRcRa4A8pjsP/CzgeeC/Qqqy6smxbrwMeB3wdmC8HuBZekjZJ1wMHlQn0CuCtmfllikR7akQcCOwNfKKc/tbM/Gr5978B12XmxvKH2U+APfqY7ihgL+Ca8k4k76OoFNivQ5x3AZ8HyMzvA1cAz4mI+wK/Vb53OR+kuN3gB4Dfo2g8N/P/2rv3aEvq6sDjX2jo5mEIIeoASkwUe0PsEfD6yEQZoqIZjJFERUacjG0WOmY0gzOiwSUJD5caE0IyOg4alZiEKER8RG00KhpfBEyuAqt5bE2CGZR2rbwmo4C3L3bPH7+6cO7pe84997yq6pzvZy0W99R57aqu36mqX+3f/pUfop8C/rqK4Vdp5xActds8tcPTqvX5NvBlyknt6ZRZBrcCB2Xmh6rvuKta/h+q99+ZZSrsXtvsb9b5fmnsMvMl1Z9PpWQKXp6ZC5Q7gqdGxAsy83OUTuB3UbL5loA3VhmEd1E6P29Y56s2U05OgzIzydXAedV3nQKcGxE/NUDIjwOeRjkBfTXwvcz8acqJ8nnVa86jtPeFzDyhivE3I2K/6j0vzszHU46fr+sYOvAkSlbgSdW2eNMA8UiN0LK2fBHlBswC8MuUNg092u5gW0CqX1vaYWbeCCxHxLaIOBg4jgfa4dOBGyijpq4GzsnMx1Jm+b0iIn6iet1jKDdsnwq8FPi7zHwcJYv+0RHxw5TZgO/NzBPtLFrNIWlTkpl3RMSxlKyDpwGfqbJs3k45AH0d+P3M3BsRUBpkp+UeHz3I6zYB12bmmSsLIuIYSkM/ufvzMvO+jsdvBy6jHBQ/mJnf6xEHAFVGw6MpF8dPBy6oxrduomQlXFZ9/xZK6p80NfPSDikdRl+grM+ngX8Bnkk5CXg5D9wp7bQ/5a4SwP2f32ubZebH1olBmpRDKSeZR0TEG6plDwJOBP4UuIDS0flfgW3VTYuN+mL1/63Ao4DLq98EgIOBk4Dr1/mMj2XmMvCdiLgb+GS1/G95oLP52cDhwDOqz99MGSK7txrC+uyIOAs4njLM+9DqfX9fnUQDfJVyl1hqmza05T8F3l61x89Qsv6gR9sdIj6pbm1ohx8GTqNkQ10LnFBl6q/cCH0S8DcrnVeZeUtEfJly7roXuLkj+++TwDUR8WOUNn1eZv5rdVNWazDDaEoi4lcovbefysxfA/6ccvfxakojeT7QNy12BNcCz6zqCxERzwJupjTQ+3jgInEfmXkdJQviXMpQlL4i4n3AmZl5JeWH5f9Rfhj+HDg7Ig6rXnox8MfDrpA0jHlph5n5LeAfKZ1Dn6Ks5/OAH83Mm4DbKXdrnlvFcnT1/Ke7P6vPNgP4Qb+4pQnZS+k8+enqTuCJlAyclSybH6Zk8+2h1N7q9xkrNnc9v9Jpugn415Xv6fiuPxggzkE7ks/p+OwnAs+PiEMpQ1weR+kQek31/pWY7+2zLlJbNL4tZxn+/W8px8efBW6OiIPo0XbXW2GpgRrfDikdRs+i3Pz8NA+0x9OAj7CxG6F/BfwE8PuUkhFfiRkq3j0JdhhNzx9RduZbI2KR0vjempm7KRer1+WE6oBk5q3Ay4ArI+Im4A3Ac6oshVuB70fEV+h9wvkHwF2ZefMAX/cG4EXV99zAA5kO76YMq7k+Im6hjDPdPvxaSUOZl3YIpe09BPhaZt5BucD8cBXLMvALwDkRcTPlDsvFVepxtzW3WfXcB4DPR8S2AWOSRvUDSsfJ9cD/AIiIwynDLk+vXvMeyg2JlwB/UqWaw+qO2X8AHl+9/ycpx6S1JHBvlGL4K1mBO4FxnVz+OfDKiNgcEftT0v7fTDkpP4xSg+FjlLukWyhtUZoFrWjLEXEdcFJmvpdyDD+cUoesV9uV2qQV7RC4jpKA8GzKOeungFcBX8/Mf6KUcTguIp5Yfe5jKMPB/6L7gyLiN4Ffz8yPAOcAt1DqAd4HbKqGhKuDRa9rVt1F/ALwisxcL7196iLiAMpF5hWZeVXd8UiTYDuU2iEi3k85sXwupV7IIyh3Mt+fmRdGxCsoxS2flJnLEXEZcERmnhkRv0XJpPsV4DuUGQgPpGTcHUKp33B1dBVzj4gTKHWHjqhe/z8zs2+mX5Si1zsz85Lq8fcoqfzfjFLM+sLMfHxVj+ESSofQJkp9h5dR7oa+i1JbYolShPMxlBP6Jaoi+dVn/0znY6kNWtSWn1K9Z39KBsMVmXlpr7abFr1Wi7SlHVbveydlgpnHRSk6/8+UyVtWyp08tVqHQyjZUBdlmdhlO/D8zHx29bojq1gfRjme3lStw32Ugt4PAU6uOqKEHUa1ioifBd4P/O/MPL/ueLpVPcRfplyonp2ZeyLih3hgHGq372Zmdy0WqdFsh5IkSZK0LzuMJEmSNiBKtc5e2X7ZWdxeUnPZlqX62Q6bzQ4jSZIkSZIkrWLRa0mSJEmSJK1yQN0BrFhcXNwCPAHYRanYLrXZJsoUlH+1sLDQPbVya9lONWNsp1Lz2U6l5rOdSs03VDttTIcRpTH2KuIqtdXJwJfqDmKMbKeaRbZTqflsp1Lz2U6l5ttQO21Sh9EugK1bt7J58+a6YwFg586dbNvWvllqjXu61op79+7dfP3rX4dqv54hE2unbf33H6d53wbTXn/b6Wzvc7O8bjDb69e5brbTyWji/tO0mIynP9tp8zRtH1lPm+JtU6ww3uvTJnUY/QBg8+bNbNmype5Y7tekWDbCuKerT9yzlr460Xba1n//cZr3bVDT+s91O53lfW6W1w1me/3WWLe5bqeT0MT9p2kxGU9/ttPmaUucK9oUb5tihfFdnzapw0iSJElqpIj4eeAC4FDgU5l5TkScClwKHAxclZnn1xmjJEnj5CxpkiRJUh8R8UjgHcAvAI8FHhcRpwGXA6cDxwNPqJZJkjQT5q7DaPfynjX/ljSbutu57V6aDbZtTdkvUjKIvpWZy8CZwD3ANzLzjsy8D7gCOKPOIAd13PEP1Law7UjqxWtnzd2QtM0H7s9pr7sJgE+8+YSao5E0aZ1tHmz30qywbWvKjgV2R8RHgR8DPg7cwuriobuAh2/kQ3fu3Dm2ADdiYWFh1fnw4uJiLXF0a0ocK4ynv6bFo/Hz2llz12EkSZIkbdABwL8Hfgb4HvBR4F5gb8dr9gM2dAt+27ZtjSikurCwUHcILC4uNiKOFcbTX2c8S0tLtXV+SpqskTqMZqn43+7lPWw+cP99/pYkSdPV65js8Vk1+g7wmcz8B4CI+DBl+FnnbDNHAnfVEJskSRMx9FnXrBX/W0m3O+11N3kyKs0wx2JLzdfrmNy5XJqyjwM/GxGHR8Qm4DTgaiAi4thq2VnAJ+oMUpKkcRqlZ2Smiv9Jmg92DkuSNiozbwB+C/gScCvw98BlwHbgg9Wy2ymdSJIkzYRRhqS1svhf99jflWJtvZa3tZibcU9XW+OWJEmDyczLKZn0na4FrAQrSZpJo3QYzUTxv17F4xYWFhpXXG5Qxj1da8Vt8T9JkiRJUpuN0mHUiuJ/FsiUJEmSpNkXERcDz6ckMbwnMy9t66RMUhOM0mH0ceAPI+Jw4Ls8UPzvvIg4FriDUvyvO3V3qlbqlaz4xJvNGpYkSZKkWRIRpwBPo0zIdCBwa0RcS7kePQW4E9gREadlpgXqpQEMnXpj8T9JkiRJUhNk5ueBp1aTLz2UkhxxOE7KJA1tlAwji/9JkqShdA4Zd/i4JGkcMnM5Ii4CzgU+ABzNiJMySfNspA4jSZKkYXQOGXe4uCRpXDLzgoh4C/AxYCsjTsrUpolsxj1zc6+ZxPs57vhtHHpImcTq7nuWuP223tuvTTNNtylWGF+8dhhJkiRJklotIo4DDsrMGzPznoj4EKUA9kiTMk17Fu9hTWPG6UE/v/OGUK/3tGmG7DbFCuOdxdv8b0kzZ/fyhm4c7fOeYd4vqTl2L++5/0TJ9ixJc+ORwLsiYktEbAZOB94JREQcGxGbKJMyWfBaGpAdRpJmzspQl84ZEjfyHmupSO1me5ak+ZOZ1wA7gK8Bi8B1mXklTsq0j2FulHpzdT45JE2SpJpFxMWUtPm9wHsy89KIOBW4FDgYuCozz68zRkmSmi4zLwQu7FrmpExdhqkjaO3B+WSHkTQHIuIS4MGZuT0iTgTeDRwGfAF4eTXNqKQaRMQpwNOAxwIHArdGxLWUWUhPAe4EdkTEaZlpGr0kSZKmwjxtacZFxNOBF3csugJ4ZWZupcwU8dJaApMEQGZ+Hnhq1XH7UMrNnMOBb2TmHdXyK4AzagxTkiRJc8YMI2mGRcQRwBuBNwEnRMQjgIMz8/rqJe8FLgIuqydCSQCZuRwRFwHnAh8AjgZ2dbxkF/DwjXzmoDNh1DVNbL+pens9t94MJb1e12sdNzL1bxO1bYrfjZjldZMkqS3sMJJm2zuB1wPHVI9HvgiFwS9EN2ojFwidF3oAS7t/wJbNm4b+zkEvMCdt3i+S5nn9M/OCiHgL8DFgK6We0Yr9gA1VmBxkGuAmTRPbL45BY+z1un7vH2Tq3yZq0r/duHWu27DTAEuSpNHZYbSG3ct72Hzg/qum5HWWFbVNRJwN3JmZ10bE9mrx/ox4EQqDXYhu1DAXP52zoH3izScMVYhvmAvMSZnlC8BBTHv9m3IhGhHHAQdl5o2ZeU9EfIhSAPsHHS87ErirlgAlSZI6dF8fe708u+wwWkNnBXiwCrxa60zgqIi4ETgCeBCls+iojtd4ESrV75HARRHxFEobPZ2SHfjbEXEscAdwFqUItiRJUq28Xp4fdgNKMyozn5GZ2zLzROA3gI9m5kuA70fEk6uX/RLgrEtSjTLzGmAH8DVgEbguM68EtgMfBG4FbgeuritGSZIkzR8zjKT58yLgXRFxGPBV4K01xyPNvcy8ELiwa9m1gLfsJI2FQ0ak+eZvgIZhh5E0BzLzvZQZ0cjMm4An1hmPJA3KE1xpPDqHkDh8RJo//gZoGJ6BSZKkxlo5we2slSBJkqTJm+sOo93LG54cSpIkSZIkTUD3NbrX7PWa6yFppuVJkjQbHLomSVL7OQNbs3hmJUmSWs+ha5IkSeNlh5EkSZI0gxzKIc03fwM0KjuMJM2tXgfR7uVLHY898ErtZfvVvDHzTppv/gZoVHNdw0jSfOtVx2ytsdPWO5Paz7oIGlVEXAI8ODO3R8SJwLuBw4AvAC/PzPtqDVCSpDEyw0hSa5gdIEmqS0Q8HXhxx6IrgFdm5lZgP+CltQQmSWPk+bY6zWSHkTu5NJtMq5Uk1SEijgDeCLypevwI4ODMvL56yXuBM+qJbrx2Owxbmmueb6vTyEPSmpia22uYiSRJkjSEdwKvB46pHh8N7Op4fhfw8I1+6M6dO0ePrMNxx2/j0EO2bPh9i4uL9/+9sLCw6jy687lJm+Z3DcJ4+mtaPJLGb6QOo47U3B3VoiuAszPz+oh4DyU197LRQpQkSW20e3kPmw/cv+djqQ0i4mzgzsy8NiK2V4v3B/Z2vGw/YMPpONu2bWPLlo138PQzTJ2uhYWFoZ4bp8XFxal91yCMp7/OeJaWlsbe+anBrRxbFxYWWFrewxaPsxqjoTuMulJzT+iRmnsRdhhJkjSXLDKtGXEmcFRE3AgcATyI0ll0VMdrjgTuqiE2SXOue3SNx12N0yjdjyupuf9SPR5Laq4kSZLUFJn5jMzclpknAr8BfDQzXwJ8PyKeXL3sl4BP1BakJEkTMFSG0SRTc8eRzjjudM3OFPq771ni9tuan3LZ1jHFxi1JGieHwWmCXgS8KyIOA74KvLXmeKS5FxEXAC+oHu7IzNdGxKnApcDBwFWZeX5tAY6JxzZNy7BD0iaWmjuJsdyj6k7za9L44bU0bYzzoGYpbsdyS1IzOBGGxikz30spu0Bm3gQ8sc54YLIXjp2f7QWqmq7qGHomcBLl2vSTEfFC4C3AKcCdwI6IOC0zW50R6LFN0zLUr76puZIkSVL9JjkFdudn21mkFtgFvDozd2fmMnAbsBX4RmbeUc3efQVwRp1BSm0y0ixpazA1V5IkranJGQpmUkhSu2XmLSt/R8SjKUPT3saIdXabOGpg3KMyOstrDPPZ/Y6bvUp3HHf8tvv/7iz7stb3N6H8RxNi2IhxxTtyh1ETU3MlSVLzNDmFvsmxSZIGFxGPAXYArwHuo2QZrdhwnd0mlkwZt1E7oPodQ/t99qBlX+ouW9K20injLJky7gwjSZK0QfNSpFOSpEmqyqN8EHhVZl4ZEacwhjq70rwy31qSpBp1Fek8EVioinReDpwOHA88ISJOqy/Kydq9vOFJVSVJWiUijgE+ApyVmVdWi28oT8WxEbEJOAvr7E5N9/Hd4337mGEkSVK97i/SCRARq4p0VstWinTO5EluZyo7OCRMkjSUc4GDgEsjYmXZO4DtlKyjg4BrgKvrCG4eeXxvPzuMJEmqUd1FOidZxLFN4/27taG4ZRtiHNYsr9ussFC8miYzzwHO6fG0PRXSEOwwkiSpAeoo0tm2Io7T1PTtMsv/dp3rNmyRTk2eheKlyZpWp6wdvurHDiNJjeZBTPPAIp2SJKnTtDplHTamfuwwktRo3sHUrOso0nlmZn62Wnx/kU7gDkqRzstrCnFNve582skrSZI0G+wwkiSpXq0s0tmrM9dOXmk22RksSfPHDiNJkmpkkU5JbeCwFUmaP94mkCRJkiRJ0ip2GEmSJEmSJGkVO4wkSZIkSVItdi/vqTsE9WCHkSRJkjSnvFCTVLeVGmmdddLUDBa9liRJkuaUxawlSb3YYTQhnVOPOg2p6hQRFwAvqB7uyMzXRsSpwKXAwcBVmXl+bQFKkiRJ8rpRjePeOCGdaXU2etWl6hh6JnAScCKwEBEvBC4HTgeOB54QEafVF6UkSZIkh2apaezJ2KDOcd6O+VYL7AJenZm7M3MZuA3YCnwjM+/IzPuAK4Az6gxSkiRJktQsDknboM5x3o7xVtNl5i0rf0fEoylD095G6UhasQt4+JRDk6RWcsi5JEmaF3YYSXMgIh4D7ABeA9xHyTJasR+woXS5nTt3ji+4DouLi/ssW1hYmMh3DavzAvHue5a4/bbxbou1tsE8mff1V/N540iSJM0LO4ykGRcRTwY+CLwqM6+MiFOAozpeciRw10Y+c9u2bWzZsmWMUZaOgqZ1Dq2l+2JxnDG3ZRtMyrTXf2lpaWKdn5IkSVLbmUctzbCIOAb4CHBWZl5ZLb6hPBXHRsQm4CzgE3XFKElN012j0JqFahr3SWl2DNKebfOF9YSnzwwjabadCxwEXBoRK8veAWynZB0dBFwDXF1HcJLURJ2ZhODQMzWPQyOl2TFIe/a4VPjbN312GEkzLDPPAc7p8bS/spIkDSgiLqBMHgGwIzNfGxGnApcCBwNXZeb5tQUoSdKYjdRh5IFTkiRp3xnTnEFttlTnt88ETgL2Ap+MiBcCbwFOAe4EdkTEaZnpMG9JmjBnLZ2OoTuMmnbgdCeRJEl1cbjAzNsFvDozdwNExG2UGUe/kZl3VMuuAM7AuoCSNHEOT5uOUTKMGnXgdIeRJEnSJGTmLSt/R8SjKRn2b6OcD6/YBTx8I5877EyNTZxRc3FxsRGfMU7G01/T4lkREYcB1wHPzsxvOgJGGt7QHUaTOnBK0iwwTVbzZF738Xld73kWEY8BdgCvAe6j3CxdsR+woWl7tm3bxpYtW8YXYI1G7cRaXFxsVEeY8fTXGc/S0tLQnZ/jFhFPAt5F1TYj4mDgchw62ggeN9tn5KLX4z5wtulOS/cOf/c9S9x+284145l2D3xTe/zXY9yaFWY9ap7M6/4+r+s9ryLiyZQZRl+VmVdGxCnAUR0vORK4a1Lf3+QLLW+SqEFeCrwC+OPq8RNx6GhjeNxsn1GLXo/9wNmmOy1r1Svo1XE1zQ6tpt2BGNQsxd2kOy2S2sEUeqm5IuIY4CPAmZn52WrxDeWpOBa4AziLkskwEU2+0GpybJovmXk2QESsLDoaR8BIQxul6HXtB05JkmaBKfRS450LHARc2nEh+g5gO+Xm6UHANcDVdQTXRt1ZSdKE7E+ZoGnF1EbA9HLc8ds49JB2JEg0xcpojn7JBZMe8dG2ESXjineUDCMPnF1MwZUkDckUeqnBMvMc4JweT5tSMwSzkjQl36KBI2CcVXNjBhmFMsmRKm0bCTPOETCjFL32wNnFA58kaRiTSKEf9KRg2DtQbTpxGodB7m72es+ktO1u50bM8rpJmipHwEgjGLnotSSNm9l60ugp9IPcEW3bHbM6DbOdvNs5nKbOvqTBWABbTZKZ34+I7czpCJhZ4O9IvewwktQ4ZutJo6fQS1IdPIarCTLzxzv+vpY5HQEzC/xNqZcdRpI0pM47HkvLe9ji3Q+Njyn0kiRJqpUdRpI0pO47HhYw1LiYQi9JkjQch8aOjx1GkiQ1hCn0kmZJ94WaF26SpsFhbONjh5EkSVIfo17keqdT86rzog28cJOktvGMRZIkqY+Vi97OC99h329nkSRJ9di9vKfvY+3LDCMwzs7pAAANWUlEQVRJkiRJkjTTzHrcuFbf5pqFHsHOdZiF9ZEkScPxnECSJDVJqzOMZqGY1SysgyRJGp3nBJIkbZz1ASen1R1GkiRJ0izyAkiSBuNQs8mxw2gKOg/4S8t72OLBX5LUMl68jofnBBqUF0CSpLrZYTQF3SnmHvwlSW3jxet4eE6geWbHsyS1i7/YDWXhS82T3ct7WFhYqDuM2h13/Lb7/7bdS1rhNMCaFSsdpp0dpZKk5jLDqKEsfKl5YuZCceghW2z3kvbhb6QkSaqDGUaSauEdckmSJEmT5DXHaOwwklQL09Kl5nAYtCRJmkVec4zGDiNJY2WtjX2NejHuNtWkdZ5MWZBW0rR11jLsd4yzc1uSpssaRpLGylob+xq1JpnbVJI0ywY9TlrjU9I4dc7cuLS8hy1r/L1eJ/bmjtfN4k232VujOeDdFbWV++u+3CZqms59csn9U9KUdR8X/R2SNCmdGdZbevzdrxNoHjK0zTBqIe+uqK3cd/flNlHTdO+T7p+SpmmtrFp/hySpHrPZDdZS4840MBNJap5ptcV+d2iX/G2QJM0Yz3tVh351Jt0nZ9O8/VuaYdQgvTINhh0POUjmQvdnz+rYS6kpplWPaL07tN6tlZpt0OPxqPUT5qH+gubDIOfR/fbx7tdJg+h3XmcW+Wyat39XO4xaYJIXmBbTlZppIye1XuBJs2daRYDn7cRX88eC2pI0vIl0GEXEWcD5wIHA72Xm2yfxPVq7svvKtKTT+M61HqsdbKfN1u/E1ZPa+WE7lZrPdto8w2ToabZNq5322qfc1+ZLv+vlXjOz9XtdnTeSx95hFBEPA94ILABLwHUR8bnMvHXc36XexUlXHk/6Oyf5PZqcabZTD5CT1e+gM87PHvRA1T0Naa/3T3K/mJUMrEm205XtMukbDPNs0vv4Wv92g7a5JrSLpsUzLM97m8msInWaZjvttU+5r82XjQxVHHVI46T3rUlkGJ0KfDYz/xkgIq4Gng9cvM77NgHs3r275wuW79vDgQesPqE4/JC9ACwtLa3796CvG+Y9k/zsccTzvbvvvX/bdW/H7vestb1337eHzT3e0+uzu7+n8zPWet22bdv47t33rvqetf7N69Rr/Tq3G6zajzdNMbyNmFg7hdX7x949y7zwDbcB8N7XHt+odlH3e8bx2d3bd61tvd5n99P5ef1e2iuGzvesLO/3eevFM6hB4p73dtr97zFL7aIJ8Uzqt6/fZ3cv7973ez036O/BuPWLdSWWeW+ns9YuxvmezvPP7u01jves/L/7XHSYc9Ne59T9Pqv7nLMzrmnpd17fGY/ttDntomntdFbi6fw/9P/d6H5Nv+fWiqfX6/pZ63Xd7xm2ne63d+/e9V+1ARHxOuDQzDy/enw28MTMfFm/9y0uLj4F+OJYg5Hqd/LCwsKX6g6im+1UWsV2KjWf7VRqPtup1HwbaqeTyDDaH+jshdoPGGSqgb8CTgZ2AT+YQFzSNG0CjqLs101kO5Vsp1Ib2E6l5rOdSs03VDudRIfRtygNa8WRwF3rvWlhYWEJaFyPtDSCv607gD5sp1JhO5Waz3YqNZ/tVGq+DbfTSXQYfQa4MCIeAtwNPA/om+4naepsp1Lz2U6l5rOdSs1nO5WGNPZqwpn5beD1wOeAG4H3ZeZXxv09koZnO5Waz3YqNZ/tVGo+26k0vLEXvZYkSZIkSVK7NWe+ckmSJEmSJDWCHUaSJEmSJElaxQ4jSZIkSZIkrWKHkSRJkiRJklY5oO4A6hIRZwHnAwcCv5eZb+96/kTg3cBhwBeAl2fmfRHxZOB3gc3APwG/nJl/3/S4O54/Cbg+M7dMK+bqe4fd3kdVy48G7gFelJnfbEHcPw78UbX8/wIvnuZ+0mQDbNPTgYuA/YA7gJdk5r9MPdAJWm8bdLzu54D/lZk/Mc34Jm2AfSCAdwI/AnwH+I+ztg/UJSIOA64Dnp2Z34yIU4FLgYOBqzLz/FoDHFJEXAC8oHq4IzNfOyvrBhARFwPPB/YC78nMS2dp/QAi4hLgwZm5fb1zGY2u+7eg5lj2ab81x7NPe6sznhWdbaTmOD4HPBRYrhb9l8y8ocaQ5lLbjnttPI615bi0VpsEHsUA1xqDmMsMo4h4GPBG4CnAicDLIuInu152BfDKzNxKuXB9abX8T4CzM/PE6u+3TifqkeMmIg4B3kbp7JqaEeP+Y+BjmXlS9fdbphP1yHG/AXh/tZ98sPqcubfeNq1OYC8Dfi4zTwBuBi6sIdSJGXC/IiL+DXAJZb+aGQPsA/sBHwV+s9oHvgacV0essyYingR8CdhaPT4YuBw4HTgeeEJEnFZfhMOpTjifCZxE2acWIuKFzMC6AUTEKcDTgMcCjwd+NSJOYEbWDyAing68uGNRz3MZja77t6DmWNZqv79YYzxrtbeoK54Va7SRuuLYj7LfnJCZJ1b/2Vk0ZW077rXxONaW49JabRL4FgNcawxqLjuMgFOBz2bmP2fm3cDVlB5PACLiEcDBmXl9tei9wBkRsQU4PzNvrpbfDPzY9MIeLu6O9/8O8HtTirXTsNv7wcAJlEwDgD+g9JROyyjbexOlBxrgUODeqUTcfH23KaUX/BWZ+e3q8bTb2DSstw1WvJuSaTVr1lv/xwF3Z+Ynq8dvAoa+K6JVXgq8ArirevxE4BuZeUd1l+wKVh8z2mIX8OrM3J2Zy8BtlJOnWVg3MvPzwFOr9XgoJTv8cGZk/SLiCMqJ7Zuqx+udy2h03b8FdVqr/dZ23O/R3u6uKx7Yt43UbKXz7FMRcVNEvLLWaOZXq457bTuOtey4tFabHPRaYyDzOiTtaEpDW7GLcuLc7/mHZ+YSZWcmIvanZD58ZKKRrjZU3AAR8RzgkMy8uoYbJcPG/Sjg/wC/ExEnU4amTPPANPT2Bn4duC4i/hslo+vfTTDONum7TTPzn4APw/3ZD+dRsuJmyXr7FdV+81XgembPeut/LPCdiHgP5c7ZbcCvTi+82ZWZZwN0HAP6/Ya1RmbesvJ3RDyakqL/NmZg3VZk5nJEXAScC3yAGfm3q7wTeD1wTPV4ltatkdb4LahNj/b75PoiWrO9fXudt0xadxup048A11KOywcCfxERmZmfrjes+dLG417LjmNtOi7t0yaBq1jnWmMj5jXDaH/K+MkV+wF7Bn0+IjZThqMdwHR7+4eKOyKOpGTm1HXRNez2PoBywfjZzHwC8GfAH0421FVG2U/+EHhZZj4MeDnw4SplcN6tt00BiIgfBnYAN2XmNP/Np2G935dtwPMowxpn0Xr7wAHAzwCXZebjgL+jjG/X+A3UHtsiIh4DfBp4DWW/mZl1A8jMC4CHUE5gtzID6xcRZwN3Zua1HYtnar/UYDrbb2Z+o+54utpbbUNPerSR2mTmX2bmf87Mf83MfwTeAzyr7rjmVduOe204jrXtuNSjTV7MGOOd1w6jbwFHdTw+ktVpuT2fj4gHAZ+kXNScXqUBTsuwcT8b+FHgCxFxI0BE3BgRPzTZcNeNa73nvwN8NzM/Xi1/HyP0jg5hqLgj4iHAcZn5ZwCZ+cHquQdPNtxWWG+bUhU6/yJlONrZ0wttatbbBmdUz/81cA1wdER8cXrhTdx66/8dSoryX1eP38902/08Wbc9tkWUCSmuBc6rOplnad2Oq4ptkpn3AB+idKrOwvqdCTyzOje5GHgO5Xd/FtZNA1qj/dYZy1rt7bE1hrRPG4mI360rmIh4SlXbZcV+PFBoV1PUpuNey45jrTou9WiT32SM8c5rh9FngKdHxEOqQtDPo3QCAZBlNqvvVw0R4JeAT1R/XwH8DXBmNURtmoaKOzPfnZmP6iiERfX3dxse998C3+oogPbzwOKUYh46buAfq+Unw/0/6N/NzH+YYuxN1XebRsQm4GPAn2bmqzJzb4/PabP19qsLMnNr1VafBdyVmSfXFOsk9F1/yqw9D6mKIcL02/08uYEyKd2xVds7iweOda0REcdQhoeflZlXVotnYt0qjwTeFRFbqgzn0ynp8q1fv8x8RmZuq37vfgP4aGa+hN7nYJoxPdpvndZqb1+qK5gebeS/1xUPpe7Mb0fEQdWN5xdTlRLQ9LTwuNea41gLj0trtcn/RP9z7Q2Zyw6jqqDu64HPATcC78vMr0TENRHx+OplLwJ+NyJuBx4EvDXKlPSnU8ZWf7XK0rmm6XFPK75eRoz7ucCvRcRO4Bzgl5sed9XJ8Vzgkoi4GfgtSkOdewNs0+dQih4/v2pfN0bEu2sMeewG3K9m1nrrn5n3Ar9IObG4hTKrxqvri3h2Zeb3ge2UmRxvBW6nFEZsm3OBg4BLV343KOu1nfavG5l5DWWI7tconafXVRcI25mB9euhcecymph92m9EvLyuYPq0NwFV1n/n9rk8M/+y3qjmUquOezNyHGvkcalHm/wya5xrD/sd++3dO4s38CVJkiRJkjSsucwwkiRJkiRJUm92GEmSJEmSJGkVO4wkSZIkSZK0ih1GkiRJkiRJWsUOI0mSJEmSJK1ih5EkSZIkSZJWscNIkiRJkiRJq9hhJEmSJEmSpFX+P9sJwjtzkH56AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1080 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograma dos features\n",
    "cancer.hist(bins=50, figsize=(20,15));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Antes do treino\n",
    "Supondo que estamos contentes com os dados do jeito que estão, vamos finalmente para machine learning propriamente dito.\n",
    "\n",
    "Agora que temos alguma familiaridade com os dados vamos separar o dataset em dois, 'train set' e 'test set'. O dataset 'train' é aquele que vamos utilizar para treinar o algoritmo de machine learning, após isso precisamos de alguma medida da qualidade de nossas previsões fazendo o algoritmo prever o resultado de dados desconhecidos por ele, ou seja, o dataset 'test'. É comum que cerca de 20% dos dados seja aleatoriamente definido como o 'test set'. Um jeito simples de fazer isso é utilizar a função 'train_test_split' da bibliotec de machine learning 'scikit-learn'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(cancer, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(455, 31)\n",
      "(114, 31)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>B</td>\n",
       "      <td>9.029</td>\n",
       "      <td>17.33</td>\n",
       "      <td>58.79</td>\n",
       "      <td>250.5</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>0.14130</td>\n",
       "      <td>0.31300</td>\n",
       "      <td>0.04375</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>10.31</td>\n",
       "      <td>22.65</td>\n",
       "      <td>65.50</td>\n",
       "      <td>324.7</td>\n",
       "      <td>0.14820</td>\n",
       "      <td>0.43650</td>\n",
       "      <td>1.25200</td>\n",
       "      <td>0.17500</td>\n",
       "      <td>0.4228</td>\n",
       "      <td>0.11750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>M</td>\n",
       "      <td>21.090</td>\n",
       "      <td>26.57</td>\n",
       "      <td>142.70</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>0.11410</td>\n",
       "      <td>0.28320</td>\n",
       "      <td>0.24870</td>\n",
       "      <td>0.14960</td>\n",
       "      <td>0.2395</td>\n",
       "      <td>...</td>\n",
       "      <td>26.68</td>\n",
       "      <td>33.48</td>\n",
       "      <td>176.50</td>\n",
       "      <td>2089.0</td>\n",
       "      <td>0.14910</td>\n",
       "      <td>0.75840</td>\n",
       "      <td>0.67800</td>\n",
       "      <td>0.29030</td>\n",
       "      <td>0.4098</td>\n",
       "      <td>0.12840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>B</td>\n",
       "      <td>9.173</td>\n",
       "      <td>13.86</td>\n",
       "      <td>59.20</td>\n",
       "      <td>260.9</td>\n",
       "      <td>0.07721</td>\n",
       "      <td>0.08751</td>\n",
       "      <td>0.05988</td>\n",
       "      <td>0.02180</td>\n",
       "      <td>0.2341</td>\n",
       "      <td>...</td>\n",
       "      <td>10.01</td>\n",
       "      <td>19.23</td>\n",
       "      <td>65.59</td>\n",
       "      <td>310.1</td>\n",
       "      <td>0.09836</td>\n",
       "      <td>0.16780</td>\n",
       "      <td>0.13970</td>\n",
       "      <td>0.05087</td>\n",
       "      <td>0.3282</td>\n",
       "      <td>0.08490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>B</td>\n",
       "      <td>10.650</td>\n",
       "      <td>25.22</td>\n",
       "      <td>68.01</td>\n",
       "      <td>347.0</td>\n",
       "      <td>0.09657</td>\n",
       "      <td>0.07234</td>\n",
       "      <td>0.02379</td>\n",
       "      <td>0.01615</td>\n",
       "      <td>0.1897</td>\n",
       "      <td>...</td>\n",
       "      <td>12.25</td>\n",
       "      <td>35.19</td>\n",
       "      <td>77.98</td>\n",
       "      <td>455.7</td>\n",
       "      <td>0.14990</td>\n",
       "      <td>0.13980</td>\n",
       "      <td>0.11250</td>\n",
       "      <td>0.06136</td>\n",
       "      <td>0.3409</td>\n",
       "      <td>0.08147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>B</td>\n",
       "      <td>10.170</td>\n",
       "      <td>14.88</td>\n",
       "      <td>64.55</td>\n",
       "      <td>311.9</td>\n",
       "      <td>0.11340</td>\n",
       "      <td>0.08061</td>\n",
       "      <td>0.01084</td>\n",
       "      <td>0.01290</td>\n",
       "      <td>0.2743</td>\n",
       "      <td>...</td>\n",
       "      <td>11.02</td>\n",
       "      <td>17.45</td>\n",
       "      <td>69.86</td>\n",
       "      <td>368.6</td>\n",
       "      <td>0.12750</td>\n",
       "      <td>0.09866</td>\n",
       "      <td>0.02168</td>\n",
       "      <td>0.02579</td>\n",
       "      <td>0.3557</td>\n",
       "      <td>0.08020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "68          B        9.029         17.33           58.79      250.5   \n",
       "181         M       21.090         26.57          142.70     1311.0   \n",
       "63          B        9.173         13.86           59.20      260.9   \n",
       "248         B       10.650         25.22           68.01      347.0   \n",
       "60          B       10.170         14.88           64.55      311.9   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "68           0.10660           0.14130         0.31300              0.04375   \n",
       "181          0.11410           0.28320         0.24870              0.14960   \n",
       "63           0.07721           0.08751         0.05988              0.02180   \n",
       "248          0.09657           0.07234         0.02379              0.01615   \n",
       "60           0.11340           0.08061         0.01084              0.01290   \n",
       "\n",
       "     symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "68          0.2111           ...                    10.31          22.65   \n",
       "181         0.2395           ...                    26.68          33.48   \n",
       "63          0.2341           ...                    10.01          19.23   \n",
       "248         0.1897           ...                    12.25          35.19   \n",
       "60          0.2743           ...                    11.02          17.45   \n",
       "\n",
       "     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "68             65.50       324.7           0.14820            0.43650   \n",
       "181           176.50      2089.0           0.14910            0.75840   \n",
       "63             65.59       310.1           0.09836            0.16780   \n",
       "248            77.98       455.7           0.14990            0.13980   \n",
       "60             69.86       368.6           0.12750            0.09866   \n",
       "\n",
       "     concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "68           1.25200               0.17500          0.4228   \n",
       "181          0.67800               0.29030          0.4098   \n",
       "63           0.13970               0.05087          0.3282   \n",
       "248          0.11250               0.06136          0.3409   \n",
       "60           0.02168               0.02579          0.3557   \n",
       "\n",
       "     fractal_dimension_worst  \n",
       "68                   0.11750  \n",
       "181                  0.12840  \n",
       "63                   0.08490  \n",
       "248                  0.08147  \n",
       "60                   0.08020  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.drop('diagnosis',axis=1)\n",
    "X_test = test.drop('diagnosis',axis=1)\n",
    "y_train = train['diagnosis'].copy().reset_index(drop = True)\n",
    "y_test = test['diagnosis'].copy().reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pré-processamento\n",
    "\n",
    "É importante analisar a distribuição dos dados para verificar se estão 'normalmente' distribuídos e se há dados ruins 'outliers'. Para que o modelo que vamos criar seja estatisticamente acurado devemos aplicar algumas transformações a estes dados:\n",
    "-  normalization - faz com que todos os features possuam valores entre 0 e 1, ou seja, a faixa variação dos valores de cada feature deve ser a mesma. Por exemplo, 'area_mean' varia aproximadamente entre 0 e 2500, enquanto 'fractal_dimension_mean' vai de 0 até 0.2;\n",
    "-  standardization - faz com que todos os features tenham média zero e desvio padrão igual a 1.\n",
    "\n",
    "Muitos algoritmos acabam enviesando o treinamento se os dados não passarem por estes processos. Para contornar isso vamos apenas utilizar a função 'StandardScaler' do scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_columns = cancer.columns[1:]\n",
    "X_train = pd.DataFrame(X_train,columns=feature_columns)\n",
    "X_test = pd.DataFrame(X_test,columns=feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.440753</td>\n",
       "      <td>-0.435319</td>\n",
       "      <td>-1.362085</td>\n",
       "      <td>-1.139118</td>\n",
       "      <td>0.780573</td>\n",
       "      <td>0.718921</td>\n",
       "      <td>2.823135</td>\n",
       "      <td>-0.119150</td>\n",
       "      <td>1.092662</td>\n",
       "      <td>2.458173</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.232861</td>\n",
       "      <td>-0.476309</td>\n",
       "      <td>-1.247920</td>\n",
       "      <td>-0.973968</td>\n",
       "      <td>0.722894</td>\n",
       "      <td>1.186732</td>\n",
       "      <td>4.672828</td>\n",
       "      <td>0.932012</td>\n",
       "      <td>2.097242</td>\n",
       "      <td>1.886450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.974096</td>\n",
       "      <td>1.733026</td>\n",
       "      <td>2.091672</td>\n",
       "      <td>1.851973</td>\n",
       "      <td>1.319843</td>\n",
       "      <td>3.426275</td>\n",
       "      <td>2.013112</td>\n",
       "      <td>2.665032</td>\n",
       "      <td>2.127004</td>\n",
       "      <td>1.558396</td>\n",
       "      <td>...</td>\n",
       "      <td>2.173314</td>\n",
       "      <td>1.311279</td>\n",
       "      <td>2.081617</td>\n",
       "      <td>2.137405</td>\n",
       "      <td>0.761928</td>\n",
       "      <td>3.265601</td>\n",
       "      <td>1.928621</td>\n",
       "      <td>2.698947</td>\n",
       "      <td>1.891161</td>\n",
       "      <td>2.497838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.399982</td>\n",
       "      <td>-1.249622</td>\n",
       "      <td>-1.345209</td>\n",
       "      <td>-1.109785</td>\n",
       "      <td>-1.332645</td>\n",
       "      <td>-0.307355</td>\n",
       "      <td>-0.365558</td>\n",
       "      <td>-0.696502</td>\n",
       "      <td>1.930333</td>\n",
       "      <td>0.954379</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.295284</td>\n",
       "      <td>-1.040811</td>\n",
       "      <td>-1.245220</td>\n",
       "      <td>-0.999715</td>\n",
       "      <td>-1.438693</td>\n",
       "      <td>-0.548564</td>\n",
       "      <td>-0.644911</td>\n",
       "      <td>-0.970239</td>\n",
       "      <td>0.597602</td>\n",
       "      <td>0.057894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.981797</td>\n",
       "      <td>1.416222</td>\n",
       "      <td>-0.982587</td>\n",
       "      <td>-0.866944</td>\n",
       "      <td>0.059390</td>\n",
       "      <td>-0.596788</td>\n",
       "      <td>-0.820203</td>\n",
       "      <td>-0.845115</td>\n",
       "      <td>0.313264</td>\n",
       "      <td>0.074041</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.829197</td>\n",
       "      <td>1.593530</td>\n",
       "      <td>-0.873572</td>\n",
       "      <td>-0.742947</td>\n",
       "      <td>0.796624</td>\n",
       "      <td>-0.729392</td>\n",
       "      <td>-0.774950</td>\n",
       "      <td>-0.809483</td>\n",
       "      <td>0.798928</td>\n",
       "      <td>-0.134497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.117700</td>\n",
       "      <td>-1.010259</td>\n",
       "      <td>-1.125002</td>\n",
       "      <td>-0.965942</td>\n",
       "      <td>1.269511</td>\n",
       "      <td>-0.439002</td>\n",
       "      <td>-0.983341</td>\n",
       "      <td>-0.930600</td>\n",
       "      <td>3.394436</td>\n",
       "      <td>0.950213</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.085129</td>\n",
       "      <td>-1.334616</td>\n",
       "      <td>-1.117138</td>\n",
       "      <td>-0.896549</td>\n",
       "      <td>-0.174876</td>\n",
       "      <td>-0.995079</td>\n",
       "      <td>-1.209146</td>\n",
       "      <td>-1.354582</td>\n",
       "      <td>1.033544</td>\n",
       "      <td>-0.205732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0    -1.440753     -0.435319       -1.362085  -1.139118         0.780573   \n",
       "1     1.974096      1.733026        2.091672   1.851973         1.319843   \n",
       "2    -1.399982     -1.249622       -1.345209  -1.109785        -1.332645   \n",
       "3    -0.981797      1.416222       -0.982587  -0.866944         0.059390   \n",
       "4    -1.117700     -1.010259       -1.125002  -0.965942         1.269511   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0          0.718921        2.823135            -0.119150       1.092662   \n",
       "1          3.426275        2.013112             2.665032       2.127004   \n",
       "2         -0.307355       -0.365558            -0.696502       1.930333   \n",
       "3         -0.596788       -0.820203            -0.845115       0.313264   \n",
       "4         -0.439002       -0.983341            -0.930600       3.394436   \n",
       "\n",
       "   fractal_dimension_mean           ...             radius_worst  \\\n",
       "0                2.458173           ...                -1.232861   \n",
       "1                1.558396           ...                 2.173314   \n",
       "2                0.954379           ...                -1.295284   \n",
       "3                0.074041           ...                -0.829197   \n",
       "4                0.950213           ...                -1.085129   \n",
       "\n",
       "   texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0      -0.476309        -1.247920   -0.973968          0.722894   \n",
       "1       1.311279         2.081617    2.137405          0.761928   \n",
       "2      -1.040811        -1.245220   -0.999715         -1.438693   \n",
       "3       1.593530        -0.873572   -0.742947          0.796624   \n",
       "4      -1.334616        -1.117138   -0.896549         -0.174876   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0           1.186732         4.672828              0.932012        2.097242   \n",
       "1           3.265601         1.928621              2.698947        1.891161   \n",
       "2          -0.548564        -0.644911             -0.970239        0.597602   \n",
       "3          -0.729392        -0.774950             -0.809483        0.798928   \n",
       "4          -0.995079        -1.209146             -1.354582        1.033544   \n",
       "\n",
       "   fractal_dimension_worst  \n",
       "0                 1.886450  \n",
       "1                 2.497838  \n",
       "2                 0.057894  \n",
       "3                -0.134497  \n",
       "4                -0.205732  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 755,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Devemos lembrar que os valores de \"y_train\" e \"y_test\" não são número, são as letras \"M\" e \"B\". Se deixarmos assim o algoritmo não saberá como lidar com estas letras. Resolvemos isso classificando cada letra com números, usaremos \"B\"=1 e \"M\"=0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    B\n",
       "1    M\n",
       "2    B\n",
       "3    B\n",
       "4    B\n",
       "Name: diagnosis, dtype: object"
      ]
     },
     "execution_count": 756,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 757,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = y_train.replace(['B','M'],[1,0])\n",
    "y_test = y_test.replace(['B','M'],[1,0])\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "metadata": {},
   "outputs": [],
   "source": [
    "# salvando o dataset pronto\n",
    "X_train.to_csv(r'.\\datasets\\Wisconsin\\X_train.csv')\n",
    "X_test.to_csv(r'.\\datasets\\Wisconsin\\X_test.csv')\n",
    "y_train.to_csv(r'.\\datasets\\Wisconsin\\y_train.csv')\n",
    "y_test.to_csv(r'.\\datasets\\Wisconsin\\y_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dados estão prontos para serem utilizados no treinamento. A partir de agora vamos nos aprofundar um pouco nos algoritmos de machine learning mais conhecidos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors\n",
    "\n",
    "<img src=\"images/kneighbors_01.png\" alt=\"kneighbors\" style=\"width: 500px;\"/>\n",
    "<h1><center><font size=\"0.5\">Fonte: https://github.com/amueller/introduction_to_ml_with_python</font></center></h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# verificar: http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 1 1 0 0 0 0 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0\n",
      " 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 0\n",
      " 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 0 1 1 0\n",
      " 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métricas para avaliar modelos de classificação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9210526315789473\n"
     ]
    }
   ],
   "source": [
    "# accuracy\n",
    "print(knn.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x178e50a3c8>"
      ]
     },
     "execution_count": 764,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "clf = knn\n",
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "# try n_neighbors from 1 to 10\n",
    "neighbors_settings = range(1, 11)\n",
    "for n_neighbors in neighbors_settings:\n",
    "    # build the model\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    clf.fit(X_train, y_train)\n",
    "    # record training set accuracy\n",
    "    training_accuracy.append(clf.score(X_train, y_train))\n",
    "    # record generalization accuracy\n",
    "    test_accuracy.append(clf.score(X_test, y_test))\n",
    "plt.plot(neighbors_settings, training_accuracy, label=\"training accuracy\")\n",
    "plt.plot(neighbors_settings, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"n_neighbors\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix\n",
    "\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html\n",
    "\"Thus in binary classification, the count of true negatives is $C_{0,0}$, false negatives is $C_{1,0}$, true positives is $C_{1,1}$ and false positives is $C_{0,1}$.\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion_matrix(classe real , classe prevista)\n",
    "cm_knn = confusion_matrix(y_test, y_pred)\n",
    "plot_confusion_matrix(cm_knn, ['0','1'],\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import accuracy_score, log_loss, make_scorer, confusion_matrix, f1_score, precision_score,\\\n",
    "        recall_score, precision_recall_curve, roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision, recall e F1 score\n",
    "\n",
    "A partir da confusion matrix nós obtemos as quantidades de TP, TN, FP e FP. Com estes valores podemos estabelecer métricas mais específicas que podem ser úteis para o modelo.<br>\n",
    "\n",
    "Precision $\\rightarrow Precision = \\frac{TP}{TP+FP}$<br>\n",
    "\n",
    "Recall $\\rightarrow Recall = \\frac{TP}{TP+FN}$<br>\n",
    "\n",
    "F1 $\\rightarrow F1 = \\frac{2}{\\frac{1}{Precision}+\\frac{1}{Recall}}$<br>\n",
    "\n",
    "Para um caso ideal teríamos $FN=0$ e $FP=0$ o que levaria a $Precision=1$ e $Recall=1$ mostrando que o modelo criado é perfeito, mas supondo os seguintes valores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 767,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision = 0.83\n",
      "Recall = 1.00\n",
      "F1 = 0.91\n"
     ]
    }
   ],
   "source": [
    "TP=50.\n",
    "TN=50.\n",
    "FN=0.\n",
    "FP=10.\n",
    "Precision = TP/(TP+FP)\n",
    "Recall = TP/(TP+FN)\n",
    "F1 = 2./((1./Precision)+(1./Recall))\n",
    "print('Precision = {:.2f}'.format(Precision))\n",
    "print('Recall = {:.2f}'.format(Recall))\n",
    "print('F1 = {:.2f}'.format(F1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision = 1.00\n",
      "Recall = 0.83\n",
      "F1 = 0.91\n"
     ]
    }
   ],
   "source": [
    "TP=50.\n",
    "TN=50.\n",
    "FN=10.\n",
    "FP=0.\n",
    "Precision = TP/(TP+FP)\n",
    "Recall = TP/(TP+FN)\n",
    "F1 = 2./((1./Precision)+(1./Recall))\n",
    "print('Precision = {:.2f}'.format(Precision))\n",
    "print('Recall = {:.2f}'.format(Recall))\n",
    "print('F1 = {:.2f}'.format(F1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision é inversamente proporcional ao número de falsos positivos e Recall ao número de falsos negativos, então podemos utilizar estas métricas baseados em nossas prioridades. Voltando ao nosso problema um FN significa que classificamos tumor como maligno e erramos, enquanto FP classificamos como benigno e erramos. Neste caso desejamos que o número de FP seja o menor possível para que um paciente não seja mandado para casa sem tratamento adequado. Assim, podemos modificar os parâmetros dos algoritmos com o objetivo de obter um Precision alto, não importando muito o valor de Recall.  \n",
    "\n",
    "Caso não tenhamos uma preferência entre Recall e Precision podemos utilizar o F1 como um meio termo entre os dois, visando que ente seja o mais próximo possível de 1.\n",
    "\n",
    "Se você rodar o algotimo nos dados algumas vezes e verificar a confusion matrix perceberá que os valores das métricas podem mudar algumas vezes. Isto ocorre principalmente por causa da escolha aleatória dos samples para o train set. Um maneira prática de eliminar este problema é usar a função cross_val_predict, vejamos.\n",
    "\n",
    "<img src=\"images/cross_validation.png\" alt=\"cross_validation\" style=\"width: 800px;\"/>\n",
    "<center><font size=\"1\">Fonte: https://www.kaggle.com/dansbecker/cross-validation</font></center>\n",
    "\n",
    "O cross_val_predict divide o dataset em partes chamadas 'folds' (cv dentro da função) e faz o cálculo das métricas em cada uma e então faz a média. Quanto maior o número de samples em um dataset menor será o erro das métricas e mais folds podem ser utilizados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cross_val_predict(knn,X_test, y_test,cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[41  2]\n",
      " [ 4 67]]\n"
     ]
    }
   ],
   "source": [
    "cm_knn = confusion_matrix(y_test,y_pred)\n",
    "print(cm_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f1_pre_rec_acc(true_labels,prediction_labels):\n",
    "    f1 = f1_score(true_labels,prediction_labels)\n",
    "    pre = precision_score(true_labels,prediction_labels)\n",
    "    rec = recall_score(true_labels,prediction_labels)\n",
    "    acc = accuracy_score(true_labels,prediction_labels)\n",
    "    report = pd.DataFrame({'Precision':np.around([pre],3), 'Recall':np.around([rec],3),'F1':np.around([f1],3),'Accuracy':np.around([acc],3)})\n",
    "    print(report)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Precision  Recall     F1  Accuracy\n",
      "0      0.971   0.944  0.957     0.947\n"
     ]
    }
   ],
   "source": [
    "f1_pre_rec_acc(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thresholds\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "metadata": {},
   "outputs": [],
   "source": [
    "precisions, recalls, thresholds = precision_recall_curve(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 774,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot precision and recall as functions of the threshold value\n",
    "def  plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n",
    "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
    "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
    "    plt.xlabel(\"Threshold\", fontsize=16)\n",
    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
    "    \n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(knn,X_test, y_test,cv=3)\n",
    "fpr, tpr, thresholds = roc_curve(y_test,y_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Curva de ROC e Área sob a curva (AUC)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 776,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def  plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 777,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9485751719620047\n"
     ]
    }
   ],
   "source": [
    "print(roc_auc_score(y_test,y_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este modelo pode paracer adequado, mas de qualquer maneira vamos usar mais alguns algoritmos: Support Vector Machines (SVM), Stochastic Gradient Descent e Decision Trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 778,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 779,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 779,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 780,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 780,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 781,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 781,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SGDClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Melhorando o modelo\n",
    "\n",
    "Esse modelo pode ser suficiente para nós, mas nem utilizamos todo o potencial do algoritmo. Agora vamos ver alguns métodos para encontrar os melhores parâmetros o GridSearch. Primeiro vamos verificar que parâmetros o algoritmo possui e que valores podem ser utilizados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 782,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 782,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mais detalhes em http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\n",
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 783,
   "metadata": {},
   "outputs": [],
   "source": [
    "# escolhemos alguns parâmetros para a procura\n",
    "parameters = {'weights':['uniform','distance'],'leaf_size':[25,30,35], 'n_neighbors':[1,2,3,4,5]}\n",
    "\n",
    "# escolhemos uma métrica\n",
    "scorer = make_scorer(precision_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/gridsearch_optimization.jpeg\" alt=\"AI Ven Diagram\" style=\"width: 500px;\"/>\n",
    "<center><font size=\"1\">Fonte: https://cs.hse.ru/en/bayesgroup/education/moml</font></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importando o GridSearch\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "# desabilitar pra mostrar o resultado\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# rodamos o grid search no training set\n",
    "grid_obj = GridSearchCV(knn, parameters, scoring=scorer)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=25, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=2, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 785,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# definimos o classificador knn com os melhores parâmetros\n",
    "knn = grid_obj.best_estimator_\n",
    "\n",
    "# e então treinamos o algoritmo com esta combinação\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Precision  Recall    F1  Accuracy\n",
      "0      0.957   0.944  0.95     0.939\n"
     ]
    }
   ],
   "source": [
    "y_pred_knn = knn.predict(X_test)\n",
    "f1_pre_rec_acc(y_test, y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_knn = confusion_matrix(y_test,predictions)\n",
    "plot_confusion_matrix(cm_knn, ['0','1'],\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Stochastic Gradient Descent -  SGDClassifier***  \n",
    "Este algoritmo foca o seu treino em reduzir o erro (alguma métrica), esse erro é chamado de função custo. Na figura abaixo cada combinação de parâmetros gera um modelo, o algoritmo então verifica em qual direção o erro diminui, \"anda\" até lá e define novos parâmetros. Isso ocorre até que o erro seja o menor possível.\n",
    "\n",
    "<img src=\"images/gradient_descent.png\" alt=\"Gradient Descent\" style=\"width: 600px;\"/>\n",
    "<center><font size=\"0.5\">Fonte: https://stats.stackexchange.com/questions/241715/gradient-descent-and-cost-function-trouble</font></center>\n",
    "\n",
    "\n",
    "Parâmetros importantes:\n",
    "-  loss - é a função custo que queremos definir;\n",
    "-  learning_rate - a taxa de aprendizado define o passo que o algoritmo dá na direção que minimiza o erro;\n",
    "-  max_iter - é o número de passos que o algoritmo dará até parar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGDClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='hinge', max_iter=None, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 789,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Precision  Recall     F1  Accuracy\n",
      "0      0.972   0.986  0.979     0.974\n"
     ]
    }
   ],
   "source": [
    "# escolhemos alguns parâmetros para a procura\n",
    "parameters = {'loss':['hinge','log','perceptron'], 'max_iter':[5,7,10]}\n",
    "\n",
    "# escolhemos uma métrica\n",
    "scorer = make_scorer(precision_score)\n",
    "\n",
    "# rodamos o grid search no training set\n",
    "grid_obj = GridSearchCV(sgd, parameters, scoring=scorer)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# definimos o classificador sgd com os melhores parâmetros\n",
    "sgd = grid_obj.best_estimator_\n",
    "\n",
    "# e então treinamos o algoritmo com esta combinação\n",
    "sgd.fit(X_train, y_train)\n",
    "\n",
    "y_pred_sgd = sgd.predict(X_test)\n",
    "f1_pre_rec_acc(y_test, y_pred_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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4FCvJxsbG5c3NzUsTTnkGMNzMngKGAoMIE+TwkmOGASuSYktz42ZDMUECuPs/UJNbRCooaSB5mgUwSrn7ce5+gLtPBL4GtLj7NGC9mR0RHXYO8GDSueIWuDg0+vFpM/sBYft+EzAVeDx9uCIi8dJXktvtLOAnZjYYeBKIXcgH4pvbnZvTJ5b83AFoFSARqYgsF7hw91sI72Tj7k8Dh5Xz+bgFLsZsW0giIuXKb9phksQbN2a2O2HbfRDh3aA6YJy7n5VxbCJSI3rlorsl7gLWAROAXxEOKu9qnJKIyDYppBgnmVeSTHN3e293PxF4APgBcASwb6ZRiUhNKSbJpFcusaU45qXoz8XAAe7+IuEMHBGRiujtqwC9bGZfAn4PNJvZamCnbMMSkZoSkHzjJuiI35+RNJXk+cA77v4Y8CfgauDyTKMSkZrSqytJd3+ZaMClu1+OEqSIVFi6Gzf5ZMm4GTdv8d7J4O/h7oMziUhEak5AisHkPRLJ1uIqyQN6LIou3HTagfTrt2OeIWRu4dNt3DH1Q3mHkbkhky7OO4TMPTZzGrtP/lzeYWRq2G6DmPvt0zM5d7ppiVVWSbr733syEBGpXQWSb5CkuYGSBT3QS0RyVygE1HUk9EnmNJpcSVJEctfbpyViZgOAccCzhM+IWJtpVCJSU6q5TzLN4xs+DLwAzAdGAsvM7PCsAxOR2lHpRXcrGluKY74LNACvuftywhWBbsw0KhGpLWkGkldxktzJ3RcV37j7A6gvU0QqqECKBS5yypJpkl27mQ0hGlhuZpZtSCJSa3r7EKBvAL8FhpnZHcDHgc9mGpWI1JQsH9+wvdLM3Z5nZs8TLrZbB1zt7s9lHpmI1IxeOXe7yMyGAq8Dd5Zuc/fXswxMRGpHoRBWYEnH5CFNc/tVtl7oYiUwqvLhiEgt6tWVpLtvyd9m1g9oAnTzRkQqppr7JMsqYN19Q/QM2+OyCUdEalE1DyZP2ydZFAAfAoZkFpGI1KSgwuMgzexq4DTC7sKfuvv1ZtYAXA8MAO5096uSzlNOn2TxG7wMXLJNUYuIdKFAigUuyjifmR0FHAscRPjgwkVm9jAwCzgKWAbMN7MT3P3BuHOlSZKT3L2tjPhERMpSCFIMJi+j0HT335rZMe6+0cxGEua6XYHF7r4EwMxmA6cD250kZwP7pQ9PRKQ8QRAkNreLqwC1tLSMampq6rx7lbuvKt3g7u1m1gxcBtwNjCAcmVOUapROmiT5FzNrAh4D3i4JQOMkRaQiyqkkW1tbH+1idzMwo/NGd/+6mX0buB8Yz3uHMwbA5sTYkg4ATiasJpcS9k++CryS4nMiIumUsQpQQ0PDFGBMp9cNpaczs33NbCJAtP7tz4GjgeElhw0DViSFFve0xB3d/R1375/+m4qIlG+HQsCmhMc37BA1txsbG5c3NzcvTTjlWKDZzI4krB5PBn4MfNfMxgFLCMd8z0qKLa6S/H3Sh0VEKiGpikwz2LxUtKTjfODPQBvwhLvPAaYC9wCLgOeBuUnniuuTzOsxtyJSYwokrxdZ7nqS7j6DTv2U7v4wcHA554lLkv3N7BC6SZbu/mQ5FxIRiZPXtMMkcUlyLGFZ2lXoHdF+EZHtlurudo9EsrW4JLnI3Q/psUhEpGaleTxDNT++QUQkU0GQfBMkr9Z4XJL8XY9FISI1rVdWku5+aU8GIiI1LEUlmRc1t0Ukd3VBwOaENFlXbZWkiEhPKZl1GHtMHpQkRSR3vbJPUkSkp6iSFBFJkDjjpvMzW3uIkqSI5C5cwCJh0V3IJVEqSYpI7gr0zmmJIiI9IgiCFJVkoEpSRGqTbtyIiMRIXUnmQElSRHJXCALqEpKkxkmKSM1Sc1tEJEaaZ9goSYpIzSoQ0KFpiSIiXVMlKSISI4j+STomD0qSIlIVNHdbRKQbhRT3t9UnKSI1K1WfZE6dkkqSIpK7QoosWSgzS5rZ14F/jd7Od/cvm1kDcD0wALjT3a9KjK2sq4qIZKAQpHulFSXDjwOHABOBejM7E5gFnAzsB0wysxOSzqVKUkSqQtq71y0tLaOampo6b17l7qtK3q8EvujuGwDM7DlgPLDY3ZdE22YDpwMPxl1PSTInf1iwgP9xyYU8vqAt71CkTGefNJlzGj8MQP9+O3CQjWLpkhf43X9cxsZNm2j9/fN88//E/r2TToIU8xKLre3W1tZHu9jdDMwovnH3hcWfzWwfwmb39wmTZ9FKYFRSbGpu5+B/X/cdLjx/Ohs2bMg7FNkGs+9fwPHn3cjx593Ik88t44vfmcvo0aM598pbOHba95h04AeYuG/i3z0pEaT8B6ChoWEKMKbT64auzmtmE4BfAV8C/sZ7BxIFwOak2JQkczB27AeZc/fP8w5DttOh+49m/w8O5+5fthEEAUuWvwpA6xPPccxhlnN0vUs5fZKNjY3L3X1pp9eqzuc0syOAh4Er3P1WYDkwvOSQYcCKpNgybW6b2WDgCeAT7r40y2v1Jp885VT+vnRp3mHIdvryZz7OtT9+gMED+7N587sFyVtr1zNm5O45Rtb7BCnGSZYz48bM9gJ+AZzh7r+ONi8Id9k4YAnQRHgjJ1ZmSdLMJgM/IewsFelTdhk0gPFj9uR3f1rMzgP7Uyi82yjbeaf+vPnWuhyj633K6ZNM6TKgP3C92Zaq/kfAVOCeaN8DwNykE2VZSZ4HXATcluE1RHJxZP04HlngALy1Zj0dHR2MGbU7S5a/SsPh+/HNHz+Qc4S9S5r8V06OdPdLgUu72X1wGafKLkm6+3SAkixelsXPPVvReKrNihVhV8jCp/v+3e3HZk7LO4SK23PPPeno6GDy+PC7/eMf/+APd1xGEASsXr2a733+2Jwj7F3SPL4hryk3VTsEaJ/9DqBfvx3zDiMzEw6uZ8SIEUw4uD7vUDI3ZNLFeYeQucdmTmOPKVfkHUamhu02iLnfPj2Tcwdb/qf6VG2SFJHakebGTV5ZVElSRHKX5sZNXpWmkqSI5K5KW9pADyRJd/9A1tcQkV5OlaSISPfUJykiEkN9kiIiCar0ETdKkiJSBVRJioh0r5Bixk1HECSva5YBJUkRyV2aQjIvSpIikj81t0VEule68njcUXlQkhSR3KV57rYqSRGpWeqTFBFJUqVZUklSRKpCUp+kBpOLSM1K0yeZ08LkSpIikr8qHgGkJCki+UvzjJvEZ+BkRElSRHKn5raISAw1t0VE4lRxllSSFJHcpZmWmDxtMRtKkiKSu6z6JM1sMPAE8Al3X2pmDcD1wADgTne/KukchfIvKyJSeUHCq1xmNhl4DBgfvR8AzAJOBvYDJpnZCUnnUSUpIvkro0+ypaVlVFNTU+e9q9x9Vadt5wEXAbdF7w8DFrv7EgAzmw2cDjwYd1klSRHJXTl9kq2trY92sbsZmFG6wd2nA5hZcdMIYGXJISuBUUmxqbktIrkrBOleAA0NDVOAMZ1eN6S5DO+dAh5A8hMhVEmKSHVI2fHY2Ni4vLm5eek2XGE5MLzk/TBgRdKHlCRFJHc9NARoAWBmNg5YAjQR3siJpea2iOSuOAQo6bU93H09MBW4B1gEPA/MTfqcKkkRyV2WE27c/QMlPz8MHFzO55UkRSR3WuBCRCRW9U7eVpIUkfzpaYkiIt2r3jpSSVJEqoD6JEVEYujxDSIiMdTcFhGJoea2iEgMrUwuIhKnitvbSpIikrsqzpFKkiJSHfLqc0yiJCkiuQsryaQ+yXwoSYpI7nR3W0QkhpKkiEgMDQESEYmhSlJEJIaGAImIxKniLKkkKSK5U5+kiEgcrUwuItK9Km5tK0mKSBWo4ixZjUmyDqB9w4a84+gRGza8k3cImRu226C8Q+gRff177jFkp+KPdZU+96b2jWxMaG9vat9Y6cumEnR0dORy4e60tbUdCTyadxwi0q0p9fX1j1XiRG1tbUOBvwJDUn7kDWBcfX3965W4fhrVWEn+EZgCrAQ25RyLiLyrDhhO+He0Iurr619va2sbBwxO+ZHVPZkgoQorSRGRalLIOwARkWqmJCkiEkNJUkQkhpKkiEgMJUkRkRhKkiIiMZQkRURiKEmKiMSoxhk3fZaZjSWcWbAQ2OzumlEkUuU046aHmNkpwLXAcuBl4Elgpru/mWtgss3M7BBgpbu/lHcskh01t3uAmQ0Ezgaa3P044D5gb+BKM0s7Z1WqhJkVl6v5InCXme2ZZzySLSXJntEB7AGMjt7PBe4H+gOfNrOKLz0l2XH3YvNrM3AAcJsSZd+lJNkD3H0t8FPgBDM72N03A78G/gR8FOiXZ3yyzf4OfIRwxao7lCj7JiXJnjMfWAV8KkqUm9z9NmAoMC7f0GQbzQGWuvu5wIsoUfZJunHTg8xsL+BiYHfgIcJm+FeA49z95Txjk+1nZj8DDgJO0O+z71Al2YPcfRnwXeBh4BzgJOBc/YXq3cysAODu0wgXpB2Qb0RSSaokc2Jm/YAOd2/POxbZfmZWiPqapY9RkhQRiaHmtohIDCVJEZEYSpIiIjGUJEVEYihJiojE0FJpfYCZfQB4AXimZHMA3Ojus7bz3POAue5+i5k9BRzt7qu6OXYX4F53P7bMa5wGXOzuR3fafjTwA3c/IOHzHcAe7v5qGde8BXjW3a8rJ1apPUqSfcc6d59YfGNmI4FnzexP7v6XSlyg9PzdGAIcVolriVQLJck+yt1fNLPFwHgzOxT4N2Ag8Ka7H2Nm/wZcSNjl8hphJfe8mY0AbgVGEC7g8P7iOUsrNjP7n8C5wEZgMTAV+BkwIKo464HxwI3AbkAdcFOxsjWzq4GzomsvTvo+ZjYe+CGwMzAceAo4w93XR4dca2aTou9zlbvPiz7X5fcs61+m1DT1SfZRZvYRwoUzFkSbJhA2lY8xs6MIE9wUdz8E+A5wb3TcD4H/dPcJwCXAvl2cu5EwKX4kagovIZyTPo13K9qAcEm4K9y9HjgKuMzMPmxmJwOnAhOBw4FdUnyl84Bb3f3D0fcaA5xYsv9v7n4o4bqdt5rZHgnfUyQVVZJ9R7GCg/D3+ipwlrsvMzOAv7j76mj/iYSJ5oloH8AQMxsKNACXAbj7X83s111cqwG4293fiI77AmzpGy0aD3wQmFVyjQHAIcD+wM/d/a3oc7MIE3Kcy4HjzOzL0blHAINK9v8oiuVZM1tEuITZkTHfUyQVJcm+Y11Cn+HbJT/XAbe5++WwZYGGEcAbhCsTBSXHbuziXBuj44g+vyuwa6dj6gib9qX9pHsCbxIu8pF0jc7uIPz/612Ey86N7nSO0ucFFYB24r+nSCpqbtemXwJnmtnw6P0FhCsTAfw/4LMAZjYaOKaLz7cCp5Q8emIG8AXCZFcXPd7AgXVmdnZ0rr2AZwn7Kh8ETjezXaPEdU6KmI8Hrnb3O6P3kwmTYNHU6DqH8m43Q9z3FElFlWQNcveHzOzbwK/MbDOwGjjF3TvM7CLgZ2b2HOFDy57q4vMPmNn+wONRM3YhYZ/hWuAP0fspwMnAjVET+X3AV939cQAzO5BwZfY3gKcJH28R50rgXjNbQ1iN/pb3LlY81sz+TFjhfsrdXwfivmc5/8qkhmkVIBGRGGpui4jEUJIUEYmhJCkiEkNJUkQkhpKkiEgMJUkRkRhKkiIiMf4/AQHC82GPbPMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_sgd = confusion_matrix(y_test,y_pred_sgd)\n",
    "plot_confusion_matrix(cm_sgd, ['0','1'],\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Support Vector Machines - SVC***  \n",
    "É um algoritmo que pode trabalhar com modelo lineares e não lineares tanto para regressão quanto para classificação. É particularmente bom para datasets complexos de tamanho pequeno para médio.\n",
    "\n",
    "<img src=\"images/svc_example.png\" alt=\"AI Ven Diagram\" style=\"width: 900px;\"/>\n",
    "<center><font size=\"0.5\">Fonte: Géron, A., 2017. Hands-On Machine Learning with Scikit-Learn and TensorFlow. 1st ed. United States of America: O'Reilly Media, Inc.</font></center>\n",
    "\n",
    "<img src=\"images/svc_example_02.png\" alt=\"AI Ven Diagram\" style=\"width: 500px;\"/>\n",
    "<center><font size=\"0.5\">Fonte: Géron, A., 2017. Hands-On Machine Learning with Scikit-Learn and TensorFlow. 1st ed. United States of America: O'Reilly Media, Inc.</font></center>\n",
    "\n",
    "\n",
    "Parâmetros importantes:\n",
    "-  C - é o parâmetro de erro da margem que separa as classes, quanto maior este valor menor será a margem;\n",
    "-  kernel - é o tipo de função que será usada no aprendizado;\n",
    "-  propability - utiliza ou não probabilidades para ajudar no aprendizado;\n",
    "-  max_iter - é o número de passos que o algoritmo dará até parar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 793,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 794,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Precision  Recall     F1  Accuracy\n",
      "0      0.972   0.986  0.979     0.974\n"
     ]
    }
   ],
   "source": [
    "# escolhemos alguns parâmetros para a procura\n",
    "parameters = {'C':[0.1,1.,10.],'degree':[1,2,3,4]}\n",
    "\n",
    "# escolhemos uma métrica\n",
    "scorer = make_scorer(recall_score)\n",
    "\n",
    "# rodamos o grid search no training set\n",
    "grid_obj = GridSearchCV(svc, parameters, scoring=scorer)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# definimos o classificador sgd com os melhores parâmetros\n",
    "svc = grid_obj.best_estimator_\n",
    "\n",
    "# e então treinamos o algoritmo com esta combinação\n",
    "svc.fit(X_train, y_train)\n",
    "\n",
    "y_pred_svc = svc.predict(X_test)\n",
    "f1_pre_rec_acc(y_test, y_pred_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_svc = confusion_matrix(y_test,y_pred_svc)\n",
    "plot_confusion_matrix(cm_svc, ['0','1'],\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Salvar o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9736263736263736\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# salvando o modelo\n",
    "filename = 'breast_cancer_SGD_model.sav'\n",
    "pickle.dump(sgd, open(filename, 'wb'))\n",
    " \n",
    "# carregando o modelo\n",
    "loaded_sgd = pickle.load(open(filename, 'rb'))\n",
    "result = loaded_sgd.score(X_train, y_train)\n",
    "print(result)"
   ]
  }
 ],
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