{ "cells": [ { "cell_type": "markdown", "id": "099a20c2-12d1-4e67-9838-0b9197ca2a85", "metadata": {}, "source": [ "# Distribuições\n", "\n", "A estatística descritiva é limitada para resumir dados, visto que dados com características completamente distintas podem ter os mesmos valores para média, mediana e variância, por exemplo. \n", "\n", "Uma forma aprofundada de reconhecer as características dos dados é inspecionar a sua _distribuição_. Neste capítulo, discutiremos algumas distribuições e outras técnicas de exploração de dados." ] }, { "cell_type": "code", "execution_count": 1, "id": "1db5c275-6d02-4f08-87f1-8085cb779e0b", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import scipy as sp\n", "import seaborn as sb\n", "import matplotlib.pyplot as plt\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "id": "c2d4b080-a0f4-4ca1-9bc3-86b09c4e73f3", "metadata": {}, "source": [ "## Função massa de probabilidade \n", "\n", "A _função massa de probabilidade_ (FMP) associa uma probabilidade à ocorrência de um certo valor dentro de um _espaço de resultados_. Podemos entender a FMP como um histograma normalizado. Isto é, todas as frequências são divididas pelo número de amostras. \n", "\n", "Para estudar a FMP e os demais conceitos neste capítulo, usaremos um banco de dados que contém dados sobre adultos americanos, tais como idade, nível de escolaridade, ocupação, estado civil, entre outros. \n", "\n", "Primeiramente, carregamos o _dataset_. " ] }, { "cell_type": "code", "execution_count": 2, "id": "b860d4b6-b8cb-4ef7-95c1-2ee1a82835d2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agetype-employerfnlwgteducationeducation_nummaritaloccupationrelationshipracesexcapital_gaincapital_losshr_per_weekcountryincome
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States<=50K
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States<=50K
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States<=50K
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States<=50K
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba<=50K
\n", "
" ], "text/plain": [ " age type-employer fnlwgt education education_num \\\n", "0 39 State-gov 77516 Bachelors 13 \n", "1 50 Self-emp-not-inc 83311 Bachelors 13 \n", "2 38 Private 215646 HS-grad 9 \n", "3 53 Private 234721 11th 7 \n", "4 28 Private 338409 Bachelors 13 \n", "\n", " marital occupation relationship race sex \\\n", "0 Never-married Adm-clerical Not-in-family White Male \n", "1 Married-civ-spouse Exec-managerial Husband White Male \n", "2 Divorced Handlers-cleaners Not-in-family White Male \n", "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", "4 Married-civ-spouse Prof-specialty Wife Black Female \n", "\n", " capital_gain capital_loss hr_per_week country income \n", "0 2174 0 40 United-States <=50K \n", "1 0 0 13 United-States <=50K \n", "2 0 0 40 United-States <=50K \n", "3 0 0 40 United-States <=50K \n", "4 0 0 40 Cuba <=50K " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adults = pd.read_csv('../database/adults.csv',skiprows=1)\n", "adults.head()" ] }, { "cell_type": "markdown", "id": "b2207b30-8937-42ab-8e2f-f459c80aa656", "metadata": {}, "source": [ "Comparemos a distribuição da variável _idade_ por histograma e pela PMF." ] }, { "cell_type": "code", "execution_count": 3, "id": "ba2c6037-4896-45c4-a9bc-77d0c0863d9a", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,5))\n", "\n", "# histograma\n", "plt.subplot(121)\n", "idade = adults['age']\n", "idade.hist(alpha=.6,bins=10,\n", " color='#aa88bb',edgecolor='w',\n", " grid=False);\n", "plt.xlabel('Idade')\n", "plt.title('Histograma')\n", "\n", "# FMP\n", "plt.subplot(122)\n", "idade = adults['age']\n", "idade.hist(alpha=.9,bins=10,density=True,\n", " color='#aa88bb',edgecolor='w',\n", " grid=False);\n", "\n", "plt.xlabel('Idade')\n", "plt.title('FMP');" ] }, { "cell_type": "markdown", "id": "5b27ce8f-9ce0-469c-9fee-772c1943c311", "metadata": {}, "source": [ "## Função distribuição cumulativa \n", "\n", "A _função distribuição cumulativa_ (CDF) calcula a probabilidade de uma variável com uma dada distribuição de probabilidade ter um valor menor ou igual a $x$. Em outras palavras, para calcularmos $CDF(x)$ para um valor particular $x$, devemos computar a fração dos valores na amostra que são menores ou iguais a $x$. A CDF é similar ao conceito de _percentil_, mas a resposta é um valor no intervalo [0,1], em vez de um _ranque_ na faixa 0-100.\n", "\n", "Considerando um _array_ de amostras `t` e o dado valor $x$, que não necessariamente está em `t`, podemos implementar uma função `CDF` da seguinte forma:" ] }, { "cell_type": "code", "execution_count": 4, "id": "3bff89ca-d9bf-4ff7-b1b9-9cdab970c231", "metadata": {}, "outputs": [], "source": [ "# implementação da CDF\n", "def CDF(t,x):\n", " c = 0 # contagem\n", " for ti in t: # cada valor na amostra\n", " if ti <= x:\n", " c += 1\n", " prob = c / len(t) # probabilidade\n", " return prob" ] }, { "cell_type": "markdown", "id": "8a3152d8-be8d-45a1-b494-95191bd93d20", "metadata": {}, "source": [ "Vejamos um exemplo simples." ] }, { "cell_type": "code", "execution_count": 5, "id": "56ac2c3c-4388-4163-bb26-6124f8cb4cb9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.3333333333333333, 0.6666666666666666, 0.8333333333333334)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# amostra\n", "t = [1,2,3,3,5,7]\n", "\n", "CDF(t,2), CDF(t,3), CDF(t,5)" ] }, { "cell_type": "markdown", "id": "617d2443-c37b-4956-9e00-f689b10c6023", "metadata": {}, "source": [ "Comentários:\n", "\n", "- A probabilidade de se encontrar um valor menor ou igual a 2 em `t` é de 33,3%.\n", "\n", "- A probabilidade de se encontrar um valor menor ou igual a 3 em `t` é de 66,6%.\n", "\n", "- A probabilidade de se encontrar um valor menor ou igual a 5 em `t` é de 83,3%." ] }, { "cell_type": "markdown", "id": "f8d91cee-3a96-448d-b1ba-d17bbf985735", "metadata": {}, "source": [ "Para valores fora da amostra:" ] }, { "cell_type": "code", "execution_count": 6, "id": "8b712546-5209-4406-8d06-dcc1785de51e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.6666666666666666, 0.16666666666666666)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CDF(t,3.45), CDF(t,1.11)" ] }, { "cell_type": "markdown", "id": "3837ce1a-3e50-429d-acf8-ad9667ee7bbf", "metadata": {}, "source": [ "Se $x$ for menor do que o menor valor de `t`, `CDF(x) = 0`; se $x$ for maior do que o maior valor de `t`, `CDF(x) = 1`." ] }, { "cell_type": "code", "execution_count": 7, "id": "0f62ee38-9f43-4da2-a17c-f8e4af4e72e9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 0.0, 1.0, 1.0)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CDF(t,-4), CDF(t,0.8), CDF(t,7.5), CDF(t,12)" ] }, { "cell_type": "markdown", "id": "ab385ee0-57ed-41e6-ab8c-ad7f542cd7e5", "metadata": {}, "source": [ "A CDF é uma função _step_:" ] }, { "cell_type": "code", "execution_count": 8, "id": "43948aba-862a-4c3e-a0f1-e78e7c190e1c", "metadata": {}, "outputs": [], "source": [ "sex = adults['sex']\n", "im = idade[sex == ' Male']\n", "fm = idade[sex == ' Female']" ] }, { "cell_type": "code", "execution_count": 9, "id": "4820a5e9-c4ac-440e-9d06-d9d80093fe4f", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "im.hist(density=True,cumulative=True,\n", " histtype='step',grid=False,linewidth=2,\n", " label='Male')\n", "fm.hist(density=True,cumulative=True,\n", " histtype='step',grid=False,linewidth=2,\n", " label='Female')\n", "\n", "plt.xlabel('Idade')\n", "plt.ylabel('CDF')\n", "plt.legend(loc=8);" ] }, { "cell_type": "markdown", "id": "7c9fe7e7-82ba-4b0b-b285-ff546060470e", "metadata": {}, "source": [ "## Medição de assimetria\n", "\n", "A assimetria das distribuições pode ser medida pelo _coeficiente de Pearson_, dado por:\n", "\n", "$$p = 3(\\mu - \\theta)\\sigma,$$\n", "\n", "onde $\\mu$ é a média, $\\theta$ é a mediana e $\\sigma$ é o desvio padrão.\n", "\n", "Podemos defini-lo como:" ] }, { "cell_type": "code", "execution_count": 10, "id": "df189fb4-da74-4574-80f9-b561abfcfaed", "metadata": {}, "outputs": [], "source": [ "# coeficiente de pearson\n", "def p(x):\n", " return 3*( x.mean() - x.median() )*x.std()" ] }, { "cell_type": "markdown", "id": "0672a649-b008-45be-af28-57da2038d5d5", "metadata": {}, "source": [ "A seguir calculamos o coeficiente de Pearson para a série de idades para homens e mulheres." ] }, { "cell_type": "code", "execution_count": 11, "id": "dc3584f5-3835-4dca-bd2c-07dc4b427daf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(57.50230041174153, 78.12203531109172)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p(im), p(fm)" ] }, { "cell_type": "markdown", "id": "d1d827ae-6a66-452b-a800-bfbc25b0dc0d", "metadata": {}, "source": [ "Comparemos com os histogramas." ] }, { "cell_type": "code", "execution_count": 12, "id": "eea492a1-6ca7-40dd-9b9e-d492b96cc53f", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "im.hist(grid=False,label='Male')\n", "fm.hist(grid=False,label='Female')\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "9dc995ea-0350-4453-b215-e742a7a4b0ab", "metadata": {}, "source": [ "A interpretação da assimetria é a seguinte:\n", "\n", "- Na assimetria à direita (ou positiva), a moda e a mediana localizam-se à esquerda da média. Isto significa que o histograma é mais denso à esquerda. \n", "\n", "- Na assimetria à esquerda (ou negativa), a moda e a mediana localizam-se à direita da média. Isto significa que o histograma é mais denso à direita.\n", "\n", "- Coeficientes de assimetria positivos explicam assimetria à direita.\n", "\n", "- Coeficientes de assimetria negativos explicam assimetria à esquerda.\n", "\n", "- Coeficientes de assimetria nulos explicam simetria (distribuição normal)." ] }, { "cell_type": "markdown", "id": "c7bd6316-e646-45bc-8745-45c27aa99611", "metadata": {}, "source": [ "Comentários:\n", "\n", "- No exemplo anterior, o coeficiente de Pearson maior para a distribuição de idades entre mulheres mostra que ela é mais assimétrica do que a distribuição de idades para mulheres." ] }, { "cell_type": "markdown", "id": "3358b0c4-a959-4550-95db-41c0030abf5e", "metadata": {}, "source": [ "## Distribuições contínuas\n", "\n", "Distribuições baseadas em observações de amostras finitas são chamadas de _distribuições empíricas_, a exemplo da FMP anterior. Em muitas situações, necessitamos de _distribuições contínuas_. No caso de funções contínuas, a FMP é generalizada para a _função densidade de probabilidade_ (PDF). A PDF é definida por meio de integração." ] }, { "cell_type": "markdown", "id": "4cc06cef-886d-4b8e-a744-2cb6417f902d", "metadata": {}, "source": [ "### Distribuição exponencial\n", "\n", "A _distribuição exponencial_ (DE) serve para medir tempos entre eventos, que são chamados de \"tempos de chegadas\" (_inter-arrival times_). Se os eventos têm a mesma probabilidade de ocorrer a qualquer momento, a distribuição dos tempos de chegadas tende a se parecer com uma distribuição exponencial.\n", "\n", "DEs são úteis para descrever: o tempo de realização de uma prova, o tempo de vida de aparelhos, o tempo de espera em restaurantes, o tempo para realizar uma prova.\n", "\n", "A CDF e a PDF de uma distribuição exponencial são definidas por:\n", "\n", "$$CDF(x) = 1 - e^{-\\lambda x}, \\ \\ \\ PDF(x) = \\lambda e^{-\\lambda x},$$\n", "\n", "onde $\\lambda$ é um parâmetro que define o formato da distribuição.\n", "\n", "Uma DE tem: \n", "\n", "- média = $1/\\lambda$\n", "- variância = $1/\\lambda^2$\n", "- mediana = $\\ln(2)/\\lambda.$" ] }, { "cell_type": "markdown", "id": "0920fa8d-4e9f-423e-9556-fab22b43b1dd", "metadata": {}, "source": [ "Para plotar uma DE aleatoriamente, podemos utilizar o _numpy_" ] }, { "cell_type": "code", "execution_count": 13, "id": "3c8a185e-63fa-487d-9efa-50e0916d9b5d", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# usando numpy\n", "lamb = 1.3\n", "n = 300\n", "de = np.random.exponential(scale=lamb,size=n)\n", "sb.distplot(de);" ] }, { "cell_type": "markdown", "id": "68188c2b-188b-49b4-99da-d80842965cf6", "metadata": {}, "source": [ "ou o _scipy_" ] }, { "cell_type": "code", "execution_count": 14, "id": "e041200e-e138-40e9-a6c2-39997f8f9850", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# usando scipy\n", "des = sp.random.exponential(scale=lamb,size=n);\n", "sb.distplot(des,color='#aa88bb');" ] }, { "cell_type": "markdown", "id": "edf04f7a-5139-481c-9664-1718415ea363", "metadata": {}, "source": [ "No próximo exemplo, usamos um valor bem maior de $\\lambda$:" ] }, { "cell_type": "code", "execution_count": 15, "id": "886946ec-f991-4f74-a4bc-477e2cfafd05", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lamb = 50.3\n", "n = 100\n", "de = np.random.exponential(scale=lamb,size=n)\n", "sb.distplot(de);" ] }, { "cell_type": "markdown", "id": "c5dbf2be-aafe-430c-93d2-fe9544ca7c3a", "metadata": {}, "source": [ "### Distribuição normal\n", "\n", "A _distribuição normal_, ou _distribuição Gaussiana_, possui um formato particularmente simétrico. Ela é a distribuição mais comumente empregada para representar fenômenos econômicos, naturais, sociais, entre outros. Enquanto a CDF para a distribuição normal dependa de integrais que não podem ser expressas em termos de funções elementares, a sua função densidade de probabilidade é dada por\n", "\n", "$$PDF(x) = \\dfrac{1}{\\sqrt{2\\pi\\sigma^2}}e^{- \\frac{(x-\\mu)^2}{2\\sigma^2}},$$\n", "\n", "onde $\\mu$ é a média e $\\sigma$ o desvio padrão de uma série de dados. \n", "\n", "À medida que os valores de $\\mu$ e $\\sigma$ são alterados, a forma de \"sino\" da distribuição \"alarga\", \"afina\", \"estica\" ou se \"contrai\".\n", "\n", "Vejamos como gerar DNs através do _scipy_." ] }, { "cell_type": "code", "execution_count": 16, "id": "29d12edd-0858-4284-bdd8-aca5245ad5f6", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAD4CAYAAAAq9brQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABO80lEQVR4nO3dd3xc1Zn/8c+Zot6LLVsuso3BNtg0gzHgQEILJZQlCSUEzKYsSSBtk196tqWxSchmk2woIQQIhBISWui9G9u49y7LTc2yuqad3x9XsmVblkbSXM1c6ft+vfwaacq9j2SdmWfOPOc5xlqLiIiIiIjEz5fsAEREREREvEZJtIiIiIhIPymJFhERERHpJyXRIiIiIiL9pCRaRERERKSfAskOoL9KSkpsRUVFssMQSRmLFy+utdaWJjuOI9GYFTlA41XEW3obs55LoisqKli0aFGywxBJGcaYbcmOoTcasyIHaLyKeEtvY1blHCIiIiIi/aQkWkRERESknzxXznGocDhMVVUV7e3tyQ7FczIyMhg3bhzBYDDZocgI0tDQwK5du5IdRkrT2JRUofGaOBrXw4/nk+iqqipyc3OpqKjAGJPscDzDWktdXR1VVVVMmjQp2eHICFJbW0tFRQWZmZnJDiUlaWxKKtF4TQyN6+HJ8+Uc7e3tFBcXK4HuJ2MMxcXFmsGXIRcOh8nIyEh2GClLY1NSicZrYmhcD0+eT6IBJdADpN+bJIv+9nqn34+kEv09JoZ+j8PPsEiiRURERESGkpJoEREREZF+UhI9RBYsWMDpp5/OvHnz+NrXvpbw469cuXL/8W+88UastQk/h8hI0draysUXX8zZZ5/NZZddRkdHR0KPv3PnTk466SQyMjKIRCIJPbaIHM7tMe32a7ykJs935+juwQWVg3r8tXMmJCiSw02cOJFXXnmFjIwMPvWpT7FixQpmzpyZsOMfc8wxvPPOOwDceOONLFq0iFNOOSVhxxdxQ6qO2eeee445c+bwwx/+kB//+Mc899xzXHbZZQk7flFRES+//DJXXHFFwo7pBQ8uqHT1eVbclarjNR5uj2m3X+MlNWkmuh9CoRCXX345AHV1dVx33XVxP7asrGz/CudAIIDf7+/xfj/4wQ8444wzyM/PJy8vjyVLlsR1/O59J9PT0xk/fnzcsYkMR4MZr1OmTNk/U9XQ0EBxcXGP9xvoeM3IyKCwsDDueESGu8GM13jEO6ZhYOM63td4GV6G1Uy029atW8e0adMAWLFiBcceeywAlZWVXH/99Qfdt7y8nAceeOCwYyxfvpza2lpmzJhx2G1vvPEGO3fu5O233+bhhx+msrKSE088Me7jP/nkk3z3u9/l6KOP7vUJQmQkGMx4nTp1KgsWLODYY49l1KhR3HrrrYcdf7DjVUQOSMTra2/iGdMw+HHd22u8DD9Kovth5cqV+z+e6f5RzYQJE3jttdf6fHx9fT0333wzjzzySI+3P/fcc8yfPx8Av9+/f3Y53uNfeumlXHrppdxyyy08/fTTI+6jYpHuBjNe7733Xi644AK++c1v8otf/II///nPh72ADna8isgBg3193b17N1dfffVB15WVlfHQQw8B8Y1pGNy47us1XoYfJdH9sGrVKubNmwfA008/zR133AHE9045Eolw3XXX8fOf/5yysrL919XV1TF69GgAOjo6CIVCxGIx7rvvPn75y1/GffyOjg7S09MByMvL0+5SMuINZrxaaykqKgKgpKSEffv2JXS8iuqj5WCDGa/gJMy9JbnxjGkY+Lju6TVehj8l0f2wdu1a1q9fz9133017ezvvvfceFRUVcb1DffTRR1m4cCHf+ta3APjpT39KaWkpt956K3fddRcA8+fPZ/78+WRnZ/O5z32OqVOnAvG9A37uuee47bbbAOdjq/PPP39wP6yIxw1mvF577bVcddVV3H///QSDQR5++GG2bt2asPEaDoe58MILWbZsGRdccAE/+clPmDNnTiJ+bBFPGsx4jUc8YxoGPq57eo2fO3fuoOOW1Daskmi3ZzVaWlp49tlnB/TYa665hmuuueag6x577LGDrps5cyaLFy8e0PEvu+yyhK40FhkKbo7ZwYzXgoICnn/++YOue/XVVxM2XoPBIC+99NKAHiuSLKk6XuMRz5iGgY/rnl7jZfgbVkm0m9ra2khLS0voMa+88sqEHk9EHBqvIt7hxniNh8a0DJZa3MUpMzOTJ554ItlhiEgcNF5FvEPjVbxKSbSIiIiISD8piRYRERER6Scl0SIiMqTue3crt724nj2N7ckORURkwFxNoo0xHzXGrDPGbDTGfLuX+51ijIkaYz7uZjwiIpJcsZjlh0+sora5g5fXVCc7HBGRAXMtiTbG+IHfARcCM4BrjDGH7YPZeb9bgecPvU1ERIaXNbsb93+9Yse+JEYiIjI4bs5EnwpstNZuttaGgIeAnhoZ3wI8BmhKQkRkmFu6vQGArDQ/OxrakhuMiMgguJlElwPbu31f1XndfsaYcuAK4PbeDmSM+bwxZpExZlFNTU3CAxWRxNKYlSNZu6uJ3PQAk0qyqdrbmuxwBI1XkYFyM4k2PVxnD/n+f4BvWWujvR3IWnuntXa2tXZ2aWlpouIbUjt37uSkk04iIyODSCRyxOvccNttt3HmmWe6dnyRQw2HMfu1r32NefPm8ZWvfCXhx966dSujR4/m7LPP5vzzz0/48VPZ2t2NTBuTS056gL0toWSHI3h/vPb0WrpgwQJOP/105s2bx9e+9rWEn9Pt44s3uLljYRUwvtv344Cdh9xnNvCQMQagBLjIGBOx1j4+oDMuumdADzsQzY2De3wvioqKePnll7niiit6vS7ROjo6WLZsmWvHFxmUFB2zH3zwAS0tLbz55pt84QtfYOHChZxyyikJPcd5553Hn//854Qe0wvW72nmkllj2NnQzr62MLGYxefrac5FUk6KjteeXksnTpzIK6+8QkZGBp/61KdYsWIFM2fOTNg53T6+eIObM9ELganGmEnGmDTgauDJ7new1k6y1lZYayuAvwJfHHACPQRCoRCXX345AHV1dVx33XVxPzYjI4PCwsI+r+vJD37wA8444wzy8/PJy8tjyZIlcZ/3D3/4AzfccEPc9xcZLgYzXt99913OPfdcAM4991zee++9Hu83mLH56quvMm/ePH71q1/F/Rivaw9H2dcWpq45RFaan5iFpnb3PoUT70j062tZWRkZGRkABAIB/H5/j48d6BiO9/gyvLk2E22tjRhjbsbpuuEH/mitXWWMuanz9l7roFPRunXrmDZtGgArVqzg2GOPBaCyspLrr7/+oPuWl5fzwAMPDPqcb7zxBjt37uTtt9/m4YcfprKykhNPPDGuc4bDYV5//XW+9KUv8cMf/nDQsYh4yWDGa0NDA1OmTAEgPz+fVatWHXb8wYzNMWPGsH79etLT07nssss455xzmDVrVmJ+8BS2ry0MQH5WkFjMqe7b2xoiPyuYzLAkBbj1+rp8+XJqa2uZMeOw5mCDGsPxHF+GPzfLObDWPgM8c8h1PSbP1tr5bsaSCCtXrtz/cU33j24mTJjAa6+95so5n3vuOebPnw+A3+8nGAzGfc7777+fa6+91pW4RFLdYMZrQUEBjY1OK7bGxkYKCgoOu89gxmZ6ejrp6ekAXHLJJaxcuXJEJNENrU4SXZAZpC3sLIVp6EysZWRz4/W1vr6em2++mUceeaTH2wczhuM5vgx/ribRw82qVauYN28eAE8//TR33HEHkNiZ6EgkQl1dHaNHjwacmuZQKEQsFuO+++7jl7/8ZdznXLduHUuXLuX2229n1apV/OY3v+GWW27pd0wiXjSY8Tp37lzuuOMOPvnJT/LSSy8xf/78hI7NpqYmcnNzAXj77bdHzLhs7JqJzgzSuRaGva1aXCiJf32NRCJcd911/PznP6esrGz/dYkawz0dX0YeJdH9sHbtWtavX8/dd99Ne3s77733HhUVFXG9aw2Hw1x44YUsW7aMCy64gJ/85CecdNJJh11XXFzMrbfeyl133QXA/PnzmT9/PtnZ2Xzuc59j6tSpQHzvlG+99db9X5955pkj5oVaBAY3XrtW+s+bN4/jjz+eU089lY0bNyZsbL755pv84Ac/ID09nTPPPJM5c+Yk4kdOea2ds89ZaQFC0RhwILGWkS3Rr6+bN29m4cKFfOtb3wLgpz/9KaWlpQkbw48++uhhx587d+7gfgniOcMriXaxuwZAS0sLzz777IAeGwwGeemllw67/tDrHnvsMa655pr938+cOZPFixcP6JzdvfXWW4M+hkjCuThmBzNeAX79618f9P2yZcsSNjYvuugiLrroogHH5lVtoQh+nyHoN6QHnIVYraFeO5xKKknR8drT6+ucOXMOGq+Q2NfXa6655rDjy8gzvJJoF7W1tZGWlub6ea688krXzyEy3LkxXjU2B681FCUr6McYQ3rAaQ7V0qHuHCOdXl/Fq9xscTesZGZm8sQTTyQ7DBGJg8ZramoLR8lMc2agg37n5Ucz0aLxKl6lJFpERIZEW+hAEu33ObPRmokWEa9SEi0iIkOiLeyUc3TJTg/QElISLSLeNCySaGttskPwJP3eJFn0t9e74fr7aQ1FyUw7sBQnK81Pa4fKOVLdcP17HGr6PQ4/nk+iMzIyqKur0x9nP1lrqaur279tqchQCQaDtLe3JzuMlDWcx2ZbOEpW2oGZ6Jz0AM0q50hpGq+JMZzH9Ujm+e4c48aNo6qqipqammSH4jkZGRmMGzcu2WHICFNSUsLWrVuTHUZKG45jMxqzhCIx0oMH5m6y0vxaWJjiNF4TZziO65HO80l0MBhk0qRJyQ5DROJUUFDQ4zbaMrx1bfOd5j+QRDe2R6hp6khWSBIHjVeRI/N8OYeIiKS+ts4Z52C3JDro9xGOqhRPRLxJSbSIiLiuvYeZ6KDfEO7c/ltExGuURIuIiOu6yjmCgW5JtM9HJKaZaBHxJiXRIiLiugPlHGb/dQHNRIuIhymJFhER1+2fiT6sJlpJtIh4k5JoERFxXU/dOQJ+QyRq1edfRDxJSbSIiLiu/QjdOSwQ0my0iHiQkmgREXHdgXKOAzXRQZ/zdUdESbSIeI+SaBERcV3XzoRpge7lHM7XXe3vRES8REm0iIi4rv0ICwsBOsKaiRYR71ESLSIirut5x0KnnEMz0SLiRUqiRUTEdW3hKH5j8Pu61UTvL+fQTLSIeI+SaBERcV1bOEowYA66LtA1Ex3RTLSIeI+SaBERcV17OHpQKQc423533SYi4jVKokVExHVtoR6SaJVziIiHKYkWERHXtYWjB+1WCN3KOTQTLSIepCRaRERc1xaOHbTRCnSfiVYSLSLeoyRaRERc195jOYd2LBQR71ISLSIirmvrYWFhoHNhYUhJtIh4kJJoERFxndPi7uCXnK6e0aGokmgR8R4l0SIi4rq2UJQ0f899ojUTLSJepCRaRERc11M5h88YfEZJtIh4k5JoERFxnTMTffhLjt9nVM4hIp6kJFpERFxlre2xJhqcxYWaiRYRL1ISLSIirupqYXdoOQdAwGfU4k5EPElJtIiIuKot5GymcuhmKwB+v9FMtIh4kpJoERFxVVvnjoQ91UQHVBMtIh6lJFpERFzVlUT3XM7hIxTRtt8i4j2uJtHGmI8aY9YZYzYaY77dw+2XGWOWG2OWGmMWGWPOdDMeEREZegfKOY7QnUPlHCLiQQG3DmyM8QO/A84DqoCFxpgnrbWru93tZeBJa601xswCHgGmuRWTiIgMvfaumejA4TXRKucQEa9ycyb6VGCjtXaztTYEPARc1v0O1tpma63t/DYbsIiIyLDSW020FhaKiFe5mUSXA9u7fV/Ved1BjDFXGGPWAv8A/rmnAxljPt9Z7rGopqbGlWBFJHE0ZqW73so5AirnSDqNV5GBcTOJPvxzux5mmq21f7fWTgMuB/6rpwNZa++01s621s4uLS1NbJQiknAas9JdXwsL1Sc6uTReRQbGzSS6Chjf7ftxwM4j3dla+wYwxRhT4mJMIiIyxHrtE62aaBHxKDeT6IXAVGPMJGNMGnA18GT3OxhjjjLGmM6vTwLSgDoXYxIRkSG2vya6x22/Vc4hIt7kWncOa23EGHMz8DzgB/5orV1ljLmp8/bbgSuB640xYaANuKrbQkMRERkGeivnUIs7EfEq15JoAGvtM8Azh1x3e7evbwVudTMGERFJrvZQFGOcWedDBfwq5xARb9KOhSIi4qq2cJTMoJ/O6r2DODsWKokWEe9REi0iIq7qSqJ7onIOEfEqJdEiIuKqtlCMjCMk0QGfIRKzxGJaDiMi3qIkWkREXNUejpKZduQkGlBdtIh4jpJoERFxVa/lHJ0dO7Thioh4jZJoERFxVVvoyEn0/ploJdEi4jFKokVExFVt4SgZKucQkWFGSbSIiLjKmYnu+eXGr5loEfEoJdEiIuKq3mqiA5010UqiRcRrlESLiIir2uLpzqEkWkQ8Rkm0iIi4qj0UPWKf6P3lHNHoUIYkIjJoSqJFRMRVbeEoWX3MRKvFnYh4jZJoERFxTTgaIxKzanEnIsNOXEm0MeYxY8zFxhgl3SIiEre2sFOmccRyDi0sFBGPijcp/j1wLbDBGPMzY8w0F2MSEZFhoj3kJNHa9ltEhpu4kmhr7UvW2k8BJwFbgReNMe8YY240xgTdDFBERLyrayZa5RwiMtzEXZ5hjCkG5gOfBZYAv8ZJql90JTIREfG8vpJobbYiIl4ViOdOxpi/AdOA+4GPWWt3dd70sDFmkVvBiYiIt7V1lnNkpPmhNXzY7V2brag7h4h4TVxJNPAHa+0z3a8wxqRbazustbNdiEtERIYBlXOIyHAVbznHj3q47t1EBiIiIsNP10x0n+UcWlgoIh7T60y0MaYMKAcyjTEnAqbzpjwgy+XYRETE4/bPRGuzFREZZvoq57gAZzHhOOC2btc3Ad91KSYRERkm+pqJNsbg9xmVc4iI5/SaRFtr7wXuNcZcaa19bIhiEhGRYaK9j81WwJmN7ohEhyokEZGE6Kuc4zpr7Z+BCmPM1w+93Vp7Ww8PExERAfou5wAnidZMtIh4TV/lHNmdlzluByIiIsNPW8hJjo9UzgFOmzvVRIuI1/RVznFH5+V/DE04IiIynLSFo6QFfPu7cPREM9Ei4kVxtbgzxvy3MSbPGBM0xrxsjKk1xlzndnAiIuJt7eFor7PQAAG/aqJFxHvi7RN9vrW2EbgEqAKOBr7pWlQiIjIstIXiSKJ9Ps1Ei4jnxJtEBzsvLwL+Yq2tdykeEREZRtrC0V4XFUJnOYc2WxERj4l32++njDFrgTbgi8aYUqDdvbBERGQ4aAtHe21vB+D3GzrCSqJFxFviSqKttd82xtwKNFpro8aYFuAyd0MTERGvaw9HaemI8OCCyiPeRzPRIh626J4DX8++8ci39XS7x8U7Ew0wHadfdPfH3JfgeEREZBhpDUUJ+o/cmQOcmmjNRIuI18SVRBtj7gemAEuBriXUFiXRIiLSi7ZQlKC/9+U3Ab/ZvymLiIhXxDsTPRuYYa21bgYjIiLDS3s4SnZ67y81zky0kmgR8ZZ4u3OsBMrcDERERIaftnCUtL5molUTLSIeFO9MdAmw2hjzPtDRdaW19lJXohIRkWGhLRwlGOijJlrdOUTEg+JNov/dzSBERGR4iqsm2mfo0Ey0iHhMvC3uXjfGTASmWmtfMsZkAb03/hQRkREtFrN0RGJxLCx0diy01mJM77PWIiKpIq6aaGPM54C/And0XlUOPO5STCIiMgy0R5zFgvHURAOqixYRT4l3YeGXgDOARgBr7QZgVF8PMsZ81Bizzhiz0Rjz7R5u/5QxZnnnv3eMMcf3J3gREUldbSEniQ4G4kyiI0qiRcQ74k2iO6y1oa5vOjdc6bXdnTHGD/wOuBCYAVxjjJlxyN22AGdZa2cB/wXcGW/gIiKS2rp6P6f1sdmKv3OmukNJtIh4SLxJ9OvGmO8CmcaY84BHgaf6eMypwEZr7ebOBPwhDtkq3Fr7jrV2b+e37wHj4g9dRERSWXtnEh3PwkLQTLSIeEu8SfS3gRpgBfAvwDPA9/t4TDmwvdv3VZ3XHclngGd7usEY83ljzCJjzKKampo4QxaRZNGYFYC2kJMUx5tEayY6OTReRQYm3u4cMWPM48Dj1tp4R1hPn9/1WAJijPkwThJ95hHOfyedpR6zZ8/WrokiKU5jVuBAOUc83TlAM9HJovEqrrAWtr0DVQth1Aw46txkR5RwvT6zGce/G2NqgbXAOmNMjTHmh3EcuwoY3+37ccDOHs4xC/gDcJm1ti7+0EVEJJW1hiJA3zXRB2aitfW3yLDx+q2w4hFob4B1/4DKd5IdUcL1Vc7xVZyuHKdYa4uttUXAHOAMY8zX+njsQmCqMWaSMSYNuBp4svsdjDETgL8Bn7bWrh/IDyAiIqlpf020unOIjCwr/wav/RTGnQof+QEUToINL0Ik1PdjPaSvJPp64Bpr7ZauK6y1m4HrOm87ImttBLgZeB5YAzxirV1ljLnJGHNT591+CBQD/2eMWWqMWTTAn0NERFJMf8s5VBMtMgzUb4Ynv+wk0LOuAuNzSjnaG2DD88mOLqH6qokOWmtrD73SWltjjAn2dXBr7TM4ixC7X3d7t68/C3w2zlhFRMRD+ruwUDPRIh4XjcCjN4LPDx+/Gza+7FxfOg2CWbDmaZj+seTGmEB9JdG9zbsPrzl5ERFJqAN9ovuaiVZNtMiwsPge2LUUPvEnKJhw4HqfH0YfC+ufhVjU+X4Y6Kuc43hjTGMP/5qAmUMRoIiIeNOBPtF9bLaiFnci3hduh1d/DJM+BDMuP/z2kmOgfR9Urx7y0NzS60y0tXZ4vFUQEZEh1xaKYjiQJB9J0KcWdyKet2MRtO2Fc/4NTA9jvmiyc7ntXSgbHvOwcfWJFhER6a+2cJS0gA/T0wtqN36/ZqJl8B5cUAlAa0eE97fW09wR4cTxhZQXZu6/z7VzJhzp4TJY2xc4yXH5yT3fnlUEeeOcVndzPj+0sblESbSIiLiiLRztc1EhaGGhJEYkFuO9zfW8snYPHeEYfp9hwZZ6PnvmJCYWZyc7vOGtrQH2bYdTPtPzLHSXiXNhy5vORix9vLn2gni3/RYREemX9lC0z3pogIBPLe5kcF5es4dfv7SBZ1bsYnxhFl8+ZyrfvnAauRkB/vbBDqIxbcToqq4656kX9H6/8XOgeTc07nA/piGgJFpERFwR90y0XzPRMjDt4Sj/+sgyPnPvInw+ww1zK7jxjEmMzssgKy3ARceNoaa5g7W7G5Md6vBWtwnS82DU9N7vVzbLudy13P2YhoDKOURExBVdNdF98RlDwGfU4k76xVrLvz6yjH+s2MWXP3IUpbkZhy1inT4mj/zMIO9vqefYsflJinQEaNgGhRV9l2iMPhYwsHs5TLtoKCJzlWaiRUTEFa2h+GaiAdIDPtrDmomW+H3nbyv4x4pdXDBjNGX5mT12gfH7DCeML2BTTTNtIb1Jc0WoBVprD+4LfSQrHoXsUljzFCy6x/3YXKYkWkREXNEejq8mGiAzzU+7ZqIlTtZaXlpTzZj8DOYdXdrrfaeV5RKzsKG6aYiiG2EanK4oFEyM7/755cOmJlrlHCIi4oq2UHzlHOAk0ZoplN50tbAD2FjdTG1zB584eRy+PkoIxhdlkZXmZ/2eZrdDHJkatgEG8sfHd//88bBziTOD7XGaiRYREVe0haN9bvndJTOoJFrit3T7XjKCPo4r77vO2WcME4uz2Vbn/aQtJTXudEo0ghnx3T+v3LncV+VeTENESbSIiLiiPc7uHNCZRIeVREvfYtaydncT08ry4v77mliURV1LiJqmDpejG4Ga90DO6Pjvnz/OuRwGJR1KokVExBVtcfaJBpVzSPy217fSGooyrSw37sdMLM4CYPG2vW6FNTJFI9BSCzmj4n9MWrbTDq9pl3txDREl0SIiknDWWqdPdLw10ZqJljhtqXXKMo4qzYn7MeUFmQR8hsXb6t0Ka2Rq2AY22r8kGiB3DDTtdiemIaQkWkREEq4jEiNmib8mOk1JtMRna10Lo3LTyUqPvzdCwO+jvDCTRZqJTqzaDc5lv5PoMieJjnl7zCuJFhGRhOsqzYi7O0cwoHIO6VPMWrbVtVJRnN3vx44vzGL1zkbCUfUjT5ja9c5l9gBmomNh2Ls14SENJSXRIiKScK2ds8rxz0T7NBMtfapvCdERiTGuMLPfjy0vyKQjEmP9HvWLTpi6DZCW49Q590fuGOeyek3iYxpCSqJFRCTh2kIRgP7VRGsmWvrQ1V1jdF6c7dS66Uq8l1ftS2hMI1rthv6XcoBTzgFKokVERA7VGurvTHSAtnCUWMy6GZZ4XHVjOwCluen9fmxRdhr5mUGWVzUkOKoRrHZD/0s5AALpkFUM1asTH9MQ0o6FIiKScF1JdH/6RIOzIDEzze9aXOJt1U0d5GcGyQj2/2/EGENpbjqvravZv/vhtXMmJDrEkaO1HlprYeLpB65bdE/8j88t00y0iIjIofq/sNC5n+qipTfVTR2MGsAsdJdxBZnsaWzX4sJEqNvoXPZno5Xucsc4NdWRUOJiGmJKokVEJOH6W86RlRbofFzEtZjE22LWUt3UPqBSji7jCjOJWdjV0JbAyEaorvZ2AynnAMgpg1jkQDLuQUqiRUQk4bqS4XhnojM6SzjaNRMtR7CvNUw4ahmV2/9FhV3KC52dC6uURA9e7XrwBSGraGCPz+vs0FHj3ZIOJdEiIpJwXWUZcW/73Vnj2hbSx+zSs+omZ1HhYMo58jIC5KYH2LFXSfSg1W2EosngG+AahuxRYPyerotWEi0iIgnX35rorM6ZaJVzyJFUd7a3G0wSbYyhvDCTKiXRg1e7HkqmDvzx/iAUT1ESLSIi0l1/u3N0dVvQwkI5kuqmDnLSA/3a7rsn4wozqW3uUOnQYETDUL9lcEk0wKjpnm5zpyRaREQSri0cJSPow2fiK+c4MBOtxEZ6Vt3YPqhZ6C7jCrOwwA7VRQ/c3m3Ott0lRw/uOKNmOMl4qDUxcQ0xJdEiIpJwraHI/jrneOR0zi42d6icQw5nrXXa2+UNPokuL3B2LlRd9CDUdXbmKE7ATDQWatcNOqRk0GYrw0RX4/ju1EReRJKlNRTd37YuHvuT6HYl0XK4PY0ddERig+rM0SU7PUBhVpCqvd6c/UwJXe3tSo6CPSsHfpxRM5zLPath7ImDj2uIaSZaREQSri0U7dfOg9mdSXSLZqKlBxuqm4DBLSrsrrwwS+Ucg1GzzumukVk4uOMUTgJ/umfropVEi4hIwjkz0fEn0WkBH+kBn8o5pEcb9jQDDGqjle7GFWSytzVMXXNHQo434tSshVHTBn8cfwBKj1ESLSIi0qUtFO1XTTQ4JR1KoqUnG2uayQz695f9DNa4QqcuevmOfQk53ohirTMTXZqAJBpg9LFOOYcHqSbaYx5cUIm1lo01zSytbKChLUxZfgZnTS0lLzOY7PBERACnO0dJTlq/HpOToSRaerZxTzOj8tIxcXZ76cvYgkwMsHz7Pj58zAC3rR6pGndAqMmZQR6sRfdAqAWad8M7v4XTbx78MYeQkmiPaWwP8/iSHazd3URWmp/i7DQWbqlnSeVebjx9EuOLspIdoogIraEIWWn9ez7KTgtoYaEcxlrL+uompo7KTdgxM4J+SnLSWV7VkLBjjhg1a53L0umJOV5u5/bfjTsTc7whpCQ6yXrqqgGHd9aw1vLYBzv49UsbiMRiXHRcGadNLibg91Hb3ME9b2/hvve28aWzp1CQ1b/ZHxGRRGsLRfdvoBIvzURLT+paQjS0hhO2qLDLuMJMllXtw1qbsBnuEaG6K4lOUDlHVxLdtCsxxxtCqolOcR2RKC+v2cNVd7zHNx5dRmluOrd8eCpnTi0l0LkTWElOOjecXkEoEuUfK7z3Rygiw099a4id/eh+8OCCShrbwkqi5TBdiwoT0SO6uwnFWdQ2d7C5tiWhxx32qtdAVglkFyfmeBn5EMzyZBKtmegUEo1ZNlY3sa2ulVfXVbO1toVt9a2EIjFG56Xz03+aSTRme9wBbFRuBh8+ZhQvrN7DpppmppTmJOEnEBFxPjnrCMdID/ZvniYt4KOxLexSVOJVG/e3txt8j+juuspDXltXo9fM/ti1DMYcn7jjGePMRiuJloFauWMfz6zYRUNbGJ+BKaU5TC7N5iPTRnHqpCLmTS0lLeA7YvkHwJlHlfDe5jpeXVetJwQRSZrWUBQLZAT6V86REfCzs0O9e+VgG6qbyU0PkJeR2JSlKDuNyaXZvL6+hs+cOSmhxx62wu1QswaOPj+xx80dAzsWOp0/PFRa42oSbYz5KPBrwA/8wVr7s0NunwbcA5wEfM9a+ws340lF1lqeX7WbNzbUMjY/g0tmjWXq6ByC/gMzOHsaO/jr4qo+jxXw+zjjqBKeXblbOzGJSNI0dS4O7O9MdHpQfaLlcBurmzlqdI4rdctnHz2KPy/Y1u/NgUasPasgFoExJyT2uHljYFsHNFRC4cTEHttFrtVEG2P8wO+AC4EZwDXGmBmH3K0e+DIw4pLnLi+t2cMbG2o5dVIRXzj7KGaMzTsoge6vUyqKSAv4WLClPoFRiojEr7nDKcno78LC9ICP9nCMcDTmRljiURuqm5k6yp1PVz88rZRQJMbbG2tdOf6ws2uJc5nIcg6A3LHOpcc2XXFzYeGpwEZr7WZrbQh4CLis+x2stdXW2oXAiCyCe2XtHl5dV8PsiYVcdvxY/L7Bv8vOCPqZVZ7Piqp9mtERkaTomonOCPRzJrqz/ENbf0uXhtYQNU0dHOVSEr2ltoX0gI/bX9/Ua7mkdNq51Nnqu2BCn3ftl64OHXtWJfa4LnMziS4Htnf7vqrzun4zxnzeGLPIGLOopqYmIcElW3VjO994dDlj8jO49PixCf2YanZFEaFojGeWe69IX4aH4ThmJX77k+gBzEQDmgAYYqk8XjdWO505EtkjuruAz8cxZbms2d1EzFpXzjGsVL4H405JfN1yMMNJzjUTvV9Pv+EB/YVaa++01s621s4uLS0dZFip4b/+sYaWjghXzR6/v1VdoowvzKQoO42nlnuvcbkMD8NxzEr8upLg9H4uLEzvTLqVRA+tVB6vGzqTaLdmogGmj8mjpSPC9nqtJepV026o2wAV89w5fu4Yz23/7WYSXQWM7/b9OEBZHbB4Wz1PLdvJv5w1hVF5iW3ZA2CMYWZ5Pu9sqmNvSyjhxxcR6U1Te1dNdP9eYrrKP1TOIV027GkmM+invCDTtXMcMzoXvzGs3tXo2jmGhW1vO5cVZ7pz/NwxTpIe8U7e4mZ3joXAVGPMJGAHcDVwrYvn8wRrLf/59BpG56Vz01mTeXyJO+8rjivP5/X1NTy/ajdXn5rg2qUui+7p/fbZN7pzXhFJafu7c/R3JroziW7S1t/SaUN1E0eNysGXgDVDR5IR9DO5NJs1SqJ7t/UtSMuFslnuHD9vrNP5o3Y9lB3nzjkSzLUk2lobMcbcDDyP0+Luj9baVcaYmzpvv90YUwYsAvKAmDHmq8AMa+2w/Ut+ZW01y7Y38N8fn0VWmnvvYcbmZzCxOIt/rNjlXhItItKDgbe4UzmHHGxTdTOnTU7Qzni9mD4mjyeX7XTa6blYOuJpW9+CiXNhyf3uHD+vc9nc7uVKogGstc8Azxxy3e3dvt6NU+YxIlhr+e2rGxlXmMkVJw5ojWXcjDFcNHMMd76xmYbWEAVZaa6er0eaqRYZkZo7IqQFfD3urtqb/QsLNRMtwB/f2sLOfe00d0Rc75zRlUS/uHqPkuieNO1xZohPvM69c+SMgrQc2PEBnOCNwgXtWDiE3t1Ux5LKBn50+XGD6gUdr/NnjOb3r23i1XXVXHGii+9VIh1Qsw72boHmPdDRBOE28PnBF4T0XMgsgNwyKJwM+eM8tSORiPRPU3u43+3tADI7Z6L3aetvAaqbOgAY7cLaoUPlZwYpL8jkhdW7+cLZU1w/n6csugd2fuB83dYA6XnunMf4nP7TO5e4c3wXKIkeIg8uqORP72whNz1ANGaHpB/l8eMKGJWbzgur9riTRDdUwpY3YNcyiIXBF3DeSabnQXaJs31nNAwdjbCvEirfdR6XVeys7j3+GqetjYgMK03tkf2lGf3hzF5DY7uSaIE9+9qBoUmiAY4py+W1ddU0tofJywgOyTk9o24jBNIPlFy4ZeyJ8P5dTu7gT/3/AyXRQ6SuuYMNe5r58LRRQzILDeDzGc6dMZrHl+ygPRztd8/WHsVisOEFePt/nKTYnw7jT3W2AC2a5CTSR9LWALXrYPv7sPpx593mx++BcScPPi4RSRn1LSGyB7DmwxhDRtCvmWgBYHdTO2l+HwVZQ5NMTS7J5pW1sGhrPR+ZNnpIzukZdRuhaIrzCbObxp4I0Q6nX3Sid0V0wdBkc8KCLfUYA6dWFA3pec+fMZrWUJR3Ng1yS9NIByx7CH5/OvzlKthXBTMuh3P/HWZ+Akqm9p5Ag1PSMX4OnH4LnPZFZ6b6jxfA8kcHF5uIpJS9rSGy0wf2YpsZ9LOvTTXRAnsa2xmdl97v2vqBGl+URVrAx3ub64fkfJ7R3gjN1VB8lPvnGnuic+mRkg4l0UOgLRRl8ba9zBibT17m0H48MXdKMTnpAV5cvaf/D7bW2Z3oqa/CL46Gv/+LU8t8xR3w5SUw+WwIDrB3Z8nR8C+vO0n13z8PKx8b2HFEJOXUt4QH3H0oM81Po2aiBaecY6hKOQCCfh8nji/g3U11Q3ZOT6jf7FwWTXb/XJtfd/KKZQ+5f64EUDnHEHhq2U7awlFOmzy0s9Dg9Gk965hSXlxdzY8vtwf32jxS94yWWqhaCDVroWEbBLNg2iVw/NUw5SOJWxS4+gmY/jFo2gWPfQ52LYfCigO3q3uHSOK11sPmV2HxvRBudRbz5JRB8RTIH3/w+B7AGLTWsrc1xHFpA1t8lKlyDgFqmztoCUWHNIkGOG1yMb95ZYPqorur3+w0Ccgf3/d9B8sYKKg4kLinOCXRQ+DB9ysZlZvOpOLspJz//Bmj+cfyXSzZ3sDJEwuPfMfGHbDuWdizEjAw+Sw4+zsw/RKnw4YbAukw+5/hrducpP5D33DvXCIjWcN2eOO/YfkjEGk/0DknFoHtC5z75I6FKR+G8pOd5HoAGtsjRGOWrPSBvbxkBDUTLbB+dxMwdIsKu5w6qYiYhSWVDZx1dGptgZ40ezc7E1xu10N3KZ4Ca9c4JSQ5o4bmnAOkJNplG6ubWbq9gQuPK8Mkqa3b2ceMIuAzvLB6d89JdCziJM+bXnG6ZUy9ACbMdWqYI+2w4q/uBpiW3ZlI/wqWPwyzP6MWeCKD1fVJU1dZ1prHnYXB42bD+NOcVpNdL4rt+5yFPFvegKUPOJsqzPzEgE67t8XZsjc7beA10Tsb2gb0WBk+1u5PotOH9LwnjC/A7zMs2lqvJBqceuh9O2Dq+UN3zq7a621vw7FXDN15B0BJtMse+6AKv89wwviCpMWQnxlk7pRiXly9h+9cOP3gG0PNzott/SbnhXXGpU75xlDLK4djLoY1T8COxc4LvYgMTmu988a0dh0UT3VKsrJ62P0tI9954zz+NGf8rX7ceVObXQJzburXm9r61s4keoAz0Zlpfhrbw1hrkzbxIMm3bncTWWl+cgb4dzRQ2ekBZozJY+FWLS4EnNJO7NDUQ3fJH+90/tr6lpLokSwas/z9gx18aGoJuUmurTpvxmh++MSqg7c07WiGd38DrXVw4qedj3CTafJZznafq/4Go6b3fX8R6Zm1sO0d502pxZlVnnB638mwMc4b2FHTYdmD8Ny3nQU+J1wL/iPsenpI3XR9s5NEZw1iJjoctbSFowNenCjet3LnPsYWZA75G6kHF1SSmxFg4dZ67n93G5+eO3FIz59yKt9zSrsKh/D34PM7LXO3vj105xwgdedIsAcXVO7/96N/rGZ3Yztl+QPsYJFA5053el6+sHq3c0X7Pljwf85M1Zybkp9AgzNQZ37CKSFZ949kRyPiTQ2VcP/lsOIRyJ8AZ/0/mHhG/0qk0rJh9mdh2seczZTe+z/nTXcc9s9EDzABztCuhSNeezjKut1NlBck57VzYnE24ahVWRE4+0HklUNgiDdGKz4KajrrolOYkmgXLalsICPoY1pZchbKdU/oX1tXQ3lBJn9ZUOnURf79Jmja7dQiD0Xvx3jljYWKM2Hbu57pEymSEiIhePM2+N0cqFrkvCE97Ys9l2/Ewxg46hw4eb5TE/n2/zide/pQ1zz4cg6ARvWKHrHW7m4iErNJTKKdksZtdS1JOX/KiISc55KiSUN/7tJpzuX654b+3P2gJNol7eEoq3buY9a4giHbobAv08fksX1vG80v3wrrnoEZl6Vm2cTRFzozYc9800n4ReTIYjFY8zTcfga8/B9OG8ovvtv/2ecjGXM8zP0ShNvgnd/0OTO0p7GdjKCPtMDAnvcyNRM94q2oagBgXGFykui8jCBF2WlsrWtNyvlTxq5lEGlzdiocannlzidpa58Z+nP3Q2pkd8PQyh37CEctJ0/opaXcEJsxJo8zfCvIfvtWZ5aq4kPJDqlnwUyYfqmzoGHZX5IdjUhqamuAhX9wkueHP+V02bn2Ubj6ASiYkNhzFVY4ibSNwru/dT7FOoLqpvZBrQHJCDovS0qiR67lVfsozk4jf4g3J+tuYlEW2+pbsdYmLYakq3zXuSxMwky0MTDtIqdrWJylZMmgVRsu+aByLyU56Ul7J92TcZkhrk27k12BcYz92K+dfrGpatxsp2PIiz+EaRc77fZERoIjbYIEzoLB+s3QUuN00Ii0w+iZ8E93wbH/BH4Xn9LzxsLcm5366Hd/65SK5I097G6797WTlzHwODQTLSt27GPmuPykdmepKM5myfYGtta1MqkkOXs8JF3lu05XjoyBbZw0aNM/Bgtudz45n/XJ5MTQB81Eu6C+JcTWulZOmlCQ9BZNUyof3f/v7MVfYhT1fLntczQvfjSpcfXJ+OCiX0BbPbz6k2RHI5Jc4TZnO9zXf+Z01Fn3jNMx4/OvwU1vOi8wbibQXXLLnBlp44d3fweNOw+7y57GjkHt9NZVE60kemRqC0VZv6eJWeX5SY1jQmdd9IhtdReLOZ05JpyevBgmnO58qrb0geTF0Acl0S74oHIvBpLaG/pQ+U0bGNWwlBV5Z7EoehSv7zlCu6pUMmaWs/Bx4V2we0WyoxEZepEO2PACvPJfsPrvzgr546+Bf10Ll/wKxp449BsT5YyG0292kvb3fgd7Vu+/yVqbgHIOP36fob6lIxHRiscsr2ogZmHWuIKkxlGam05m0M/irXuTGkfS1K53JrEmnJa8GHw+OP5aZwKhoTJ5cfRCSXSCWWtZUrmXyaXZFGSlRqLqj7YxeefTtKaX0jr2dArTYjy3Y2h3gRqwj3wfMgudRYYjuTZNRp7dK52Z53XPOB+pnvl1OPNrMH6Os/A2mbJL4bSbwQTg3o9B9RoA9raGCUcteZkDnxX3GUNxdho1TUqiR6Kumd/ZFcldT+QzhonFWSzcNkJnorvqoSfMTW4cJ37KmSh4/67kxnEEqolOsK11rextDe/vy5wKJux+kWCkmfUTrsLnD3DxuHYe3ZrJvpAhPy3FE9PMQjj33+HJW5yd146/OtkRibgr0uFsOLR9QWf5xC1QfMjq+N7qpodKTinM/aITy70fg/n/YE9kDMCgN5cqzU2ntrNVnowsC7bUM60sNyUmoSYWZ/P8qt3UNXdQnOORiadE2fYOZI9ynnu2vpm8OAomwIzLYfGf4EPfTF599hFoJjrBllTuJc3vY8bY1PiPzmvezKiGpewqmUtLprMI6OpJ7XTEDI9XDnHz9IE64Toonw3Pfxeaa5IdjYh76rfAW7fB9vfhqPNg3jcPT6BTSc5ouOEpwMCfLqGpahXAoBYWApTkpGsmegSKRGN8sG0vp1QUJTsUACo666IXbRthJR3Wwra3YWIcu5y6adE9zr/CCuhohCduPnBdKkwkoCQ6odrDUVbs2Mdx5XmkBwa25W0i+aNtTNr1D9rTCqkqPWv/9ccVRjiuIMxftmSkdoVE10D54F5n04f2BvjzP8HCP6bMABJJmKpF8IdzoaMJTvuC05XGl/znkT6VHg3znwYsx730aSaZXeQNsjWZMxOtJHqkue3F9bSEooSjMR5ckPwa2PKCTNICPhaPtCS6oRIadzi95lNBwQSnV/WW1yEWTXY0B1ESnUDPr9pNRyTGiSnSG3rmht+TEdrLlrGXYH0Hv6hdPamNtfuCfFDvkYqe3DFw9EWwezns/CDZ0Ygk1uon4E8XQ3oOnPEVKDk62RH1T+kxcMPTEI3wl7QfUR49vGtHf5TkOEn0iO7ROwJtqnb6AadKS7mA38es8vyR16Hj9Z85ly01qTNhNeUjzkTarqXJjuQgSqIT6IH3KinKTkuJJ4DCfauZtvU+qgtOpDH78Ebpl0/oID8Y4451yY81blM+DAUTYeVf49p+WCTlWQvv/BYeuQHKZsJnX3ZKJLxo1DT+d/wvSTdRzl/4GXJaBj6TOCo3nXDUUt+iuuiRZN2eJsbmZwy6pj6RZlcUsXLHPtrDqTUD6qq6zRDMctZkpIpR053nxk2vpFSTASXRCbJ2dyPvb63n1IoifEnuDW1iEeas/Hc60gqpLDu3x/vkBC03HNXGCzvT2dDogY+MwekdfeKnna8X/xFCI3xLVvG2aASe+Qa88D1nU4EbnoLskmRH1X/dahQXVfv4ceY3CUaauOCdazh2w++ZUtn/nvTji5xa1O172xIdraSofW1hKutbOXp0brJDOcgpFYWEo5YPKkdQSUfdRqcjkEmhFNH4YPKHnTKTug3Jjma/FPoNedsD71WSFvBx8sTkl3JM23ofRY1rWDTju0T9R94xcf5RrWT6Lb9fmzWE0Q1SdomTSDfugqe/mlLvSEXi1tEED13rbNt9+pfhE/c629173OZGQ42vhDUV12OIMmPLPWS37uj3cSZ0JtEPvZ/8ulgZGm9vrCVmSbkkes7kYtICPl5cvSfZoQyNuk3QWgslxyQ7ksOVnwzpuc5sdIpQEp0ADa0h/vZBFZfMHEN2enJrjPObNjBrw+/YPvoctped1+N9FmypZ8GWejbsrOPcknr+XpnBQ6tahjjSQRg1A47+qNPy7rWfJjsakfgtugfevA1+eypsfBFmfsKZ8fng3pRacT4QoRjsDQcoTQvTljGa1ZNuJOrLYPq2+yireadfxxpX6LyhUDnHyPHcyt1kBv37P4VIFTnpAc4+upRnVuwiFhsBkzYbX3IuR01Pbhw98QedXQxr1kFbanwyoCQ6Af749lZaQlE+f9bkpMbhi4Y4fdl3CAVyeP/YH8T1mCvK6sgNRPnT9tHemtSder7T+u71W+Hd/0t2NCLx2bvVaWHXVg+nfj51Vr8nwK5WPxZDaZqzXXdHWhGrJ91Ie1oRZy/+EhU7no77WNnpAUpy0pREjxCtoQgvrt7DzPJ8/L7klkP25OJZY9jT2DEySjo2vOBsppSqpWXjTwUsVC1MdiSAkuhBa2wPc8/bW7jg2NFMK0tub+hZG35LYdM6Fsz8TzrSi+N6THYgxtVja1jbnMXfvNI3GpzelR/7tVNL+vx3UnY3I5H9VvwV3v0t+NPgjK9C6bRkR5RQ21ucl5PS9PD+68LBHNZU3EBN4Ymcvvw7zFr/v2BjcR1vUkk21eoVPSK8uHoPbeEos8bnJzuUHp0zfTQZQR9/XVyV7FDc1d4IW950Pu1NVVnFUDzV6aWfAjN/SqIH6e43t9DUHuGWj0xNahxjq99g+pY/sXH8lewcdVbfD+jmwyX7mJbTyr8tyaGy2UN/Ev4AXHk3HH2hs0DrtVtTYlCJHCTcDk9/HR77jNPv9Myvpdaq9wTZ2OSUspVnHJz4Rv0ZvHrKHWwcdyXHbbqLeR98lUCk70XB08fksbuxfWR8hD7CPfbBDsryMqgoTs1uUTnpAa44sZzHl+6goXUYfzqy9h8Q7YCxJyQ7kt6NP8Wp2+7amjyJPJQxpZ7KulZuf30Tl8waw3HlyXsHndOyndOXfZuG3KNZPP1b/X68z8Atk3ZiDNy8IJ/WiAtBuiWQDlfdD8dfA6/9BJ76irNtskgqqNsEfzwfFt0Nc2+G074EaTnJjsoVGxr9ZPuj5AcObwUW8wV5/7h/Y9H0b1Ne/TrnvffpPlvgzRiTRygSY/tedeEZztbvaeKN9TVcd9qEpHe26s31cytoD8d4eOH2ZIfijkX3wNv/A5lFUFCR7Gh6V3Y8+NNhyQPJjkRJ9EBFojG+8ddlBHyG71+cvI8+/JFW5i35KtYY3jjpV7124+hNSVqEX57SyMq9Ab7wbj6h+D5xTQ3+IFz2f3Dm150FWn+62OneIZIssRgsuBNuP9Opg776L3DBj72xA+EAbWwMUJ7RceRdgo1hfcWneG3278lq382Fb3+SiTufOeLxpo9xyuPW7Gp0IVpJFXe/uYX0gI9r50xMdig9enBBJQ8uqGRJZQNTSrO5843NNHd4aaYpTm0NULseyk9K7lbf8Qikw9jjYfXjEEpuUwQl0QN063NreX9LPf91+XGU5SenltgXC3PBO9dQ0LSBLWMuoaz2PaZUPnrQv/44f2yIn5zcxOt70vnntwpoCKX4QOrWn5YP7nU+Kj9pPuxaBnd8yKntEhlqtRvhvkvh2W/CxNPhi+/BtIuSHZWrYhbW7AswPrPvT4F2l57Os2f8lb15R3PGsm8xZ8UP8fdQ3nFMWS4GWL1TSfRwtbG6ib9+UMUnZ4+nKDst2eH06fwZZdS1hPjjW1uSHUribXvbKYecMDfZkcRn3BwINcOa+Bcsu8Ejez6njljMctuL67nrzS18+rSJ/NNJ45ISh7FR5i7/LgUtm9k89mPsyz0qIce9elI7PuB7H+Ry6ctF/OSkRoKtPffHnDOpKCHnTKixJzi7Gq38K9z7MZj7JfjIDyDooUWT4k0dTfDGz51uMcFM+Nj/wknXp/6sTgJsaPTTGPZxTE7Pm6P09IZ+y5iLCftzmFz1d8ZWv8FbJ/6CmqLZ+2/PCPoZlZfOsqp9rsUtyWOt5Uf/WENW0M9Xzk3umqJ4jS/K4oJjR3PnG5u55tQJlOamJzukxAi3QeU7MPpYZ+GeFxRNgsIKWPoAHH9V0sJQEh2HBxc4tXtN7WEeX7qTNbsaOXliIceU5e6/bSj5YmHmrPgBE3c9R+Xoc6gpPDGhx//kpHam5Eb4+sI8rnuzkDkFfq4ur2VshkcWVOSNgZvehBe+73RD2PgyXP47p1G7SKLForD8EXjp36F5t9N68dx/g5xRyY5syCysdbZpPia7HzsMGh9Voz9MY04Fk3Y8xXkLbmTD+I+z9Jiv7X9enVCUzQeVe4nFLL4UbH0mA/fooipeW1fD9y+eTkmOd5LR//fRaVz4P2/yH0+t4rfXnpTscBJj4R+csojJH052JPEzPjj+WmeviIbtUDA+KWEoiY5DJBbj3U11vLK2mkjUcvHMMZw+pRiThBmmQKSVeUu+xpjad1h69JfpCBYk7NgLttQf9P2Pjq7hyd1FPLmnmIUNuXykpIGPj62lMHj4wqGUk5YNl/wKjrkInrgZ7joHTp4P5/wQslJwBl28JxZzavJe+xnUroP88U7rusIKZ5X7CPLSrnRK08KM7tbeLl6N2ZNYcdRN5LTv4pgt9zN+90usOupf2DDhKiYWZbFwaz2bapqZmmI72cnArd3dyL8/tYq5k4v55zMmJTucflmwuZ4PHV3C08t3UZi1kv+6/LhkhzQ47Y3OBlAlx0DxlGRH0z/HX+00FFhyP3z4u0kJQTXRvbDW8vKaPfz6pQ08u3I3FcXZfOWcqZxxVElSEui8pk2c/+6nGF23gPeO+09WT/mcq+dL81k+PraO/z1uE+eVNvBqbQFfWTmFh3aU0Br1yJ/O1PPg5vfhtC/AB/fBb06GBXeog4cMXEez05f8t7Phrzc65RqfuNdpXVdYkezohty+kOHtPWmcVtg44MqVmC+NJdO+wXNnPExD3jGcvOZWLn7zMj4afoE0wizeNgI2uRgh9jS2c+M9C8nNCPCrq07w5CcMHzq6lNF56TyxdAf7Wvv/xjGlvPIjZ/e/aRcnO5L+K5zoTJS9f2fSFhhqJvoINlY38Z9Pr+GN9TWU5KRzw9wKjilL0kyIjXHU9r9y0pqfEwlk8drJ/8fu0tOH7PQFwSj/PGEPF42u5+Edpfx9dwkv1RTwddvG9Ue14U/F58BDt08uORrm/Sus+hs8+//g7f+Fs77pfBwUSP0FLZJkC+922tXtWAy7lkKkvXMh6/Uw5gTnRch45I1lgr2wM52wNZxW2DSo43TVTW8bfT4N2ZMZX/0q5274EW+lF7LojY9AaBac+dUERCzJcucbm7n7rc3sbQnz+Q9NTtqi/MEK+HxcedI47nh9M19/ZCl3XT/bk28GqFrsJKCnfs55PvOiM74C655xXvNPv3nIT68k+hC1zR387tWN3P/uNjLT/Hz/4umkB/xJ24p01rpfU7HrWXLadrAvexKbyi8nu21HvztvJEJZepivTN7Jx1rqeHDHKP5jWS5/r8zgZyc3MaPAAy1/8sY6fXpr1ztbhj71FXj1JzD7MzD7xhFVwyp9iHRA7QYnad7yBmx43lk46E+HsplQcQYUeutjaLf8bVsGE7KjTMlqT8wBjWFf7lT25RxFXstm7I4lXNT8GPalv2G2vgXHXQlHnQs5pYk5nwyJqr2t3PXmZhpaQ3z6tArGFgysHWuqGFeYxYUzy3h6+S5+9+pGbjnHG4sj92tvhL9/HnLHOIvvVz6W7IgGZsJpTi336/8NMz8+5BtZKYnu1NAa4u63tnD3W1toD0e56pTx/Ov5x1CSkz70iwetpXTvB8zY/EfKa94gFMhmU/nl1ObPTImV/pOzO/je1O1UB8fwn0tz+djLhXz+6Fa+MqOFjFRvg2sMlB7jzEyXn+wkSK/9BF6/FUZNh7EnOiuUT/tCsiOVRDj0EwlrnbZIrfXQ3uB8XTwVWmo6/9U6iwPrt4DtrP3PKXP+XkYdC2XHOdt2CwDbmv28W5PGN49tTvxTkzE05kxh89Hz+NKSFh496nlKqlfB4y8CxunEM/40p6/tmBOcj3YD3lmgNlJYa3ly2U7+7clVtIWi3HB6BZNLnA2HkrEwP5HmTi4m6PfxyxfXU5CdxqdPS81e14eJhuFvn3Oe5254CjLykh3R4Fz0C7hjHjz0KbjuMcgsGLJTu5pEG2M+Cvwa8AN/sNb+7JDbTeftFwGtwHxr7QduxtRdOBpj4dZ6/vbBDp5atpOOSIyZ5fmcM30Uo3IzeGFVz63dXGFjFDRtoLz6NSp2Pk1+y1bag4VUlZ7F7uI5RP2p9bGXMXDp+A4+NDrEj5fn8Pt12TxdlcGXprVw+YR2byTTo6Y7/5qrnfY+O5fAnpXgCzoLwyrOhDHHOwlU/niVfXiBtU5pxd6t0FAJm15xEua2+gOX0R66zGQUQHYpZJfAqBkw43Lnb6NsFpRMhcV/GtqfwyPu2ZiJ31iurGhn2253znHO2BDfX1LG7XyS73/1Sti9DDa8BJtedv5fFvy+857GmVUrnOhcZhU5u69lFTtf7/++8zI9NyUmJYajvS0hllU1sGjrXp5avpNtda3MLM/nvBmjPdWJoy/GGP7747PY1xbmB4+vZFttC9+44Bgygin8AtjWAH+/CdY/Bxf/0vlUzetKjoJ/uhMevRF+NwdO/SxUfMiZEEt3d4dY15JoY4wf+B1wHlAFLDTGPGmtXd3tbhcCUzv/zQF+33k5aNZawlFLKBojHInREoqwp7GDPY3tbNjTzIod+3h/Sx2N7RGy0vxcefI4SrLTE1ujZS3GRvDZCL6Yc+mPtpEeaiA91EB2+07ymreQ37KZooaVZIQbANhTNJu1k25g69iLqdiR3EbifSlIs/x8dhNXTGjnpyty+PbiPH68LIdzxoY4sSjM9PwIozJjFKbFyA1aUrJsLGeUkzRNv9RJvnYtcxKx134G2AP3C2Y5yVYgzfloH5ykDevUwxq/syOdL+AsMDvoRbvQ+Toj33nxzsiD9M5/gXTnMfv/jczaWsD5fdqY0zYuFnFmg2MR5/uORuf33tHkfBTZUgNNu6Bxh7NDZeMOJ3EONR98zGCm87vPGeV8CtGVVGUUOP8Xp31Rb5D6KWbhzT1pPLApk09WtFOWGWObS+calRHjsvHt/HlzJuc9/yCnloQx2SUw6yo47uPOJweNO5w3Sa11zpviuo3OQqNwGweN4e58wcPHaNffRvdke/91hc7uqN3HqvGNyETcWktHJEZrKMre1hCVda1sqnFeV5dub2BbnbN5jgEml2bzydnjmTUuP6W39R6ooN/HHZ8+mf98ajV/eGsLz6zYxSdPGc9pk4uZXJJNXmYwuUl1NOK8njVsg02vwsK7nHFy0S/glM8mL65EOPSTxrlfgjVPOYslu+RP6JwwmwalnZdFU5wOXgnYQdbNmehTgY3W2s0AxpiHgMuA7kn0ZcB91loLvGeMKTDGjLHWDnjP5n97YiV/WbidUOTI+1YbA5NLsvnocWV8ZNpo5k0tITs9kJCPlkrrF/PhhTc5ibPtu0444kunKbuCnaM+xJ7iOewunkNbxuhBxzHUTh8V5smP7OWd6iBPbM/glV3pPF55+BuSgLH87OQmPl6RoPrJRDI+KJrs/Jt9I7Tvg5p1zr+m3c7H/20Nzkxm/ebOx3S+KFjbmfB1Jn3RMFSv6Zz93HugNCC+QJwXa+N3ku5vrEvwD5pCNr8Of7n6QKLcr99Tp+xRTm/wwgqY9CFngUzBROdy65vOm5/eLH1gQKGPVFELc54uprbDz+ScCN88rrnvBw3St2a2sKA2jateLyTLH+Pdi+vIT7POi2BeufOvJzYG4VYnoQ51XoZbnE8bWuu6fUKx11k4WrXQuT7WjzUevoAzVrsS6msegslnJeYHT0Gzf/QSdS0dzvzBIcryMjhhfAFXnzKBE8YXsHZXI+mpPCubIEG/j/+6/DgunjWGX7+0gf95aQOwYf/tAZ/hM2dO4jsXTR/awF74Przzm4Ovm3SW08d+OO6bUFgBp9/iTLTs3QK5Y6FmDVSvdT6VjB3SScUXdFrfXvyLAZ/S2J5GQgIYYz4OfNRa+9nO7z8NzLHW3tztPk8DP7PWvtX5/cvAt6y1iw451ueBz3d+ewxwpKyiBKhN6A8yNBT30BpucU+01qbUKqt+jNneeOn/SbEmnlfihP7FOlzH61Dw0t9Eb/RzpJa+fo4jjlk3Z6J7+tzm0Iw9nvtgrb0TuLPPExqzyFo7u6/7pRrFPbQUt/viHbO98dLPq1gTzytxgrdi7UkixutQ8PrvuYt+jtQymJ/DzeLLKqD7PozjgJ0DuI+IiIiISEpxM4leCEw1xkwyxqQBVwNPHnKfJ4HrjeM0YN9g6qFFRERERIaCa+Uc1tqIMeZm4HmcFnd/tNauMsbc1Hn77cAzOO3tNuK0uLtxkKdN+Y+jjkBxDy3F7Q1e+nkVa+J5JU7wVqxeNlx+z/o5UsuAfw7XFhaKiIiIiAxXI7ghrYiIiIjIwCiJFhERERHpJ88n0caYTxhjVhljYsaY2Yfc9h1jzEZjzDpjzAXJivFIjDEf7YxtozHm28mOpzfGmD8aY6qNMSu7XVdkjHnRGLOh87IwmTH2xBgz3hjzqjFmTeffyVc6r0/p2I0xGcaY940xyzrj/o/O61M67kQwxvzcGLPWGLPcGPN3Y0xBt9tSZkx77bknlZ9vvPL84tXnE6/z2ljrTSqPw954ZYz2xo3x6/kkGlgJ/BPwRvcrjTEzcDqCHAt8FPg/42xFnhLMgW3RLwRmANd0xpyq/oTze+zu28DL1tqpwMud36eaCPCv1trpwGnAlzp/z6keewfwEWvt8cAJwEc7O9iketyJ8CJwnLV2FrAe+A6k5Jj2zHOPB55v/oQ3nl+8+nzidZ4Za73xwDjszZ/wxhjtTcLHr+eTaGvtGmttT7srXQY8ZK3tsNZuwekAcurQRter/duiW2tDQNe26CnJWvsGUH/I1ZcB93Z+fS9w+VDGFA9r7S5r7QedXzcBa4ByUjx26+jaTznY+c+S4nEngrX2BWtt177L7+H0j4cUG9Mee+5J6ecbrzy/ePX5xOs8NtZ6k9LjsDdeGaO9cWP8ej6J7kU5sL3b91Wd16WKVI8vHqO7+np3Xo5Kcjy9MsZUACcCC/BA7MYYvzFmKVANvGit9UTcCfbPwLOdX3tlzKRinKkYU19S+m/da88nw5TX/q69Fm9fPPt3n6jx6+a23wljjHkJKOvhpu9Za5840sN6uC6V+vmlenzDijEmB3gM+Kq1ttGYnn79qcVaGwVO6KwJ/rsx5rgkh5Qw8YxpY8z3cD5+e6DrYT3c39UxM4yee1IxJs/y4vNJqhtGY603Xot3WErk+PVEEm2tPXcAD0v1LcVTPb547DHGjLHW7jLGjMGZMU05xpggzoB5wFr7t86rPRE7gLW2wRjzGk49mmfi7k1fY9oYcwNwCXCOPdDMfsjHzDB67knFmPqSkn/rXn8+SVXDaKz1xmvx9sVzf/eJHr/DuZzjSeBqY0y6MWYSMBV4P8kxdRfPtuip7knghs6vbwCONFuQNMZ5i3k3sMZae1u3m1I6dmNMaVdXCmNMJnAusJYUjzsRjDEfBb4FXGqtbe12U6qP6S6pGKcXn29S7m/dq88nw1gqjrXeeHEc9sZTf/eujF9rraf/AVfgvLvrAPYAz3e77XvAJmAdcGGyY+0h9otwug9swvnIKukx9RLrX4BdQLjz9/0ZoBhnJeuGzsuiZMfZQ9xn4nxcthxY2vnvolSPHZgFLOmMeyXww87rUzruBP3sG3HqBrv+v27vdlvKjGmvPfek8vONV55fvPp84vV/XhtrffwsKTsO+4jbE2O0j58h4eNX236LiIiIiPTTcC7nEBERERFxhZJoEREREZF+UhItIiIiItJPSqJFRERERPpJSbSIiIiISD8piRYRERER6Scl0SIiIiIi/fT/ASc0DRzmevLeAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "n = 300\n", "mu, sigma = 2, 3\n", "\n", "fig, ax = plt.subplots(1,3,sharey=True,figsize=(12,4))\n", "lab = lambda mu,sigma: f'$\\mu={mu},\\sigma={sigma}$'\n", "\n", "dn = sp.random.normal(mu,sigma,n) \n", "sb.distplot(dn,ax=ax[0],label=lab(mu,sigma));\n", "dn = sp.random.normal(mu+9,sigma+1,n) \n", "sb.distplot(dn,ax=ax[0],label=lab(mu+9,sigma+1));\n", "ax[0].legend(loc=2,fontsize=8)\n", "\n", "dn = sp.random.normal(mu+6,sigma-2,n) \n", "sb.distplot(dn,ax=ax[1],label=lab(mu+6,sigma-2));\n", "dn = sp.random.normal(mu-2,sigma+2,n) \n", "sb.distplot(dn,ax=ax[1],label=lab(mu-2,sigma+2));\n", "ax[1].legend(loc=2,fontsize=8)\n", "\n", "dn = sp.random.normal(mu-10,sigma-1,n) \n", "sb.distplot(dn,ax=ax[2],label=lab(mu-10,sigma-1));\n", "dn = sp.random.normal(mu+10,sigma-1,n) \n", "sb.distplot(dn,ax=ax[2],label=lab(mu+10,sigma-1));\n", "ax[2].legend(loc=2,fontsize=8);" ] }, { "cell_type": "markdown", "id": "9b408cd5-d6a1-41a6-9787-3bdb73e7a03b", "metadata": {}, "source": [ "A CDF para essas distribuições pode ser plotada adicionado `kde_kws={'cumulative':True}` na função `distplot` do _seaborn_. Para remover o histograma, basta configurar `hist=False`. " ] }, { "cell_type": "code", "execution_count": 17, "id": "38682f51-b6d2-4b08-a86c-43c724cd5c82", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1,3,sharey=True,figsize=(12,4))\n", "\n", "dn = sp.random.normal(mu,sigma,n) \n", "sb.distplot(dn,ax=ax[0],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu,sigma));\n", "dn = sp.random.normal(mu+9,sigma+1,n) \n", "sb.distplot(dn,ax=ax[0],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu+9,sigma+1));\n", "ax[0].legend(loc='best',fontsize=8)\n", "\n", "dn = sp.random.normal(mu+6,sigma-2,n) \n", "sb.distplot(dn,ax=ax[1],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu+6,sigma-2));\n", "dn = sp.random.normal(mu-2,sigma+2,n) \n", "sb.distplot(dn,ax=ax[1],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu-2,sigma+2));\n", "ax[1].legend(loc='best',fontsize=8)\n", "\n", "dn = sp.random.normal(mu-10,sigma-1,n) \n", "sb.distplot(dn,ax=ax[2],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu-10,sigma-1));\n", "dn = sp.random.normal(mu+10,sigma-1,n) \n", "sb.distplot(dn,ax=ax[2],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu+10,sigma-1));\n", "ax[2].legend(loc='best',fontsize=8);" ] }, { "cell_type": "markdown", "id": "583980f0-f8d4-4447-8cdd-862cc8d75412", "metadata": {}, "source": [ "### Distribuição logística\n", "\n", "A _distribuição logística_ (DL) é interessante para fundamentar aplicações de aprendizagem de máquina, principalmente em problemas envolvendo _regressão logística_ e _redes neurais_. A DL é utilizada para descrever fenômenos associados a crescimento ou queda. Modelos biológicos são um exemplo. A função de distribuição geral de uma DL é:\n", "\n", "$$PDF(x) = \\frac{e^{-\\frac{(x-\\mu)}{s}}}{s(1 + e^{-\\frac{(x-\\mu)}{s}})^2},$$\n", "\n", "para uma a média $\\mu$ e o parâmetro $s$.\n", "\n", "Com o _scipy_, podemos gerar DLs da mesma forma que as distribuições anteriores." ] }, { "cell_type": "code", "execution_count": 18, "id": "734e9367-a636-4176-9248-4e2f3afa5fb5", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mu, s = 2, 3\n", "\n", "fig, ax = plt.subplots(1,3,sharey=True,figsize=(12,4))\n", "lab = lambda mu,s: f'$\\mu={mu},s={s}$'\n", "\n", "dn = sp.random.logistic(mu,s,n) \n", "sb.distplot(dn,ax=ax[0],label=lab(mu,s));\n", "dn = sp.random.logistic(mu+9,s+1,n) \n", "sb.distplot(dn,ax=ax[0],label=lab(mu+9,s+1));\n", "ax[0].legend(loc=2,fontsize=8)\n", "\n", "dn = sp.random.logistic(mu+6,s-2,n) \n", "sb.distplot(dn,ax=ax[1],label=lab(mu+6,s-2));\n", "dn = sp.random.logistic(mu-2,s+2,n) \n", "sb.distplot(dn,ax=ax[1],label=lab(mu-2,s+2));\n", "ax[1].legend(loc=2,fontsize=8)\n", "\n", "dn = sp.random.logistic(mu-10,s-1,n) \n", "sb.distplot(dn,ax=ax[2],label=lab(mu-10,s-1));\n", "dn = sp.random.logistic(mu+10,sigma-1,n) \n", "sb.distplot(dn,ax=ax[2],label=lab(mu+10,s-1));\n", "ax[2].legend(loc=2,fontsize=8);" ] }, { "cell_type": "code", "execution_count": 19, "id": "f8ad2162-40a9-45d8-b623-5dd6aaf1080a", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1,3,sharey=True,figsize=(12,4))\n", "\n", "dn = sp.random.logistic(mu,sigma,n) \n", "sb.distplot(dn,ax=ax[0],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu,sigma));\n", "dn = sp.random.logistic(mu+9,sigma+1,n) \n", "sb.distplot(dn,ax=ax[0],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu+9,sigma+1));\n", "ax[0].legend(loc='best',fontsize=8)\n", "\n", "dn = sp.random.logistic(mu+6,sigma-2,n) \n", "sb.distplot(dn,ax=ax[1],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu+6,sigma-2));\n", "dn = sp.random.logistic(mu-2,sigma+2,n) \n", "sb.distplot(dn,ax=ax[1],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu-2,sigma+2));\n", "ax[1].legend(loc='best',fontsize=8)\n", "\n", "dn = sp.random.logistic(mu-10,sigma-1,n) \n", "sb.distplot(dn,ax=ax[2],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu-10,sigma-1));\n", "dn = sp.random.logistic(mu+10,sigma-1,n) \n", "sb.distplot(dn,ax=ax[2],hist=False,kde_kws={'cumulative':True},\n", " label=lab(mu+10,sigma-1));\n", "ax[2].legend(loc='best',fontsize=8);" ] }, { "cell_type": "markdown", "id": "ab66e79c-3fc9-491e-90c6-7eb05cf777a7", "metadata": {}, "source": [ "## Estimadores de densidade de _kernel_" ] }, { "cell_type": "markdown", "id": "7f2f365a-e50f-4fe6-884a-b3542157ecf1", "metadata": {}, "source": [ "Histogramas possuem o ponto de fraco de definir intervalos de classe (_binning_). Dependendo dos dados com que trabalhamos, métodos diferentes de _binning_ podem levar a interpretações diferentes, visto que a plotagem visual pode ser diferente.\n", "\n", "Um _kernel_ especifica o formato da distribuição em cada ponto e a _largura de banda_ controla o tamanho do _kernel_. O propósito de um _kernel density estimator_ (KDE) é buscar uma distribuição contínua do conjunto de dados, quando nosso interesse é apenas fazer uma inspeção geral nos dados.\n", "\n", "Há diversos tipos de _kernel_ e rotinas implementados em Python. Entretanto, vamos utilizar os KDEs do módulo _scikit-learn_. \n", "\n", "Antes disso, vejamos o efeito do _binning_ sobre os dados." ] }, { "cell_type": "code", "execution_count": 20, "id": "baf6d97e-b585-4d23-9ea3-a84e130da676", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1,2,sharey=False,figsize=(12,4))\n", "sb.distplot(im,ax=ax[0],bins=5,kde=False,label='5 bins')\n", "ax[0].legend()\n", "sb.distplot(im,ax=ax[1],bins=25,kde=False,label='20 bins')\n", "ax[1].legend();" ] }, { "cell_type": "markdown", "id": "e19600ed-effb-4803-958d-557296b6e88d", "metadata": {}, "source": [ "Os _kernels_ disponíveis no _scikit-learn_ são obtidos a partir da classe `KernelDensity`." ] }, { "cell_type": "code", "execution_count": 21, "id": "3355e394-218c-4582-9b6e-fc7568da922f", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.neighbors import KernelDensity\n", "\n", "X_plot = np.linspace(-6, 6, 200)[:, None]\n", "X_src = np.zeros((1, 1))\n", "\n", "fig, ax = plt.subplots(2, 3, sharex=True, sharey=True,figsize=(8,5))\n", "fig.subplots_adjust(left=0.05, right=0.95, hspace=0.2, wspace=0.2)\n", "\n", "\n", "def ff(x, loc):\n", " if x == 0:\n", " return \"0\"\n", " elif x == 1:\n", " return \"h\"\n", " elif x == -1:\n", " return \"-h\"\n", " else:\n", " return f\"%ih\" % x\n", "\n", "\n", "for i, kernel in enumerate(\n", " [\"gaussian\", \"tophat\", \"epanechnikov\", \"exponential\", \"linear\", \"cosine\"]\n", "):\n", " axi = ax.ravel()[i]\n", " log_dens = KernelDensity(kernel=kernel).fit(X_src).score_samples(X_plot)\n", " axi.fill(X_plot[:, 0], np.exp(log_dens), \"-k\", fc='#aa88bb')\n", " axi.set_title(kernel)\n", " \n", " axi.xaxis.set_major_formatter(plt.FuncFormatter(ff))\n", " axi.xaxis.set_major_locator(plt.MultipleLocator(1))\n", " axi.yaxis.set_major_locator(plt.NullLocator())\n", " \n", " axi.set_xlim(-2.9, 2.9)\n", "\n", "plt.suptitle(\"Kernels disponíveis :: scikit-learn\");" ] }, { "cell_type": "code", "execution_count": 22, "id": "20d645d2-bb75-40de-a808-29cd663caa73", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy.stats import norm\n", "\n", "N = 100 # amostras\n", "band = 0.5 # largura de banda\n", "kernels = [\"gaussian\", \"tophat\", \"cosine\"]\n", "\n", "np.random.seed(1)\n", "X = np.concatenate(\n", " (np.random.normal(0, 1, int(0.3 * N)),\n", " np.random.normal(5, 1, int(0.7 * N))))[:, np.newaxis]\n", "\n", "Xp = np.linspace(-5, 10, 1000)[:, np.newaxis]\n", "\n", "true_dens = 0.3 * norm(0,1).pdf(Xp[:, 0]) + 0.7 * norm(5, 1).pdf(Xp[:, 0])\n", "\n", "fig, ax = plt.subplots()\n", "ax.fill(Xp[:, 0], true_dens, fc=\"blue\", alpha=0.2, label=\"distribuição original\")\n", "colors = [\"red\", \"orange\", \"darkgreen\"]\n", "\n", "lw = 2\n", "\n", "for color, kernel in zip(colors, kernels):\n", " kde = KernelDensity(kernel=kernel, bandwidth=band).fit(X)\n", " log_dens = kde.score_samples(Xp)\n", " ax.plot(\n", " Xp[:, 0],\n", " np.exp(log_dens),\n", " color=color,\n", " lw=lw,\n", " linestyle=\"-\",\n", " label=\"kernel = '{0}'\".format(kernel),\n", " )\n", "\n", "ax.text(8, 0.37, \"N={0}\".format(N))\n", "\n", "ax.legend(loc=\"upper left\")\n", "\n", "ax.set_xlim(-5, 10)\n", "ax.set_ylim(-0.02, 0.4)\n", "plt.title('Aplicação de KDE');" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }