2017-10-26_scikit-learn/mini-curso_scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mini-curso de scikit-learn @ SciPy-SP\n",
    "\n",
    "*Machine Learning em Python!*\n",
    "\n",
    "por Danilo J. S. Bellini"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importante**: Há uma versão mais recente e completa deste curso/tutorial de Machine Learning [neste link](../2018-03-31_scikit-learn/sklearn_tutorial.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parte 0 - Contexto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objetivo do curso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Expor as convenções da biblioteca\n",
    "* Ilustrar alguns conceitos básicos de Machine Learning\n",
    "* Apresentar o vínculo com outras bibliotecas do Python\n",
    "* Hands on!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instalação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pelo gerenciador de pacotes de sua distribuição, `pip`, ou `conda`.\n",
    "Maiores informações em http://scikit-learn.org/stable/install.html\n",
    "\n",
    "Outros pacotes explicitamente utilizados neste mini-curso:\n",
    "\n",
    "* NumPy\n",
    "* SciPy\n",
    "* Matplotlib\n",
    "* Seaborn\n",
    "* Pandas\n",
    "* Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dicas gerais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Se acabou o exercício, ajude o colega!\n",
    "* Sempre olhe as docstrings (`Shift + TAB` no Jupyter, `?` ao final no IPython/Jupyter)\n",
    "* Não use \"copiar\" e \"colar\", a menos que você já tenha decorado e esteja seguro sobre o conteúdo, senão isso pode atrapalhar o aprendizado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## O que é o scikit-learn?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biblioteca FLOSS (BSD) para Statistical/Machine Learning (data mining, data analysis), contendo recursos para:\n",
    "\n",
    "* **Aprendizado supervisionado**: classificação, regressão\n",
    "* **Aprendizado não-supervisionado**: *clustering*, estimativa de densidade\n",
    "* **Workflows estatísticos**: comparação, validação, escolha e combinação de parâmetros e modelos\n",
    "* **Pré-processamento**: redução de dimensionalidade, normalização, cálculo de *features*\n",
    "\n",
    "Além de bases de dados (datasets) e diversos outros recursos auxiliares."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![](ml_map.png)](http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html)\n",
    "\n",
    "Imagem obtida em: http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os \"scikits\" são bibliotecas para processamento científico que podem ser vistas como add-ons para o SciPy: https://www.scipy.org/scikits.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apesar do nome no PyPI seguir a convenção dos scikits, a importação do scikit-learn é realizada pelo nome `sklearn`, e o conteúdo da biblioteca está organizado na forma de módulos e subpacotes:\n",
    "\n",
    "* `sklearn.base`\n",
    "* `sklearn.calibration`\n",
    "* `sklearn.cluster`\n",
    "* `sklearn.covariance`\n",
    "* `sklearn.cross_decomposition`\n",
    "* `sklearn.datasets`\n",
    "* `sklearn.decomposition`\n",
    "* `sklearn.discriminant_analysis`\n",
    "* `sklearn.dummy`\n",
    "* `sklearn.ensemble`\n",
    "* `sklearn.exceptions`\n",
    "* `sklearn.feature_extraction`\n",
    "* `sklearn.feature_selection`\n",
    "* `sklearn.gaussian_process`\n",
    "* `sklearn.isotonic`\n",
    "* `sklearn.kernel_approximation`\n",
    "* `sklearn.kernel_ridge`\n",
    "* `sklearn.linear_model`\n",
    "* `sklearn.manifold`\n",
    "* `sklearn.metrics`\n",
    "* `sklearn.mixture`\n",
    "* `sklearn.model_selection`\n",
    "* `sklearn.multiclass`\n",
    "* `sklearn.multioutput`\n",
    "* `sklearn.naive_bayes`\n",
    "* `sklearn.neighbors`\n",
    "* `sklearn.neural_network`\n",
    "* `sklearn.pipeline`\n",
    "* `sklearn.preprocessing`\n",
    "* `sklearn.random_projection`\n",
    "* `sklearn.semi_supervised`\n",
    "* `sklearn.svm`\n",
    "* `sklearn.tree`\n",
    "* `sklearn.utils`\n",
    "\n",
    "A Documentação da API possui informações sobre todos esses subpacotes: http://scikit-learn.org/stable/modules/classes.html\n",
    "\n",
    "Obviamente não veremos tudo neste tutorial introdutório, mas tentaremos entender as interfaces para utilizar os estimadores e outros recursos presentes no scikit-learn!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parte 1 - Aprendizado supervisionado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aprendizado supervisionado é aquele em que nós possuímos a informação do resultado/alvo para as entradas (variáveis independentes) fornecidas. Aprendemos a partir de pares `(vetor_de_features, alvo)`, em que o vetor de *features* de uma entrada está associado a um valor ou classe/rótulo `alvo` mensurado/fixado, que queremos ser capazes de predizer a partir de um novo vetor de *features*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos dividir o aprendizado supervisionado de acordo com a natureza da variável alvo:\n",
    "\n",
    "* Classificação (variável dependente categórica): processo de identificação da classe-alvo a partir dos *features*\n",
    "* Regressão (variável dependente quantitativa): cálculo de um número a partir das variáveis de entrada, estando presente alguma noção de métrica/distância/similaridade entre os possíveis alvos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dependendo do contexto, há outros nomes possíveis para essa variáveis ([Dependent and independent variables na Wikipedia](https://en.wikipedia.org/wiki/Dependent_and_independent_variables)):\n",
    "\n",
    "> [...], an independent variable is sometimes called a \"predictor variable\", \"regressor\", \"controlled variable\", \"manipulated variable\", \"explanatory variable\", \"exposure variable\" (see reliability theory), \"risk factor\" (see medical statistics), \"feature\" (in machine learning and pattern recognition) or \"input variable\"\n",
    "\n",
    "> [...], a dependent variable is sometimes called a \"response variable\", \"regressand\", \"predicted variable\", \"measured variable\", \"explained variable\", \"experimental variable\", \"responding variable\", \"outcome variable\", \"output variable\" or \"label\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Datasets no scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os datasets que podem ser abertos diretamente com o scikit-learn são dicionários com pelo menos os seguintes valores associados às seguintes chaves:\n",
    "\n",
    "* `DESCR`: Descrição, em texto, do conteúdo do dataset\n",
    "* `data`: `np.ndarray` $2D$ com os *features* ou variáveis independentes (entrada), em que cada coluna é um *feature*\n",
    "* `target`: `np.ndarray` $1D$ com os alvos ou variáveis dependentes (saída)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando aplicável, há ainda:\n",
    "\n",
    "* `feature_names`: nomes dos *features* (colunas do `data`)\n",
    "* `target_names`: rótulos/*labels* para cada valor possível de `target`\n",
    "* `images`: `np.ndarray` com a imagem de entrada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos abrir um dataset famoso!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dict = datasets.load_iris()\n",
    "iris_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DESCR': str,\n",
       " 'data': numpy.ndarray,\n",
       " 'feature_names': list,\n",
       " 'target': numpy.ndarray,\n",
       " 'target_names': numpy.ndarray}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{k: type(v) for k, v in iris_dict.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris_dict[\"DESCR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dict[\"data\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dict[\"feature_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_dict[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'],\n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saída categórica com índices para esses nomes\n",
    "iris_dict[\"target_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 50, 50])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(iris_dict[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "50                 7.0               3.2                4.7               1.4   \n",
       "100                6.3               3.3                6.0               2.5   \n",
       "\n",
       "        species  \n",
       "0        setosa  \n",
       "50   versicolor  \n",
       "100   virginica  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.DataFrame(iris_dict[\"data\"], columns=iris_dict[\"feature_names\"]) \\\n",
    "         .assign(species=[iris_dict[\"target_names\"][idx] for idx in iris_dict[\"target\"]])\n",
    "iris.iloc[[0, 50, 100], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seriam esses dados linearmente separáveis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LIOrHyjf+B/fsuqfP96tRW13qt1ZQVSDi0yYW6W1xxirDcf+P/ok5f70RN0+8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\np8PMzMzMzIbKnQ4zMzMzMxuq/wBNal7K7LsjdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e25329438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue=\"species\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há uma classe/espécie que é linearmente separável das demais! Você conseguiria colocar alguma reta acima em algum dos gráficos para separar uma distribuição das outras duas?\n",
    "\n",
    "As outras duas espécies criam um bloco só, parecem mais difíceis de separar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esse dataset, na versão atual, contém erros de transcrição, felizmente irrelevantes para este tutorial. Veja o artigo *The use of multiple measurements in taxonomic problems* (1933) do R. A. Fisher e a análise em https://github.com/danilobellini/scientific-literature para mais informações sobre o Iris, discriminantes lineares e o fundamento estatístico de parte do que será visto a seguir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classificadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No scikit-learn, há um padrão para todos os **estimadores**, objetos que são treinados para estimar/predizer valores. No caso da classificação, fazemos isso com os métodos `fit` e `predict`, um para treinar o classificador, o outro para utilizá-lo.\n",
    "\n",
    "```python\n",
    "from sklearn import módulo_com_classificador\n",
    "estimator = módulo_com_classificador.ClasseDoClassificador(*args, **kwargs)\n",
    "estimator.fit(X_train, y_train)\n",
    "y_pred = estimator.predict(X_test)\n",
    "```\n",
    "\n",
    "Cada entrada pode ser tanto um *array* do NumPy como um *dataframe* do Pandas:\n",
    "\n",
    "* `X_train` e `X_test` são $2D$ e possuem um evento por linha, uma feature por coluna, como as matrizes do campo `data` dos datasets que vêm com o scikit-learn\n",
    "* `y_train` é $1D$ e possui o resultado de cada respectivo evento, i.e., `y_train[i]` é o resultado do vetor de features `X_train[i]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obs.: Os nomes `X_train` e `X_test` não seguem a [PEP8](https://www.python.org/dev/peps/pep-0008/). Eles decorrem do uso tradicional de letras maiúsculas para representar matrizes e letras minúsculas para representar vetores, em matemática. A variável `X` é tradicionalmente utilizada para as matrizes de \"entrada\" e a `y` para o vetor (matriz coluna) de \"saída\". Porém, isso é apenas uma convenção de nomes, o contexto pode sugerir nomes mais descritivos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segue uma lista não-exaustiva dos classificadores disponíveis no scikit-learn:\n",
    "\n",
    "* `sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis`\n",
    "* `sklearn.ensemble.AdaBoostClassifier`\n",
    "* `sklearn.ensemble.RandomForestClassifier`\n",
    "* `sklearn.gaussian_process.GaussianProcessClassifier`\n",
    "* `sklearn.linear_model.LogisticRegressionCV`\n",
    "* `sklearn.linear_model.Perceptron` $\\rightarrow$ possui `partial_fit`\n",
    "* `sklearn.naive_bayes.GaussianNB` $\\rightarrow$ possui `partial_fit`\n",
    "* `sklearn.neighbors.KNeighborsClassifier`\n",
    "* `sklearn.neural_network.MLPClassifier` $\\rightarrow$ possui `partial_fit`\n",
    "* `sklearn.svm.LinearSVC`\n",
    "* `sklearn.svm.SVC`\n",
    "* `sklearn.tree.DecisionTreeClassifier`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É possível identificar a espécie de Iris a partir das $4$ medidas (largura e comprimento das sépalas e pétalas)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = iris.iloc[:, :4]\n",
    "y = iris[\"species\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'virginica', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm # SVM = Support Vector Machine\n",
    "svm_estimator = svm.SVC(kernel=\"linear\") # Classificador linear\n",
    "svm_estimator.fit(X, y) # Treino!\n",
    "svm_estimator.predict(X) # Parece com 50 setosa, 50 versicolor, 50 virginica?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qual é a proporção de acertos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99333333333333329"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_estimator.score(X, y) # Realiza o predict internamente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando temos um classificador, como podemos avaliá-lo?\n",
    "\n",
    "Poderíamos usar os dados que temos para realizar essa avaliação, porém se esses dados são os mesmos utilizados para treinar o estimador, talvez estejamos propagando um vício sem saber, obtendo um resultado demasiado otimista devido à falta de variabilidade. O nome dado a esse resultado é **overfitting**.\n",
    "\n",
    "Para auxiliar o entendimento do conceito, podemos fazer uma analogia disso com a aplicação de uma prova depois de uma aula. Durante a aula, o aluno pode encontrar um padrão como \"*as respostas desse tipo de exercício formam uma P.A.*\", por isso ter ocorrido nos exemplos mostrados, e achar que essa regra generalizava o que foi ensinado. Mas, se o padrão encontrado era meramente contingencial, esse aluno poderá se sair mal na prova por responder com os detalhes irrelevantes que memorizou ao invés de tentar aprender o conteúdo.\n",
    "\n",
    "Por outro lado, em uma aula o professor explicita o que é esse conteúdo, enquanto que em generalizações realizadas a partir de dados, qualquer padrão, mesmo que não seja considerado o mais simples por algum critério, pode ser o conteúdo, e o que queremos testar é se o padrão encontrado faz sentido como generalização dos dados e dos eventos futuros. Uma forma de testar isso é reservando uma parte dos dados que temos para validar/testar a generalização realizada a partir dos demais dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dividindo o dataset em 2 partes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma prática comum em *machine learning* é a de dividir os dados fornecidos em $2$ conjuntos: um para a realização do treinamento (`fit`) do estimador, outro para a realização de uma avaliação do estimador obtido. Um conjunto para o \"treino\", outro para \"testes\". Entre os diversos modelos, adota-se o que minimiza o erro olhando apenas para o conjunto de testes:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![](overfitting.png)](https://en.wikipedia.org/wiki/File:Overfitting_svg.svg)\n",
    "\n",
    "(Imagem obtida no Wikipedia)\n",
    "\n",
    "As curvas do gráfico acima representam a taxa de erro quando avaliamos o modelo com o conjunto de testes (vermelho) ou com o conjunto de treino (azul). O eixo das abscissas representa os modelos, por exemplo os resultados de cada iteração de um processo de treinamento. Continuar um treinamento pode nos levar a uma contínua melhora na acurácia do modelo quando olhamos somente para o conjunto de treinamento, mas em algum instante o resultado aplicado ao conjunto de testes indica que o modelo começou a perder acurácia: overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = \\\n",
    "    model_selection.train_test_split(X, y,\n",
    "                                     test_size=.25,\n",
    "                                     random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isso separou $25\\%$ dos dados para teste e $75\\%$ para treino. Esse é o *default* do scikit-learn, o argumento nominado `test_size` não precisava ter sido passado nesse caso, mas é útil explicitar esse número, e torna prático mudá-lo, caso necessário.\n",
    "\n",
    "Poderíamos fazer esse particionamento manualmente, mas isso já particionou nossos dados embaralhando-os, sem perder a correspondência de índices entre as respectivas entradas e saídas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Chamar o `fit` novamente sobreescreve os dados, apagando o treino anterior!\n",
    "# Classificadores que permitem aprendizado incremental possuem um método `partial_fit`!\n",
    "svm_estimator.fit(X_train, y_train)\n",
    "y_pred = svm_estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como podemos avaliar se esse resultado `y_pred` é similar ao `y_test`?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos realizar avaliações sumarizando os erros e acertos de cada natureza em uma matriz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13,  0,  0],\n",
       "       [ 0, 15,  1],\n",
       "       [ 0,  0,  9]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As linhas representam os resultados coletados, as colunas representam os resultados estimados. Cuidado para não se confundir: isso é o transposto do [exposto no Wikipedia](https://en.wikipedia.org/wiki/Confusion_matrix), mas é a forma descrita na documentação do scikit-learn.\n",
    "\n",
    "O scikit-learn sempre ordena os dados ao apresentá-los, de maneira que as 3 linhas e as 3 colunas referem-se às espécies *setosa*, *versicolor* e *virginica* (ou 0, 1 e 2, se estivéssemos usando o `datasets.load_iris()[\"target\"]`), nesta ordem. Para fixar uma ordem qualquer, usa-se o argumento nominado `labels`.\n",
    "\n",
    "Colocando rótulos com o Pandas para melhorar a visualização:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">y_pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>setosa</th>\n",
       "      <th>versicolor</th>\n",
       "      <th>virginica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y_test</th>\n",
       "      <th>setosa</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  y_pred                     \n",
       "                  setosa versicolor virginica\n",
       "y_test setosa         13          0         0\n",
       "       versicolor      0         15         1\n",
       "       virginica       0          0         9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [\"setosa\", \"versicolor\", \"virginica\"]\n",
    "pd.DataFrame(metrics.confusion_matrix(y_test, y_pred, labels=labels),\n",
    "             index=[[\"y_test\"] * 3, labels],\n",
    "             columns=[[\"y_pred\"] * 3, labels])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resumo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O que fizemos até o momento pode ser re-escrito da seguinte forma, sem utilizar o Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13,  0,  0],\n",
       "       [ 0, 15,  1],\n",
       "       [ 0,  0,  9]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets, model_selection, svm, metrics\n",
    "\n",
    "iris_dict = datasets.load_iris()\n",
    "\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    model_selection.train_test_split(iris_dict[\"data\"], iris_dict[\"target\"],\n",
    "                                     test_size=.25, random_state=0)\n",
    "\n",
    "# Extra: o \"fit\" devolve o próprio objeto estimador\n",
    "svm_estimator = svm.SVC(kernel=\"linear\").fit(X_train, y_train)\n",
    "y_pred = svm_estimator.predict(X_test)\n",
    "metrics.confusion_matrix(y_test, y_pred) # Implicitamente labels=[0, 1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_estimator.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sem fixar o `random_state`, o resultado talvez não seja o mesmo, pois chamar `train_test_split` re-embaralha os dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando as técnicas vistas, treinar um classificador para separar os seguintes blobs gerados aleatoriamente, avaliando o resultado obtido:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_blob, y_blob = datasets.make_blobs(n_samples=2000, n_features=2, centers=4, random_state=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l/FyQVFrAvudsyaR2xwehcAoB98SLhB9P3Gukp+G9atEevd+7Q9dNRzAIxBOZ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\nDsUgTkRE5FD/HyXQo7eP6iAmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e203fb8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"darkgrid\"):\n",
    "    sns.FacetGrid(pd.DataFrame(X_blob).assign(y=y_blob), hue=\"y\", aspect=1, size=7) \\\n",
    "       .map(plt.scatter, 0, 1, s=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parte 2 - Combinando blocos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para o que foi feito até agora, é suficiente saber que o classificador `sklearn.svm.SVC(kernel=\"linear\")` e o `sklearn.svm.LinearSVC` encontram um discriminante linear para separar as classes, maximizando a distância com relação às amostras (margem larga), a depender do valor do `C` (quanto maior, mais tenderá à margem máxima).\n",
    "\n",
    "Porém, um discriminante linear é um hiperplano no espaço de *features* que consegue apenas separar duas classes (espécies, no caso do Iris dataset). Para funcionar com múltiplas classes, o `sklearn.svm.SVC` resolve isso agindo como um \"one-on-one classifier\". O que é isso? Tem alternativa?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essas classes são *decorators* (no sentido do *design pattern*) para classificadores binários:\n",
    "\n",
    "* `sklearn.multiclass.OneVsOneClassifier`: Classifica todos os pares de classes separadamente, selecionando o que tiver o maior número de votos\n",
    "* `sklearn.multiclass.OneVsRestClassifier`: Cria um classificador para cada classe, avaliando se pertence à mesma ou ao conjunto de todas as demais classes\n",
    "\n",
    "No caso do SVM, se quiséssemos usá-lo como *one-vs-rest*, teríamos de instanciar o estimador desta maneira:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92105263157894735"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import multiclass\n",
    "one_vs_rest_svm_estimator = multiclass.OneVsRestClassifier(svm.SVC(kernel=\"linear\"))\n",
    "one_vs_rest_svm_estimator.fit(X_train, y_train)\n",
    "one_vs_rest_svm_estimator.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temos acesso aos classificadores binários:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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='linear',\n",
       "   max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "   tol=0.001, verbose=False),\n",
       " SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "   decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "   max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "   tol=0.001, verbose=False),\n",
       " SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "   decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "   max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "   tol=0.001, verbose=False)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_vs_rest_svm_estimator.estimators_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O método `decision_function` do estimador (classificador) binário é usado como critério de desempate, ou a primeira coluna do `predict_proba`, que representa a probabilidade da classe \"positiva\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.58459897, -2.15411929,  1.89199335, -4.52609987,  1.39684192,\n",
       "       -4.29544529,  1.60323992, -2.65028866, -2.86540149, -1.98461469,\n",
       "       -3.74016002, -2.38375773, -2.64026621, -2.6974942 , -2.68086321,\n",
       "        1.29117385, -2.52210662, -2.41636546,  1.25101273,  1.74739869,\n",
       "       -3.18923262, -2.45158845,  1.00481639,  1.26350425, -3.02357691,\n",
       "        2.02104714,  1.10714364, -2.23785545, -1.35409061,  1.20373409,\n",
       "       -3.57813571, -2.44243775,  1.43990798, -3.01486208, -4.0207076 ,\n",
       "       -1.94176612,  1.32668652, -3.27471138])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No caso do SVM, estas são as distâncias com relação ao\n",
    "# hiperplano que separa as classes\n",
    "one_vs_rest_svm_estimator.estimators_[0].decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -3.58459897,  -0.92348443,   2.33275035],\n",
       "       [ -2.15411929,   0.75997811,  -2.27721574],\n",
       "       [  1.89199335,  -2.78555586, -10.22889684],\n",
       "       [ -4.52609987,  -0.47091046,   2.40364536],\n",
       "       [  1.39684192,  -1.36923269,  -9.01408977],\n",
       "       [ -4.29544529,  -1.66880518,   3.61569843],\n",
       "       [  1.60323992,  -1.64733995,  -9.32226525],\n",
       "       [ -2.65028866,  -0.97173427,  -1.138744  ],\n",
       "       [ -2.86540149,  -0.36455685,  -0.97910331],\n",
       "       [ -1.98461469,  -0.47295318,  -2.24734191],\n",
       "       [ -3.74016002,   0.14842487,   1.43498217],\n",
       "       [ -2.38375773,  -1.18956441,  -1.37695212],\n",
       "       [ -2.64026621,  -0.26532064,  -0.96415879],\n",
       "       [ -2.6974942 ,  -0.46934076,  -0.96076673],\n",
       "       [ -2.68086321,  -0.56340739,  -0.6694658 ],\n",
       "       [  1.29117385,  -0.78384098,  -8.8948682 ],\n",
       "       [ -2.52210662,  -0.66725593,  -0.82027139],\n",
       "       [ -2.41636546,   0.01877505,  -0.93981856],\n",
       "       [  1.25101273,  -0.75379295,  -8.57956781],\n",
       "       [  1.74739869,  -2.40605501,  -9.76799887],\n",
       "       [ -3.18923262,  -0.71939978,   1.34778637],\n",
       "       [ -2.45158845,  -0.84385308,  -0.55478585],\n",
       "       [  1.00481639,  -1.28676671,  -8.00670245],\n",
       "       [  1.26350425,  -0.51925711,  -8.35223057],\n",
       "       [ -3.02357691,  -0.61408366,   0.26330678],\n",
       "       [  2.02104714,  -1.82480794,  -9.86920946],\n",
       "       [  1.10714364,  -2.10762963,  -8.1841191 ],\n",
       "       [ -2.23785545,  -0.58466089,  -1.77602793],\n",
       "       [ -1.35409061,   0.43462248,  -2.97123269],\n",
       "       [  1.20373409,  -1.4717427 ,  -8.43703669],\n",
       "       [ -3.57813571,  -0.99137515,   1.34269723],\n",
       "       [ -2.44243775,  -0.84478845,  -0.38564368],\n",
       "       [  1.43990798,  -1.54302373,  -9.25603074],\n",
       "       [ -3.01486208,  -0.94315382,   0.41184156],\n",
       "       [ -4.0207076 ,  -0.69306628,   2.50563849],\n",
       "       [ -1.94176612,  -0.38496645,  -1.43921874],\n",
       "       [  1.32668652,  -2.08484402,  -9.29441408],\n",
       "       [ -3.27471138,  -0.25438126,   0.76643981]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quando não é um classificador binário, cada coluna do\n",
    "# `decision_function` refere-se a um classificador binário\n",
    "one_vs_rest_svm_estimator.decision_function(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para mais informações sobre classificadores multi-classe, veja http://scikit-learn.org/stable/modules/multiclass.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pré-processamento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dependendo do estimador, um ajuste aditivo/multiplicativo para normalização da entrada pode ser fundamental para treiná-lo, influenciando o comportamento do mesmo. O fluxo do pré-processamento é similar ao de classificação, dessa vez com os métodos `fit` e `transform`:\n",
    "\n",
    "```python\n",
    "from sklearn import preprocessing\n",
    "pre_processor = preprocessing.ClasseDePréProcessamento(*args, **kwargs)\n",
    "pre_processor.fit(X_train) # Encontra os parâmetros para pré-processar\n",
    "X_data_p = pre_processor.transform(X_data) # Processa um conjunto de dados\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando aplicado nos dados de treino, esse passo de transformação/pré-processamento passa a ser parte do pipeline de classificação, sendo necessário aplicá-lo nos dados antes do `predict` do classificador:\n",
    "\n",
    "```python\n",
    "X_train_p = pre_processor.transform(X_train)\n",
    "X_test_p = pre_processor.transform(X_test)\n",
    "\n",
    "estimator.fit(X_train_p, y_train) # Classificador do dado pré-processado!\n",
    "y_pred = estimator.predict(X_test_p) # Predição também exige dado pré-processado!\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalização"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classes comuns para pré-processar colunas/*features* independentemente incluem:\n",
    "\n",
    "* `StandardScaler`: subtrai a média e divide pelo desvio padrão\n",
    "* `RobustScaler`: subtrai a mediana e divide pelo IQR\n",
    "* `MinMaxScaler`: transformação afim do intervalo `[mínimo, máximo]` dos dados para o intervalo `[0, 1]`\n",
    "* `MaxAbsScaler`: transformação afim do intervalo `[-m, m]` para o intervalo `[-1, 1]`, em que `m` é o máximo do valor absoluto do *feature*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "iris_scaler = preprocessing.MinMaxScaler()\n",
    "iris_scaler.fit(X_train)\n",
    "X_train_p = iris_scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris_measurements = [cname.rsplit(None, 1)[0] for cname in iris_dict[\"feature_names\"]]\n",
    "iris_p = pd.DataFrame(X_train_p, columns=iris_measurements) \\\n",
    "           .assign(species=[iris_dict[\"target_names\"][idx] for idx in y_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2bcuKOkFglxHyBKczj7/yS70k6fnLgZ9fb1o/8ux9WD3n3y1nHLfc7yyyv/Lc\nlfjezu9xhmaylpnT5EBWt6mYwLqdnnp+3seXgASw8tyVWRktxT/9bPuri5PqVVEAtw5/EVElFThj\nbC5Ouy4FwhG0/OIrWDO3HZExUxE79BdE/t8KBC67z7Q9Titk5YNdRsgT8pp5PAI0S9b6gd+sQsuc\nG21nHLeq7pOZ/Tu235GaMBBgV0PKkJnT0adl5TBSP8qmnc7M8cjHl/pgPTr+2IFbzrklVV2w448d\n+Oacbxb9Utj2Vxfbkw4R+QVyjN1QSl1i9zMiclEgcKxmeo7a6U4Zl6/T65unLl9r6tjBR2kIHH0T\nTd//EACgCdA/CRvs07t1Gc8X78c1H7wmq2Z7LB41zQWSL7vSikRlY3RvTJ/HwOh+Ut9s3i81Td83\nrNY/+iZU/Sgg1GB6eqOSXFPY/A9fevYBYH9sf/bjhrubEGXl9OCrWTmMDR61bvfj/WgyTWGh9Bzn\nOAmJJWKYOHqiadnE0RNLlkm2/dUj16nitwH8R44vIqoCtpevE0PmvusiepeR9EvvCx7Uu5akrdeA\nIBZMXWAqn7hg6gI0ZPyDReQ7Trs3Gn3qn703q5qV9pkfoTcQRNtvbkTr+mMlc6+bfp1eSW773TnL\nT7O7CY0oM6dGd7+0HEYkYF3JUMHc7vcf0CcWzJHJhmCDdZsfZJtPZiPOSO41vppRlDOSu813M4p6\nNb9Zs8IqQeDhz2XPTPtPjwHJIf2T3ME+/QCWsV7/DTux9Lk7yjEjst8xv35kVb0qs/tJ+szOZy44\nVr1qqB/9gYDlTOQd53egcfvdCGxJm8DNZjZoq1mcK9DdhPn1sqycRvSCI2m51aD0/BjVqySEwP/5\nTHa7/4nVwN2zj93PyGR/vB9Ln17qtzbfd/mtBk5K5la1s9aeldf6L175YsW3w61toNqVdflaadZ9\n10N1evlFAGg4znK9yOgJ+ZW3dfJPHJFXZHZvNLpRWZXQBfRqVi89rp+gf+MAIoDl/tEYbkTgN6vM\nv8uYzTxzE9jdhEZi1Q034zYApLq8NtU127f7J04z38/IJEuak1M8shNRNrvSvJmlEy3Wix3Z67y8\nrV0J0iJmNCcqG7v82s4AHbUvARp3uM8RucUut0deM9/PyCRLmpNTPOkgomxO+65brBepPx6rz/23\n7NK6yuJqtl0J0iJmNCcqG7v8StB2/4kosS49jUDJy2ET5cVu5vJgQ85M2mbaqs2nmsbqVUSUzWlp\nXov1AuEIWv5vu3Vp3Uy2JUj5jxb5gF1+ww16ZSqL/ScQbkDLL1ZY7x82jyEqi3AD8NTt+hiOE6fp\nVa+euh247H7gGwdsM5kz00R3ZVWbAAAgAElEQVRpco3p+HbZtqKCXty9t9KbkDevjEOhKmIxrkKD\nhpgoRIDhWw1QyB7Amtl3eLDPurSuUVY03UglSD1C0xSi8SQa64KIDiXRGA4iEOCneFaq9m+Vvo8M\n9ulzcwz1A8vfBg7+WZ99/KXH7UvoGoaitvtHMhxBTIBGAFEBIlAIlvt1Uoons+x0DJyW1PNpFP2o\nawICI6RpKAocfevYoHHA3Hbbtck2mdbiA/qxw4WCBx4ppkB5sn2HlFK/yfXl5MlFZL6IvCoiu0Tk\naznW+4yIKBFpLeRFELmhZvJr0S9di/ejd+Awlm65ATPXz8TSLTfg6FA/egd6sfTppfqyp5eid6AX\nmsoYf5HPrOkuzLBeapqmcKh/CEvWduH0WzdjydouHOofgqZ5u/JfJfLr17/ViDL3kY2L9X7uz94L\nHNmnlySddxtw3q0j59cm88lwA3oHetG2pQ0z189E25Y29A70Iqkly/c6PaTS7a8ns+x0DJyW1Evd\nblx8LK/9B/TluRTaHls8TvvH9ehNRkc+XhRAU5qzYxF5zoglc0VkKoA7AZwBIFV0WSk1eYTHBQH8\nGcCFAPYB6ASwSCn1SsZ6owD8CkAdgOuVUjnr2ZW85F0+ZW2B/Erb5vHcZ02akN925KGKr3S49pGT\nb/JbCunlPYf1f20vlv7mX0wlEH/16V9hxfYVzsoi5lORyuPVq/oGE1iytgvbew6lls2ePAYPXNmK\n5vqiCgBWXX5d/FtVlsU+kiol+kT7sduFDwN1zSPn1yLzfYko2mxK6TYXMammi6ouv+k8mWW7HGaW\nVR54Vz/RyFxv4cN61cFcCm2PMx7XHxDXyuiWqERvFVx+9R8nR/YfAbgHQALA+QDWAVjv4HHnANil\nlOpRSg0B2AjgUov17gCwGsCAoy0mKo/aya9Fv/RI/aisEoinNJ9iXxZxsE8vtzjYl3/lKaN7lgzf\nlumEQ9MU+gYT0NTwrc0nmI11QXTu6TUt69zTi8Y6T3d8qUh+8/lbOf37l41R+tYqx3ZjN06cZr6t\nb0ZqKGSu57PIfGO40baUbg2qePubK8sVy6zTMXD1zdbr1Q+XxR14Vy98YKXQ9jjjcW6W0WWJXv9y\nkqaIUuop6FdF/qqUWgHgAgePOwVAWp017BteliIiMwCcppT6pcPtJSqX2smvRZnE2ODRrBKIr/e9\nbl0Wcagv+3L/4BFPl8HNp+tEdCiJWRNbTMtmTWxBdMjT3V4qkl+nfyvPdV0ZqduKXQnpg6+ab3v3\nDHdjSeRdCjoaj1ruX9HarORW8fbXLst7D0Url1nbUsz95mWDfdbrDRxJ62510P7EowTcLKPLEr3+\n5eSkY0BEAgD+IiLXi8inAYxz8DirS1epvXP4Ob8L4KsjPpHI1SLSJSJdBw4ccPCriYpWO/m1KJMY\nAbB6zipTCcTRdaOxes6/m8sizlmFyLP3ZpcMjR72dBncaDyJtg3d2N5zCAlNYXvPIbRt6EY0nn0i\n0RgOomPRDMyePAahgGD25DHoWDQDjWFPX+moSH6d/q3y+fuXxUilm636ul/6PX0sR/rtlpXA41/W\nT1LyLAUdCUWwKmOfWzVnVa1+elvx9tcqy9/67AfwnV//uXKZtStpKxltUbAOWPAD83oLfgA8/5Nj\neTRy6pJIKILVc1ebjxdzV5fsSodbz03uctIx8SvQi2m0Qb+UeQGAKx08bh+A09LunwrgjbT7owCc\nCeAZEQGAkwBsEpFLMvtlKqXuB3A/oPfJdPC7iYpVO/m1KJMY+NVX0fLpe7Hm/LsQCTchFu9HJBSB\n6vwJOj72HTTWH4fo4LuI1DUjcPDPwLXbj5VY3PYd4IS/Mf8Oj5XBzacbUCAgGNNUhweubPVWFZvc\nKpJfp38rz3VZS++2cuYCYO5Nep7jUf3qRGZpaGMG8g//L/32jE/q+5Ax87hd95aMfSC9As9AcgAn\nNJyAjvM70BhuRDQeRSQUQXCkikPVqeLtb1aWB5NY/rMXsen5Y5tR9szmKmmbLlQHvPJz4HPrgMjx\nel5f+wMweS5wW++xdtrFCoEBCaCloQVrLliTqjDVEGwoScUpq+dm9Sp/GPGkQynVCaQ+WWhTSh11\n+NydAKaKyCQArwNYCGBx2vMeAXCicV9EngFw00gDwYjKpHbya1MmMTDUj6aN/wTs3Y6mCbOhLf4J\nDp/x92j/zY3ofrsbM8bPwOo5q9By0Z0IPPZF/Z+qCbP1T976Mj5RNLoA1I8q72uzYXSdSB8kanQD\nshokGghIarlPBkRXLL9O/lb5/v1dZ3Sfah6nV6H6+fXH8vyZB/UTjvTS0CLAuk/p/9St+1R2yWej\ne0uOUtBGBZ72re3H9qe5q9HS0IKABLw6eLxcPNH+pmcZArz97qDp52XP7EglbdPX+9MvgM3t+v2v\nvQaMnQb87FpzOx2P6aV0XRKQQGpgdyQUyZn3Yp672IHpVD4jvtMi0ioiLwJ4AcCLIvK8iMwc6XFK\nqQSA6wE8CeBPAB5RSr0sIreLiH8nFlwx2vkX+VbV5teKVdeRBQ8Cz95n6h4Si0fR/vtvoPOtTiRU\nAp1vdaJ92zLE4n3mbiQ/uxaoi4zcBaCCfNplyjGv59dzf39jHzh/uX7CMVK3KKOby+7fWndjCdWP\nWHo0loihfWu7eX/a2s5+6fBmfj2RWaclbTPXSwzo7XJmO63K1zWMeSfAWcncFwBcp5TaNnz/owDu\nVkp9oAzbl6XiJXNdwpK5BfF0/xYrniyZC1iUSYwAK8eZBhpqt/Vi5o9bkVDHloUkhB2XdyFwe9qA\ny0AIWL5fv4Sf3uXqsvv1yiYe4YGJv2o6vx74+2dukP6O3DHWPMA2ENJnY07PrtKAn14NfPK7+r5i\nXMWLvQO8+ChwzhJ9BEKO0qOa0jBz/czs/enzO/zSTaTm8uuJzDqeHDBtPcBZrt3cbO/l3Xf5rQZO\n3umjxgkHACilfgvAaRcrIioHu/KcWlIvj5gqk5jMXcrTEM+u1hM7ste6YsiRvebHTpgNHP6r3gXg\n9hb99uhbrgxaLKbsqtF1IiDDtyX458FzZWAryPhbJDUNRwfiWX8TN/7+RQkEzFWqzlygj1Va/nbG\n/pLQby+7X1/2pyeAf58A/O8TgNWT9G4tg33H/qVRsCw9ygo83pdMHsvu0YE4lFLeyqxhpDbdrprV\nYF/ZNpF5J8DZScdzInKfiJwnIh8TkbuhD946W0TOdnsDiWgEtuU+E9az0sb7Ry5xqyWyLuNHGo63\nrhjScHz25f7GE1yfZdxrZVe9tj2VZPwtfritB68fHsDV63b4429idEs571Z9bMcrv9RnHDf2oQ0L\n9VKjz947vE/9EzDhHOCz683dq4yf5yiVywo83pZMajjUP5TK7tXrduBQ/xCSyQqW/rZq6wePWLf/\n6W16337rboBlLO7BvBPgrHvVlhw/VkopJ3N2lAy7V+WP3au8w5XuVXaz1C582HpW2n9cD6yaaF72\nDx3AmhnmZYsf0T85S7uMrwmyq49YdSOBxbIST/rntRmDC9ieqs2v8bdYccn7sWLTy555jxzRNGCo\nT993jJnGrWYiNwbzGvtafbO+Lz57L/DMv5nXz5wx2vhVadWrfFiBp2rzCwBHB+K4et2OrOzef8VM\njGoIu7WJuVm19Uu7gV+0ZWc0vU1v3w089wO9yprR5fWVX+rV10aaobyEPJZ33+W3GjipXnV+OTak\nUiYOPJzX+nsaFo+8ksectfYsx+tW8QlK9bKbpdaubGfD6OxlViVuww3H+vsO/8MUALIrhsixn5v+\nsbJaVoRkUkM0nkRTfQj9gwlEwvnNfu2kL3YxfbY9Vwa2goy/xZRxzam/ySUffA+uO38KpoxrRmwo\niURCQyxx7P1sDAcRDHrgH26jSlX6TOPpjOXp99NncN66Knt9m0+UWYHHu5rqQxh/XD2e/MpcTBnX\njF37+3DPM7vQlHGynNkuuZpjq7b+hL+xzmh6mx45Xs9l+slwIAR87CZ3ttMG805OqleNF5EHRWTz\n8P0zROQq9zeNiByxmy0516y0mcsO/zV7mYsTR+XLqqtDb/8Q1iyablqvmNmvi+0e5dOZy11h/C12\n7e/DrIktuOSD78FNfzcNKza9jGnLN2PJui70Rofwo9/u9k7XlXTGPmXMNJ7OWJ5+3+gbb7cvemhf\nImcGhpK46aJjmV2x6WXcdNE0DKTtz2XvgmWVr8N/tc5cepsee6fiYzqIAGdjOh6CXrbuPcP3/wx9\nwkAi8gK7Mop1jdb9eI3vyzwGoxjReBI3bNxpmsH6ho078dGpY0s2+3Wxs2R7oqSmRxh/iydfehOr\nFnwAN154OpY9/kLW+3fRmSeb7ldsRvJMxj5lzDSeuQ+98suMvvFN5sd5eF8iZ5JK4eZHzZm9+dEX\nkEzrkm7XLrmWY6t8NZ5gnbn0Nt2utLOLc3QQWXHSqfZEpdQjInILoNfPFhGPHBmIKGu25PQxFE1j\nzf3N65oASPa6gPXjPaKpPmTZdampPlSy2a+L7R7l05nLXWH8Lb40ZzIi4QBExPJvO2Vcs+l+ZteV\nijH2qdnX6iVxjX1oKKp3O/zw/9K7phj7lDFreK59kXwlV5uTzzolZZcvwMGyhuxjQW3Odk8V5GTP\n6BeRMdAL/0FEPgzgSO6HVK98xoDkM/7jxd17R15pmJuDzsmn0mdLTh9DEQgeGyiYPmCwDGMwSql/\nMGE5g3X/YCI1qLPY2a9LMUu2D2cud0363+LoQNzyb7trf5/pfvr7WXHp+5Sx72TetxqEa7cvkq84\naXOcrFNydvlysixXbonKwMnHLzcC2ATgvSLyOwDrACx1dauIiNI0hoO4a+F0U9eluxZOd9R1KRIK\nWD42EjI3f+we5Z5IyPr9e/KlN/N+P4nKwS6zkdCxjBbTLhHVIifVq/4oIh8DMA16nZpXlVJx17eM\niGhYMBjAmKY63H/FzLyrxMQSGjY+txcrLnl/qgrNxuf24ktzJqM57fHsHuWegWT2e7Djr7344kcn\n4fp5U71VvYoI1plNtRvDH1gU0y4R1aIRTzpE5LMA/ksp9bKILAdwtoisVEr90f3NI6KK0TTX59rI\nh4hAREzfOylx21gXRMfTu/Cd//5LalkoILh+3tSs35FP96hiyuvWEk1TaKwLYnRjGOOPq4cIMP64\nejzbcxBNw7M7e6ZLVS4e2x+otDL350g4gJ6D/aZ1eg72Z43xCgYDGDV8kuGLHKdjpqnMnHQ6/oZS\n6lER+SiAiwB8G8A9AD7k6pYRUeUYM98+dpVe833C7OGKKGMrclAyytm2behG555ezJrYgnsvPxtD\nSQ1tG3amlnUsmoExTXWmf/5LMVbDyfZY/e5aZ/ydQkHBxWeejH/+8R9Tf6+7Fk7HUDyJhjofjH3x\n2P5ApWW1P99z+dlon/+3+Oojz6eWfeuzH8DAUBKN1TBei5mmCnCSLKNS1d8DuEcp9XMAde5tEhFV\nXDyqH4z2bAO0hH772FX68gqwKmd7OBpH24adI5a4dWOsRrHldWuF8XcKQCxLi8YdzoFScR7bH6i0\nrPbnd6JxfPWR53OWzPU1ZpoqwMnp+usich+AjwNYJSL1cHayQkR+ZTfLuc3Mym6zKmd7WkujoxK3\nbozV4Ozjzhh/p1GRMpcWLTWP7Q9UWvm0L77J7EiYaaoAJycPn4M+OeB8pdQ7AFoA3OzqVhGRezRN\nr9Ouhm81i9lzKzyzsqYp9A0moCn9diCeRNsFU/DkV+bif775CTz5lbnY/+5AUTOAJxIajg7EoSmF\nowNxJBLOZxHm7OPORIf0961vIJH192q7YAr6097jZFIzvedOZ4IfkZO8j4QzjVc1q/35td6o5T5u\nZPboQBzJpIZk0tyOJJNaVvtVsizbKSTjzDRVwIgnHUqpqFLqp0qpvwzff1Mp9f/c3zQiKjmjH++G\nhcAdY/Xb6IHsg1QFZ1Y2+lcvWduF02/djCVru9A/mMAVH5mIFZtexrTlm7Fi08sY1RByVArX6vmi\nQwn0Rodw9bodOP3Wzbh63Q70Roccn3iwvK4zkVAAC8+ZgP85cNT0Xt348alYeM6E1N9/ydouHOof\nwg+39ZjuF/3PmtO8j4QzjVc1q/35hMYw7lqU3b786Le7U23G0cEEDmW0I0cHEzjUP2hqb0qSZTuF\nZpyZpgoQ5WL/RBGZD+AuAEEAP1BK/XvGz28E8GUACQAHAHxJKfXXXM/Z2tqqurq6SraNE7/2q5I9\nV6Z8JgfMh5uTA7545YuuPbcLXB2x64f85m2wTz8o7dl2bNnEOfrMtZkTmVWosknfYAJL1naZBn7P\nnjwGd152Fs779jOpZc/cdB427XwdF515cqqk5ZMvvamXtEzrAmH1fDtvuxD//OM/Zv2O+6+Y6bgC\nTQmqV1V9fo2//YpL3o+eA0cx+70n4rhIGH0DCVyzfkfW33/FJe/HRf+5NXX/gStbi5tkMZ+8j4SV\nfjJVVX4z9+cAgB9s60m1L/2DCTz0u92mKnjP3HQebvnpi6YcWy0rSZbtFJPx2s40K35UgGudE0Uk\nCOD7AC4EsA9Ap4hsUkq9krZaN4BWpVRURP4ZwGoA/+jWNhE5VZH8FnMAcPrYfPrxFjGzcj7/kGet\nazNe4rSWRjz5lbmpE4xTT4jYlrTsG0yYSl9mPt9xkXDR/bW9PPt4Jdtf4/2MhAOAAn785Q8hNpTE\nsz0H8fa7gxjVEEZzg/UYjynjmk33ix4jkyvvWftMBIjH7PchzjReNpXIb+b+rCllKvMcEMHoxrCp\nDbIa95FrrFl6u1TwuDItCQz16xkc7APqmgofm8FMU5m5eUp7DoBdSqkepdQQgI0ALk1fQSm1RSll\ndCB8FsCpLm4PUT7Km99iuoHk89gy9OO16s5k173Aal2r/v+zJragbzBh6l7VGx3C8k++z7Tspoum\nITaUzPrdbRdMMT3fu7G4bX/tKlGR9td4P3+4rQevHx7AknVdmLZ8M3742x5cfObJqfdq7yHr/vK7\n9veZ7hc9RsYu7/GB7H2m/wCw/e7iumFRqVT8/4eheDJV5vn0WzdjybouXHzmyXjypTdT7U3fYHZb\nZTcWpG8gUXyXKy2p53TjYj2nGxcD/QeBucvM63FsBnmUmycdpwB4Le3+vuFldq4CsNnqByJytYh0\niUjXgQMHSriJRLbKm99iyhfm89gy9OPNp5ys1boP/W53dl/qRdOx9ne7zSVXN+xE30Ayq6Sl8X36\nel84d5Lp+UIBsRkPUjVjMirS/hrv50Vnnoxlj7+Qeh8uOvNkU8nc7/z6z/jWZz+Q9fd/8qU3SztG\nxi7vKpm9zzz+ZeCMT7J8qDdU/P+HuKYsyzxfdObJqftrf7c7qx1pbghatl8PZbRfBZXYHurXc2rK\n7VXAh67h2AzyBTf7BFhdN7Q8rReRywG0AviY1c+VUvcDuB/Q+2SWagOJcihvfospX5hXF5JGaI0n\nIrZ4IyLhRsTiUURCEQRK2I+3sS6I8cfVm7oh3PPMLjSEAjg6EEdTfQj9gwk0hoOWXak6nt6F6y6Y\nYipxGwkH0PH0LtN6RperzGWjGkJZy5obQlklcxtCQdx/xczU9kRCQYRCVdOfuSLtr/F+ThnXbHpf\np4xrxvwzx+Oey8/GcZEw3o3FsfO1w3jgilZE6oKIDb/HX5ozGdfPm1q6Gd4DAX2ys0Ubzd2mBMCo\nk4BrtwMnTgMOvgps+47+vaHI8qGa0hBLxBAJRVK3AamafLmt7PlNJjVE48lj7UHYuptnehfAjqd3\n4drzp2DFJe9PtXUrf/kn/MfnPuio/cq7+2B9s3VuG0ZlZ9zFsRnMNhXKzZTsA3Ba2v1TAbyRuZKI\nfBzArQAuUUoNurg9RPkob36L6faURxcSbfAIegcPY+mWNsxcPxNLt7Shd/AwNFW6biQD8SRuumia\nqdvT/770/ejtN1d5OdQ/hKG4denZWFxDc30IAdH7WduVqH2tN5q17N1YPGuZMfu48XyBgCAUCmBU\nQxgBEYxqCFfTCQdQofbXeJ927e8zvV/vRAdNXVX++cd/xBknj8ZgIoHLf/AHKCgEg4Gs96gkjH7r\nMnwbCOj7xrzbgCfagZXj9Nt5twFH0/5ERXRR0ZSG3oFeLH16qb6fPb0UvQO9Jd3PqlxZ85tMajiU\n0T719g9hzaLppvWsugDuOxzDRf+5Fe/9+hO46D+34u13B7Par1hcK02J7XjMOrfxWHbGXcJsUzHc\nPMp2ApgqIpNEpA7AQgCb0lcQkRkA7oPeYOx3cVuI8lXe/BbT7SmPLiSxgXfQvrUdnW91IqES6Hyr\nE+1b2xFLxIra/HSaBtz86AumrgRKwXZGaielZxvDwezuUIum4/jGcFYXnfQuVDVcyrYi7a9RevTJ\nl97EqgXHuk+FgkHL9z8UDFamW5tKAj+71txN5WfXAgiUpItKLBFzfT+rcmXNbzSetMznuVPHjtAF\ncDpOyGiD7NqvkpTYVpp1bsv4Dz+zTcVwrXuVUiohItdDn1gwCOCHSqmXReR2AF1KqU0AvgWgGcCj\nIgIAe5VSl7i1TUROlT2/dt1AnHxilasLSUa3q8joCeh+u9u0rPvtbkRCkYI220pjfXa3hFzVoprq\nMOJs4cFgAGOa6kzdoYwDduYyESnp7ON+VKn215j9/UtzJiMSDqS6T4nA8v0f1RBCU7gC3drsKv4c\ndzLwjQNFd1GJhCKu72fVrNz5baq3rqbWXB8ydZ3a/NKb+NJHzV0AgZHbL2O/KLpdsq1U1ZT3ay4U\ns03FcLXOo1LqCQBPZCy7Le37j7vxe92ceyMfEwcedryuW3N6UOHKnt9iyhdaPXawT+8ikla/PXZk\nL2aMn4HOtzpTy2aMn4FYIoamcGkOXMYs1OnzZ0SHq7yk1643qkWNagg7Kj0rIhj+5yL1fSAgGBXU\n/zFMn1/Dq6Vsy6lS7W966VGIhu8//Rd84dxJeHXlxdi1vw/f37ILm55/w/T+AyWZ98Q5o0ti+twG\nRneq+uaiy4fGEjHX97NqV8789tu0T9GManbjRtVDZQwtcVo6uyQltkfKbRkw21SMqurETERpLLpd\nRRqOx+q5qzHrpFkISQizTpqF1XNXl/RTKmMW6vQxHdF4EvdcfnZWVwWn3QvyKcNL3mFk4Zr1O46V\nNf67abjx41NN73/Z31+Xq7hFQhHX9zMqHavum/dcfjai8aSpHZv5Ny2pGckr0gZ5YBZxZpuK4eqM\n5G5wMiOuV6505COfKx2ckTzFd31myj4juVX1KoGrlUfsZhR/4IqZUICpK1Qw6Oz32j6nW7P8lkfV\n59fufbv/ipmm978i76/LszHXQIWfqspvZvUqAbBk3Y6sTK645P246D+3pu6XvQ3ywCziVZJt3+W3\nGvj2aE1EDlh0uwoAqcvgblwOt5tRvHG4mgtg7gpV1HMWO2M1ucrufWtKy0Ku9Vx9f12ejTkgAVf3\nMyqtYDBg6qqpKTViydyKtEEemEWc2aZC+e7UlIi8za68bTGzS7vxnOQ+p+8b31/yGrtMZpbMZUaJ\nnONJBxGVVMnKQ7r8nOQ+p+8b31/yGqtMZpfMZUaJ8sHuVURUUiUrD+nyc5L7nL5vfH/Ja6wyGQkF\n8KU55pK5zCiRc7zSQUQlZ5SHTJ9dWtMU+gYT0NTw7XDVF7vlVB2UUqYvCCzfZ6vMEFVSKrOp7yu8\nQUQ+xysdPvTi7r15re9Wtauz1p6V1/o+q4xFJWSURG3b0I3OPb2YNbEFHYtmoKUxjN5oPGv5mKY6\n0z+ddo/PXI+8JZnUcKh/CBuf24tPzTgVyx5/ge8f+YKR3Rs27kxl9q6F07Hjr71YumEnM0xUAF7p\nICLXReNJtG3oxvaeQ0hoCtt7DqFtQ3fO5U4fT94VjSdxw8aduOjMk7Hs8Rf4/pFvGNlNz+wNG3di\n9ntPZIaJCsSTDiJyXa7SqU5KpbJkrj8Z7++Ucc18/8hX7Nqm4yJh031mmMg5nnQQkevsyk/2DyZY\nUrWKGe/vrv19fP/IV+zapndjcdN9ZpjIOY7pqAH5jAHJd5wGkRNG+cnMMRm5ljt9PHlXYziIuxZO\nx8bn9mLVgg9kjeng+0deZWQ3c0zH9v85iFBAmGGiAvCkg4hcl6skKkuqVq9gMIAxTXX44kcnobEu\niPuvmImm+hDfP/I8I7tGZvsHE4iEgphz+jj8+d8uZoaJCsDuVURUFKuSt1bLii2JypKq/pJMajg6\nEAeG3yYRgYgACnz/yBesSuSyDSIqHK90EFHBrEvZTkddMID/9eM/jlgelaVwqxNL5ZLfJRIaeqPZ\nJXNbGusQCvHzWqJCuLrniMh8EXlVRHaJyNcsfl4vIj8Z/vkfRGSim9tDlA/md2TWpWx34nA07qg8\nKkvhuqeS+WWpXCpWpdvfWMK6ZG4swewSFcq1Kx0iEgTwfQAXAtgHoFNENimlXklb7SoAh5VSU0Rk\nIYBVAP7RrW2iyspnkHqlJxJkfp2xK2V76gkRPPmVuZgyrhm79vfhnmd2WZaWZClcd1Qiv5qmEI0n\n0VgXhEAw/rh6lsqlglQ6v9GhZM4y30RUGDevdJwDYJdSqkcpNQRgI4BLM9a5FMDa4e8fAzBPRHjN\nnbyA+XXAqpRt2wVT0Ns/hBWbXsa05ZuxYtPLuOmiaRiwutLBUrhuKWt+jW5yS9Z24fRbN2PJui7c\ndNE0vHUkxveXClHZ/K7t0rt9XjDFtJ5R5puICuPmSccpAF5Lu79veJnlOkqpBIAjAMa4uE1ETjG/\nDhilbGdPHoNQQDB78hh84dxJWd0Sbn70BWias8ezDGVJlDW/Vt3kbn70BQREsGrBB/j+Ur4qnt8b\nNuzEF86dZMruXQunIxJidokK5eZ1QqtPHDJrQThZByJyNYCrAWDChAnFbxnRyJhfByxL2dp1marP\nPlizFK5ryppfu/d8/OgGKKVYKpfy5Yn8NjeEskrmchA5UeHcPOnYB+C0tPunAnjDZp19IhICMBpA\nb8Y6UErdD+B+AGhtbRcn0bgAACAASURBVLUoYkelktdEgpOq6x/oDMyvQ0YpW0AvJ9k3PJPv9p5D\nqXWMLjXNFv2hMx9PJVHW/Brd5Oze81EN+j9qfH/JIc/kd1RDGABSt0RUODdP2TsBTBWRSSJSB2Ah\ngE0Z62wCcOXw958B8LRSVpWxicqO+S0Qu0x5Qlnzy/ecSoz5JapCrn3spJRKiMj1AJ4EEATwQ6XU\nyyJyO4AupdQmAA8CWC8iu6B/QrHQre0hygfzWzh2maq8cueX7zmVEvNLVJ1cvdatlHoCwBMZy25L\n+34AwGfd3AaiQjG/hWOXqcord375nlMpMb9E1Yd7lkdMHHjY8bp7Gha7uCVERERERKXFMgxERERE\nROQqnnQQEREREZGreNJBRERERESuEr9V+BSRAwD+OsJqJwI4WIbNqZRqf32As9d4UCk1vxwbUyrM\nL4Dqf30A81vN72+1vz6A+a3m97faXx9QpfmtBr476XBCRLqUUq2V3g63VPvrA2rjNdqp9tde7a8P\nqI3XaKfaX3u1vz6gNl6jnWp/7dX++oDaeI1+xe5VRERERETkKp50EBERERGRq6r1pOP+Sm+Ay6r9\n9QG18RrtVPtrr/bXB9TGa7RT7a+92l8fUBuv0U61v/Zqf31AbbxGX6rKMR1EREREROQd1Xqlg4iI\niIiIPIInHURERERE5Cpfn3SIyHwReVVEdonI1yx+Xi8iPxn++R9EZGL5t7JwDl7fF0TkgIjsHP76\nciW2s1Ai8kMR2S8iL9n8XESkY/j1vyAiZ5d7G93E/DK/fsb8Mr9+xvwyv1QBSilffgEIAvgfAJMB\n1AF4HsAZGetcC+De4e8XAvhJpbe7xK/vCwC+V+ltLeI1zgVwNoCXbH7+CQCbAQiADwP4Q6W3uczv\nL/Pr4S/ml/llfv35xfwyv/yqzJefr3ScA2CXUqpHKTUEYCOASzPWuRTA2uHvHwMwT0SkjNtYDCev\nz9eUUlsB9OZY5VIA65TuWQDHi8jJ5dk61zG/Psf8Mr9+xvwyv35W4/n1LT+fdJwC4LW0+/uGl1mu\no5RKADgCYExZtq54Tl4fACwYvnT4mIicVp5NKxunfwM/Yn51zK8/Mb865tefmF8d80tl5eeTDqtP\nHDLr/zpZx6ucbPsvAExUSn0AwH/j2Kcy1cLP799ImF/m18+YX+bXz5hf5pcqwM8nHfsApJ+Znwrg\nDbt1RCQEYDRyX47zkhFfn1LqkFJqcPjuAwBmlmnbysXJe+xXzC/z62fML/PrZ8wv80sV4OeTjk4A\nU0VkkojUQR/otSljnU0Arhz+/jMAnlZK+eVMd8TXl9E/8RIAfyrj9pXDJgBXDFeh+DCAI0qpNyu9\nUSXC/DK/fsb8Mr9+xvwyv1QBoUpvQKGUUgkRuR7Ak9ArNfxQKfWyiNwOoEsptQnAgwDWi8gu6J9Q\nLKzcFufH4etrE5FLACSgv74vVGyDCyAiGwCcB+BEEdkH4F8BhAFAKXUvgCegV6DYBSAK4IuV2dLS\nY36ZXz9jfplfP2N+mV+qDPHPiTsREREREfmRn7tXERERERGRD/Ckg4iIiIiIXMWTDiIiIiIichVP\nOoiIiIiIyFU86SAiIiIiIlfxpIOIiIiIiFzFkw4iIiIiInIVTzqIiIiIiMhVPOkgIiIiIiJX8aSD\niIiIiIhcxZMOIiIiIiJyFU86iIiIiIjIVTzpICIiIiIiV/Gkg4iIiIiIXMWTDiIiIiIicpXvTjrm\nz5+vAPCLXwo+xPzyK+3Ld5hffqV9+Q7zy6+0L6oA3510HDx4sNKbQFQw5pf8jPklP2N+iSrLdycd\nRERERETkLzzpICIiIiIiV/Gkg4iIiIiIXMWTDiIiIiIicpVrJx0i8kMR2S8iL9n8XESkQ0R2icgL\nInK2W9tClC/ml/yM+SU/Y36JqpObVzoeAjA/x88vBjB1+OtqAPe4uC0VpWkKfYMJaGr4VmO1Nh94\nCMwv+ddD8Eh+2f5RAR6CB/LL7BKVlmsnHUqprQB6c6xyKYB1SvcsgONF5GS3tqdSNE3hUP8Qlqzt\nwum3bsaStV041D/ExsvjmF/yM6/kl+0fFcIL+WV2iUqvkmM6TgHwWtr9fcPLqko0nkTbhm5s7zmE\nhKawvecQ2jZ0IxpPVnrTqDg1kV+qWmXJL9s/conr+WV2iUqvkicdYrHM8iMEEblaRLpEpOvAgQMu\nb1ZpNdYF0bnH/IFN555eNNYFK7RFVCI1kV+qWmXJL9s/conr+WV2iUqvkicd+wCclnb/VABvWK2o\nlLpfKdWqlGodO3ZsWTauVKJDScya2GJaNmtiC6JD/LTE52oiv1S1ypJftn/kEtfzy+wSlV4lTzo2\nAbhiuArFhwEcUUq9WcHtyZuTQWaN4SA6Fs3A7MljEAoIZk8eg45FM9AY5qclPuf7/FJNK0t+09u/\nT01/D5656Tz8nyUfAhTYN56KUfL8Zh7PI6EAj91EJRZy64lFZAOA8wCcKCL7APwrgDAAKKXuBfAE\ngE8A2AUgCuCLbm2LG4xBZm0butG5pxezJragY9EMjGmqQyBw7MpvICAY01SHB65sRWNdENGhJBrD\nQdM65D3Vnl+qbl7Jr9H+PfiFVvQPJtC2YWfO9pIIKH9+7Y7nLY1hHruJSkiU8tenTa2traqrq6vS\nm4G+wQSWrO3C9p5DqWWzJ4/BA1e2ornetXM5MvNd6++V/JIn1Ex+2V5WparJL/NZk3yX32rAGckL\nxEFmRETOsL0kL2M+icqDJx0F4iAzIiJn2F6SlzGfROXBk44CcYA4EZEzbC/Jy5hPovJgZ8UClXqA\nuKYpRONJDlgjIl+za8tYUIO8yi6fgD7eg5klKg2edBQhEJDUILNiBps5rYRFRORlI7VlpWgvidyQ\nmU8el4lKj92rPCAaT6JtQze29xxCQlPY3nMIbRu6EY2zPykR+QfbMqoWzDJR6fGkwwNYOYOIqgHb\nMqoWzDJR6fGkwwNYOYOIqgHbMqoWzDJR6fGkwwWaptA3mICmhm+13BMwsnIGEVWDfNqyfNtJonJy\nmmXmmMg5juYrsUIGn7GyCxFVA6dtGQfpktc5yTJzTJQfXukosUIHnxmVMwIyfMsGi4h8yElbxkG6\n5AcjZZk5JsoPTzpKjIPPiIhyYztJ1YA5JsoPTzpKjIPPiIhyYztJ1YA5JsoPTzpKjIPCiYhyYztJ\n1YA5JsoPB5LnKZHQEEsk0VQfQv9gApFQEKHQsXO3fAaFa5pCNJ6sycHjmtIQS8QQCUVStwEJ2C4n\nIn9Lb+/6hxI4IRLG/VfMNLWltdoelhvbX2cyj9ENwYDp+N8YDqKl0Zxj5tae03wxh9WL72IeEgkN\nvdEhXL1uB06/dTOuXrcDvdEhJBKaaT0nAymNqhdL1nbh9Fs3Y8naLhzqH6qJcnua0tA70IulTy/F\nzPUzsfTppegd6EVSS1ou15Q28pMSkWdltndG2/mj3+7G6bduxo9+uxu90dpsD8uN7a8zmZnd9uf9\nWcf/o4MJi/8J4sytBbvcZebL6XrkT66edIjIfBF5VUR2icjXLH4+QUS2iEi3iLwgIp9wc3uKFUsk\nccPGnaZKFTds3IlYIv/+m7Vc9SKWiKF9azs63+pEQiXQ+VYn2re251xeCdWWX6otXsqvVXt3w8ad\nuOjMk5HQFC468+SstrVW2sNyY/vrTGZmZ7/3xKyMvhONo20Dc+uE03x5LYdUWq6ddIhIEMD3AVwM\n4AwAi0TkjIzVlgN4RCk1A8BCAHe7tT2l0FQfsqxU0VSffy+1Wq56EQlF0P12t2lZ99vdaAw3Wi6P\nhCLl3DwA1Zlfqh1ey69dezdlXDMAYMq45pptD8uN7a8zmZk9LhLOyuhpLY3MrUN2ucvMl9P1yJ/c\nvNJxDoBdSqkepdQQgI0ALs1YRwE4bvj70QDecHF7itY/mLCsVNE/mMj7uWq56kUsEcOM8TNMy2aM\nn4FoPGq5vEKfcFRdfqmmeCq/du3drv19AIBd+/tqtj0sN7a/zmRm9t1YPCujr/VGmVuH7HJndaXD\nQzmkEnPzpOMUAK+l3d83vCzdCgCXi8g+AE8AWGr1RCJytYh0iUjXgQMH3NhWRyKhIO5aON1UqeKu\nhdMRCeX/qUYtV72IhCJYPXc1Zp00CyEJYdZJs7B67uqcyyug6vJLNcVT+bVq7+5aOB1PvvQmQgHB\nky+9mdW21kp7WG5sf53JzOz2/zmYldHjG8PoWMTcOuE0Xx7LIZWYKOXOgCcR+SyAi5RSXx6+/3kA\n5yillqatc+PwNvyHiMwG8CCAM5WyHzHU2tqqurq6XNlmJ+yqV6VXuRiIJ6FpQGM9q1fZKVH1FNf+\nWNWaX/KUmsqvVdtp3I8OJREJBRBLaDXZHpYb219ncmXWqFQF6OM/0pcFg6zRY8Vj1avYuFSAmyVz\n9wE4Le3+qci+/HkVgPkAoJTaLiINAE4EsN/F7SqYpikcjsXRtqEbnXt6MWtiCzoWzUBLYxi9UX35\n+OPqcdNF03Dzoy+Y1hnTVJd1ADWqXAFI3daKgATQFG4CgNRtruUVUHX5pZriqfwalf9u2Lgz1S7e\ntXA6WhrrUlX+AKB5+J+1WmsPy43t78iSSevMjmnSMzuqIZyqcJX5P4HV8Z6c58tDOaQSc/N0vBPA\nVBGZJCJ10Ad6bcpYZy+AeQAgIu8D0ADAs/1PclWcMpb/83lTcPOjL7Cahf9VXX7ddNbasxx/UVl4\nKr+lrPxHNaHi+Y3GrTObfiyv5SqURIVw7aRDKZUAcD2AJwH8CXqViZdF5HYRuWR4ta8CWCIizwPY\nAOALyq3+XiVgV4ElvaoVq7BUh2rML9UOr+W3lJX/qPp5Ib9OMlvLVSiJCuFqi6+UegL6AK/0Zbel\nff8KgHPd3IZSMqpZbO85lFpmVK8ylhtVWDLXiQ4l2WXAZ6otv1RbvJTf9DbSYLSdoxrC5dgE8plK\n59dJZu3+J+DxnsgaRzs5oGkKfYMJNNYFcd/nZ+LGj0/NqlRhVLm455ld+NZnP1D11Sw0paE/3m+6\nJSLKpGkK4YCUrPIfOcd2unCNYetqlenH8lquQulFzLv38VR8BFYDxe5aNB3XXTAFsbiWqrAypqkO\nD1zZmqpe9cAVrSNWr/IrTWnoHehF+9Z2dL/djRnjZ2D13NVoaWhxo8IEEflUevs5/8zxuOfys3Fc\nJGyq/EfuYDtdHBFBY10wldl3Y3GEAgKRY8fyzGN/NR7v/YJ59we+EyOwGih2w4adiMU1NNeHUo2L\nUYkqIILGuhCaG0KpqizV1gDFEjG0b21H51udSKgEOt/qRPvWdk7eQ0Qm6e3nv256BdNv/zX+6YE/\nQER4wuEyttPFicaTWLJuB6bf/mtMvuUJTL/911iybkfWIPH0Y381Hu/9gnn3B7b6I+BAsWyRUATd\nb3eblnW/3c3Je4jIhO1n5bCdLg6z6y/Muz+MeNIhIvUislhEvi4itxlf5dg4LzAGiqUzBorVqlgi\nhhnjZ5iWzRg/g58o0P/P3puHR1Glff/fU713EmICETAaECFAlCVCHFGDIDM/nWdcnhFUcFB0GHR0\nBJd3DOOo6KDPq8QZZ0RlxGVUYAQV5nFf5h1ciIoadjCsxrBElpAOIUkvSXed3x/V3emlqrt6S3d1\n7s915equ06dOn0p9+65zqur+FkEEQfEzfVCcTgzSrrYgvWsDNVc63gZwFQA3gI6Av14BJYqFY9Fb\nUD2xGhUDKqBnelQMqED1xGo6o0AQRBAUP9MHxenEIO1qC9K7NmDRbK0ZYzs45+f0UH+iMn78eL5h\nw4Ye/U63W4TD7UGOSY8Olxs6xmD2JoyLIqImjIsih73Lk1WJZiIX4XA7YNFb/K9pSNbS3D8xHfrt\nCWJ56N/2WdtT2BNNkdX69XhE2Lu646bFoIMjYNlq1AWZcRDJJ8VxWnM7Ldb4G3rst+h1QctWgw46\nnbr/ZzaOAzKNGPVO//w0oObX8hVjrNc+RtjjEWGzd+KWZRtRev+HuGXZRtjsnVi+vgG2jk7MWbYB\npfd/iDmvbkBzRydEMXgS53NvmfNq5HpaQ2ACcgw5Qa8EQRCAFDebO7rj5stf/ABbR3AcbWxx4h81\n9VkRDzMVitPxo3Tsf/mLH/zLzR2d8Hii27Jm6zgg0yC9Zz7KU0DGtjPGtgG4CMAmxthuxti2gPJe\ngb3LgztXbQlyr7r3zW24amwx7n1zW1D5vJWbw5wt5Nyv5OoRBEFkC6Fx89JzBobF0flrtuHScwZS\nPCQyErlj/52rtuDScwYGLavRLo0DCEIi0nM6Lu+xXmQwOSa9rINFH4tBlbMFOWAQBNHbCI2bQ0/N\nlY2DvnKKh0SmoXTsH3pqbtByjoonj9M4gCAkFK90cM73c873A3jU9z6wrOe6mF46XG5ZB4uTji5V\nzhbkgEEQRG8jNG7uO9YuGwd95RQPiUxD6di/71h70HKHyx21LRoHEISEmieSnx24wBjTARiXmu6k\nnkjJXHKfWQ3SE0lP2LtwRqEVB2125Jp1eHtLI564ZjTufXOb/0nli2eMhdWgC0qg5JzjhRvHYc6y\njQH1kuOAEZo0JTABJp0pnYndBEEQsBp0eGr6WNy5agtqG2z4eMfhoOWKwYX48zVjsGbjQSyeMRYC\nk+6hd7hFSrSVITTWm3VmOD3OdBt5ZDVWgw7PzTwXLQHH/lOsBiz7qgF6gaFicCGemj4WFr0Obc6u\niMnlPieseSs3J30c0BuQSxAHkAlmNkSMKLpXMcbuA/BHABYAdl8xgE4Az3PO7+uRHoaQiPuPL5kr\n9IffN8cIALKfFVgMsDk6cefK7oPlUzPGotBqRHNHJywGHXJMehy02VFgNSDHqIfN3hl0cH1q+lj0\nMethNCTvYCpyETanDVXrqrD56GaU9y/Hoxc+isWbFuOY4xiqJ1aj0FyY7T9CzY1IyL2K3KsCyGr9\nBp58cXR64PaIQQO4AqsBANDpFrFhvw3jBhUGxU1fbO7tEw+5WL+ochHW7F2DpVuXorx/ebriveZ2\nTCz6dbtF2WN/nkkPk0GHk44umPUCTjrdYcf7vjnGsIkHuVfFh5z+qydWwyAYcPdndweVxfgboH9+\nGoh0e9VjnPM8AE9wzvt4//I4533TNeFIlEjJXEqfOdwe3LkyJJls5RZ839SBn/zftbh1+UbsO9aO\nSX/+DL9dsUmqL5N81iVyCIwh16RPSqBxuB2oWleF2iO1cHM3ao/U4oEvH8DsUbNRe6QWVeuq6KE4\nBEGkDd+g61cvfAOPyPHbFZsw6c+f4aw/fuCPl4wxzFu1BRPO6hcWNynRVkIu1s+vmY8pJVP8yxTv\nk4/Ssd8tcgy57wOMXfj/4HSLssd7Od0Kgvf4n8RxQG9ATv9V66rQ6moNK6PfQOajeHsVY+xc79s3\nA9774ZxvSlmvUkS0ZC65z6Ilk4W+V6qvJtksFix6CzYf3RxUtvnoZgzJH+J/Tw/FIQginfjiYa5Z\nPi76ytUac/RGosV63zLF++Si5liupNtkH+97M0r6L84tDiuj30DmE+k61F+8f88C+AbA8wBe8L5f\nrKZxxthlXqvdfYyxPyjUuZYxVscY+44x9lps3Y+NSMlcSp9FSyYLfa9UX02yWSw43A6U9y8PKivv\nX4761nr/e5r1J0am6ZcgYiET9OuLh+1O+bjoK1drzNEbiRbrfcvZFu/TrV81x3Il3Sb7eN+bUdJ/\nY3tjWFm2/QaykUi3V03mnE8GsB/AuZzz8ZzzcQDKAeyL1rA34fxZAD8HUAZgBmOsLKTOMAD3AbiQ\nc342gLvi3hIV+JK5JgzpC73AMGFIX38yl9JnFr2UEBlY/sQ1o/H3z/aFvVeq/9T0sUlPGLPoLaie\nWI2KARXQMz0qBlTg0QsfxUvbX0LFgApUT6ymWX8CZKJ+CUItmaJfX0L5901tsnHx+6Y2LJo6Guu/\nPx72OSXaSsjF+kWVi7D2wFr/crbF+0zQr9KxvMPV5V/WC6xHjve9GTn9V0+sRr4pP6wsm34D2Ypi\nIrm/AmNbOOdjo5XJrDcBwMOc80u9y/cBUq5IQJ1qAHs45y+q7XCiibixulcJAgtKiHR2euDh3O9U\nYRBYWIJ4YH0lN4tkEOrowMBg1pth77LDordAJygHPo/ogcPtgNVgVVU/Q0nZTbGZqt9MhRLJ46JX\n6NcXDy16HRzu7rgot+z0kHuVHEruVSadCU63MyyOy7n9pCDJPOv163aLUTXKOQ873jPGKGlcJWq0\nqta9inMey7iGdkgaUHPj4U7G2IsAVgDgAGYC2KlivWIABwOWDwH4SUidUgBgjH0JQAcpyHykou24\n8SVzAfC/RvtMpxOQpxMgihwdnZ5w9yu9TrY+AOSZDanbFiYgx5Dj/0GGujsoOTl4RA9sThvm18wP\nckMpNBdqceKRKjJSv9lALBMUgCYpcZIx+g2Kh3rpNceoV3QS9CXaEt34Yj0A/6sZZtk4XmAuwAnX\nCdXHgwwlI/Sr1wt+zfqO5bne5W6NsqDjfSSXTJp4BKPkTBWqVTn9B77PMeTQuEYjqIlANwP4DsCd\nkC5f1nnLoiH36wq9rKIHMAzAJAAzALzIGDslrCHGbmGMbWCMbWhqalLx1akhkvtVOlFyd1C6v9Hh\ndmB+zfwwNxS6HzKIrNMv0avIaP1maizVEkpx3Ol2xnQ8yFAyWr+RIG2rJ9axS7S2aFyT+USddHDO\nnZzzv3LOf+n9+yvn3Kmi7UMAzghYPh3AjzJ13uacd3HOfwCwG1IQCe3D896ckvFFRUUqvjo1RHO/\nShdK7g5K9zdaDVbZ+laDNWV91CBZp1+iV5HR+s3UWKolIsXxWI4HGUpG6zcSpG31xDp2iQSNa7SB\n4qSDMfaG93U7Y2xb6J+KtmsBDGOMnckYMwKYDuCdkDpvAZjs/Z5+kC6X1iNDieR+lU6U3B2UZvj2\nLrtsfXuXXbZ+LyXr9Ev0KjJav5kaS7VEpDgey/EgQ8lo/UaCtK2eWMcukaBxjTaIdKXjTu/r5QCu\nkPmLCOfcDeAOAB9DygF5g3P+HWNsIWPsSm+1jwE0M8bqAHwK4F7OeXNcW9IDRHK/SidK7g5KZwss\negsWVS4Kc0PR2JmwlJKN+o2VUa+OUv1HZBaZrt9MjaVaQimOm/XmmI4HmUim6zcSpG31xDp2idYW\njWsyHzXuVb8GUMM539szXYqMWvcJJSeqSO5VatpydnkgioDVlEJXClEEuuyA0Qp02gGDFRAi3wkX\n6O7g8rggcjGiG0Soe5Ve0MOoM4bXV+hLD7mjRENzWXlacq/KlMlEFieSZ6V+ffHSYhCkGOmNtTkm\nfcKxWPP44qnBAnR2AKbciDE+WpwNjeNmvRkuj0vR1SrJaG5nJcO9Sq+PfpzTjLbjGGsoNyVp0WKw\nwuHVnKBCc2rGEmrdNmN05czAHZL9qFHXYABLGWPfM8beYIzNZYxFtMtNNz73iDmvbkDp/R9izqsb\n0NzRCY9HlC0XReWJV2hbs1/ZAEeXB+CSe0VKJhz2JmDldOCRIunV3iSVRyDQ3aGjqwNzP5mLccvH\nYe4nc2Fz2iDy4PV1gg65RulJ6k6PE7evvT28vkJfRK9LRLTvIAiid+GLl/+oqUdjixMvf/EDGluc\nuGXZxrCY63ML9LlVZeSgLJn44un6JUDrQWDV9RFjvM/ZJ1Kc9cVxgQmwGqw44TqB5XXLcaTjCOZ9\nOg/jlo/DvE/nocXVQvE5RtxuETZ7p1+7tyzbCJu9E2539P+jJrQd51hDvinvmMCrubmfzpO0Kka/\npcw3dgl8DcTnShWoZ5vTBo9M24G/h1xjLrlWZSBqEskXcM4vAXAOgC8A3AtgY6o7lgiR3CNidZXo\ncSeKLjuwejbQUAOIbul19WypXAXxOFkp1lfoSzIdJwiCyB588fLScwZi/ppt/ldy8kF3PC27HHj7\njqgxPt5YPqVkChZ8tYDic4I43B7cuWpLkHbvXLUFDneWaDfBsUYgDrcDVSHOUVVJco4iV6rsIqoZ\nOmPsAQAXAsgFsBnA7wHUpLhfCaHkHpFj0sfsKtHjThRGK3BgfXDZgfVSuQpidYOIWl+mL5bscEch\nCCLJ+OLl0FNzg14D6bVOPr7Y3m+4qhgfbywfkj+E4nMSUBov5GTLM2QSHGsEojgmSIJzFLlSZRdq\nbq+6GkBfAP8B8C8A73DOD6e0Vwmi5B7R4XLH7CrR404UnXagZEJwWckEqVwFsbpBRKyv0BdHdrij\nEASRZHzxct+x9qDXQHqtk48vnh7frSrGxxvL61vrKT4nAaXxQofLnaYeJZkExxqBKI4JkuAcRa5U\n2UXURHIAYIzlAbjI+3ctgKOc84tS3DdZ1CYyyj0RtNBqQLvLjRZ7F84otOKgzY4CqwF5ZkPYPZce\njwh7V3cC2Rd7mzB35ZbUP13Ud5/l6tnSWYeSCcC0lwBrkaoEL7VP+AxM+mrvasdrO1/D0q1LceuY\nW3H9yOuRa8iVksE8XRBevyGoL6K1H2yulkx44m0G3igbGUokjx1KJM8counXF3tXfrMf/11+Ot7a\nfAj/XX465q/ZhlsvPhPnlhQi1xxbUm7W4IvtG14Fxlwr3WIVGOONeRD1Jjg8Tr8hSEdXR1iczTHk\nwKQz+RNlnd76AGBz2rB6z2pcPuRyLPhqQarjc1bpN/SYb9Hr0N7pxomA8cIpVgNyjfrs0K3iWKOv\nNPEw5QKudsCYA0TJjfDldFQFPA282vs0cDXJ5JHwiB60OFtQVRPwO6isxinmU+DyuBIxs9GcfrMB\nNe5V5wCoBHAxgPEADkJys1qQ+u6Fk4h7FQA0d7gwb+WWgMnIWPTNMQVNIHwJ53eu6q731PSxKMwx\nwukWU+9EkYCjhMhFtHW2odXViuLcYjS2NyLflI88Y57/BykfIKQf8QnniZAf9yIU6nMgGMzkXpUE\naNIROzTpyBzida9yu0W0Ot3hMdVqzI4BnBpEEXC1AvYWoKAEcLUB5nzg5GHgPw9B7FcKW8XNQXH5\nr5P+Cr2g98dZ7f3EzQAAIABJREFUt+jG3Z/d7f984QUL8V79e5hWOg2FZumsvMPtgFln9rv4pDA+\nZ41+lY75VqMOc5ZtjDhe0DRhYw0z0HEcWPOb7onI1BeBnKKIEw814464uyjTdqG5EE6PM9ETn1my\nE7WFmknH+wA+h5REXss57+qJjimRyKCt3eXGnFc3YH19t5X3hCF98cKs8cgNuE+zzdmFW5ZtDKv3\n/I3jkGc2xN/5HsDnXFV7pNZfVjGgAk9f8nS3u1VnO+Z+Oi+szuLJT2Hep3eGrzt5MXK8TlcZhuaC\nBk06YocmHZlDvPrVckxNGq52ySGoISAlcnAl8F/VwJIJ6PjdN5i7qVoxdivF9vvOuw+PfftYUIzv\nIbJGv0r6/PvMczF24f8LKgsdL2QVzpOSq1qoRqe/Bpj7KK6mZtwRL3Jtv//L9/Hw+ocT/T7N6Tcb\niPrL4Zz/oic60hOoTQrXcgKZmuRDpaQvqyEnZclgBEH0XrQcU5OGUuJuv+EAAEvfYRFjt1Js9yWO\nU6J4/Cjps4/FEFaW1SYIplx5jZoin3SM1fQgFuTaLs4tJrMEjdJLrmtLqE0K13ICmZrkQ6WkL3tX\nR8qSwQiC6L1oOaYmDaXE3eO7AQCO5r0RY7dSbPcljlOiePwo6fOkoyusLKtNEFzt8hp1tUdcLVbT\ng1iQa7uxvZHMEjRKr5p0WA06LJ5RjglD+kIvMEwY0heLZ5T78z0C6z01fWxQvaemjw2rl4lY9BZU\nT6xGxYAK6JkeFQMqUD2xOvhKh96C6spFwXUqF0UsJwiCiBeLXj6mWvSZH1OThsEqJeoOrgQEvfQ6\n9UWg7j1A0MNS9054/A2I3XKxfeEFC7H2wNqwGE/EhtIx3/c+0nghqzDmSJoM1agx8i1LasYd8SLX\ndr4pP2XfR6QWVe5VmUSke4qDksddHggCYDZ0J5ILApNNMPclhQWv74bAGMxGHTpcbhgFBoMhfJ2Y\nCUzc6nIC3CP9oAMTxgPqiF1OOBj3JxIKYDDpzZKzlN4i6wwRmOTt8rjgET2wGqywd9lh1puhF/RB\n7lWBbSmVy7UrcjHuRPIkJaJr7p5MyumIHcrpyBxUx99ODyx6AQ63CLNegCPAFaj7cx2cnh4w5kgV\nvjhtsACdHcpuP0HJuh0A00kuVd4463ehcjtg1pvh9LgUE8E93vhsDVzP44RJZ4LT7QwrN+vMfner\nFCSUa26nRdKv2y3C4Q52rwIQVub0iLLjh4wjLEncAnQ5gg1qgHDTGohB7lWi0QqHCpcot+gO0qBv\nrBHUJZnjPoCoZXL6ZowlOobI0B2X3SjeUMsYexeA4oyEc35lSnoUJ3I2uU9cMxp//ng3jp50Bdnc\n+pLAApPBFNf/X2l9pbZi7GS3RV3eAGDKAuCt20Ps6voB9uPA6tkQ8wbC9v89jKqvHvQ7NDx64aNY\nvH4xjjmOKVrSCUxAjiEHbtGN9s52zA9wQ1lUuQgF5gLoBb0/OTwwSVwQdLLlgVa8p1pOxbxz5+GB\nLx+IyzlCra0voR22/3BAdd1RZ5aksCdEOpCLn09NH4vGE3YUn2INcwXauN/WMxbkqSKS/W2g24+M\nLal43XLYPPag+BfoQlVgKkCLjCW5UvkpplPQ4mwJivMLL1iInc07MfbUsUHlFGflEUWOFkdXiM3+\nWBh0Am5bsQm1DTbMu2Qopp9XEqTljNWunB3u1BeBjcuAdYuk5euWA57OcMtcnRHw2uSLF88Pc1ST\n05BbdIdpMHCsASgf9w2CIciRLbTs1jG3YuqwqbI69iWN97CBApEAkSLPnwH8JcJfRmHv8mDeys1Y\nX98Mt8ixvr4Z9765DbdNGor19c2Yt3Iz7F0RHgIYZf1Y2lKkyy79wBtqgMp7pAlHQw0guqXX1bOl\nM2HeOo6J/wdVXz2I2iO1cHM3ao/U4oEvH8DsUbNRe6QWVTXzI97D6HQ7Mb9mftD682vmw+l2xtx1\nh9uBqnVVqD1Si9mjZuOBLx8IardqXZXq+ykD24pnfYIgMgu5+Hnnqi04qygPd67aElY+4ax+/uW4\n42k68cXyssulCUdgHF/zGymOB9YL+NzhPBEW/xZ8tQBTSqb446BSfJQrl4vzC75agIqBFWHlFGfl\nkdPvvJVbcMLe5S+79JyBYVrOWO3K6A5rfiPp1bdsbwmvs3q2VO4tc5RdiSoVGlIz1lDSb6urNWLZ\nlJIppOMsQvFKB+f8857sSKIoOVMNPTXX/z6S64Sa9dW2pUige0m/4couEd5yJTeTIflD/O8jOUtZ\nFV2qYnejCnSQ8LmlhLar9n7KVDpdEATR8yjFz1xzdFcgTToC+WJ5pDgeWC8AS35JRBeqSHE7lvI+\nxj4UZ1WipN8zCruPlUNPzVXlfpkRRHFKAwAUDJKvUzDIvxjNUc2HmrGG0nG/OLc4Ylmi4w0is4h6\njZUxNowxtpoxVscYq/f99UTnYkHJmWrfsXb/+0iuE2rWV9uWIoHuJcd3K7tEeMuV3EzqW+v97yM5\nS9kVXapid6MKdJDwuaWEthvLlQ5yniCI7EEpfrY7o7sCadIRyBfLI8XxwHoBOFoPRHShihS3Yyk/\n2XmS4qxKlPR70NZ9rNx3rF2V+2VGEMUpDQDQsl++Tst+/2I0RzUfasYaSsf9xvbGiGWJjjeIzELN\nwwG/APAQgL8CuALAzd71Hkp998JRSgRTuqe4MMeIQy0OFFgNyDMbFO+9jJYTkrScDv8TaQcBne1A\npwPILZJ+6NYCwNQnOKfj0oWo+vL+4JyOTQE5HYY8CHqjbCK622BWvs+yyxnzk84j5nRUVqPAXOBP\nWox0z3ASczoy7Eba6GRrInmm5HRoLOk8a/SbVTkdYQm4MnEyMKdj/CzA1RH8lHFXu7S+uxPobAvP\n6YAnbTkdOYYcmHSmZCSWZ8gOU08s+s26nI7r35Buo/IZHuhNgPNEeE6HwQq0NwEFgyC2N8Gm16nK\n6TjhPIGqmqqgMcEp5lNSmtORYG5Shu203oGaScdGzvk4xth2zvkob1kN57wyauOMXQbgKQA6AC9y\nzh9XqDcNwJsAKjjnEUdkat1T2p1uvPLlD1j8yT5/AOmbY4oYHBTdrxScsGJGLhD89xJg7UKg7Yg3\nkbxIqut1RRGdJ+FwnYAlvwQOhw2CMcfrXtUBCweElTMUE9FxYD3cN38EZ/8RsBpyYO/qgPnoLuhf\nviygflFMEw8596r2rna8tvM1LN26VHVA0IJ7VU/rN9OgSUfKySr9yrlXuTwiRJHDLXL0sRhw0tEF\nvcAgCCzxeJoK5GK0UpwURcDtlCYVSgnlG5cBzXuASX8ECgcDJw9LroSmPnB4T9AEuk354qBSfFQq\nV3K1CnWvcovusAFeAoO3rNdvu8uNFnsXzii04qDNjn65RgAMVlMGajeUwMmzpwtwtEh5HUGGB/0k\nF81Ah6uA8YNvkuzQGyMeq0Uuoq2zDa2uVhTnFqOxvRH5pnzkGfOC6sbrXpUiF7YM3XHZjZpJx5cA\nKgGsBvAJgEYAj3POh0dZTwdgD4CfATgEoBbADM55XUi9PADvAzACuCMZg7Z2lxtzXt2A9fXN/rIJ\nQ/rihVnjgxyrehxXO7ByupSk5WNwJfBf1cCSCdL7Gau67wdWqj9jlfRe7rPprwGrrldfHvh9cdDR\n1YG5n8xF7ZFaf1nFgAo8fcnTPeEokbKgkU79Zgo06Ug5Wa/fjI3FSkSKuXJx0lf/v6qBD6qUY3vg\n8gdVCcfdeEhBrM5q/WpOu5FwnlQeF5j7dJfFqn8vaR4HxAtNOtKAmqniXQCsAOYBGAfgBgCzVKx3\nHoB9nPN6znkngFUArpKp9wiAagCxWyopoJQUlvaEr2jJXQfWS3Wi1TdalT8LSERXVW6MPak8kCxO\nCk+bfgkiCWSEfjM2FisRKeZGqq+UUB6YuBtYL8G4Gw8ai9Vp16/mtBuJSOOCQGLVvxeNaYtII1En\nHZzzWs55O4CTAOZxzq/mnH+tou1iAAcDlg95y/wwxsoBnME5fy+GPkdFKSks7Qlf0ZK7SiZIdaLV\n77QrfxaQiK6qvDP2pPJAsjgpPG36JYgkkBH6zdhYrESkmBupvlJCeWDibmC9BONuPGgsVqddv5rT\nbiQijQsCiVX/XjSmLSKNqHGvGs8Y2w5gG4DtjLGtjLFxKtqWu3Tlv5eLMSZASk7/Pyr6cAtjbANj\nbENTU1PUL7YadFg8oxwThvSFXmCYMKQvFs8oh9WQ5jMUBqt0f/DgSkDQS6//vQSoeVJ670viilTf\nV0fpM2NObOVx2OcGYtFbUD2xGhUDKqBnelQMqED1xOpsOMORNv0SRBLICP1mbCxWIlLMjVS/7j3g\nqmeC15v6olTuW77qGWk5CXE3HjQWq9OuX81pNxJGq6THUH2GXsGIVf9eNKYtIo2oyenYBuB3nPMa\n7/JFAJZwzkdHWW8CgIc555d6l+8DAM75Y97lfADfA/BNtQcAsAG4MtJ9mWrvyQxNCsuUhC/Rm/Bn\nMVilZHCmh6A3QexywME4LHprcKJUYDJYlzPIfUI0WuFwO71tScmDgqCDR3TD4XZ6kwo7YBGM0OkM\n0gOrmA4wmGXdriK6tUTappDkMIEJyXJHiUYq7ylOq34zAcrpSDm9Qr+ZGosViRYPAz/3uVR12qUT\nO50d3e5AvvLAZbcL4CJEgzdB1hvvrQZrTAmzconjOiF8MBwam5OckJv1+tWcdgMJ07E5XI8QwrUO\nHqLjHEBGW6HIaZIxFlV/QPREcqXk9QTNaDSyI7MLNXuozTfhAADO+RcA2lSsVwtgGGPsTMaYEcB0\nAO8EtNPKOe/HOR/MOR8M4GtECRixIAgMuSY9BOZ9zYBAIXIRNlcL5n46D+OWj8PcT++EzWmD59sX\nYXOdwNxPvOWfzIXNaYPIRelgZ8oFOAdc3mSwR4ogfvMcbM7AtubB5mqBW3TD5mzBPG/5vE/vhK3z\nJDzfvgisnCE5rXBIbfomHPYmKXnskSLp1d4klatEYII/WczhduB3a38Xvh3aI636JYgEyRj9ZmIs\njogv5jKhO076CI2Xq64HWg8BXz8nuf4YvesZcwF7sz9eY9X1knNVpx3ivk9hc7Zg+c5/4nDHYX+s\nXl63HDanDXM/mRsxfnpED2xOW0CMnycdR8Tg2358FqWB7bW4WvyDsxxDTipPCCVKRuhXc9r1EarT\nYzuBjuPBenS2yR/7A8YZWHW9pOso4wGRi2hxtQRpssXVgrbONr/+5PTd1tmmqiz0dyCnbQ2PNXoV\naiLOt4yxpYyxSYyxixljSwB8xhg7lzF2rtJKnHM3gDsAfAxgJ4A3OOffMcYWMsauTE73tYXD7UDV\nuirUHqmFm7tRe6QWVV89CMeYa1H11YPB5euqgu+H7OyQ7O4aagDRDUfZlaiqmR+2jtPtxPyQ8vk1\n8+EYc6207urZ0pkNH112qczbrmydRLYvdDs0Auk3c2jb+bjqP0KC9Jsi5OLl23cAZZcHx025em/d\nBrja4DhrIqpq5mNKyRQs+GqBP15OKZkSFrvl4qfD7ZCP8TL1tBqPSb8JEqq/otKg8QMaaiQLXblj\nv70l5vGAktZaXa0R9d3qag1bT64sVLda1nZvR43v21jva+jDAC+AdM78EqUVOecfAPggpGyBQt1J\nKvqiaZQcHqymPtGdH0LcJyx9h8m3ZbAqfgeA2ByyYiTbHCxIv4SWIf2mgEgOhIFxU6lewSBYGMPm\no5sxJH9IULwMXQbk46dijA+5717r8Zj0mwCh+jPlheuxYJCiRsPK4nSvKs7tzv2X03dxbrGqslDd\nal3bvRk17lWTI/wpTjiIcJQcHuyuk9GdH0LcJxzNe+Xb6rIrfgeA2ByyYoQcLAiCyGoiORAGxk2l\nei374fDG+/rW+qB4GboMyMdPxRgfcjaa4nEvJlR/rrZwPbbsV9RoWFmc7lWN7Y3+ZTl9N7Y3qioL\n1S1pW7uoca/qzxh7iTH2oXe5jDE2O/Vdyz5kHR4ueASWrW+g+oJHIjs/GHOC3Ccsde+gunJR2Dpm\nvRmLQsoXVS6CZesbsTtkJWP7yMGCIIhsQS5eyrlSyToV/h0w5cHy/TpUVy7C2gNrsfCChf54ufbA\n2rDYLRc/LXqLfIyXqUfxuJcSqr+mPeHuVZYC+WO/tSBp7lX5pvyI+s435YetJ1cWqlvStnZR4171\nIYCXAdzPOR/DGNMD2Mw5V29tk0Qyyv0nDtenYPcqOyxgEAwWeDxdcIidsBpyJMcpvQU6QR/8He5O\nwNMZ7F7lcYW5RnV6OuEW3d0uEkwPnd6o3McE3auCmkrcUSIWNJLV101G6TcKmeJeFUuuRsPjv4ip\n7TRD+tUSokfKrQtzqfIu+xwG5VwBffXcLoB7ZN2r7F12mPVmOP3Og3ZY9GbpOBBCvO5VSY7HpN90\noPZ47dNrJDc1sHAtg4W0bwG6HFG/T06TAML07QoYs5B7Ve9DzR7qxzl/A4AI+BO8NPh0nCQTj+uT\nKEKwH0fOa9MhPFIkvXa2S84PnScx79M7/Y5TLc4WiKI7+Dv+OQ1wO6RMGnMfCIJe1jXq9rW3w+mR\nHtCaa8yFzmCWd2LxEcmtJUYCXVEy3B2FIAhCHaIH6GiS3HwePVV6bT8muVb5llsPAeuXdB8HAuOq\nuY9kO2q0AqY8CIIeFkNOkOPPip0r0OIMcQBytkAUww+3OkGHXGMuBCZIMV7B0pTicZahdtwhipLr\nVKALVcdxSa++ZVdbeB37cWl9n24NVqksyvfJuVe1d7WHlZ1wnQhzT5PTqBrdkra1iZq91MEY6wvv\ng3kYY+cDaE1pr7RAPK5PCus43I5wJyqfG4mK7yAnB4IgiBQS4h6IhhppuezyyC5WEQiN27LuVTKu\nVEQvRu24Q65eqF7tCu5Vcbhbyo1B1LhQEb0PNe5V90Dyxz6LMfYlgCIA01LaKy0Qj+uTwjoWQ468\nE4MhR9V3kJMDQRBECglxDwTQ7VoVuqzS/S80biu6V6Xh6eVEhqJ23BHJZc2HkntVHO6WcmMQNS5U\nRO9DjXvVJgAXQ7LIvRXA2ZzzbanuWMYTj+uTwjqOrg55J4auDlXfQU4OBEEQKSTEPRBAt2tV6LJK\n97/QuK3oXhXHM5OILEXtuCOSy5oPJfeqONwt5cYgalyoiN6HmkTyawB8xDlvY4w9AOBcAI96JyM9\nTsYkgvnurVw9W5r5l0zwOj8UKSdqGyzSfZVrvOtMnA+cfytEYw5szhZU1czH5qObUd6/HNWVi1Bo\nLoBgb/Z/h3jxfDjO/y0sxtygxCnf0zmr1lV1rz+xGoXmwmy/z1FziWAZo181PJyf7h4AAAY7X1Nd\nlxLJU4um9BsrkRJ0fTkda34THO9FDvy/B4C2I5KL1dY3gPGzAGMeYDBHTLwNjdu3jrkVU4dNxfyw\n40AhhJCcjR427FCC9NvTqB13yNWb+hKw8VVg3SJp+brlEAE4nCdgyS+Bo/UALOZTIJjyA3Sv7vvk\nxiB/nfRXdIldmTwu0Zx+swE1k45tnPPRjLGLADwG4M8A/sg5/0lPdDCUjAoaalwkQn+03okGjLnd\nE5C8ARB/8Rc4AFhMeXC42mBhAgRvkji67BANFu/ERP4HnCEHoZ5Gc0Ejo/QbDZp0pBrSb6agZnAV\n6AZkawA++7/SZGPaS1I815ul44HoBl6/IfrJKIRPHsw6E5xuZ7e7od4iO+HIkJNMpN+eRhQBV6uU\nj1EwSLpaYS0AAicKgKRV50npqeO+epYCQG/yT4ZFgwU2V0t0Hal0y5IbgwDRXajSiOb0mw2o2fs+\n64xfAPg75/xtAMbUdUlDqHF9Ck3E+ux/gFW/8iYmessr74Gw6lfIebwEwp8KpNeVM6R1vd/h8DhR\nVaOclEVODgRBEHGiJmFW0Emx/tUrgafLge1vdtfjXPocTJpwqDQYCY3bOkGPHK8rVY4xN2zCAZBx\nSK+myy7p6+lyYGGh9Pr6DeH66uwA3gip98YNkiZZwJhCjY5UulvG60JF9C7UKKCRMbYUwLUAPmCM\nmVSuRwDKiViBiYm+5MPQOgHJWpQsThAEkSISTdD11YvHYCRG6FjQi1GrLyXjA1Ouf5F0RKQDNe5V\n1wK4DMCfOecnGGMDAdyb2m5lEb5ErIaa7rKSCd2JiQ013cmHoXV8D/JBd6JW7ZFafxVfUpbvWR0E\nEY2YHviXwn4QREahFKcDYrCqemrbSQA6FvRi1OorcHwRWM/VLj0zBqQjIj2oca+yc87/xTnf610+\nzDn/d+q7liUYrNI9vYMrAUEvvU57SXryp6+85kngv5eE1zEEX+monliNigEV0DM9KgZUoHpiNZ2V\nIAiCSBSlOB1qVxutntp2EoCOBb0Ytfoy5gBTXwyuN/VF7xPHJUhHRDqImkieafRUIphiYrbKpKrg\nxgISEF3t0g9f0AW31eUEuEf6TKHdSMniouiRyiIkIKptS0NoLhEs3YmMMV3p+OFACnuiHkokzxzS\nrd+4UBuzVddTiOch7YgGS3dMVoqxsdQNXC0sAd0Mp8fZ0/Gc9JsO5HQKyJTxEJ16xxkBdUQmk+jN\nZdpSMRYBwtuSK8ugcYbm9JsNZMzezyR87iBzP5mLccvHYe4nc2Fz2iCKHsnhZOV04JEi6dXeJAUB\nxcZEwH4cWHW9tM6q66VlUQxO0DJaAVNexGQtpaQsUfRI/f10ntTfT+d19zeW7eMRtoMgCEJr+Fyp\n1MRsNQmzkeJ5QDui0Qqbq6U7JsvFWG/fxK+XwNbeGLluaFcDjgEWvQUtrhaK572FUJ0C8hoHk26l\nYoLkrmZvDqsjcASPKbhCWwH6lhs/tHW2yY4p2jrbSJdEECmddDDGLmOM7WaM7WOM/UHm83sYY3WM\nsW2MsbWMsUGp7I9aIrqDRHM4CUWNK0oy+lszP7i/NfMV3UzI/UQdWtUvQQCkXwDJj78q21MVY71t\nOcquRNX6h+OOx9kaz0m/KlGjSbW/AxX15PTW6mqV1WCrqzXrdEkkRsomHYwxHYBnAfwcQBmAGYyx\nspBqmwGM55yPBrAaQHWq+hMLiq4OhjicSXrCzcRgVe6vXH1yrYiKlvVLEKRfL8mOvyrbUxVjvW1Z\n+g5LKB5nYzwn/caAGk0my50N8norzi2W1WBxbnFYmZZ1SSROKq90nAdgH+e8nnPeCWAVgKsCK3DO\nP+Wc+6bQXwM4PYX9UY3P1SGQ8v7lcHR5nSMC8TlHKNEZxzox4uiyK/dXrr7S9tEZiEA0q1+CAOlX\nItnxV2V7qmKsty1H896E4nGWxnPSr1rUaFLt70BFPTm9NbY3ymqwsb0xrEzjuiQSJJWTjmIABwOW\nD3nLlJgN4MMU9kc1EV0dYnUmMViBGSuB+Q3AQy3S64yVyXczqVwU3N/KRYpnFMi1QhWa1S9BgPQr\nEcntRxSlBFvufQ3N8/B97nu6MxcBcOC65VGPAapirLdvlrp3UD3h4bjjcZbGc9KvEoF6dJ6UnjB+\n3XJg7mZggU16vW55sCaT5c4Geb3lm/JlNZhvys82XRIJouY5HfEi5wwga5XFGJsJYDyAixU+vwXA\nLQBQUlKSrP4pInCgEDo8fd6DsOSXwNF6ABboIIAB1iJgxqoY3Ku8DhJrfiNdpiyZIFnXJXHSIQg6\nFJoL8fTkxarcqwQmSPUveTpTXSUyAc3qlyBA+pUQBPmYDUgJsqtnd8flaS9JdQWhOwF9w6vAmGuB\nt+8Irnf9G9JgT+EYoCrGevsmnH87Cg2W7vgdYzzO0nhO+pVD9AAdTcHjiWuXA2In8O68YI0GovQ7\nCB27qKinpDcAqso0rksiQVK59w8BOCNg+XQAP4ZWYoz9FMD9AK7knLvkGuKcP885H885H19UVJSS\nzgbRZYfw+g3IeWoshIWF0uvrN0jJVGocTgLxTTgCE7PW/EYqTyKCoEOOMVdyoTDmRrTLBZSdsAg/\n2tUvQZB+u5GL2dESZn2fl10uTThC63Ex6jFAVYz19i0ofscRj7MwnpN+5ZAbTzha1CWJqx27qKgn\npze1ZUTvJpUKqAUwjDF2JmPMCGA6gHcCKzDGygEshRQwjqWwL7GRzORDU658W0l6Oi2RMrSrX4Ig\n/UYmWoz3fd5veMqNQAhZSL9yyI0nCgaRRgnNkLJJB+fcDeAOAB8D2AngDc75d4yxhYyxK73VngCQ\nC+BNxtgWxtg7Cs31LMlMPnS1y7flao+/f0TK0bR+iV4P6TcK0WK87/Pju1NuBEKEQ/pVQG480bKf\nNEpoBnoiuRy++3mV7veNqS2ZezCnvgjkFAU/xZaIB809UTTdT8SlJ5JnFKTfdBEtxkfL6YjnWJB9\nkH57mkg5HckYr/QuNKffbCCVieTaJSyZqgNgOkmirnYVyeOBbemAnH7A9NekS6OudqnNZE84RFG6\nh1N1gjtBpIdYJhGpJJYJGABsn7U9RT0hehxBAKyhcTmnO2b6jgETbgcMlu56nXZpOVKspVhMpAql\n8QSE6EniokcaywTpnU58Ej0LRUIlfMlUHNIB5LVrgUeKgJXTpTNgofaKSogiYG8GVl0vrb/qemlZ\n7fqqv6NJ6ls8fSQIguhNiCJgPx4Sl48Hx0zfMUDQAeY+UmKtwSrVU4q1FIuJVKI0ngAiJ3/7rpAE\nrtfRJJUTRA9Ck45oRHM5SfX6mfIdBEEQ2UK8MVOt6xXFYiIVxKuvHnLRJIho0KQjGok6WSXTCSud\n30EQBJEtxBsz1bpexdouQaghXn2RiyaRIVBORzR8LiYNNd1lPmcINT/YRNfviT4S2uXh/Jiqpyor\nIVPyNAhCFfHGzGjrUSwmUkm8+vK5XoWu52qXbh0kiB6CrnREw2CVnCAGVwKCXnqd9pL6J4onun6m\nfAdBEES2EG/MjLYexWIilcSrL2OO5JoZuN7UF6VyguhB6EpHNMKcrGJ0I0l0/UgEuqQY84AZK6Ug\nQo4pBEEQysQbl6OtF/q5zyWoi2IykQTi1q1OsukPc2uTca8i9zUihZCS1OBzMVFyhkj1+nKEuqS8\ndq0UKDhBD313AAAgAElEQVSS9x0EQRDZSrxxOdp6gtflyucW9Oip5GJFJI+4dRvgwmbuozzhIPc1\nIoXQlQ6tEuhiAXS7WMxYRfcOE72WWJ690bbz8RT2hOjVUHwmtAjplkgxdDpcq5BLCkEQRGZC8ZnQ\nIqRbIsXQpEOr+FwsAvG5WBAEQRDpg+IzoUVIt0SKoUmHViGXFIIgiMyE4jOhRUi3RIqhnA6tkkpX\nLILQKJSnQWQEFJ8JLUK6JVIMTTq0jM/FAqAkL4IgiEyC4jOhRUi3RAqh6StBEARBEARBECmFJh0E\nQRAEQRAEQaSUlE46GGOXMcZ2M8b2Mcb+IPO5iTH2uvfzbxhjg1PZH4KIBdIvoWVIv4SWIf0SRPaR\nspwOxpgOwLMAfgbgEIBaxtg7nPO6gGqzAbRwzocyxqYDWATgulT1KW5EUXpoDiVW9RqySr9EUhj8\nh/dV1214/Bcp7El0SL9xQHE+YyD9RoB0SmiYVCr1PAD7OOf1nPNOAKsAXBVS5yoAr3rfrwYwhTHG\nUtin2BFFwN4ErJwOPFIkvdqbpHIim8kO/RK9FdJvLFCczzRIv3KQTgmNk8pJRzGAgwHLh7xlsnU4\n524ArQD6prBPsdNlB1bPBhpqANEtva6eLZUT2Ux26JforZB+Y4HifKZB+pWDdEponFROOuTOOPA4\n6oAxdgtjbANjbENTU1NSOqcaoxU4sD647MB6qZzIZrJDv0RvhfQbCxTnMw3SrxykU0LjpPI5HYcA\nnBGwfDqAHxXqHGKM6QHkA7CFNsQ5fx7A8wAwfvz4sKCSUjrtQMkE6YyCj5IJUjl5WGcz6dPvw/nx\n9ZggusmO+NtTUJzPNEi/cpBOCY2TyisdtQCGMcbOZIwZAUwH8E5InXcAzPK+nwbgE855ZgUFgxWY\n9hIwuBIQ9NLrtJekciKbyQ79Er0V0m8sUJzPNEi/cpBOCY2TsisdnHM3Y+wOAB8D0AH4B+f8O8bY\nQgAbOOfvAHgJwHLG2D5IZyimp6o/cSMIgLUImLGK3CJ6EVmjX6JXQvqNEYrzGQXpVwHSKaFxUnl7\nFTjnHwD4IKRsQcB7J4BrUtmHpCAI3Zcu6RJmryFr9Ev0Ski/MUJxPqMg/SpAOiU0DE2PCYIgCIIg\nCIJIKTTpIAiCIAiCIAgipaT09iqCIAiCIAiCyHQ2btx4ql6vfxHAOaCT8okiAtjhdrt/M27cuGO+\nQpp0EARBEARBEL0avV7/4oABA0YWFRW1CIKQ3U5oKUYURdbU1FR25MiRFwFc6StnWnOYY4w1Adgf\npVo/AMd7oDvpItu3D1C3jcc555f1RGeSBekXQPZvH0D6zeb9m+3bB5B+s3n/Zvv2AXHqd+vWrfWj\nRo2iCUeSEEWRbd++vWDMmDFDfGWau9LBOS+KVocxtoFzPr4n+pMOsn37gOzdRtJv9m8fkL3bSPrN\n/u0DsncbSb/Zv31AQtso0IQjeXj/l0G3qdE9awRBEARBEARBpBSadBAEQRAEQRCERrn44ouHHj9+\nXJfufkRDc7dXqeT5dHcgxWT79gG9YxuVyPZtz/btA3rHNiqR7due7dsH9I5tVCLbtz3btw/oHdsY\nxOeff74v3X1QQ1Ze6eCcZ7Xgsn37gN6xjUpk+7Zn+/YBvWMblcj2bc/27QN6xzYqke3bnu3bB2Tu\nNp48eVKYNGnS0OHDh5cNGzbs7BdeeKGguLh41G233VY8atSokaNGjRq5Y8cOEwD8+OOP+ksvvfSs\nc845Z+Q555wz8t///ncOALS2tgrTpk0bXFpaWlZaWlr2yiuvnAIAxcXFow4fPqwHgCVLlhSOGjVq\n5IgRI8quv/76QW63G263G1OnTh08bNiws0tLS8v+9Kc/nZqO/0G2XukgCIIgCIIgiIzgX//6V58B\nAwZ0ffbZZ/sAoLm5Wffwww+jT58+nu3bt+985pln+s6dO/eMTz/9dN+tt956xj333HP00ksvbd+7\nd6/x0ksvHVZfX//dH/7wh4F9+vTx7Nmzpw4Ampqagm6p2rRpk3n16tWFGzZs2GUymfjMmTNLnnvu\nub5jxoxxHD582LB3797vACBdt2Jp+koHY+wyxthuxtg+xtgfZD43McZe937+DWNscM/3Mn5UbN9N\njLEmxtgW799v0tHPeGGM/YMxdowxtkPhc8YYW+zd/m2MsXN7uo+phPRL+tUypF/Sr5Yh/ZJ+e5pz\nzz3XUVNT0+e2224r/uijj3L79u3rAYBZs2bZAGDOnDm2zZs35wLAl19+2efOO+8sGTFiRNkVV1wx\ntL29XdfS0iKsW7euz9133+1/2F5RUZEn8Ds++uijvB07dljHjBkzcsSIEWVffPFFn/r6etOIESNc\nBw8eNM2aNeuM1atX9ykoKAhar6fQ7JUOxpgOwLMAfgbgEIBaxtg7nPO6gGqzAbRwzocyxqYDWATg\nup7vbeyo3D4AeJ1zfkePdzA5vALgGQDLFD7/OYBh3r+fAPi791XzkH79kH41COnXD+lXg5B+/ZB+\ne5DRo0e7Nm3aVLdmzZr8+++/v/g///nPSQAQhO7z/4wxDgCcc2zYsGFnbm5ukIUv5xyMMcXv4Jyz\na665pvnZZ59tDP1sx44ddf/7v//bZ8mSJae+/vrrhW+++WZDkjZNNVq+0nEegH2c83rOeSeAVQCu\nCqlzFYBXve9XA5jCIu2tzELN9mkazvk6ALYIVa4CsIxLfA3gFMbYwJ7pXcoh/Woc0i/pV8uQfkm/\nWkaL+m1oaDDk5eWJt99+u+2uu+46umXLFisALFu2rBAAXnrppYLy8vIOALjoootOLlq0yJ938dVX\nX1kAYNKkSSeffPJJf3no7VWXXXbZyffee6+gsbFRDwBHjx7V7dmzx3j48GG9x+PBTTfddOLRRx9t\n3L59uzX1WxyOlicdxQAOBiwf8pbJ1uGcuwG0AujbI71LHDXbBwBTvZcOVzPGzuiZrvUYav8HWoT0\nK0H61SakXwnSrzYh/UqQfnuQjRs3WsaOHTtyxIgRZYsWLRq4YMGCwwDgcrnY6NGjRyxZsqT/4sWL\nDwLA888/f3DTpk05paWlZWedddbZzzzzTBEAPPbYY4dPnDihGzZs2NnDhw8v++CDD/ICv2PcuHHO\nBx54oHHKlCmlpaWlZZdccknpwYMHDQ0NDYaLLrpo+IgRI8p+/etfn7lw4cJDPf8f0PDtVQDkzjiE\nPklSTZ1MRU3f3wWwknPuYoz9FtJZmUtS3rOeQ8v7LxqkX9KvliH9kn61DOmX9NvjTJ069eTUqVND\nb3HD73//+6a//OUvhwPLBg4c6H7//ffrQ+vm5+eL//rXvxpCyxsbG7f73s+ZM6dlzpw5LaF16urq\ndsbd+SSh5SsdhwAEzsxPB/CjUh3GmB5APiJfjsskom4f57yZc+7yLr4AYFwP9a2nULOPtQrpl/Sr\nZUi/pF8tQ/ol/RJpQMuTjloAwxhjZzLGjACmA3gnpM47AGZ5308D8AnnXCtnKqJuX8j9iVcCSPss\nNsm8A+BGrwvF+QBaOeeHo62kEUi/pF8tQ/ol/WoZ0i/pNyNobGzcPnDgQHe6+9FTaPb2Ks65mzF2\nB4CPAegA/INz/h1jbCGADZzzdwC8BGA5Y2wfpDMU09PX49hQuX3zGGNXAnBD2r6b0tbhOGCMrQQw\nCUA/xtghAA8BMAAA5/w5AB8A+C8A+wDYAdycnp4mH9Iv6VfLkH5Jv1qG9Ev6JdID087EnSAIgiAI\ngiCSz9atWxvGjBlzPN39yCa2bt3ab8yYMYN9y1q+vYogCIIgCIIgCA1Akw6CIAiCIAiCIFIKTToI\ngiAIgiAIQkMsXry4b0NDgyHd/YgFmnQQBEEQBEEQhIZYsWJFvwMHDtCkgyAIgiAIgiCyFVHkhe0u\n9yiR83HtLvcoUeSFibZ58uRJYdKkSUOHDx9eNmzYsLNfeOGFgpqaGmtFRcXws88+e+RFF100bP/+\n/YaXX365YMeOHdYbb7xxyIgRI8ra29vZ22+/nTdy5Miy0tLSsmuuuWaww+FgAHD77bcXn3XWWWeX\nlpaW3XLLLacDwGuvvZY/evToESNHjiy74IILSg8ePNgjbrbkXkUQBEEQBEH0amJxrxJFXtjc4Ro0\nb+UWobbBhorBhVg8Y6zYN8e0XxBY3A+RfOWVV0756KOP8letWrUfAJqbm3U//elPh73//vv7Tjvt\nNPcLL7xQ8O9//zv/zTffbDjvvPOG//nPfz44ceJEu91uZ0OGDBn173//e/fo0aNdv/zlLweXl5fb\nb7311uaf/OQnI+vr63cIgoDjx4/r+vXr52lqatL17dvXIwgCnnzyyX47d+40v/DCC4fi7bcS5F5F\nEARBEARBEHFi7/IUz1u5RVhf3wy3yLG+vhnzVm4R7F2e4kTaPffccx01NTV9brvttuKPPvoot76+\n3rB3717LJZdcUjpixIiyJ554YuCPP/4YdkvV1q1bzaeffrpr9OjRLgC46aabmr/44ou8wsJCj8lk\nEqdPnz7o1VdfPSU3N1cEgB9++MFYWVk5rLS0tGzx4sUDdu3aZUmk32qhSQdBEARBEARBqMRq1Blr\nG4IvaNQ22GA16oyJtDt69GjXpk2b6kaNGuW4//77i1etWlUwdOhQx65du+p27dpVt2fPnrovv/xy\nb+h6SnctGQwGbNmyZefUqVNPvPXWW6dMmjRpGADccccdJbfffvuxPXv21D3zzDP7XS5Xj8wHaNJB\nEARBEARBECqxd3o6KwYHp3BUDC6EvdPTmUi7DQ0Nhry8PPH222+33XXXXUc3bNiQY7PZ9P/5z39y\nAMDlcrENGzaYASA3N9fT2tqqA4CxY8c6GxsbjTt27DABwLJly/pWVla2tba2CjabTXfddde1Pvfc\ncwd37txpBYC2tjZdSUlJFwC88sorfRPpcyz0SOIIQRAEQRAEQWQDVoOucfGMsWE5HVaDrjGRdjdu\n3Gi57777ThcEAXq9ni9ZsmS/Xq/n8+bNK2lra9N5PB522223HR0/frzzxhtvPD537txB9957r7hh\nw4adzz33XMM111xzlsfjwZgxY+y///3vm44dO6a//PLLh7pcLgYAjz766EEAuP/++3+cMWPGWf37\n9+8cP358x4EDB0zJ+L9EgxLJCYIgCIIgiF5NLInkgJRMbu/yFFuNOqO909NpNegaE0kiz0ZCE8np\nSgdBEARBEARBxIAgMFuuSW8DgFwTDafVQDkdBEEQBEEQBEGkFJp0EARBEARBEASRUmjSQRAEQRAE\nQRBESqFJB0EQBEEQBEEQKUVzk47LLruMA6A/+uPQIKRf+gv40xykX/oL+NMcpF/6C/gj0oDmJh3H\nj6t2MyOIjIP0S2gZ0i+hZUi/RG/jrrvuOu2tt97Ki3W99957L2/y5MlDk90f8vgiCIIgCIIgCA0i\niiI459DpdGGf/e1vf/uxJ/rQ1dUFg8EQtZ7mrnQQBEEQBEEQRFoRxUK42kaBi+PgahsFUSxMpLnb\nbrut+PHHHy/yLd9zzz2nPfTQQ/0ffPDB/uecc87I0tLSsrvvvvs0ANi9e7dxyJAhZ8+cObPk7LPP\nLvv++++NU6dOHTxs2LCzS0tLy/70pz+dCgBTp04d/PLLLxcAwOeff24tLy8fMXz48LJRo0aNbGlp\nEex2O5s2bdrg0tLSspEjR5a9++67YVdFjh49qvvpT396VmlpadmYMWNGfPPNNxZf/2bMmDHowgsv\nHHb11VefqWYbadJBEARBEARBEGoRxULYmwZh5QwjHikCVs4wwt40KJGJx8yZM21r1qzxr//2228X\nFBUVufft22fetm3bzp07d9Zt2bLF+uGHH+YCQENDg/nmm29u3rlzZ93Ro0f1hw8fNuzdu/e7PXv2\n1P3ud79rDmzb6XSyX/3qV2f97W9/O7B79+66zz//fHdubq64aNGiUwFgz549da+99lr9LbfcMthu\nt7PAdauqqk4bM2aMfc+ePXWPPPJI46xZs/wTjG3btlk//vjjfe++++4ParaRJh0EQRAEQRAEoZau\njmKsni2goQYQ3UBDDbB6toCujuJ4m7zwwgsdzc3N+oaGBsP69est+fn5nm3btlnWrVvXp6ysrMx7\nRcO8a9cuMwAMHDiwc8qUKR0AMGLECNfBgwdNs2bNOmP16tV9CgoKPIFtb9u2zXzqqad2XXzxxXYA\nKCwsFA0GA7766qvcG2+8sRkAysvLnaeddlrn9u3bzYHrfvvtt3mzZ89uBoArr7yy7cSJE/rm5mYd\nAFx22WUncnNzVSfmp2zSwRj7B2PsGGNsh8LnjDG2mDG2jzG2jTF2bqr6QmQeIhfR0dUR9JpJkH6z\nn0zXYCKQfgktQ/rt3WgiNhtzjDiwPrjswHqpPAGuuOKKlhUrVhT885//LJw6daqNc4677rrr8K5d\nu+p27dpVd+DAgR133333cQCwWq3+f0xRUZFnx44ddZMnT25bsmTJqdOnTx8c2C7nHIyxsMkB59Hn\nC3J1fG3l5OTEtHNSmUj+CoBnACxT+PznAIZ5/34C4O/eVyLLEbkIm9OGqnVV2Hx0M8r7l6N6YjUK\nzYUQWMZcfHsFpN+sRSMaTIRXkCb9Dv7D+6rrNjz+i2R8JZF9vAKKv70SzcTmzo5OlEwwoqGmu6xk\nglRuitksys8NN9xgmzNnzuCWlhb9559/vnvjxo2Whx9++LRbbrnFlp+fL/7www8Go9EYNgs4fPiw\n3mQyiTfddNOJ0tJS169//eugHIsxY8Y4jx49avz888+tF198sb2lpUXIzc0VL7roovYVK1YUXnnl\nlW3btm0zHT582Dh69Gjn2rVrc33rnn/++W0vv/xy3yeeeOLwe++9l1dQUOAuLCyMayaYsj3IOV8H\nwBahylUAlnGJrwGcwhgbmKr+EJmDw+1A1boq1B6phZu7UXukFlXrquBwO9LdNT+k3+xGCxpMBNIv\noWVIv70XzcRmQ04jpr0kYnAlIOiBwZXAtJdEGHIaE2l2/Pjxzo6ODqF///6dgwYN6rr66qtPXnPN\nNbaKiooRpaWlZb/85S/POnHiRJhNVUNDg+Giiy4aPmLEiLJf//rXZy5cuPBQ4Odms5n/85///H7e\nvHklw4cPL5s0aVKp3W4Xqqqqjnk8HlZaWlp23XXXnbV06dIGi8USNKlZtGjRj5s2bbKWlpaW3X//\n/cWvvPKKqvwNOdJpmVsM4GDA8iFv2eHQioyxWwDcAgAlJSU90jkidVj0Fmw+ujmobPPRzbDoLWnq\nUVyQfjVMlmgwEUi/hJYh/WYpmonNgmCDtQiYsbIYxhwjOjs6YchphCBEmiyrYs+ePXWByw8++OCx\nBx988Fhovb17937nez9hwgRHXV3dztA6a9asafC9v/jii+1bt27dFamOj8svv7zt8ssvbwOA/v37\ne9auXft9aJ0nn3wyZjvedF6rYjJlsjeXcc6f55yP55yPLyoqkqtCaAiH24Hy/uVBZeX9yzPvTEZk\nSL8aJks0mAikX0LLkH6zFE3FZkGwwZS3HUzYCFPe9mRMOLKddE46DgE4I2D5dAA98hATIr1Y9BZU\nT6xGxYAK6JkeFQMqUD2xOvPOZESG9KthskSDiUD6JbQM6TdLodic3aTz9qp3ANzBGFsFKQGslXMe\ndmmUyD4EJqDQXIinL3kaFr0FDrcDFr0ls5LEokP61TBZosFEIP0SWob0m6VQbM5uUjbpYIytBDAJ\nQD/G2CEADwEwAADn/DkAHwD4LwD7ANgB3JyqvhCZh8AE5BhyAMD/CkjOFb4gk85gQ/oltAzpl9Ay\npN/sRO3xXWl8QGiflE06OOczonzOAfwuVd9PaI9Mssoj/WY3maS1VED6JbQM6Tf7yPaYS6iD9jSR\nMMl6kI9mrPKIIDTxIKcQSGsEQRDJI9pxgGIuAdCkg0gQ39mLuZ/Mxbjl4zD3k7mwOW1xDTw1Y5VH\n+Enm/u9JSGsEQRDJQc1xgGJufDQ0NBguu+yyIbGud9111w3auHGjOVKd6urqomeeeaZv/L2LHZp0\nEAmRzLMXmrLKIwBo9+wVaY0gCCI5qDkOUMyNj8GDB3d99NFH9aHlXV1dEdd7/fXX948bN84ZqU5V\nVVXTHXfc0ZxgF2OCJh1EQiTz7AVZ5WkPrZ69Iq0RBEEkBzXHgWyMuSIXCzu6OkaJXBznfS1MpL3b\nbrut+PHHH/c/TOaee+457aGHHuo/bNiwswFg8eLFfX/+858PueSSS4ZWVlaWejwezJw5s2To0KFn\nT548eejFF1889OWXXy4AgPPOO2/4unXrrABgtVrL586dWzx8+PCyMWPGjDh48KDe1/6CBQv6A8CO\nHTtMF1xwQenw4cPLysrKRn733Xem1tZWYcKECaVlZWUjS0tLy1asWHFKItsH0KSDSJBknr0ItMrb\neMNGPH3J05RkluFo9ewVaY0gCCI5qDkOZFvMFblYaHPaBs39ZK7Re0uZ0ea0DUpk4jFz5kzbmjVr\n/Ou//fbbBeeff35HYJ1Nmzblrly58oevv/56z7JlywoOHjxo3L1793evvvpqw+bNm3Pl2nU4HMKE\nCRPad+/eXTdhwoT2p59+Ouwpmddff/2Zv/3tb4/t3r27bsOGDbtKSkq6rFar+P777++rq6vb+fnn\nn+/54x//eLooJnbrtDb3NpF2fMliFr0Ff5v8N/xu7O+6z15UVsOsM0dMKlZKOvNZ5QW+EplD6H4z\n68yyZ68C939PJ5erTWwnrREEQSSO0lWM0OMAgLhirtqY3pOmJg63o7hqXZUQckuZ4HA7iuNt88IL\nL3Q0NzfrGxoaDOvXr7fk5+d7hgwZ0hlYp7Ky8mT//v09AFBTU5N79dVXt+h0OpSUlLjPP//8Nrl2\nDQYDnz59eisAjBs3rmP//v3GwM9bWlqEo0ePGm+88cYTAGC1WnleXp4oiiK76667Ti8tLS2bPHly\n6bFjx4yHDh1KyPU2nQ8HJDSKrPVdZTXmjJqDDncHXtv5GpZuXapoiUfWedpEab8VmAqCHuRk1pnR\n4mpJy/4lbREEQfQscg/0S9ZxQG1M7+nYb9FbjAq3lBkVVlHFFVdc0bJixYqCI0eOGKZOnWoL/dxq\ntfpnUpJzdHT0ej0XBMH3Hm63mwV+rtTO0qVLC5ubm/Xbt2/faTKZeHFx8SiHw5HQP5OOwllCD8/w\nw5PGaqSksbs+vQvPbnk2YlKxVpOPeztK+83pcQadvXJ6nDHtXzntxqtn0hZBEET66RQ7kxKL1cb0\nno79DrejU+GWsk6FVVRxww032NasWVP43nvvFcycObMlUt3Kysr2t956q8Dj8eDgwYP6b775Ji+e\n7ywsLBQHDBjQuXz58lMAwOFwsLa2NqG1tVXXr1+/LpPJxN999928H3/8MaEJFUCTjqygp21LlZLG\nrAarqqRirSYf93bU7rdY9q+Sdts62+LSM2mLIAiiZ5GL4x1dHTjVcmpQvXhicSqOO8nAorc0Vk+s\nFkNuKRMtektjIu2OHz/e2dHRIfTv379z0KBBES2qZs2a1TJw4MDO0tLSs2+++eZBY8aM6TjllFM8\n8XzvihUrfnj22WdPLS0tLRs/fvyIgwcP6n/zm9/Ytm7dmnPOOeeMXLFiReGZZ54Z0Q1LDXR7VRYQ\nOMMH4J/hP33J08gx5ERdX+QiHG6H/7KoRW+RvRzpq8c5R3n/cv/3AdIM395lly13uB1B/fAlnUWr\nR2QWavdbLPtXSbsPT3g4Lj2TtgiCIHqWSHH8/R/e99eTi8XRxh9qY7rS+MPeZUeuUTa/OiEEJti8\nt5QVW/QWo8Pt6LToLY0CE8JuiYqVPXv21PneDx8+vHPv3r3fAcC8efOaAfgtbnU6Hf7+978fys/P\nF48cOaKrqKgYOW7cODsAfPvtt7t99ex2u382dvPNN7fcfPPNLQDw5JNP/ugrHzVqlOvrr7/eE9qX\nLVu27Ep0ewKhKx1ZQCIzfLVXSQLrmfVmLLxgYVDS2MILFqq2xMtG67zegNr9ZtaZsahyUVC9RZWL\nYNaFP6dISbvFucVhZWr0QdoiCILoWZTi+Ol5p0eMxWrGH2qPJxa9RXFckioEJthyDDnbBSZs9L4m\nPOGIlZ/97GfDRowYUXbhhReOuPfeew+XlJS4e7oPsUBXOrKASGcCAs8eBJ5FCDy7YO+yo5+lX9B9\nkKFnlQPPZPzQ+gPWHliL+867D0Pyh6C+tR7v1b+HG8puCEsmk7tqIpd0pnR1hUg+aq9shaJ2vzk9\nTmw5tgVPTnoSfYx9cLLzJGoP1+KC4guQIwRfbVA6O9XYHnyFWu3VCtIWQRBE8lBzvIg0BokUiyPd\npQHAv96avWuCxhtr9q7BDWU3BB1PnB4n3qt/T3ZcEnrcySYCr2hoAToSZwGR7OrkziJ4RE9Q+cPr\nH8a88nn4+Zk/BxD9fslvD3+LqcOm4rFvH8P4FePx2LePYeqwqTDrzKptSMmuND0kmv+jZr+ZdWaM\n7DsS93x2D8YtH4d7PrsHI/uOVLzSEXp26n8u/B/km/LjvlpB2iIIgkgctceLSFcjIsXiSHdp+L7T\nYrBg6daluPqdqzF2+Vhc/c7VWLp1qewYZVrptKBxybTSabFe6RBFUWTRqxFq8P4vg8RCVzqyAKWz\nu0pnERZPXozNRzeHnYmeM2oOPvzhw6j36Z838DxVZx6IzCPR/B+137HgqwVB37HgqwVYPHlx2L21\ncmen3v7+bdx89s1YPHkxrAYr7F12mPVmmjwQBEH0IGqPF06PM64xgcPtwK1jbsWUkin+9dYeWItD\nbYf831l/ol5VTofABBSYCoKOG3Fc5d7R1NRUVlRU1CoIgjo/WkIWURRZU1NTPv5/9s49TIrqzP/f\nty8z3TODyADGBB2RiG5MVBAwYoLxkmy8RZJgEsiKJotiVgUTNxnUGJdFkw3kYsTLek1UskIMGmW9\nrFkVF6KYgAHj7achSBBvIIPITHfP9OX8/qjunqrqqu7q6a7ururv53n66enT51Sdmv6eU3Uu7/sC\nL5FrlrMAACAASURBVOrTOejwCbmZBAD5d7tZhEgoggn7TcClT12a92e9eNpijIiMKGmH0b2mG+OG\nj8NXn/8qbtx0Y/77kIQw98i5Ll8lqZRaePiw82LWFm6zrM9Zh55l8K2+9MSl+GDgAyxYu8Cgz85I\nJ4KBYNXqSQghxJ5yPEfd8vwtZT8TRIIRzBg/o6Cv/9+t/5vPc9sLt2HRcYtw1TNXGeJvmOuQUZmK\n44KkUqnz3nnnndvfeeedT4A7gSolA+DFVCp1nj7R1UGHiJwC4DoAQQC3K6V+bPq+C8BdAPbN5rlM\nKfWIm3VqJuz2WSZSCSxYu8Awe7Fg7QIsPXFpfrWkmB1GIp1oCg9BftRvLbw7leNFxGqVTillq083\nvJD4FT/qlzQP1G/9ccNjoZ5E2vpZZOHUhUD2UI++/ijGDR+XX8EoZldS6Sr+pEmTdgA401FmMiRc\nG8mJSBDAjQBOBXA4gFkicrgp25UA7lVKTQQwE8BNbtXHDxQLmKZPS6QS6B3oRTQUxXUnXoeff+bn\nhr3xxWaindhhNIOHIL/qt5j9j5NgfOlMGr0DvcioDHoHepHOFLoEj4ai1vt7Q9bnMNtgFNNnrQJg\neh2/6pc0B9RvbSgVhNVtj5TRUBT7RffD/Wfej02zN+H+M+/HftH9MKZjDB7+0sPYNHsTHv7Sw5j1\nD7PQFm4z3CvM9yLGaPIGbq50HANgs1JqCwCIyAoA0wG8rMujAOyT/Xs4gLdALMkZdFktHQLIf/fZ\nAz+Lz439XMFy5fqz16M/3Z/3VlWJP+sm8RDkS/1a/XaRYMTRsnTOAUGpbU/BQBCdkc4Cm4z3+993\ntPRtp8++ZB8uWX3JkJfOmwxf6pc0DdSvyxR7psj1qU7v9UN9JuhP92P+0fNx5dNX5uvwk+N/gt39\nu7Fw3UJDvfRY3Yt+ceIvmmIHhtdx8249BsAbus/bs2l6FgI4W0S2A3gEwDwX6+Np9EuHete28VTc\n8N1pHz0tv1yZy7dg7QIkUoOBJEOBkOVMdCjgfAzaBB6CfKtf82+XSCdstaUnnopbasucDwBEBCKS\n/7s/3Y+Vr63E5cdcjg1nb8Dlx1yOla+ttCxrt1Ly7FvPlqwjyeNb/ZKmgPp1mWLPFHqc3uuVUlBK\nFfxdjIzK4MqnrzTUoS/ZV7JeVveie165B0um+XsHhh9wc6XDyu2YWYWzANyplPqZiEwFsExEPqGU\ncY1PROYCmAsAXV1drlS20Sm1dJj7bp+WfWy3ppz3+/PyMxn/u/V/Dd6rHvnbI5j5sZm1uRhv0DT6\ndbos7dRA3GoG7bbP3YYzxp1hMAZcdNwiSze6disl3Wu6S9aR5Gka/RJfQv26TDW3IzldBXdShzEd\nY0rWy+pedMvzt+D8I873+w4Mz+Pmr7EdwIG6zwegcPlzDoB7AUAptQ5ABMAo84GUUrcqpSYrpSaP\nHj3apeo2NjlDLT0XHHUBYskYAODBLz6IUw8+FX3JvoJ8ua0puRmDWDKGx994HNNWTMNRdx+FaSum\n4fE3HuessZGm0a+VtnLL0npy257M+XIa1B/PPFPVm+zNu9HNpV31zFW2mgsGguho6UBAAuho6UB/\nuh8XHHWBYe/vBUddQM3a0zT6Jb6E+nUZp/2+02M5XQUvVYc3e9+0vc/kbE8SqYRt3X2+A8PzuPmL\nrAcwXkQOFpEWaIZeq0x5tgE4GQBE5GPQOo2dLtbJs5gNtS6acBFmjJ+B+avn5wP8ffvob9tuTdGv\niDSDIXgVaBr9lmMsWExb+nzmWaiOlg7HbnStyLlWtApISSxpGv0SX0L9ukw1nwPKcZNeqg7DW4cX\npC2ethi/fuXX+QCFsVQM137m2pL3ItJ4iJN9d0M+uMhpAH4BzZ3dL5VSPxSRRQA2KKVWZb1R3Aag\nA9rSabdS6vfFjjl58mS1YcMG1+rcyGRUJr9kGEvGMH/1fIPR1JT9p2DpiUvxt/f/hnH7jkN7uB19\nyT5seX8L2sJt+PKqL2PK/lMsgwh6dBnS1cihzaRfvbaK6SGdSSOeihuCL5mXz/uSfZj35DyDNh/+\n0sNYuG5hgV6datHqmLnyHjYS9KV+x172sOM6bv3x6Y7zkobDl/ptJpz2+6XoHei1fR4p5ZzGqg4A\nDM86v37l14YYILm+XylV9F5UAkYerwOuDjrcgJ2GRkZlMGnZJKRUKp8WkhCem/0cdsV3WQbbefyN\nx/3m8cdznUYz6NfKpuPaE65FMpMs8JQyonWEI89ZxfTuYS37Ur8cdDQNvtQvKZ+h2nQ4wcW+33P6\n9QOMSO5R7ILxxJIxQ7CdUdFRiKfimPmxmTjzkDO9uqJBPERAAhjROsJgCB4NRSEilisbTgI61SKo\nISGEkPKxcv5htfIwlJWVSl38k8aCT58epdh+zNzeylMPPhXzJ87HwnULMWnZJMxfPR+7+3czqBpx\nlYzKYHf/7ry9UU53AAqM/Jx6UKEdEiGENC5m5x9WA46eRA/mPTkvb5vRk+gp+TwSDUWx6LhFhr5/\n0XGL2Pd7FA46PIo+GM9zs5/D9Sddj85IJxLpQa8O5x9xfoHHIMY2IG7j1P97Lq8TDyp2eueqHSGE\nND7l3Bf0JNIJPLTlIUOMp4e2PIREOlG0HGlMSt6xRaRVRL4uIleIyFW5Vy0q10ykM2n0DvQiozLo\nHehFOpPWvshkgP5eQGXfM4OzAlZBe/QzwuOGjyvbD3fOJZ3+nTQnmUwafVlN9g30IpNJO9JHsdUL\nc9lyVjCaICAlIYQ0FkWeQUoWNfX1Q4kLEg1FcdahZxk8F5516FmIBCN8VvEgTu7aDwKYDiAFoE/3\nIlUiZ4Sl347Sk+hBOpMCYjuB5TOBq0dr77GdRRu9fkZYv+qRo5gf7qEufxL/kclqcl5Wk/NWz8fe\ngV5H+rBbvehN9haUBcAVDEIIaUQymbKfQfJFTc8T2/duH3JckHAgjIVTF2LD2RuwcOpCRIIR7O7f\nzWcVD+Lkzn6AUuprSqklSqmf5V6u16yJsA+skwBWzgG2rgUyKe195RzAFIwNMM4o5Ay0yt0HP9Tl\nT+ItnKxWxFNxdJs0uWdgjyN9WOpu2hLc88o9lmW5gkEIIQ1IMub4GcSM+Xnihk034JpPXVO2XV48\nFcd3nvoOTv/d6ZiwbAJO/93pee+IfFbxHk68Vz0jIkcopV5wvTZNStHAOtvWGTNvWwe0GAPuWLko\nzbkdzc0iO/EWMdTlT+IdimlFr4uohSbHdIxxpA/9altOd5FgBLc8f0vJsoQQQhqEFmfPIFaYnyce\nff1RBBBw/DxidxzA+b2INB62v7aIvCAifwHwaQB/FpFXReQvunQyBKxmmXMu4fTkXMKha+pg2U/M\nQN+8DcgAhhnqeCqOla+tNBharXxtZdmzyE6Neol3cbqaFbfQ5Ju9bw5ZHwOZAWqLEEIanUwaSHww\naMMx41fG77umAgOFKx1mu9REqnB799jhY1FubDir55JK7kWkvhQbYp4B4AsATgVwCIB/zH7OpZMy\nsbOZiIQiWDxtsWHZcfG0xYiGIsBZdwBjpyFzxFfR8/mrMe9PVxfsYYwEIzhj3BkGQ6szxp2BSDBS\nVv2iwQiWmOqxZNpiRMs8DmlcnK5mWWmyM9JpqVOzzqx03pfsw7WfuZbaIoSQRiWTBvp2Aiu+rtlw\nrPg60HUM8JVlQCAEjJ2mPZOEjSsdVnapsWQMPz3+p/k+/6IJF2HG+Bn5POW4zDVv1x3eOpwu1D1K\nyYjkIrJMKTW7VFqt8HJE0b5kH+Y9Oc8Q5GbK/lNw/UnXIxKMIJ6KDwbWkRCCoRZkkgnEkUE03Ibt\ne7fjhk034NHXHwUAXDThIpz9sbPRZvHdlP2nYOmJS50Fz8lktD2aLW3I7N2BeGYA0X3GIL7rr4i+\nvAqBYy8EWhsyCI/nIorWW799A72Yt3p+oQZPXIp2nVb6kn1Y9vIynNx1MsYNH4cte7ZgWMswPLD5\nAUPaE9uewOzDZxsC9FmVfWLbE5g9bjqQ2IPoyPFGbYXb8vrDQEz7HGgKuw5f6pcRyZsGX+q3qdDd\n+zEQA0SAe76m2W7kGDsNmHmP9gxg0z/3DvRi3VvrMOXDU7BPyz74YOADrH97PT75kU/i3b53MW74\nOOxN7sWlT11q+fxTKsCrVVBBAGUHGjThOf36ASc2HR/XfxCRIIBJ7lTH3xSbZc4F1EEmg46BPmDl\nHGSGfRg9/7gQ3c/8IL//ftFxi/Jlzxh3Buavnl/w3aOvPzpoE1KKnHeKlXOAbesQ6JqK9uk3APef\nj/YX79NmN47/blX/D6R+RENRLJm6EN3rFg7adExdaBmM75bnb8GNm27Mp206Z1NBWkhCmHvkXEPZ\n3MrbVc9cZdBmpH00gj/7BwBAO6Bpa9q/GvSHrqnaTFrb6GYZeBBCSO0x3fvzfe+w/Y35tq3TBhwS\nsJ18jIQimLDfBFz61KX5Pn/xtMVoC7XhP/70H9j47kZsmL1hyHYYue3hAAwDFKs00tgUs+m4XET2\nAjhSRD7IvvYC2AHNjS4pE0c2EzpvEfHj/xXdz/zAsP/+qmeuwvlHnG8Z+C/3Xf64xTxM5HxvJ/sK\nvVM8ePHgQOP4BUP20U0aj0Ayjs6NK3D90d147uwNuP7obnRuXIFA0mTTYaHV3oFee9sjU1krbcaT\nMeDCdcBVPdr78QuAAQv9OfSOQgghxIZS8TXsPFOdcIUxX9dUrXwREqmEpQfORCqRd4ceT9JmlBQZ\ndCil/kMpNQzAT5RS+2Rfw5RSI5VSl9ewjr7BkQtbnbeI6MjxljMD44aPw7h9rQP/jRs+bnC/vN0M\ngt73tp2HrFGHASd8H5h0zuD+zjJ8dJMGJdyGwKRz0f7wdxG4Zj/tfdK5BXt0rbTaEe7AouMWGdIW\nHbeoQGe23thaOoBHuoFr9tPeJ52jzZwN0TsKIYQQC5zE17DzTNU5VttSlbPhmHF7yf64mAfOnAOb\ntnAb7TCIo+1VvxWRo01pewD8XSmVcqFOvsXKlWjBPsSBrMeqrWsR3/VXTPzQRMMeyIkfmohEOgGV\nTtp+d/2JS7XjBoLWFdHPcLz3av58ebqmanmO/ZY24Mh9l5sJmbWiUW08SCkCAW3r0qwVRW0orLSa\nSMXx0JaHcPkxl+dtNR7a8hBmf+yf0N4yLF82t0pi1mb8/b+jXa+l+87T9gpb6a+/F4js4+q/ghBC\nfIn+Hg9Y37v7e6373sQHwGlLtInH914FnrsbmFrcrjPngdPc58eSsbxdqaPnH+J7nPzaNwF4FsCt\nAG7L/r0CwGsi8o8u1s2XlHRhG27Le6yKrvkZlhx3tcnjz48RDbairX8vlhxnHWinvaVjcMBhWGLd\nqz1k6mc41vwUmH6DcWbjrDuAcDtnof1KIGDco2tjO2HWajQUwVnjZxi8pJ01foY2U6Vbxo8GI9Yz\nWk/+yHiCbeuAlvZC/U2/QUsnhBBSPsXia+S2W7W0a/f6eRu1La/zNmqfzYOLXa+VvOdHi3ng1MFA\nsMTJSsdWAHOUUi8BgIgcDuB7AK4GcD+A37tWu2ZENxMdaGlDZzIxODMwsBfRZ29B4P8WA11T0XnW\nr7RVjXCb9ayBlaHYF2/SPFTkZjhevE/L+4Wl2rKqfubbbiZkIMaVjiYkkIyj8/V1uP4zP0e0dR/E\n+z9A9G9rEPjoZ4AV/5TXWOBry9CJIK4/5geIDu9CfM82RFUQgZHjjQfsmqrZdDx/r3Fm7fl7S86s\nEUIIsUG3YyJP11SgZytw4xTt76+vANJJ4L/nDz4ffOlmIJnQtr/qnxmSiaIDj2AghM7ICCw9cemg\nB85QBMGAk0dM0kw4GWb+Q27AAQBKqZcBTFRKbSlVUEROyQYV3Cwil9nk+aqIvCwiL4nIPc6r7i5W\nQfyqfxIbQy/dTHQgFEF7RiEw0If25f+EwOof5o2+Aiu/ifZMBgFAy2P2fmxlKPbAhZovbv3scu8O\noLUdUDDOfOtWXYr56PYrDavfUgaCpYoPVdsSROCgqWjv60FAKe39oKna+U9bAly5Q3sfiCHwm9lo\nv24CAos6tfd7ZwPHXlCopZZ2YPK5RluPyYU2JqR8Gla/hDiA+q0Ay3v3r7R7u66fxu++ZXw++N23\ngHQ/8NW7gR/s1N7f3ASotOHwVveQYCCEjpaOvCdODjiIFU5U8aqI/Ce0LVUA8DVoW6taASTtCmVd\n694I4HMAtgNYLyKrsoOWXJ7xAC4H8Cml1G4R2W+I11FVcsHNutd0D7oVPX4JOiOd1VsOtHNXp3cV\nqs9zzoM2y6XtmqGYVXm7JdbhBwL3nw+c9hNg9GH2sREc7v/3Iw2rXye6KVa8Em2HWoHYLtPM2C1A\nMGScGTt3lbXuWodZa6lJNeYmDatfQhxA/VaIuV9NJrS++8GLS/fT0RHAXWcO5ptxOxAeNPauyfMR\n8S1OFPINAJsBfBvAdwBsyaYlAZxYpNwxADYrpbYopQagDVqmm/KcD+BGpdRuAFBK7Sin8m4RT8XR\nvabb4P6te0330F27Wc1M27mr07sKtTL41tM1VUvPpICO/YD+Pi3cTe4cuSVWqzIv3gc88r3BrVJ2\nD3kO9//7kMbUr51uUglHqx9FtV2q/EAf8MC/mGbGLgBiu41pPVutdZfTmllLzasxN2lM/TYhR9x1\nhOMXyUP9VpNMqrDvtuune7Ya8913ntZ3Z6n68xFpKkre3ZVScaXUz5RSX1JKfVEp9VOlVEwplVFK\nFXPePAbAG7rP27Npeg4FcKiIPC0iz4rIKeVfQvUpFsSvbOxc1xUz9MpRyuB7+g1a+idmACdfpc1A\n688RjhYusX7xJmDtz5tuq9QQaEz9Wulm2P7AwN7i7hGzFNV2qfJ2jgVGHGRMe+pHTb0tr0FoTP0S\n4gzqtxLMzx0t7YV9t10//ZSFww+dfV1Vn49I01Fye5WIfArAQgAH6fMrpcaVKmqRZrY6CAEYD+AE\nAAcAWCsin1BKvW+qw1wAcwGgq6urVJUrxtblZypefuRLO9d1dq5C9UbaemMws8F3fy/w7M1a+oXr\ntGXT3LFyqx7to7UHva/fC4Qj2ky1BIEv38ptLKVpTP1aGQiecEVp94hZHLuztSpvZ5zYu1PTYM4Q\n/OWHgBabrVSkVjSmfglxBvVbCebnjsQeLRjr4WeY+ukOYz8tAux9x3gskwvzqj4fkabDyVPAHQB+\nDuDTAKboXqXYDuBA3ecDALxlkedBpVRSKfU6gFehdSIGlFK3KqUmK6Umjx492sGpK8NRED+n2K5o\ntJeeDTYbg+kNvlvateBqY6dpnUjuHAWrHrO0WXAFbU99Sxu3sTijMfVrZSDYOdaxa2NbbVu6szWV\ntzr315ZpNypz0L9QC7dM1ZfG1C8hzqB+K8H83BHZR+uXC/rpVmM/HYpoNhwFwQEHBxNVfT4iTYcT\nQ/I9SqlHh3Ds9QDGi8jBAN4EMBPA1015HgAwC8CdIjIK2nJpSa9YblPVIDbm2eFPzABOvFJ7UIvs\nq614tHZk/WZnZ4NzNh8tbdkZ4+Vaoze7s33ubs0LRVJ3juO/a1z1YEC/odKY+rUyvB7oc+za2FLb\nShDY+7bxPF1TNeNDlTGuVpjPrTLAfaZVllzQP0Cn7XbALlglcYPG1C8hzqB+K2EgZlzZGOjT+mWr\nflofhDUQBNpHWTyXDPbdDPJHKsGJSlaLyE9EZKqIHJ17lSqUjVZ+MYDHALwC4F6l1EsiskhEzsxm\newzALhF5GcBqAN9TSu0a4rVUlaoFsdHPDh/xFeCzC7VViD/dBsR3axG/rx6tvfe9pxlv6fdi3vNV\nbVBhdmfb0gasWQzcNBX470sG7T30qx45GNCvbBpav2bD67CDVTN9cbO2QxHrFQwrOxHAeG47O4/W\nDpO2d2qumklNaGj9ElIC6rdCwlHjyoaVTYfJVgNA1hZkl7Hvju0qsO9jkD8yVEQp8zZJUwaR1RbJ\nSil1kjtVKs7kyZPVhg0b6nHqoZNbuYDStjttXQt0vw7ce45xdnrsNG2GYcXXC9PNKxX9vdqDoHkF\npWP04DmKlfcHVvt+GxrX9KtfHRuKDYW5vF6rOax0lPjAWq9fWwYsHmtMM8+qEV/qd+xlDzs+3tYf\nn15plRqecrxSvXDuCy7WpOr4Ur++wPx8UOx5Q98nm8vl8vH5gVQJJ96rTrR41WXA4VlyM9P62Ybo\nvsaZh0/M0OJmtHZo75+YMfidk/31OXuPJg/o17RU6nbWXL6lXfOKdeE64Koe7X3Y/toMmt61bku7\ntVe11mHG41vNqhFCCCkPJ8FhrWw6rPrplvbi5QDulCBVxYn3qg8B+BGAjyilThWRwwFMVUrd4Xrt\n/IZ+733ig8G/c8bf+sA902/Qyrx4n/X+/GJB1RhsjVRKMqFp8oELdYEAb9Z0e+/swbSZ/wU8f69m\nW5TzivL8vcARZxmPZ/KAQgghpEycBoc125K+95rmrcrcTx/7LWOfbOeh0MI+kJCh4MSQ/E4AvwLw\n/ezn1wD8BppXK+KETFobcLR2aA9pf/s/IJPUvELcdx5w/PesH9yO/562gpFbqbDaQpPrCMwDEqt0\n4l/K2V5llRcwpklAG3DoDQ9/9y3gy7cbdfq3/wMmnasZk+cj2N6hlR87bTDtizdpM2yEEEKGhp0L\nfvP2p3CbtsU1tluLo7T3HeDYf9Fs6wAgGAGOmVPo3CO3U8I8qOFOCVIlnDwFjFJK3SsilwOagZeI\n0CLUKZm01tDvO0/3UHY7EB0BrL8D+OrdQGQ4cNRXC1c6hh+odSa5Bu9khoM0H05nv+zyfm0ZkB4o\nLD9sf2PZYfsDgax7XL1O20eaVtaiWqRy/eDkiUVafBhCCCFDo5ztT+kBzWnNtnWaJ6tJ5wx+zk0O\nRVqNZbhTgriMEyX1ichIZAPziMixAPa4Wiuv4GRvpd5VXSY16KpuoA945b+BJQdrgXtyKx1X7tDe\nn793cHUkEDDOcOSOs3JO1kCdNDXlaMMqb2y3dfnPXWO06fjsvxfme/DiwaX3nD1IMq7NrN00FVjU\nqb3vfSdroE4IIWRI5LY/6cltf9KTjAEb7hp8pjjmPIvnkDlaOfMzTKX2gYQUwYmaLgWwCsBHReRp\nAHcDmOdqrbxAbsbY7FLUPPCwdSk6TNtyMnaatqfyqK8aA/cc9VWjkRcNvIgd5WjDKu+IgwrT9Ksa\nOU3u82FnbhfpzIAQQqqP0741HDU+U0SG2/fdpZ5hCKkiJbdXKaX+LCKfAXAYNBdjryqlkq7XrNFx\nureyv9cYpOe9VzWDrv5eoG2UFvhvIFYY0O/Bi43u7GjgRewopo1wW6Gthjnv7r8Xpp1wRaG+e7Y6\n0yCX6AlxHR+74iV22PWtwGAgv4GYFnxY/0zx3qvWfXf/XgYSJjXF9ilARL6cewE4E9qg41AAX8im\nNTdOZ5db2oxBeh7p1j63tGmv1mHFA6zl4OwxscNWG9HC1biBvZoNhz5v24jC8p1jCzX51I+ca5BL\n9IQQUn3MfStg0c/3Gm3y1vy00GXujNuBN/5kPDZ3TxCXKbbS8YUi3ykA91e5Lt7C6cpDMjG4lxIY\ntOnQzyY4ORZnj4kddtqwXY1bbj1Tpk/r7y3U5N53gJYOapAQQuqFVSBXq37+C0uBF36rpb14HzDq\nH7TdE60dWv8eagWevs54bO6eIC5jO+hQSn2zlhXxHE5dyzlZEXF6LLrCJXZYacNWe+3aLJk+r/7v\n1g7N61rOpbPe61ooMuhmkRokhJDaYeV98NxV1v1851ij2/LJ52qTRhLQtm1nMnSPS2oOHecPFacr\nD1YzxuZAaVzFIG7gRHt2BIJA+2jjzFhLe6Ffd0IIIbXBavXa1taur/gzBZ87SB2guirByb71lvbC\nvZTTb9DS9W52uQeeVJti2nNCIKgNTnIzY3YDDieuowkhhFSG1eq1ra1de6HtB93jkjrDlQ63Scat\no40f/gXgke8xwB9xDzvtTb2welujyglMSAghZOhY2X/ufQdoGVZ8xYL9NGkQbAcdpTxUKaWax5Dc\nbLhVzhJkuE3bS6lv7NNv0CI06w17IVzabHYq0ZkVVtqz27M71HM7dR1NCCGkMuzsP0ORwf7aqt9l\nP00aBHqvKkWlMwTmfZM7X9UGHC/ep32/bZ3Wkdw9nTMPzYwbM1FO9+xWcm4GrSSEkBqhgECL5plq\nxEFajKVAi5ZeDPbTpEGg96pSVGOGILdvsr9X21JlNvh671XOPDQ7bs1EOfF4Vsm5GbSSNBsLh5eX\n/+Aud+pBmo+BPuDe2cb+duw0YyBhy3Lsp0lj4GgKVUROF5FuEbkq93JY7hQReVVENovIZUXynSUi\nSkQmO614zajmDIFVELfpN2iBeyo5LnGFmuq3njNRlZybQSsbFl/0v6RpoX4tcBJI2Ar206RBKGlI\nLiI3A2gDcCKA2wGcBeBPRQtp5YIAbgTwOQDbAawXkVVKqZdN+YYBmA/gj2XX3k30e9wv+QuADDDs\nI9qqxMsPDW2GwLzdpWercasVZx4ahprrt9KZKDubDCe2GpWcm24XGxLP97+kqaF+bRiqG3T206RB\ncKK445RS5wDYrZT6dwBTARzooNwxADYrpbYopQYArAAw3SLf1QCWAEg4rLP75Pa4L58JXD0a+N0F\ngFLa+yPdwKRzgHB0aMfObXdRAFrbgd4dnHloTGqr30pmosx6XT5T+5xJ26SbXNpWOgtGt4uNiHf7\nX0KoX2ta2rQgrfq+esbtzlal2U+TBsCJ6uLZ95iIfARAEsDBDsqNAfCG7vP2bFoeEZkI4ECl1EPF\nDiQic0Vkg4hs2Llzp4NTV4h+j3smpb0/cCEw7VLt7/vO09yRVoJ+5uEHO7V3GpE3ErXVbyV6sNLr\nyjna/l+r9GSseucmjYp3+19CqF9rBmLAc3drbtCv3KG9P3e3lk6IB3ASp+MhEdkXwE8A/BnaHP3t\nDsqJRVrexYKIBABcC+AbpQ6klLoVwK0AMHny5BJuGqqA3R73UYcN/l2NvfZOjHxJvai9foeqAnIF\nvgAAIABJREFUBzu92u3/tdIuteg3vNv/EkL9WtPaAaxZDDz1w8G0QAj4zHfrVydCysDJVOYSpdT7\nSqn7ABwE4B8AXOOg3HYYt2EdAOAt3edhAD4B4CkR2QrgWACrGsIYLLfHXU/Oy1Tub84s+B3v6NdO\nr7n9v+Z0arcZ8I5+CSmE+rXCrk/v761PfQgpEyeDjvxUqVKqXym1R59WhPUAxovIwSLSAmAmgFW6\nY+1RSo1SSo1VSo0F8CyAM5VSG8q6Ajew2uP+xZuAtT+n7UXz4B392tlktLTTY0nz4h39ElII9WtF\nS7uNTUd7vWtGiCOKRSTfH9oeymh272RuuXMfaN6siqKUSonIxQAeAxAE8Eul1EsisgjABqXUquJH\nqCMFnh76AAkCX76VXh+aBE/pt5hnEnosaUo8pV+f88Lr2+pdBc9B/doQCALto7W4HLnYXy3tWjoh\nHqCYTcfnoe2XPADAz3XpHwC4wsnBlVKPAHjElGYZ40MpdYKTY9YMwx73YYPp4bbSLkiJL/CUfu1s\nMqzSnbjRJZ7HU/olxAT1a0MgOOgeN/fOPp14hGIRye8CcJeIzMjac5Cca9KVczSD3K6p2nYVevoh\nXoEaJoQQ/8A+nXgIJ4p8WkTuEJFHAUBEDheROS7XqzGxc01qdkFKSKNCDRNCiH9gn048hJNBx6+g\n7av8SPbzawC+7VqNGhk716TVcJ9LSC2ghgkhxD+wTycewsmgY5RS6l4AGUAz8AKQdrVWjYqda1K6\nICVegRomhBD/wD6deAgng44+ERmJbGAeETkWwB5Xa9Wo2LkmpQtS4hWoYUII8Q/s04mHcBKR/FJo\n/rE/KiJPAxgN4CxXa9Wo0AUp8TrUMCGE+Af26cRDlBx0KKX+LCKfAXAYtFgdryqlkq7XrFGxc01K\niFeghgkhxD+wTyceoeSgQ0QiAC4E8GloW6zWisjNSqmE25UjhBBCCCGEeB8n26vuBrAXwPXZz7MA\nLAPwFbcqRQghhBBCCPEPTgYdhymljtJ9Xi0iz7tVIUIIIYQQQoi/cGJptDHrsQoAICKfBPC0e1Ui\nhBBCCCGE+AknKx2fBHCOiGzLfu4C8IqIvABAKaWOdK12hBBCCCGEEM/jZNBxiuu1IIQQQgghhPgW\nJy5z/16LihBCCCGEEEL8CaPHEEIIIYQQQlzFyfaqISMipwC4DkAQwO1KqR+bvr8UwHkAUgB2Avhn\nrqyQRoH6JV6G+vUeR9x1RL2r0DBQv4T4D9dWOkQkCOBGAKcCOBzALBE53JRtI4DJWWP0lQCWuFUf\nQsqB+iVehvolXob6JcSfuLm96hgAm5VSW5RSAwBWAJiuz6CUWq2UimU/PgvgABfrQ0g5UL/Ey1C/\nxMtQv4T4EDcHHWMAvKH7vD2bZsccAI+6WB9CyoH6JV6G+iVehvolxIe4adMhFmnKMqPI2QAmA/iM\nzfdzAcwFgK6urmrVj5BiUL/Ey1C/xMtQv4T4EDdXOrYDOFD3+QAAb5kzichnAXwfwJlKqX6rAyml\nblVKTVZKTR49erQrlSXEBPVLvAz1S7wM9UuID3Fz0LEewHgROVhEWgDMBLBKn0FEJgK4BVqHscPF\nuhBSLtQv8TLUL/Ey1C8hPsS1QYdSKgXgYgCPAXgFwL1KqZdEZJGInJnN9hMAHQB+KyKbRGSVzeEI\nqSnUL/Ey1C/xMtQvIf7E1TgdSqlHADxiSrtK9/dn3Tx/uWQyCrFkGm0tQcQG0mgLBxEISNl5iD/w\nmn79TrXbnt/bMvVbBguH17sGxEQj6JfPBIRUF1cHHV4ik1HY1TeA+cs3Yv3WHkwZ24mlsyZiZHtL\nvgNxkocQUn2q3fbYlgkhxeAzASHVx02bDk8RS6Yxf/lGrNuyC6mMwrotuzB/+UbEkumy8hBCqk+1\n2x7bMiGkGHwmIKT6cNCRpa0liPVbewxp67f2oK0lWFYeQkj1qXbbY1smhBSDzwSEVB8OOrLEBtKY\nMrbTkDZlbCdiA2lDnvknHYLHvn08/vaj0/DYt4/H/JMOMeQBgHQ6g72JJDJKYW8iiXQ6Y3nOTEah\ntz+FjMq+ZyzdkBPSdJjbRqy/dPssB9v23p9mmySE2PYRfQ76pUSS/QghVtCmI0tbOIilsyYW7M1s\nCw/OWERDAcw8pguXrNiUz3PdzAmIhgbHbul0Brv6BgryjGxvQTA4mI97QQmxxqpt3Hz20bhu1gRc\nsty+7ZVDNBTAdTMnGNvprAlIZzL41q//zDZZI8Ze9nBZ+bf++HSXakKIEcs+YuYE/OoPr2Ppk5sx\nZWwnbjtnUkGe/zz7aPT1pzBf11exHyFEg4OOLIGAYGR7C247d7KtF4p4KoNLVmzCui27AADrtuzC\nJSs24bZzJ6MjO6CIJdOWeW49ZxKG6QYd+r2guXzzl2/UjtXKn4U0L1Zt41u//jN+MXMCFp75cRyy\nXwc27+jFij9twz9PG5dve+UQT2Ww4k/bDMdTCvjWr//MNkkIKegj9iaSuPuZrfj5438FoPUP7/UO\nYNWmNw39SHwgjUvvfZ79CCEWsAXoCAQk3ynoO4dUKoN4Ko321lDJ/Zt2edpNnQ33ghI/U4kbSbu2\nMaqjFe/Hkvm0Le/1IRoOoLc/VfZ52lqCWPrk5vwDBAD87UensU0SQgBofcSW9/ryn0OBgOEzABzY\n2VZWPzKUvooQP0GbjhKkUhn0xAYw9+7n8Nd3e233gefo60/Z7gPV48SGhBAvktsedf5dG3Do9x/F\n+XdtwK6+Acf7mu3aRm9/CgtXvYTDrnwUC1e9hCvP+NiQz2O1F/uNnhjbJCEEAJAYSOO7nz8s3+ec\nf/cGfPfzh+HMoz6Sz2PVZ9j1I72J1JD7REL8gijlLdFPnjxZbdiwYcjlnc7AptMZxJLa6sbeRAqp\ndBrDoy3oMdtrzNLsNWIDaYQDgnAogL2JFN6PJXFgZxve6Ilh37YwhrWGaNNRfTz3j6pUv16gt1+7\nuea2FwDA1HEjcec3JmMgo9DeGkJffwrRUBAhC5sMq7Zx3awJ6E0kMaojgo5ICL2JFJRSuOuZrfj8\nJz6c39rw2Itva1uuTCuL+tXKvv4UWgOCDwZS6E2k8+10RHsY/amMwW6kWJusQlAwX+q3XDuNcnDN\npqNBggMecXCXK8d94dwX3Disr/Rr7iNaAoI398Qxelgkn9bXn0QkHMKwSBhv9MQwqqMFvf1pfOc3\ng33GDV+fiLRSRvuzWROw4o/bDCsiU8eN5Jar+uI5/fqBplK70wd9O2Pw//rj37EnlsTNsydhWGsI\nvf0p3Pn0oFHZdTMn4OW39+DQD+2Dy+9/wdDhmHFiQ0KIF7HaHnXBZw7GnkSqoE11trUUDDys2kYk\nGAAUcMGy5wzG5TMmHYjv/vb5fNriGUciGjYeL7daaZ4sCAcCuPz+wbSffuUofGifVkdtkpMGhPgH\nyz5i5gTsNyyC8+9+zpDW09ePydc8nm3zExANB/AfXz4iP3mRySh0mvqvaDiApU9uNpyTWzdJM+Lb\n7VVW7mhLBfLJlZGAIDaQxuhhrfl8l6zYhOkTxuDnj/8V31r2HHr7U7hg2XP4+eN/NeQ5uqsT/5o1\nIsunL9+EWDJd4D4vZ0MSkOw7H1aIBzG3tUQyjetnTcCmqz6HLf9xGjZd9TlMGTsy72BB317iqbRl\nWzW3jXgqXVB+dyyJ7/7W2NYW3PcXxAaM7iqtyl6yfBP2xJOGtO/+9nnEk2lHbdK2Lxmgq0xCvIZl\nH7FiU/7v3PuKP23D6GERvHrNqVh45sex/I/bsDuWxAk/fQofveIRnPDTpzB/xaaCfiSezHDrJiHw\n6UqH7SxkR4utgZdVmcUzjgQArHr+Lazf2oN9ouF8mY6ItcG4XXpbSwiHfv9RzogSX2HVbm47ZxIm\nHdSJfzG4np2AD+3Taiibc7Cwq7f0ioGVg4YDO9tsnTb8021/zB/vv87/pGW+AzvbLMs6wdYRRGvQ\ncG62dUIaHzsHMLl7PgCcedRH8MWJB2CubuVj8Ywj8ZF9IwXlChzHOHDJT0gz4MuVDrtZyGJG3lZl\nFtz3F1x04iH5fB/Ek/m/exPWx7JL37yj13J1hRAvY9VuUhlVMGs4f/kmfPuzhxrK5tpesdXHHFZt\n185g8wPTCsYH8aRlvjd6YgVpTmce7Yzdt+2KlbwWQkhjYfdskLvnA8BFJx6CBff9xXJl1VzO7DhG\nv2X0tR+eitvOnczJCNKU+HLQUcwd7eIZR2LquJEIBQRTx43E4hlHoq0laFvmkP06MHXcSFw3cwIe\n3PRm/u83evpw3cwJhmNdN3MC/rytBz/5SuE5bly9uaAuhHgdq3azTzRs2Za6RrYVtJdoyJnr6Ggo\nWNDeRrSFLdtgR4txlvHBTW8W5ps1ASPawoa0cmYeczOXxvIT8IvHXyt5LYSQxsKqf7lu5oT836GA\n4JD9Omx3N5jLWfUj3E5NiE+3V+VmLfTec3KzmA9s3G4I5PPAxu345qcPhohYlokPpHHz7ElIpdM4\n+9ixOHbcKC0o2afHoT+V0ozKIyHsjaeQgcLxh+6HxEAat54zKe/x4ld/eB2rnn/LcNy+/hSGRQaX\nbqvgCYeQmpOb8de3m9zKgrkt9fWnDO0iGgoiniosb9U+4qk03nw/hltmT8p7rwoGBHc9s7UgYOA5\nx4011PF/XnwXX5t8YMG5AwFx7MjBqn2ajd0DArz7Qb+hXK7foYcaQhqXUCiAzrYWQx8RDgje2hM3\npDnp19rCQYOnSkLIIL5sGcVWNGYe02Xw9T/zmC60hYOWM5c/+cqRiIQDmHT1/2LSNU/go1c8gs//\nYg2WPrkZ0ZYgLr5nE/Ymkrj0N5vwrV8/h1RaAQpoa9Vc6gVE0NYSxBcnHmBZlxyVxjUgpF5YtZtQ\nQCxnDaPhYL5dDIuEEQoFirZV83nG7NuGC5Y9h0O//yguWPYcotkAf5//xRpD29wnWriC0RIqPLfT\nmUe79gnAUD4Sslr94L5tQrxAKBQw9hGBADpaw5h7t9bn/OoPrxf0a7m+Sl+OAw5C7HE1ToeInALg\nOgBBALcrpX5s+r4VwN0AJgHYBeBrSqmtxY7pxE/83kQSv/rD6wX++7/56YPRFg7m42+YZyUMs5n9\naQQCQCYDnH93YcyB286ZDAAIBIBI2H6mtLc/hV+u3VI0loBdXAP68C6Jq0tB9dKv1zCvAgQE+L9X\nd2DqR0dhn2hYs7H423uYduh+BXou1lb1Kx2AMXZObs/03LufK2g3t54zCZId8Fdj1bCc9lnmiqUv\n9dswcToaJPZGObgVp6NcHMb18KV+c+xNJAv6l0s/Ox7nHDcWwyLhonGBiCfgVpI64NqQXESCAG4E\ncCqAwwHMEpHDTdnmANitlDoEwLUAFldyzpzrzfbWEL7xqYPx2ItvF6xoBIMB21kJw8xnJIS2lhCi\nLQHLmdhoSyCfp9hMaVs4iFmfPMiwujLrkwcZZj+L2aCQ+lAP/XoV84pBJBTEcR8dhfdjSSgFvB9L\n4riPjrKc8c+tBOrbxxcnHmCpfXPbbQtb78NuCwerune6nPbZKPu2qV/iZer5/JBzd23V7pc+uRnD\nImHbezkhpDhuDs+PAbBZKbUFAERkBYDpAF7W5ZkOYGH275UAbhARUUNYfrGLYnzRSYfkZxyHsuwZ\nT2Ys7UC02Y3Sx3MSBNBqXzz3gtedmurXbwykM4YAmUstAmQClbWvYDCAke0tru+n9mj7pH6Jl2mI\n54f5Jx1iiCKutfsUXvvhqbS9JGQIuLn5cAyAN3Sft2fTLPMopVIA9gAYOZSTWbnuvGT5JsSTmYr2\nWTpZqShFqdlPa0843AteZ2qqXz+htcVCl7lWrmMrbV/FVi6rhUfbJ/VLvExDPD9841MHW66k1nsl\nkxCv4uY0nVVrNM9AOMkDEZkLYC4AdHVZ73l1a4uSk5WKSqnFOUjZ1FS/fqLc7UiNrn0v1NEC6pd4\nmYZ4fuiIhOiZipAq4mbr2Q7gQN3nAwC8ZZdHREIAhgPoMeWBUupWpdRkpdTk0aNHW57MLliX02Bf\nxajFPu1G2QtO8tRUv36i3LboBe17oY4mqF/iZRrm+YGeqQipHm62oPUAxovIwSLSAmAmgFWmPKsA\nnJv9+ywATw51P7FHt0CQxqWm+vUTbIsNAfVLvAyfHwjxIa5tr1JKpUTkYgCPQXN590ul1EsisgjA\nBqXUKgB3AFgmIpuhzVDMHOr5PLoFgjQotdavn2BbrD/UL/EyfH4gxJ+46npFKfUIgEdMaVfp/k4A\n+Eq1zpfbAgGgkb3KEI9Qa/36CbbF+kP9Ei/D5wdC/AdbFiGEENIAvPD6Nsd5GyWQICGEOIVWUYQQ\nQgghhBBX4aCDEEIIIYQQ4iocdBBCCCGEEEJcRbzmIVFEdgL4e4lsowC8V4Pq1Au/Xx/g7BrfU0qd\nUovKVAvqF4D/rw+gfv38+/r9+gDq18+/r9+vD/Cpfv2A5wYdThCRDUqpyfWuh1v4/fqA5rhGO/x+\n7X6/PqA5rtEOv1+7368PaI5rtMPv1+736wOa4xq9CrdXEUIIIYQQQlyFgw5CCCGEEEKIq/h10HFr\nvSvgMn6/PqA5rtEOv1+7368PaI5rtMPv1+736wOa4xrt8Pu1+/36gOa4Rk/iS5sOQgghhBBCSOPg\n15UOQgghhBBCSIPg6UGHiJwiIq+KyGYRuczi+1YR+U32+z+KyNja13LoOLi+b4jIThHZlH2dV496\nDhUR+aWI7BCRF22+FxFZmr3+v4jI0bWuo5tQv9Svl6F+qV8vQ/1Sv6QOKKU8+QIQBPA3AOMAtAB4\nHsDhpjwXArg5+/dMAL+pd72rfH3fAHBDvetawTUeD+BoAC/afH8agEcBCIBjAfyx3nWu8e9L/Tbw\ni/qlfqlfb76oX+qXr/q8vLzScQyAzUqpLUqpAQArAEw35ZkO4K7s3ysBnCwiUsM6VoKT6/M0Sqk1\nAHqKZJkO4G6l8SyAfUXkw7WpnetQvx6H+qV+vQz1S/16mSbXr2fx8qBjDIA3dJ+3Z9Ms8yilUgD2\nABhZk9pVjpPrA4AZ2aXDlSJyYG2qVjOc/g+8CPWrQf16E+pXg/r1JtSvBvVLaoqXBx1WMw5mV1xO\n8jQqTur+3wDGKqWOBPA4Bmdl/IKXf79SUL/Ur5ehfqlfL0P9Ur+kDnh50LEdgH5kfgCAt+zyiEgI\nwHAUX45rJEpen1Jql1KqP/vxNgCTalS3WuHkN/Yq1C/162WoX+rXy1C/1C+pA14edKwHMF5EDhaR\nFmiGXqtMeVYBODf791kAnlRKeWWkW/L6TPsTzwTwSg3rVwtWATgn64XiWAB7lFJv17tSVYL6pX69\nDPVL/XoZ6pf6JXUgVO8KDBWlVEpELgbwGDRPDb9USr0kIosAbFBKrQJwB4BlIrIZ2gzFzPrVuDwc\nXt98ETkTQAra9X2jbhUeAiKyHMAJAEaJyHYA/wYgDABKqZsBPALNA8VmADEA36xPTasP9Uv9ehnq\nl/r1MtQv9UvqAyOSE0IIIYQQQlzFy9urCCGEEEIIIR6Agw5CCCGEEEKIq3DQQQghhBBCCHEVDjoI\nIYQQQgghrsJBByGEEEIIIcRVOOgghBBCCCGEuAoHHYQQQgghhBBX4aCDEEIIIYQQ4iocdBBCCCGE\nEEJchYMOQgghhBBCiKtw0EEIIYQQQghxFQ46CCGEEEIIIa7CQQchhBBCCCHEVTjoIIQQQgghhLgK\nBx2EEEIIIYQQV+GggxBCCCGEEOIqnht0nHLKKQoAX3wpeBDqly/dy3NQv3zpXp6D+uVL9yJ1wHOD\njvfee6/eVSBkyFC/xMtQv8TLUL+E1BfPDToIIYQQQggh3oKDDkIIIYQQQoircNBBCCGEEEIIcRUO\nOgghhBBCCCGu4tqgQ0R+KSI7RORFm+9FRJaKyGYR+YuIHO1WXZqBjMqgL9lneK9m/mqXb3So3/qS\nzqTRO9CLjMqgd6AX6UzacVm/a9MJ1G9zYKf13Gd9O/JSW6B+CfEnbq503AnglCLfnwpgfPY1F8B/\nulgXX5NRGfQkejDvyXmYtGwS5j05Dz2JHtsbTLn5q13eI9wJ6rcupDNp9CR6MH/1fExaNgnzV89H\nT6LH0cCjSbTphDtB/foaO63n2s+yl5fh7b638+3IY23hTlC/hPgO1wYdSqk1AHqKZJkO4G6l8SyA\nfUXkw27Vx8/EU3F0r+nG+nfWI6VSWP/OenSv6UY8Fa9K/mqX9wLUb/2Ip+JYsHaBQV8L1i5wpK9m\n0KYTqF//U0zr3Wu6cXLXybjqmas82RaoX0L8ST1tOsYAeEP3eXs2rQARmSsiG0Rkw86dO2tSOS8R\nDUWx8d2NhrSN725ENBStSv5ql/cJ1K9LtIXbLPXVFm4rWZbadAz163HstJ5rP+OGj/NzW6B+CfEg\n9Rx0iEWaZZRIpdStSqnJSqnJo0ePdrla3iOeimPihyYa0iZ+aGLRlY5y8le7vE+gfl0iloxZ6iuW\njJUsS206hvr1OHZaz7WfLXu2+LktUL+EeJB6Djq2AzhQ9/kAAG/VqS51pxLj12goiiXHL8GU/acg\nJCFM2X8Klhy/pOhKRzn5q13eJzSlfivRqdOy0VAUi6ctNuhr8bTFiIQiJctTm45pSv02Ok7bSDqT\nhkBstb7k+CV4YtsTWHTcIr+2Bdf0O/ayhx2/CCHlEarjuVcBuFhEVgD4JIA9Sqm361ifupEzCOxe\n042N727ExA9NxJLjl6Az0omAOBsXhgNhLJy6EGM6xuDN3jcRDoSrml9PQALojHTi+pOuRzQURTwV\nRzQUdVxXn9B0+q1Ep+WUDQaC6Ix0YumJS9EWbkMsGUMkFMH7/e+XLE9tOqbp9NvoOG0jOUPxBWsX\nYL/oflg4dSEOGHaAQeudkU7MPnw2IsFIvh35rC1Qv4R4EFHKckWy8gOLLAdwAoBRAN4F8G8AwgCg\nlLpZRATADdA8VMQAfFMptaHUcSdPnqw2bCiZzVP0Jfsw78l5WP/O+nzalP2n4PqTrkd7uL3q5Ss9\nXwNhtcRenQNTvwVUoptaa9wjUL8kj1ON9w70Yv7q+QX5lp64FB0tHbWssi/1W84KxtYfn+44L2k4\nXNMvsce1lQ6l1KwS3ysAF7l1fi9Ra8NuGtuWhvotpBLd0HlBbaF+vYdTjVfiaMErUL+E+BNfrLN6\nnVobdtPYlgyFSnRD5wWEFMepxitxtEAIIfWEg44GoNaG3TS2JUOhEt3QeQEhxXGqcTtHC2wLhJBG\nxzWbDrfw657ijMrkDf2cGvyZywQkgNZgK/rT/cioTNFjVVK2Xtdrgef2ZHpdv05/N6t8SinEU/G8\ncXg0FEUwEEQ6k7ZMN+M0n4egfokBs8YjoQj60/2IBCP59HgqjtZgKxKpREFbMLe7SDCCRDrhVn/u\nS/3SpqNp8Jx+/QBXOhqEgATQHm43vBcj5+lk3pPzMGnZJMx7ch76kn1QSuUNEnPpPYmeAteLufMA\n2rL+RU9chCvWXoHdid0ly1aKVd3dOA+pPk50avf79iY1A9hJyyZh/ur52N2/G6lMCj2JHkN6T6IH\n6Uy64Ji7+3cXlKdmiF8wa/zXr/wauxO7sezlZXi77+18+rwn5+H9/vfRFm5DQALoaOnIDzj07W7Z\ny8vYzxJCGgoOOjxKPBVH95purH9nPVIqhfXvrEf3mu6i6aWOM+eIObjy6Ssdl3Wj7sT72P2+e/r3\nFKQlUgksWLvAkL5g7YICLVAzxO+YNX5y18lYsHYBTu46GVc9c1VJ7duVZ5shhDQKHHR4FDtPJ3ae\nTZx4sho3fFxNPATRE5G/sft9x3SMKUhz6omHmiF+x6zxXH/stF+2K1+qHCGE1AoOOjyKnacTO88m\nTjxZbdmzpSYeguiJyN/Y/b5v9r5ZkObUEw81Q/yOWeO5/thpv2xXvlQ5QgipFRx0eJRink6uPeFa\nPPylh7Fp9iY8/KWHce1nrkUkGEFfsq9gP6/+OHe8cAeu+dQ1rnsIoici75JRmbyOrPQE2P++w1uH\nF6RFQhFHnnjc0IyTayGkVpg1/sS2J7B42mI8se0JLDpukUH7155wLYBBDaczaQQkYFm+nDbDNkEI\ncRN6r/IwVh6CAKAn0YPuNd3Y+O5GTPzQRPzo0z/CL577BXbEd2DJ8UvQGek0GADrj1ML71UZlcHe\ngb3Y078HYzrG4M3eNzG8dTiGtQwr91ye8z7hZf3mDFX12rLTk9Xv2xHusPSk49QrVZU8npV1LS5D\n/RIDdt6n9N6r+tP96Ev2GbS7eNpi3PfX+7B1z1ZcOOFCHDDsgLK9Vw2hTfhSv/Re1TR4Tr9+gCsd\nHsbKk5CVwe0Vf7gCc46YY2tIqC8fDUXL8qI1FOKpOL7z1Hdw+u9Ox4RlE3D6707Hd576Dpf9Gxyn\nxtx2v28inbDUVjAQREdLh8ETjxXlenirxrUQUkvMGg8Ggvn3XBvJqEyBdnMG5w+//jBO/93pOO/3\n5wFAvryTNsM2QQhxGw46fIadwe244ePyf9d7GxONgr2J09/NC7+vF+pIiBWl+vjc53K1zDZBCHEb\nDjp8hp3B7ZY9W/J/13vmikbB3sTp7+aF39cLdSTEilJ9fO5zuVpmmyCEuA0HHT7DyuD2R5/+Ee54\n4Q6DIeFQDAarZWRIQ3Jv4vR3s3RmcMK1Zf2+bhu0UoPEq9hpd9/WfXH5lMvx8Jcexu3/eDsAlNVu\n2CYIIW5DQ3KfYWnE2zIcHS2DRrxAobF5KSPaahveVsko2HOGYF7Wr1MHAJVqpVZG3tU0TB8i1C8p\nG6t22BZqw8q/rsSM8TNw31/vwy3P3zKkdlNmm/ClfmlI3jR4Tr9+gCsdPsPSiPf/jEa8QzEYrLaR\nYTWNgkltcOoAoFKt1MqglRokXsSqHXav7TZEMB9qu2GbIIS4ias9ioicIiKvishmEbnM4vsuEVkt\nIhtF5C8icpqb9WkGnBgDDsVgsBmNDKlfI7UyJG9GrbkB9etPihmSV8OgvFGgfgnxH652Mo6kAAAg\nAElEQVQNOkQkCOBGAKcCOBzALBE53JTtSgD3KqUmApgJ4Ca36tMsODEGHIrBYLMZGVK/hdTKkLzZ\ntOYG1K9/KWZIXg2D8kaA+iXEn7i50nEMgM1KqS1KqQEAKwBMN+VRAPbJ/j0cwFsu1qchqLWB7EUT\nLsIvTvwFoqFo/nxDMRislZFhA0XEpX5N1MqQvAm15gbUrwew02AxbVq1j0XHLTJEMC/VbjygfeqX\nEB8ScvHYYwC8ofu8HcAnTXkWAvi9iMwD0A7gsy7Wp+7UwkA2IAF0Rjpx/UnXIxKMYHdiN769+tsF\n58vlcWpEqz+uW4a3DRIlOgf1a0E4EMbCqQvzBqzhQNgyXzKTxMJ1Cw2/o1OaUGtuQP02OHYaHNE6\nArv7d9tq09w+YskYoqEoZh8+G5FgBLMPn425R861bTce0T71S4gPcbOHsfIMYHaVNQvAnUqpAwCc\nBmCZSGGvJyJzRWSDiGzYuXOnC1WtDbU2kE2kE+hea32+oRgMum1k2GARcalfE7UyJAeaTmtuQP02\nOMU0WEqb+nbR0dJhiFxeqt14RPvULyE+xM1Bx3YAB+o+H4DC5c85AO4FAKXUOgARAKPMB1JK3aqU\nmqyUmjx69GiXqus+tTaQ9ZpBboPVl/o1wYjknoL6bXDsNNgWbnNVmx7RPvVLiA9xc9CxHsB4ETlY\nRFqgGXqtMuXZBuBkABCRj0HrNHw7FVFrA1mvGeQ2WH2pXxOMSO4pqN8Gx06DsWTMVW16RPvUr0sc\ncdcRZb0IqSauDTqUUikAFwN4DMAr0LxMvCQii0TkzGy2fwVwvog8D2A5gG8or0UrLINiBrKVGvZZ\nla9HhNlKrqORIuL6Wb9OfyNzvkgw4tiQvFF+Rzu8UMdK8LN+/YKdBkOBUNW02Sj3hXKhfgnxJ4xI\nXmOsIr4C5UcINx/TrjyAmkVdroaBIiPiuovT38guXyQYQU+ip2hE8lz5Okf7LkkD1JH6bXL0GtwR\n24GQhNC9thv7RffDhRMuxAHDDhiyNmtwX/Clfv0ekbzc1YsXzn3BpZrUHc/p1w801lNAE2BlIOtm\nBOdaRpj1ggFxs+P0N7LL15PoKWlIDnjjd/RCHYm/yWnuvN+fh70De/OOPx5+/WGc/rvTcd7vzzPk\nK4dGuS8QQkgON13mEof4JYJzo9SD2FOpMfiYjjElyxJCnJNra7mI4noqaV/sj4kVL7y+rd5VIE0M\npzcaAL9EcG6UehB7KjUGf7P3zZJlCSHOybW1XERxPZW0L/bHhJBGgzYdDYDl3ttpS9AZGQGk+hEX\nld97G5AAWoOtiKfiiAQjSKQT+WjjiVQCnZFO9CR6EAlF0B5uLyiT27tb6X52c/ncOXqTvbjnlXtw\ny/O31CLolOf2ZNZbvxmVwd6BvdjTv6eoXUY5Nh0d4Y68DnOayKgMEqkE2sJtiCVjiIQi+a2EZs1l\nMmntc7gN8Wygs0AgWLf/UQ2hfpuUdCaFuKl9xFIxpNIpfHfNd23trdLZtpIrFw1FEQwEC/pj/b1h\n+97tuGnTTdgR34FrT7gWoUCoWnZMvtSv3206sHB4mfn3uFOP+uM5/foBbq9qAAoiMA/0IvrszcB7\nf0XPPy5E9zM/yN+ErvnUNVj656UYO3wsZoyfgQVrFxi++83/+w1O6DoBC1YvKCizI77DUcTbUlg9\nkBrOMW0Jzj/i/PxNj/uFGwun0cLN0cdzDzL6steecG2Blq494Vok00l0rzUOosPBML7z1HdM0Zf3\nxe7EbnTrdLxk2mJNi80x8CBNRjqTQk9it6HvXjxtMTbt2ISJ+03EDSfdgEgoUjAoSGfS6En0FJQb\nERmB9/vfz7fBC466oODesOT4JegId6A32VvQBhssEjkhxMc46mlEJCgiHxGRrtzL7Yo1G3mDvoEY\n2pd/HYHVP0T8+H9F9zM/MBgCXvn0lZhzxByc3HUyFqxdUPDdaR89DVc+faVlmXIi3hbDqrzhHGu7\nkUgnaKDYgJRjSG6OPp4baOrL7unfY5221nSOtd3Y07/H4rwJdJt03L12AbeAEN8STyUK+u4Faxdg\nyoenoHttNzIqY2ngHU/FLcslUglDG7S6N3Sv6UYqk/JCJHJCiI8pudIhIvMA/BuAdwHkHPorAEe6\nWK/mpaUN2LYOABAdOd7SEHDc8HH5v83f7dOyT8kylUa8tTNQ1J+DxoqNSSWG5GM6xgw5zc4I3VaL\n4TbnF0WIh7DTfK7vbrPRvl05c7qdQbrbkc4JIaQUTqahLwFwmFLq40qpI7IvDjjcYiAGdE0FAMR3\n/dXSEHDLni22RocfDHxgWyb3d6URb+0MFPXn4OxZY1KJIfmbvW8OOc3OCN1Wi8mY84sixEPYaT7X\nd8dstG9Xzpxud29wO9I5IYSUwsmg4w0AvrUksqPSCOFDJtwGnHUHMHYaomt+hiXHXW2IHHvNp67B\nHS/cgSe2PYHF0xYXfPfI3x7BNZ+6xrKMPvJsJRFprcpbnYM0Hk5/e6t8w1uHO0+bZjrHtCUY3jrc\n4rwRLDHpeMm0xdQP8Tx295BoKFLQdy+ethjr316PxUW0Hw1FLctFQhFDG7S6N1Sj3yeEkEqx9V4l\nIpdm//w4gMMAPAygP/e9UurnrtfOglp4T6lGZO3KKpABkjGgpQ2ZZMKR9yr9d/3pfmRUxrKM296r\nahzZ2XPeJ+rt/cep96pcXrNGgMJIxlZp9F7lCOrXp5S6h1h5r0qkEnlvVHYMxXtVNft9E77UL71X\nmfP7ds7Zc/r1A8VsOoZl37dlXy3ZF6DZdPgWvbEtgLzB3fUnXY/2cLv7FQgEgNYO7c+WNuTOqD93\n7u/2QHvBd/qZK6sywKDhujndcRVtytfk/0OGTM5APKdtAJiy/xRLbZf6jYulBSSAjhZNw7l3u7KB\nQBDt2TzturyEeJVS95BgIFTQPjocaD8YCFrmt2qrVveGSvt9QgipBNtBh1Lq3wFARL6ilPqt/jsR\n+YrbFasnjORK/Aq1TYj7sJ0RQkghTtZVL3eY5hsYyZX4FWqbEPdhOyOEkEJsBx0icqqIXA9gjIgs\n1b3uBJCqWQ3rQEMZ3GUyQH8voLLvmRoZtJegbob2pCLK0nYF2qM+SDMz5HvIENsc2xshxAsUs+l4\nC8BzAM7MvufYC+A7blaq3hRECK+tcfQgmQwQ2wmsnKPF7uiaqnm2ahut2X3Uibob2pMh41jbFWiP\n+iDNzpDuIUNsc2xvhBCvYNsjKaWeV0rdCeAQpdRdutf9SqndtatifdBHhK1bZO1kTLsBbV0LZFLa\n+8o5WnodqTSiOakvjrRdgfaoD0KGcA8ZYptjeyOEeIVi26teEJG/AHhORP5ifjk5uIicIiKvishm\nEbnMJs9XReRlEXlJRO4Z4nX4E1108jzb1mnpdaRZjCSbWr8VaK9Z9NHoNLV+vcgQ25xf2xv1S4j/\nKDb1cgaALwD4n+zrn7KvRwCsLHVgEQkCuBHAqQAOBzBLRA435RkPzSj9U0qpjwP49hCuwb/oopPn\n6ZqqpdeRZjCSbHr9VqC9ZtBHo9P0+vUiQ2xzfmxv1C8h/qTY9qq/K6X+Dq1BdyulXsi+LgPweQfH\nPgbAZqXUFqXUAIAVAKab8pwP4Mbcdi2l1I6hXYZP0UUnRyCkvZ91h5ZeRxrK0N49mlu/FWivSfTR\n6DS3fr3IENucT9sb9UuIDylmSJ6jXUQ+rZT6AwCIyHEAnEQVGgPgDd3n7QA+acpzaPaYTwMIAlio\nlPof84FEZC6AuQDQ1dXl4NQeRxeRHC3DgJn3aMEC+3uBlvZBo0J9voE+QIJAOKLNjIXbtHyGPBWk\n62gYQ3t3aVz9OviNKi4fCGgGrLNWGPMBWR2a0nTHC4Tb0Nk6AtefuNQYZVwpoP8Dk5abIvJ4PWhc\n/foN2740rfXLeb23Acm4qZ/Ovrd2AAO9QNvIwjZXom37tD+mfgnxIU56pTkAbhSRrSKyFcBNAP7Z\nQTmrEPPmSOYhAOMBnABgFoDbRWTfgkJK3aqUmqyUmjx69GgHp/YwOQ8my2cC988FYu8BK74OXD1a\ne4+9p+XR57t6NLB8lvbd/XO1tNhO7aZnyDPU9EL3iw1haO8ujanfgt/d/jequHwgoD0MSfYdKCzb\nv8fyeIH+D9B+z0wErh6tvfd/APSZtNyX1Rxxg8bUr9+wbU8pTd8Gvb8HbF492E+vu8miTbwHhKOD\nbc7hZIIP+2PqlxAfUrJnUko9p5Q6CsCRAI5SSk1QSv3ZwbG3AzhQ9/kAaG54zXkeVEollVKvA3gV\nWifSvOg9mEy7FHjgQmtvJlaeTh64UCuTyzfQZ+0Npdz0OnvLqhONqd9KPZpVUt6qbGy39fFiuwvz\n3WfKd995muaIGzSmfv2GXXsaiGn6Nuv94E8P9tOHn2Gdh20CoH4J8SXFvFednX2/VEQuBXAegDm6\nz6VYD2C8iBwsIi0AZgJYZcrzAIATs+cZBW25dEv5l+Ej9B5MRh1m783EztPJqMMG/27tsM5Tbnqd\nvWXVicbUb6UezSopb1V2xEHWxxtxkLN8uRUUUm0aU79+w6492fWl0X0H+2m7/p1tAqB+CfElxVY6\ncnYbw2xeRVFKpQBcDOAxAK8AuFcp9ZKILBKRM7PZHgOwS0ReBrAawPeUUruGdCV+Qe/B5L1X7b2Z\n2Hk6ee/Vwb/7e63zlJteZ29Z9aBh9VupR7NKyluV3f136+Pt/ruzfP29zupNyqJh9es37NqTXV8a\nf3+wn7br39kmqF9CfIooZd4macogElFKJWpUn5JMnjxZbdiwod7V0KjUoNfumLmotMP2B06+SluO\n37YOmRm/Qnz8iYiG2xFP9iGqgMDyWYPRa794E/DEImDvO9lItqO0/cMFEW5N6ccvAI69AGgdBvTv\nBZ69BVizWMv/tWWaJ5WW9updY/Ww2vfb0FSs30qj1NuUz7SNQjydMBqiKpj0HdUMwWO7tZWL3X8H\nOkYPbjHR1yfcBvTuNOYbiGlbrHL5ZtwOtI8GINVvR96g+fTrZaz6e8CmPY7U7DPuO0/rx0+4Augc\nC8R3A8FWzYC8vxeI7AP0bAWe+pHWb+faRAkHCxmVybfTOhqO+1K/Yy972PHxtv749EqrVHsWDi8z\n/x536lF/PKdfP+Bk0LEZwLsA1gJYA+BppVTdVNgwN71KH/5KHTt3c0smAJVGJhxFT2I3utcuwMZ3\nN2LihyZiybTF6AwPQyDUMnTvVeFo9uaofxi8A2gfBaT6gYG97lxjdfBcp1EV/VbZe1UmHEVP/250\nr+ke1NbxS9CJIAK/mT34239tGZAesB7EJuPGwYnlYHfkoKeenPcqiHvtqPFpTv16kWL9PWDvvSqV\n0LxS6ct96WYgGC48VksHEIo4GnD0JHoK22uks9YDD1/ql4MOc34OOkj1cGJIfgg0zxAvQAsY+LyI\nbHK7Yg1PpQa9xdB7DWppA1qHIZ5KoHvtAqx/Zz1SKoX176xH99oFiKtU1tPJMC2v2euJ2QOROT0Z\ntzDwnaOlqwwNyxsRu990iOXj6QS613QbtbWmG/HE+86MxpNxY32ScZt8CW1mVwLaeyDobjsipFoU\n06ltHxsE/n979x4mR1Xue/z7dvfcenKBBBQFNIDokcMlIUFFBXGrW7wRFJBEucSDKHIw+CAqynnY\nbNQtInI2qBhBPELAhJsXRNx4RRERCSQkXLaajQiIW2ICuc21p9f5o6pnqrurumump6e7an6f5+ln\nuqpXVa3qftearup6azlXvdz3zoDBndXrci7WLaT7C/3h7TXBgwGKyPRQd5wOM9sLeB1wBHAI8Ajw\nmybXq/01mtA7Tj0dedb+fW3ZvLV/X0tPowMF1tsPJZanXk+uJzy2Zlfc0z4qGbwyHsbTNqa4HYlM\nyETjNGq5yhstjCPmI9trsgcDFJFpIM4p0ieBjwE/ds4d7px7h3PuC02uV/trNKF3nPqH+1jwwgVl\n8xa8cAH9jZ4RrrUfU7yP0hr9hf7w2Nr6ZHnBqGTwyngYT9woxiQJJhqnUctV3mhhHDEf2V71S4eI\ntLk4Bx0LgOuA95nZvWZ2nZmd1uR6tb+OvHcd7rwjvETreUeMJdBWKo7AwDbvcqWhnWPPB7d5067o\nzSsWIjfXk+vhkiO+yGF7HEbOchy2x2FccsQX6bGcv94+Lwm88vngjrGB34r+dHB+rf0Yzz5KawVj\nbGBb7UH3KuKgJ9vNJUdeUh5bR15CT/cu5Z99fteIeOgu33ZHd/y4UYxJu6nXT77/FjjvSTj1Nq9M\nsTDW5sraYAHMquP73Sugq3fCMd+T6wlvr/qlQ0TaXN1EcgAzmwG8Hu8Sq5MA55yb19yqhWurRMY4\nCb3FEW9k2tJdTAJ3o6q649Rx3/QSuDMhV70VixSHd9LvivR0zaR/cDs9ZMj87uuw+Y9j6w3bRs07\nWdVIhIy7j62TuESwpsRvMMYq7wxVeY143LtXZbu9UcSDd6qa9WIvX6M/MK9nV2+9N51cvu2eXWHr\nX8fK5XeFrtnhsdPeMdZMit92Uy9hvDjsxX9lW3vy9/Dig+EHZ5XPf+A6r38u3b1q5z+8OM/1eCec\numZMKOZ196qJUSI5SiQfk7j4TYO6vZSZrQHuBd4N/CdwZKsOONpOnITeoZ1jo86GjTAeHEX81g9G\n/8Q+3Edm1VJ6L34JmX/d1fu7eqk3qm1wvVGjmNcabbzWfjSatCzNF4yxeiMbRyTEZob76e3oJWMZ\n7+9wP9x4MnxlAVw0x/s7MugdXATn3XSy9yWsctuFwfJyN54cnRyuGJN2US9hvDAYPdL4D86qnn/A\nO2HDzV4buPYY78TPd5Z4B++lmypMIOZH22ngr4hIu6ubSA68zTm3qek1SavgyLRRI9BWjiIeJs4I\n5LW2odHG06vWCPOV4ibEhpXrmhk/KbZy24o1SYJ67aPeSOOV80v9c3BabUFEpqk4t8zVAUcjgiPT\nRo1AWzmKeJhaI5AH11trlFsl7KZTrRHmK8VNiA0rN7g9flJs5bYVa5IE9dpHvZHGK+eX+vbgtNqC\niExT+k222Tp7vWt75x0Bd1/m5XAEEwiPvdKbf8JKWPId70xaWCJwWMLtcd+ER28vX2/YNo6/xquH\nEnbTKRhjwdjo7K0uG5m4HZIMfuJK+OhauGCL9zfbFbKda7z8jcpt57rKlz1xpWJN2l+tGxsUi968\nqn74Gvjzb2DxV8P759L04q/Cpo2w5Abvl47gTT5ERKaBWInk7SRxiYzFIgzv9K7z7Z7tneFyxbFR\nmc0Co4LXSAQuFmFwa3li7ww/ubGzd3Tk8qrncUYnT67EJYI1LX6LI2OJqaXRvqMGGquKg+7q+Hvv\nSiiGjD7eNdvL7eia6f3ykemAXGfFSON56Ns8XUcZHw/FbzsK6ydhLMF85h5jieHb/ua9XhiEGbt5\nbaJ79lg7qJzeudkbfDUd7SKV8atE8sry8RPJD7r2oNhlN5y6YXz1mHyJi980iMzpMLP31FrQOffd\nya9OCg33waqlXmJhybwjvF81Vr/Pm//JP48lJ8JYEuKS73jJhqX13Hhy9XqWrh4bubwk+Dx4bX0p\nYbdyviRfJjsWK6W/kWUr4mBgW3X89T8HP1xePu+W0+BdV3hJsSWlGAxue3DHWDJucNmlqxV30v7C\n+snKmN5wsxf7b78Evvdh7+9lfv5Gaf6Vh49NL13tnQy6Ve1CRKavWonk76rxmgN00BFHVGJiMCEx\nKgkx+I9IIzdLs4Qlx0aNPh5nJGXFqqRNrRt5RCWMB6dLsa92ISLTWORvus65D9R4/K+prGSiRSUm\nBhMSo5IQg8m4GrlZmiUsOTZq9PE4IykrViVtat3IIyphPDg91Kd2ISLTXqwLSc3sHWb2STO7oPRo\ndsXaVthotbV05KsTck9cWZ7YveHm+onAGrlZJiIsXivndear468nYvTx/K71Y1CxKu1kvH122PI4\nbwTyj66Fg04YSwx/9PbqhPHK6VLsq12IyDRXd5wOM1sB5IE3At8Ejgd+H2flZnY0cDmQBb7pnLs4\notzxwM3AYc659s1SrDVaba1EwJEh7/r44DKYt9zS1d6XvsLQ2N2rwhKBM5ny8ulIBG9riY/fqHjN\ndnr5QcF5vbtVxx9WHW9QPwYVq20h8fE7GSbaZ9dbvnMG5Lrh8DO9G4Ecfia84Vw/1iunA7GvdhGb\n4lckfeL0dq91zp0CPOec+1fgcGDveguZWRb4GvA24ABgqZkdEFJuJrAcuG88FW+JWqPVTmSZ4EjM\nHd1jI9R2zwq/85BGbp4yqYjfqNjrey4kHgeq4y8s3uLGoGK1pVIRv5NhIn12nOWd89pI14yxv6Ox\nXjkdiH21i1gUvyLpFKfH6/f/9pnZi4FhYJ8Yy70K2Oice9w5NwSsBhaHlPsscAkwEGOdrTWRBFkl\n1SZV8uM3KvbiJINL0iU/fidDo/2v+u9WUfyKpFCcg47bzWwX4EvAg8ATeB1APXsCTwWmn/bnjTKz\nBcDezrnbY9W21SaSCKjkwaRKfvxGxV6cZHBJuuTH72RotP9V/90qil+RFIpz0HGJc+5559ytwEuB\n/wF8LsZyYQOvjI5EaGYZ4P8CH6+7IrMPmdkaM1uzadOmGJueBGXJh9u9gdc683DiDXDU+fETAeuN\ncFvaxlCft52JJjvKZJv6+G0k4TVs2ajYC00G76mfcK6YTJJk97+TpWb/O+KNUeOK3t9ioXraMvH6\nb7WPyab4FUmhuonkwL3AoQDOuUFg0MweLM2r4WnKcz/2Ap4JTM8EDgTuMjOAPYDbzOyYymQw59xV\nwFXgjSgao86NCUsePPZK+PlFsP2/4bhr4MiPw3B/vETAbKc3qFppJPFsJ+Cg7x9jI9y+6QL4/plp\nGak2DaY2fhtJeI1cdi5kKmIv0+mNJl6WzNozFoul5U9c6d0AQaOKJ1Vy+9/JFHVTAxzs3OQNivnk\nvXDkp2DhKeHTM/fw2tCced7Jpw7/roKNJKhLPYpfkRSK7B3NbA8zWwj0mNkCMzvUfxyFdzereu4H\n9jezfcysE1gC3FZ60Tm31Tm3m3NunnNuHvA7oKrDaImw5MHvnwlHnOOPFn6a988nTiJgaSTxryyA\ni+Z4f2882Vu+tI0jzvHWP9FkR2mGqY3fRhJeo5Yd6oObKmLvppO9+cFk1uH+6uX7nmssAVdaLbn9\n72QLS94e2ukdUJTi+4B3Rk9vuNlrO9ceA5i3fKMJ6lKP4lckhWr90vFWYBneGYbLAvO3AZ+pt2Ln\nXMHMzgLuxLvl3becc4+Y2UXAGufcbbXX0EK1Rp8tPQ+OFj6RdQVHgS6NaltZRsmKLTPl8dtIwmqc\nGKucX2/5qBHJFZOJkOj+dypUto3KPrhen6wE86ZS/IqkU60Rya91zr0RWOace2Pgsdg59904K3fO\n3eGce7lzbj/n3Of9eReEdRjOuaPa5ixFrdFnS8+Do4VPZF3BUaBLo9pWllGyYktNafw2krAaJ8Yq\n59dbPmpEcsVkYiS2/50KlW2jsg+u1ycrwbzpFL8i6RPn4tN7zOwaM/sxgJkdYGanNblerRWWfHjs\nlXD3ZeGjhY93XcdfUz4i+d2XeevXSLXTVyOjFUfGWMhI42GxG7Z8PmJEcsWkpEFnb3nbKI0sHjVd\nGf8aXVxEZNzMudp5Vf7Bxv8DznfOHWJmOWCtc+6gqahgpUWLFrk1a6bghEax6F2f25n3rv+1zNgd\nfipHCx/XugKj0AbnDw+AG/HWrZFq4wq7w0lbqxm/UXESR2SMjYzlH9WK3bDlYeL1kTjSFb9JU9U2\n8mP5TqXp4YHo+G+kvaZDKuN33nk/ir2+Jy5+R6NVmnoXzh5n+a2xix50bfyvhRtO3TC+eky+xMVv\nGsS5e9VuzrmbzOzTMHqt5UiT69V6peRD8O72U9I9q8F1zQifH7wWOG6+iKRLVJw0smwmOxaztWI3\navmJ1kek3YW1jcq/teK/kfYqIjINxTkts9PM5uLfI9vMXgPEP/QVEREREZFpLc4vHefg3apuPzO7\nB9gdOL6ptRIRERERkdSoe9DhnHvQzN4AvALvGrg/OOeGm14zERERERFJhboHHWbWDZwJvB7vEqu7\nzWyFc26g2ZUTEREREZHki3N51XXAduAr/vRSYCVwQrMqJSIiIiIynrtiQVvcGUsixDnoeIVz7pDA\n9C/N7KFmVUhERERERNIlzt2r1vp3rALAzF4N3NO8KomIiIiISJrE+aXj1cApZvakP/0S4DEz2wA4\n59zBTaudiIiIiIgkXpyDjqObXgsREREREUmtOLfM/ctUVERERERERNIpTk6HiIiIiIjIhOmgQ0RE\nREREmkoHHSIiIiIi0lRNPegws6PN7A9mttHMzgt5/Rwze9TM1pvZz83spc2sz0QVi44dgwWKzv9b\ndK2ukkyBtMRvmqltRmvX+NVnJnG0a/yKyMQ17aDDzLLA14C3AQcAS83sgIpia4FF/m13bwEuaVZ9\nJqpYdGzeOcTp167h5ef/mNOvXcPmnUP6R5lyaYnfNFPbjNau8avPTOJo1/gVkcY085eOVwEbnXOP\nO+eGgNXA4mAB59wvnXN9/uTvgL2aWJ8J6RseYfmqtdz7+GYKRce9j29m+aq19A2PtLpq0lypiN80\nU9usqS3jV5+ZxNSW8SsijWnmQceewFOB6af9eVFOA37cxPpMSL4zy/1PbCmbd/8TW8h3ZltUI5ki\nqYjfNFPbrKkt41efmcTUlvErIo1p5kGHhcwL/Q3dzE4CFgFfinj9Q2a2xszWbNq0aRKrWF/f0AiH\nzZtTNu+weXPoG9KZuZRLRfymmdpmTW0Zv/rMJKa2jF8RaUwzDzqeBvYOTO8FPFNZyMzeDJwPHOOc\nGwxbkXPuKufcIufcot13370plY2S78hyxdIFHL7vXHIZ45w37883Tl5IvjOrJMh0S0X8JlWcZOPK\ntnn4vnO5YukC8h06a06bxm/wMzt2/ou569yjuOH0V4NDfakEtWX8ikhj6o5I3imncdcAABbqSURB\nVID7gf3NbB/gr8AS4H3BAma2APgGcLRz7tkm1mXCMhljbm8nV5+6iJ6ODJt3DvHhlQ9w/xNbOGze\nHK5YuoC5vZ1kMmEnZiTBUhG/SVRKNl6+am3NdhZsm/nOLH1DI+Q7smqLnraM39Jnds2yRewcLLB8\n1Tr1pRKmLeNXRBrTtF86nHMF4CzgTuAx4Cbn3CNmdpGZHeMX+xIwA7jZzNaZ2W3Nqk8jMhljRleO\n/uEiZ69apyTIaSBN8Zs040k2LrXNjPl/9YUVaO/4zWSMooPl6kslQjvHr4hMXDN/6cA5dwdwR8W8\nCwLP39zM7U82JUFOL2mL36RQO5sc7Ry/+oylnnaO37Zz4exW10AkFo1IPg5KghRpPrWz9NNnLCIy\n/eigI4ZSUmtPR4bLl86vSFydX5W4OjJSZPvAMEXn2D4wzMhIsUU1F0meqATx7mxmwu0qLDE97jyZ\nHMF+MQNcEdKXZgy99yIiKdXUy6vSIJjU+sJZXfyfd76SL7znIPaek+epLX10ZDM45yjd4W9kpMjm\nnUOcvXosQfLyJfOZ29tJNqtjPJF6whLEu7MZtvRNrF2FJaavOOlQhkaKFYnM8+nMZjjj+geV3DzJ\nKvvF5f/0Mk557bzRvvTZbQN0ZDOc9u01eu9FRFJKBx11BJNa7/zYkXz0O17yY8nh+87lqlMWMtP/\n4tM3PMLZq8fK3Pv4Zs5eva6sjIjUVkoQB5jRlWP7wPCE21WwDZeWfa5vmE9/d0PZvOWr1vGF9xxU\nMW8tV5+6aLQuMjGV/eJbD3wRH7n+wdHpOz92JOfc9JDee0mUeef9aFzln7j4HfELpzxP46BrD2p1\nFaQF9C24jmDC48teMCM0+bE38E+xtytXt4yIjE8j7SosaXnvOfnQ9e09J181T8nNjav8/Cr70qi+\nVe+9iEh66KCjjmDC48Znd4QmP+4cLIxO7xws1C0jIuPTSLsKS1p+aktf6Pqe2tJXNU/JzY2r/Pwq\n+9KovlXvvYhIeky7g45aiaJhr+U7sqw46VDuOvco9tu9l8uXlCc/Xl6RSJ7vyFaXWVKdbF6vLiJJ\nNtmxHdWuenLZusnlYYnpu+Y7QhOZd813aITzSVKKgZGi95nccPqrWf8v/8zGz7+NXfIdZTfluPPh\nv1V9vnrvRUTSZVpd81NrpGMg9LU5+Q6GRop8+rsbRhMgV5y8kBldOZ7a0kdnxfXk2WyGub2dXHXK\nQnq7cuwcLJDvyFYlu8YddVkkaZoR22ZGvjPL1086lFk9HWzrH6Y7Fy+5PCox3Q0Vqm4K0duZ0wjn\nk6AUA6vu+wvHLtiLT926fvQz+uJxB/P9tU9zymvncdl7D+EFs7p5aksf+c4sV5+yiHyX3nsRkTSa\nVr901BrpuNZrwZFzL/vZnzhj5QNsfHYHR116Fx+5/sGqUXSz2QwzuzvImDGzuyP07jrjGXVZJEma\nEdt9wyOcft0DzL/op+z76TuYf9FPGSgUR5OTS9s5e/W6WCOX9xdG+Mj1D3LUpXex32fuGG3L/YUR\njXA+CUox8NYDX8Snbl1f9hl96tb1o4nk2wYKo+//6dc9AIbeexGRlJpWv3TUGwU3KlE1bP7LXjCj\nrMxk10UkqZoR22HrnNXTMeHkct3woblKn1dUgnhpfqkfLc1X/ycikl7T65eOGqPgRr0WlcC68dkd\nZWUmsy4iSdaM2A5b57b+4Qknl+uGD81V+ryiEsRL80v9aGm++j8RkfQyb2C75Fi0aJFbs2ZN3XKF\nQpH+wshoXkVPzrs+uG+oQKHoRq8Lz2WMfKd3dnP7wDDP9Q2PXuO9S76DGV05dgwWeD4wf0Z3ls/d\n/hj77tbLstftw4xubxtZM7o7swwMjTDiXNm2g3XJd2Qxs7Lr3pf/08tG16XrmWNL3BsUN36TpFh0\n9A2PjOZB9Pi5FpUD783Jd9JfKFblS4S11VyuOgdquDDCUHGsXXVljL5Csaxt7pLvYEZnjoGR8u04\n59UxuI3xDDZYuY9R7TNuOV8q43dkpDj6Xm/aPkhhxHHuzQ+VxUJvZ46MwdaBAl+44zH+vm2QFScd\nSjaTUU5HciTuw4kTv+Mde2M8EjlOx4VbYxdtl7E3Npy6IU6xxMVvGqTyWoJCoRj6hWJOvpO+oZGq\n+T3+QUAwYfyweXP48nsP4Qfr/spbDtijbP7lS+dz6QmHsKVviA+vfGB0/pdOOJifP/B33vTKF/KJ\nm9eXbWP175/kil9sLPtyU0pu7enIsHln+bqUVC5JEDXad0c2U5WkvWOwUDXa9649HZFtNXjgUSw6\ntg4UysstnU9XNlPVNncOlW/n6lMWhrb7Wd25ssT0XMYwCz+QiJMYr5tDVI88XoqH0o01ntzcx+d/\n5B1kXL5kPjsHhzn/Ha8kl80wOFLkbI0GLyKSWqm8vKq/MBKaYBo1fyyRvPy1j9/0EIvn78nH/ZFy\nR5dZtc77ElNR/hM3r2fx/D35xM3rq7bx1gNfVLXNUnJr/3Cxal1KKpckCEsaf65vODRJ+7m+4aoY\nr9VWg0LLrVpXtc6weYWiC93GQKFYlph++nUPhLa5uInxujlE+cjjpffgjOsfBOD9V9/HUZfexffX\nPTP6Gew+s5vlq9ZhoD5QRCTlUvlLR60k0VrJo2GvRSWrzugOX1dU+cqEyWDCqpLKJakaHe07bkJ3\nVLmwdVbOi2qTs3o6quaFtbm47VPteGJ9b61+czq9dyIiaZfKXzpqJYlGzY9Kfo1KVt0xEL6uqPKV\nCZPBhFUllUtSNTrad9yE7qhyYeusnBfVJrf1D1fNC2tzcdun2vHE+t5a/eZ0eu9ERNKuqYnkZnY0\ncDmQBb7pnLu44vUu4DpgIbAZONE590StddZKBAsmce4YKPDte/5clkcxJ98ZmTxamdhduj58bm8n\nO4dGuOdPm/joqnWjyzz6t628/IWz+PhNYwmSX37vIfz00f+OndNRSljVteAT1tQ3Z6rjN4micjoc\nVCV4d2YzDI0Uy3IouiMSuitzOkLztPycjmD+Rti8qJyOfGeW06+rn0cV1T7n5DvKEuO9BPrh8bTj\nVMVvsejoGyqQ93M3/v1nfxzN3dgxOMwLZnZX3cTj2e0D5DJZHnnmeRa+dE7ZZ6Q+sO2lKn5LlEhe\n7qB9XtLqKoybEsnbV9MOOswsC/wReAvwNHA/sNQ592igzJnAwc65M8xsCfBu59yJtdYb1WmEfTEo\nHTR4Xwi8O+IE76xSOVp43YOW3k76h0foyBgduQzbBwpVX6xmducYHC7WvXtV2Ajl47jrjXia9gZN\ndfwmWdy7V3VkM3wkeIDgH3w7R927V0H4HenMqGrPZlbVlirvXhVVLqrNhe9j9QFG5YFIq+5e1Q79\nb+kuVZ3ZDIVikW2VNwJYMp/Z3TlyuQz9w0V6cpnxvHfSeqmJ3yAddJTTQYdMpmZeXvUqYKNz7nHn\n3BCwGlhcUWYxcK3//BbgTRZ2+5gYwpI4z161jv7hIjO7O0a/xNQaLbyU2L1zsMCHVz7AZT/7U3ly\n6/AIM7s76O70kr9DRzQeLpLvypVtI5erP0J55YjJ+mfbclMav0lWPdp3seqmDMtXreP5yqRv/4YK\nle0j7IADCC0X1p7D2lLccuPbx+qk8f5CsV3accv73+Wr1lHE+9yGI5L5h4qObCbDjK4c2WymXd47\naT31vyIp1MyDjj2BpwLTT/vzQss45wrAVmBu5YrM7ENmtsbM1mzatCl0Y5OZxBknuVVJo6k3pfGb\nJlFtIyqRPIkS0P7bqv/VCPAyTup/RVKomQcdYWccKq/lilMG59xVzrlFzrlFu+++e+jGJjOJM05y\nq5JGU29K4zdNotpGVCJ5EiWg/bdV/6sR4GWc1P+KpFAzDzqeBvYOTO8FPBNVxsxywGxgCxOQ78hy\nxdIFHL7vXHIZ4/B953LF0gXkO8Z/5jHfkeXyJfPL1nX5kvll65rM7UlbmtL4TZPwtjGfXfIdNdtU\nkiSg/bdV/9uTC+9Te3Jt835Je1H/K5JCzfxt+35gfzPbB/grsAR4X0WZ24BTgXuB44FfuAlmtmcy\nNjrCd6OJiNlshrm9naOj6IYlf0/m9qQtTWn8pklU23DO1WxTSZKA9t9W/W8ul2FOvrxPjbphgAjq\nf0VSqWkHHc65gpmdBdyJd8u7bznnHjGzi4A1zrnbgGuAlWa2Ee8MxZJGtllK9gRG/05UNpthpv+F\naGZ3R2iZydyetJdWxG+ahLcNq9umkqSd23879r+5XIaZufR8/tI86n9F0qmp/ymdc3cAd1TMuyDw\nfAA4oZl1EJkoxa8kmeJXkkzxK5I+7XV6TkRERCSFxjMGyBPdTazIOGz485NNWW8Sx/+QxumCWhER\nERERaSoddIiIiIiISFPpoENERERERJrKknaHOTPbBPylTrHdgH9MQXVaJe37B/H28R/OuaOnojKT\nRfELpH//QPGb5s837fsHit80f75p3z9IafymQeIOOuIwszXOuUWtrkezpH3/YHrsY5S073va9w+m\nxz5GSfu+p33/YHrsY5S073va9w+mxz4mlS6vEhERERGRptJBh4iIiIiINFVaDzquanUFmizt+wfT\nYx+jpH3f075/MD32MUra9z3t+wfTYx+jpH3f075/MD32MZFSmdMhIiIiIiLtI62/dIiIiIiISJtI\n9EGHmR1tZn8ws41mdl7I611mdqP/+n1mNm/qazlxMfZvmZltMrN1/uODrajnRJnZt8zsWTN7OOJ1\nM7Mr/P1fb2aHTnUdm0nxq/hNMsWv4jfJFL+KX2kB51wiH0AW+C9gX6ATeAg4oKLMmcAK//kS4MZW\n13uS928Z8NVW17WBfTwSOBR4OOL1twM/Bgx4DXBfq+s8xZ+v4reNH4pfxa/iN5kPxa/iV4/WPJL8\nS8ergI3Oucedc0PAamBxRZnFwLX+81uAN5mZTWEdGxFn/xLNOfdrYEuNIouB65znd8AuZvaiqald\n0yl+E07xq/hNMsWv4jfJpnn8JlaSDzr2BJ4KTD/tzwst45wrAFuBuVNSu8bF2T+A4/yfDm8xs72n\npmpTJu57kESKX4/iN5kUvx7FbzIpfj2KX5lSST7oCDvjUHkrrjhl2lWcuv8QmOecOxj4GWNnZdIi\nyZ9fPYpfxW+SKX4Vv0mm+FX8Sgsk+aDjaSB4ZL4X8ExUGTPLAbOp/XNcO6m7f865zc65QX/yamDh\nFNVtqsT5jJNK8av4TTLFr+I3yRS/il9pgSQfdNwP7G9m+5hZJ16i120VZW4DTvWfHw/8wjmXlCPd\nuvtXcX3iMcBjU1i/qXAbcIp/F4rXAFudc39rdaUmieJX8Ztkil/Fb5IpfhW/0gK5VldgopxzBTM7\nC7gT704N33LOPWJmFwFrnHO3AdcAK81sI94ZiiWtq/H4xNy/5WZ2DFDA279lLavwBJjZKuAoYDcz\nexr4F6ADwDm3ArgD7w4UG4E+4AOtqenkU/wqfpNM8av4TTLFr+JXWkMjkouIiIiISFMl+fIqERER\nERFJAB10iIiIiIhIU+mgQ0REREREmkoHHSIiIiIi0lQ66BARERERkabSQUcbM7OjzOz2uPMnYXvH\nmtkBgem7zGzRZG9HppeJxquZvdjMbol4bTQ2zewzgfnzzOzhiddW0szMlpnZi2OU+7aZHR93/iTU\nSzEssTUaxzGWO8PMTgmZPxqbZjbfzN4eeO1CMzt3vNuS6UUHHRJ0LHBA3VIiU8A594xzLs4/zM/U\nLyICeGMR1P2y1gKKYRmPZTQxjp1zK5xz19UpNh9vHAyR2HTQ0QAz6zWzH5nZQ2b2sJmd6M9faGa/\nMrMHzOzO0sif/tnZfzez3/rlX+XPf5U/b63/9xXjrMO3zOx+f/nF/vxlZvZdM/sPM/uTmV0SWOY0\nM/ujX5+rzeyrZvZavFFJv2Rm68xsP7/4CWb2e7/8EZP01kkbaVUcm9kdZnaw/3ytmV3gP/+smX2w\n4qxaj5mtNrP1ZnYj0OPPvxjo8WP2Bn/VWT+uHzGzn5hZTxPeNmkxPz7+08yu9ePiFjPL+69Vxa5/\nxncRcIMfLz1mdoHfdz5sZleZmY1j+7Xaxxcr+00zy5vZTaUYNrP7zGyRYnh6m+o4NrMXmNkD/vND\nzMyZ2Uv86f/y43T0Vwu/Dg+Z2b3A//bndQIXASf6dTjRX/0Bfvw/bmbLm/WeSYI55/SY4AM4Drg6\nMD0bb0TM3wK7+/NOxBsNFOCuUnngSOBh//ksIOc/fzNwq//8KOD2kO2Ozgf+DTjJf74L8EegF+9M\nyON+nbqBvwB7450deQKY49f1buCr/vLfBo4PbOcu4Mv+87cDP2v1e65HquL4PLx/YrOA+4E7/fm/\nBF4BzAus+5zA9g/GG0V3kT+9I7DOef5r8/3pm0rtQ490PfzP2gGv86e/BZwbI3YXBdYxJ/B8JfAu\n/3lZXxgo823g+BjbqOo3/bp9w39+oGJYj8DnPdVx/Ijf757l973vB14K3Ou/fiFwrv98PfAG//mX\nAn3yMvzvDoFlfgt0AbsBm4GOVr+/erTXI4c0YgNwqZl9Ee9L1d1mdiDeP5Sf+icbssDfAsusAnDO\n/drMZpnZLsBM4Foz2x+v8+kYRx3+GTjGxq6l7AZe4j//uXNuK4CZPYrXqewG/Mo5t8WffzPw8hrr\n/67/9wG8zlHSp1VxfDewHPgz8CPgLf4ZvnnOuT+Y2bxA2SOBK/xtrjez9TXW+2fn3Dr/ueI23Z5y\nzt3jP78eL57+g9qxG/RGM/skkMc7EfMI8MMY231FnW2E9ZuvBy4HcM49rBiWgKmO498Cr8PrV/8N\nOBowvD55lJnNBnZxzv3Kn7USeFuN9f7IOTcIDJrZs8ALgadrlJdpRgcdDXDO/dHMFuKdzfqCmf0E\n+B7wiHPu8KjFQqY/C/zSOfdu/4vWXeOohgHHOef+UDbT7NXAYGDWCN7nHfvyAV9pHaXlJWVaGMf3\n410m8DjwU7wD4tPxvmTF2WaUyrjXpSnpFRaHRu3YBcDMuoEr8c4YP2VmF+KdtImj3jbC+s3x9L2K\n4ellquP4buAIvBORPwA+5W+z8oYfFlK3WsK+c4iMUk5HA8y7e0Sfc+564FLgUOAPwO5mdrhfpsPM\n/mdgsdL18q8Htvq/RMwG/uq/vmyc1bgT+GjpGk4zW1Cn/O+BN5jZrmaWw7u0pmQ73tlqmUZaFcfO\nuSHgKeC9wO/w/hGeS8XZNt+v8S4BwP8V5uDAa8NmNp5fByU9XlKKUWAp8Btqx26wjyt9MfuHmc3A\nu2wqrnrtI8xv8GId8+4SeFDgNcXw9DbVcfxr4CTgT865IrAF76TTPcFCzrnnga1+Pw9+HxxSB5FY\ndNDRmIOA35vZOuB84HP+F6njgS+a2UPAOuC1gWWeM7PfAiuA0/x5l+CdYb4H7yfU8fgs3mUs681L\nuv1srcLOub/i/Zx6H/Az4FFgq//yauAT5iX17hexCkmfVsbx3cDfnXN9/vO9CD/o+Doww78k5ZN4\nB88lV+HF/w0hy0m6PQac6sfFHODrdWL328AKP9YHgavxLi/8Pt4vb7HEaB9hrsT7Erke78zyesb6\nXsXw9Dalceyce8J/+mv/72+A551zz4UU/wDwNT+RvD8w/5d4iePBRHKRmsy58fxyJo0ws7vwkrPW\ntLgeM5xzO/xfOr6Hl5z2vVbWSZKjXeJYpjf/Er7bnXMHtrgqsZhZFi+xdsA/qfNz4OX+l0uZppIW\nxyKN0PV209OFZvZmvJ9lf4J3dkRERJonD/zSv4zKgI/ogENEphP90iEiIiIiIk2lnA4REREREWkq\nHXSIiIiIiEhT6aBDRERERESaSgcdIiIiIiLSVDroEBERERGRptJBh4iIiIiINNX/B204Ea+CvdLK\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e2036f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris_p, hue=\"species\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como exceção, a classe `sklearn.preprocessing.Normalizer` realiza um pré-processamento em cada linha, isoladamente, para esta ter norma unitária. Por não ter estado, o método `fit` de tal classe não faz nada, porém ele existe para que essa classe possa fazer parte de uma pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_p_normalized = preprocessing.Normalizer().transform(X_train_p)\n",
    "iris_p_normalized = pd.DataFrame(X_train_p_normalized, columns=iris_measurements) \\\n",
    "                      .assign(species=[iris_dict[\"target_names\"][idx] for idx in y_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lvQnQzdWrCjZ+48XH8q6Dfu556RPWvf8FAH/8ybkseGZrt+IyGuv5HMud4kQr\nDGdZ+zHdNDFDLQSUtrY5vBv3yzUYH66F067AvHQlTWF/rFFSP7ueqserCOkQz0x9hmXvLutcB85b\nSbmrT29c5V38FoJ0Ivyw1np9xksihOgbpmkNaXr62vYlMmc8Ap7BsZNCdFK5qc2k43PjJ4mv37ke\np3KyefZm++VzXV4CF9TgBaibZf3ds2+F8dfA2u+3l2Paz+H9p6BqTkJ5co231InH5WTo10r58YXf\n6HDnfRxlJY68uCuZDXZ3cu+bOQ6Ala/uiN3Z9ZS093REe0NGDym37RXxljq77C3Je6aZdLy8xzUA\n48O1+H64heo3qll94Wrb7aITywOhAF6HG8Kt7Rs4yuDiZfDi7UmTAsZnRpelru1jefn0sQCse/8L\nTqjwdDsu0+n5y3WGBm9rM/zme3g7nmPQ4DuAsfba2O+8035uvfDU6fDtRQR8e6l+d0msYdH4Zfuc\nj2hyy3jRJdpF/kt+C0OpM5RSZwCvKaXuUkpNij4XeV4IkYvslsh8+lrr+Q6Sjc/1tx2xfz7op3Jo\nJVNGTuGZqc+wZfYWnrvsOfb59uEeeLy1FGf07558idXgiC/HczdZzycpT67wtYb4vMnPzRecxK1r\nP+gwH2GLzOtIwW4Oxw/XbOFf/mEkn9w5hdVzqjoNJfG3hZkwooId+5qZMKIi4f0mjKjA1xqyfd7f\nVkD/h6CfwJef2dav1iN/hRGTcR91Ig17G5IuY72neQ+1k2txawWhFmiN5OdYOsT6HmqFC5dKUsA0\n2cXyrWs/4MbzRgPweZO/23EZjfXuvCbnJDvHhFqs4Xtrr+183D/7FjhvITx7Q6eM5au3ro4tpbvz\n8M6k5x6R/1LdZvw/ka8zgSrgP+OeuzudN1dKXayU2qaU2qGU+mmK7WYopbRSqir9oguRWXkbv0mX\nyOx8p8guL0Dt5OW4931Crd0k8R2vcu8593LzGTfzyq5X2Hl4J8MGDMNb4qUt3Jb4d4/5hn05os8n\nuduaCzwlDo7ylDB8UPfvZOaKbMVvsju53lInhlKUlzo79RJ5ShysnFXJix/+leXTxzJp1CCchmLS\nqEGxXpGVsyptny8YLg/uV/+Te/9hOTefcTPL3l1G1RNV1Gyswecqw7zqKVrDrTx32XP2izicXctg\nh5uKsqMx3vq5dcH3tWHwnVo4eVp7fo5Sb6dJ5LkoF46/yWJ59JByJo0axNGeElbOGtetuCyIWE52\njtFha76Q3e8GfwMqRsCujQQObk9YzGTuaXP5uvfr3HfefYwcOJLayZ1z1ZQ5JUFgIUg6vEprfR6A\nUmqU1rox/ndKqS4nkSulHMAxMeWCAAAgAElEQVT9wIXAbmCTUmqd1vrjDtsNAOYD73S/+EJkRl7H\nb5pLZEIkL0Dp0aw6dwXuEi+Bg9utyeBnzKGi7GhWnb/KGpMb9OPe+ADGa3fivH0P67at45JRl7Do\nrUXtkwkn1+I651aM1+603vzANvtyRJ+3KU+ucDgMBpQ6Y3fY48dfR+9K5vL462zGb/RObnf2mWEo\nBnld/OvkUbhLDB66ZjzeUif+tjCeyFC2QV4Xq+dU4XE5Ep4vGG1+jCN/xRkMsPCd9vka0TwF93/7\nfnxBn+0iDv62I7gb6nD8z/Pwvd/A6VfCmqsShzUCfPycNa8qx/Pn5MrxN1ksB9rCVixGGgrdicuC\niOVk5xiXF/YnOe7v/5/Yz+7Xa6m9sIanP33B9jzSsK+BBRMXMGrgKBoPN7J2+1pmnzwbp5G7x1yR\nnnSOPE/bPPe7NF43EdihtW7UWrcBa4BpNtstAWqBljTeU4j+kr/xW+Kxxtd2sURmlBEM4K27CmNx\nBd77z7QaDWuvxQi2xCUV9GC8vhwAt9PDBcMvYNFbixITRG2oJvDNH7T/3Y+fh+kPJ5Zj2s+t51OU\nJ1c4HAZelzNf70pmLX57eifXMKxeEIdhMKCspFOvSPT3yXpL8l6k3rq/Nsx2TLupzYSEa/dvuZ+b\n/3gzgea9lC8fZTU4pj8M4RZrOEun4S0/iTT2fVn6gN2SE8ffpLHscsRisCdxmfexnOwc0+aDj39v\nHefjfzf9YWsRgzfuhmk/x2jeR8XLS7j672fZnkdGDhzJ5esuZ9zj47h83eU8+P6DsopagUjabFRK\n/T1wCjBQKXV53K++BqTTzzUM+Dzu8W6soVrxf6MSOEFr/bxS6icpynIdcB3A8OHD0/jTQvRa/sav\nYVgT+matSbp6VYJUw7Fam9u/R+5eBQ7vZuRRI+0noLvKO/xdd/vj6OpV3/yB9T1HJ5HHMwxFhack\ndufd1xrKl7uSWYvf/rqTG7+qUF7eLe4oUm8DIX9C0k2wxrQnTcpWPhTu2G/VL4cLHCXJhzXOeASi\nE8S7WOEuy3Li+NtVLBdcDKYr2TkGrEVC6uOWaG5thsbX4cO17a+/dCVGxQg8YBvTI48amfBcbHEE\nWdwg76U6wnwDuAQ4Crg07usMYG4a721X82Lr8yqlDOBe4N+7eiOt9UNa6yqtddXgwYPT+NNC9Fp+\nx69hWEOXVOR7qouJaFd5vOGTrEmo0SR/b/8y1mvhfmUJvjZf8gRRCX/XYZ2MfPut4R5Lh1jf/Qfy\nImGgaWqa/EGue2wzJ92+nuse20yTP5gPybyyGr+ZvpMbXVVo7qP1nHT7euY+Ws9BX1s+/F9SMwzc\nJZ7O86wiydFs61xbs/WfLfua1ehoa7avz22+9hXjoivc5W4Sz5w5/iaL5YKNwXQlO8c4XHDaDKvB\ncegz67nhZ7b3fDTvs+YVaQhEFiaJVzm0El/Q1yn+paejMKSTp2OS1npjyo2SvA6o0VpfFHm8AEBr\nvSzyeCDwF6A58pKvA03AVK110oW0JU9HZkmejsgbF1P82i2xO/0RK6HYH+9s3+7c261eitJywqE2\nDgWbqd6QmCCqoqyi87r+rc3WRU38GN8Rk/MiYWCG19SX+O2hQsh1kIpdvgyApkBTYp37Zg0VW9Zg\nfPOG9rpkhsF3wFpBKMmS2X1UJ4s6fgs9BnskWVxd/rDVI1JantCrZpphKxnmhlvj5nQs5+iyo2kJ\nt6abL6aniqBLKvekUzOuUkrN6vDcYaBea/1citdtAsYopUYCe4CZwFXRX2qtDwPHRB8rpf4I/CTV\nAUOIflQ88WvbVe6GN5YnbvfGcjjnJ6AMHCVlVDhd7RPNO54Y4oduoGHA1xPfK8dXr4rK4zX1Czp+\n8/j/kpZO+TIi9ami7GhWnXMPbtcAa9GHl2owPn7Omq8Re7EDvF0Mr+zGCndZkvPxW+gx2CPJ4qp8\nMCjV3isCYJoYwQAVZRWsOu8+ayGToN86jxgOvJFJ4zKkqrCk03QsBcYB2yNfY4EK4Fql1IpkL9Ja\nh4CbgBeBPwNPaa0/UkotVkpN7XXJhcigoovfjl3lwUCSIRrta6VHL4ziL5B8QR+mNvEFmzHffiAy\ndGMWfHuRlRgqyXvlqvg19aeefhwv3nw225ZOwdcayulhFPkUv6apaW4NYerI9zT2a0HkOuiCVY8S\n65OxdAjeNVdjHP4c7+u1VoZnu7rU1fDKZEMqc6RO5mL8dozTlmDhx2C3JYurQ58lxlbc8D5j6RC8\nT87C8O3H6/SAUnFxb30XhSOd4VWvAv8YOQiglHICf8Baym6r1vrkjJcyTq507xcqGV6VWXkTv2lk\nNU/YXJs0tTTx9CdPc8HwCxh51Eh8bc14G+pw/PetVhf7pSvh/gldvlcuiY7brnvnMy6rPL5TZvJB\n3tLezFco6vg1TU1LKIyvNcT8ui1x+7WyU/JAu9d2zBSdzuvyRdgM4wv58JZ42fnlTl7e9TIzRnyH\nipdqrIbGiMnWRN0XbulZXepm/U4i73Z0T+PXLt7umzWOUofBD554ryBjsEfs4uqyB6B0AJQO7HJ4\nn3nVUxzRQQ63HmZY+TD2NO9hYOlABrgGyPCqApFOo2MbMDHSnRkdS/mO1vrvlVINWuvKlG/Qx/Lm\noi1PSaMjs3I1fu3GkBuazqvbYD1nlkS2K/EQCAUwlMGjHz1qs+b6z6h44afWEJA79luvz72VclIy\nTY2vLcR1j23uPH77mirKy3o8frto4zd6EedrDbHgma09GhdfqCsHmdrsNHdj8VmLeb7xeeaMuASz\nrRn3oDEEwi24tcJwlsXqkm09Tnax1vvVq/JuZ/c0fpPN33hw9ngMpfCUFlYM9oppQtBnrVDY5gPl\ngLgYBUCbVi+4GWp/znASuG03h1oOsfDNhQlzBT1OD6UYOJyuvjx/FPk/KjvS+a/VAluUUr9WSv1f\noAG4WynlBV7OZOGEEJkX7aWY9+o8xj8+nnmvzqOppQlTkThEA8C/H/PtB2hq3sO81+bHtvcFfbEG\nR+Ka6z8lcE51+9CNdFbTyjGGofCWOu3Hb5cW8fjtXvAHw8yva+CEip5nfM/7XAdJBEIBqjdUJ9Sj\nRW8t4pJRl+BzlTHvvVrGP1Fl1dOw36qnpKjHyYandGeFuyKXbP6Gt9QJioKLwV4xDKtnQ0W+u2wa\nCEmGYZnaZOGbCxPPIW9UcyBwgKbgEcLvPpyLK62JbujyKKO1fgQ4C3g28vUPWuuHtdY+rfUtmS6g\nECKzAqFAQtKx6IE+EAokbhj0w9PXEjh5KtUbazptf1z5cfZ5BAaNyYtkgKn4W5OM324t4vHbvRC9\niNuxr1nGxXeQLB/HceXHpaynaddj0W3J5hDt2Ndc3BPHeypJcsFksT+sfBi3briVwOlXWkOynr7W\nOh+JvJPuuAAD2B/ZfrRSarTW+o3MFUsI0V+SJh3ruC56ZGUS96AxtttH8wh0TGoWCAXw5sH8jVQM\nA+66YizPbN7NRacey+gh5fhaQ5Tk70fKquhF3P2v7WD59LE825C4X8ucBs35k4ixT6WqR6nqadr1\nWHSbp8TBfbPGseadXbE4bW4J0XjgCL7WEN5Spwyv6o4kyQWTxX7j4UYa9jbgKf2a9WRurbQmuqHL\nU6ZSajnwJnA7cEvkK2n2TyFEfkmadKzjHdJIl3jg4Hbb7Q1lUDu5c1Izdx7N3+goumJNWYmDge4S\nrjlrBDXrPuIbC9dz/eObOdwSIhSSbv7u8pQ4WDmrkv1HWmk8cISZZw5P2K9ffNnCrzY02iZb68lq\nV/nE7XR3Tg44+WcYrfaJ1OJ7OtKqx6LbDENR4XYlxOkPntjMsKM8/PpPOzOeHLAgY95meJ9d7C8+\nazGrt66mcmgl/tavrNfm0EpronvSnUg+Vmvd2j9FSi1XJ+IWCplInlm5GL/RseDVb3SR6C+yMom5\n+VGaKmdSvbGm0/ZA+hNZc5zdijV3XTGW2v/exrr3vwCsyaQPXTMer6tH47kLPn5TTfaOrl4VCmuu\nf7zzJP2aqadQs+6jhEnlhb5qVVTChPC2Ztxv/xIObKfpH2uofusO23qadj3uO3m3w3uzelVzayhp\nnF604o3Y475ODlgsMR8VH/u7j+zmgS0PsC+wj+WTl1Ox9f/h+PO6vlr9sPB2Xh5Ip9GxHrhCa92c\ncsN+kosXbYVEGh2ZlRPxa7NqjanSbCxEXttx9ap8blwkk2zFmviLDKeh+OTOKfhbwz1Zxaqg4zed\ni6Xm1hDuEgffWLieUNzd2+h+3b63mdFDvDgiFxdFmQU6rr6awRYCSietp12uXtX7FaviFXT8xksV\np9uWTuHvbnsh9viTO6dgqPaGdW9XWCu6mI/EaLikjECoBU+JB3/Qj1s5ZfWqApDOf82PtXrVg0qp\nldGvTBdMiKJjmtb65TryPROrc8QlZbIS91krgRiahER/SRsQkS5xw3DgdZV3vX0eS7Zizegh5bHH\nE0ZUcKQlJKtY2YiuULWx8SAhU7Ox8SDz6xrwB9sniXtcjqSTybfvbaZm3UcJQ1aKMgt03DAUw+VJ\nWU87Juy066nsWPdjx5n+OP7kqVRxumNfc8Lj6CII0Ub33EfrezX8qqhi3gyDz4pRx9KhlD85E8O3\nn3KnB0dJmay0VgDS+c+tA5YAbwGb476EEH2lqwuCvhJZgYpPN1hrpEdWAjFDLZIFtoNkK9Z83uTH\naSgmjRrEXVeMJRw28bWGkrxL8UrnYsnfFubFD//K8uljmTRqUGy/Lp8+lvtf28HGxoP8sG5LrKFS\nDJnIO0rMTN6Lupmk7hP099/xJ08li9P7Zo7jxQ//Gnu8clYlnhIrvtNpdKf7t4si5k0T2pphbZIY\nTect+qquiIzpsm9Oa/2oUsoNDNdab+uHMglRfOIvCKD9YDtrTXuOjL4QWYEqnjngWJrC/k5jwb0l\nXkodpQU7fKor0cnO8cOD7r7idAaUOWhYdCHeUidHAiG80sthK3qxFB0WMvX04/jxhScBxFam8pQ4\nmHXmidS98xk1U09hzNBytu9t5u4/WPNmai49mcsqh+FxOTjSEsTt7Pw/ib/QKzSp5mlAN+dP2dT9\n2CpAbf10/Mlh4bCJPxjGW+rEF4lPh8PANDWGgllnDqfunV3UTD0ltnpVw64mLjr1WG769hhrFau4\nuV191UNhdxwqyJgP+sFVnjxGI5INIczCnCbRA102OpRSlwJ3Ay5gpFJqHLBYaz0104UTomikuiDo\nS9GkTNGLCyBw/m2x9f2B2Pr+NZNqmPbstKI9eBuGYpDXxeo5VXhcDnytIXbsO0KJw8MP12yJXQDc\nN3McHpcDj1IFObGzp+IvloZ+rZSfXPQNbvndB7H99surz8BhGAwqd/Ev/zASj8vBV4EgNes+YmPj\nQWouPZnvnHYs//bEewn7usLT/j8p9GVK43NvQHvdXHX+qtjv0r7Asqn7sVWA+uv4k6PCYZODvrZO\n9brC4+JQIBiL4ZsvOInhgzzsORTgrhethnE0M3k0OWB0HgeahEY3tPdQdGcuRsfjUMHGvMsD+7cl\nj9HS8pQNi1R1xVvizdKHEh2lcwVRA0wEvgTQWm8BRqbz5kqpi5VS25RSO5RSP7X5/Q+UUluVUluU\nUn9SSp3cjbILkVH9Gr9JMrT2+bKANkmZ3EedmDQhU7EnGYvPeu11ORk9ZAA/XLMlYcjED9dsIazp\n9rCJTMv28Tf+YmnpZadxy+8+iO23wQNKOdIaYu5j1nj36x7bTJOvDaehuOsKawjLZZXDbPd1IBQu\nyEzkdlLl3uh2IsAkCdko8fTf8acb+jN+/cFw0liLDpF6dssXnHv3H/mn1e8QNjUvbP2rNcRq1ji8\nLkeswRGdx7Hw2a2xWLYbftUd8cehgo35Nj98/HuY9vPEGJ3enlg2VQJMyVOTH9Jpboe01oeVSgjy\nLmdCKaUcwP3AhcBuYJNSap3W+uO4zZ7UWv8ysv1U4B7g4nQLL0Sm9Hv8Ri8Inr7WusM4fFJmsnjb\nJGVKlZApqtPBu29XwckLhqHwljpth0wM6P7KVRmVK8ff6MWSqXXCfrvxvNGxRggQGe++hdXXVFHh\ndbH6mio8pfbDU7yFuGJPEj1KFGia9nUxSUI2DKP/jj9p6u/4TVavkz0/fJDHWrWuQ69D/DwOAFPD\nsstPY/ggT+H2UPSVEg9UzYH6R+E7d8ExY2I9HNFYTdWwSJmcVno6ckY6VwkfKqWuAhxKqTFKqVVY\nk8q7MhHYobVu1Fq3AWuAafEbaK2/invoJY3GjBD9pH/jN/6C4I791vdMZfHukJTJLiHT0m8tZfXW\n1bGXJCQZK+JJp77WkO2kTl9rKNcmdubU8bfjZNjRQ8rtx7uXOvC4nJSXOVPu62Jhmyjw7FoMZdgn\nAvzys9R10SYhW+z5/jr+pKdf4zdVrCWbxG3X69BxHse697/ggnteByjcHoq+Eo3BSTfAMSeB7yCs\nuSrhHJMqAWayuiI9HbklnSPKPOAUoBWoA74Cbk7jdcOAz+Me7448l0ApdaNS6i9ALTA/jfcVoj/0\nf/wmuyDIMEMZVJRVsOr8VWyevTk2BvZA4ID9wTvVKjgFrsRQ3DdzXKcVbEoMlWsTO3Pq+Bud3xHd\nb583+W0v5lqC4Vjm5WT7Osf2c0bZ1c2KsgpKHaWdL7C+WYP7lSU9r4tZOv4k0a/x6ylx2MZadOGC\n+Od/efUZoLHNDl40K01lSjQGgwHbVazcWiVtWCSrK8U0DzEfdJkcsMdvrNQVwEVa6+9HHs8GJmqt\n5yXZ/qrI9nNsfncdcB3A8OHDx3/22WcZKbOQ5ICxNy7y+E2ZZEyb1t0nM+6Os+G07pAW+AE+HDYJ\nhk2CpsZb6qS5JcRf9h/h+KM9DPKWdvdOZlHFb3yitJZgGF9riPl1WxImlgMc8gc5ocLD501+hgwo\nJax1bEUht9OB01nYMZauWB11lBE4uB3367UYH67tz7pYUPGbavWqVHEbn/Ay3ezhpqmt4ValDvyt\nYQwDypwy9ComxTnGpJurtiUnOzsLkg6OVUr9nhTdlWmsXrUbOCHu8fHAFym2XwP8Isnfegh4CKyM\nol38XSH6QvHGr2liBP14I+O+vSWexAuYVKvgFPDymqZp3dlsDZkJq9wsnz6Wund28a+TR+VShuCc\ni9/o/A4Aj8tJmdORsCKPATT521jwzNbYvr3rirGUOAwqF7+U9AKuWBnKwGtq+M00vOnUxfyah9Xv\n8etwGAxwWPtjQFlJ7Pn4uDU1zK/b0mEuUkMsO3g6K01ZDZPWhIbLXVeMZUCpkwFlJcUd2/ExeuMm\neG0pfLjW+l0kro3S8tgcDZmrkX9SnSHv7uV7bwLGKKVGAnuAmcBV8RsopcZorbdHHn4X2I7oUnd6\nI0SPFWf8RudrdJxQGj++O8cmnfYXfzDMIX+QBc9sTbjouHXtB9RMPSXXMgTnXPzG3zGOXoyVlzpj\nw1NMTafJ5bf87gNWX1OVkFwteoEnSL8uplOvc0vOxS/0LvdG/FK6HRsut/zuA5ZdfhoOh1G8sW0X\no5c9YN3wOvK3ojjHFIOk0a21fr03b6y1DimlbgJeBBzAr7TWHymlFgP1Wut1wE1KqQuAIHAI6NQ1\nKkQ2FG38ppOkMNUqOAXM43JwQoXH9qIjmiwsVyaL5lr8Jht2UuEpoclv5UH4zdwzk04uT3icW427\n7Eq3LvZX8tE+kmvxG9Ux4SUk5t5IJ86f+L59nJ9Q4UFl/9CRPXYx+uwNMKsOUEVxjikGGW1Sa61f\nAF7o8NyiuJ9/mMm/L0RvFGX8ppskLDrhD3LyoiUT/G1hDhxptb3oaG4J8ehbO3NqiFUuxW/HpUSj\nvRYPXTM+9vyeQwHbfbvnUCDhcXeTqxW8dOpiHib/y6X4jeoqO3g6cb5jX7NtnH/e5OeYAaXFG9tJ\nY9Rb8HMFi4n8J4UQ7XIwSVguME0NGk6ocHda5WbFzHH8v4bdrHx1h9yFTyLZsBRvqZNfXH0Gf/nP\n72AouOd7p3dIpjYOl9PgsnHH9Sq5WtGTet1rpqlpCYXxuhysvqaKT+6cwuprqhjkdQHQ3BpKGefR\n5+9/bQd3X5EY53ddMZajPSVpx3Z0fpndClp5S2K0KBRpk1oIYatI52uk0nHIxKpZ43jomvF4S518\nFQjybMMean7/MZNGDcLXGsLryo0hVrkk6bCU1jBf+oPUrPuIvV+1snLmOFbOGseg8lJ2HfRz53/9\n2Xp+1ji8pdbkc2i/wJOEa2mSet0luzlH0bgyTc2RliBHWkPc8rsP4no5xuEuMWJDp2qmntIpzuef\nPxpfa4htS6ewY18z97+2g+X//T/cc+XpDB1Y1u3Vq9JdISvvSIwWhUyuXiWEyDdFOl8jlfghE1NP\nP45TjjuK6x7bnLB61VeBIJePP55w5G6oxyX3c+LZDUu564qxLHx2K3u/amX59LHc/YdtzF+zhQdn\nj+efVr+TeOFWt4XVc6oACvOCK9OkXqfU1YV8skUk5tdtSRg6df9rO1g+fSy3rrUaJvPPH83MicM7\nHS+ebdiN02GAhvKy7h0rkg3hyvsFFiRGi0ImV68SQuSjIpyvkUr8kIkbzxvNrWs/6LR61S+uPoNF\nz33E/iOtrL6mKpvFzUkdlxLdddBP7X9vY9371iqo0RXAvrtyA+VlzqQrBPnbCvSCqz9IvU6qqwv5\nVItIxA+disZzzdRTGDO0HF9riOse29zpePHQNeN73CPamxW0cp7EaMHL2OpVQghRCOKHBo0eUm57\nwh9QVsK697/AaaiE1ZZEu2i+A1NrLrjndUJx49CjK4BNGFHBV4Fg0hWCCvqCS2RNV3GVahEJX2so\n4fl1739h3XyYU5XQIIl/X2+pE6OHS1V1tYKWELmsy34rpdQYpdTTSqmPlVKN0a/+KJwQQmRbdGjQ\npFGDYivPxJswooId+5pjP/tbw9koZt7wt4Zt9+HnTX7umzmOjX85wPLpYztMKLcmkEcvuDq+1t8m\n+1z0XFdx5SlxcLSnhLuu6BiX4xKOD/0Rr6n+nhC5TmmdetUDpdSfgP8A7gUuBf4l8rr/yHzxOquq\nqtL19fXZ+NM5I1eSA26dszXbRci7QdwSv/kpOsnUXWJw0NfGDztkE777xW2xCc+DvKXpDpsoyvi1\ny8gcnShe6jAIhEzcJQb+tjDeUmfCpN6CnUSbn/JuhyeL33TiKrp6lWmCp9SBv9XqeYvGpd0k9EzF\na6pJ7yJtssOyIJ1Gx2at9Xil1Fat9WmR5zZorSf3Swk7kIs2aXTEybuDhsRv/ks44UdXninp0cm/\naOO3NxdNcsGVM/Jup6eK30zFlcRrzpJ/QhakMwCwRSllANsjGUL3AEMyWywhhMhN0bkJkLjyjIyn\nTl/CPuzmfuvNa4VIJlNxJfEqRLt01iK7GfAA84HxwGxgTiYLJYQQQgghhCgcXTa7tdabACK9HfO1\n1kcyXiohhMhhMmQid8n/RmSSxJcQPZfO6lVVSqmtwAfAVqXU+0qp8em8uVLqYqXUNqXUDqXUT21+\n/+PIqlgfKKVeUUqd2P2PIERmSPwKO9HJoXMfreek29cz99F6DvraMM3U8+P6WzHGb778b0TXcjF+\nJb6E6J10Bhj+CrhBa70BQCn1D8CvgbGpXqSUcgD3AxcCu4FNSql1WuuP4zZrAKq01n6l1L8BtcD3\nuv8xisvWnbsy9t6njRyeuffuxgT4bE9Sl/gVyeRDRuBijd98+N+IruVq/Ep8CdE76czpOBJtcABo\nrf8EpDPEaiKwQ2vdqLVuA9YA0+I30Fq/prX2Rx6+DRyfXrGFyDiJX2ErTxLUFWX85sn/RnQtJ+NX\n4kuI3kmn0fGuUupBpdS5SqlzlFIPAH9USp2hlDojxeuGAZ/HPd4deS6Za4H1aZRHiP4g8Sts5UmC\nuqKM3zz534iu5WT8SnwJ0TvpNDrGASdhJQisAf4XcBbwf4C7U7zObmaV7cBHpdTVQBVwV5LfX6eU\nqldK1e/fvz+NIgvRaxK/wlaeZAQuyvjNk/+N6FpOxq/ElxC9k87qVef18L13AyfEPT4e+KLjRkqp\nC4DbgXO01q1JyvAQ8BBYyX16WJ7cVjMw2yXotlxJUpghEr/ClmEoBnldrJ5Tlcsr2BRl/ObJ/0Z0\nLSfjV+JLiN5JZ/WqoUqpR5RS6yOPT1ZKXZvGe28CxiilRiqlXMBMYF2H964EHgSmaq33db/4QmRM\nQcevqU18QV/Cd5G+aMIvQ0W+595FR0HHbyp58L/pN3lcz3M2fiW+ckcex3fRSmd41f8FXgSOizz+\nBCthYEpa6xBwU+S1fwae0lp/pJRarJSaGtnsLqAc+J1SaotSal2StxOiXxVy/JrapKmliXmvzmP8\n4+OZ9+o8mlqa5IBdQAo5fkV68rmeS/yKruRzfBczpXXq3kal1Cat9QSlVIPWujLy3Bat9bh+KWEH\nVVVVur6+Pht/OrNyZHhVJpfM7Y40l8zNu1tMuRC/vqCPea/OY9PfNsWem/D1Caw6fxXeEm8WS1Z0\nJH5FxvRDPZf4FVnTB/Gdd/FbCNLp6fAppQYRmcSllPomcDijpRJCZIzb6aZhb0PCcw17G3A73Vkq\nUX4zTU1zawhTR75LorA+Jfu3Z6SeZ57EZvZIfOendBodP8YaS/l3Sqk3gceAeRktlRAiYwKhAJVD\nKxOeqxxaSSAUyFKJ8pdkKM4s2b89J/U8syQ2s0viOz912ejQWr8HnIO1TO71wCla6w8yXTAhRGa4\nnW5qz65lwtcn4FROJnx9ArVn18odoh6Iz1AcMnUsQ7Gs2989ye4YJ92/Qdm/XZF6nln+NonNbJL4\nzk9dLpmrlLoC+O/IJK6FwBlKqaWRxogQIs8YyqCirIJV56/C7XQTCAVwO90YKp2OTxEvaYbiUgem\nqWVlmzRE7xjPr2tg06dNTBhRwcpZlQzyuiQDdC9IPc8c09R4SiU2s0niOz912egA7tBa/04p9Q/A\nRVgJAX8BnJnRkhWZETt1mhwAACAASURBVC1PZuy9Py27Ku1tt+7clfa2uTLpXHSfoYzYZDuZPN5z\n0QzFGxsPxp6bMKKCXQf9HDOglPLSdA6xxS2+NwOI3TFePacKwHb/+tvCsm/TIPU8M/zBMAeOtEps\nZpnEd/5Jp0kY7Sv8LvALrfVzgCtzRRJCiPxgZSgel5ChePn0sax4+RO545mmVL0ZkgFa5CKPy8GK\nlz9h+fSxHWJznMSmECmk0xzfo5R6ELgAWK6UKiW9xooQQhQ0w1B4S50su/w0TqjwsGNfM3f/YRv7\nj7TKHc80Jestiu4/yQAtco2/Lczer1q5+w/bqJl6CqOHlPN5kx+vJAsUIqV0Gg9XYiXouVhr/SVQ\nAdyS0VIJIUSeKHM68JY6uPrhd/juyg3sP9IqdzzTZJoaQ9Gptyi+N0MyQItcE+2B23+kle+u3MDV\nD7+Dt9RJmbO9zstyukJ01uVtOK21H3gm7vFfgb9mslBCCJFPXA4j1tvxeZMfl0M6g7sSP4F86NdK\nWXb5aQwf5MHfGsbjkt4MkbsMQ6XsgUu1OILEtShm0vcvhBC94A+G+cET7yUMD5o0ahCr51TJ8KoU\nOk4gf3bLF7H9JhdmItdFe+CATvU81eIIckwQxUxuxwkhRC/Isq49I/tNFCqJbSHsSaNDCCF6IToR\nOl50IrRITvabKFQS20LYy2ijQyl1sVJqm1Jqh1Lqpza/P1sp9Z5SKqSUmpHJsoi+t3Xnrm595RuJ\nX5GOXF3WNdfjN1f3m8gNuR6/qUhsC2EvY4MLlVIO4H7gQmA3sEkptU5r/XHcZruAfwZ+kqlyCNET\nEr8iXV1NKs2GfIjfXNxvIjfkQ/ymIrEthL1MzmiaCOzQWjcCKKXWANOA2EFDa/1p5HdmBsshRE9I\n/Iq0pZpUmiV5Eb85uN9EbsiL+E1FYluIzjJZE4YBn8c93g2c2ZM3UkpdB1wHMHz48N6XTGRFng2x\nkvgV+UziV+QziV8hClAm53TY9SP2KDuO1vohrXWV1rpq8ODBvSyWEGmR+BX5TOJX5DOJXyEKUCYb\nHbuBE+IeHw98kcG/J0RfkvgV+UziV+QziV8hClAmGx2bgDFKqZFKKRcwE1iXwb8nRF+S+BX5TOJX\n5DOJXyEKUMYaHVrrEHAT8CLwZ+AprfVHSqnFSqmpAEqpCUqp3cAVwINKqY8yVR4hukPiV3SHaWqa\nW0OYOvLd7NFIkD5TCPGba/tU9J98jF+JVyG6ltElFbTWLwAvdHhuUdzPm7C6TYXIORK/Ih2mqTno\na2N+XQObPm1iwogK7ps1jkEeFw5H9vKv5nr8mqbGHwzbLilqt09XzqpkkNcly44WiVyP3yjT1LSE\nwvhaQ8yv2yLxKkQKkpFcCCF6wR8MM7+ugY2NBwmZmo2NB/lh3RZ8bWG525lEtFEx99F6Trp9PXMf\nreegry22v+z26fy6BvxByegsckc0jvd91cr8ui0Sr0J0QRodQgjRCx6Xg02fNiU8t+nTJrylTrno\nsGGaGl9bKGWjItk+9bgko7PIDfFxfEKFR+JViDRIo0MIIXrB3xZmwoiKhOcmjKhgx75muejoIHpn\n2ONyprxIS7ZP/W3SiBPZ1zGOd+xrlngVIg3S6BBCiF7wlDi4b9Y4Jo0ahNNQTBo1iOXTx/Lih3/F\n1xrKdvFySnTYVFcXaZ4SBytnVSbs05WzKvGUSCNOZF/HOL7/tR0snz5W4lWILmR0IrkQQhQ6w1BU\neFz88urxlJc52bGvmWcbdnNZ5fHS09FBdNhU9CLt1rUfJEy8jV6kGYZikNfF6jlVthPNhcgmuzi+\n56VtLLv8NIYP8uBvtRZIkHgVIpE0OoQQopdaQiaPvrWTi049ltFDyuHUY3m2YTf/OnkU5aXSoRwV\nHTa17n0rz1vN1FMYPaQcf1sIr8uZcJFmGIryUusUFf0uRC5IFcdoKC+TeBXCjpwNhRCilzwlDmad\neSI16z7iGwvXU7PuI2adeaIMr+ggftjUC1v/Ss26j2jytXVqcAiRyySOhegZaY4LIUQvyXCg9Mh+\nEoVA4liInpFGhxBC9AEZDpQe2U+iEEgcC9F9MrxKCCGEEEIIkVHSPO+m0x49Le1tt87ZmsGSpG9E\ny5MZed9Py67KyPsKIYQQQojCktGeDqXUxUqpbUqpHUqpn9r8vlQp9dvI799RSo3IZHmE6A6JX5HP\nJH5FPpP4FaLwZKzRoZRyAPcDU4CTgVlKqZM7bHYtcEhrPRq4F1ieqfII0R0Sv6InTFPT3BrC1JHv\nps5KObIRv7ny2UX+k/gVojBlsqdjIrBDa92otW4D1gDTOmwzDXg08vPTwLeVUrL8g8gFEr+iW0xT\nc9DXxtxH6znp9vXMfbSeg762bF289Gv85thnF/lP4leIApTJOR3DgM/jHu8Gzky2jdY6pJQ6DAwC\nDmSwXL1y5M8/y3YRRP8oyPgVmeMPhplf18DGxoMAbGw8yPy6BlbPqcrG6jb9Gr859tlF/pP4FaIA\nZbKnw+6OQ8fbBulsg1LqOqVUvVKqfv/+/X1SOCG6IPErusXjcrDp06aE5zZ92oTHlZUEgf0avzn2\n2UX+k/gVogBlstGxGzgh7vHxwBfJtlFKOYGBQFOHbdBaP6S1rtJaVw0ePDhDxRUigcSv6BZ/W5gJ\nIyoSnpswogJ/WzgbxenX+M2xzy7yn8SvEAUok42OTcAYpdRIpZQLmAms67DNOmBO5OcZwKtaaxlE\nKXKBxK/oFk+Jg5WzKpk0ahBOQzFp1CBWzqrEU5KVu6X9Gr859tlF/pP4FaIAZWywYmSM5U3Ai4AD\n+JXW+iOl1GKgXmu9DngEeFwptQPrDsXMvvjbI376X33xNqKIZTN+RX4yDMUgr4vVc6rwuBz428J4\nShwYRv+vLdDf8ZtLn13kP4lfIQpTRmdIaa1fAF7o8NyiuJ9bgCsyWQYhekriV3SXYajYxNNsT0Dt\n7/jNpc8u8p/ErxCFJ6PJAYUQQgghhBBCGh1CCCGEEEKIjJI+xAySuSVCCCGEEEJIT4cQQgghhBAi\nw6TRIYQQQgghhMgolW9pBZRS+4HPutjsGOBAPxSnu3KxXLlYJkivXAe01hf3R2H6Sp7Hb18p9M8H\nEr+5+v/N5bJBbpevY9kkfvtfrpYtV8sFycuWd/FbCPKu0ZEOpVS91roq2+XoKBfLlYtlgtwtV38o\n9M9e6J8PiuMzJpPLnz2Xywa5Xb5cLltfyuXPmatly9VyQW6XrRjJ8CohhBBCCCFERkmjQwghhBBC\nCJFRhdroeCjbBUgiF8uVi2WC3C1Xfyj0z17onw+K4zMmk8ufPZfLBrldvlwuW1/K5c+Zq2XL1XJB\nbpet6BTknA4hhBBCCCFE7ijUng4hhBBCCCFEjpBGhxBCCCGEECKj8rrRoZS6WCm1TSm1Qyn1U5vf\nlyqlfhv5/TtKqRE5UKYfK6U+Vkp9oJR6RSl1YqbLlE654raboZTSSql+WWIunXIppa6M7LOPlFJP\n9ke5+kMuxm9fSuPz/bNSar9Sakvk6/vZKGdPKaV+pZTap5T6MMnvlVJqZeTzf6CUOqO/y9jXehOz\nSqkFkee3KaUuykLZkh57lVLhuDhcl4WyJa0LSqk5Sqntka85fV22NMt3b1zZPlFKfRn3u4zuu76U\nq/ErsZuxshVE3BYUrXVefgEO4C/AKMAFvA+c3GGbG4BfRn6eCfw2B8p0HuCJ/PxvmS5TuuWKbDcA\neAN4G6jKhXIBY4D/n707j3Oyuhc//vk+ySzJzLAMi1oQAQUVQVlV9OJuXQtW1IIX5fYqWhdw+bUg\nFblzweqF9l4Viq1bK2oFFVqlLrVaN1RUwEFBrEoHRBDZBpCZZJbMc35/PEkmmUlmMjDJJJnv+/XK\nK9uT5CTznfM85znnfE8p0Dl4v3tbx14Kv3tK47cNvt9/AL9t67IexHc8DRgKrIvz/IXAK4AAJwMf\ntnWZU/A3jRmzwIDg9nlAn+D7uFJctrh1L1DRxr9bzP8FoBgoC153Dt7unOryNdh+MvCHVPx27SF+\nNXY1btvTJZN7Ok4ENhhjyowxNcBiYEyDbcYAC4O3lwBni4i0ZZmMMW8aY3zBux8APZNYnoTLFTQb\nmAtUpaBMiZZrErDAGLMHwBizI0VlS7Z0jN/WlGjMZSxjzDtAeRObjAGeMI4PgE4iclhqSpcUBxOz\nY4DFxphqY8xGYEPw/VJWtjaqexMqWxPOA14zxpQH68DXgNZeRbml5RsPLGrlMqRCusavxm5qypap\ncZtVMrnR0QP4JuL+luBjMbcxxgSAfUCXNi5TpGtwzoQmW7PlEpEhwOHGmBdTUJ6EywX0B/qLyHsi\n8oGItPYOt62kY/y2pkT/F8YGhwwsEZHDU1O0lGlpfZDuDiZmk/1bHGzdmy8iq4J1zCWtWK6WlC3W\n/0IqYijhzwgO6+kDvBHxcDJ/u9aUrvGrsZvcsmV63GYVd1sX4CDEOuPbMP9vItu0poQ/T0QmAMOB\n05NYnvDHxXgsXC4RsYD7cLpJUymR38uNM8TqDJyzO8tFZKAxZm/DF2aYdIzf1pRI2f8KLDLGVIvI\nz3DOMJ6V9JKlTib//WI5mJhN9m9xsHVvL2PMtyLSF3hDRNYaY/6VwrLF+19IRQy15DPGAUuMMXUR\njyXzt2tN6Rq/GrvJK1tIJsdtVsnkno4tQOSZ0Z7At/G2ERE30JGmh0OkokyIyDnAncBoY0x1EsuT\naLmKgIHAWyKyCWf8+TJJ/mTyRP+GLxhjaoPd2l/gNEIyXTrGb2tq9vsZY3ZHxP8jwLAUlS1VEqoP\nMsjBxGyyf4uDqnuNMd8Gr8uAt4AhqSxbE/8LqYihlnzGOBoMUUnyb9ea0jV+NXaTVLYImRy32aWt\nJ5Uc6AXnDHgZTpdZaBLRcQ22uYnoSWHPpkGZhuBMfuqXTr9Vg+3fIjUTyRP5vc4HFgZvd8XpTu3S\n1vGXou+e0vhtg+93WMTtHwMftHW5D+B79ib+RPKLiJ5I/lFblzcFf9OYMQscR/RE3DJadyL5Ade9\nOJNc84K3uwJf0cSE1CSVLeb/As4k3I3BMnYO3i5O9d81uN3RwCaCiwqn4rdrD/Grsatx254ubV6A\ngyq8kx3my+A/453Bx2bhnAkAyAeew5n09RHQNw3K9DqwHVgTvCxLh9+qwbZvkYJGR4K/lwD/B6wH\n1gLj2jruUvjdUx6/Kf5+9wKfBXcWbwLHtHWZW/j9FgHbgFqcs27XAD8DfhZ8XoAFwe+/NlX/U238\nN40bszhnaf+F01t5QRuULWbdC5wS/Pt8Ery+pg3KFvd/AfjP4O+5AfhpW/xdg/dLgP9p8Lqk/3bt\nIX41djVu28tFgn8ApZRSSimllEqKTJ7ToZRSSimllMoA2uhQSimllFJKJZU2OpRSSimllFJJpY0O\npZRSSimlVFJpo0MppZRSSimVVNroUEoppZRSSiWVNjqUUkoppZRSSaWNDqWUUkoppVRSaaNDKaWU\nUkoplVTa6FBKKaWUUkollTY6lFJKKaWUUkmljQ6llFJKKaVUUmmjQymllFJKKZVU2uhQSimllFJK\nJZU2OpRSSimllFJJlXGNjvPPP98AetGLIQNp/Ool4pJxNH71EnHJOBq/eom4qDaQcY2OXbt2tXUR\nlDpgGr8qk2n8qkym8atU28q4RodSSimllFIqs2ijQymllFJKKZVU2uhQSimllFJKJZU2OpRSSiml\nlFJJlbRGh4j8QUR2iMi6OM+LiMwTkQ0i8qmIDE1WWZRqKY1flck0flUm0/hVKju5k/jejwO/BZ6I\n8/wFQL/g5STgd8HrA2bbBl9tHd5cF76aOrw5LixLDng71a49TorjN90MWjgo4W3XTlybxJKoA/A4\nWv+qzPU4aRq/B7q9UiqJPR3GmHeA8iY2GQM8YRwfAJ1E5LAD/TzbNuyurGHSwlX0v/MVJi1cxe7K\nGmzbxNzuD8vL+Gp7BZ4cFxXVAerq7AP9aJWFUh2/SrWmdK9/Jy1cxe3PrGHX/moQqKgKNNpWtV/p\nGr+xttc4VipxbTmnowfwTcT9LcHHDoivto4pi0pZUbabgG1YUbabKYtK8dXWNdpu0Ydfc8mQnpQs\n+4z/9+wayitrEEuarTBs21BRHcA2JtxQibyvlU270qrxq1SKtVn9O2VRKd2K8rj93KOZ/ue1zkHe\nE6vYVVlNnW2H69KG9a3WrypCm8Rv5PaLPvya31x+PHdedCzT/7zWaXxU1Dc+9PhAqcbastERqx8y\n5n+liFwnIqtEZNXOnTtjvpk318XKTdEnRlZuKseb62q03XkDD2Pa0k9j7vjind1oeCbkD8vLGt3X\nCqZdadX4VSrF2qz+XbmpnJvOPIppSz+NOsi7ZdEaNuyoZNLCVeyvqmV3ZbWeSVbxtEn8hnhyLC4Z\n0hO3y6Kyuo6nrj2J/x49kGVrtkYdS/xheVlCPSdKtRdt2ejYAhwecb8n8G2sDY0xDxtjhhtjhnfr\n1i3mm/lq6hjRuzjqsRG9i/HV1DXa7qjuhXF3fPHObjQ8E3LewMO4ZfEaVpTt5sJBh3HJkJ5c/+Tq\ng6pg6ups9lfVYhvD/qpaHfKV3lo1fpVKsTarf0f0LubIbgWUjD6Of91zIa/eehqjT/gBKzeVc1T3\nQlaU7WaPr5Ypi9a06MSQalfaJH5DvW/+GpvnS7dgCUz/81qOnvEKP3tqNZcM6cmFgw5zGtGL13De\nwMMS6jlRqr1oy0bHMuDqYBaKk4F9xphtB/pm3hwX88YPYWTfLrgtYWTfLswbPwRvjqvRdpXVAUb0\nLg43PiLFO7vR8ExI5Gtb0niJp67OZndlDdc94TRc/vjuRipr6rTnJH21avwqlWIpr39t24CBP006\nCV9NHa+u28bRM16hZNln3HXxsbx3x1l8t88PwOHF3hafGFLtSqvGr8dt8cC4wVHx+8C4wXjc9YdI\ndXU2u4K9b57giIkpi9ZExea0pZ8y7fyjefXW03jq2pPo0cnD6BN+ADTdc6JUe5G07FUisgg4A+gq\nIluA/wJyAIwxvwdeBi4ENgA+4KcH83mWJXQpyOWRicObzCZhWUJBrosHxg/mm3IfI3oXs6Jsd/j5\n0NmNwrzonyZ0JiS07YYdFeH7iTRemst04autC/ecjD7hB+Gek5WbyhnRu5jfTxiKy7Lw5jmv97gt\n/AFbM2ckSarjV6nWlG71rzM8tZopi9aE67Q5Y49nw85Kln3yLVMWreHeSwdRlO9m+dQzEYHXbz+d\nnp09LRr2EvoszSqU2VIdv/6AzeKPNlMy+jiO6l7Ihh0VLP5oM/85qi9eESeO8lz4quu4/rQ+VFYH\n6HdIIb+bMJTnS7dS8tf1ABzSIY9ct0XJc59GxTnAUd0KqKwOUJDn1rhU7VbSGh3GmPHNPG+Am1rz\nMy1Lwo2Fho2GSC6XRdeCPKry6pg3fnDUjjBW7wjUn8mbsqiUlZvKeXXdNh4YN5hbFq+JaoCERDZe\nQvNBQq8NfU6XgtxwpVOQ5+aQDnm8eutp9DukkM27fXQryiNgG7oV5bG/OsAvIiqyR64eFh7AWmcb\nqmqdXhFvnpvK6gDeHBcul679eKDaIn6Vai3pUv+GGgAYEz4rDITPCpeMPo5ln3zLyk3l9Ozs4bvv\nq5i6JLqeW33XuRTlu/neX8vzpVv53l8b9+AtkbpWpb9Ux68310VHbw6HdMhDxGk8dPTm4M11sbvC\niadDOuTxX6OPY8APOkadEHxg3GAASv66nlvP6d8ozp8v3cLsSwZSlO9m824fdz2/ju3fV2tcqnYp\nmet0pDXLEry5bvLdrmZ7R0LbNzyT53FbPDJxOJ4ciwfGD+aWOI2XyPkgQHiIwCMTh4d3zlU1dfz8\nvKOjGhahMyTTzj+a2jrDU9eexIYdFWzb68NX4/SMRFZ8FdW1FNt5dPDkUFkdwGPA7daGh1Ktofcd\nLyW87ab/uSiJJckMkQ2AP006KWaPxVHdCwGYctZR+GrqOLRDPr+bMJSi/Bx27a8mYBtueOrjqHrO\nm+ti0hOrYzYqEqlrlWqopraOCwYe1ijWahpkW7NEwiMSgPDcjYevHsYZR3enVxdvVJyHRi38LKKR\nMmfs8bzxz+0E6uxwcgTLgny39nyo7Nfuj0hDZ+csCV438U/fcFuXy3KuLafn5JGJw/nyVxfwyMTh\nUWcwEsmMYRvDL577NOb40Fy3FZ6sVrLsMwb06MjijzZHZ35ZvIZuRfnc8NTH9L/zFa57YjXlvhoC\nAZ2crpRKvcgGgK869kTdDTsq+N2/D2HcSb14/L2NfLu3ihue+pijZ7zCrc+soaI6EO7xDdVzodux\n5ni0NAuRUgC1tgk3JiJjrdY24blFn327l6J8d8z4KshzM/3Pa9lfFYiK81hzkp4v3cKFgw7j9mc/\nCSdHKK+scfbTOndTZbl23+hoLU01XhLJjOHNi12ZdfTkNpqsdssiJytGw20L8tyNKk1fbV14cvp1\nT6xmd2WNNjyUUkkX2QDY56/h15cf32ii7pHdCjj1qG7hOq3hAdovnvuUm848KvyeKzeV08GTE/U5\nkY2KRLMQKRWpIM7+tyDPHc62dsqR3di82xczvr7317KibDcL39sYNSE91nzPyMyXkXG+x1ebcIIE\nXcNGZSptdKRAIpld4u0svXmxz9yFhiVEbvu9v7bRdvEaIpG0AlNKtSbbNlRWB/ji7gt49dbTeG39\ndvJcFvdeOogv7r6A300YyuKPNrNtX1X4gC9eQo7Iui5WPRfZqEg0i6FSkUIZLSON6F1MZXWA308Y\n6szPzHdz/+tfMmfs8Q3iazDPl24FYN4bG+hSmMvcy47ny19dwP6q2kbvGy/ODy/2JtQj19LV05VK\nJ9roSIHI+SCxhl+Bs7N8YHx0yr45Y49n+76quJVhw7OGK/61q9F2G3ZURD0WaohErqreuAKrxlej\njRClVMuFDoque2J1eEjoWcccwqqvyykuyEUEcl0WY4cdzg86ecKJOELXkUb0Luabcl9UPRe6HXnQ\nZ4nzuYnUtUo15M1xxUmZ66KmzqYgz82GHRVs/76a3/z9C0pGH8cXd1/AvZcOAghnr3L2zXVMXfIp\nxkDJss8aNVLiNXC+Kfcl1CPX0tXTlUonOrMuRZrLrBXaWUam7PvN37/AEhpl2Hpg/GDyXMLDVw+j\nIM/J6rJ1j49TjuzGv+65kA07Knh13TbGndSLxR9ujvqcEb2L2bzbxzn/9zYjehfz0FXDYky8XMND\nVw3jyVWb+Nu67ZplQymVsFiTuact/ZTfTRjKzBeczD0PXTWMpau/4epTevPqum3MGXs8z5duYc7Y\n45m2tD6Zxv/95ASK8tx8+asLqKwOIDgTbiPrvhfWbG1UTyWSxVCpEJfLoktBbjiuQhkg/QGbKYvW\nUDL6uHCcTlv6KRfNWx7eFy/6cDNuS+oTHeQ4yWn8NXVRjZSjuhfyTbmPXJc02qf/+vLjKcpzx+2R\ni0wDjXGya0XSeUsqU2iNnEb8tTYlyz6LSr07sm8XCnLdjSpDl8siD6cyEqCjN5efPbU6qmFS7Mll\n3Im9WFFWHlW5zf3bF+EzJE2NZb1g4GEYg2Z/UUolLN5k7qL8HJ5f4ywa/fh7Gxl3Ui+eeH8Tlwzp\nyfOlWzhv4GH8oFO+U9fluvlqRwXPrfyGS4b05A/vbuSn/9aH5V/t5LgfdIpqmMwZezyrv96r9ZQ6\nKC6XRVEwzXxRvjNvyGsJKzeVs+DNDfz8h0fzfOmWcAOisjrAv3bu57yBh3HTWf3Ca3v8x6l9wjEY\nSrMfaqTMGz+EXLeLYpcVtU/PsYTcONmrYqWB/vXlx2MbWPaJ8/8Ub30xpdKNRmgaabgWSKiSyo9I\n4xuqDEMsS3BZVjjzFRCebB4aWhCq3HzVdcx4fm24ogLirjGyYUcFJcs+4/dXDePulz7XsyhKqYQ0\nXEgVGg/1nPfGBm466yj+c1RfPDkWP/23PuGei427KujbtciZyzHwMJ4v3cK4k3rxx3c3Rk02h+j1\nPi6at1zrKdWqQrEc2mfedOZRHNW9EF9NgII8F5f//gMCEcOP3ZZw01n98NU6DYBYC2YClPtqG68l\n444du7F6Dn/x3Kfce+kgXl67rcn1xZRKNzqnI40c6HjkeJPNvblOj0hRfg6WCAhs/746artX122L\nOZdkwZsbgmcn3Zr9RSmVsFiTuX99uVOnhIzoXYy/1g6nHBcR/v2RDynKz+Hy33/AXS+sY8OOCo7q\nXsh5Aw+jS0Eu897Y0ORkc62nVGuLjOWX126jZNlnbN3j586/rGPDjsq4aaBDjd9YWS1bOicjXs9h\nry5enbekMo72dKSZAxmPHO/MYsPu1lg9KeNPOoJibw4PXz0Mb647PJdk2SffMrJvF3zVdXoWRSmV\nsEYLqVbXUWfb7NxfHR773rBOCR1YhXpel33ybfjs8si+XXj46mFRk80b1nXflPu0nlKtrmEsb97t\nC+8fAR4YNzhqkd7Q3KQenfvG3X+3dC2Z5vbvOqRKZRKN1iwQb1hWwx1wrFXVQyuwuwI22/b5KVn2\nGSs3lYfPTlqCnkVRSrVI1MmTfDe2bWLWOyGhA6sFb25oNJk8VJfNGz+ERR9+HeP5wRTkuXVFZ5UU\noVi2bUNBnivceN65vxpvrouHrhoWzm71fOkWxp90RJON30RPEoYkun9XKhOIMZmVDnX48OFm1apV\nbV2MtBOZ3SLWTj2R1++vqmWPr5bDi718U+6jszfHGZqVvjvytC1YPJkUv4MWDkp427UT1yaxJOmh\n9x0vJbztpv+5KJHNNH6DIifLHtIhj1vP6U+vLl581U6dZlkSruM8ORa+mjpnntoB1HWq1WTcj36w\n8RtrPwu0aN8ba2J4cxkiD3b/rmLSH7ANaE9HljjYNJGWJRTl5+ByWYhA16I8rdiUUikRqxcW4/SS\nRG4TqtuK8p3piDq0RKVSvP1sS/a9TY04aOnnKpVpNHpVmFZsSqm2ovWPai801lV7pdmrlFJKKaWU\nUkmljQ6llFJKPkYJRAAAIABJREFUKaVUUiW10SEi54vIFyKyQUTuiPF8LxF5U0RKReRTEbkwmeVR\nqiU0flUm0/hVmUzjV6nsk7RGh4i4gAXABcAAYLyIDGiw2QzgWWPMEGAc8GCyyqNUS2j8qkym8asy\nmcavUtkpmT0dJwIbjDFlxpgaYDEwpsE2BugQvN0R+DaJ5VGqJTR+VSbT+FWZTONXqSyUzEZHD+Cb\niPtbgo9FKgEmiMgW4GVgcqw3EpHrRGSViKzauXNnMsqqVEMavyqTafyqTKbxq1QWSmajI1bS6YYr\nEY4HHjfG9AQuBJ4UkUZlMsY8bIwZbowZ3q1btyQUValGNH5VJtP4VZlM41epLJTMRscW4PCI+z1p\n3P15DfAsgDFmBZAPdE1imZRKlMavymQavyqTafwqlYWS2ehYCfQTkT4ikosz0WtZg202A2cDiMix\nOJWG9n+qdKDxqzKZxq/KZBq/SmWhpDU6jDEB4GbgVeBznCwTn4nILBEZHdzs/wGTROQTYBHwH8aY\nhl2oSqWcxq/KZBq/KpNp/CqVndzJfHNjzMs4E7wiH5sZcXs9cGoyy6DUgdL4VZlM41dlMo1fpbKP\nrkiulFJKKaWUSiptdCillFJKKaWSShsdSimllFJKqaTSRodSSimllFIqqZqdSC4iecBYoHfk9saY\nWckrllJKKaWUUipbJJK96gVgH7AaqE5ucZRSSimllFLZJpFGR09jzPlJL4lSSimllFIqKyUyp+N9\nERmU9JIopZRSSimlslLcng4RWQuY4DY/FZEynOFVAhhjzPGpKaJSSimllFIqkzU1vOrilJVCKaWU\nUkoplbXiNjqMMV8DiMiTxpirIp8TkSeBq2K+UCmllFJKKaUiJDKn47jIOyLiAoYlpzhKKaWUUkqp\nbBO30SEi00VkP3C8iHwfvOwHduCk0VVKKaWUUkqpZsVtdBhj7jXGFAG/NsZ0CF6KjDFdjDHTU1hG\npZRSSimlVAZrKnvV0ODN5yJuhxljPk5aqZRSSimllFJZo6k5Hf8bvCwAPgQeBh4J3p6XyJuLyPki\n8oWIbBCRO+Jsc4WIrBeRz0Tk6ZYVX6nk0fhVmUzjV2UyjV+lsk9T2avOBBCRxcB1xpi1wfsDgZ83\n98bBCecLgHOBLcBKEVlmjFkfsU0/YDpwqjFmj4h0P5gvo1Rr0fhVmUzjV2UyjV+lslMi2auOCTU4\nAIwx64DBCbzuRGCDMabMGFMDLAbGNNhmErDAGLMn+N47Eiu2Ukmn8asymcavymQav0ploUQaHZ+L\nyKMicoaInC4ijwCfJ/C6HsA3Efe3BB+L1B/oLyLvicgHInJ+YsVWKuk0flUm0/hVmUzjV6ks1NSK\n5CE/BW4Abgnefwf4XQKvkxiPmRif3w84A+gJLBeRgcaYvVFvJHIdcB1Ar169EvhopQ6axq/KZBq/\nKpNp/CqVhZrt6TDGVBlj7jPG/Dh4uc8YU5XAe28BDo+43xP4NsY2Lxhjao0xG4EvcCqRhmV42Bgz\n3BgzvFu3bgl8tFIHTeNXZTKNX5XJNH6VykJNLQ74bPB6rYh82vCSwHuvBPqJSB8RyQXGAcsabPM8\nEJqw3hWnu7TsQL6IUq1M41dlMo1flck0fpXKQk0NrwoNp7r4QN7YGBMQkZuBVwEX8AdjzGciMgtY\nZYxZFnzuhyKyHqgDfmGM2X0gn6dUa9L4VZlM41dlMo1fpbJTUylztwVvng0sN8Z81dI3N8a8DLzc\n4LGZEbcNcHvwolRa0fhVmUzjV2UyjV+lsk8iE8l7AxNE5AhgNbAcpxGyJpkFU0oppZRSSmWHRCaS\nzzTGnAUMBN4FfoHT+FBKKaWUUkqpZjXb0yEiM4BTgUKgFGc18uVJLpdSSimllFIqSyQyvOpSIAC8\nBLwNfJBgylyllFJKKaWUSmh41VCcyeQfAecCa0Xk3WQXTCmllFJKKZUdEhleNRAYBZwODAe+QYdX\nKaWUUkoppRKUyPCqOTjDquYBK40xtcktkkoq24ZaH+R6ocYHOV7n8YaPWc12gimllEq1yDq8tgpM\nHeQWaN2tUivWsUQo9pp6TrVriQyvusgYM9cY8742ODKcbYNvJywaB7O7OdfV+5zHVjwIO7+AHA/U\n7Ae7rq1Lq5RS7YttQ3UFmOC1bTd+PlSH//k68O2CRePr63PfzsavUaq1xTqWCMVeU8+FXttUjKus\npk3P9qTWB0uugU3LwQ441749sGohnHAFvPwLuLs7LP53qNyllYFSSqVKcwdrEF2Hj7odnr8xuj5f\nco2zjVLJFOtYIhR7TT2XSIyrrJbI8CqVLXK9sHlF9GOdj4ABP4IXbnYqB3Cul14D4xdDXmGrFsE2\nNv6AH4/bE762RNu+6Wjtxs1tXQSl2o/IgzXALuyOv9aHR8BfW+nUlZF1eNejG9fnm1c49bxSrSDu\n/jrWsURk7MV7riY6xsMNkiQca6j0pEd77UGoOxPgppUwcGz9c3u+hq79U7Lzso1NeVU5k9+YzLAn\nhzH5jcmUV5VjGz3LoZRq5yIO5OyBYyk/t4TJH82uryv95djG1Nfhu76AXiOj36PXSOfATqmD1OT+\nusYXP/aaei6ysTJwLNy4Aq5+ATDa29FOxG10iMhfRWRZvEsqC6kOQsPuzL9OgXNKYNDl0HsUeDtD\nTUWcSqKyxWMubWNTWVsZdR3iD/iZ+s5UVn63koAJsPK7lUx9Zyr+gL91vqtSSmWqiIM1/+lTmfpB\nCSu/W8m5vc9l+onT6ezpjK9mP/a6JU4dvnE5XPKgU49bbuf6ssfqk4ModRDi7a+r66qptAR74jIq\nb1mDPeiK6NjL8Tq3G8Wlxzme6DXSaXCcPRNenuoM6V40XodZtRNNDa/6TcpKoZLC6Rr14fF2xX/R\nb/C8PRdr3VJnHPD4RYAEd1A2jH3MGVK1eYVTKVzyILx4G+z/zqkwvN2azT4ROjMy9Z2plG4vZcgh\nQ5h72lyK84uxxMLj9lC6vTTqNaXbS/G4PRFvolkvlFLtUOhgbck15Hc5il+e+Ev6dOpDRU0Fi/65\niCv+eoVTp55cQvGaxVgn3wBWDox72hmaUl3hZLHS+lIdLNuOub/u7ulOZW1l9D7+wrkUu7xY7vz6\n2PN2bRCXXifpwaqFMOa3zn6+4ZDu4DArO9erQ7CzWNxGhzHm7VQWRLWumA2Ac0soBqz1Lzg7J7GC\nPSG7YfVCuPDXzlCr6v3wyWJY+5zzZqExlzneJhsEkWdGgPCZkflnzacgpwB/wM+QQ4aEnwcYcsgQ\n/AE/BTkF9b0ySyIaPwk2eFTbGrRwUMLbrp24NoklUSqDuXKxL32UPVV7ueeje8J196xTZlG2r4xX\nNr7C1A9KmD90KgU5XqeXOrfQyTy4/q8wfKLWl+rgBPfD/lpfo/31jYNvjL+PDx8bVDoTyJ+5qn4/\nPvYx5xjjrV/Brn/C2EdjDum2czxNnrhUma/Zv6KI9BORJSKyXkTKQpdUFE4duJhdox+U4D+nJHrc\nb2jy4lu/ggdPhlnF8MwE6Hta/ZttXgE5HuzqfVT6dmIb41xX74vqDm2uJ8Pj9jD3tLmMOHQEbnEz\n4tARzD1tbn1PR1NZL5RSKpvV+uCZq6g2AXwBH4+c+wjP/uhZunq6MvP9mUwaNAkI1qld+zsZBhf/\nuzM85eVfOBkIVy3U+lIdnOB+2POP2cw9uSRqf92zqGf8fXw4I9V458RlYff6/fjSa5yENYANVFZ/\njz1jB5U3fYgdmmPaa6QOwW4HEsle9Ufgv4D7gDOBnwKSzEKpgxe3AdCxJ/ZPnsQKjfuNl4Wi69H1\n93uNxA5UUR7wMfWj2fVnIE6ZTbErz8moAs32ZFgGil1e5p81P3bXaXMZMZRSKlvlerGLDqMyN5+S\niDO9s06ZxW9Lf0vfjn2BYJ1aW0nB0gZZgF642emt1vpSHYzgftiyAxQD80+fiqdLP/x1VfH38Xu/\npiAyFp+/ES6cC+uWOo9tXgFd+4cTJEx9+/b644gfllDc7RisYRPx5HibH4KtMloi/VUeY8w/ADHG\nfG2MKQHOSuTNReR8EflCRDaIyB1NbHeZiBgRGZ5YsVVzQpVDpCGHDGHL/i343bn13e/xMk3U+uDG\nD7Avf5LK8U/jF5j6/l3RZyDevwu/mPDLYvZkjJqLx5Uf7rK1nr6Cgnt7YS0cTUF1BZaJ+Nymsl60\ngWyM30ELByV8UZktG+M3q9X48J/1y0Zneme+P5MbB9/ItsptvPTjl3j0h4+CWNhFh0W/Pnhgly3Z\nqzR+20jEfthat5SCBSdhPTGGAtvEH63wxj3R7xHzxGU1vtEPhBMkhI8jVpTgH3kjeLvhCw7pijTk\nkCH4tPcuayTS6KgSEQv4SkRuFpEfA92be5GIuIAFwAXAAGC8iAyIsV0RMAX4sEUlV02KVTnMOmUW\nD655MPqsQaxME8FJ5Pbnf6W8z0gmv3Ur+a78Zs9AWGJRnNeZ+Wfcz+oJq5g/dCrFK/+A5dsFgSpn\nqFRhd/jZcidNXnUlBPz1K5PmeOJkvUj9mTuNX5XJNH4zUI4XT6cjYtazPYt6UpRbRMmKkvr0pT8s\nqR+aAs6BYqgezXAav22oiexTVo2PYnch80+/j9VXrWb+mQ9QbAvW/m3R79FrpJOOP/h6+7I/Ul69\nN35PRnB+qMftYdYpsxodt2hPR/ZIZHjVrYAX5x97Nk4vx8QEXncisMEYUwYgIouBMcD6BtvNBuYC\nP0+wzCoBllgU5BRQMrKEHoU9KNtXxrzSeezy7woPd7KNjb/Oj6egG/4rFztDnfZ8Da+XwLql+G/6\nkKnLp7Hyu5WU7StrehJ46HNr/RQsuhI2LSf86MZ3nGxZRYfCWTOcYQCRE8X/ektElqyuzqT1ts9e\npfGrMpnGb6axLPy1lTHr2YqaCm576za6erry7I+epW/Hvmyt2ErB+ffiWf9CcLLuo1D2Nhx1VjYs\ntKbx21Ysy0lGELUf9oSzT1nDJ1IQTPZSENqHX/I7eP6G6P16bhHctRPKNzlzNT4sYfqJ02PGt6/W\nhzfHyVr1+e7PmX7idPp27EvZvjJeLHuRqwZcRYFV0EShVaZo9mjOGLPSGFMBfA9MMcZcaoz5IIH3\n7gF8E3F/S/CxMBEZAhxujHmxBWVuv0KL/IV6BprIaW0bG9vY9CzqydaKrTy29jF2+XeFJ243Wvjn\nzSmUV+2hquMPsMc+SuWta/F07R8+K/HI2kcanYEITwKPLBfGaVyEFv6ZWe6MM87xwhm/rE+TFzlR\nfNTtEZPG/c4OUyznuu2ysGj8qkym8ZuBYvVQzxk1h4KcArp7ujNlyBTu/ehehj81nJIVJVS6XNTN\n2I79499RWVeDfexFzhoKofWR7Dqo+t6pm6u+d+5nBo3ftmQF97+h4c8izsiEE66InexFcOZwzNjh\nnGDMLcR25zlrdRX3xtPRmYAe6zhizqg5PPX5Uwx7chhT3pzC4O6D8dX6qKit4MhORzLh2Am4xIWd\nObGrmtBsT0dwnOQfgaLg/X3AfxpjVjf30hiPhUfwB4ds3Qf8RwJluA64DqBXr17NbZ6dWpBONt56\nGQU5BeS58rDECufajkp9t3wq959xPyXvl7DDv4P7z7w/fFbilY2vAFAysoSeRT2dSeBGnFZrzX74\n4CF4Z06wXH+Euhr4y/XRKfM6HxF7oni3Y5zGya4v02logMavymQavxkq35XP/WfcT0FuARv3bmTp\nV0u58tgruXHwjcx8f2ajdKXzzpxHVU4+U5dPi04zmtcJq3IXLL02oh5+FAq6geVq42/ZLI3fthbr\nmGPistj78A49wBjw7wU7gP3xQsqHjGPqihJKt5fywiUvMOSQIeHjiOknTqdvp774an386fM/sWDN\nAsCJ6WnLp3H/mfdz65u3huN5zqg5eNwevG4PVvrHrmpCIqeR/wDcaIzpbYzpDdyE0whpzhbg8Ij7\nPYFvI+4XAQOBt0RkE3AysCzWZDBjzMPGmOHGmOHdunVL4KOzUAvSycZLO2cbO5wpKl52q4LcAq4Z\ndA0rv1vJ058/zZxRc8JnJXb5d+F154OxnUngT1/hpMhb/O/OGZABY5xyVe93GhzBstqF3akM+LBF\nqJy8qvE45PJN9WkfK3ely6qkGr8qk2n8ZiB/wE95VTm3vnUrg58YzI+X/ZgFaxbw9OdPx0xX2t3T\nHRGhs6eY6SdO59ze50anGV16bfQ+Y+m1zjoK6U/jt63FOuYo34R9+jQn1e3Mcuf69Gn1+/CqfbD0\nGvwDRjN1Rf2E8d+u+S13n3o3Iw4dwWubXuPej+5le+V2vDleHvrkoaiPLd1eSkFOQdTxy7Tl06gz\ndfgDvmZHeaj0lsicjv3GmOWhO8aYd0VkfwKvWwn0E5E+wFZgHHBlxPvsA7qG7ovIW8DPjTGrEix7\n+9KCdLKJrPwdL/Xdxr0bw6kZH/rkIa4ddG14fOXGfRvpnN8Fq6ayvjKCiHSNwRR5ET0a4RR5H5TU\nn4U7bzbF4nImn13yoDOHJDKf9/jF6TAmWeNXZTKN3wzkcXvoUdijUf390CcPcdWAq6Lq7Av6XMCU\noVOY/MbkqPS6AK9teg1PTkHsfUbb162J0PhtazGOOewNr1E+4qfRvWqj5lD86VIsOxDe93u69IuK\n4Vc2voKFFZUuf/aK2Vwz6Jq4xyGRSreXUpRb5NxZOFoXDc5gifzFPhKRh0TkDBE5XUQexDm7MFRE\nhsZ7kTEmANwMvAp8DjxrjPlMRGaJyOjWKX470oJ0sv44aef8Eb0iHreHuaMaZ7d6ffPrlO0rC79m\n476NXLrsUqa/O51cVy4iQqXLhf/Hv8ceOBZ74FjnbMfVL1DZ+QinF2PP1+Gy+k+f2jhF3vt34R99\nvzP28x+z6nN5Q9qsy6HxqzKZxm9msI3tjHsPXvsDfrZWbG1Uf19/wvUYY3j0h4/y0o9f4qI+F3Hz\n4JuZ8d6MRul1Jw2aFF7LI+Y+o7oihd/wwGj8poEYxxz+/j8MJ5cJ78+XT8N/5BnOBru+cBb52/1V\noxje4d8BFTuw9m0BY7PDv4OPtn3EfWfcx5qr1/CX0X/hpsE3MXfUXF7f/HrUa4ccMoT9NfvxV3+v\niwZnODHGNL2ByJtNPG2MMQmt2dFahg8fblataocnM1oyp8Ouo7xia3g85ZBDhjB3ZAnFhT2ixkPW\nBaqptKspyHHGDr+++XVGHzmaeR/PY4d/B3NGzWHpV0vZtG8TU4ZOYcZ7M8Lvd/epd1Ng5YGxue3d\nadELBuZ0wKreB3/5GfbVLzDsqeEETCD8uW5xs/qq1Vg1PmcV003L6wvfe1RLejoybpHKto7flqy/\nsXbj5sTft0/iY6XXTlyb8LbppPcdLyW87ab/uSiRzTR+27FYc+/uO+M+ACprK8P17fUnXM/YfmOZ\n1nDORn4xw54c1qhuXXXVKsr95RTv/RZX0SEx5nR0dVKZHjyN32xm21C50xl9EIwfe+KymDG3esIq\nrFnFTgKZ8+6h7puPKO89Mipm54yaQ7EB1yvTsLsdw/6TrqcyUBl1XDFn1ByKcovYX7O/0Ws9bg/e\nj5/C+ts0J37v2ukknDlwGRe/2aDZmscYc2YqCqKaETONXex0slatn+LSxcwfGlxJdPdXeEoXY518\nY9TBvMuVQ+Hqx/Gf8BP6durLhIIJuCwX94y6B3/AT54rj/HHjMdtuZn8xuSoCYwz3ptByciS8P3Q\n9dT372L+vznZVvjRPPx1VU2k2g3mA2/YkGqDdTmUUiqVIufegVN/3vbWbSw4ewG5rtzwUBRfrY8p\nb06JOYE8Vt26v2Y/S79aylU/OIOCz56HK54ET0dnku/Gd7Mlpa5KNstyGqjjnobcAtj1Bf59W2Lv\nz/dtpsByQ8UOyCmg6qgzWfr5n6JS34Zjct1SLMuN+5SbmPH2jKi4nrZ8GiUjS9i8bzMPnPkABTkF\n+Gp9uC03uasedxocUD/KQ+M44zTbTBSRQ0TkMRF5JXh/gIhck/yiqUZCaeyaSyeb48UaNpGCl36O\ndXd353rYxMYH8zU+rM//SsH/HIH1350pvKcHnqcux6rx4XF72Fu1l9veui3uwoA9CnvQo7BHo8c9\n3i6Q3wEKu+Nx5TcaxjX3lNl4AjUA2N6uVF65GPuunc61t6uO01RKZb14c+/yXHnkufLwB/xc+/dr\n426X745Rt542F1+tj037NuHp0g/+Ng2evQp2fgHPXg1HnKQndVTC7OBwaluEyhwP+V/8jbmnzG6c\nOr/gEKfnYdyf4IPf4ckp4KFPHuLSZZcy+MnBXLrsUh765CEnJgF6jYwb1z2KetC7U29uefOWcBrd\nitoKqP6+zRcNVgcvkT7Wx3GyVd0ZvP8l8AzwWJLKpBJgG9tJWxuclOVxe8KZqZrsFbFtZyxkrtdp\nvET0NNinT8N/8s/w5Hrx1fqYutw5C7etclvMsxtbK7Y2KlfUgoF5hVhAsdvL/BPvwtOxl9Pr8upd\nWBU7sK98lvI6X6PUvsUuL1ZOflsvDqiUUkkTL5mHP+AHCPeCxFuYtbquGm+ON9wjsmX/Fn6z8jfs\n8O/g7lPvpjrgx3PGnTBsonPGevzi6Po0cl+gda1qIGbq/VFz6ZzfiflnPoAnpyD62KO6wslkeczF\n+GsqYsf27q8oCDYa4sW/r9bHi2UvNurZm3/mPApO+zl2bRV+MXgE/LWV0cc+Ku0l8pfqaox5FrAh\nPMFLV2lJlRgLAjZa2O+NyZRXldcvCAWxe0VC80IWjXNS3T59BbhyYfwi7Bk7KB/xn0x+61aGPTkM\nT45zFuKCPheQ78oPp7sLnd2459/uoWNOIR1dnuizHqPmku/Kj5ociTuPgvnDsWYVU7DgJKx1S2Hz\nCvxiYqb29Vdud8q3aJxTXk2Pp5TKMrEWAgwtuBp5FjjWgmr3/Ns9+Gv93PzGzWyr2Ma1f7+Wi/5y\nES9tfCk8/NWu3g8n/6x+XY4cr9PIMLaT1rx6X/2+QOta1UDM1PvLp1K1ewMFT4/HqtxJgSvigD/X\n6ywMfMI4PB/8nrknlzDi0BFc1OciXvrxSzz6w0eh0+HYVz4L3m4x43/WKbP40+d/4uK+F3NBnwvC\nZSndXoonx4sNlNf5mj72UWktkZ6OShHpQnBhHhE5GdiX1FIpR5zJ4/68wsYL+70zlflnzXd6GOKJ\nzLsNzvUzV8H4xfjrqsI9GwBle52za5MGTWLq8ql09XQNj8/cWrGVDrkdyF92C5g65p9eP3ckP78T\ne6r3NDo7Unz6NKw3f1Vflia6Vz0de0WvRZIeKXSVUqrVWGJRnF8clUY0dNa2srYy7sKsW/ZvwTY2\nU9916us+HfvErkcLujsrSYsVe19yyYNQ2F3rWhVT3P1zl36x46XGB2f8EvKKsN6eQ/HOf7Lg3FlU\n5uY3Hs2Qkx+O/3lnzsOT46FsbxnzSufxysZX+Oi7j5h+4vRw7MfqAYQWHPuotJFIT8ftwDLgSBF5\nD3gCmJzUUilHnAUBE1mHI6Ym1vpo+J6hs2t9O/WldHspr2x8JTw+c8zzY8h352Pt34a1bqnTezGr\nmIKXfk5VoCrm2RH/yT9zxmJGjMkMda9GCnXBNiyfUkplG0ssCnIKoq6hcS/ILv8uvMEx7GOeH8Mh\n3kPC9XVo+FWk0OReanxOg6OmovG+5Pkb4bSf179I61oVodn9c8N4yfFCcW/Y9SX0Gom1bil2TUXs\n0Qy1TlxaYuHN8TL8yeFcuuzScCOjdHspfTv1bbIHMCShYx+VNpptdBhjPgZOB04BrgeOM8Z8muyC\nKeI2EuJWBsEzAXE1sdZHw/d8ZeMrvFj2Iv7aJj7rsscaNSQ8Od7YlUJuoXNW5K6dznWc7tW5p8zG\n8/bcRuVTSqn2IrIXZPVVq5l/1nyK84vD9XRkQyPW8Ku5p8zGk98JcjxOD0dunIUCux5df1/rWhUh\n5v755JL6/XPDeLEsZ7X79X+FMb+F3qMaLRIIEY2E4HA+XxPriq2esCoc+5ZYB37so9JGs8OrRORy\n4G/BhXlmAENF5O5gY0QlU6iRELmORa+ReIww97S5LPlyCef0Ooc+nfpQWVtJvis/6uWNJpvneLAi\nU9SeNg1Ovt7p6aj1M/fUXzH1vTvD3aCX9f0RHnEz97S5jYZL5bvyqcwDz5XPRk36bmpyZEFeoVMm\nS8jH4K/10zmvM/POnIc3+FpPoAarYofTkNEUukqpdsoSC4/bQ3VddfgxYwy/Pu3XPPflc8w6ZRYz\n35/Ja5teo2/Hvtx/5v0U5hTiq/XhslzgyqMy4MdjG6z938bcl7Dna61rVUyNhv/VVOD54PcgLipv\nWYOn0xHOPtvY9fM6cgpg+ERYtRAu/DX+uiquP+F6zu51djh17j82/8OZUP7Sz2H8YvJznCxsU5fX\nH2PMGTWH/E0f4O92VP3nBHs6Gh2PBHtBoJkEOyotJLI44KfGmONF5N+Ae4HfAL80xpyUigI21K4W\n92liQcA6DHuq9kT9o4YWjLLEip154rS5FOd1xqr1O2fAKndFLfxT95Mn8VsW3twifMGsEFV11eS7\n8qmqq8Lj9lBRW8HTnz/NQ5881OgzIU7Gi+A2AOVV5Sz5cgkX972Yme/PbFx2Q0syqmTc4j5tHb+6\nOOCB08UB2z5+2xPb2Oyv2R+1UGDopI/B0Dm/czhTYGVtJV63F1/AR01dDb945xf125/6K4pxY2E3\n3pfkFsGBZwrU+G1PbBs7UBU742TEMUBkVrS6QA3ltY0X+iv+dCmuv9+JPWM75VV7WPLVUsYcOYYO\neR2c445aH3UYvDne8MLFl/W/LHwcEath0dSxR5yGR8bFbzZIpIYJZaq6CPidMeYFIDd5RVJhkalv\nI4YlYVlURUz8jhorGexmjJl54p2p+OuqnIlftX6nwREc42sXdmdPwMeUt24L5sa+hW2V3/H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ZRKqqQOr1JKKaWUUirdrV69urvb7X4UGIielD9YNrAuEAhcO2zYsB2hB7XRoZRSSiml2jW32/3o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\nOI7jOE6meKPDcRzHcRzHcZxM+T8AXZTRcVG7VQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e24e2c9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris_p_normalized, hue=\"species\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É um pouco burocrático ter de realizar o pré-processamento manualmente antes do classificador:\n",
    "\n",
    "```python\n",
    "from sklearn import preprocessing, módulo_com_classificador\n",
    "\n",
    "pre_processor = preprocessing.ClasseDePréProcessamento(*args, **kwargs)\n",
    "pre_processor.fit(X_train)\n",
    "\n",
    "X_train_p = pre_processor.transform(X_train)\n",
    "classifier = módulo_com_classificador.ClasseDoClassificador(*args, **kwargs)\n",
    "classifier.fit(X_train_p, y_train)\n",
    "\n",
    "X_test_p = pre_processor.transform(X_test)\n",
    "y_pred = classifier.predict(X_test_p)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na prática, queremos um objeto classificador que abstraia o processo inteiro de pré-processamento e de classificação, isto é, um classificador que já realiza o pré-processamento, para não ser necessário pré-processar cada entrada antes de usar o classificador. A realidade é que isso existe! O `sklearn.pipeline.Pipeline` conecta blocos de pré-processamento (objetos com `fit` e `transform`) a um único classificador recebendo uma lista de pares:\n",
    "\n",
    "```python\n",
    "from sklearn import pipeline\n",
    "pipeline_classifier = pipeline.Pipeline([\n",
    "    (\"Nome da etapa 1 de pré-processamento\", objeto_pre_processador1),\n",
    "    (\"Nome da etapa 2 de pré-processamento\", objeto_pre_processador2),\n",
    "    # ...\n",
    "    (\"Nome da etapa N de pré-processamento\", objeto_pre_processadorN),\n",
    "    (\"Nome da etapa de classificação\", objeto_classificador)\n",
    "])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94736842105263153"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import pipeline\n",
    "pipeline.Pipeline([(\"normaliza\", preprocessing.StandardScaler()),\n",
    "                   (\"classifica\", svm.LinearSVC())]) \\\n",
    "        .fit(X_train, y_train) \\\n",
    "        .score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício: OCR de números!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O scikit-learn possui um dataset de dígitos escritos à mão:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "digits_dict = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos visualizar os primeiros 10 dígitos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0opABAAB0opABAAB0opABAAB0opABAAB0MlIhy8yPZOavMvOpzLx73EMBAADMgkMLWWZe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\nZLVVPLLDkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e203b9c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plt.subplots(2, 5, figsize=(15, 6), sharex=True, sharey=True)[1]\n",
    "for idx, ax in enumerate(axes.flat):\n",
    "    ax.imshow(digits_dict[\"images\"][idx], cmap=plt.cm.gray_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Parte 1*: Bisbilhotar/fuçar/investigar esse dataset. Nesse passo é preciso, no mínimo, identificar o tamanho de cada imagem, o número total de imagens e a quantidade de cada dígito presente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Parte 2*: Utilizando algum dos classificadores disponíveis com o scikit-learn e as técnicas já vistas, criar um OCR para dígitos a partir desse dataset, reservando $30\\%$ do mesmo para a realização de uma avaliação exposta na forma de uma matriz de erro."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parte 3 - Aprendizado não-supervisionado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Até agora estudamos apenas o processo de classificação supervisionada, no qual tínhamos um rótulo categórico para cada vetor de *features*, e gostaríamos de predizê-lo quando novos dados surgissem. Se tivéssemos apenas os *features*, poderíamos inferir alguma informação a partir dos mesmos? Sim, e esses são os processos de aprendizado não-supervisionados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering com o k-means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provavelmente o algoritmo mais famoso de clustering é o k-means. Ele está implementado no scikit-learn na classe `sklearn.cluster.KMeans`. Como classificador, ele possui os métodos `fit` e `predict`, mas dessa vez o `fit` recebe apenas uma matriz de features para treinamento, e os labels são apenas números de $0$ a $k - 1$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O k-means realiza um ciclo para a obtenção de \"centróides\":\n",
    "\n",
    "1. Define $k$ pontos como centróides\n",
    "2. Classifica cada ponto (linha da matriz de entrada) com o centróide mais próximo do mesmo (distância euclideana, tipicamente)\n",
    "3. Atualiza os centróides com os novos centróides de cada conjunto classificado\n",
    "\n",
    "A repetição dos passos $2$ e $3$ até a convergência é o que caracteriza o algoritmo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![](K-means_convergence.gif)](https://en.wikipedia.org/wiki/File:K-means_convergence.gif)\n",
    "\n",
    "(Imagem obtida no Wikipedia)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para exemplificar na prática, vamos aplicar o k-means aos blobs gerados anteriormente!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# O primeiro parâmetro é o número de clusters\n",
    "kmeans_model = cluster.KMeans(4, random_state=42)\n",
    "kmeans_model.fit(X_blob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_blob_pred = kmeans_model.predict(X_blob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Mesmo sem conhecer as classes de cada amostra, o algoritmo encontrou $4$ clusters a partir apenas dos pares de coordenadas de cada ponto. Vamos comparar visualmente o resultado desse processamento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SXHcPgKIlVvkEk5sXVYNMJRnxo+iL6ysQzC+2pSsGJB9jEvCCIBwoK38XBRBvnelxZKkG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\niIiIiGwYJBMRERER2fz/xOLsE1kwxskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e24450eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_blob_dframe = pd.DataFrame(X_blob)\n",
    "full_blob_dframe = pd.concat([\n",
    "    X_blob_dframe.assign(y=y_blob, origin=\"Original\"),\n",
    "    X_blob_dframe.assign(y=y_blob_pred, origin=\"K-Means\")\n",
    "])\n",
    "with sns.axes_style(\"darkgrid\"):\n",
    "    sns.FacetGrid(full_blob_dframe, hue=\"y\", col=\"origin\",\n",
    "                  aspect=1, size=5, sharey=True) \\\n",
    "       .map(plt.scatter, 0, 1, s=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parecem bastante próximos! As cores diferentes apenas representam que o índice arbitrário de cada cluster não são iguais. Podemos fingir que esse processamento foi supervisionado e calcular uma matriz de erro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[500,   0,   0,   0],\n",
       "       [  0, 493,   7,   0],\n",
       "       [  0,  11, 489,   0],\n",
       "       [  0,   0,   0, 500]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Casamento/reordenação das classes realizada empiricamente de forma a\n",
    "# fazer a diagonal da matriz ter os maiores valores de cada coluna\n",
    "metrics.confusion_matrix(y_blob, np.array([3, 0, 1, 2])[y_blob_pred])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apenas $18$ dos $2000$ pontos usados para gerar os blobs não coincidiram. Mesmo sendo uma técnica de aprendizado não-supervisionado, pode-se dizer que ela foi eficaz em identificar as classes nesse caso artificial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aplicar o k-means ao Iris dataset e verificar o resultado através de uma matriz de erro. O k-means consegue classificar corretamente as $3$ espécies? E se usássemos outro valor para o $k$, como $2$, $4$ ou $6$, o que acontece?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separação de fontes de áudio com ICA - Independent Component Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dado um arquivo de áudio stereo, por exemplo:\n",
    "\n",
    "https://freesound.org/s/380165/\n",
    "\n",
    "Podemos separar os lados esquerdo e direito do áudio, convertendo em dois arquivos WAVE, `left.wav` e `right.wav`, usando o FFmpeg:\n",
    "\n",
    "```shell\n",
    "ffmpeg -i arquivo_baixado.mp3 -map_channel 0.0.0 left.wav -map_channel 0.0.1 right.wav\n",
    "```\n",
    "\n",
    "Os lados esquerdo e direito são muito similares nesse arquivo de áudio, mas seria possível separar as duas fontes sonoras (os $2$ cantores), ou pelo menos criar dois arquivos de áudio em que algum dos cantores está em maior evidência que o outro?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos tentar encontrar componentes independentes no áudio usando o `sklearn.decomposition.FastICA`.\n",
    "\n",
    "Para ler os arquivos de áudio como um *array* do NumPy e armazenar depois os resultados em arquivos WAVE, vamos utilizar o módulo `scipy.io.wavfile` do SciPy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44100"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.io import wavfile\n",
    "rate, left = wavfile.read(\"left.wav\")\n",
    "right = wavfile.read(\"right.wav\")[1]\n",
    "rate # Amostras de áudio por segundo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5178240, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio_input = np.stack([left, right]).T\n",
    "audio_input.shape # Par (amostras por canal, número de canais)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int16')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio_input.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O FastICA no scikit-learn é um algoritmo de decomposição, o qual se comporta como uma rotina de pré-processamento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FastICA(algorithm='parallel', fun='logcosh', fun_args=None, max_iter=200,\n",
       "    n_components=2, random_state=42, tol=0.0001, w_init=None, whiten=True)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import decomposition\n",
    "ica = decomposition.FastICA(n_components=2, random_state=42)\n",
    "ica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim como outros modelos de pré-processamento, há um método `fit_transform` que unifica os dois passos `fit` e `transform` já vistos, e pode ser mais eficiente caso o processo do `fit` já exija o cálculo do resultado esperado pelo `transform`. A expressão:\n",
    "\n",
    "```python\n",
    "processor.fit_transform(X)\n",
    "```\n",
    "\n",
    "é funcionalmente equivalente a:\n",
    "\n",
    "```python\n",
    "processor.fit(X).transform(X)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output = preprocessing.MaxAbsScaler().fit_transform(ica.fit_transform(audio_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wavfile.write(\"result_0.wav\", rate, output[:, 0])\n",
    "wavfile.write(\"result_1.wav\", rate, output[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os resultados desse processamento estão disponíveis nos links:\n",
    "\n",
    "* `result_0.wav`: https://freesound.org/s/406216/\n",
    "* `result_1.wav`: https://freesound.org/s/406215/\n",
    "\n",
    "As vozes não estão totalmente separadas, mas, por exemplo, a partir do instante 1:37 (últimos 20 segundos) é possível notar que cada áudio destaca mais uma das vozes, distintamente, sem distorcê-las.\n",
    "\n",
    "É possível obter resultados melhores usando técnicas específicas para essa finalidade, mas é importante notar que esse processo de segregação em componentes independentes não utilizou nada além do arquivo de áudio stereo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para maiores detalhes sobre ICA no scikit-learn, vejam estes dois exemplos na documentação do scikit-learn:\n",
    "\n",
    "* http://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html\n",
    "* http://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_vs_pca.html\n",
    "\n",
    "O primeiro link contém, em particular, uma decomposição de sinais sintéticos, o qual ilustra bem o processo de decomposição realizado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parte Final - O aprendizado não acaba aqui!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Há muitas coisas importantes que não puderam ser apresentadas neste tutorial introdutório, desde questionamentos gerais de ciência de dados:\n",
    "\n",
    "* Como coletar os dados, e como esse processo pode influenciar os processos que seguem?\n",
    "* Como identificar quais dados são relevantes para um estimador?\n",
    "* Como tratar dados incompletos, espúrios e outliers?\n",
    "\n",
    "Como questionamentos mais diretamente vinculados ao scikit-learn:\n",
    "\n",
    "* Como selecinar o algoritmo de classificação?\n",
    "* Como podemos fazer para encontrar o valor ideal do `k`?\n",
    "* Como encontrar os melhores coeficientes do SVM?\n",
    "* Como tratar *features* categóricos?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parte desse conteúdo exige conhecimento não apenas do scikit-learn, mas com outras bibliotecas. Por exemplo, para tratar features categóricos pode-se usar o `sklearn.preprocessing.OneHotEncoder`, mas talvez o `pd.get_dummies` do Pandas seja mais adequado para o que se deseja fazer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Minha sugestão para o autodidata que deseja continuar aprendendo sobre scikit-learn é que procure por material sobre:\n",
    "\n",
    "* Regressão: Os modelos de regressão são similares aos vistos para a classificação, com a diferença de que o resultado não é mais categórico\n",
    "* PCA: Decomposição fundamentada nas dimensões de maior variabilidade comumente utilizada para redução da dimensionalidade\n",
    "* Grid Search: busca exaustiva por parâmetros para os estimadores\n",
    "\n",
    "E veja os exemplos disponibilizados em http://scikit-learn.org/stable/auto_examples/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# FIM! Espero que tenha gostado! =D"
   ]
  }
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