octave1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "octave1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Octave",
      "language": "octave",
      "name": "octave"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/obabilonia/test1/blob/master/octave1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "gKcwMiObiRwC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lLlT-f1miRwG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Atividade1"
      ]
    },
    {
      "metadata": {
        "id": "Y4ubRhXZiRwI",
        "colab_type": "code",
        "colab": {},
        "outputId": "9aa4c5c4-254a-444b-dd44-0a45f3c8176f"
      },
      "cell_type": "code",
      "source": [
        "# % FEEC/Unicamp\n",
        "% 25/10/2018\n",
        "% Classificação linear para os dados MNIST usando Ridge Regression\n",
        "% Pode-se monitorar o erro quadrático junto ao conjunto de validação\n",
        "% ou monitorar a taxa de acerto junto ao conjunto de validação\n",
        "clear all;\n",
        "rand('seed', 1500); % Força o gerador de números pseudo-aleatórios a gerar a mesma sequência\n",
        "disp('Como criterio de escolha do coeficiente de regularizacao e usando o conjunto de validacao:');\n",
        "opcao = input('(1) Monitorar o erro quadratico; (2) Monitorar a taxa de acerto: ');\n",
        "if opcao ~= 1 && opcao ~= 2,\n",
        "    error('Opcao invalida!');\n",
        "end\n",
        "load data; % Carrega os dados de treinamento e validação\n",
        "load test; % Carrega os dados de teste\n",
        "[N,m] = size(X);\n",
        "X = [ones(N,1) X]; % Acrescenta-se uma coluna de 1's\n",
        "[Nt,mt] = size(Xt);\n",
        "N_classes = length(S(1,:));\n",
        "Xt = [ones(Nt,1) Xt]; % Acrescenta-se uma coluna de 1's\n",
        "new_order = randperm(N); % Define uma permutação de inteiros de 1 a N\n",
        "X = X(new_order,:); % Embaralha os dados de treinamento e validação\n",
        "S = S(new_order,:);\n",
        "part = 0.8;\n",
        "Xtr = X(1:(part*N),:); % Toma os 80% primeiros para treinamento\n",
        "Xv = X((part*N+1):N,:); % Toma os 20% últimos para validação\n",
        "Str = S(1:(part*N),:);\n",
        "Sv = S((part*N+1):N,:);\n",
        "v_c = [];\n",
        "v_EQ = [];\n",
        "EQ_min = 10^30;\n",
        "v_tx_acerto = [];\n",
        "tx_acerto_max = 0;\n",
        "for p=-4:1:12, % Esse intervalo de busca deve ser ajustado a cada aplicação\n",
        "    c = 2^p; % Varia o coeficiente de regularização em potência de 2\n",
        "    w = (Xtr'*Xtr+c*eye(m+1))\\Xtr'*Str; % Encontra os parâmetros dos 10 classificadores lineares\n",
        "    S_est = Xv*w; % Obtém a saída para os dados de validação\n",
        "    if opcao == 1,\n",
        "        EQ = sum(sum((Sv-S_est).^2)); % Calcula o erro quadrático de validação\n",
        "        v_c = [v_c;c];\n",
        "        v_EQ = [v_EQ;EQ];\n",
        "        if EQ <= EQ_min, % Armazena o melhor desempenho junto aos dados de validação\n",
        "            EQ_min = EQ;\n",
        "            c_min = c;\n",
        "            w_min = w;\n",
        "        end\n",
        "    elseif opcao == 2,\n",
        "        acerto = 0;\n",
        "        for i=1:(0.2*N),\n",
        "            [vmax,indmax] = max(S_est(i,:));\n",
        "            if Sv(i,indmax) > 0.5,\n",
        "                acerto = acerto+1;\n",
        "            end\n",
        "        end\n",
        "        tx_acerto = acerto/(0.2*N);\n",
        "        v_c = [v_c;c];\n",
        "        v_tx_acerto = [v_tx_acerto;tx_acerto];\n",
        "        if tx_acerto >= tx_acerto_max,\n",
        "            tx_acerto_max = tx_acerto;\n",
        "            c_min = c;\n",
        "            w_min = w;\n",
        "        end\n",
        "    end\n",
        "end\n",
        "figure(1);\n",
        "if opcao == 1,\n",
        "    semilogx(v_c,v_EQ); % Plota o gráfico de [Coeficiente de Regularização] X [Erro Quadrático junto aos Dados de Validação]\n",
        "    ylabel('Erro Quadratico na Validacao');\n",
        "elseif opcao == 2,\n",
        "    semilogx(v_c,v_tx_acerto); % Plota o gráfico de [Coeficiente de Regularização] X [Taxa de Acerto junto aos Dados de Validação]\n",
        "    ylabel('Taxa de Acerto na Validacao');\n",
        "end\n",
        "xlabel('Coeficiente de Regularizacao');grid;\n",
        "St_est = Xt*w_min; % Obtém a saída para os dados de teste\n",
        "EQt = sum(sum((St-St_est).^2)); % Calcula o erro quadrático de teste\n",
        "fprintf('Valor do coeficiente de regularizacao = %g\\n',c_min);\n",
        "if opcao == 1,\n",
        "    fprintf('EQ de validacao = %g\\n',EQ_min);\n",
        "elseif opcao == 2,\n",
        "    fprintf('Taxa de acerto de validacao = %g\\n',tx_acerto_max);\n",
        "end\n",
        "fprintf('EQ de teste = %g\\n',EQt);\n",
        "% Apresentação, a seguir, do desempenho em termos da taxa de acerto na\n",
        "% classificação, para os três conjuntos, sempre usando o classificador que\n",
        "% minimizou o EQ junto aos conjunto de validação.\n",
        "acerto = 0;\n",
        "S_est = Xtr*w_min;\n",
        "for i=1:(0.8*N),\n",
        "    [vmax,indmax] = max(S_est(i,:));\n",
        "    if Str(i,indmax) > 0.5,\n",
        "        acerto = acerto+1;\n",
        "    end\n",
        "end\n",
        "fprintf('Taxa de acerto - Treinamento = %g\\n',acerto/(0.8*N));\n",
        "acerto = 0;\n",
        "S_est = Xv*w_min;\n",
        "for i=1:(0.2*N),\n",
        "    [vmax,indmax] = max(S_est(i,:));\n",
        "    if Sv(i,indmax) > 0.5,\n",
        "        acerto = acerto+1;\n",
        "    end\n",
        "end\n",
        "fprintf('Taxa de acerto - Validacao = %g\\n',acerto/(0.2*N));\n",
        "acerto = 0;\n",
        "S_est = Xt*w_min;\n",
        "for i=1:Nt,\n",
        "    [vmax,indmax] = max(S_est(i,:));\n",
        "    if St(i,indmax) > 0.5,\n",
        "        acerto = acerto+1;\n",
        "    end\n",
        "end\n",
        "fprintf('Taxa de acerto - Teste = %g\\n',acerto/Nt);\n",
        "% Obtendo a taxa de acerto por classe\n",
        "erros = zeros(N_classes,1);\n",
        "n_amostras_classes = zeros(N_classes,1);\n",
        "mat_conf = zeros(N_classes,N_classes);\n",
        "for i=1:Nt,\n",
        "    [vmax,indmax] = max(S_est(i,:));\n",
        "    if St(i,indmax) < 0.5,\n",
        "        erros(indmax,1) = erros(indmax,1)+1;\n",
        "    end\n",
        "    n_amostras_classes(indmax,1) = n_amostras_classes(indmax,1)+1;\n",
        "    [vmax2,indmax2] = max(St(i,:));\n",
        "    mat_conf(indmax2,indmax) = mat_conf(indmax2,indmax)+1;\n",
        "end\n",
        "taxa_acerto_por_classe = 1-(erros./n_amostras_classes);\n",
        "disp('Taxa de acerto por classe');\n",
        "[[1:9 0]' taxa_acerto_por_classe]\n",
        "disp('Apresente toda resposta entre aspas simples');\n",
        "input('Quais sao os dois digitos mais faceis de classificar? ');\n",
        "input('Quais sao os dois digitos mais dificeis de classificar? ');\n",
        "figure(2);\n",
        "imagesc(mat_conf);\n",
        "colorbar;\n",
        "title('Matriz de confusao (Digito #0 virou #10)');\n",
        "input('Quais sao os dois digitos que mais recebem classificacao errada de digitos da classe #9? ');\n",
        "disp('O grafico da Figura 3 a seguir plota os coeficientes obtidos para cada classificador');\n",
        "figure(3);\n",
        "colormap('default');\n",
        "for i=1:10,\n",
        "    subplot(4,4,i);\n",
        "    param_class = reshape(w(2:(m+1),i),[28,28]);\n",
        "    imagesc(param_class);\n",
        "    if i<10,\n",
        "        title(sprintf('#%d',i));\n",
        "    else\n",
        "        title(sprintf('#%d',0));\n",
        "    end\n",
        "end\n",
        "disp('Explique como usar cada imagem da Figura 3 para se obter a classe de um digito recebido na entrada.');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Como criterio de escolha do coeficiente de regularizacao e usando o conjunto de validacao:\n",
            "(1) Monitorar o erro quadratico; (2) Monitorar a taxa de acerto:1\n",
            "Valor do coeficiente de regularizacao = 64\n",
            "EQ de validacao = 4663.3\n",
            "EQ de teste = 3827.78\n",
            "Taxa de acerto - Treinamento = 0.857083\n",
            "Taxa de acerto - Validacao = 0.855083\n",
            "Taxa de acerto - Teste = 0.8604\n",
            "Taxa de acerto por classe\n",
            "ans =\n",
            "\n",
            "   1.00000   0.83008\n",
            "   2.00000   0.91295\n",
            "   3.00000   0.84579\n",
            "   4.00000   0.82080\n",
            "   5.00000   0.88544\n",
            "   6.00000   0.88105\n",
            "   7.00000   0.86217\n",
            "   8.00000   0.84022\n",
            "   9.00000   0.84342\n",
            "   0.00000   0.90316\n",
            "\n",
            "Apresente toda resposta entre aspas simples\n",
            "Quais sao os dois digitos mais faceis de classificar?'2''0'\n",
            "Quais sao os dois digitos mais dificeis de classificar?'4''1'\n",
            "Quais sao os dois digitos que mais recebem classificacao errada de digitos da classe #9?'4''7'\n",
            "O grafico da Figura 3 a seguir plota os coeficientes obtidos para cada classificador\n",
            "Explique como usar cada imagem da Figura 3 para se obter a classe de um digito recebido na entrada.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjAAAAGkCAIAAACgjIjwAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAHXRFWHRTb2Z0d2FyZQBHUEwgR2hvc3RzY3JpcHQgOS4xNnO9PXQAABymSURBVHic7d1hbCPpfd/xR3vOXYpuKXCDxnYhwgmD0yIl6zTmoQDVA1ygXJ8U+JV54gAOECwRiIu+Ut0VSr3ovhEKWDLEYveN25UKsOgLI6KOeZcsbTEIAlyHbiDGwGWYYgOs7NywBa5xJOx4b+3TrZZ58cRjgqS4Wol65j+z38+LxTzUcJ7/jLTz0zPzaDjV6/UUAABBuxJ0AQAAKEUgAQCEIJAAACIQSAAAEQgkAIAIBBIAQAQCCQAgAoEEABCBQAIAiEAgAQBEIJBCo9lsTk1NTU1N7e7uKqWOjo50c3V19bS3rK6uNpvNkdsZfv20LUxNTV2k7BcqFArj9+KCDg4Orl27dnBw4B9A7a233mq32+pFB6T/qyOP50g3btw4ODhYXV3V36zV1dVr165du3bthbv5Ut+dly1DKXV0dHTjxg294yMLW11dLRQKE+kdeGk9hMTe3p5SKh6Pl8tl3YzH40op3Rxp5FcPDw/39vYODw/P0mm5XL7sHxKlVKlUevTo0SVtv1QqlUql3s8PYKlU2tvbW19fj8fjyWSy96ID0v/V8Ufbt7+/rw9aPB7f29vT/ZbL5fX1daXU3t7emPe+1Hfnpcp49OhRuVxOJpN+DSMLOzw8fGGRwCUhkEJDnz4WFxdzuVyv1yuXy7lczj9FHh4e6qZSKpfLHR4e6ixJJpPf/va39WrxeNzfzt7enl5h5K8m+kvxeHxxcdH/kr/+8EnZ/5LuWr+i8zKZTO7v7/f68kCvef/+ff+NyWTyG9/4hn8e9FNweKeGXxnZl+/Ro0dKqf4C/OLv37+vlKrVav4B6a/H3/GBw6W7GNNjuVzOZDLxeFzXmcvl+kN94OgtLi7qUOz1erpTvzu/Wv1dG97N4bLHl6EDSTeH39VfmB/hgGFcsguZZDKpr+e02+1MJuO/vrW11Ww2a7Xa/fv3m83m7u6uPqXmcrnPf/7zSqnd3V0/DDQ9VqjVakqpgU1tbGyUy2W9Kf3ixsbGxsZGrVar1Wp6eXj9Wq3Wbrc3NjaazebGxoY+vcbj8Rs3bvgrHx0d7e3tZTKZW7duKaX8Ir/0pS8N76y/U7Vardlsbm1tDe/mmL6UUrr+/r3rP5L6MPavvLGxUSqV1tfXhy+a+aW6rjumR50xi4uLmUwmmUyWy+V2u6370p3295jL5Q4ODtrt9sHBwcHBgZ+1Pv+7Nn43hw2XkUwm19fX+w/FaYVlMhn/Eh9gEoEUMvqc1Ww22+12//mrXC7v7++32219Kjk4ONCnnng8fvXqVaXU+vq6vjLjSyaTmUxmY2MjHo/rWNLa7XY8Hl9fX19cXNRnYd2jPmfpr/afrw8ODvT29elycXFRf/X+/ft6fHB0dOSvXyqVcrmcX7lf5Oc+97nhndUDgtXV1Xa7XavVyuXy8G6O6UuvMHyWP42/qcXFxYHw7i/1+9///pgeM5mM32kmk9ELekf6F/yjEY/Hdayqn2deP/+7NrybP/jBD8bsy8gyho0sLJlMHh0dHR0djdk+cBkIpPDJ5XK7u7tHR0f9Z5mNjY233npL9V0TGzBwKtRu3brVbrf39vb835SVUv1nopHvKpVKw6dOLZPJjByOnI8ew+Vyua2trUKhsLq6+sLdPDudoxOsVpuammq324VCYWNjY3d398aNG/2Dj/5Biabzu9lsLi4uDh/tkcf/fGUMrzO+MAIJ5hFI4aOvqAycSfUIplwu6/PsWejJV6VSSf+O75+AMpnM0dHR7u7uwcGBf+lGv6jHKAcHB8Ohpbdw7dq1W7du6VPb1taWUmp3dzcej5/xvK8vXvmdrq6ubm1tra+vHx4eJpNJPR4a2M3xfcXj8f5LZLqL1Z/z7xX178jW1lZ/DcPG96hv7fR6vWQyqW8F6a/u7u7qbQ4cilwu1263m83m+JHccKdvvvnm8BEbU8bwNk8rTH9zB/IJMCHom1g4K/8mtr68pm9B+wv+Nbf+mQ76DPutb31L9c2b8rczcAb0Vzg8PPTPTXph+MX+aWD9Ew2SyaSeL9c/gtHzF067Ca+rPTw89EcDemu9Xm9/f7//mtL+/v7I3Rzuy6cnNeiSBk7KuVyuf7KDnts2vOP9Zeti9vb2xvSor6rpuWr+UeqfiDH8ndWb1SsPTGron+020OnIIza+DP/I+5sdWZgeAY/+KQQu01SPjzCPCj1w6f8F/OjoSM99OMeVHz0WGfg1udlsnjbcabfbA1cR9Stn711XO7JTpZS/neHdHN/XjRs3MpnMwP2zkfTMAr0dPYFCn9AHKtS9nNaj3oK+GjZwNNSFrxAOdHraERtTxshtDhR27dq19fX1i18UBV4WgYSIazabhULh0aNHL8xFvaae67G1teX/gc4rZWtra3d3d+QlPuCyEUiIPj2MOMtATd+m0kO9ic93CAU9i5IbSAgEgQQAEIFZdgAAEQgkAIAIBBIAQAQCCQAgAoEEABCBQAIAiEAgAQBEIJAAACJ8JugCLsv169eDLgGAIA8fPgy2gImflALfo4mLbCAppf7Pnxl6CMU7/+y3Xmr9Zu+93NS7l1RMICK2RxHbHRW5PTrH7nz45geXVMxLmeBJ6Te/PDWpTcnBJTsAgAgEEgBABAIJACACgRSAKF3N1yK2RxHbHRW5PYrY7sAX5UkNACDK094nk9vYL09uU1IwQgIAiEAgAQBEIJAAACIQSAAAEZjUAACGPH3+LOgSRGOEBAAQgUACAIhAIAEARCCQAAAiEEgAABGYZQcAhjztPQ+6BNEYIQEARCCQAAAiEEgAABEIJACACExqAABDPu4xBhiHowMAEIFAAgCIQCABAEQgkAAAIjCpAQAMefr8l4IuQTRGSAAAEQgkAIiORqNRqVT8ZqVSyefz+Xy+0+kMN0e+EiACCQAiolgsLi8v+81Wq+W6br1eX1lZ2d7eHmgOrxBc4f+AQAKAiKhWq6VSyW/atp1KpZRS2WzWtu2B5vAKAVX9C0xqAABD/tWv/Y2//Oc/+sK536uUmp2dPcu70um0XtDBM9Ac+UqACCQAMORPf/imv/xx7/zvVUrdeudM73JdN5vNKqU6nU46ne5vDq/wcgVdAi7ZAUA0pVIp13WVUt1uN5FIDDSHVwi2WhXtEdLvpP+NmY5+tfWSv+qcy4+//NRAL0qp58fHZjoy47WrV8101DN13KZef91MRydPnhjo5Yqp3XkFzc/P66kKtm0vLS0NNIdXCLZapdRUr2fiZGre9evXf/3vvmymr1/5IwJJLgLp3KIUSD/6wv7Dhw8NdDTG9evX7393YueKW+9MnXGPWq1WIpGYmZkZ2Rz5SlCiPEICAOhbRKc1R74SFAIJAAz5+Pkbk9tYpK5kaExqAACIQCABAEQgkAAAIhBIAAARmNQAAIZ83GNSwziMkAAAIhBIAAARCCQAgAgEEgBAhLBOavA8b3l52fM8pZRlWYVCIeiKAAAXEtZAajQa6XT69u3bnud97WtfI5AAyPd0ko8O+snkNiVFWAMpkUjoT++IxWJB1wIAmICwBpJ+PG23271z507/Z8j3++7fbfc33/mV4D/tA4Ax3/vkO/7yrDrTB34jWGENJKVUpVJxHKdUKp327HQSCHiVfeWNr/vLP1L7AVaCMwprINVqNdd1q9Vq0IUAACYjrIHkOI7rusViUTdJJgDyTfTzkCIorIG0trYWdAkAgEniD2MBACIQSAAAEQgkAIAIYb2HBACh8/Tk9aBLEI0REgBABAIJACACgQQAEIFAAgCIQCABAERglh0AGPL0ObPsxmGEBAAQgUACAIhAIAEARIjyPaTek4/NdPTjL5voZW7/Jya6Uer9L0bqCfknT56Y6ejK64ZuD/SOj810ZMbzaO0OLiLKgQQAojw9idRvexPHJTsAgAgEEgBABAIJACACgQQAEIFJDQBgyNPnvxR0CaIxQgIAiEAgAQBEIJAAACIQSAAAEQgkAIAIzLIDAEN+esLnIY3DCAkAIAKBBAAQgUACAIhAIAEARGBSAwAY8tMTHh00DiMkAIAIBBIAQAQCCQAgAoEEABCBSQ0AYMjPLnlSQ7FYVEp5nre0tDQ/P6+UqlQqtm0rpdbW1lKp1EDzUos5B0ZIABAFjUYjnU5Xq9V79+7t7OwopVqtluu69Xp9ZWVle3t7oBl0vSMQSAAQBYlEwnEcz/Mcx5menlZK2bath0HZbNa27YFmwOWOwiU7ADDkD//1f/WXv/a//t2536uUmp2dHVghkUh4nre8vOy6bqlU0i+m02m9oKNooCkNgQQAhrxsCI15r1P8k4EVtre3LcsqFAqe5xWLxUKhoJRyXTebzSqlOp1OOp3ub567ksvDJTsAiIhYLKb/ffz4sVIqlUq5rquU6na7iURioBlsqSMxQgIAQ352comnXMuybt682el0HMdZWFhQSs3Pz+vJC7Zt63l3/c3Lq+TcCCQAiIKZmZlms9lqtSzLmpmZ0S/W6/X+Vwaa0hBIABAd+hbRmFeGV5CDe0gAABEIJACACFyyAwBDfvaMU+44HJ0JeH58bKCX97/4hoFelFJvf/CJmY6M7ZEZZn4MTHrt6lUDvZw8eWKgF4QCl+wAACIQSAAAEQgkAIAI3EMCAEM+ucwnNUQAIyQAgAgEEgBABAIJACACgQQAEIFAAgCIwJQPADDkmEcHjcUICQAgAoEEABCBQAIAiBDuQPI8L5fLBV0FAGACwn2HbXNz0/O8oKsAgDM5Pnkt6BJEC/EIqdFoTE9Pp1KpoAsBAExAWEdI3W630WjcvXu3WCyets73PvlOf/Mrb3z98usCIEWz956/PKtmA6wEZxTWQNrc3IzFYpVKxXXdSqViWdbMzMzAOiQQ8CrLTb3rL3+oPgiwEpxRWANpaWlJ3z2ybXtubi4WiwVdEQDgQsIaSP6to1gsls1mgy0GAHBxYQ0kX7VaDboEADiTT58xy26cEM+yAwBECYEEABCBQAIAiEAgAQBECP2kBgAIi2fPGAOMw9EBAIhAIAEARCCQAAAiEEgAABGY1AAAhpxM7vOQIvnIB0ZIAAARCCQAgAhcsguNz3z2s2Y6st86MtPRD//gnxvo5Td+768M9KKUen58bKaj165eNdNRz8geXXn9dQO9IBQYIQEARCCQAAAicMkOAAx5PrlHBzHLDgCAy0IgAQBEIJAAACIQSAAAEZjUAACGPH82FXQJojFCAgCIQCABAEQgkAAAIhBIAAARmNQAAKZM7kkNkcTRAQCIQCABAEQgkAAgIiqVSj6fz+fztVpt4JVOpzPclIZAAoAo6HQ6juPU6/Vqteo4jlKq1Wq5rluv11dWVra3tweaQdc7AoEEAFHQaDTm5uZarVan01lbW1NK2badSqWUUtls1rbtgWbA5Y7CLDsAMORvfr/sL39h61sv997Sf+xvzs7ODq+zs7PjeZ5S6sGDBzqT0um0/pKOooGmNAQSABjysiE07r2b/314nYWFhdu3byul8vm8fsV13Ww2q5TqdDrpdLq/ee5KLg+X7AAgChKJhL/8+PFjpVQqlXJdVynV7XYTicRAM6g6x2CEBABRUCgU9MDIcRzLspRS8/PzevKCbdtLS0sDzWCrHYlAAoCIqNfrrVbLsqyZmZmRrwyvIAqBBACmXP7nIelbRGNeGV5BDu4hAQBEIJAAACIQSAAAEQgkAIAITGoAAEOmLn9SQ6gxQgIAiEAgAQBEIJAAACIQSAAAEQgkAIAIUZ5l9/z4OOgSJunZRx+Z6ejK66+b6eg3fu+vDPTyL/+3oR+Dv/htM/2okydPDPVkhLGfNwmYZTceIyQAgAgEEgBABAIJACACgQQAECHKkxoAQJQrz4KuQDZGSAAAEQgkAIAIBBIAQAQCCQAgApMaAMCQqROe1DAOIyQAgAgEEgBAhBBfsqtUKrZtK6UsyyoUCkGXAwC4kLCOkDqdjuM49Xq9Wq06jhN0OQCAiwrrCKnRaMzNzbVaLaXU2tpa0OUAAC4qrIGklNrZ2fE8Tyn14MGDkZnU7L3X38xNvWuoMgACfO+T7/jLs2o2wEp8PDpovBAH0sLCwu3bt5VS+Xx+5AokEPAq+8obX/eXf6T2A6wEZxTWe0iJRMJffvz4cYCVAAAmIqwjpEKhoAdGjuNYlhV0OQCAiwprICml6vV6q9WyLGtmZiboWgAAFxXiQFJKZbPZoEsAgLOamtykht7EtiRIWO8hAQAihkACAIhAIAEARCCQAAAihHtSAwCEyASf1HAysS0JwggJACACgQQAEIFAAgCIQCABAEQgkAAAIjDLDgAMmeCjgyKJERIAQIQoj5Beu3rVTEcnT56Y6Shinh8fG+jlL37bQCdKKfX2B5+Y6ej9L75hpiMz/4P47wMfIyQAgAgEEgBAhChfsgMAUa5E8oE/k8MICQAgAoEEABCBQAIAiEAgAQBEYFIDABjCkxrGY4QEANHheV4ul/OblUoln8/n8/lOpzPclIZAAoDo2Nzc9DxPL7daLdd16/X6ysrK9vb2QDPYOkcikAAgIhqNxvT0dCqV0k3btvVyNpu1bXugGWShp+AeEgAY8pd3/4O//C/+/X8593uVUrOzswMrdLvdRqNx9+7dYrHov5hOp/WCjqKBpjQEEgAY8rIhNOa9n/zxfxtYYXNzMxaLVSoV13UrlYplWUop13Wz2axSqtPppNPp/ua5K7k8BBIAGHLlMmfZLS0t6btHtm3Pzc3FYrFUKqWDp9vtJhKJgeYllnJeBBIARIF/FS4Wi+lh0Pz8vJ68YNv20tLSQDPAUk9DIAFApFSrVX+5Xq+3Wi3LsmZmZoab0hBIABBlerR0WlMUpn0DAERghAQAhlzqpIYIYIQEABCBQAIAiEAgAQBEIJAAACIwqQEADOHzkMZjhAQAEIFAAgCIQCABAEQgkAAAIhBIAAARmGUHAIZcedYLugTRGCEBAEQgkAAAIhBIAAARuIcUGq9dvWqmo97xsZmOzOzRyZMnBnpRSr3/xTfMdPTT7/26mY7+8Vf/r5mOAI1AAgBD+Dyk8bhkBwAQgUACAIhAIAEARCCQAAAiEEgAABGYZQcAhlz5lEcHjcMICQAgAoEEABCBQAIAiEAgAQBEYFIDABjC5yGNxwgJACBCiEdIxWJRKeV53tLS0vz8fNDlAAAuJKwjpEajkU6nq9XqvXv3dnZ2gi4HAHBRYQ2kRCLhOI7neY7jTE9PB10OAOCiwnrJLpFIeJ63vLzsum6pVBq5znd/8j/6m+/8k5sGCgMgRLP3nr88q2YDrMTHpIbxwhpI29vblmUVCgXP84rFYqFQGF6HBAJeZbmpd/3lD9UHAVaCMwrrJTulVCwW0/8+fvw46FoAABcV1hGSZVk3b97sdDqO4ywsLARdDgDgosIaSDMzM81ms9VqWZY1MzMTdDkAgIsKayBp2Ww26BIAAJMR7kACgBDh85DGC/GkBgBAlBBIAAARCCQAgAgEEgBABCY1AIAhPDpoPEZIAAARCCQAgAgEEgBABAIJACACkxoAwJArnz4PugTRohxIJ0+eBF3CJBnbnc989rNmOnr20UdmOjLD2HH7R1/5oZmOrr7/Tw304r39twZ6QShwyQ4AIAKBBAAQIcqX7ADg1eF53vLysud5SinLsgqFglKqUqnYtq2UWltbS6VSA81gCx7GCAkAoqDRaKTT6Xq9Xq1Wt7a2lFKtVst13Xq9vrKysr29PdAMut4RGCEBgCFTlznLLpFIJBIJpVQsFtOv2Lath0HZbHZ5eTmRSPQ3L6+ScyOQAMCQP3n/P/nL//bt/3zu9yqlZmdnB1bQn6Dd7Xbv3LlTKpX0i+l0Wi/oKBpoSkMgAYAhLxtCY97r/v//ObxOpVJxHKdUKulwUkq5rquXO51OOp3ub567kstDIAFAFNRqNdd1q9Wq/0oqldLB0+129fW6/mZghZ6OQAKAKHAcx3XdYrGom9VqdX5+Xk9esG17aWlpoBlkracgkADAkEt9dNDa2trwi/V6vdVqWZY1MzMz3JSGQAKAKPPvJ41sisLfIQEARCCQAAAiEEgAABG4hwQAhkx9ehJ0CaIxQgIAiEAgAQBEIJAAACIQSAAAEQgkAIAIzLIDAEOYZTceIyQAgAgEEgBABAIJACACgQQAEIFJDQBgyNSnz4IuQTRGSAAAEQgkAIAIBBIAQAQCCQAgApMaAMCUCU5qeG1iW5KDERIAQAQCCQAgAoEEABCBe0gY9Oyjj4IuIZSid9y8t//WQC+/+/D/Gejlm1810AkuihESAEAERkgAYMqnn05sU8yyAwDgkhBIAAARCCQAgAgEEgBABCY1AIApx5N7dNAvT2xLcjBCAgCIQCABAEQgkAAAIhBIAAARmNQAAIb0Pj0OugTRQjZCajQalUpFL1cqlXw+n8/nO51OsFUBAC4uTIFULBaXl5f1cqvVcl23Xq+vrKxsb28HWxgA4OLCFEjVarVUKull27ZTqZRSKpvN2rYdaF0AgAkI8T2kdDqtF3QyDWv23utv5qbevfSaAIhRnP3Fr6qzs7MBVoIzCnEgua6bzWaVUqfdQyKBgFdZ9a/n/OVvfvXHAVaCMwprIKVSKZ1D3W43kUgEXQ4AnMHx5D4PKYrCGkjz8/N6LoNt20tLS0GXAwC4qJAF0u3bt/3ler3earUsy5qZmQmwJADARIQskAboe0gAgAgI07RvAECEhXuEBAAh0jvm0UHjMEICAIhAIAEARCCQAAAiEEgAABGY1AAAhjxnUsNYjJACMPDU1wiI2B5FbHdU5Pao/6mpiBICCQAiIuwfW0ogAUAUROBjSwkkAIiCCHxsaZQnNXz45gdBlzDarJr9UAmt7XwitkcR2x0le4+++dWXfsvs7GxIP9/osk9KL/zYUuEiG0gPHz4MugQA+AUDJ6UXfmypcFyyA4AoSKVSruuqMH9saWRHSADwSonAx5ZO9Xq9oGsAAExGq9VKJBIh/dhSAgkAIAL3kAAAIhBIRnmeVywW9Z9S12q1oMuZGM/zcrlc0FVMhv+37tH4BhWLRf0j12g0gq7lohqNRqVS8ZthfyoBhhFIRjUajXQ6Xa/Xq9Xq1tZW0OVMzObmpud5QVcxAZ1Ox3Ec/Q1yHCfoci5K/7xVq9V79+7t7OwEXc6FFIvF5eVlvxmBpxJgGIFkVCKRmJubU0rFYrGga5mYRqMxPT0d0j/EG9BoNObm5lqtVqfTWVtbC7qci0okEo7jeJ7nOM709HTQ5VxItVotlUp+MwJPJcAwpn0bpf9mrdvt3rlzp/9/V3h1u91Go3H37t1isRh0LZOxs7OjR3sPHjwIeyYlEgnP85aXl13XjcbPW7+wP5UAwwgk0yqViuM4pVJJh1PYbW5uxmKxSqXium6lUrEsK6TzTX0LCwu3b99WSuXz+aBruajt7W3LsgqFgr55WSgUgq5oksL+VAIMI5CMqtVqrutWq9WgC5mYpaUlPZ6wbXtubi7slyITiYT+W3el1OPHj4MtZiL0dyQWi0Vjd3ypVErnUHifSoBhBJJRjuO4rutf3YpAMvlXS2KxWATGfIVCQQ+MHMexLCvoci7KsqybN2/qmRoLCwtBlzNJEXgqAYbxh7HAoFD/rfuwiO1Ovwjv2quJQAIAiMC0bwCACAQSAEAEAgkAIAKBBAAQgUACAIhAIAEARCCQAAAiEEgAABEIJACACAQSAEAEAgkAIAKBBAAQgUACAIhAIAEARCCQAAAiEEgAABEIJACACAQSAEAEAgkAIAKBBAAQgUACAIhAIAEARCCQAAAiEEgAABEIJACACAQSAEAEAgkAIAKBBAAQgUACAIhAIAEARCCQAAAi/D2R2njgdmkQigAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6vIlgRqQiRwO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Atividade 2"
      ]
    },
    {
      "metadata": {
        "id": "Z5_d2fnTiRwQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "ba757349-314f-41ad-c92d-9a0418ebefc8"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "% FEEC/Unicamp\n",
        "% 25/10/2018\n",
        "% Classificação por ELM para os dados MNIST usando Ridge Regression\n",
        "% Pode-se monitorar o erro quadrático junto ao conjunto de validação\n",
        "% ou monitorar a taxa de acerto junto ao conjunto de validação\n",
        "clear all;\n",
        "rand('seed', 1500); % Força o gerador de números pseudo-aleatórios a gerar a mesma sequência\n",
        "randn('seed', 1500); % Força o gerador de números pseudo-aleatórios a gerar a mesma sequência\n",
        "disp('Como criterio de escolha do coeficiente de regularizacao e usando o conjunto de validacao:');\n",
        "opcao = input('(1) Monitorar o erro quadratico; (2) Monitorar a taxa de acerto: ');\n",
        "if opcao ~= 1 && opcao ~= 2,\n",
        "    error('Opcao invalida!');\n",
        "    \n",
        "end\n",
        "load data; % Carrega os dados de treinamento e validação\n",
        "load test; % Carrega os dados de teste\n",
        "[N,m] = size(X);\n",
        "X = [ones(N,1) X]; % Acrescenta-se uma coluna de 1's\n",
        "[Nt,mt] = size(Xt);\n",
        "N_classes = length(S(1,:));\n",
        "Xt = [ones(Nt,1) Xt]; % Acrescenta-se uma coluna de 1's\n",
        "new_order = randperm(N); % Define uma permutação de inteiros de 1 a N\n",
        "X = X(new_order,:); % Embaralha os dados de treinamento e validação\n",
        "S = S(new_order,:);\n",
        "part = 0.8;\n",
        "Xtr = X(1:(part*N),:); % Toma os 80% primeiros para treinamento\n",
        "Xv = X((part*N+1):N,:); % Toma os 20% últimos para validação\n",
        "Str = S(1:(part*N),:);\n",
        "Sv = S((part*N+1):N,:);\n",
        "nhidden = input('Numero de neuronios na camada intermediaria (Sugestao: 500) = ');\n",
        "w1 = 0.2*randn(nhidden,m+1);\n",
        "Ytr = [ones(0.8*N,1) tanh(Xtr*w1')];\n",
        "Yv = [ones(0.2*N,1) tanh(Xv*w1')];\n",
        "Yt = [ones(Nt,1) tanh(Xt*w1')];\n",
        "v_c = [];\n",
        "v_EQ = [];\n",
        "EQ_min = 10^30;\n",
        "v_tx_acerto = [];\n",
        "tx_acerto_max = 0;\n",
        "for p=-5:1:10, % Esse intervalo de busca deve ser ajustado a cada aplicação\n",
        "    c = 2^p; % Varia o coeficiente de regularização em potência de 2\n",
        "    w = (Ytr'*Ytr+c*eye(nhidden+1))\\Ytr'*Str; % Encontra os pesos das 10 saídas da ELM\n",
        "    S_est = Yv*w; % Obtém a saída para os dados de validação\n",
        "    if opcao == 1,\n",
        "        EQ = sum(sum((Sv-S_est).^2)); % Calcula o erro quadrático de validação\n",
        "        v_c = [v_c;c];\n",
        "        v_EQ = [v_EQ;EQ];\n",
        "        if EQ <= EQ_min, % Armazena o melhor desempenho junto aos dados de validação\n",
        "            EQ_min = EQ;\n",
        "            c_min = c;\n",
        "            w_min = w;\n",
        "        end\n",
        "    elseif opcao == 2,\n",
        "        acerto = 0;\n",
        "        for i=1:(0.2*N),\n",
        "            [vmax,indmax] = max(S_est(i,:));\n",
        "            if Sv(i,indmax) > 0.5,\n",
        "                acerto = acerto+1;\n",
        "            end\n",
        "        end\n",
        "        tx_acerto = acerto/(0.2*N);\n",
        "        v_c = [v_c;c];\n",
        "        v_tx_acerto = [v_tx_acerto;tx_acerto];\n",
        "        if tx_acerto >= tx_acerto_max,\n",
        "            tx_acerto_max = tx_acerto;\n",
        "            c_min = c;\n",
        "            w_min = w;\n",
        "        end\n",
        "    end\n",
        "end\n",
        "figure(1);\n",
        "if opcao == 1,\n",
        "    semilogx(v_c,v_EQ); % Plota o gráfico de [Coeficiente de Regularização] X [Erro Quadrático junto aos Dados de Validação]\n",
        "    ylabel('Erro Quadratico na Validacao');\n",
        "elseif opcao == 2,\n",
        "    semilogx(v_c,v_tx_acerto); % Plota o gráfico de [Coeficiente de Regularização] X [Taxa de Acerto junto aos Dados de Validação]\n",
        "    ylabel('Taxa de Acerto na Validacao');\n",
        "end\n",
        "xlabel('Coeficiente de Regularizacao');grid;\n",
        "St_est = Yt*w_min; % Obtém a saída para os dados de teste\n",
        "EQt = sum(sum((St-St_est).^2)); % Calcula o erro quadrático de teste\n",
        "fprintf('Valor do coeficiente de regularizacao = %g\\n',c_min);\n",
        "if opcao == 1,\n",
        "    fprintf('EQ de validacao = %g\\n',EQ_min);\n",
        "elseif opcao == 2,\n",
        "    fprintf('Taxa de acerto de validacao = %g\\n',tx_acerto_max);\n",
        "end\n",
        "fprintf('EQ de teste = %g\\n',EQt);\n",
        "% Apresentação, a seguir, do desempenho em termos da taxa de acerto na\n",
        "% classificação, para os três conjuntos, sempre usando o classificador que\n",
        "% minimizou o EQ junto aos conjunto de validação.\n",
        "acerto = 0;\n",
        "S_est = Ytr*w_min;\n",
        "for i=1:(0.8*N),\n",
        "    [vmax,indmax] = max(S_est(i,:));\n",
        "    if Str(i,indmax) > 0.5,\n",
        "        acerto = acerto+1;\n",
        "    end\n",
        "end\n",
        "fprintf('Taxa de acerto - Treinamento = %g\\n',acerto/(0.8*N));\n",
        "acerto = 0;\n",
        "S_est = Yv*w_min;\n",
        "for i=1:(0.2*N),\n",
        "    [vmax,indmax] = max(S_est(i,:));\n",
        "    if Sv(i,indmax) > 0.5,\n",
        "        acerto = acerto+1;\n",
        "    end\n",
        "end\n",
        "fprintf('Taxa de acerto - Validacao = %g\\n',acerto/(0.2*N));\n",
        "acerto = 0;\n",
        "S_est = Yt*w_min;\n",
        "for i=1:Nt,\n",
        "    [vmax,indmax] = max(S_est(i,:));\n",
        "    if St(i,indmax) > 0.5,\n",
        "        acerto = acerto+1;\n",
        "    end\n",
        "end\n",
        "fprintf('Taxa de acerto - Teste = %g\\n',acerto/Nt);\n",
        "% Obtendo a taxa de acerto por classe\n",
        "erros = zeros(N_classes,1);\n",
        "n_amostras_classes = zeros(N_classes,1);\n",
        "mat_conf = zeros(N_classes,N_classes);\n",
        "for i=1:Nt,\n",
        "    [vmax,indmax] = max(S_est(i,:));\n",
        "    if St(i,indmax) < 0.5,\n",
        "        erros(indmax,1) = erros(indmax,1)+1;\n",
        "    end\n",
        "    n_amostras_classes(indmax,1) = n_amostras_classes(indmax,1)+1;\n",
        "    [vmax2,indmax2] = max(St(i,:));\n",
        "    mat_conf(indmax2,indmax) = mat_conf(indmax2,indmax)+1;\n",
        "end\n",
        "taxa_acerto_por_classe = 1-(erros./n_amostras_classes);\n",
        "disp('Taxa de acerto por classe');\n",
        "[[1:9 0]' taxa_acerto_por_classe]\n",
        "disp('Apresente toda resposta entre aspas simples');\n",
        "input('Quais sao os dois digitos mais faceis de classificar? ');\n",
        "input('Quais sao os dois digitos mais dificeis de classificar? ');\n",
        "figure(2);\n",
        "imagesc(mat_conf);\n",
        "colorbar;\n",
        "title('Matriz de confusao (Digito #0 virou #10)');\n",
        "input('Quais sao os tres digitos que mais recebem classificacao errada de digitos da classe #7? ');\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Como criterio de escolha do coeficiente de regularizacao e usando o conjunto de validacao:\n",
            "(1) Monitorar o erro quadratico; (2) Monitorar a taxa de acerto:1\n",
            "Numero de neuronios na camada intermediaria (Sugestao: 500) =500\n",
            "Valor do coeficiente de regularizacao = 16\n",
            "EQ de validacao = 3490.47\n",
            "EQ de teste = 2805.44\n",
            "Taxa de acerto - Treinamento = 0.913875\n",
            "Taxa de acerto - Validacao = 0.910167\n",
            "Taxa de acerto - Teste = 0.9163\n",
            "Taxa de acerto por classe\n",
            "ans =\n",
            "\n",
            "   1.00000   0.94835\n",
            "   2.00000   0.93949\n",
            "   3.00000   0.89244\n",
            "   4.00000   0.90307\n",
            "   5.00000   0.90909\n",
            "   6.00000   0.91278\n",
            "   7.00000   0.91098\n",
            "   8.00000   0.90085\n",
            "   9.00000   0.90293\n",
            "   0.00000   0.93615\n",
            "\n",
            "Apresente toda resposta entre aspas simples\n",
            "Quais sao os dois digitos mais faceis de classificar?'1''2'\n",
            "Quais sao os dois digitos mais dificeis de classificar?'3''8'\n",
            "Quais sao os tres digitos que mais recebem classificacao errada de digitos da classe #7?'1''2''9'\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gNq2iYaPiRwV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Lb9L4bgIiRwX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Atividade 3"
      ]
    },
    {
      "metadata": {
        "id": "ibgUIyb1iRwY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DIZ6DeWxiRwd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#mnist = tf.keras.datasets.mnist\n",
        "#(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
        "# load da base e separação em treino e teste\n",
        "mnist = np.load('mnist.npz')\n",
        "\n",
        "x_train = mnist['x_train']\n",
        "y_train = mnist['y_train']\n",
        "\n",
        "x_test = mnist['x_test']\n",
        "y_test = mnist['y_test']\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1Adzv95EiRwh",
        "colab_type": "code",
        "colab": {},
        "outputId": "c1aee642-9e01-4c75-9a34-0b69acd87617"
      },
      "cell_type": "code",
      "source": [
        "#modelo com dropout\n",
        "model = tf.keras.models.Sequential([\n",
        " tf.keras.layers.Flatten(),\n",
        " tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "tf.keras.layers.Dropout(0.5),\n",
        " tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "model.compile(optimizer='adam',\n",
        " loss='sparse_categorical_crossentropy',\n",
        " metrics=['accuracy'])\n",
        "model.fit(x_train, y_train, epochs=5)\n",
        "model.evaluate(x_test, y_test)\n",
        "model_json = model.to_json()\n",
        "json_file = open(\"model_MLP.json\", \"w\")\n",
        "json_file.write(model_json)\n",
        "json_file.close()\n",
        "model.save_weights(\"model_MLP.h5\")\n",
        "print(\"Model saved to disk\")\n",
        "os.getcwd()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 33s 554us/step - loss: 0.2002 - acc: 0.9414\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 32s 541us/step - loss: 0.0822 - acc: 0.9752\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 33s 546us/step - loss: 0.0531 - acc: 0.9827\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 32s 535us/step - loss: 0.0362 - acc: 0.9882\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 32s 535us/step - loss: 0.0266 - acc: 0.9914\n",
            "10000/10000 [==============================] - 1s 116us/step\n",
            "Model saved to disk\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C:\\\\Users\\\\suzukij\\\\Desktop\\\\Atividades\\\\deep_learning'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "Yu9q21UwiRwk",
        "colab_type": "code",
        "colab": {},
        "outputId": "6e0f0b85-438a-4e38-9975-9365ac378a1c"
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          multiple                  0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              multiple                  401920    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              multiple                  5130      \n",
            "=================================================================\n",
            "Total params: 407,050\n",
            "Trainable params: 407,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Kr07T2nZiRwp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#modelo sem Dropout\n",
        "model_2 = tf.keras.models.Sequential([\n",
        " tf.keras.layers.Flatten(),\n",
        " tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "#tf.keras.layers.Dropout(0.5),\n",
        " tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])\n",
        "model_2.compile(optimizer='adam',\n",
        " loss='sparse_categorical_crossentropy',\n",
        " metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-d7oN2kMiRwt",
        "colab_type": "code",
        "colab": {},
        "outputId": "9614404d-0b89-455b-82c0-a4ed3ee4d634"
      },
      "cell_type": "code",
      "source": [
        "model_2.fit(x_train, y_train, epochs=5)\n",
        "model_2.evaluate(x_test, y_test)\n",
        "model_2_json = model_2.to_json()\n",
        "json_file = open(\"model_MLP_s.json\", \"w\")\n",
        "json_file.write(model_2_json)\n",
        "json_file.close()\n",
        "model_2.save_weights(\"model_MLP_s.h5\")\n",
        "print(\"Model saved to disk\")\n",
        "os.getcwd()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 33s 555us/step - loss: 0.1998 - acc: 0.9407\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 35s 580us/step - loss: 0.0796 - acc: 0.9756\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 32s 528us/step - loss: 0.0515 - acc: 0.9836\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 33s 546us/step - loss: 0.0373 - acc: 0.9878\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 31s 516us/step - loss: 0.0274 - acc: 0.9910\n",
            "10000/10000 [==============================] - 1s 141us/step\n",
            "Model saved to disk\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C:\\\\Users\\\\suzukij\\\\Desktop\\\\Atividades\\\\deep_learning'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "Gcy_N2ruiRwx",
        "colab_type": "code",
        "colab": {},
        "outputId": "592fd821-919c-4a7d-c6ec-57d6d867a789"
      },
      "cell_type": "code",
      "source": [
        "model_2.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_9 (Flatten)          multiple                  0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             multiple                  401920    \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             multiple                  5130      \n",
            "=================================================================\n",
            "Total params: 407,050\n",
            "Trainable params: 407,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iq9osUnPiRw0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Atividade 4"
      ]
    },
    {
      "metadata": {
        "id": "IYcW8fXDiRw1",
        "colab_type": "code",
        "colab": {},
        "outputId": "0faa80ed-ba15-4424-f3f1-f6628d9eec57"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "#mnist = tf.keras.datasets.mnist\n",
        "#(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
        "mnist = np.load('mnist.npz')\n",
        "\n",
        "x_train = mnist['x_train']\n",
        "y_train = mnist['y_train']\n",
        "\n",
        "x_test = mnist['x_test']\n",
        "y_test = mnist['y_test']\n",
        "\n",
        "# reshape to be [samples][width][height][pixels]\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Conv2D(32, kernel_size=(3, 3),\n",
        " activation='relu',\n",
        " input_shape=(28, 28, 1)))\n",
        "model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(tf.keras.layers.Dropout(0.25))\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "model.add(tf.keras.layers.Dropout(0.5))\n",
        "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
        "model.compile(optimizer='adam',\n",
        " loss='sparse_categorical_crossentropy',\n",
        " metrics=['accuracy'])\n",
        "model.fit(x_train, y_train, epochs=5)\n",
        "model.evaluate(x_test, y_test)\n",
        "model_json = model.to_json()\n",
        "json_file = open(\"model_CNN.json\", \"w\")\n",
        "json_file.write(model_json)\n",
        "json_file.close()\n",
        "model.save_weights(\"model_CNN.h5\")\n",
        "print(\"Model saved to disk\")\n",
        "os.getcwd()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 336s 6ms/step - loss: 0.1849 - acc: 0.9435\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 624s 10ms/step - loss: 0.0812 - acc: 0.9763\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 337s 6ms/step - loss: 0.0624 - acc: 0.9811\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 338s 6ms/step - loss: 0.0496 - acc: 0.9847\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 342s 6ms/step - loss: 0.0414 - acc: 0.9872\n",
            "10000/10000 [==============================] - 15s 2ms/step\n",
            "Model saved to disk\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C:\\\\Users\\\\suzukij\\\\Desktop\\\\Atividades\\\\deep_learning'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "NlON34laiRw6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JDwvdHjjiRw9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Atividade 5"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "182a3810-aab7-4d65-8ff7-0ad50183d769",
        "_uuid": "c8c2cca5ed3dcf9ff50ba2da4c9db56329bfc2cb",
        "id": "_ojMtfEGiRxA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Author:** Raoul Malm  \n",
        "\n",
        "**Description:** \n",
        "\n",
        "This notebook demonstrates the future price prediction for different stocks using recurrent neural networks in tensorflow. Recurrent neural networks with basic, LSTM or GRU cells are implemented. \n",
        "\n",
        "**Outline:**\n",
        "\n",
        "1. [Libraries and settings](#1-bullet)\n",
        "2. [Analyze data](#2-bullet)\n",
        "3. [Manipulate data](#3-bullet)\n",
        "4. [Model and validate data](#4-bullet)\n",
        "5. [Predictions](#5-bullet)\n",
        "\n",
        "**Reference:**  \n",
        "\n",
        "[LSTM_Stock_prediction-20170507 by BenF](https://www.kaggle.com/benjibb/lstm-stock-prediction-20170507/notebook)"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "1426ca5e-ed0c-4766-ac74-cd1dd38c680c",
        "_uuid": "8ec772a02eb88c3446fe0f689b2e1ee8ec9deb6b",
        "id": "4E2RteQbiRxB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 1. Libraries and settings <a class=\"anchor\" id=\"1-bullet\"></a> "
      ]
    },
    {
      "metadata": {
        "_cell_guid": "5a142d3f-96b5-4378-bb01-903367721e08",
        "_uuid": "78e6c0015907947ff5fade7ad76317633d27177a",
        "id": "nL2lzBziiRxB",
        "colab_type": "code",
        "colab": {},
        "outputId": "25d04256-50ec-4216-f36e-4dce11162f21"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import math\n",
        "import sklearn\n",
        "import sklearn.preprocessing\n",
        "import datetime\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "\n",
        "# split data in 80%/10%/10% train/validation/test sets\n",
        "valid_set_size_percentage = 10 \n",
        "test_set_size_percentage = 10 \n",
        "\n",
        "#display parent directory and working directory\n",
        "print(os.path.dirname(os.getcwd())+':', os.listdir(os.path.dirname(os.getcwd())));\n",
        "print(os.getcwd()+':', os.listdir(os.getcwd()));\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/obabilonia: ['.texmf-var', 'octave-4.4.1', '.bash_logout', '.local', '.vnl', 'gromacs', 'Public', 'colab.ipynb', '.ssh', 'CaSO3.png', '.rasmol', '.dmrc', 'Desktop', 'missfont.log', '2', 'Downloads', 'AP_on_Calcite_surface_final.png', 'BDT-out-1000.pdb~', 'Pictures', 'MachineLearning', '.quantumwise', '.xsession-errors.old', '.gnome', '.thunderbird', 'anaconda3', '.gconf', '.VESTA3-obabilonia:0.pid', '.jupyter', 'Documents', '.bashrc.swp', 'Untitled.ipynb', '.gitconfig', 'Untitled1.ipynb', 'energy-potencial.xvg', '.bashrc-anaconda2.bak', 'Dropbox', 'test_main.py', '.viminfo', 'work', '.bashrc', 'Unconfirmed 356541.crdownload', '.Xauthority', '.x-formation', '.compiz', '.ipynb_checkpoints', 'NA', '1', '.pcsx', '.adobe', 'selenium', 'octave-4.4.1.tar.gz', '.Rhistory', '.mozilla', 'test.pdf', 'AP_on_Calcite_surface_final2.png', 'Templates', '.conda', '.dropbox-dist', '.ninja_ide', 'pexpect-4.6.0.tar.gz', '.mcq_log', '.config', '.rstudio-desktop', '.octave_hist', '.ICEauthority', 'Music', '.pki', '.profile', 'result.txt', 'octave1.ipynb~', '.keras', '.dbus', 'anaconda2', '.aptitude', '.wine', 'thermal-graf.ps', 'phonopy', '.bash_history', '.kde', 'energy.xvg', 'lib', 'dwhelper', '.dropbox', 'VirtualBox VMs', 'examples.desktop', '.hplip', 'R', 'TfinalTQMC.pdf', '.gphoto', 'VESTA-x86_64', 'conda-bld', 'Videos', '.cache', '.xsession-errors', 'QuantumWise', '.pip', '.continuum', '.ipython', '.mcq_history', 'BDT~', '.skdata']\n",
            "/home/obabilonia/Dropbox: ['PHPmailer.zip', 'Screencast 2017-04-10 12:59:02.mp4', 'aws-ses-forwarder.zip', 'Moneed.apk', 'm82n28qt9p1dl6fceh0sa8bpca1tqu31ehbsbuo1', 'hardinfo_report.html', 'conf3.png', 'Sao_Paulo_RIO.pdf', 'pristino_modelo_2', 'Programa_ML_ICTP.png', 'disp.yaml', 'post.php', 'OBrine', 'Swim_Lane.pdf', 'Public', 'AirBrush_20160923150100-magic.png', 'octave2.ipynb', 'AWS.pdf', 'marcos.jpg', 'factura.zip', 'Proj_BolsMS_OABP.pdf', 'Relatorio_petrobras.docx', 'PDOS.png', 'DropsyncFiles', 'Moneed', 'Swim_Lane.sdr', 'conf4.png', 'atulButtePodcast.mp3', '#photos-1.dropbox.com (1).url', 'Novas_conformações.pdf', 'Proof_of_address.pdf', 'metodologia-quali-yuri.pdf', 'Exploration_Outliers_detection.pdf', 'post.php~', 'database_ift_new.csv', 'PDOS_AP_Dolo.png', 'ping_pong.png', 'conf2.png', 'foo.zip', 'octave3.ipynb', 'Group_Meeting_03-05-2018 4.pdf', 'q75laofd3bv0p49nt5g0qr87nle059hqf0f8pcg1', 'status_reports_2018-03-15T18-44-45+00-00.csv', 'Yuri-MD&ML-James-Oscar.rev.docx', '921n5j5haqm76rai3lg3hmpijtv2112t5rjnaio1', 'GitHub', 'u5av57l2t9c8n24gdsn4js1nqkq6ljtlfskbl101 (1)', 'Untitled.ipynb', 'Untitled6.ipynb', 'factura_funciona.zip', 'Building_machine_learning_force_fields_for_nanoclu.pdf', 'pluggable.php', 'machine_learning', 'ITAu_TCC', 'Untitled5.ipynb', 'moneed.txt', 'Untitled1.ipynb', 'AirBrush_20160923150100-anti-jpeg.png', 'octave-workspace', 'moneed125.apk', 'fin.pdf', 'octave2.ipynb~', 'El_Amor_de_los_2', 'Doctorado', 'environment.yaml', '.ipynb_checkpoints', 'BabiloniaPerezOscarRel2.pdf', 'Nuevo documento de texto.txt', 'TEMPLATE_PETROBRAS_INTRO.doc', 'Paper-v0', 's3_config.php', 'CUV_BABILONIA_OSCAR_0452166472718.pdf', 'j3gtupd21bt8lalemcf6akts0jirm7v4p90t7r81', 'presentacion.png', 'OSCAR_RIO_Passagem.pdf', 'pdos1.png', 'header.php', 'Space_Feature_Construction.pdf', 'EOR_Low_Salinity_Mecanisms.pdf', 'Projeto.pptx', 'Q-learn.png', '#photos-1.dropbox.com.url', 'Gauss-Newton method to Parameter optimization Aplied in Hamiltonian.txt~', 'Relatorio_petrobras_metodologia.docx', 'Untitled3.ipynb', 'Nova_PDOS_Conformações.pdf', 'petrobras-fev2018..pdf', \"test1 (name-VirtualBox's conflicted copy 2017-10-19).odp\", 'Brochure.docx', 'test2.png', 'Aplicaciones', 'u5av57l2t9c8n24gdsn4js1nqkq6ljtlfskbl101', 'oscar.jpg', 'AP_on_Calcite_surface_final.xsf', 'AMAZON_SES_SETUP_NOTIFICATION', 'Articulo_estimacion_de_parametros', 'PbBiI - Articulo', 'Raphael_Oscar_paper', 'form-denuncia.php', 'Paper_Machile_learning_oil_recovery', 'test1.odp', 'PA_dolomite.energy.xlsx', 'test.dat', 'Relatorio_petrobras_proposta.docx', 'Untitled2.ipynb', 'TEMPLATE_PETROBRAS_PROP.doc', 'KAZEMINIAKORRANI-DISSERTATION-2014.pdf', 'Articulo_Mi_proyecto', 'test1.pptx', 'boleto_invoice_00457663.pdf', '.dropbox', 'ANP.pdf', 'ITAu', 'conf1.png', 'Yuri-MD&ML-James.rev.docx', 'Jupiter', 'PDOS_AP_Calcite.png', 'factura (cópia).zip', 'Data Mining Process.pdf', 'octave1.ipynb', 'AirBrush_20160923150100.jpg', '.dropbox.cache', 'umml0k4q1ro2o5o2iv0ic1j78iivmnojn6n3o0g1', 'Data_transformation.pdf', 'TorniRepuestosjpg', 'Babilonia', 'Untitled4.ipynb', 'Paper_Raphael_Oscar', 'asesoria.png', 'server-aws', 'Mask.png', 'Ingles.pdf', 'salida.pdf']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "5416ce8a-24ff-4903-a09b-b2941eae71d9",
        "_uuid": "9422490f417bc1fcac8e6a0a392faa7451173dac",
        "id": "4aihZWY1iRxH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 2. Analyze data <a class=\"anchor\" id=\"2-bullet\"></a> \n",
        "- load stock prices from prices-split-adjusted.csv\n",
        "- analyze data"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "60dbc696-dda8-476e-8227-010a79df3aa2",
        "_uuid": "aef1074dd4f86a9fe5a380c83994123ce56cce9d",
        "id": "_cVww1rziRxH",
        "colab_type": "code",
        "colab": {},
        "outputId": "1c40d3c8-1857-42f5-ded1-7bb62ec3bd8c"
      },
      "cell_type": "code",
      "source": [
        "# import all stock prices \n",
        "df = pd.read_csv(\"./ITAu/ZuZu/prices-split-adjusted.csv\", index_col = 0)\n",
        "df.info()\n",
        "df.head()\n",
        "\n",
        "# number of different stocks\n",
        "print('\\nnumber of different stocks: ', len(list(set(df.symbol))))\n",
        "print(list(set(df.symbol))[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 851264 entries, 2016-01-05 to 2016-12-30\n",
            "Data columns (total 6 columns):\n",
            "symbol    851264 non-null object\n",
            "open      851264 non-null float64\n",
            "close     851264 non-null float64\n",
            "low       851264 non-null float64\n",
            "high      851264 non-null float64\n",
            "volume    851264 non-null float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 45.5+ MB\n",
            "\n",
            "number of different stocks:  501\n",
            "['GIS', 'PGR', 'GD', 'STZ', 'SYF', 'FAST', 'ED', 'LEN', 'ABBV', 'MMC']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "962ea6e1-51ff-4bba-ad30-77ca5c765d59",
        "_uuid": "c06e709a1bfecb963213943dc7ce8cd559780d9b",
        "id": "tmXdTIXIiRxO",
        "colab_type": "code",
        "colab": {},
        "outputId": "df39d92b-e113-4b4d-847a-6ec724892bdd"
      },
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>symbol</th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "      <th>low</th>\n",
              "      <th>high</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-12-30</th>\n",
              "      <td>ZBH</td>\n",
              "      <td>103.309998</td>\n",
              "      <td>103.199997</td>\n",
              "      <td>102.849998</td>\n",
              "      <td>103.930000</td>\n",
              "      <td>973800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-30</th>\n",
              "      <td>ZION</td>\n",
              "      <td>43.070000</td>\n",
              "      <td>43.040001</td>\n",
              "      <td>42.689999</td>\n",
              "      <td>43.310001</td>\n",
              "      <td>1938100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-30</th>\n",
              "      <td>ZTS</td>\n",
              "      <td>53.639999</td>\n",
              "      <td>53.529999</td>\n",
              "      <td>53.270000</td>\n",
              "      <td>53.740002</td>\n",
              "      <td>1701200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-30</th>\n",
              "      <td>AIV</td>\n",
              "      <td>44.730000</td>\n",
              "      <td>45.450001</td>\n",
              "      <td>44.410000</td>\n",
              "      <td>45.590000</td>\n",
              "      <td>1380900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-30</th>\n",
              "      <td>FTV</td>\n",
              "      <td>54.200001</td>\n",
              "      <td>53.630001</td>\n",
              "      <td>53.389999</td>\n",
              "      <td>54.480000</td>\n",
              "      <td>705100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           symbol        open       close         low        high     volume\n",
              "date                                                                        \n",
              "2016-12-30    ZBH  103.309998  103.199997  102.849998  103.930000   973800.0\n",
              "2016-12-30   ZION   43.070000   43.040001   42.689999   43.310001  1938100.0\n",
              "2016-12-30    ZTS   53.639999   53.529999   53.270000   53.740002  1701200.0\n",
              "2016-12-30    AIV   44.730000   45.450001   44.410000   45.590000  1380900.0\n",
              "2016-12-30    FTV   54.200001   53.630001   53.389999   54.480000   705100.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "d1fc9207-adcd-44f4-85a1-065d34fd82f2",
        "_uuid": "60ee56dd22397b9c545ea7dcc318f5c733d419ba",
        "id": "_QnJYHjZiRxS",
        "colab_type": "code",
        "colab": {},
        "outputId": "4ae7f16c-9755-4c4c-c843-e8d89a045acf"
      },
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "      <th>low</th>\n",
              "      <th>high</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>851264.000000</td>\n",
              "      <td>851264.000000</td>\n",
              "      <td>851264.000000</td>\n",
              "      <td>851264.000000</td>\n",
              "      <td>8.512640e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>64.993618</td>\n",
              "      <td>65.011913</td>\n",
              "      <td>64.336541</td>\n",
              "      <td>65.639748</td>\n",
              "      <td>5.415113e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>75.203893</td>\n",
              "      <td>75.201216</td>\n",
              "      <td>74.459518</td>\n",
              "      <td>75.906861</td>\n",
              "      <td>1.249468e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.660000</td>\n",
              "      <td>1.590000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.810000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31.270000</td>\n",
              "      <td>31.292776</td>\n",
              "      <td>30.940001</td>\n",
              "      <td>31.620001</td>\n",
              "      <td>1.221500e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>48.459999</td>\n",
              "      <td>48.480000</td>\n",
              "      <td>47.970001</td>\n",
              "      <td>48.959999</td>\n",
              "      <td>2.476250e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75.120003</td>\n",
              "      <td>75.139999</td>\n",
              "      <td>74.400002</td>\n",
              "      <td>75.849998</td>\n",
              "      <td>5.222500e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1584.439941</td>\n",
              "      <td>1578.130005</td>\n",
              "      <td>1549.939941</td>\n",
              "      <td>1600.930054</td>\n",
              "      <td>8.596434e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                open          close            low           high  \\\n",
              "count  851264.000000  851264.000000  851264.000000  851264.000000   \n",
              "mean       64.993618      65.011913      64.336541      65.639748   \n",
              "std        75.203893      75.201216      74.459518      75.906861   \n",
              "min         1.660000       1.590000       1.500000       1.810000   \n",
              "25%        31.270000      31.292776      30.940001      31.620001   \n",
              "50%        48.459999      48.480000      47.970001      48.959999   \n",
              "75%        75.120003      75.139999      74.400002      75.849998   \n",
              "max      1584.439941    1578.130005    1549.939941    1600.930054   \n",
              "\n",
              "             volume  \n",
              "count  8.512640e+05  \n",
              "mean   5.415113e+06  \n",
              "std    1.249468e+07  \n",
              "min    0.000000e+00  \n",
              "25%    1.221500e+06  \n",
              "50%    2.476250e+06  \n",
              "75%    5.222500e+06  \n",
              "max    8.596434e+08  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "8e1e0b13-c1da-4fe1-b971-bb2ebcad453b",
        "_uuid": "ef59e62b988d2b0bfd00f834a7ecc17cb927dfc9",
        "id": "GeCYOr98iRxX",
        "colab_type": "code",
        "colab": {},
        "outputId": "d63be4de-7252-4563-9f83-532873181c24"
      },
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 851264 entries, 2016-01-05 to 2016-12-30\n",
            "Data columns (total 6 columns):\n",
            "symbol    851264 non-null object\n",
            "open      851264 non-null float64\n",
            "close     851264 non-null float64\n",
            "low       851264 non-null float64\n",
            "high      851264 non-null float64\n",
            "volume    851264 non-null float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 45.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "ce84999a-306f-4586-b8d6-dc3a0ab9ab67",
        "_uuid": "1235c99db35b562f09d6fb5721368bcab68e307d",
        "id": "G2MxHaG_iRxb",
        "colab_type": "code",
        "colab": {},
        "outputId": "ace4fd5d-e114-4926-f56b-b6bc0e5611f9"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 5));\n",
        "plt.subplot(1,2,1);\n",
        "plt.plot(df[df.symbol == 'EQIX'].open.values, color='red', label='open')\n",
        "plt.plot(df[df.symbol == 'EQIX'].close.values, color='green', label='close')\n",
        "plt.plot(df[df.symbol == 'EQIX'].low.values, color='blue', label='low')\n",
        "plt.plot(df[df.symbol == 'EQIX'].high.values, color='black', label='high')\n",
        "plt.title('stock price')\n",
        "plt.xlabel('time [days]')\n",
        "plt.ylabel('price')\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "plt.subplot(1,2,2);\n",
        "plt.plot(df[df.symbol == 'EQIX'].volume.values, color='black', label='volume')\n",
        "plt.title('stock volume')\n",
        "plt.xlabel('time [days]')\n",
        "plt.ylabel('volume')\n",
        "plt.legend(loc='best');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbYAAAFNCAYAAABsXEqqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "73ad0def-f162-42b9-a1d6-aac45be27e81",
        "_uuid": "777e047511205f929abf0867c7c8a2edee2531ca",
        "id": "r3I5vFvGiRxf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 3. Manipulate data <a class=\"anchor\" id=\"3-bullet\"></a> \n",
        "- choose a specific stock\n",
        "- drop feature: volume\n",
        "- normalize stock data\n",
        "- create train, validation and test data sets"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "e577ab98-344e-4a8e-88c2-09a7d45834b9",
        "_uuid": "3333a7ef79ad756a4b0190e8aef2ed8b3624d7d8",
        "id": "qAhnoU9HiRxf",
        "colab_type": "code",
        "colab": {},
        "outputId": "b9abac43-c05e-45b8-a8fe-46b6b80b867d"
      },
      "cell_type": "code",
      "source": [
        "# function for min-max normalization of stock\n",
        "def normalize_data(df):\n",
        "    min_max_scaler = sklearn.preprocessing.MinMaxScaler()\n",
        "    df['open'] = min_max_scaler.fit_transform(df.open.values.reshape(-1,1))\n",
        "    df['high'] = min_max_scaler.fit_transform(df.high.values.reshape(-1,1))\n",
        "    df['low'] = min_max_scaler.fit_transform(df.low.values.reshape(-1,1))\n",
        "    df['close'] = min_max_scaler.fit_transform(df['close'].values.reshape(-1,1))\n",
        "    return df\n",
        "\n",
        "# function to create train, validation, test data given stock data and sequence length\n",
        "def load_data(stock, seq_len):\n",
        "    data_raw = stock.as_matrix() # convert to numpy array\n",
        "    data = []\n",
        "    \n",
        "    # create all possible sequences of length seq_len\n",
        "    for index in range(len(data_raw) - seq_len): \n",
        "        data.append(data_raw[index: index + seq_len])\n",
        "    \n",
        "    data = np.array(data);\n",
        "    valid_set_size = int(np.round(valid_set_size_percentage/100*data.shape[0]));  \n",
        "    test_set_size = int(np.round(test_set_size_percentage/100*data.shape[0]));\n",
        "    train_set_size = data.shape[0] - (valid_set_size + test_set_size);\n",
        "    \n",
        "    x_train = data[:train_set_size,:-1,:]\n",
        "    y_train = data[:train_set_size,-1,:]\n",
        "    \n",
        "    x_valid = data[train_set_size:train_set_size+valid_set_size,:-1,:]\n",
        "    y_valid = data[train_set_size:train_set_size+valid_set_size,-1,:]\n",
        "    \n",
        "    x_test = data[train_set_size+valid_set_size:,:-1,:]\n",
        "    y_test = data[train_set_size+valid_set_size:,-1,:]\n",
        "    \n",
        "    return [x_train, y_train, x_valid, y_valid, x_test, y_test]\n",
        "\n",
        "# choose one stock\n",
        "df_stock = df[df.symbol == 'EQIX'].copy()\n",
        "df_stock.drop(['symbol'],1,inplace=True)\n",
        "df_stock.drop(['volume'],1,inplace=True)\n",
        "\n",
        "cols = list(df_stock.columns.values)\n",
        "print('df_stock.columns.values = ', cols)\n",
        "\n",
        "# normalize stock\n",
        "df_stock_norm = df_stock.copy()\n",
        "df_stock_norm = normalize_data(df_stock_norm)\n",
        "\n",
        "# create train, test data\n",
        "seq_len = 20 # choose sequence length\n",
        "x_train, y_train, x_valid, y_valid, x_test, y_test = load_data(df_stock_norm, seq_len)\n",
        "print('x_train.shape = ',x_train.shape)\n",
        "print('y_train.shape = ', y_train.shape)\n",
        "print('x_valid.shape = ',x_valid.shape)\n",
        "print('y_valid.shape = ', y_valid.shape)\n",
        "print('x_test.shape = ', x_test.shape)\n",
        "print('y_test.shape = ',y_test.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "df_stock.columns.values =  ['open', 'close', 'low', 'high']\n",
            "x_train.shape =  (1394, 19, 4)\n",
            "y_train.shape =  (1394, 4)\n",
            "x_valid.shape =  (174, 19, 4)\n",
            "y_valid.shape =  (174, 4)\n",
            "x_test.shape =  (174, 19, 4)\n",
            "y_test.shape =  (174, 4)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/home/obabilonia/.conda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:12: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "27e07a3b-89ea-48cc-bd86-023785634d1e",
        "_uuid": "c5ce171e4160f5e0175e4e5ab8e242f181262440",
        "scrolled": true,
        "id": "1mWDM1KmiRxl",
        "colab_type": "code",
        "colab": {},
        "outputId": "f7fa4bf6-dba9-4a24-9222-a4bbe2347d97"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 5));\n",
        "plt.plot(df_stock_norm.open.values, color='red', label='open')\n",
        "plt.plot(df_stock_norm.close.values, color='green', label='low')\n",
        "plt.plot(df_stock_norm.low.values, color='blue', label='low')\n",
        "plt.plot(df_stock_norm.high.values, color='black', label='high')\n",
        "#plt.plot(df_stock_norm.volume.values, color='gray', label='volume')\n",
        "plt.title('stock')\n",
        "plt.xlabel('time [days]')\n",
        "plt.ylabel('normalized price/volume')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAM8AAAEWCAYAAADBx8MMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncS4iTx7nIvr/0R950cv5wzAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAFNCAYAAAC5cXZ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nij358OsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "4375362b-7a7c-4572-947c-91f46c4fd77c",
        "_uuid": "a495f69f3a265f419cd5aa0a4119a8b0392365fd",
        "id": "XXIol0PliRxp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 4. Model and validate data <a class=\"anchor\" id=\"4-bullet\"></a> \n",
        "- RNNs with basic, LSTM, GRU cells\n"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "dde96de3-fef3-45cf-959c-22171a28a3b6",
        "_uuid": "b5015613883c52563da5f8e5be8d6adeb274e420",
        "id": "DiZ3utl4iRxq",
        "colab_type": "code",
        "colab": {},
        "outputId": "fb7c64e8-3918-4a51-9255-eae83e57c0f5"
      },
      "cell_type": "code",
      "source": [
        "## Basic Cell RNN in tensorflow\n",
        "\n",
        "index_in_epoch = 0;\n",
        "perm_array  = np.arange(x_train.shape[0])\n",
        "np.random.shuffle(perm_array)\n",
        "\n",
        "# function to get the next batch\n",
        "def get_next_batch(batch_size):\n",
        "    global index_in_epoch, x_train, perm_array   \n",
        "    start = index_in_epoch\n",
        "    index_in_epoch += batch_size\n",
        "    \n",
        "    if index_in_epoch > x_train.shape[0]:\n",
        "        np.random.shuffle(perm_array) # shuffle permutation array\n",
        "        start = 0 # start next epoch\n",
        "        index_in_epoch = batch_size\n",
        "        \n",
        "    end = index_in_epoch\n",
        "    return x_train[perm_array[start:end]], y_train[perm_array[start:end]]\n",
        "\n",
        "# parameters\n",
        "n_steps = seq_len-1 \n",
        "n_inputs = 4 \n",
        "n_neurons = 200 \n",
        "n_outputs = 4\n",
        "n_layers = 2\n",
        "learning_rate = 0.001\n",
        "batch_size = 50\n",
        "n_epochs = 100 \n",
        "train_set_size = x_train.shape[0]\n",
        "test_set_size = x_test.shape[0]\n",
        "\n",
        "tf.reset_default_graph()\n",
        "\n",
        "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
        "y = tf.placeholder(tf.float32, [None, n_outputs])\n",
        "\n",
        "# use Basic RNN Cell\n",
        "#layers = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.elu)\n",
        "#          for layer in range(n_layers)]\n",
        "\n",
        "# use Basic LSTM Cell \n",
        "#layers = [tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons, activation=tf.nn.elu)\n",
        "#          for layer in range(n_layers)]\n",
        "\n",
        "# use LSTM Cell with peephole connections\n",
        "#layers = [tf.contrib.rnn.LSTMCell(num_units=n_neurons, \n",
        "#                                  activation=tf.nn.leaky_relu, use_peepholes = True)\n",
        "#          for layer in range(n_layers)]\n",
        "\n",
        "# use GRU cell\n",
        "layers = [tf.contrib.rnn.GRUCell(num_units=n_neurons, activation=tf.nn.leaky_relu)\n",
        "          for layer in range(n_layers)]\n",
        "                                                                     \n",
        "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)\n",
        "rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)\n",
        "\n",
        "stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons]) \n",
        "stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)\n",
        "outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])\n",
        "outputs = outputs[:,n_steps-1,:] # keep only last output of sequence\n",
        "                                              \n",
        "loss = tf.reduce_mean(tf.square(outputs - y)) # loss function = mean squared error \n",
        "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) \n",
        "training_op = optimizer.minimize(loss)\n",
        "                                              \n",
        "# run graph\n",
        "with tf.Session() as sess: \n",
        "    sess.run(tf.global_variables_initializer())\n",
        "    for iteration in range(int(n_epochs*train_set_size/batch_size)):\n",
        "        x_batch, y_batch = get_next_batch(batch_size) # fetch the next training batch \n",
        "        sess.run(training_op, feed_dict={X: x_batch, y: y_batch}) \n",
        "        if iteration % int(5*train_set_size/batch_size) == 0:\n",
        "            mse_train = loss.eval(feed_dict={X: x_train, y: y_train}) \n",
        "            mse_valid = loss.eval(feed_dict={X: x_valid, y: y_valid}) \n",
        "            print('%.2f epochs: MSE train/valid = %.6f/%.6f'%(\n",
        "                iteration*batch_size/train_set_size, mse_train, mse_valid))\n",
        "\n",
        "    y_train_pred = sess.run(outputs, feed_dict={X: x_train})\n",
        "    y_valid_pred = sess.run(outputs, feed_dict={X: x_valid})\n",
        "    y_test_pred = sess.run(outputs, feed_dict={X: x_test})\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.00 epochs: MSE train/valid = 0.068963/0.319524\n",
            "4.99 epochs: MSE train/valid = 0.000329/0.001008\n",
            "9.97 epochs: MSE train/valid = 0.000379/0.001560\n",
            "14.96 epochs: MSE train/valid = 0.000224/0.000704\n",
            "19.94 epochs: MSE train/valid = 0.000178/0.000586\n",
            "24.93 epochs: MSE train/valid = 0.000119/0.000324\n",
            "29.91 epochs: MSE train/valid = 0.000104/0.000299\n",
            "34.90 epochs: MSE train/valid = 0.000093/0.000234\n",
            "39.89 epochs: MSE train/valid = 0.000131/0.000507\n",
            "44.87 epochs: MSE train/valid = 0.000075/0.000200\n",
            "49.86 epochs: MSE train/valid = 0.000084/0.000230\n",
            "54.84 epochs: MSE train/valid = 0.000099/0.000315\n",
            "59.83 epochs: MSE train/valid = 0.000066/0.000194\n",
            "64.81 epochs: MSE train/valid = 0.000120/0.000340\n",
            "69.80 epochs: MSE train/valid = 0.000072/0.000230\n",
            "74.78 epochs: MSE train/valid = 0.000063/0.000204\n",
            "79.77 epochs: MSE train/valid = 0.000062/0.000191\n",
            "84.76 epochs: MSE train/valid = 0.000061/0.000206\n",
            "89.74 epochs: MSE train/valid = 0.000075/0.000221\n",
            "94.73 epochs: MSE train/valid = 0.000067/0.000227\n",
            "99.71 epochs: MSE train/valid = 0.000066/0.000227\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "9035de79-0f5c-4b6b-bc86-0d8d4e2f392e",
        "_uuid": "a88583a9effb6f1f84e13b8853adca62578c2992",
        "id": "fM1bgUsdiRxw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 5. Predictions <a class=\"anchor\" id=\"5-bullet\"></a> "
      ]
    },
    {
      "metadata": {
        "_cell_guid": "412b33a3-1e02-4ed5-9846-9984022492bd",
        "_uuid": "2923055320991790fe0c8c0ffa3e75558e5c313a",
        "id": "XDy16q3ziRxy",
        "colab_type": "code",
        "colab": {},
        "outputId": "1bf14193-d62d-4ea9-94c3-345c33cfb8a2"
      },
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1394, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "1bb898b9-943e-4e6f-b43d-8bdb6331cc4e",
        "_uuid": "6fa5eb60c8e8a5d3542547629c3e0df14e5c33a3",
        "id": "e6UQWhEIiRx2",
        "colab_type": "code",
        "colab": {},
        "outputId": "d0cda41a-7208-4847-f55a-f7ec8f53e574"
      },
      "cell_type": "code",
      "source": [
        "ft = 0 # 0 = open, 1 = close, 2 = highest, 3 = lowest\n",
        "\n",
        "## show predictions\n",
        "plt.figure(figsize=(15, 5));\n",
        "plt.subplot(1,2,1);\n",
        "\n",
        "plt.plot(np.arange(y_train.shape[0]), y_train[:,ft], color='blue', label='train target')\n",
        "\n",
        "plt.plot(np.arange(y_train.shape[0], y_train.shape[0]+y_valid.shape[0]), y_valid[:,ft],\n",
        "         color='gray', label='valid target')\n",
        "\n",
        "plt.plot(np.arange(y_train.shape[0]+y_valid.shape[0],\n",
        "                   y_train.shape[0]+y_test.shape[0]+y_test.shape[0]),\n",
        "         y_test[:,ft], color='black', label='test target')\n",
        "\n",
        "plt.plot(np.arange(y_train_pred.shape[0]),y_train_pred[:,ft], color='red',\n",
        "         label='train prediction')\n",
        "\n",
        "plt.plot(np.arange(y_train_pred.shape[0], y_train_pred.shape[0]+y_valid_pred.shape[0]),\n",
        "         y_valid_pred[:,ft], color='orange', label='valid prediction')\n",
        "\n",
        "plt.plot(np.arange(y_train_pred.shape[0]+y_valid_pred.shape[0],\n",
        "                   y_train_pred.shape[0]+y_valid_pred.shape[0]+y_test_pred.shape[0]),\n",
        "         y_test_pred[:,ft], color='green', label='test prediction')\n",
        "\n",
        "plt.title('past and future stock prices')\n",
        "plt.xlabel('time [days]')\n",
        "plt.ylabel('normalized price')\n",
        "plt.legend(loc='best');\n",
        "\n",
        "plt.subplot(1,2,2);\n",
        "\n",
        "plt.plot(np.arange(y_train.shape[0], y_train.shape[0]+y_test.shape[0]),\n",
        "         y_test[:,ft], color='black', label='test target')\n",
        "\n",
        "plt.plot(np.arange(y_train_pred.shape[0], y_train_pred.shape[0]+y_test_pred.shape[0]),\n",
        "         y_test_pred[:,ft], color='green', label='test prediction')\n",
        "\n",
        "plt.title('future stock prices')\n",
        "plt.xlabel('time [days]')\n",
        "plt.ylabel('normalized price')\n",
        "plt.legend(loc='best');\n",
        "plt.show()\n",
        "\n",
        "corr_price_development_train = np.sum(np.equal(np.sign(y_train[:,1]-y_train[:,0]),\n",
        "            np.sign(y_train_pred[:,1]-y_train_pred[:,0])).astype(int)) / y_train.shape[0]\n",
        "corr_price_development_valid = np.sum(np.equal(np.sign(y_valid[:,1]-y_valid[:,0]),\n",
        "            np.sign(y_valid_pred[:,1]-y_valid_pred[:,0])).astype(int)) / y_valid.shape[0]\n",
        "corr_price_development_test = np.sum(np.equal(np.sign(y_test[:,1]-y_test[:,0]),\n",
        "            np.sign(y_test_pred[:,1]-y_test_pred[:,0])).astype(int)) / y_test.shape[0]\n",
        "\n",
        "print('correct sign prediction for close - open price for train/valid/test: %.2f/%.2f/%.2f'%(\n",
        "    corr_price_development_train, corr_price_development_valid, corr_price_development_test))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4MAAAFNCAYAAAC66roXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIJeZvdcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "correct sign prediction for close - open price for train/valid/test: 0.55/0.36/0.39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "581be505-fdc8-4aec-a3a3-19cd28f4e324",
        "_uuid": "2dc96fc968aa367482f1b7dc2018848af84bdb75",
        "id": "JpP2cMu6iRx5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mxgBMo0EiRx8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Atividade 6"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "6dfe74fc-d444-e723-4639-18b3e0fff20e",
        "_uuid": "5f038994eea67358eba2a3a5ded7a61845cb126a",
        "id": "kKPbgtmdiRx9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Manifold Learning And Autoencoders\n",
        "\n",
        "Author: Alexandru Papiu"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "6f86e2ca-4c94-0460-2c3b-99fb6001a18f",
        "_uuid": "afd0a6e2846ee420593b40e9b4a9f4921c0d9904",
        "id": "gNk_9H3wiRx-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The MNIST competition is slowly coming to an end so I figured I'd try something slightly different - let's try to see if we can get some intuition about the geometry and topology of the MNIST dataset."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "1745b4fe-3fb0-d876-2120-5049a9d7162a",
        "_uuid": "1ccb0d2eed43b91b9a2cc7b6dad624fad6da4cf3",
        "id": "-VnBTpGviRx_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Loading required packages and data:"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "ca8de246-c0bd-52c0-e358-1185a9575b3c",
        "_execution_state": "idle",
        "_uuid": "a97f15834f540b6b50042b69595e1d2271f5e382",
        "id": "U7sQiq7eiRyA",
        "colab_type": "code",
        "colab": {},
        "outputId": "773a5f8c-9243-4efa-ae88-f74d278d8690"
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Dropout, Convolution2D, MaxPooling2D, Flatten, Input\n",
        "from keras.optimizers import adam\n",
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "%config InlineBackend.figure_format = 'retina'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "ce56722d-f117-e84b-dbd2-1cd3b4c8c19d",
        "_execution_state": "idle",
        "_uuid": "091d951a2dce8e600a8cffd0d7b4630127e75f81",
        "collapsed": true,
        "id": "h4esHVFMiRyD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train = pd.read_csv(\"./ITAu/ZuZu/train.csv\")\n",
        "\n",
        "X_train = train.iloc[:,1:].values\n",
        "X_train = X_train.reshape(X_train.shape[0], 28, 28) #reshape to rectangular\n",
        "X_train = X_train/255 #pixel values are 0 - 255 - this makes puts them in the range 0 - 1\n",
        "\n",
        "y_train = train[\"label\"].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "99de6496-7669-4384-7400-dd25018c6eef",
        "_execution_state": "idle",
        "_uuid": "b1effa9e9637e61b174ebca06b850bdfb2837823",
        "collapsed": true,
        "id": "zQ5CnI6eiRyH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#define a function that allows us to see the digits:\n",
        "def show(img):\n",
        "    plt.imshow(img, cmap = \"gray\", interpolation = \"none\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "7bd3500e-0e42-fb0d-3f63-c822ec983fc9",
        "_uuid": "212371d17dd9e2b90702905253cea4e440b7ef75",
        "id": "34rXf-aQiRyK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's pick one image to be our default image - just so we have a reference point. We'll call this the **\"eight\" image** - simply because, well it's an eight:"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "9d15656d-80c2-201d-9003-beaee13152c4",
        "_execution_state": "idle",
        "_uuid": "d2660d1f22ecb66851b6a1d23487b03cd69b2d00",
        "id": "eoNXV2vqiRyL",
        "colab_type": "code",
        "colab": {},
        "outputId": "ad492da6-0fa4-4235-8ab3-7c2a2eb1c034"
      },
      "cell_type": "code",
      "source": [
        "img = X_train[10]\n",
        "show(img)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH0CAYAAADVH+85AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 250,
              "width": 253
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "83b69df9-798c-4e93-8ff1-c58e33cf506f",
        "_execution_state": "idle",
        "_uuid": "a16333d9545a0aab6de84249f0121bba0c607956",
        "id": "NfQ7xLmViRyR",
        "colab_type": "code",
        "colab": {},
        "outputId": "c17a105b-3696-4647-e140-14276b24290b"
      },
      "cell_type": "code",
      "source": [
        "pd.DataFrame(img)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.992157</td>\n",
              "      <td>0.992157</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.705882</td>\n",
              "      <td>0.188235</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.992157</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.890196</td>\n",
              "      <td>0.113725</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.890196</td>\n",
              "      <td>0.309804</td>\n",
              "      <td>0.870588</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.505882</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.792157</td>\n",
              "      <td>0.043137</td>\n",
              "      <td>0.705882</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.466667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.168627</td>\n",
              "      <td>0.941176</td>\n",
              "      <td>...</td>\n",
              "      <td>0.956863</td>\n",
              "      <td>0.494118</td>\n",
              "      <td>0.788235</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.027451</td>\n",
              "      <td>0.831373</td>\n",
              "      <td>0.992157</td>\n",
              "      <td>...</td>\n",
              "      <td>0.407843</td>\n",
              "      <td>0.992157</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.992157</td>\n",
              "      <td>0.803922</td>\n",
              "      <td>0.082353</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.098039</td>\n",
              "      <td>0.874510</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>...</td>\n",
              "      <td>0.843137</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.992157</td>\n",
              "      <td>0.874510</td>\n",
              "      <td>0.219608</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.388235</td>\n",
              "      <td>0.964706</td>\n",
              "      <td>...</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.678431</td>\n",
              "      <td>0.098039</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.164706</td>\n",
              "      <td>...</td>\n",
              "      <td>0.854902</td>\n",
              "      <td>0.423529</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.054902</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.168627</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.074510</td>\n",
              "      <td>0.643137</td>\n",
              "      <td>0.964706</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.313725</td>\n",
              "      <td>0.909804</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.168627</td>\n",
              "      <td>0.964706</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.784314</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.635294</td>\n",
              "      <td>0.992157</td>\n",
              "      <td>0.752941</td>\n",
              "      <td>0.043137</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.019608</td>\n",
              "      <td>0.698039</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.466667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.027451</td>\n",
              "      <td>0.729412</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.890196</td>\n",
              "      <td>0.721569</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.043137</td>\n",
              "      <td>0.564706</td>\n",
              "      <td>0.890196</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.188235</td>\n",
              "      <td>0.537255</td>\n",
              "      <td>0.949020</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>28 rows × 28 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     0    1    2    3    4         5         6         7         8         9   \\\n",
              "0   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "1   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "2   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "3   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "4   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "5   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "6   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "7   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "8   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "9   0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.333333   \n",
              "10  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.168627  0.941176   \n",
              "11  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.027451  0.831373  0.992157   \n",
              "12  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.098039  0.874510  0.988235   \n",
              "13  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.388235  0.964706   \n",
              "14  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.164706   \n",
              "15  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "16  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "17  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.168627   \n",
              "18  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.074510  0.643137  0.964706   \n",
              "19  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.313725  0.909804  0.988235   \n",
              "20  0.0  0.0  0.0  0.0  0.0  0.000000  0.168627  0.964706  0.988235  0.784314   \n",
              "21  0.0  0.0  0.0  0.0  0.0  0.000000  0.635294  0.992157  0.752941  0.043137   \n",
              "22  0.0  0.0  0.0  0.0  0.0  0.019608  0.698039  0.988235  0.466667  0.000000   \n",
              "23  0.0  0.0  0.0  0.0  0.0  0.027451  0.729412  0.988235  0.890196  0.721569   \n",
              "24  0.0  0.0  0.0  0.0  0.0  0.043137  0.564706  0.890196  0.988235  0.988235   \n",
              "25  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.188235  0.537255  0.949020   \n",
              "26  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "27  0.0  0.0  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "\n",
              "   ...         18        19        20        21        22        23   24   25  \\\n",
              "0  ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "1  ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "2  ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "3  ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "4  ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "5  ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "6  ...   0.992157  0.992157  1.000000  0.705882  0.188235  0.000000  0.0  0.0   \n",
              "7  ...   0.988235  0.988235  0.992157  0.988235  0.890196  0.113725  0.0  0.0   \n",
              "8  ...   0.890196  0.309804  0.870588  0.988235  0.988235  0.505882  0.0  0.0   \n",
              "9  ...   0.792157  0.043137  0.705882  0.988235  0.988235  0.466667  0.0  0.0   \n",
              "10 ...   0.956863  0.494118  0.788235  0.988235  0.988235  0.588235  0.0  0.0   \n",
              "11 ...   0.407843  0.992157  1.000000  0.992157  0.803922  0.082353  0.0  0.0   \n",
              "12 ...   0.843137  0.988235  0.992157  0.874510  0.219608  0.000000  0.0  0.0   \n",
              "13 ...   0.988235  0.988235  0.678431  0.098039  0.000000  0.000000  0.0  0.0   \n",
              "14 ...   0.854902  0.423529  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "15 ...   0.054902  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "16 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "17 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "18 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "19 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "20 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "21 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "22 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "23 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "24 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "25 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "26 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "27 ...   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.0   \n",
              "\n",
              "     26   27  \n",
              "0   0.0  0.0  \n",
              "1   0.0  0.0  \n",
              "2   0.0  0.0  \n",
              "3   0.0  0.0  \n",
              "4   0.0  0.0  \n",
              "5   0.0  0.0  \n",
              "6   0.0  0.0  \n",
              "7   0.0  0.0  \n",
              "8   0.0  0.0  \n",
              "9   0.0  0.0  \n",
              "10  0.0  0.0  \n",
              "11  0.0  0.0  \n",
              "12  0.0  0.0  \n",
              "13  0.0  0.0  \n",
              "14  0.0  0.0  \n",
              "15  0.0  0.0  \n",
              "16  0.0  0.0  \n",
              "17  0.0  0.0  \n",
              "18  0.0  0.0  \n",
              "19  0.0  0.0  \n",
              "20  0.0  0.0  \n",
              "21  0.0  0.0  \n",
              "22  0.0  0.0  \n",
              "23  0.0  0.0  \n",
              "24  0.0  0.0  \n",
              "25  0.0  0.0  \n",
              "26  0.0  0.0  \n",
              "27  0.0  0.0  \n",
              "\n",
              "[28 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "343a1e51-0f5f-6538-8c66-fe12860ab485",
        "_uuid": "5344c5ca7ce137a787236e3d0199704363d12d81",
        "id": "E9B5GrrLiRyW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Ok so our digits are in a space in which every pixel is a variable or a feature. Since there are 28*28 = 784 pixels per image we can thing of the images as sitting in $\\mathbb{R}^{784}$ - a real 784 dimensional vector space.\n",
        "\n",
        "This space is very high dimensional and most dimensionality reduction techniques try to exploit the assumption that not all of these dimension are needed to distinguish between the digits (or more generally extract features or achieve some learning task).\n",
        "\n",
        "Where does this intuition come from? To see this let's generate a point uniformly at random in the unit 784 dimensional hypercube and see what it looks like. We pick points in the hypercube simply because we normalized the pixel intensities. \n",
        "\n",
        "Maybe a 784-hypercube sounds intimidating but it has a very easy mathematical definition: it's the set composed of all  vectors in $\\mathbb{R}^{784}$ that have all coordinates less than or equal to one: $$\\{x \\in \\mathbb{R}^{784} | |x_i| < 1\\} = [0,1]^{784}$$"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "befda8b4-94d8-1e73-2668-22c14ed27033",
        "_uuid": "4bf41988a3e55dc65bce641f035831252b021595",
        "id": "nv03GtbKiRyX",
        "colab_type": "code",
        "colab": {},
        "outputId": "d152fe82-9628-48bb-a273-61372355f8b3"
      },
      "cell_type": "code",
      "source": [
        "#generating a random 28 by 28 image:\n",
        "rand_img = np.random.randint(0, 255, (28, 28))\n",
        "rand_img = rand_img/255.0\n",
        "\n",
        "show(rand_img)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH0CAYAAADVH+85AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzt3XmUlNWdxvHnBygIQ7OLIiioLCou\nASLKqjghoGJQQYlGOAbccYMoiUrENTFxYgyIEsUFnNEQTIgiblFUDLjBEeEAQSKLgCyKLA1ibLjz\nR1Vnekg3y/0VVHP7+zmnT9FV9dTvUrzdD2931ftaCEEAACBNlfK9AAAAsPdQ9AAAJIyiBwAgYRQ9\nAAAJo+gBAEgYRQ8AQMIoegAAEkbRAwCQMIoeAICEUfQAACSMogcAIGEUPQAACaPoAQBIGEUPAEDC\nKHoAABJG0QMAkLAq+V7A3mBmiyUVSFqS56UAABCrqaSNIYRmngdJsuglFVStWrVukyZN6sY+wPr1\n66OH16tXLzorSYWFhdHZbdu2uWYfeOCB0dkaNWq4ZletWjU6u3LlStfszZs3R2dDCK7ZTZo0ic56\n/r0kqaioKDq7dOlS1+yjjz7alV+3bl10tm7d6G8NkqStW7dGZ1esWOGavWXLluhsQUGBa7bnefM8\nZ5JUq1at6OzatWtds7/66qvobM2aNaOzmzZt0vbt26PzxfJa9GbWWNKdknpIqifpc0mTJN0RQoh/\nZqUlTZo0qfvQQw9FP8CkSZOisz/60Y+is5I0Y8aM6OyGDRtcsz2lc+qpp7pmH3HEEdHZ22+/3TXb\n85x7ylKSPNtpo0aNXLM9/6EdNGiQa/aLL77oyo8fPz46e/HFF7tmz58/Pzo7bNgw1+zZs2dHZzt3\n7uya3b9//+is5zmTpF69ekVnR44c6Zo9YcKE6KznOZ82bZo2bNiwJPoBsvJW9GZ2lKTpkg6W9BdJ\nCySdLOl6ST3MrGMI4ct8rQ8AgBTk88V4o5Up+etCCL1DCD8NIXST9ICklpLuyePaAABIQl6KPrs3\n312ZF8vt+HPL2yVtlnSJmfl+6QsAQAWXrz3607OXr4YQ/t8rDUIImyT9TVJ1Safs64UBAJCSfP2O\nvmX2cmEZt3+izB5/C0mvl/UgZjazjJtaxS8NAIB05GuPvvh9EmW9RLz4+tr7YC0AACRrv34ffQih\nbWnXZ/f02+zj5QAAUO7ka4++eI+9rCMgFF8f/yZfAACQt6L/e/ayRRm3N89elvU7fAAAsBvyVfRT\ns5fdzez/rcHMakrqKGmLpHf39cIAAEhJXoo+hPAPSa8qc8D+a3a4+Q5JNSSNDyHEH4AcAADk9cV4\nVytzCNzfmdkZkuZLaq/Me+wXSro1j2sDACAJeTsEbnavvp2kJ5Up+KGSjpL0oKRTOM49AAB+eX17\nXQjhM0mX5nMNAACkzLzn0i6PzGxmlSpV2tSvXz/6Mc4+++zo7EEHHRSdlXyn/zzzzDNds+fNmxed\nHT58uGt2q1bxBzT0nKNbkhYujH+Dx1FHHeWa3bhx4+jsoYce6prtyXft2tU1+6WXXnLl//jHP0Zn\nq1Tx7eNMnjw5OnvBBRe4Zh9wwAHR2eeee841e9SoUdFZ7ymVzznnnOis99/722+/jc4+/fTT0dnz\nzz9f8+bNm1XWMWN2Vz7PXgcAAPYyih4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEgYRQ8AQMIoegAA\nEkbRAwCQMIoeAICEUfQAACSMogcAIGEUPQAACaPoAQBIWF7PR7831a5dW717947Ot2jRIjq7YsWK\n6Kwk/fjHP47Ozpo1yzX7yiuvjM527949b7M9p5GUpNNPPz066z1F7owZM6Kzv/71r12z161bF52t\nVMm3n1BQUODKT5o0KTr71FNPuWa/++670dlhw4a5Zl988cXR2VWrVrlm33TTTdHZlStXumYXFRVF\nZ2vWrOmaPWbMmOjsDTfcEJ1dvnx5dLYk9ugBAEgYRQ8AQMIoegAAEkbRAwCQMIoeAICEUfQAACSM\nogcAIGEUPQAACaPoAQBIGEUPAEDCKHoAABJG0QMAkDCKHgCAhFH0AAAkjKIHACBhFkLI9xpyzsxm\nfuc732njOWf04MGDo7O33nprdFaSOnbsGJ09//zzXbPnzJkTnV24cKFr9hVXXBGd7d27t2v27Nmz\no7P16tVzzW7fvn10tk2bNq7ZV199dXS2VatWrtn33HOPKz98+PDobNeuXV2zx44dG5394Q9/6Jrd\nrl276OzcuXNdsz1fJ5Uq+fYrr7322ujszTff7Jo9bdq06OwPfvCD6OzgwYO1aNGiWSGEttEPIvbo\nAQBIGkUPAEDCKHoAABJG0QMAkDCKHgCAhFH0AAAkjKIHACBhFD0AAAmj6AEASBhFDwBAwih6AAAS\nRtEDAJAwih4AgIRR9AAAJCzZ09SeeOKJbaZOnRr9GLVr147ONmzYMDor+U5DeeKJJ7pmr1+/Pjp7\n4YUXumYXFhZGZwcOHOiaPWTIkOis59SdkjRz5szo7Lp161yzPafYXbp0qWu293StX331VXT2mWee\ncc32fH+oW7eua/asWbOis506dXLNPuecc6Kzr7zyimt2hw4dorO33Xaba/YHH3wQnb3vvvuis/Pm\nzdOWLVs4TS0AACgbRQ8AQMIoegAAEkbRAwCQMIoeAICEUfQAACSMogcAIGEUPQAACaPoAQBIGEUP\nAEDCKHoAABJG0QMAkDCKHgCAhFH0AAAkjKIHACBhVfK9gL2lUqVKql69enT+8MMPj86++OKL0VlJ\nGjZsWHT2xz/+sWt2165do7OLFy92zfacb7pHjx6u2XPmzInOjh071jX7+eefj86uXbvWNduz9hde\neME1+8Ybb3TljznmmOjs0Ucf7Zo9YMCA6Kz37+353nTVVVe5Zq9atSo6++ijj7pm9+/fPzq7adMm\n1+xbb701Ouv5GuvXr5/mz58fnS+Wtz16M1tiZqGMj/itCQAA/Eu+9+g3SPptKdcX7uuFAACQonwX\n/foQwog8rwEAgGTxYjwAABKW7z36qmb2I0mHS9os6WNJb4cQtuV3WQAApCHfRX+IpPE7XLfYzC4N\nIby1q7CZzSzjplbulQEAkIB8/uj+CUlnKFP2NSQdL2mMpKaSXjKzE/O3NAAA0pC3PfoQwh07XDVX\n0pVmVihpqKQRks7dxWO0Le367J5+mxwsEwCA/Vp5fDHeI9nLLnldBQAACSiPRV98qK8aeV0FAAAJ\nKI9Ff0r28tO8rgIAgATkpejN7Bgz+7c9djNrKmlU9tOn9+WaAABIUb5ejHehpKFm9rakpZI2STpK\n0lmSqkmaIun+PK0NAIBk5Kvop0pqKek7kjoq8/v49ZLeUeZ99eNDCCFPawMAIBmWYp+a2cyDDjqo\nTYsWLaIf47XXXovOPvzww9FZSa7T677//vuu2Z999ll09ne/+51r9jfffBOdnT59umt23bp1o7NL\nly51zR4zZkx09s4773TN9pzq9cwzz3TNPuCAA1x5M4vOep5zSerZs2d09j//8z9ds7/73e9GZ1u2\nbOmaXa1atejsSy+95Jo9bty46Oxhhx3mmt2kSZPo7OWXXx6d/fWvf63ly5fPKuut5LurPL4YDwAA\n5AhFDwBAwih6AAASRtEDAJAwih4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEgYRQ8AQMIoegAAEkbR\nAwCQMIoeAICEUfQAACSMogcAIGFV8r2AvSWEoKKiouj8Aw88EJ1dsmRJdFaSCgsLo7OjR492zf7r\nX/8ana1Xr55rdvfu3aOzzzzzjGv2/fffH5298sorXbNr1qwZnd2wYYNr9tatW6OzCxYscM32fI1J\n0uuvvx6d9XyNSVL16tWjs2vWrHHNXrRoUXS2X79+rtmNGzeOzrZq1co1+5hjjonOXnPNNa7Z06ZN\ni86OGDEiOrtx48bobEns0QMAkDCKHgCAhFH0AAAkjKIHACBhFD0AAAmj6AEASBhFDwBAwih6AAAS\nRtEDAJAwih4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEhYsqepbdq0qcaNGxednzx5cnR26NCh0VlJ\nOuCAA6KzV111lWt2+/bto7MtWrRwzfacYve9995zzX733Xejs57TUEpS5cqVo7Oe0+tK0vXXXx+d\n/e///m/X7C5durjyderUic527NjRNdtzqlnv10nLli2js95TKj/xxBPR2bPOOss1+5e//GV01vOc\nSdLhhx8enW3WrFl0dvDgwa7TEhdjjx4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEgYRQ8AQMIoegAA\nEkbRAwCQMIoeAICEUfQAACSMogcAIGEUPQAACaPoAQBIGEUPAEDCKHoAABJmIYR8ryHnzGzmIYcc\n0ubSSy+NfoyLLrooOvv4449HZyXplFNOic7eeeedrtl/+ctforMDBgxwzd6wYUN0dtiwYa7ZRxxx\nRHR2/vz5rtkzZsyIznq2FUmaPHlydLZXr16u2VOmTHHlK1WK30/p1q2ba/b9998fnT3yyCNds9es\nWROdXbt2rWv2aaedFp39xz/+4ZpdrVq16OzQoUNds4uKiqKznvPRX3LJJVqwYMGsEELb6AcRe/QA\nACSNogcAIGEUPQAACaPoAQBIGEUPAEDCKHoAABJG0QMAkDCKHgCAhFH0AAAkjKIHACBhFD0AAAmj\n6AEASBhFDwBAwih6AAASViXfC9hbCgoK1KNHj+h8kyZNorN9+vSJzkpS1apVo7OdOnVyzR4yZEh0\n9n/+539cs//0pz9FZ1977TXX7J/97GfR2f/6r/9yzT7ggAOis/PmzXPNvv3226OzmzZtcs3+61//\n6so///zz0dn33nvPNfvRRx+Nzh533HGu2Z7TOY8ePdo1e/369dHZP/7xj67ZEyZMiM6uW7fONdvz\nb+Y5Re7y5cujsyXlZI/ezPqY2Ugzm2ZmG80smNnTu8h0MLMpZrbOzL42s4/N7AYzq5yLNQEAgNzt\n0d8m6URJhZKWS2q1szub2Q8kPSdpq6Q/SFonqZekByR1lNQ3R+sCAKBCy9Xv6G+U1EJSgaSrdnZH\nMyuQ9KikbZJOCyEMDCHcJOkkSTMk9TGzfjlaFwAAFVpOij6EMDWE8EkIIezG3ftIaiDp2RDChyUe\nY6syPxmQdvGfBQAAsHvy8ar7btnLl0u57W1JWyR1MLP4V6QBAABJ+Sn6ltnLhTveEEIokrRYmdcO\nHLkvFwUAQIry8fa6WtnLst4jUnx97V09kJnNLOOmnb4YEACAioID5gAAkLB87NEX77HXKuP24ut3\neWSGEELb0q7P7um32fOlAQCQlnzs0f89e9lixxvMrIqkZpKKJH26LxcFAECK8lH0b2QvSzs+bRdJ\n1SVNDyF8s++WBABAmvJR9BMlfSGpn5m1K77SzKpJujv76cN5WBcAAMnJye/ozay3pN7ZTw/JXp5q\nZk9m//xFCOEnkhRC2GhmlylT+G+a2bPKHAL3HGXeejdRmcPiAgAAp1y9GO8kSQN2uO5I/d974ZdK\n+knxDSGESWbWVdKtks6XVE3SIklDJP1uN4+wBwAAdiEnRR9CGCFpxB5m/ibpzFzMBwAApbMUd57N\nbOaxxx7bZuLEidGPcc4550RnGzVqFJ2VpH794s/pM3NmWccQ2j0tWvzbmyF22/Tp012zH3nkkehs\ngwYNXLPbti31nZq75eOPP3bN3rhxY3T2rrvucs0+7LDDorMvv1zaUax3n+d88pL0+OOPR2evvPJK\n1+xZs2ZFZ4uKilyzt27dGp1dtmyZa/bgwYOjs+PGjXPNvv7666Oza9ascc1+9dVXo7Oef+9BgwZp\n4cKFs8p6K/nu4oA5AAAkjKIHACBhFD0AAAmj6AEASBhFDwBAwih6AAASRtEDAJAwih4AgIRR9AAA\nJIyiBwAgYRQ9AAAJo+gBAEgYRQ8AQMIoegAAEpbsaWpr1qzZpn379tGPsWXLluis9xSYntnDhg1z\nzfac2rdDhw6u2XfffXd09rLLLnPNnjp1anS2f//+rtmnn356dPbBBx90zR41alR01rut3XHHHa78\nvffeG53t1auXa3bz5s2js5MmTXLN3rZtW3T2wAMPdM1u3bp1dPbggw92zZ4wYUJ09oQTTnDN9pwa\n+MYbb4zOjhw5UitXruQ0tQAAoGwUPQAACaPoAQBIGEUPAEDCKHoAABJG0QMAkDCKHgCAhFH0AAAk\njKIHACBhFD0AAAmj6AEASBhFDwBAwih6AAASRtEDAJAwih4AgIQlez761q1bt5k8eXL0YzzyyCPR\n2ZYtW0ZnJcnzb7J582bX7Ntuuy06u337dtfsDz74IDr75ptvumafddZZ0dlvvvnGNfuyyy6Lzlau\nXNk123N+ce+2Nm7cOFe+ffv2rrxH06ZNo7Pvvvuua3bz5s2js507d3bN/sUvfhGd9W6r7dq1i842\nbNjQNXvjxo3R2Q0bNkRnP/zwQxUWFnI+egAAUDaKHgCAhFH0AAAkjKIHACBhFD0AAAmj6AEASBhF\nDwBAwih6AAASRtEDAJAwih4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEhYlXwvYG/5+uuvNWfOnOj8\n3Llzo7MDBw6MzkrSzTffHJ0999xzXbP79OkTne3SpYtr9vDhw6Ozo0ePds0eMWJEdPaII45wza5f\nv350tkePHq7ZW7Zsic6OGTPGNXv+/Pmu/KRJk6Kzd911l2u25xTYzz33nGt2QUFBdPaTTz5xzf7q\nq6+is7Nnz3bNXrhwYXTWc3pdyXfq8UsvvTQ6O2TIEBUWFkbni7FHDwBAwih6AAASRtEDAJAwih4A\ngIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEgYRQ8AQMIoegAAEkbRAwCQMIoeAICEUfQAACSMogcAIGEU\nPQAACUv2fPTffvutVq5cGZ3/85//HJ3dsGFDdFaS+vfvH51dvXq1a/Y333wTnb3yyitdsw877LDo\n7NKlS12zGzduHJ1dvHixa/bll18enW3durVr9kcffRSdnTJlimv2kiVLXHnPOcI7d+7smj1nzpzo\n7IQJE1yzR48eHZ3dvHmza7bn3+zaa691zd60aVN09vjjj3fN9uSnTp0anc3FueilHO3Rm1kfMxtp\nZtPMbKOZBTN7uoz7Ns3eXtbHs7lYEwAAyN0e/W2STpRUKGm5pFa7kZktaVIp18/N0ZoAAKjwclX0\nNypT8IskdZW0Oz+r+CiEMCJH8wEAQClyUvQhhH8Vu5nl4iEBAEAO5PPFeI3M7ApJ9SR9KWlGCOHj\nPK4HAIDk5LPov5f9+Bcze1PSgBDCst15ADObWcZNu/MaAQAAkpeP99FvkXSXpLaS6mQ/in+vf5qk\n182sRh7WBQBAcvb5Hn0IYY2kn+9w9dtm1l3SO5LaSxok6cHdeKy2pV2f3dNv41wqAAD7vXJzZLwQ\nQpGkx7KfdsnnWgAASEW5KfqstdlLfnQPAEAOlLeiPyV7+WleVwEAQCL2edGbWRsz+7e5ZnaGMgfe\nkaRSD58LAAD2TE5ejGdmvSX1zn56SPbyVDN7MvvnL0IIP8n++TeSmpvZdGWOpidJJ0jqlv3z8BDC\n9FysCwCAii5Xr7o/SdKAHa47MvshSUslFRf9eEnnSvqupJ6SDpC0WtIESaNCCNNytCYAACq8XB0C\nd4SkEbt537GSxuZiLgAA2Llkz0dfo0YNdejQITq/atWq6OzNN98cnZWk6667LjpbrVo11+x33nkn\nOvu9731v13faiV/96lfRWc+57CXp4osvjs6ed955rtmeba1v376u2X/605+is7Nnz3bNvuuuu1z5\n3//+99HZ7du3u2Z36tQpOvvSSy+5Zl9yySXR2QsvvNA1+957743O/sd//Idr9imnnLLrO5WhoKDA\nNfu+++6LzoYQorMbN26MzpZU3l51DwAAcoiiBwAgYRQ9AAAJo+gBAEgYRQ8AQMIoegAAEkbRAwCQ\nMIoeAICEUfQAACSMogcAIGEUPQAACaPoAQBIGEUPAEDCKHoAABKW7GlqV65cqVtuuSU6/8QTT0Rn\nGzZsGJ2VpH79+kVnr776atfsevXqRWebNGnimu1Ro0YNV/6hhx6KzjZu3Ng1+6CDDorOvv76667Z\nEydOjM4WFha6Zjdo0MCVP/zww6OzgwYNcs1esGBBdPbuu+92zb7nnnuis95T5HpOY+09rXHLli2j\ns55/L0kaNWpUdHbevHnR2W7duunjjz+Ozhdjjx4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEgYRQ8A\nQMIoegAAEkbRAwCQMIoeAICEUfQAACSMogcAIGEUPQAACaPoAQBIGEUPAEDCKHoAABKW7Pnoi4qK\n9OWXX0bnO3XqFJ31niP82GOPjc7Wrl3bNdtzPnrv+abffvvt6Oxjjz3mmn3LLbdEZ+fPn++a3bBh\nw+jsyJEjXbMPOuig6Owxxxzjmv3Tn/7UlT/55JOjs97nbePGjdHZAQMGuGZ78j/84Q9dsytVit83\n9H5/6NmzZ3S2ffv2rtkXXHBBdHbKlCnRWc92VhJ79AAAJIyiBwAgYRQ9AAAJo+gBAEgYRQ8AQMIo\negAAEkbRAwCQMIoeAICEUfQAACSMogcAIGEUPQAACaPoAQBIGEUPAEDCKHoAABKW7Glq69evryuu\nuCI6f9FFF0Vnb7jhhuisJPXu3Ts6e9NNN7lmL1u2LDpbUFDgmr19+/bobN++fV2zx48fH51ds2aN\na7Yn36FDB9fsfv36RWebNWvmmt2iRQtX/sMPP4zOXnXVVa7Zo0ePjs5edtllrtmerxNPVpIaNGgQ\nna1bt65r9u9///vo7IIFC1yz77///ujsV199FZ0tKiqKzpbEHj0AAAmj6AEASBhFDwBAwih6AAAS\nRtEDAJAwih4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEgYRQ8AQMIoegAAEkbRAwCQMIoeAICEUfQA\nACQs2fPR16pVSz179ozOt2vXLjr785//PDorSTNmzIjOXnzxxa7Zo0aNis4ec8wxrtnr16+Pzq5a\ntco123O+6l69erlm9+3bNzrbpk0b1+wzzzwzOtupUyfX7G3btrnyixcvjs56zi8uSZ9//nl0tkeP\nHq7ZF110UXS2cePGrtkTJ06Mzp566qmu2Y0aNYrOXnLJJa7Z06ZNi842bdo0OrtmzZrobEnuPXoz\nq2dmg8zsz2a2yMy+NrMNZvaOmQ00s1JnmFkHM5tiZuuymY/N7AYzq+xdEwAAyMjFHn1fSQ9L+lzS\nVEnLJDWUdJ6kxyT1NLO+IYRQHDCzH0h6TtJWSX+QtE5SL0kPSOqYfUwAAOCUi6JfKOkcSS+GELYX\nX2lmt0h6X9L5ypT+c9nrCyQ9KmmbpNNCCB9mrx8u6Q1JfcysXwjh2RysDQCACs39o/sQwhshhBdK\nlnz2+lWSHsl+elqJm/pIaiDp2eKSz95/q6Tbsp9e5V0XAADY+6+6/zZ7WVTium7Zy5dLuf/bkrZI\n6mBmVffmwgAAqAj22qvuzayKpP7ZT0uWesvs5cIdMyGEIjNbLOk4SUdKmr+LGTPLuKnVnq0WAIA0\n7c09+l9Kai1pSgjhlRLX18pebigjV3x97b21MAAAKoq9skdvZtdJGippgSTfGxh3IoTQtoz5MyX5\n3mAMAEACcr5Hb2aDJT0oaZ6k00MI63a4S/Eeey2Vrvj6+KOnAAAASTkuejO7QdJISXOVKfnSDlf2\n9+xli1LyVSQ1U+bFe5/mcm0AAFREOSt6MxumzAFvPlKm5Ms6dt8b2cvSjgPZRVJ1SdNDCN/kam0A\nAFRUOSn67MFufilppqQzQghf7OTuEyV9Iamfmf3rgPJmVk3S3dlPH87FugAAqOjcL8YzswGS7lTm\nSHfTJF1nZjvebUkI4UlJCiFsNLPLlCn8N83sWWUOgXuOMm+9m6jMYXEBAIBTLl513yx7WVnSDWXc\n5y1JTxZ/EkKYZGZdJd2qzCFyq0laJGmIpN+VPC4+AACIZyl2qpnNbNq0aZt77703+jGmT58enW3Z\nsuWu77QTDz8c/5uL22+/3TX71ltvjc62bt3aNbtOnTrR2XHjxrlmP/nkk9HZF154wTW7c+fO0dkp\nU6a4Zt9zzz3R2TFjxrhmf/HFzn7Dt2svv1zawTV3z6OPPuqaffbZZ0dnX3rpJddsz2mRb775Ztfs\n8ePHR2f79++/6zvtxAUXXBCdff/9912zP/00/rXhDRo0iM6OGTNGn3/++ayy3kq+u/b2IXABAEAe\nUfQAACSMogcAIGEUPQAACaPoAQBIGEUPAEDCKHoAABJG0QMAkDCKHgCAhFH0AAAkjKIHACBhFD0A\nAAmj6AEASBhFDwBAwih6AAASViXfC9hbtmzZog8++CA6X7NmzehsYWFhdFaS7rvvvujsSSed5Jr9\n2WefRWdXrVrlmj1z5szobLt27VyzDz744Ois5/zgklS9evXo7NChQ12zly1bFp09+eSTXbMXLVrk\nys+fPz86+9FHH7lmv/nmm9HZdevWuWYfeuih0Vkzc80ePnx4dLagoMA1e8yYMdHZTz75xDX7wgsv\njM6uXr06Ort9+/bobEns0QMAkDCKHgCAhFH0AAAkjKIHACBhFD0AAAmj6AEASBhFDwBAwih6AAAS\nRtEDAJAwih4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEhYsqeprVKliurWrRudX7FiRXT2kUceic5K\nUufOnaOzb731lmv2U089FZ2dOHGia3bXrl2jsy+//HLeZnfp0sU1+6KLLorOHnnkka7Z11xzTXT2\njDPOcM2uXbu2K+85Ra/3FLnt27ePznq+viWpWbNm0dkhQ4a4Zo8dOzY6O3r0aNfsU089NTpbv359\n1+zjjz8+OtukSZPo7IQJE6KzJbFHDwBAwih6AAASRtEDAJAwih4AgIRR9AAAJIyiBwAgYRQ9AAAJ\no+gBAEgYRQ8AQMIoegAAEkbRAwCQMIoeAICEUfQAACSMogcAIGEUPQAACUv2fPQHH3ywrr/++uj8\n6tWro7PHHXdcdFaS1q5dG51dt26da7bnvO4/+9nPXLNDCNFZzzndJenRRx+Nzr733nuu2Z5zZX/2\n2Weu2Z7zXTdq1Mg1e+DAga40MPt1AAAMoklEQVS859zoHTt2dM0uKiqKzj7//POu2XPmzInObtmy\nxTX72GOPjc56vzctWbIkOvu3v/3NNXv9+vXR2WuvvTY6W7Vq1ehsSezRAwCQMIoeAICEUfQAACSM\nogcAIGEUPQAACaPoAQBIGEUPAEDCKHoAABJG0QMAkDCKHgCAhFH0AAAkjKIHACBhFD0AAAmj6AEA\nSJh5Tg1aXpnZzEqVKrWpXr169GO0atUqOnv55ZdHZyWpcuXK0dkePXq4Znv+3vXq1XPNfuqpp6Kz\n5513nmv25MmTo7MrVqxwzfZ8DU6fPt01e+rUqdFZz7+XJPXs2dOV//73vx+d7datm2t27dq1o7Pn\nnnuua/aiRYuis56vb0mqU6dOdHbbtm2u2b/97W+js96/t+drdN68edHZ4cOHa8mSJbNCCG2jH0Q5\n2KM3s3pmNsjM/mxmi8zsazPbYGbvmNlAM6u0w/2bmlnYycez3jUBAICMKjl4jL6SHpb0uaSpkpZJ\naijpPEmPSeppZn3Dv/+XaLakSaU83twcrAkAACg3Rb9Q0jmSXgwhbC++0sxukfS+pPOVKf3ndsh9\nFEIYkYP5AACgDO4f3YcQ3gghvFCy5LPXr5L0SPbT07xzAADAnsvFHv3OfJu9LCrltkZmdoWkepK+\nlDQjhPDxXl4PAAAVyl4rejOrIql/9tOXS7nL97IfJTNvShoQQli2t9YFAEBFsjf36H8pqbWkKSGE\nV0pcv0XSXcq8EO/T7HUnSBoh6XRJr5vZSSGEzbsaYGYzy7jJ914KAAASsVcOmGNm10kaKmmBpEtK\n3hZCWBNC+HkIYVYIYX32421J3SW9J+loSYP2xroAAKhocr5Hb2aDJT0oaZ6kM0II63YnF0IoMrPH\nJLWX1CX7GLvKlHoQgeyefpvdXjQAAInK6R69md0gaaQy74U/PfvK+z2xNntZI5frAgCgospZ0ZvZ\nMEkPSPpImZJfE/Ewp2QvP93pvQAAwG7JSdGb2XBlXnw3U5kf13+xk/u22fGwuNnrz5B0Y/bTp3Ox\nLgAAKjr37+jNbICkOyVtkzRN0nVmtuPdloQQnsz++TeSmpvZdEnLs9edIKn4LBPDQwi+M3UAAABJ\nuXkxXrPsZWVJN5Rxn7ckPZn983hJ50r6rqSekg6QtFrSBEmjQgjTcrAmAACgHBR99nj1I/bg/mMl\njfXOBQAAu5bs+eibN2/eZvTo0dGP8dBDD0Vn27Z1nTpYw4cPj856z/FdUFAQnT3hhBNcs7/4osyX\nduyS95zwhx12WHT2jjvucM2eM2dOdHbp0qWu2d27d4/ODhkyxDXbez776667Ljp71llnuWafffbZ\n0dkDDzzQNfsXv/hFdHbr1q2u2bVq1YrOer9O2rdvH529/vrrXbOnTYv/QfPUqVOjsx988IEKCwvz\nfz56AABQflH0AAAkjKIHACBhFD0AAAmj6AEASBhFDwBAwih6AAASRtEDAJAwih4AgIRR9AAAJIyi\nBwAgYRQ9AAAJo+gBAEgYRQ8AQMLc56MvrzZt2qQ33ngjOu85HaNnriQtWbIkOvvKK6+4Znfr1i06\n6zktsCT95je/ic56TnErSb/61a+is57nTJJq1qwZnf3DH/7gmj1w4MDobJ8+fVyzGzVq5Mq3aNEi\nOjt37lzX7CpV4r91Hn744a7ZdevWjc7ecsstrtmVKsXvG44dO9Y1+8wzz4zOer7GJKmwsDA669nW\nvKcVLsYePQAACaPoAQBIGEUPAEDCKHoAABJG0QMAkDCKHgCAhFH0AAAkjKIHACBhFD0AAAmj6AEA\nSBhFDwBAwih6AAASRtEDAJAwih4AgIRZCCHfa8g5M/uySpUqdevXrx/9GP/85z/zkpWkpk2bRmc3\nbtzomu05nePatWtdsxs3bhydLSoqcs1evXp1dHbTpk2u2Z5Tfx555JGu2UuXLo3O1qlTxzV7+fLl\nrny1atVceY/NmzdHZw888EDX7EMPPTQ6u2LFCtdsM4vOek/Pu2jRouis59S+krR9+/borOcUt9nv\na+tCCPWiH0TpFv1iSQWSlpRxl1bZywX7ZEFp4DmLw/MWh+dtz/GcxSnPz1tTSRtDCM08D5Jk0e+K\nmc2UpBBC23yvZX/BcxaH5y0Oz9ue4zmLUxGeN35HDwBAwih6AAASRtEDAJAwih4AgIRR9AAAJKxC\nvuoeAICKgj16AAASRtEDAJAwih4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gBAEhYhSp6M2tsZo+b2Uoz\n+8bMlpjZb83Md2LthGWfo1DGx6p8ry9fzKyPmY00s2lmtjH7fDy9i0wHM5tiZuvM7Gsz+9jMbjCz\nyvtq3fm2J8+bmTXdybYXzOzZfb3+fDCzemY2yMz+bGaLstvOBjN7x8wGmlmp38cr+va2p89byttb\nlXwvYF8xs6MkTZd0sKS/KHPu4ZMlXS+ph5l1DCF8mccllmcbJP22lOsL9/VCypHbJJ2ozHOwXP93\nTutSmdkPJD0naaukP0haJ6mXpAckdZTUd28uthzZo+cta7akSaVcPzeH6yrP+kp6WNLnkqZKWiap\noaTzJD0mqaeZ9Q0ljn7G9iYp4nnLSm97CyFUiA9Jr0gKkq7d4frfZK9/JN9rLI8fkpZIWpLvdZS3\nD0mnS2ouySSdlt2Gni7jvgWS1kj6RlK7EtdXU+Y/n0FSv3z/ncrh89Y0e/uT+V53np+zbsqUdKUd\nrj9EmfIKks4vcT3bW9zzluz2ViF+dJ/dm++uTGk9tMPNt0vaLOkSM6uxj5eG/VQIYWoI4ZOQ/Q6x\nC30kNZD0bAjhwxKPsVWZPVxJumovLLPc2cPnDZJCCG+EEF4IIWzf4fpVkh7JfnpaiZvY3hT1vCWr\novzo/vTs5aul/KNvMrO/KfMfgVMkvb6vF7cfqGpmP5J0uDL/KfpY0tshhG35XdZ+o1v28uVSbntb\n0hZJHcysagjhm323rP1GIzO7QlI9SV9KmhFC+DjPayovvs1eFpW4ju1t10p73oolt71VlKJvmb1c\nWMbtnyhT9C1E0ZfmEEnjd7husZldGkJ4Kx8L2s+Uuf2FEIrMbLGk4yQdKWn+vlzYfuJ72Y9/MbM3\nJQ0IISzLy4rKATOrIql/9tOSpc72thM7ed6KJbe9VYgf3Uuqlb3cUMbtxdfX3gdr2d88IekMZcq+\nhqTjJY1R5vdZL5nZiflb2n6D7S/OFkl3SWorqU72o6syL6w6TdLrFfzXbb+U1FrSlBDCKyWuZ3vb\nubKet2S3t4pS9IgUQrgj+7uu1SGELSGEuSGEK5V5EeNBkkbkd4VIVQhhTQjh5yGEWSGE9dmPt5X5\n6dt7ko6WNCi/q8wPM7tO0lBl3j10SZ6Xs9/Y2fOW8vZWUYq++H+wtcq4vfj69ftgLakofjFLl7yu\nYv/A9pdDIYQiZd4eJVXA7c/MBkt6UNI8SaeHENbtcBe2t1LsxvNWqhS2t4pS9H/PXrYo4/bm2cuy\nfoePf7c2e7lf/ihrHytz+8v+vrCZMi8K+nRfLmo/VyG3PzO7QdJIZd7TfXr2FeQ7YnvbwW4+bzuz\nX29vFaXop2Yvu5dyNKSayhxAYoukd/f1wvZjp2QvK8w3C4c3spc9Srmti6TqkqZX4FdAx6hw25+Z\nDVPmgDcfKVNWa8q4K9tbCXvwvO3Mfr29VYiiDyH8Q9KryryA7Jodbr5Dmf+ljQ8hbN7HSyvXzOyY\n0l58YmZNJY3KfrrTw75CkjRR0heS+plZu+IrzayapLuznz6cj4WVZ2bWprTDu5rZGZJuzH5aIbY/\nMxuuzIvIZko6I4TwxU7uzvaWtSfPW8rbm1WU41aUcgjc+ZLaK/Me+4WSOgQOgfv/mNkIZV648rak\npZI2STpK0lnKHGVriqRzQwj/zNca88XMekvqnf30EEnfV+Z/+9Oy130RQvjJDvefqMwhSZ9V5pCk\n5yjzVqiJki6oCAeR2ZPnLfuWpubKfN0uz95+gv7vfeLDQwjFxZUsMxsg6UlJ25T58XNpr6ZfEkJ4\nskSmwm9ve/q8Jb295fvQfPvyQ1ITZd4u9rmkfypTXr+VVCffayuPH8q8teQZZV6hul6Zg0yslfSa\nMu9DtXyvMY/PzQhlDpdZ1seSUjIdlfnP0VeSvpY0R5k9hcr5/vuUx+dN0kBJk5U5omWhMod0XabM\nsds75/vvUo6esyDpTbY33/OW8vZWYfboAQCoiCrE7+gBAKioKHoAABJG0QMAkDCKHgCAhFH0AAAk\njKIHACBhFD0AAAmj6AEASBhFDwBAwih6AAASRtEDAJAwih4AgIRR9AAAJIyiBwAgYRQ9AAAJo+gB\nAEgYRQ8AQML+F2jFgbtkrhm5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 250,
              "width": 253
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "d5367fa5-7367-39fd-e0c3-664d495457a3",
        "_uuid": "23d35b55d873d4082db86edfa336a5c1b0ad9b63",
        "id": "zXrYCdCXiRyf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Doesn't look like anything to me!\n",
        "\n",
        "We can try to sample at random many times but unless we get extremely lucky all we'll get is static and nothing resembling an actual digit. This is good empirical evidence that the meaningful images - in this case images of digits, are clustered in smaller dimensional subsets in the original 784 dimensional pixel space. This is what is called the **manifold hypothesis**. And the promise is that if we better understand the structure of the manifold we will have an easier time building machine learning systems."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "c710659e-5521-046f-01b1-3ae17a75a584",
        "_uuid": "ee6c44a688ebc21e6b52b679d54bff1d3e3472d3",
        "id": "JrpcPUmJiRyg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Before we try to see how to figure out the manifold structure let's take a closer look at out space. For example what happens if we start at the point of a digit and start traveling in a random direction? Will we get any meaningful images?"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "1febcf07-9339-4e08-aafd-f6bffac763d0",
        "_uuid": "f30a9a3043bec610a5ddc400a0f02706f4a8256a",
        "collapsed": true,
        "id": "sKnH16uQiRyi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "rand_direction = np.random.rand(28, 28) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "6153ce43-3371-5d0d-2ac3-9590096b974d",
        "_uuid": "b4b5a6385f6de7ba5e24447f1feb38cd923cea58",
        "id": "f0B_tCBbiRyo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Moving in a random direction away from a digit in the 784 dimensional image space:"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "3e434fd0-7b95-7c63-4daf-dfef0b8014f7",
        "_uuid": "26510714bd1a5ab5598a9a4ee2d1b2edc91827a2",
        "id": "TvdoW6UmiRyo",
        "colab_type": "code",
        "colab": {},
        "outputId": "14483696-7b4d-4ddb-aade-c70e8ca874ef"
      },
      "cell_type": "code",
      "source": [
        "for i in range(16):\n",
        "    plt.subplot(4,4,i+1)\n",
        "    show(img + i/4*rand_direction)    \n",
        "    plt.xticks([])\n",
        "    plt.yticks([])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo4AAAHWCAYAAAD5OwjnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsnXeAVdX1/ddlCjADAwxdkSYWxMao\nUSNGQEVsqNh7b8GoEUuMX0ssSTQmthhLxF6iKKgINhAriDoiqKhYQFT6gDTpzu8Pcrdr72Emlzfz\nk2jW559sc868uW/evucd7l5n7aSyshJCCCGEEEL8J+qt7wsQQgghhBA/DbRxFEIIIYQQmdDGUQgh\nhBBCZEIbRyGEEEIIkQltHIUQQgghRCa0cRRCCCGEEJnQxlEIIYQQQmRCG0chhBBCCJEJbRyFEEII\nIUQmtHEUQgghhBCZ0MZRCCGEEEJkQhtHIYQQQgiRCW0chRBCCCFEJrRxFEIIIYQQmdDGUQghhBBC\nZCK/Ll4kSZIpAEoATK2L1xOZ6QhgYWVlZaf1fSE/VZS7642OUO7mjPJ2vdIRyt2cUe6uNzqijvI2\nqaysrPXVJElSAaC01i8kcqKysjJZ39fwUyVJkookSUoLCwvT/3bjq1evtnjlypVurH79+havWrXK\n4u+//z7+jmrHCgoKLK5Xr16183iMr4N/PhLfy4oVK9b6enztAJCfn1/tGL8mv/9ly5a5eTVdV15e\nHpYvX44kSbB69Wrlbg4kSVJRr1690uLi4vS/3TjnyNKlS91Yo0aNLF6+fLnFnOuAz5E4xp89f9Y1\n5RJfR4MGDeJbWuvvBYDvvvvO4ry8vLVee7ymOMY/l/7NAGDRokVuXk3Xlb7PxYsX4/vvv59XWVnZ\nvNrJolqSJKnIy8srLSkpAVD18+a1hD97AGjSpInFnE8x7/jzjut2UVGRxem6v7Z5nNeLFy+2mPMn\nEt8L/xxfU1wvGzZsaHF8z3wP8fufN2+em8fvK64HhYWFWLBgQbr21nrNrZMnjljzLwdtHMVPkamF\nhYWlnTqt+UcY36QAMH/+fItnzpzpxjp06GDx7NmzLY5f1PyFFheFli1bWpwupGubx4vCN998Y3Hr\n1q3dPF644ubt66+/tpi/IOfOnevmtWjRwuL4nvnnNt54Y4s//vhjN4+vKy5iTZo0wWeffQZRK6YW\nFxeX7rTTTgD8FyAAfPXVVxZ/9NFHbmz77be3mD+HBQsWuHn8Bcn3AQB06dLF4g022MDimEvNmjWz\neOLEiRZ37drVzeMvVb5fAGDChAkWN27c2OIpU6a4eek9DFTNR/65XXbZxeKRI0e6eZtuuularwn4\nIaffeOMNLFy4cCpErkwtKSkp7du3L4Cqm/XJkydb/M4777ixXr16Wfz+++9bHPOON1hz5sxxY1tt\ntZXFnDNxreO1ecyYMRbvsMMObh6vubxOA8Brr71mcdOmTS2eNGmSm7fFFltYHN8z30P77ruvxY8+\n+qib1717d4vj91iHDh0wbNgw1BXSOAohhBBCiEzU1RNHIX6yrFixAlOnTgXgnyIC/l/DXOIDfLlh\nyZIl1b7+JptsYnEs+fGToYULF1oc/+XKTxk33HDDan8X/+s6PrnhJ0osUWnVqpWbxz/HT2oA/69r\nLvPF38Vln3i9kydPdmVzkRtLlizBW2+9BQAoKytzY/y58ZMTwH9uFRUVFscnw7vuuqvFsRQ4fvx4\ni6dPn25x8+a+estPhbbeeutq3gnw+eefWxxLgTNmzLCY85afDsafq+lJ/KxZsyyu6Z7mJ1MA8Mor\nrwCoWg0Q687ChQvxwgsvAAB69+7txvgJW1w7vv32W4s5L2LuHnDAARbHEvSrr75qMT+1btu2rZs3\nduxYi3/5y19W+7s4x7lqBABffvmlxZy7/HQQ8E9I27dv78Y4d7lqxD8D+ErXzjvv7MaGDh1aRZZR\nG/TEUQghhBBCZEIbRyGEEEIIkQltHIUQQgghRCakcRQCP+hWWP8E1GzxwSfXWNvSpk0bN49P60W9\nGWseWbPDp+wAf3KVdViskQS8NnKjjTZyY3yqj7WQ8UQiv+eoXWQNG+suWZcE+Pf16aefVnn9ePJc\n1I74N+Y8YEsQwJ+2Zy3g5ptv7ubxaWzW6QLe2olPiPbp08fN69atm8WssWKNJODzJ+q/2rVrZzFr\nIb/44gs3jzWOUbvYsWNHi/l0d7xH+B5nLRy/fjx9LmpHzAX+7FjTCACbbbaZxaxHZ6cAwJ9M3mab\nbdwYrz18+v6oo45y81LHgngdqdY1hdf03XbbzY2x+wDn3Ycffujm8fdHXEv5vnzjjTfW+tqA/154\n8skn3ViTJk2cnrm26ImjEEIIIYTIhDaOQgghhBAiEypVi/958vPzq5QHUriDS7SQYcsTNgCPhsv8\n2rG0zDY+XNaLnQUYtpdgE+54vdFkm8vOsQTEcKk9difg1+cSTbQZ4k4IpaVVewPUZF8kslFYWGil\n1tgBjD+PaB/D5WnOkWiGzVYosZzIhuB77723xbGMy1YinNM9evRw8ziv2DQZ8KVLlkdEWxSWd7AN\nSnx9tvGJ9xnLObi8zcybN092UrWkQYMGVeQPKWz9FC1kWFrw3nvvWfzII4+4ebwujh492o3xWn3s\nscdaHNc6zl1er/bbbz83j3PrqaeecmOcTyyziOy4444Wx+8P/nuwjKOmZhJRdpIkSRWD89qgJ45C\nCCGEECIT2jgKIYQQQohMaOMohBBCCCEyIY2j+J8nSRJrLch6FcBb7sR2amxj06JFC4tjK6gPPvjA\n4tjSkHWShYWFFkcLFdYk8u9iCx/A6x+j9Q9fF2vioj6RdV9R+8nvuXPnzhZH/Qy3MZw2bZob++67\n76QRqwPy8vJM/xc1jqxnjVo91lrxGNveAMCIESMsjnYnrJNkPWu0dmJNL9uH8D0B+JyLul1uBcfv\nM+YQ6ytjqzrWcnL7OLZjAbyG7t1333Vj6T0ofW7tycvLs7UlrrlsJdW1a1c3xnmz5ZZbWsxrEQDc\nd999Fu++++5ujNdg1s/GtZQtrtjSZ9y4cW4e50Nsq8n3F6+z0dqN7dFi/nNLw3333dfit99+283j\na3z55ZervD63tK0teuIohBBCCCEyoY2jEEIIIYTIhErV4n+e1atXmxVDtH/g8trXX3/txrjkweWW\naLnDnTq4QwDgSy/ctSaWiLmkPX36dIu5JAz4cnQsqXHZnct88XdxiTuWkaqzIOLyEuAtJCLFxcVV\nSjVi3VmxYoXlZOx4xHY33LECALbbbjuLufQbLXdS+Qbgy8UAMGzYMIs/+eQTi2OJmPOTS26xHMc/\nx1Y/gC+7swVJ/F18r0b5RXUWRNEqqiYJRSpVWbx4cZXXF+vG8uXLrRQc18t+/fpZzJ1SAKBXr15r\nfb3YzYXXoyjVGDRokMUsR4g5ydINvo7YiaZTp04W85oIeBkHy49i1xcucce1s3379hZPmDDB4tih\njOUecd1u3bp1jRZs64qeOAohhBBCiExo4yiEEEIIITLxP1Wq5pJHdZ1CAN8J4a677srpd7Hr/DPP\nPOPGLr30UovZ/V6sf2I3Az6JFsur3GmAT+fFcjeXGmIpjMuBXP6KHTi4fMynCePpaz4xetVVV7mx\npUuXWswl7Vgq4nL6hx9+6MYGDhxoMZ/ujl0MaiqL8L0h6obddtvN/TefEI35yF1VOK9iuZtL2vE1\nOEe4tBZP1/MpZc5NPkUK+HLiTTfd5Mb4HuTyXCytc1k8nu4+66yzLOYOMzFPa+pMo7z9/8OBBx7o\n/pvzM8oWqpNFxDWMS9rxM+bvfnahiN2GuHTNJ7NZKgT4e+G8885zYxUVFRbz93489cz3CV8TAJx+\n+ukW77nnnhbHsji7JeTl5bmx/Pz8KvlcG3QnCCGEEEKITGjjKIQQQgghMqGNoxBCCCGEyMTPWuPI\n2jLA6xV79+5d7c+xFiB2ZMgKH4dnzSQAbLvtthaz7ifqNMSPQ0FBgVkvRG0h61mKi4vdGOsTa9L0\n8c9FHRnrpljnFbU9DzzwgMWscWRdC+BtHth2BPC2EaxJjPcJ5263bt3c2NNPP23xQQcdZDH/LQCv\nFYt64m+++Sbn+0r8QMOGDbHVVlsBqNr1grWp0XKGP4+aNH38c5MnT3ZjbO204447Whx1h//85z8t\nZk1atFlh+6A49otf/MJivs922GEHN4/1w3x9APDUU09ZfNJJJ1kcu0GxdVG0+0mts6LViVh3GjVq\nZJ/5lClT3Bh3ZmEbJcBrcuPax7AWNnYA4g5dffv2tfjZZ59182655RaL+Z5hKyrA2wfF8wx9+vSx\nmHW9e+yxh5vH+nm+JwGfu6zVjd1y+L6Jdj9jxoyp07zVE0chhBBCCJEJbRyFEEIIIUQmfnal6k03\n3dTiCy64wI3VVJ7OBS4P8SNkALjhhhssjqVA7sJwyimnWHz55ZfX6fWJbKxevdpKw7E0wqWx2KWF\n4VJtLGlzKYbLxQAwdepUi7m8FnN3++23t5hLHpzvgC83ssUD4MviXJI/7rjj3Lx7773X4mhBxH+P\n/v37W3z33Xe7edxVJ+0QwWPR0kKsOytXrrSyVswDXptiRwymadOmFkfLHS4Z7rTTTtWOsfXNOeec\n4+ZtvvnmFrN0omfPnm4e5y1LeQB/X3A+cv4BwGOPPWZxfC+cb1wmjKVFvs9iN5K0ZP7+++9X6cok\n1o1ly5bZulBWVubGvvjiC4u5o0qE7cBiF5XnnnvO4igVe/755y3mtWnAgAFuHq/VLGGIecfr6q67\n7urGOHe5VBxfg8vfUfbEpXZ+/VdffdXNY8ugJ5980o3ttddeVSQgtUFPHIUQQgghRCa0cRRCCCGE\nEJnQxlEIIYQQQmTiZ6FxPPTQQy3++9//bnG0WqhrWEc0cuRIN8Z2GFHjyMRWbeLHp169etYykFta\nAV4DFrUn3HKQ9YSsvQG83orbCgLAmWeeafGRRx5pMWsEgTWaoBTWWtbU/qphw4ZujFtZsb1KbF3F\n+rVox8OWQay1jPY6rFOKtiaxHZbIjby8PFvjRo8e7cZYRx3tPb7++muLt9lmG4uj/pbzIrbKvOii\niyxmTSJrGgHfLpAtQiZNmuTmsUaN22ECPvfff/99i6OWmDVfUavM1j1suxItSsaMGWNx1Ncpb+uO\ngoICy9HBgwe7MbaZiZY7rJNlCye2KAN8+0DOHwD4wx/+YDHvEeLnzZZonOO8PgL++z2u/ayFLS8v\ntzha6bz44osWFxUVuTFuM8htZOOaO2LECIt32WUXN5aXl6eWg0IIIYQQ4sdHG0chhBBCCJGJn2Sp\nOpbPuDtB48aNLf7/3Z2ia9euFg8cONCN1WTdwsSSpPjxWb16tZXUNttsMzdWUVFhMZetAV8OZEsS\nLhcD3o4nWk9wyY9LCbHDDJcvOK9jjnMpPC2/r+012XbkxBNPdPO4ZBnLc0uXLrWYy0Pc2SZex+LF\ni93Y/Pnzq5TsxbqzatUq+0x79erlxtjmKZZ+t9hiC4s5NzfYYAM376233rJ45513dmNs4cQWOVHq\nwR03uCwcS8RcCo/SHpZV8HVwLgL+3o05xzITzv3hw4e7eSzn4Hsa+KHEz3NEbqxYsQLTpk0DABxy\nyCFu7KOPPrI4dkDhLkUsB4syC7bc+dWvfuXG9t13X4s5T7iUDPjvcF5n49rFpfBoi/Xee+9ZzFY6\ncW3mMjmX2QEvCeJOc9GOh++hKGH67LPPqlir1QY9cRRCCCGEEJnQxlEIIYQQQmQiqYtybpIk5QDK\n/uPEWlC/fn2L33nnHTfGpRd2cc/a1JsfBQP+JNT+++/vxvg04BlnnGHxrbfe6ubVdB38+JqbrMfr\nyEplZWXdHZf6HyNJkvK8vLyytLwcJQb8eP+rr75yY1xGKSgosPiDDz5w8woLCy2eMGGCG+OyNp9i\njV1fqjvBzWU8wJ+YvfHGG90Y5xefujvttNPcPC6bcK4CvgRyySWXWBxPnHPHhHiqt3PnznZacunS\npcrdHEiSpLygoKAsPY0cS3pcxo0lOD6Nyh2P+LQx4HOay4KAvy+4+0aUYqTlSADo3r27xdwNA/Al\n7pdeeqna39WuXTuL+/Tp4+btt99+Fj/11FNujJ0NuDNS7DDD9yqf4AZ+KJOPGTMGCxcufLeysnI7\niHUmSZLy+vXrl6XrGOcF4D+TmAv9+vWzmE/V33fffW4e5248wc9rKd8bUe7Ba+tuu+1mcezAwms6\nS0QAvw/g3IrdkVgudOedd6I6uCNO7F7E9zK7AwBruueksoyKiopar7l64iiEEEIIITKhjaMQQggh\nhMiENo5CCCGEECITPxk7ntLSUovjUXbWabKesCb9JusXWPMDeA1EhB3fzznnnGp/F18H63wAYMCA\nARbnqmsUdUdlZaVZLEQrBIa7pgBee8WfY3T+ZwuFqIFhCxG2t4l2Cqz7YT3Y8ccf7+ax3iZa6bCV\nyYEHHmhx1FNyRyS2IwKAyy67zGLWfEbLF9YfRRuIKVOm1Kk1xP8qlZWVWLVqFYCqOkaGu6YAPpe4\nww9b5wA+R6L1x1ZbbWUxawujFpLvC87bK6+80s0bNWqUxaxPA4B99tnHYl6ro56SbVyidvH666+3\nmPW4sasRd/6Ilj6pPVHUlol1h3OXP3vA25Jx1xQAWLBggcWsJY/a9BNOOMHiIUOGuDHWVJ588skW\nv/nmm24ea7M5d++66y4378knn7Q45u7hhx9u8Xbb/SCJ7dmzp5vH5zZil6Y77rjD4vRvBlS1IGJ7\nuKg5f+GFF1wXp9qiJ45CCCGEECIT2jgKIYQQQohM/GRK1Vw+u/rqq93YLbfcYjHb9tQEd+yIpWl+\nDT6GDwDXXHONxZtsskm1r8+Pr8866yw3xu9FrH8KCwutW0XsfMESCe5KBHjrGy69xu4BXA6MnSq4\nfMEdCGJDer6Os88+2+JoEcTlxr322suNsbUUlzliWeO1116zmLsyAd5GhctDsazHdjAs7wDWlKni\ndYt1p6ioyMq10baEu6+0bt3ajY0fP95iLunGtY7vhdg1iUuI/NnHvOXr+MMf/mAxW/gAQGorBPjS\nN+DlHCyr4PsP8NZRI0eOdGNsrcPWRbE7DN8LsVtOKkd67733VK6uJY0bNzb7m8GDB7sxXj+jjIbX\npvnz51vcv39/N48lEywBAvzax6Vvts4BfN7xniNaoLFF1AEHHODGWFbHsojPPvvMzeN7LdpAsQSD\nJSJxH8GyIu6OA6zpdsN/u9qiJ45CCCGEECIT2jgKIYQQQohM/GQ6x9QEd47hx7w1vTcuSXAHDMCX\nKI455phqX4NLkDfffLMb+/vf/17DFdct6hyTOzF3Y1mViaUxPkHHp4ijXGLmzJkWx3I3n3jljgEt\nW7Z089JOK4Avi7NMA/CdiGI3ES5z8CncWObkLkh8ig8AVq5cafF3331nMXdFAPzfgH8GWFOymTJl\nCgBg2bJlyt0ciHm70047VTt34sSJ7r/51D+XoKNbxccff2xxPLW66667Wjxw4ECLY86NHTvWYnaa\nuP/++908LjXGdZvLbny6O5a7//GPf1jMsg/A3zO89jds2NDN4/uT5RbADx2Vxo0bh0WLFqlzTI7E\n3N17772rnfv666+7/+Yc5ZyMUgo+pcylZADo3bu3xUcccYTF3DEL8J2UWIIxdOhQN49fI35HcEeY\n559/3mIukQPAbbfdZnHMO5ZFsLQi3q/8N4jSoR49etjvnzdvnjrHCCGEEEKIHwdtHIUQQgghRCa0\ncRRCCCGEEJn4WWgcGdZ8nXnmmTm9BusZZs+e7ca448FDDz1kcV26sq8r0jjmTpIk5Q0bNixLrRfY\n+gDwdhCxGwVrHLlLS+y2wlY60f6DNZUXX3yxxaxHBLxdyaxZsyzu0KGDm8fXwbpIABgxYoTFN954\nI6qD31fUa7IFBusw+Zoi8TWWL19u98uqVauUuzmQJEl506ZNy1JLkxdffNGNb7vtthbHNYx1ffxZ\nx65JbKXDem4A2GWXXSw+9dRT1/p6ALD55ptbzJrE2PUlP/8HZzjuCAJ4+xNef6M+sUGDBhZH/Rdr\nz9hGjXWcEdYtAz/oxmbNmoWVK1dK45gjSZKUt2jRouzQQw8FADzyyCNunPWz3L0F8PpcXlfi58iW\nPrGbEWtmWZ8Y1zC2SmOrp/SeS+HcZasrwO8LeH2PmkzOtZKSEjfG9w13vYndolhDHDt5LViwwCzQ\n6kJXrieOQgghhBAiE9o4CiGEEEKITPzsStVc0ouPubPCDvL33nuvGzvjjDMsZouH9YlK1bmTJEl5\nYWFh2QYbbACgqo0BlyFiCZpLeatXr7Z4+vTp1b5GtDWpbt7o0aPdGNuLcHk7lka4Swh3eQGA448/\n3uIVK1ZY3LFjRzePu7qw5Q4AdO3ada3XxNcOeLsJ7mYDrCm9pBY9yt3cSJKkvKioqCy1Iovdr9ge\nKZaguUzMdkuxYwWXArmLRoTvmQcffNCNsbyDbc6GDBni5nFpMb4XLieytRNbWQG+nBi7IVXX6Sbm\nLXfjiF1LUiugZcuWobKyUqXqHEmSpLxx48ZlqYVUXC9ZchBL0Fwm5lwYM2aMm8cyhiiLYFjGcdNN\nN7kxziG2DGLbJ8DLQmI+HXLIIRbzvbb77ru7eWw7NGfOHDd25JFHrvWaogVaanEGVO1e9uSTT9pa\nvnr1apWqhRBCCCHEj4M2jkIIIYQQIhPaOAohhBBCiEzk/+cp//1069bNYj5qH/WbrG1h25Jo68DH\n2rmFG+B1L5999lmOVyz+m1ixYoXZdUQbD25X1bx5czfG1j2sE0z1kims2YkaGB7bZ599LGatIuBt\ncCZNmmRx1Lnwf7PtSLxGtuphTSPgc5x/F+D1SNyeLep9WeMYdaObbbaZ7p06YOnSpaZLbNGihRtj\n245o2TRy5EiLWScYW66xLUjMM9ZaHX744Raz/Q7g9Vovv/yyxfE+YzuS2KKyU6dOFrOOa8KECW4e\nv2du7wZ4/SbrjGNLTc7vaJ3Vs2dPAMAbb7yxXu3Xfg4sXrwYb7zxBgCgbdu2biz9OwNeUw0A//rX\nvyxm3SrbQwFeV81rLABMnTrV4tQSCAC23357N4/teZ588kmLo5UOW9/EVoKsR2etcWyluNtuu1kc\ndcKs3+TvI9bVA/7eiLrR/v3745lnnkFdoSeOQgghhBAiE9o4CiGEEEKITPxkStVcJoxdLw4++GCL\n2ULipZdecvMuuugii7mswd1m4mtwdwzAl01Ubvt5UK9ePfvMY8mPu1hwBxjAlyi4TBDtZ7i8cOGF\nF7qxjTfe2GK2t4mljJtvvtlitgG66qqr3DzO3VgO5JINX3u0CIpdQhguKXKXkPi7+L5hixNgjUyE\nuzOJ3KhXr559HrwuAcCwYcMs5nwBfGnwrbfesjjaz7C04ZRTTnFj1XVKGjt2rJvH3bX4/uG1GPCl\n8JiPLIPga49SjPi7Gf7dfB/E9Z3vx9hFKs135W7tycvLMykES80AYNCgQRZH6QPnJMsRov0M58Zh\nhx3mxlg+wZZ948aNc/P4HuKcOeecc9w8XnOj9IElHSwZ4f0H4Lt6RVi2xJ1z+HsFALbcckuL+b5O\nr5FtBmuLnjgKIYQQQohMaOMohBBCCCEy8ZMpVXPjcz5NBfgyB3fLuPzyy908HuO4S5cubl4sJzJ8\n8urFF1/8T5ctfgLUq1fPSq1ccga8RILLBIBvYM+dNeJrcAkwvgaXIaZNm2bxeeed5+ZxGZu7KcSu\nL71797aYy24AsMcee1h8/fXXWxw7LPHJ73gikcuG7D4QT4tzx5lYvpk3b16V/0+sO/n5+SatiKUp\nLk9HN4CZM2dazJ04YqmO3SZi9xnufMGnTI855hg3jx0G+L7gEjbgy3g9evRwY/369bOYZUosIwH8\n9wCfCAd8PnLeRkcN7loTnQLS+5O7LoncKCgosC5v8QQ8l6ejuwSvg3wiOr4Gf0bp6e2UAw880GI+\n9RxL2ttt90NjIM6ZwYMHu3mcd/vvv78b484xLImLsgrOwyiJ4u8ZPkkd5UF8X8eOX5MnT3Z/u9qi\nJ45CCCGEECIT2jgKIYQQQohMaOMohBBCCCEy8V+rcYxH9B9++GGLYxeDd955x2LW3ixZsiTT72I7\nif8E/y7x84D1NrGLAWtbYrcItlpgzV7UuQwcONDiqNnhri3HHXecxdEShzVbrCl777333DzunJR2\nw0l58803LWbtYtTKsA4zamVYy8h6MO6yAHjLkmjzMn36dGkc64AGDRpYZ40tttjCjXHexs+GNa2s\ne4qWO9zNIuplFy1aZPH5559vcexYwZqsDTfc0OJRo0a5eazpjTnNGjXuVhSts/i+4HsT8N8ZrCX+\n5JNP3DzO22233daNpV2UovZRrDvFxcX4xS9+AQDYcccd3RjnTOxqxR2veG3iNRaAredAzfcG25nF\nnPn0008t5vwfOnSomzdgwACLX3vtNTf2yiuvWMz5ydcHeAsqtgsC/Fo9efJki/mcBgBnt/OrX/3K\njY0bNw5Lly5FXaEnjkIIIYQQIhPaOAohhBBCiEz815aqY2cBdmePj4O5PJe1PM1wSQbwj3z5sbb4\nebJs2TIrAXA5DfDlkGiTwCVdzpO+ffu6edyd4rnnnnNj//d//2cxSyZi9wy2l+CORRdccIGbx+W7\nyspKN8a2LGzjE3Ocy0PxOhYvXmwxl89jCZ7LPlEKUlJS4srcIjcWLVqEl19+GQCwzTbbuDH++7LV\nDeA7G3FZkC2lAJ8jTz31lBvj7h5sAxKtzfj+4XX7z3/+c7W/K+Ytj3HORbkDl7jjdXD5j3M6vmcu\ntUcLovTnli9frnJ1LZk/fz6GDBkCoKr90uzZsy2OHWH4s1u5cqXFXAaOP/f444+7seHDh1vMcp6t\nt97azeO1jvP/kksucfNSucja4DI52wxFSye+N+K9zNIS/n5iuyDAy5ti16P27dtXkWXUBj1xFEII\nIYQQmdDGUQghhBBCZEIbRyGEEEIIkYn/Ko1jQUGBxVGzwLqXZ5991o2xrpFfIx7DZ4499liLe/bs\n6cZY8xX1NuLnR7169VBcXAygqt6PNWBsBQF43S3nXbSLYhufqPerzgqqe/fu7r/5ujhfY+6yTUrU\na7K2jfVgrL0BvH3OjBkz3BioAlbcAAAgAElEQVRrzFhjxLpgwGvKop6nUaNGzvZE5EZ+fr7pVuPf\nmD+b2HItzXXA522EbXziesx5wfdI1F2xDnHLLbe0OOrKOV+i1ozzkbWV3EIT8PdMap2TwhpctiWJ\nrTKrs3sBftAIK3drD1ugRb0oW0SxHhHw6w9/dpyDgNcuxn3A7bffbjHfJ9xOGPC5e9RRR1kcNZms\ni41jrKE9++yzLT7iiCPcPLbPqal9KH+3xHv3/ffft5j1mcCa1p9xja4NeuIohBBCCCEyoY2jEEII\nIYTIxH9VqZofpbJbeuSss85y/92rVy+LuXy466671vqa4iPfdekyI34a5OXluRIIw+XdaDnDHShY\n0hBzl+14YpeEJ554wmK2oTjggAPcPC6HcPljypQpbh6XbGKngKlTp671mvj/B3z5JpaAuOzO7zmW\nTfj1Yylq+fLlsrmqAwoKCsyeJJah2rVrZ3G0NCkvL7eYP9+Yt9wtI9onPfTQQxaz9QeX9ABfMmZZ\nxbhx49w8zrmYL/z6fE3xNfjn+PUAbxnEudewYUM3r3PnzhbHtT8tXSt3a09hYaFJEGLubrLJJhZH\nyc7o0aMt5lIyf+8DXhbx6quvurF7773XYu6mddJJJ7l5XDI+5JBDLH7++efdPM67mLvjx49f6zXF\n1+C1Or4Gf8/w/cpdlABgq622snj+/PlubOHChVXW8tqgJ45CCCGEECIT2jgKIYQQQohM/FeVqvmU\nVDwVx+7sbdu2dWP833ziLdcT0aeccorFsUsNd+0QPw/y8vLQqFEjAMDHH3/sxrhsEsvCnGtcxuZy\nbpzHJURgzWm3lAULFlgcu9Rw+YJPu8YT3FdffbXFY8aMcWN8cjWW3Rkei6dOuaTCpc14v/JJ2HiN\nX375ZZ2WTf5Xyc/PN0nAqFGj3FhNpzQ5H7mMHT9rlmnE7jN8UpVjPgEKeMeLtDsT4LsuAcAdd9xh\n8W9/+1s3xiXj2OmFYXeAmHPcyYhlKbFkuO2221ocuyalJf5YBhfrTkFBga19jz32mBtjmc4LL7zg\nxvLy8izmDivcFQvwecjyA8DLhTp16mRxdJdgh4p3333X4vS7IuWZZ56x+JprrnFjXE5nx4FYnuex\nKBmZO3euxfy98OCDD7p5fM/z+gusKfFH54XaoCeOQgghhBAiE9o4CiGEEEKITGjjKIQQQgghMpHU\nRWeUJEnKAZTV/nKqh7UnRx55pBs788wzLeauCGxvAgD333//Wl/7tttuc/8d7Un+26msrFQrgxxJ\nkqQ8Ly+vjPVcTNrdAKiqf6xOWxutEHbeeWeLo81Onz59LOZOL2yZAvhOL2wHMmzYMDePrUvYggTw\nljmsd2nRooWbx1rLqCnjbhqsG4u6L9aRRT3bd999Z9e2YsUK5W4OJElSXlhYWMYaWYY14S+99FK1\nY6y1+uqrr9w87u6y9957uzHu7sI58uKLL7p5bF/Geq+xY8e6eRMmTLCYcxjwljmsmWR9GuDzvXfv\n3m6M7YS4q0i0PmF9b1wT0vv6k08+wdKlS9+trKz0bXJEJpIkKW/QoEEZWysx3MFl8ODBbmyHHXaw\nmHP3008/dfN23333tcaA1z9yHg8ZMsTN49fn3P3ggw/cPF6r33nnHTfG+xHWvrM1D+D184cddpgb\n430Mn7HgewHwOmS2QwPW6NvT61y0aFGt11w9cRRCCCGEEJnQxlEIIYQQQmTiJ1OqFtWjUnXuJElS\nnp+fX5Y+2q/JBieWfrmExqXa2FmCSxTRhoEtULiEwuU0AOjSpYvFXP7jEjngrRvYJiVeF5de+GcA\n34WBSy2Al3F069bN4liq5tJgtCdatWqV2fp8//33yt0ciOW+aPPEZaxPPvnEjTVp0sRitleKecv5\nHvOWc6Ss7Ielf+LEiW4eW4RwOS7m7RdffGHxXnvt5cbYuonvx88//9zNY5uUaM/ClkR9+/Zd6+sB\na6yiUuL9nso7/t2FQ6XqHEmSpLyoqKgs7XTClmeAXy+iZIc/V7ZpirnL3VZi7vK6zV3n3njjDTev\nX79+FrPdGFsCAb50ffTRR7sxXhf5Xvvwww/dPJb2RLtBtiQ69thjLWbZEOClVPz+AWDZsmW2Jq9c\nuVKlaiGEEEII8eOgjaMQQgghhMiENo5CCCGEECIT/1UtB4VYH9SrV89alEXNE2tPYks2tkngtlas\nQQR867KoGWT92fTp0y2OOhq2yOE4tlZjLWRsMcU6MrYZihogbk0Yr4PfJ+vB2EoI8BrN+Prt27d3\n71XkRr169UyvFa052FaGNV2Ab/33y1/+0mLODwBgu5So/X355ZctZr1WvEc4D/gzjxZNPXr0sDhq\nt9i6h9vCxd/F188aTMC/T9bNpTq7FLYFiq+fWsJNmjSpyjWKdSMvLw+NGzcGUFUDze3y4no5fvx4\ni/fdd1+Lo0UO2+zE9qpsu/Pmm29azHZlgM8n1nbHa+LriO/l2WeftZhthmJu8fXH+3WfffaxmK21\nOKcB4PXXX7c4vpeePXu691pb9MRRCCGEEEJkoq5OVVcAKP2PE8X/F3SqOneSJKlIkqQ0/RdaPO3J\nT8viyT0+mcxP/vgUaHzN+PRt2bJl1V2X+29+8se/N75e/Jcmw09TeV58X3z98fX49/GJwfjkk08Q\nRgoLC7Fy5UokSYLVq1crd3Mgzdv06UTMF/7cYj5yHvCTv/iEml8zPiFZtGjRWudFOC/49WPeNmjQ\noNrX49OzPC++L85Hnhevn++5+HSHn+ZH0r/V0qVLUVlZOa+ysrJ5tZNFtSRJUlGvXr3S9MldzAXO\nGV7rAP80mqtBcb3h9TKuYdygoabc5RzinInXy08g4+txPvG9VtM9GZ9ocu7y+2cXAaCqOwbTuHFj\nLFmyBPXq1auTU9V1tXGcAqAEwNRav5hYFzoCWFhZWdnpP00Ua0e5u97oCOVuzihv1ysdodzNGeXu\neqMj6ihv62TjKIQQQgghfv5I4yiEEEIIITKhjaMQQgghhMiENo5CCCGEECIT2jgKIYQQQohMaOMo\nhBBCCCEyoY2jEEIIIYTIhDaOQgghhBAiE9o4CiGEEEKITGjjKIQQQgghMqGNoxBCCCGEyIQ2jkII\nIYQQIhPaOAohhBBCiExo4yiEEEIIITKhjaMQQgghhMiENo5CCCGEECIT2jgKIYQQQohM5NfFiyRJ\nMgVACYCpdfF6IjMdASysrKzstL4v5KeKcne90RHK3ZxR3q5XOkK5mzPK3fVGR9RR3tbJxhFASV5e\nXmmzZs1KAaBePf8gc+nSpRYvXrzYjZWWllq8ZMkSi1euXOkvND+/2rHi4mKL69evX+28goICixcu\nXGhxo0aN3LwkSSyO74V/jq/pu+++c/OKioos5vcVf47f/9y5c928eF1MgwYNMG/ePOTl5ZVWO0lk\noSQ/P7+0efPma81d/lz5sweAFi1aWMx5HfMuLy+v2jH+jBs2bGjx8uXL3bzCwkKLFyxYYHHjxo3d\nPM5d/r0A8O2331rMORjzk69p0aJFbozvoebNm1s8e/ZsN4+vi68JWJO7c+fORX5+vnI3d0ry8/NL\nW7RoUQpU/az5M+V8AYCWLVtazHm7YsUKN49zJI7x58trXU15O3/+fIubNGni5tWUt/xzfE3xu4Sv\nKd6rnLf8/mfOnOnmxeti0vtzzpw5Ve5jsU6UFBQUlLZs2XKta25NuduqVSuL+TOO+cmfd8zJkpIS\ni7Pm7rx58yxu2rSpm1dT7vLP8TXFdZWvKb5nvg5+/9OnT3fz4nUxRUVFmDVrVp2tuXW1cZzarFmz\n0kMOOQSA/wIEgA8++MDi1157zY3tv//+Fr/11lsWz5o1y81r1qyZxTNmzHBjv/jFLyzedNNNLf7q\nq6/cvLZt21r84osvWvyrX/3KzeMPP27eXnjhBYv5i7O8vNzN6969u8Wvv/66G+MNx5FHHmnxnXfe\n6eb98pe/XOs1AUCXLl3wyCOPQNSaqc2bNy899thjAfiFBADGjx9v8ciRI93YYYcdZjF/xvGG5jz5\n5ptv3Nguu+xi8ZZbbmnxlClT3Lx27dpZ/Mwzz1i85557unmcJ3FT+fTTT1vMX55vvvmmm7fjjjta\nHN8zL1wnnXSSxTfccIOb16tXr7VeEwBsvvnmGDRoEEStmNqiRYvSU089FYD/xzMAvP322xY/++yz\nbizNdQB4+eWXLf7666/dPM6RadOmuTH+fLfeemuLP//8czevffv2Fg8ZMsTiffbZx83jDSF/iQLA\n4MGDLW7durXFcV3leym+5zZt2lj861//2uI//vGPbh7fTzFv0/vz1ltvxfTp06dC5MrUli1blp55\n5pkAqn7eY8eOtXjYsGFu7MQTT7R41KhRFsf85DyZOnWqG+PPmL+nP/30UzevY8eOFvN37YEHHujm\nce7Gf3g89NBDFvP+45VXXnHzeA8S3zP/3Lnnnmvx5Zdf7ubtvffea70mYM09Gtfo2lBXG0d8++23\ntjDwZhDwG6UOHTq4sYqKCot5oxf/FXLMMcdYHP+1x4vE5MmTLeYvW8AnGidPfCLCG1jesMbX33jj\njS3mTR7gd/88D/ALEm8Q4u/if5Xsvvvubuy+++5zT5BE7lRUVNgNfuihh7ox3ijFz3HOnDkW8+IU\n8+m0006zOOYuf5nyP7B40QL8ZnG//far9neNGTPGYn6aDQCTJk2yeLPNNrO4Z8+ebh5vdPkfYoBf\nkHih5Xsc8P9q3nfffd3Ybbfd5v4lLnKjoqICd999NwC/PgJ+o7TJJpu4MX46zBu9uOaeffbZFsen\nMY8++qjFEydOtLhTJ18FGzp0qMX9+/ev5p0Ar776qsUxl95//32Lv//+e4vjP5p4o7vFFlu4MX5v\nn3zyyVp/BvB5e9BBB7mxv/3tbwD8d5bIjTlz5uCOO+4A4P8BCviNEq9TgH9CzOtPzN3zzz/f4vg0\n8v7777eYHwx06dLFzXvssccs5gc8cc3lfUXMpwkTJlhcWVlpMW/yAP89ww8QAL/m8hrOPwP4ihI/\n1ACAP/3pT+77qrbocIwQQgghhMiENo5CCCGEECIT2jgKIYQQQohM1JnGkWFxK+D1flHb1KBBA4tZ\ni8NCZ8ALoVm8D/iTr6wpSMW3Kb1797aYTzDXJKSOWgTWzrAAPR6O4fccNTvbbLONxXxIp1u3bm4e\nn0ZnXQawRocWT7KK2hNFy6w7jafe+RDY5ptvbjHnGQC89NJLFvfo0cON8QlCziHW6ABeJ8haFdZI\nAsCGG25ocdRo8SGGN954w+J4OIZzl0XmALDDDjtYzCJuzmnA5+7tt9/uxlq0aFHlgJuoHbyOAF6n\nGrVNnLesp9prr73cPF4XY07ziWbOn8suu8zN44MErE9jjSQAbLTRRhYfccQRbowPMPD9GQ9a8nvm\nNRwAdt55Z4v5sA3nM+C/S2688UY3lmrKvvnmmyqaT5E7I0aMcP/NOsF4SJYPgfF6xrpvoOYDhJy7\nvA7Gg1LpYV/AH8iN38WsR+eDZwCw/fbbW8xayNGjR7t5vEdgjScA7LrrrhbzIR0+FAz4Nfe6665z\nY61bt65y+K026ImjEEIIIYTIhDaOQgghhBAiE3VWqm7YsCG22morAN4yAfAWJNHcsnPnzhaPGzfO\n4vSofkrXrl0tHj58uBtj3zy2kIglGj5Gz4+r+ag9AKxevdpi9mECvMXJxx9/vNbXBrzFCZfPAWDV\nqlUWcwklGtpymZAfy6e/L/pUitwoKirCtttuC8BbJgA+d9mIGPBWNVxCS207UtL7AgAef/xxN8af\n4cUXX2xxLNGw3QQb35588sluHudu9AXlEhBbqMTcZXkGyzEA//fgHI+GyxtssIHF2223nRtLkgRf\nfvklRO0oKiqyUmtNa26UB/FayjKKa665xs1L7wkAVTxj2X7qqquusjhKEKrL2wEDBrh5vCbedNNN\nbozXwXfffXetrw14G7gol+K/R58+fSyORsws9YiSqPQ++eKLL6qY5ot1o7i42L77Yu6yfU60PmJp\nBXsqX3rppW4el4jvvfdeN/bFF19YfP3111scPXY5v9jq5rzzznPzOHf/8pe/uDGWTPBaGtdctqqK\n/qT89+C1uaaGFLyvANa8l88++wx1hZ44CiGEEEKITGjjKIQQQgghMqGNoxBCCCGEyESdaRzz8/PN\nDoF1VoA/Qs/WCoC3IGEtVGw1xLqXfv36uTFuBcd9L2OPyo8++shiPsoej8az1jDaNbCujbUZy5Yt\nc/NYo8aaIsBrHQ4//HCLue0W4DU2bC+Qvr5aDtYNBQUFpqNivQrg+z1H+wPWUbGmJLaMYpuH2AqK\nbXzYBif2qn7vvfcsZnuG5557zs1jzVZsg8lWUvw+2cYB8Jph1mcC3r6C+8bGntbcezXqOr/55hu1\nHKwDOG/jmst5G/VOvM5wLkVLpUsuucTi4447zo3xmsaWVVFHxes797fmvumA177zPMDfT/w+oyac\n7Ubi9wz35GabtmgFw7/74YcfdmOpHjnacol1p6CgwGxnYhtW7vfM+Qn472r+rMrKyty8gQMHWhx1\n4Lymce5yK0rAtx5mS5+4nrHWcI899nBjfE/xmhs1sqz5Zn0m4G18zjnnHIvjPcTXeN9997mxqVOn\n1mne6omjEEIIIYTIhDaOQgghhBAiE3VWql66dCk++OADAFXLbEcddZTFfIQe8BYKbIUSu7k0atTI\nYi4XA8Bf//pXi7mUFkvEnTp1spg7LUTbBS6TR3uJoqIii/kRdfxdXNaM5U++DrYg4u4JQNXyN7Ph\nhhuq3FdHfPfddxg/fjwA4NNPP3Vjp5xyisXcKQUADj30UIs5d4cOHermce5yuRgArrjiCou5DBPt\nl7irEl9HLOVwWS/aNfF1sMwh/i7ubhPLSHxvcOeODh06uHncgSPSoUMHlfvqgCVLlpjsha3BAOA3\nv/mNxU888YQbO+aYYyzmvP3Xv/7l5rHsJ0oWLrzwQot5TY8lQ5Zi8HXETjRc0otWTVx25zUv2jzt\ntttuFse85fuOLYh4LQZqztt07uzZs51Filh3lixZgjFjxgDwEjLAl5ljTrI8hnnggQfcf3Pusq0U\n4C37WKoQS8ScM3wdbOcE+JyPex/OXbYWirInLnHH3KrOgogleoAvf0dbuS5dutRppzk9cRRCCCGE\nEJnQxlEIIYQQQmSizkrVTGz0zeW/6ATPHSy43PX555+7eVzSjmUudkznx7zxhB+Xk/fdd1+Lp02b\ntpZ3sYbzzz/f/Tc/7j399NMtjqfzuARSv359N3bqqadafMQRR1jMp1kBYNKkSRbn5eW5scLCwiru\n86L28GcK+BJg7ETEJ0a5A9LkyZPdPC5px9fgbi6cu7F8w+WKgw46yGLuggD4k6ZcZge87IJLHrGU\nye+FT3oDvlTEp1NjWZy7JcU8Ve7WDUmS2N+RT1sCfu2IXYhYHsMSiJhzXNKOr8EdMVhSk8qVUlgG\nwY4CURLCazqviYBfF9ldInaz4VzlbjOAz1suhca1n+/pmKMFBQVr/f9FbqSdWS666CL3//OeYObM\nmW4sLW8DXgYR844/7/ga6WluwOdu7PDGUgjuLhdlIXyaPzq+8BhLmO6//343j3OqS5cubuz444+3\n+LLLLrM4lsXZrSXuF+rXr1+neasnjkIIIYQQIhPaOAohhBBCiExo4yiEEEIIITJRZxrHkpISO6Ye\nHdjZtX/DDTd0Y6w3YI1NrMe3a9fOYtY5AGs0UykHH3ywxdH5n217GjRoUO28o48+2uKoo+nfv7/F\nrPs58MAD3TzWTsQONqx1YH1Ht27d3Dy2DIo2LqNGjarSLULkRpMmTUxD++GHH7ox7szSvn17N8a5\nzBqbmLv8c3wvAD4PuTvHHXfc4eZde+21FqfaIAC466673DzWw9x9991ujG2xWDfG+h3Ad5KJWuMn\nn3zSYtbbRGsU1qJFu5/hw4crd+uApk2b2nr3/vvvuzHuKhGtktj2izsSxbytzr4M8HnLWtq//e1v\nbh53TWJbsltvvdXN49z8xz/+4cZYr8aasRNOOMHNYyudqDMeMmSIxVdddZXFsbsS632jtVCa+9Fe\nTaw7paWltu6kVmgp/Bmw3hoAOnbsaPE777xjccxd/rnYXathw4YWDxgwwOJrrrnGzbvyyist5u/z\nmOOs3b3hhhvc2BlnnGEx68BPO+00N4+16XH/xLn75z//2WLuzgV4y6DY8W7w4MF1mrd64iiEEEII\nITKhjaMQQgghhMhEEh3Gc3qRJClv3Lhx2c477wwASP83hV3do2M6W9Ww83k8Xs+PivnRMOAfbXMZ\npmfPnm4eO8bz0XU+1g/4UmA8ys8N2Ln7Br824B+PxzIPH+dnC5Z3333XzePH8vH4ft++fe1158+f\nL3+IHEmSpLxJkyZl6WP/mDNcMt5ll13cGJfruNQQSy+cu1xKBoAHH3zQYpZjxBIa261wd6TY0YNz\n95tvvnFjzZo1s3jBggUWc5cRwHfWiF1wuJTP9260cuGuBrfffrsbO+CAA6yUXVFRodzNgSRJyps2\nbVqWdpzYc8893TiXzHr16uXGqstbXhMBn7dsIQYAgwYNspglG7H7xnnnnWcxd0bq3r27m8f2IdHa\nqbS01GJec88991w3j2UgUWLEVit8H0QpBndGuvHGG91Y+r0zZMgQzJ07993KykqvzxCZSJKkvFmz\nZmV9+/YFAOy9995u/LrrrrOYO6oAvszMndvYYgrwsoWzzjrLjbEUgnM35uRvf/tbi0eOHGlxzHHO\n3dj1qHnz5mu93pi73IUrSox4X8DXy7INwO9jWNoErJHfPfroowCA2bNn13rN1RNHIYQQQgiRCW0c\nhRBCCCFEJrRxFEIIIYQQmagzjWPz5s3L0nY7UdPHtffYLpC1jHvttZfFrGsBvBYlWn/stttuFvOR\n8zjv+++/t5hbUrH2BvD6rNhybdGiRRazFiy2Idp1110tjn9jbh/HFgCsOwO85ohfD1ijU0p1lPPm\nzZNOLEeSJClv2bJlWdrmbNiwYW6cLZJi2zXWTXGrKW6BCXh7haj/7dGjh8V8b0QtMOfuBhtsYHG0\nmth0000tjvcQa2xYsxPf17bbbmsxW/MA3qKkOqsVwOswo/6uQYMGZm0yd+5c5W4OJElS3rp167JU\nM/v444+78W222cZibjUJ+LZ6hxxyiMWtWrVy87htYbT+YA0ut73caaed3Dxe+1ifxXZBgLcbY10Y\n4NdF/m5hHRvg9YmshQT8/cR5y3pPwH8XcFtanvvYY49hzpw50jjmSJIk5W3atClLdbNRj8prTGzD\n+9Zbb1nMFk4xd9meqnfv3m6Mc5TnRQ07r7l8doLtcQD/fR7Xfs7D559/3mLWogPesi22peV7je14\n+LwF4NvIRnvABg0a4KGHHgIAzJo1SxpHIYQQQgjx46CNoxBCCCGEyESddY5Zvnw5pkyZAgA4+eST\n3Rjbk3Tt2tWNcZmZ50WLHLbc4Z8BfBmXHw1zuQzwJT4uocQuFlyei3Ynb775psVs3cKvDfjH17Gr\nA1v88Gtcf/31bt7y5cstnjZtmhubNGmSs8sQubN8+XLrNBGtabg00qZNGzfG0gqeF3OGLXdi2YTl\nFFyiYBkE4EslXEKJJWIu38XOF6+++uparyPan/A9yvkOeAuI3Xff3eJoXcK5GS1PJk6cWKUELtad\nZcuWmQ3ShRde6Ma4u1Zcm9IuSXFetCNhy51oi8KlZS7PcbkM8F1raspbtmKLMo1Ro0at9Tpmz57t\n5rFMI3Zo4hzn+/aWW25x8zgvP/30UzeWlviVu7Vn2bJlJlO79NJL3Rhb08ROcwcddJDFvJ7FtY4t\nd9KOdiksRSsuLrZ4+PDhbh53TuL9wsqVK908trSKUiQuT3PeRSkFXxP/DOAtflILI6BqhyXOyyid\ne/vtt6tIO2qDnjgKIYQQQohMaOMohBBCCCEyUWenqhs0aFCWPm6NJ+v4NGc8tZo2OgeAkpISi2+6\n6SY3Lz//h6p6fAzL5TMuT8dTcVwy5rF4qprLjlwuBrxzPT/65ZIM4F3n2Qkf8B03+OR0fJRcVFRk\nMZ+CBdZ0MUibmteFE/z/KkmSlDds2LAsPZEZT49ySWHw4MFu7JRTTrGYT7hdffXVbh7nbmxgz+Uw\nzkM+7Qp4GUf//v0tjqVBLj1yGQbwDgF8kpo7aQDAZZddZvH//d//uTG+R/k+jLKJxo0bW5x2iUk5\n/vjjcc899wAAZsyYodzNgSRJyouKisrSkjHLBgBg/vz5FnPnLsB30uDP/ve//72bV1Pe8olrPtl/\nzDHHuHm8lvJaP2LECDfvlVdesZhlGYAvhfMJbl5HAX/ilDvWAN5hgDsqxfWd8zaenj399NMBALfd\ndhumT5+uU9U5kuZuKunh8isAVFRUWBy7qAwcONBizt34edeUu+yGwfuRk046yc1jmc5xxx231p8B\n/LodJXYsb+J7JsrjWOrz61//2o3xiXH+PuKucwDQqFEji9MuMSlnn322/Y6vv/5ap6qFEEIIIcSP\ngzaOQgghhBAiE9o4CiGEEEKITNSZxrF+/fplqTYldkBJkh9K6tGOhLVRqZ0PUNU9fcCAARbz8XfA\n2zfsvffeFkft4rvvvrvW6/j73//u5j388MMWFxYWurETTjjBYrakiDoN1pC9/vrrbix1cAe8FiPq\nI9gO4K9//asba9WqldlRrFixQjqxHElzN/1bszbs3+MWc24BPndZq8h2SwBwwQUXWBytaVgPzFYT\nsSPMuHHjLGY7lagB4v+OGrDTTjvNYra52G+//dw87jQS7yG2xWJNTbQg6tKli8VXXHGFG2vbtq39\nDuVubqS68vTvzLqwf49bfMABB7gxztvUzgeo2mGG9a2sVQR8N4u06xJQtSMMr32HHXaYxbzGAsDt\nt99ucezmwms/a2zj+2LtIlv4AN5qhbW+3CUJ8GtwtDhKbY2mT5+OFStWSOOYI6muPLVPit3kOHej\n1pvPAbBmkM85AMBVVznriwYAACAASURBVF1lcexkxx1ijj32WIt5bQO87pY1jnHezTffbDGfgQCA\nc845x2L+rue1Pl5/vF7Wsbds2dLi2BmP9yPRVq5du3b2O5YvXy6NoxBCCCGE+HHQxlEIIYQQQmSi\nzjrHNGnSxEpo3HEA8GUsLr8C3iWdH1kff/zxbt7EiRMt7ty5sxvj5vZcaoyNxLfeemuLudE9O7MD\n3lrn4IMPdmP8qLxt27YWf/jhh24ev2YsI7E9AJfZ43VwyfPwww93Y6tWrariMC9yo7S01Mpo0QaK\nOxJwZwoAeOqppyzmLhZcWgOA9957z+LYCYE7DHH+x9zl62DbkenTp7t5fG8ceuihboxLkfx6fH1x\nXryOzz77zOIePXpYzDYpgC8bnnjiiW5s5cqVVUqaYt1p3ry52d9ce+21bmyHHXawOHbreuyxxyzm\nNSZamrC0h9c6wJd4OfdjvvD6xrZksesLd87g0jcA6+oEeNuhtJNLCsub2H4H8BIR7tbF+Qz4DjNn\nnnmmG0vtTwYPHlylvCrWjebNm9u6cOWVV7ox7r7SrVs3N8YyL16nopXUO++8Y3HcL3CJl/MwLy/P\nzWM5xl/+8heL4/c5fy/E3OW1lbseRekH71uitR+vrfxe+L4A/P6BS+TAmv0C/+1qi544CiGEEEKI\nTGjjKIQQQgghMlFnp6oBWO2LT89FYqcLPiXH5ZBYauDTebHczU3M+bT0jjvu6OZxiYZPOMUTrHyC\niss1gC/nxI4YzB133GHxkiVL3NiiRYss5lIR/y2ANY/zU+JJ9T59+lhngzlz5uhkao7E3I0SCT7h\nF8ur/Hm1a9fOYj75BvgTnptssokb45PaXD7mMjAA3HfffRbzaemXXnrJzePuB/FENHeEGTp0qMXc\nLQMA7rzzTos5VwHfBYrL0XxSFfB/A+52AKw5FZ6WTWbNmqXczYGYt2lXk7Xx+OOPu//mjhvcpaV1\n69Zu3rPPPmtxdHzgk/ic07169XLzeB3kE6evvfaam3fqqadaHDtzsDSDuzfF6+XfVVPecjmaOz4B\nvtNHzNtUtjRo0CDMnDlTp6pzJOZuPAHMa248fc/7go4dO1rMnxvg1+pY7mZXivSkPADsueeebh6f\nlmYHCe4oAwBnnHGGxWk3txTuCMNj7du3d/PuuusuizlXAV/GnjZtmsWx4xfvn+bNm+fGjjjiCNx2\n220AgOnTp+tUtRBCCCGE+HHQxlEIIYQQQmRCG0chhBBCCJGJOrPjadOmDU455RQAsFp6CmsQ2d0c\n8Poq1m5NmDDBzWMLhWjDsHr1aotZRxP1EewYzzqFG264wc0rKChAdbzxxhsWs41A1LXx+4oWLNyt\n4eijj7Y46n74fbGGLr2O2EFH5MaGG26Is88+GwBw/fXXu7EDDzzQ4m222caNscaRu12wFQTguwqN\nHTvWjbEFBOcQa14Ar93lPIm5y9rd5cuXuzG2huD3yTofwOdutIbg12Bd2siRI928VatWVfsaL730\nUhUNmlh3NtpoI+tuwp0yAG/fFTtMsK6P18uo3erXr5/FcW3ijlr8+cYuXLz2sx73b3/7W7WvF3OD\n18tbbrnFYtZnAl5ny/Y+gNeqs6ZuxIgRbh7nLWvogB+08FGDJtadDh06mOb6kksucWP8nci2UoDX\nOPKay9/LgLfRi1pvznnW7sac3HfffS1m67vYxY1zN+ok2UqHNbgxtzh3uesW4LuGDRw40OKoBebc\njfn/zDPPVDknURv0xFEIIYQQQmRCG0chhBBCCJGJOrPjadq0aVlqxcBHxgH/aDiWoNnWIXXmB6qW\nvoqLiy1mR/d//36L+XfH8g27/XNXjWuuucbN22mnnSyOJWguXXIp+YADDnDz+NE2O9wD/vg+H5vn\nR96Ad4bnDiMA8MADD1ipevXq1bI0yZEkScqbNWtWlpaTp0yZ4sY572IJ+pBDDrGYczfaNLGVQ7Qr\nYbiLxe9+9zs3xh0O2C7qoosucvN+9atfWRzLFVx65NyN3Q64I87XX3/txs4//3yLuVMHl40AYNKk\nSRZzlxEAuP32260cqdzNjSRJyps3b16WfqaffvqpG6/JPoRLgSxnSO29UliywCXnf/9+i/mzjh0r\n2G6MLYOiBQtLMbjTGOC/IzhvTzjhBDfv0UcftZgtdwBvRcXXFPP2/ffftziWSVNrlYULF2L16tWy\n48mRJEnKW7RoUZaWk2MHFM5dtuED/Ge+bNkyi9lqD/C5yyXnf/9+i/nzjpZW/L3N+XryySe7eXxv\ncKek+Lu5lHzaaae5edzVJXaQ4/1JTbnLMiLewwBrOkulpepVq1bJjkcIIYQQQvw4aOMohBBCCCEy\noY2jEEIIIYTIRJ1pHOvVq1eWahmjTQLrCbl9DuCPqB900EEWf//9924eaw1r0qXwcfizzjrLzWN9\nANtLRHsF1gfEsauvvtriqOVkuJXcrbfe6sZYr9i5c2eLWb8DeAsJtioC1ujVHnnkEQDA7NmzpRPL\nkTR3Uy1jbGd57LHHWlxRUeHG2L7hyCOPtDjeU9waraioyI2x/myPPfawmHWwgNfbsP43tkXbdddd\nLY52TWzBEzVgDN+H1157rRvjtojcao71O4C3kIj36+GHH45BgwYBAGbMmKHczYE0b1M9WLTwSK3R\nAK9FBbxm6sQTT7Q4rrncwoy1vgDw8ssvW8x5G7Vb3JaSrW9iS7Tdd9/d4pi3bMFT05rL9+Dll1/u\nxlh7xhYsK1eudPPY1iXeq+lacOutt2L69OnSOOZIzN1NN93UjQ8YMMBi1nYDXqvKuRbXXLYYi7nL\nbY8579heDPBrJLcw5LMSALDXXntZHHOX9zf8eqyzBPz3TNSt85mOrl27WhzX3FdeecXimLsnn3yy\nWbd99dVX0jgKIYQQQogfB20chRBCCCFEJuqsc0xeXh6aNWsGoGqnAnZa33rrrd0YP6Z+4oknLI72\nM1xeYHsGAOjevbvFU6dOtTjap4waNcpiflR85plnunlcFo6Pg7nczde+4447unlsDRHhUj4/Ro9u\n8vx35MfQwJryX7162vfXBfn5+daRINoYXHHFFRZvv/32bozLBg8++KDFnI9xXuwswOVAtuNhmQLg\nS2ick1ySBHzuxjIc2zzw/cXlbQC47777UB3cJYQ750Trn5133tliLg0Byt26Ij8/Hy1atAAA9OjR\nw42lHWWAqjnNdjfcoSjaz3COsM0T4OUHH3/8scUxb3kN/u677yyO1if8enHN5fuC70G28AHW2DxV\nB0uCuHNOLJPy+xw+fLgbS6VYyt3aU1BQgFatWgHwXeEAWBcvwHd7A3z3rn/84x8W83oD+NyNY2xB\nNXHiRIvHjx/v5rG9Dcs4ouVUTWvuueeeazHfo1zeBoCbb74Z1cFrK3eY4e8VwFu9saUasCZ3Y3m8\nNugOEEIIIYQQmdDGUQghhBBCZKLOTlUXFxeXpSWQ2B2Gy9Pt2rVzY1yG4JNvsYvBYYcdZjGXyADf\nCYHfT//+/d08LhPyo+dYWuSTUUcddZQb49IOn5aOpwT5VFM8Ec2Pnmsq93HXjth9pmXLlla+XLBg\ngU6m5kiSJOWNGjUqS0tgb731lhvnU8TxxPVHH31kMZd7uWwN+M4s8eTqSSedZHF+/g/KkdiJiLvU\n8Ol7lnAAPmfiKcF77rnH4ttuu83ieFqc5RMxd/n3ce7G62DJyPTp091Yq1atMHr0aADA/Pnzlbs5\nkCRJeePGjcvSMvSYMWPcOK+lcV354IMPLOby1t133+3mcd6mXSdSWN7DZeb999/fzTv++OMt5hJ0\nvJc++eQTi6MbxgMPPGAxl6PjaXHuOBJP8nPpkkvVW2yxhZv3xRdfWBy7JrVp0wbAmq5g8+fP16nq\nHEmSpLykpKQsLUO/+uqrbpzL0zF3q3NQ4fUM8Cfs43cnl8I5Z2KHGXa24DWX5UWAl2dwZy3Ad4S5\n8847LZ41a5abx51uYu5yeZ5L1bHDEnePiq4Zbdu2NelFRUWFTlULIYQQQogfB20chRBCCCFEJrRx\nFEIIIYQQmagzO55GjRpht912A+B1M4DXtrCmEfA6MdYWsg0KACxbtsziqElkHdYf//hHi9n+AQAm\nTZpk8WabbWYx6xAA70j/3HPPuTHumJBauABVu+WwDiJqLFhDxnYAbLkCeNsH7kQDAKNHj67y/kRu\nlJSUWAeBPn36uDHO3cmTJ7sx/uwWLVpk8XXXXefmsd6PtWeA17akzv7x9wJeN8x6rbQDSwp3Anny\nySfdGOduauMCVLWBYh1y1Hmxdpe1PalmMYVzN2qNn3322SodFsS606RJE+yzzz4AqlqUcf7wugd4\n2xHujHXTTTe5efz5Rs0tr32s9Y6WH2zHs+2221rMViqA10I+9thjboxzK7VwAbzFDuCtoth6CvBr\nLnc1ev755928vLw8i1njCfxgccL3usiNpk2bWk5xpyrAW9qwHhcA3n77bYu5a1bUOHLOxPWHc4h1\nh/zZA17vzlr3eJ8cc8wxFse9BF9HqpEFfNctoObcZR0ma5mjXRRfP3eiAdZYHdZl3uqJoxBCCCGE\nyIQ2jkIIIYQQIhN1VqquqKjAvffeC6CqKzo3uo+dY7iky4+oo00Ql+f+9a9/uTF+fDtt2jSLYycE\nLsvwI+XoBM/H3yP86JxtA7iUDvgSd+wqw4+iudzCzcwBb1HBLvbAGpsCtiYQuTNnzhwrdfTr18+N\nsa0BlysAL0/gbgTRuoRLdNGq58MPP7R4ypQpFsdOIPPnz7eYyyux61HMeYbLzs2bN7c45u7QoUMt\njh1DPv/8c4u53BLlKfy+uLwErJGJxA4jYt2ZNWuWdeViuybArzFRHsF5u2LFCoujbRLn+/333+/G\nOFfZwibmAeftLbfcYvHJJ5/s5nF3m1ju5pLkBhtsYHHMW+7WlUpPUlhmwtZR8buK19locZRa97z1\n1lsqV9eSGTNmmKyMrXMAn1vxO5FtnDh3OQcBvw6yDRkAXHvttRazdC529WKbsuuvv95iLk0Dfh8Q\nuwqxZITL0VFmxvuRmJMs52P7qFSmksJrarQ42nLLLTF27FjUFXriKIQQQgghMqGNoxBCCCGEyIQ2\njkIIIYQQIhN1pnEsLCw0Gw/WewFei/LII4+4MW6hwy3Xon6FdQ/c3i2+Jv/uqO1hTQQf0U/bzaWw\nTjLas7BGk61PTj/9dDeP7XNeeeUVN8ZtlFh3WVBQ4OaxNizq5jbaaKMqegqRG4WFhWjfvj2AqnnH\nWpTYko11gpy7S5YscfO4FVS0TRk2bJjF/Ltryl1ujcX6ScDfJ7H9G+t52Gpk4MCBbh7fGy+88IIb\nYxsr1p4VFha6ea+//vpa5wFr7H+Uu7WnsLDQrJRqyttofVNd3kbdHmusozXNKaecYvHSpUstrilv\nWUMWW/1xbh566KFubObMmRazdcsll1zi5vE1RqsS/n3cHjbmLVtWsT4T+GHdjrYtYt2pX7++/T1j\n7vL6yRZlwJpWuymcu/H7ce7cuRZHPS23eeX7JOYu7yVYu8trIFD9ugr473fWJF555ZVuHtvnRBs1\nPt/BORlbE44aNcri2AJ50003rdO81eothBBCCCEyoY2jEEIIIYTIRJ2VqvnRc6Rbt24W77zzzm7s\nmWeesZiPrnM3GMCXk/mRLAD885//tPill16y+LDDDnPzuAzBj6ufeOIJN49LL1xqAbybPF9TfA1+\n3B5L92xpwu+5pKTEzdtuu+0sjmWTBQsWYPXq1RC1p0GDBmbREa1AuBTcs2dPNzZ48GCL+TOOuct2\nTLFTBXc8GDFihMV9+/Z187hk/Otf/9riaO/Ducv2VgDw5ptvWswWEvE1uGQZy0hcvuTcbdasmZvH\nNhpcNgLW5LJyt/Y0bNjQylgxb9lKZ4899nBjbP3B61ssfXHpjtdpwHeLqcm+iaUYF154ocV33XWX\nm8clw5i33FGLXz++Buct3weA757Dr89lewDW/QxYY9PFpOW/2NVJrDsNGzbEVltttdYxXi/jOpha\n/gF+bYprLsvZnn76aTd28803W8xreLQy47y+9NJLLY7SD/6u//77790Y2+L07t3b4tjphkva0aqH\nbfc4d+Oay68/a9YsNzZnzpwq91Vt0BNHIYQQQgiRCW0chRBCCCFEJur0VHV6MpVLx4A/MTRkyBA3\nxqcr+RF1+lopfJIpusTziaf0ZDeAKo/CudzLXQFiiZhPQXNJBvAn6mpyjOexoqIiN8anBPmUWPxd\n/Jg+XuPw4cOrlBFFbvDp1NjAnk/Lxwb2/Jlzp5dNN93UzeNORHwCD/AdLtq0aWMxyxQAf0qOJRfs\nSgAA77zzjsWxYxGfIOV7JpY5ueRRXFzsxrgLVOvWrS3mbgyAP/0ar/Hxxx9X7tYBLA/605/+5MbO\nPfdciwcNGuTG+DQql2bjSWeW4nz88cdujPOHy73xZCrLFFhuEcts3JEr5i2vn3wyO+Ytr5cxb7lr\nEncru/zyy908PhVbWlrqxlJJRyyDi3WnQYMG1nnqiiuucGMXXXSRxXfccYcb4+9fXqe6d+/u5nEn\nIu5iFX+O16bYpYalCnxKP+YFu2bEcnfjxo0t5lPVMXfZbYN/BvAuL9w5if9OgN9nRQnGPffcU6d5\nqyeOQgghhBAiE9o4CiGEEEKITGjjKIQQQgghMpFUVlbW/kWSpLyoqKisa9euAID4mnw0PloosL0C\na8aiLmGvvfaymHU5gNfLsC4n2oxw3Z+PpkfNJFvuvPbaa26M9QfcESNq0iZPnmwxd1kAgOnTp1vM\nNhGLFy9289jeiPVvwBqdZGpTsWDBAi+YEJlJkqS8uLi4LNXDxtxlTVXUP7LNCWtv3nvvPTePuwix\nFhLwNhJsVRXvE9b8sh0I5xLgLXdGjhzpxlgny3rfaJHF994555zjxr766iuLJ0yYYHHsOsKaZNYd\nA2v0ZqNHj06vQ7mbA2neRm1XCudc1J+y1orzlvWxcR5rtgF/n3BOR6sS1k2y9U9FRYWbx5Y7zz77\nrBtr0qTJWn8uWv9wPv7ud79zY19++aXF7777rsVsgxKvd6ONNnJjqU7yueeew/z589+trKz0i77I\nRJIk5Y0aNSpL9YBxze3Xr5/Ff/jDH9zYAQccYDHvF3jdi6/BekfA2/iwxdqNN97o5rGlFedu7FLD\nucv2U4DXULJmknWWADB+/HiLo+6Wu4FxN7nYkYvPdKSa/ZQvv/zSLLUqKipqvebqiaMQQgghhMiE\nNo5CCCGEECITdVaq5kfP3CkG8GUxfqwLAK1atbKYywbRnZ/LybFZN9s17L///haz/QMAHH300RZz\nOTFa6ZSXl1s8YMAAN8aPrNnhnX8G8OXzWKpjR/rf/OY3FsdSNTvG85F/YI0lRProe8WKFSr35UiS\nJOUlJSVlqRUDd4oBfGmMuxIB3taDywYxd1m2EHO3UaNGFh966KEWx24HbAs0bty4al9v7NixFke7\nBu5uw90OYpmH7SY6dOjgxh5++GGLL774YotjqZrLgR999JEbW7JkiXU2UO7mRpIk5U2aNClLZTtR\nKsN5+9xzz7mxDTfc0GJem2PesoyGLXwAn7fHHHOMxY8//ribx1IHXvtj3nKHjWjPwnnLa+Trr7/u\n5rVo0cLiTp06ubF77rnH4iuvvNLiWKpmmVKUS6W/e8aMGVixYoVK1TmSJEl506ZNy1IrsmjhxCXd\n2LGIv0tryl22d4q5xpKdE0880eJHHnnEzbvgggssZgu0goICN4+/F6IshMvivEby6wHelq9Lly5u\njC2J+PVj7rLFIMs2gDW5m8qali9frlK1EEIIIYT4cdDGUQghhBBCZEIbRyGEEEIIkYk60zg2adKk\nLD3azvoXwNvKsH4B8LqX/v37W8waKcDb4LBGBwDuvfdei1mLE7WLfFydNQCxnR+304qv8dhjj1nM\nNhTRXoJ1XdE2gnWdrKlhexfA6xm4XRywxiojtTT59ttvpRPLkSRJyps1a1aW2j3Fdk9sK8OtIgGv\nUznqqKMsjppBthOJrTS5zSRrZ2LesTUE26bwawPehiW+xn333WcxtzqcPXu2mzdx4kSL2Y4I8C2v\nWJfG2mIAuOSSSyyOuXvwwQeb7m7evHnK3RxIkqS8tLS0LP27xzWMbWVmzJjhxlj7zRqvqBlknXa0\n97j++ust5ryNejJuH8j629iGkm1WYjs21nhxW8GYt/ydwVZCgLfWYZuqgw8+2M377W9/azFbZQE/\n3ONPP/00KioqpHHMkSRJylu0aFGWtiWNa9iWW25pMbc4BYARI0ZYfMYZZ1gcNYOcu507d3Zjf/zj\nHy3m/ULU8XILQr43YrtMzqG45t5yyy0W8xoZ70m22eF7AfA68+eff97iww8/3M3jv0fM3eOPP97a\nPc+ZM0caRyGEEEII8eNQV08cK+rVq1eaPq2J/+rk3S8bbwP+dCf/K5RPLMfXjKea+Glf/Ncq07Bh\nQ4u54Xf8VwI/dYqvxye5+MlqPNXFp6niE1j+lw2/z/i0Kz15urbrKCkpwaJFi5CXl6eTqbUgSZKK\nvLy80vSJTU25Gz9jPiXHJ5E5p+Nrxqdv/NSEP+P4ebNzAL9+vN74r3eG7xN+QhXvSb434pMsvvf4\nhGv8vWxMHt9L06ZNsXDhQuTl5dXJCb//RdK8Tf/uMQ8aNGhgccxbPo3JTRGiqwO/ZnyCUdPaxBQX\nF1tcU97GJ5AMV6k4z2Le8loa85HvO37/8elRavINVH1f6T3+7bffYvXq1fMqKyubQ6wzae6mf/ua\ncjd+xmy+zSeRo6sDf8fGNZcrRzWtuZy7fG/EJ5Ocu/E1eH3PNXf53uP3z985ADBt2rRqr6N58+aY\nP38+8vPzsWzZslqvuXW1cZwCoATA1Fq/mFgXOgJYWFlZ2ek/TRRrR7m73ugI5W7OKG/XKx2h3M0Z\n5e56oyPqKG/rZOMohBBCCCF+/kjjKIQQQgghMqGNoxBCCCGEyIQ2jkIIIYQQIhPaOAohhBBCiExo\n4yiEEEIIITKhjaMQQgghhMiENo5CCCGEECIT2jgKIYQQQohMaOMohBBCCCEyoY2jEEIIIYTIhDaO\nQgghhBAiE9o4CiGEEEKITGjjKIQQQgghMqGNoxBCCCGEyIQ2jkIIIYQQIhPaOAohhBBCiEzk18WL\nJEkyBUAJgKl18XoiMx0BLKysrOy0vi/kp4pyd73REcrdnFHerlc6QrmbM8rd9UZH1FHe1snGEUBJ\nQUFBaatWrUoBIC8vzw0uXrzY4m+//daNtW7d2uKFCxdavGLFCn+h+T9c6vLly91YkyZNLC4qKqp2\nXmFhocXz5s2zuFmzZlXeUEp8L/xzBQUFa732eE3xPfN18Pv/5ptv3LyarquoqAgzZ85EQUFBabWT\nRBZKCgoKSlu3bv0fc3f+/PlurE2bNhYvWLDA4pi7nCdZc3fZsmVuXv369S2uqKiwuLS0+o8/vhf+\nOb6mRYsWuXklJSUW15S7/P5j7jZt2tTiJEncWFFREWbMmKHcrR0lBQUFpW3atFlr3vJnGvO2bdu2\nFvPnm2veFhcXW5w1b+PaxjkS38vcuXMt5vyrac2N75l/jt//119/7ebxda0tbwFgxowZVf5WYp0o\nKSgoKG3btu1/zF3+vgWADTbYwGLO3ZifNeUur01Zc5dzMK65WXM3634h5i5fB+fuV1995ebVlLvF\nxcWYPn16na25dbVxnNqqVavSc889FwDQuHFjN/j6669bPHToUDd2+umnW/zcc89ZPG3aNDevVatW\nFk+ZMsWN9e3b1+LtttvO4k8++cTN69y5s8UPPvigxQcffLCbx5tU/kAB4IEHHrCYP8RRo0a5eb16\n9bI4vmdO/gsvvNDiiy++2M3bf//913pNALDNNtvguuuug6g1U1u3bl16/vnnA/CbJgB49dVXLX78\n8cfd2IABAywePny4xV9++aWbxxuszz//3I3ts88+Fu+www4Wf/zxx27exhtvbPG9995r8eGHH+7m\ncZ7wAgkA99xzj8Wcgy+++KKbt8cee1gc33O7du0s/v3vf29x+vdLOfDAAy3mBRMAunfvjmuuuQai\nVkxt06ZN6e9+9zsAVfP25ZdftvjRRx91Y2effbbFw4YNsziuq7y+ffbZZ26M16Ydd9zR4o8++sjN\n69Kli8WDBg2y+Mgjj3TzOG/jpvKf//ynxZx//H0BAH369LE4vueN/h975xkmZbVE69VKHnIGBQYJ\niqDIGI5Zj/Eo5hxRMSEiCioqRhTFHDCDCoqAiFkxYEBUgmEMGEGJEoYsoiSBuT/mfuWqGqZP29OP\nPvee9f4q2Ht6vu5evXvPV7VXNWtm8XXXXWfxxRdf7OYdc8wxFkfdJt8t/fr1w+zZs2dBZMusJk2a\n1L366qsBlNbue++9Z/HIkSPdWK9evSx++eWXLZ4xY4abx+vbjz/+6MaOOOIIi3fbbTeLv/vuOzeP\n11zW7imnnOLmsU6idh955BGLWYOvv/66m8d7mPic+ef69etnMX//AMDxxx+/yWsCSr5brr32WuQK\n1TgKIYQQQoiMyNUdRyxatAj3338/AODcc891Y1tssYXF7dq1c2MLFiyweNq0aRZvtpnf0yZ/nQCl\nbynzHZjCwkKL27Rp4+YNHz7c4tNOO83ieFv3rbfesphTyfHxO3bsaDH/BR5/bvvtt3dj/NfA119/\nXebv4lvW8a+cfv36YfHixRDlZ9GiRbjnnnsAAN27d3djrN327du7sfnz51vMd7ejnq6//nqLY9pk\n0KBBFn/22WcWt23b1s3jO91nnnlmmb+L78LwXXoA+PTTTy0uKCiwmP8CB/wd0h122MGN8Z2hKVOm\nWBy1yymmLl26uLFrr70WixYtgigfCxcuxB133AEAuOiii9wY35nr0KGDG+OyAr5DGNfcm266yeKo\n24cfftjiTz75xOKtt97azeO73GeffXYZz8TfgYla4sffsGGDxXx3EPC6ZX0DXrdffvnlJn8G8Gsu\nf84A4IorrgBQbFA4agAAIABJREFU8rqL8lFUVIQBAwYA8HcRAaB58+YWx+9OLi3gO4RRu7fccovF\nUbsPPPCAxZMmTbJ42223dfPK0m78Xa+99prFUbsff/yxxRs3brT4uOOOc/P4DilnTQG/X2Dt8s8A\nfs3t2rWrG7v88stzqlvdcRRCCCGEEBmhjaMQQgghhMgIbRyFEEIIIURG5KzGkeETpgDQoEEDi2Oe\nnS1IuGbw6KOPdvNeeukli/kEEuCP748fP97iO++8083jk3xcn/bEE0+4eS1b/mlzFGsF+ATh2LFj\nLX7nnXfcPH7OfDoR8Ceun3rqKYt33313N+/333+3mOuNgJJainjyXJQfPqkH+DpBrscFvJUD11TF\nU/rPP/+8xZ07d3ZjrF0+CXvfffe5eaeeeqrFXKPGNZKAPwl4zjnnuLFdd93VYq6FjKeq+TlzjScA\n7LfffhZzDdAee+zh5q1atcpirvEESj4P8eS5KB/RuYHXn3S63WmnnSzmU5kA8Oyzz1p85JFHujG2\nn+JTsA899JCbd8YZZ1jM9WlcIwl43Xbr1s2Nsbb4uyWeqmbdco0n4J0C+ITsXnvt5eax/RbX1QN/\nfhbmzJlTqs5eZA+vj4CvE+TvaQCoXr26xexCcdJJJ7l5zzzzjMVRu2yFw9/bcS0966yzLOY1l2sk\nAe8cEGvkWV/sYBBPVfNz5lPUgHcLePTRRy3ed9993TzWblKPm7DFFltg1qxZyBW64yiEEEIIITJC\nG0chhBBCCJEROUtVV69e3VIKfOwc8B0JooUMH7d/4403LI63WnfZZReLOdUAeHPagQMHWhyd1dmS\ngW0X4u9av369xdGomNPObBMRbVFOOOEEizl9DvjXg218YpcOtiWIKZVUKlXK2FRkR/Xq1bH33nsD\n8O89APzxxx8WR+2yVQ1bMkR7CU4RxxQdv4c8FrXLHQlYu9dcc42bx9fPZrGAt29gG4qoXS7p4DQk\n4LXLJt9Ruy1atLB4n332cWOpVKqUwbn469SoUcPSVWxTA/j3KVofcVnFK6+8YnG09OEU8YMPPujG\neM197LHHLI7lM6xbtguJ5Qus22hUzLqdMGGCxdEWhS3WYukQvx5cBhV1m5+fbzGXFPHv+/7770t1\n/hB/jRo1atjrW1xc7MbYPieWtrF2uTzjggsucPP23HNPixObwAS2TnvyySctjuUzrC/WbiwbY+3G\nJh5cMsGNUOKay5ZlsXSIXw8uJ4kdZrjEjkuKgJLnEg3Oy4PuOAohhBBCiIzQxlEIIYQQQmSENo5C\nCCGEECIjclbjWLFiRatFiXVitWrVsjgeIedaFLZMYJsIwNffRLsGtvGpV6+exbGOauLEiRYfeuih\nFo8aNcrN47qXaP3DdW1cV8RH4QG4o+9s4QN4G4k+ffpY/MILL7h5fI1cR5Q8vloO5oZ02q1du7bF\n+++/vxvj1pRsmcD1uABw/vnnW9yjRw83xjU79evXt/jbb79187gmkS19uI0m4LXL+gH8Z4qfZ9Tu\njBkzLOb6TMDbSHA9z+jRo928ww47zOJY1zlz5ky1HMwBFStWNIuYdLrldRXw7yGvb7vttpubx1Zk\nF198sRvr1KmTxel0+8EHH1jMtijcQhPw9VqsHcDbrnDNMduVAV630R6KrVCuu+46i9m2JV5jtF1J\nHl/aLT+VKlUy2xl+TwGgTp06FvO6Cng7Jl7fopUd1wz27t3bjXFLP9Yut/8FytYu10UCvv4xtm/l\n737+jLING+C1y/WZgNfuDTfcYPGIESPcPK7djXZu06dPV8tBIYQQQgjx96ONoxBCCCGEyIicpap/\n//13s0r45ptv3Bjb3cQUBafx+Fg+d6UAfLqb03sAcN5551nMnT9iini77baz+Omnn7b4kEMOcfM4\npce3kON1LFmyxOKY5uFb7GwFEa+D00Zt2rRx82IqhmnTpg2KiorKHBeZ89tvv+HDDz8EAEyZMsWN\nsTXI0KFD3Vi0gEgYPHiw+zenDWMJBncnYHuJqCfWDF9HTOux5tkyBQBq1qxpMWs3pvX48xC1W5YF\n0dZbb+3mxfQ3s/XWW5fqZiL+OitXrjSrry+//NKN3XjjjRZH+7KePXtazGvuI4884uaxbmP5BXcy\n4s4fUUusF76O2M2DSyKizRivuVyeEy3K2NqMLUwAn1rn74htttnGzYspRCbR+Pz589U5ppysXLnS\nOmV98cUXbowt8OJaeskll1jM2o0di1i7cR/AXWa4xCamiFm73FXmmGOOcfN4rU6nXS5xiBZl6bTL\n+x0uZ4vaTWcR1a5dO9f9przojqMQQgghhMgIbRyFEEIIIURG5CxVzcRuFpxGiSmqJEUIlNxOTYgp\nw3PPPdfi2Pg8OVkIwKUQYvqGb/lyl4Hvv//ezZs5c6bFBx54oBvjjh58m/uJJ55w8zjNvNVWW7kx\nPvE1YMAAi6dPn+7mTZ482eLYJaFixYql3OdF+YndVj7//HOL461+7gjUvn17i6PuuPF91C53xWDt\nFhYWunmcKjzjjDMsjt0AuCtC7HzBnRFGjhxpMadhAJ9SiSno008/3eK77rrL4pgWZwcD7h4CAJUr\nVy6lZ1E+brnlFvfvzz77zOKoW+4GxCUQrHXAO1nMnTvXjSUnYgFg9erVm/y9gNctl2XEcib+d0zj\nsUMFnySNp/VZ39tuu60b4/WeO4nE1CJ394garVy5MoDSXT9E+bjtttvcvz/99FOLo+7effddi7nr\nXNQdp7Qz1S7/XsCXT5xzzjkWx9PXX331lcUx3c17CS7Tiyf2ed3m5wX4spBHH33U4mnTprl5fAp8\nU9rN5Zqr1VsIIYQQQmSENo5CCCGEECIjtHEUQgghhBAZkbMax7p161r9U6yV4c4srVq1cmMtW7a0\n+JNPPrE41pGwVQ3bgABAlSpVLObaBrZSAXz9Gtcgcn0B4I/bx/oLrvuZM2eOxVzHFh8/1lDykfo7\n7rjD4tjhgS2DoqXAyJEjS3WLENlRr149q7+KdS5cU9W2bVs3xrWr6epR+edeeuklN5aXl2fx5Zdf\nbvGVV17p5rGW2XYhWlmwzUmse+PuH1w3FruCsJVO7ATC1it33323xbHDEndXiNZCw4YNk3ZzQP36\n9a326uOPP3ZjXE8V61Rbt25tMdeixjWX7T74fQeAqlWrWsx2a5deeqmbx/Xu3B2Gfy/gO27EOuPL\nLrvMYq4Zix1BWLexDo2vf+DAgRbH7kpsGRTr1RIbLGm3/NSvX9/OLUTtst1YOu2mq0fl8xLPPfec\nG6tWrZrFV199tcX83Q4Affv2tXjp0qUWc40w4Pcc3JUI8Os4W/vxWg94G6h02uXaSLbwAbx1UbSq\neuKJJ0p16CkPuuMohBBCCCEyQhtHIYQQQgiRESl2X8/6QVKpwrp16xZ07twZQOlbqP3797f44IMP\ndmOc8uAUXExlrFq1yuJevXq5MU49sL0JW00APrXx1ltvWRy7IrB9CKf0AN8UnW8vx3Rf0kUHKG13\nwil5TnfGJuRs8XLTTTe5sTPOOAPDhw9Pfk7+EFmSSqUK69WrV5Bo9qijjnLj3FQ+dhjilMeKFSss\n5hQK4LUbUxRsadOkSROLO3To4OZxCnDMmDEWxzQwp2w4rQcADRo0sJi1y6kWAJg0aZLF0TaCtctp\nJE7lAF67119/vRs755xzLJVdVFQk7WZBotujjz4aQOluFpxm4zQw4Ndc1i3beQBet7F04vbbb7eY\ndcspQsDr9tVXX7U46rZChT+rpmK3roYNG1qcTrec8rznnnvKHOPP1i+//OLmsRUKv4bAnx3KhgwZ\ngqKios+Li4t3hPjLpFKpwvr16xckmj3++OPdOJc+xA5DvObye8fWaIAvW4gla2yB17hxY4tjWpy1\ny92GYgkD7xeipR5rl68p7he4ROrOO+90Y7wXYkvB2OWoY8eOFvfp08eNde/e3VLZCxYsKPeaqzuO\nQgghhBAiI7RxFEIIIYQQGaGNoxBCCCGEyIic1Tg2bdq04IILLgDgrSAAYMcd/ywFiS0HOX/Prfi4\n9gDwbX2ibQ1b1XANVjySvnHjRoubN29u8bPPPuvmcZ0L14UB3lKC6yTZViVef2wzxzUSbPcTf9eb\nb75p8bHHHuvGqlatarYFqhPLnlQqVbjFFlsUJFYMQ4YMceOsLW43Cfg61q5du1octcstCGON7847\n72wx15jFtmus3RYtWljMrQMBYIcddrCY62sAYNmyZRazturVq+fm1a1b1+L4nPfee2+LWbvc9hPw\ndZixhqlatWpme5KLepv/RVKpVOGWW25ZkNR7sxUH4GsI43vIOuNWrul0G+t7d9ppJ4vffvtti2Ob\nyw0bNljM1mvxO4JrtzLVbbzemjVrWhxr0/fdd1+LuT6TvwcAX4d5yimnuLGkNnTQoEFYsGCBahyz\nJNFuYrMUzwCk0+77779vcbdu3Szmsw2Ab9manL1I4P3I66+/bnHULq+5rF22GouPF7XL+wX+Xfn5\n+W4e1x3H9q377befxaxdtiYCvNUbt9gEStbcpEXnvHnzVOMohBBCCCH+HrRxFEIIIYQQGZGzzjGr\nVq3ClClTAJS2juHbyzE1wClYnscpPAC47777LD7ooIPcGD8m2zrwEXrAd63h29DRUZ2Pzcej99y1\nhrtlcKcYwN/a5vQH4NMo3LngkUcecfPYDiN2n5k8eXKp3ymyY9WqVZaWu/XWW90Ydwlo1qyZGzv5\n5JMtfueddyyOXX7Ycid2WGErE9YadxcCfFoinXbZ0orTygDwyiuvWMypx7Vr17p5nEaJHUPYKuWw\nww6zOKabWLvfffedG5s4caK0mwNWrVplXbpYY4BPH8c199RTT7V47NixFu+6665uHne1irrlshou\nCRo9erSbxx2/0umWrUU4rQz4FBxfBz8e4J9nUVGRG2ObFLaLe+yxx9w81m3s4JHYbEm75Ye1e++9\n97oxLgHjshwA1p0uzttjjz3cPO6aFcssateubTFrN5asZaNdTisDfh2PKXOGv1vmzZvnxli7bE/0\nV7T7wQcfuH1NedEdRyGEEEIIkRHaOAohhBBCiIzIWar6jz/+sFus8VRQlSpVLL7//vvd2FVXXWUx\n3+Y9//zz/YVSCjre8uVOHZye5hODgD8Fe+CBB27y+gDf6J5PFgL+9CinMfk2MQA7wbSpx+DTgGvW\nrLE4psX5FvuIESPc2M033+xOWInsWbduHebOnQsA+PHHH90YayOmVLgjAZ/0P+uss9w87iwQyyz4\nhCunhbt3717mPE7X8Wk8wKcvYnkDp/I4pRi7Z7D++XRifAxO38RT4JwOjKcQb7/9dpdKEtmxbt06\nO3U6depUN8a6iJ0o+vXrZzHrkdOAgNft/vvv78Z47XvuuecsTtwJErj8iMtyuAMI4NfL+Bnksh9O\n/S1ZssTNY5199tlnboxTnvxdEtPinArkzwHwZzeaG2+8EXPmzIHInnXr1tlr+MMPP7gx1i47NwAl\n33sJ7FBx4oknunms3fges3afeeYZi2NHunHjxlnMZTnRQeWhhx6yOJ7m585wrN3oLsPf79xFBvCl\nQ/y5iZ/JWrVqWRzT2AMHDizVwas86I6jEEIIIYTICG0chRBCCCFERmjjKIQQQgghMiJnNY4bN260\nej2uVwGAVOpPo/ILL7zQjbFtAluacG0A4G1SuC4S8LYjbAsSj9dzzRfXBMWaTK41Y7d3ALj00kst\n5vo3trgAvIVEpUqVynx8tmOJFkRbb721xbEmYuDAgVi8eDFE+SkuLjbtxhrczTb782+rnj17ujF+\nT9gaIjr6s60J6wfwtYFcoxVrWtnShrUba63Ycoc/TwDQp08fi7nOK+n4lMD6jPW/L774osVc7xst\niNJp96677sKiRYsgysfGjRuttjrW37Juo+b4/eBOLFG3SU0fAFx88cVujOvGWKuxI8y3335r8bRp\n0yxeuHChm8fXwfW8AHDllVdiU8TvEl7vY+0vr8dcMxYtiNq1a2dx1G1SUx6vXfx1Nm7ciNWrVwMo\nbSXF+wVeswDfhY2/m9u2bevmDRw40OKoE+4Qw5p56qmn3Dy2tGHtLl261M1j6ys+RwEA11xzjcVc\nEx4/T1xrGet/2c6P90W77767m7fttttaHC19BgwYUMqiqjzojqMQQgghhMgIbRyFEEIIIURGpIqL\ni8v/IKlUYbNmzQr69u0LwN+eBbyr+3HHHefGOD03e/Zsi6+77jo3j28bV69e3Y2xbU39+vUtZpse\nAHZrHPC3f6MdCds8cHcQoKRjSwJbsMR0NHfwiNebNHcHvLVKfC84TcipP6DE/ih57YqKisrdtPx/\nlVQqVdiiRYuCRG8xNcJlECeddJIbY7sOtmG48cYb3byvvvrK4phCYzsU7sYxfvx4N4+1+8Ybb1jM\nXQsAuPKFqF1+TLajitrlDgP16tVzY5xiYVusdNrl9B9Q0qlmyJAhAIAFCxZIu1mQSqUK8/PzCxKt\n9e7d241zKvmUU05xY1zOM3PmTIujRRLbiEWN8NrHay7b7wBet9ylJnZf4dKFWPbDP8e6rVy5spvH\nlmhseQYAPXr0sDhdBw/WLaf+gD87hgwePBgLFiz4vLi4eMcyH0iUSaLdRG8xbcup5GgRxdY3rN1o\nTffFF19YzGUbgLf94zWXbXoAr9133313k/8P+NKFqN0xY8ZYzGt91C4/ZuxQxqVEbBeVbs1t3769\nG/vjjz+shHD+/PnlXnN1x1EIIYQQQmSENo5CCCGEECIjcpaqBlCQ/Ds6sPOt4nhyiVNh3CEgphr4\nVNx2223nxvj2bZ06dSzmNDAA3H333RZz+jh2GeCuHUlKLYFPjPNpQm6IDvh0UEwnLl++3GK+3V63\nbl03j0+txpNcp59+up0cmzt3rtJ9WRK1e8UVV5Q5N2qB3y8+kdq0aVM3jztrbL/99m7sqKOOsphP\n08V0GqdiWLuff/65m8edOx555BE39sQTT1jMp19jWoO7DsQyjmXLllnM2o0p7XTaPfPMM+2z+PPP\nP0u7WRB1e/XVV5c5N3aR4NRyOt2OGjXK4o4dO7qxY445xmI+oc8dNgCf/ubOFly+AXjHAj4RC/jv\nDC5t6tSpk5s3ePBgi1esWOHGWIMzZsywmF8LwOs2dqY5++yzAZR8FufOnatUdZZE7cayNObRRx91\n/+bUMn/n8vsGACNHjrQ46oS1u2HDBot5LQaAm266yeKaNWtazE4BgE+1xy5NvEcYOnSoxdFBhT+j\nvMYCfr/ADjBRu5zijo4r5513HgYMGAAAmDNnjlLVQgghhBDi70EbRyGEEEIIkRHaOAohhBBCiIzI\nWeeY/Px83HzzzQC83QxQUtOUsNtuu7kxrklkq5LYPeCEE06wOHbEyMvLs5hrcbjbDOBrIdl1PjrX\ns/UEW/0AwM8//2wx14xxfSbg69+22WYbNzZp0iSLEwsjwHflAP60fwBKd3V48cUXS9Wfiexo2bKl\n1RBGa4ikrgnwtlKAf49Zu+PGjXPz2BaHO8wAvnaGa3H69+/v5nFdDltJcd0u4LV7+OGHuzHuhMG1\nYrE+lz+T0QZq4sSJFnNt0ujRo9081m58/Oeff97V7YjsaNWqlb3/XJcNlNQ0Jey5555ujOtRWbds\nOQJ4KxS2gAKA2rVrW8x1u/369XPzjj/+eIu5Tj3dmhtrzbjWkGvGYm066zZaQLE1G9tlcR0n4HUb\ntZ90GZF2y0/r1q2tlvXcc891Y6xltkMDMtduly5dLI7d31gnXN997bXXunknnniixS+88ILFsY6R\ntXvssce6MdYKa43tggBfrxhtoNhGjWuGuY4T8NqNe45Ro0blVLe64yiEEEIIITJCG0chhBBCCJER\nObPjadCgQUGSTp46daobZ/uQmILm29Rr1qyxePjw4W4eWzkcccQRZV4Ldzs466yz3Bg3SGfLoOhO\nz+npvfbay42xxQ83LY+N1Nm6hTviAMAdd9xhMacP2fkd8FYrsaF5//79LVW9fv16WZpkSSqVKmzY\nsGFB0l3j+++/d+Os3ZiCZkf/tWvXWsxpYMBr9+ijj46/3+JPP/3U4qhJblrP3W24hAPwdij777+/\nGytLu2yFAviOOGxdAgD33XffJq+JrYQA/1ziZ+i6666TdstJottEJ999950br1GjhsUxjcddVHjN\n5dIbwOuWU84Rfq85vQcAc+fOtZjLcmI6msuIoo3awQcfbDHbp1x66aVuHlu3RN0++OCDFrNuYyen\nTz75xOJ99tnHjSXXv3z5cmzYsEF2PFmSSqUKGzVqVHDGGWcAKG1vw9p9++233dgll1xiMXcK4jUL\n8KUUcY1kPv74Y4tjVzsuS+M0drScYi3HMdYur7mXX365m8c2f2y5A3hd8+cprrnc1Y677wAl3xlJ\nqjoXa67uOAohhBBCiIzQxlEIIYQQQmSENo5CCCGEECIjclbjuNlmmxUktQnxKDjXJSxcuNCNcYu3\naCnBcEshrjsDgNdee81irutiKxXA1yw8//zzFsfWUoceeqjFv//+uxvj1kD8eNxWEfD1lbEFI9tj\ndOjQweL169e7eWw7FJ/z+eefbxYyuWgh9L9KKpUq3HzzzU270QqB66iKiorcWO/evS1mjXMtC+Bb\nQcX3kS1K9ttvP4u7du3q5nGdLFvfLFq0yM1jC56oXa695FoZrrMEfN1xrN3lui9u/Rm1y7VJbJeV\nPGZi3TV79mxpNwuibmPbyCuvvNJirqMGfE1rXJsY1i3XnQG+jSbrlq3XAGDOnDkWs5VO1C3XiUXd\nPv300xazbiNcc3z++ee7Ma75YvugqFu2y4qf1eR169evH2bPnq0axyxJtJtYkUXtcvvMqF3eI3Ct\nd1xzmzdvbjFbngHAM888YzFrN6m5TOA1l89cxO8BrltfvXq1G2PNc21thOuO49rPexrWLtf7At52\nKGq3d+/eZp82c+ZM1TgKIYQQQoi/B20chRBCCCFERuSsc0zFihXRoEEDAP72L+C7GESLA+6Wcf/9\n91sc7Wf4Fu1OO+3kxpYuXWoxH6/fcUefSeBj/+z2HjvdVK5c2eJ169a5MU7tcPqD09tA6Y4eDHfS\nYBf7mCY94IADLObUOlByFD+mx0V2VKhQAY0aNQIAHHjggW6MSw6ixQHrkDthRPsZ1m5BQYEb43IH\n7hAQ5/3www8Wc+r3qquucvNYu9xJAPCpdbaJiB1mYsclpm3bthazdjltDQAHHXSQxUnHjQRpNzdU\nqFABjRs3BlDawubUU0+1ONoysW7ZGiyuzTvssIPF8f2dNm2axVyWwOs54K1FWC8xlZxOt1wu0rlz\nZ4vZwgfwXTUi3AWGOz7xZxPwryOnNIE/P3fSbvnhNTd+d5500kkW83cgAPzrX/+ymN/vuDazDmMq\n/KuvvrKY07usdwCYOXOmxdzZpVu3bm4e2+jF/QJrl/Uabdm4m1GES//4OqJ2+bPB5R1AiXZzqVt9\nAoQQQgghREZo4yiEEEIIITIiZ6eqa9WqVZA0JI8dNjh1x6kuwDf75o4tSQP0hKSzB1C6KwCn4Ljb\nAT8e4E++8okkvv0LAJMmTbI4pgK5sfigQYMsjqfF+RRi7E7At8Q5fdOxY0c378cff7R41qxZbqxp\n06Z45ZVXAABLlizRydQsSbSbpDr4JDsA7LvvvhZHt4DCwkKLuWMAp60Br93YmYZPBrIWYuqR3Qf4\nJCh3SACA999/32LudgCUNLpPYHeAeHKRTyFG7XIKna83pig5lRk/r82aNcMLL7wAAFi8eLG0mwWp\nVKqwdu3aZeqWy4Wibj/77DOLuQtX4tKQwLrl9B7g18V69epZzCUQAHDNNddYzGtuPPU5duxYi/v1\n6+fGuNSBO4RwJzDAr/1Rt1y2xNebriRkU7oFSlwNlixZolPVWZJoN0lD80l2wKenY/kWd/bhk/gD\nBgxw81i73NkI8JpMyusAX14DeB3ymhu1xenu/v37uzF2wMhWu7vssovFXGYRS/b4u2X69OlurHnz\n5rb+L1q0SKeqhRBCCCHE34M2jkIIIYQQIiO0cRRCCCGEEBmRsxrHFi1aFNxwww0AStsVcH1ArJXh\nOkG2TIg1Cy+//LLF0TaCj+gPGTLEYnZtB3wNIdcNcEcNwNdHxOP1EyZMsJg7HER3+pYtW1rMx/oB\nX1fB9ZTLli1z8zbffHOLo5v8qFGjrK5y3bp1qhPLklQqVZifn1+QdDJJp93PP//cjT388MMWcx0Z\nW5wAXofRNoK7CD311FMWDxs2zM3jWqxdd93V4kcffdTNO/HEEy2O2uX6IO5wELXbqlUri2OdV8OG\nDS3mzwJbYgFeu2zHBZR0YUi6L0i72ZFKpQpbtmxZcNtttwHwrzfgLW2ibrl+nGvIooUY18FGSx+u\n/WXrj7iWch3WbrvtZjFbrwHA8ccfb3HULdcS8xjXhQFet7HGK7F+AYAPP/zQ4nS65U40wJ+fzwUL\nFuCPP/5QjWOWpFKpwq222qogqQWPay5rl997ALj33nstZpude+65x8178MEHLY4Wa1z/y5ZLvHcA\ngJ133tlitgeMNezHHnusxVG7vN/h5xVr01u3bm0xW1gBfs1l7caOd6xd7kQDAEOHDrVa9rVr16rG\nUQghhBBC/D1o4yiEEEIIITIiZ6nqSpUqFTRp0gQA0KVLFzfOt15jV40HHnjAYm5Ufs4557h5fAw9\npqB/+eWXTf6uaGnCaYnJkydbzKnp+O9Uyt/Vfe+99yzmDjCrVq1y84YOHWpxdMb/5ptvLP7tt98s\nZmsMwNtmcAwAjRs3ttvWK1asULovSxLtNm3aFEDpkgDWU2I3lcDpEbYaOfvss908TktER39OGbP9\nUtQMpyW4vOGEE05w89Jpl9Mc7dq1szhqd/DgwRZHTbJ2f/31V4tjJwROi3M3J6DESirpkvPLL79I\nu1kQdRvLAdgOKZZHcCkFr/9nnHGGm8eWYlG3rPepU6daHLsQLV682GIubeD0HuA73UTdTpw40WLu\nlsGlQoBfezbnAAAgAElEQVQv24hdZb7++muLWbfxOvh7Ieo2+X4bN24cVqxYoVR1liTa3XLLLQGU\n7iLE2o1d6JLSDMDvF04//XQ3j+2euATo//7+Tf6uqBnWLq+dbAMEpNcuWwFxqVzULpc9xbWU092s\n3bj28+eEvyOAEu0mNonLly9XqloIIYQQQvw9aOMohBBCCCEyQhtHIYQQQgiRERVy9UCVK1e2mj+u\n2wJ8Pj+2tWKbhAoV/rwcrlsEfFu07t27u7EzzzzTYq7XYpseAFi7dq3FXGvJNg6Ar9mJtRN83J6P\n+XPtBQCcddZZFj/33HNujOt02IKnSpUqbh63YopH79u2bVvKgkNkR5UqVcwKKtb7rVy50uJbbrnF\njbFNAms32irNmTPH4l69erkxrivjz0k67bI1BNs+Ab5lGn8uAG97wXYq0YaF6+W4TSHg22VyzXDU\n7pgxYzY5DyixgJF2y0+VKlWsVjXqlmunb7zxRjfWuHFji1m38X1iKyZueQn4dTFT3bKNVPPmzd08\nbpcWa4RZW2xfFdvSduvWzeJYB892VryWRt2+9tprFnONG/CndRG/ZiI70mmX19zrr7/ejWWqXa4X\n5xaDgNcuf06idtesWWMx20olrScTvvvuO4tjrTHXCbMdFdc0AsCFF15ocbRi43aZ6bSbtCAGgEWL\nFrmx9u3b53TN1R1HIYQQQgiREdo4CiGEEEKIjMjZPfeqVau64+YMp9YOO+wwN8adY7hLR+XKld08\nTlG8+OKLbuy+++6zmFMU2223nZs3evRoi2+66SaLo+s83zrnI/8A8P7771vMqWq+BsAfm49H78ty\nk69fv76bx4+fdNpIWLJkiXu9RPZUrVoVnTp1AlDaToF1F+0aON3AaY2YQuDHiGULrD1OUXAnGgAY\nOXKkxbfeeqvFd955p5vHqZdotcVWUmxVFTshrFixwuKo3S+++MLidNo95JBDLOYyE6AkjSLtlp9q\n1apZGivqlq2jjjnmGDfGKV5+D9PpltdOwJc3cMeNrbbays0bPny4xazVWNqTTrfvvPOOxZ07d7Y4\nlj1xeVNMf3L3HC434i5egLfBirpN1mB+zUR25OXlWfo3apc7wx133HFujL9nuSQu7hfYFiqWLbAO\nuTtSTEHzeszrdCxZSqfdt99+e5PXFB+DtRvX3LI6J0Xt8t5q/vz5bmzhwoU5XXN1x1EIIYQQQmSE\nNo5CCCGEECIjcpaq5pOpffv2dWN8qok7xQD+pOdBBx1kMTcYB4A99tjDYu4CAHh3+Zo1a1ocu9Tw\nSSM+gRRv+fKt4Xgd/PjsIB8btXNak13sAX/KdosttrA4nrjlU7EdOnRwY4MGDSqVjhHZUaVKFUsN\nX3755W6sX79+Ft97771ujE+psXb59BzgtTtlyhQ3xtrNy8uzmNM1QEmqIYFLNWKKmFMUu+yyixur\nU6eOxbE7AcPpIdY7AMycOdPipPMDAPTo0cPN485P8RoffvjhUukY8dfhk6lx7ejfv7/FMaXLp1G5\npIBT04AvMfryyy/dGOu2WrVqFscuNazb559/3uK45vLanE63xx9/vMUxxcljUbd8QpxTkrFrCf87\nXuP9998PoHQqUfx1qlSpgvbt2wMovXZwGUMsaeA1l8sWeI0F/BrM5TWA1y6nuPfff383j8vDuFQj\n6oJdNKJ2ee3jTi9RuyeeeKLFtWrVcmPTp0+3mN0IovsAu82w4wdQ8t2VS93qjqMQQgghhMgIbRyF\nEEIIIURGaOMohBBCCCEyIhWPj2f1IKlUYY0aNQoS5/X4mFwLGF3c2SqCa28mTpzo5h1xxBEWR4d3\ndprnjhjRZoRrdrijQex088EHH1gcrX+43oY7CxxwwAFuHtdJ3nzzzW6M620mT55sceyWwx1mos3F\nrFmz8NJLLwEAlixZ4gsmRMYk2k1qYqJ2uW6qT58+boxrVrj25sMPP3TzWP+xZpbrY1hDsbaHax7Z\n+idag4wfP95irikDvHa5puzggw928z799FOLY30c19tMmDDB4uXLl7t53KkjanfmzJl2bYsXL5Z2\nsyCVShXWrFmzIFnTom5POukki2P948knn2wx65bXPcDrljtnAH7tY9uwaDPCmuY1d8OGDW4e6zZ2\nK6pbt67FXDPJ1jkA8PHHH1scuyFxJ5GPPvrI4rjmsm5jR7FE+6NHj8aSJUs+Ly4u3hHiL5NoN9Yl\nJpxyyikW9+zZ042deuqpFrN2WT+A1y53XgG8zRLbksUOS1z/y2turE8cN26cxc8884wbK0u7XJ8J\nAJMmTbI4qaVNmDZtmsX8GY1rLn9GW7du7camT59u17Zo0aJyr7m64yiEEEIIITJCG0chhBBCCJER\nObPjqVChAurVqwegtB0JW4SwhQfgbxtz2i46wXPKLDbr5uPrbAMSU3XcjYO7aMTH404FjzzyiBvj\ndAt3h3n33XfdPL7NHRuODx061GJ2wo9pE06pxI4jK1eudCl6kT2s3WhJMm/ePIujdnls6dKlFscO\nHAMGDHC/i2HbELZTYLsowKfQuBtBfLy33nrLYu7oAfiSDO4Ow48H+A4EnF4BfKeFhx56yOKoXU4d\nxfTNb7/9Ju3mgAoVKpg1SLQemzt3rsVs4RHHWLeVKlVy87jEJuqM11xe39944w03j9ORrM34ePxz\nTz/9tBtj3fKa++abb7p5XM4UdcsdygYPHmxxTPdx2pG7NQF/fmak3fJToUIFs4yJ1mM///yzxVG7\nPLZkyRKL436BO8NFrdWuXdti1j9/7wPenmfs2LFlPt6YMWMsjmtdWWvu66+/7uaxPVrsEscdynhN\nj9rlPUj8DP3666/us1NedMdRCCGEEEJkhDaOQgghhBAiI7RxFEIIIYQQGZEzO5769esXJNYlXEMA\nAB07drSY62sAX8vFrYe4BhHwViJt2rRxY9dff73FXKcTaxe5Duj999+3OKlvS+D2P/HoPds8cF0C\n1/kA3hqCLYcAID8/32KudWCrAQA466yzLI51c2effTaeffZZALk5Xv+/SiqVKmzQoEFBYlEStbvD\nDjtYzPU1gLdqYsuTWDPI+oo2CVdffbXF6bTLNlNcyxLb+bGVRdQu1wlzW0Gu1QS8NQRbDgFeu6+9\n9prFXbp0cfNOO+00i2P9Ubdu3TBixAgAwMKFC6XdLEilUoUNGzYsSNYMXh8BoFOnThZH3XL7NLaY\nijWDrK22bdu6Mf65ihUrWhzrv7gFYTrdnn766RbH9q1ca8lrc/wuYXsotiMCvCXUyy+/bHHXrl3d\nPNZ71O2FF14IABg2bBgWLVokO54sSaVShY0aNSpI1oy45rJ9DrfnBWDfeQBwxRVXWBxrBrktYNIK\nOeHSSy+1mNfcqDu2meIax6hdbg0cH4MtftgGK34m+TwDr+GAt4Xi7xw+zwH4fUbUbs+ePfHkk08C\nAIqKimTHI4QQQggh/h5ydcdx6eabb143+as3/tVZtWpVi9etW+fG+KQR/5UQT67xY8bdNN/t47ss\n8Y5L9erVN/n48XrZtDPCp/X4L6VoxMwNxeNfVHz9/Pzj7509e7bF8bnUr18fy5cvx+abb441a9bo\nrk2WpFKppRUqVMhKu3ySuFGjRhbH02vptMt3++Jfq0yNGjUsTqddvvMUNcOn9Xhe1O5vv/22yXnx\n+tNpd9asWWVeR4MGDbBs2TJptxwkuk1e93iHulq1ahZH3fJpzMaNG1ucrW7j+8tkqlvWT3w8dt7g\nefF5sW6jHvn6+XMbs00zZ84s8zqS76dly5Zh/fr1y4qLi/0Pi4xItJu89rnQLq9FgNdXzNbxnep0\n2mXHC/5sRO2yhtJpN9M1N2qX74qyduOdT24sEq+jYcOGWLp0KSpUqIDVq1eXe83N1cZxJoCaAGaV\n+8HEXyEfwK/FxcUt/+kL+X8VafcfIx/SbtZIt/8o+ZB2s0ba/cfIR450m5ONoxBCCCGE+P8f1TgK\nIYQQQoiM0MZRCCGEEEJkhDaOQgghhBAiI7RxFEIIIYQQGaGNoxBCCCGEyAhtHIUQQgghREZo4yiE\nEEIIITJCG0chhBBCCJER2jgKIYQQQoiM0MZRCCGEEEJkhDaOQgghhBAiI7RxFEIIIYQQGaGNoxBC\nCCGEyAhtHIUQQgghREZo4yiEEEIIITJCG0chhBBCCJERFXLxIKlUaiaAmgBm5eLxRMbkA/i1uLi4\n5T99If+vIu3+Y+RD2s0a6fYfJR/SbtZIu/8Y+ciRbnOycQRQs2LFinWbNGlSFwA233xzN7hy5UqL\nly1b5saaNm1q8S+//GLx2rVr3byKFSuWOVa7dm2Lq1evbvHq1avdvCpVqli8ePFii+vVq1fqCSVU\nqOBfIv65SpUqWbxixYoyryk+58qVK1vMz3/OnDluXt26dS1OpVJurFq1apg/fz4qVqxYF6I81KxU\nqVKZ2v31118tju/jFltsYfHy5cstjvpknaxZs8aN1alTx+K8vLwy57Fm0mmXdRK1u2jRok1eU7ba\n5ec/e/ZsNy+ddvPy8jBv3jxpt3zUrFSpUt2mTZv+V90uXbrUjeVat7lYc1kj8bn8nbrl69qUbgFg\n7ty5WLduHUTWZK3dLbfc0mLWbtTnP6XduOYuXLjQYtYg73XiNcXnzD/Hz3/WrFlu3n/T7ty5c1Gp\nUqWcrLm52jjOatKkSd3rrrsOAFCrVi03+O6771o8fPhwN3bZZZdZ/OKLL1o8Y8YMN483WNOmTXNj\nRx99tMW77767xV9//bWbt/XWW1v8yCOPWHz66ae7ebxJ5TcUAB5++GGLmzVrZvFrr73m5h1yyCEW\nx+fMP3fzzTdb3K1bNzfvxBNP3OQ1AcDOO++Mvn37QpSbWU2aNKl7ww03ACit3bffftvip59+2o31\n6dPH4hdeeMHin376yc3jL6qo3WOOOcbiPfbYw+J02mUNdunSxc1jnfDmDQAeeOABi5s3b27xq6++\n6uZ17tzZ4mHDhrkx1u6tt95q8bnnnuvmnXzyyZu8JgD417/+hSuuuAKiXMxq2rRp3RtvvBFAet0+\n+eSTbuyqq66yePTo0RZPnz7dzUu35h533HEWZ6rbhx56yOIzzzzTzUun24EDB1rM+ou6Pfzwwy2O\nz7lFixYW33bbbRZ37drVzTvllFM2eU1AiW6Bks/9jBkzZkFky6ymTZvWvemmmwD4DT8AjB071uKh\nQ4e6Mf7OY+3++OOPbh5vsH744Qc3dvzxx1u81157WTxlyhQ3b5tttrH4/vvvtzhqJp127733Xovz\n8/Mtfumll9w81m58zvxzd9xxh8XxM3Tqqadu8poAYLfddsOll16KXJGrjSOKiorQv39/ACh1gfwl\ntf3227uxuXPnWvztt99avNlmvvySP+zxL2NeWCZNmmRxu3bt3LzBgwdbzJu0uDvnN5UXTwCYPHmy\nxRs2bLD4hBNOcPP453baaSc3xn+VfPHFF2X+Lv7LI34x9+rVy/01I7JnwYIFSBaxyy+/3I3xF07H\njh3d2M8//2zxN998Y3HULn/Y452Ke+65x+KJEydavO2227p5jz76qMUXXHCBxem026RJEzfGnw3W\nLv+BAviNbtQu/yVfWFi4yZ8BvHbPO+88N3bxxRejqKgIonzMnz8f/fr1A4BSG3HWbadOndwYZzbS\n6Za/9OJdm7vvvtviCRMmWNy+fXs3j//I6d69u8VRt3zTIOqWPxe77LKLxfzHCeA1yPMA/0WaTrdL\nliyxmD9nAHDhhRcCKFkvRPmYP38+kj/W+Q8ZwG+Uonb5DjH/kRL1dN9991kctXvnnXda/OGHH1q8\n3XbbuXn8h/ZFF11U5u96/vnnLY7f4azdjRs3WsybPMDrMPkDJYG1+9lnn5X5u3jN5c9a8u9c6laH\nY4QQQgghREZo4yiEEEIIITJCG0chhBBCCJEROatxBP6skXnuuefc/zdu3NjiefPmuTE+1cS5/ZNO\nOsnNGzlypMV8GAbwp+u4sPbxxx93884++2yLuT6N6yEAoHXr1hb37NnTje29994Wcz3ZmDFj3LyG\nDRtazPVGAHDwwQdbzId09tlnHzfv999/t5gPEQElxb8zZ86EyC2jRo1y/2btcj0uULZ2Y/3KiBEj\nLD722GPdGJ8gfOuttyyOxf1c48o1alyHBgBt2rSx+OKLL3ZjZWk3HjLg58z1yYA/9MWHHf7973+7\neb/99pvFvXv3dmPSbu7h9RHw72F0a+D1Z7fddrP4tNNOc/NYg7GGm9fcN954w+J4mOr888/f5HVw\njSQAtG3b1uJevXq5MdYWH0J75ZVX3Dx+znHNZd1y7dr+++/v5rEDSPz8JAdzZs6cWeoErsieeHiU\na/fimluzZk2LWbvxkGCm2n399dct5nUa8HWCXFvJNZKAPwAWz3ewdrkWMh6O4breqN1DDz3UYj6k\nc8ABB7h5rN0ePXq4sWbNmpU6/FYedMdRCCGEEEJkhDaOQgghhBAiI3KWqq5Rowb2228/AN7qA/AW\nJGzmCgA77rijxZzi5hQH4P2WYnpu6tSpFrPXXjR3ZbsJPrp+yy23uHnr16+3ONpc8LF5Psofj+iz\n1xOnIAH/evBtdDY0BYBGjRpZHG9Lp1IpZ18ksqdmzZr2+rJlAuCtn6J2d955Z4tZu1wSAfgShLvu\nusuNff/99xY/88wzFsdULpvksmUIeykCXrubKm9IGD9+vMXRhoW1++abb7oxfj3Yxidqt1WrVhYf\neOCBbmyzzTaTdnNAzZo17bXNVrfPPvusxdEXjnUb03Pfffedxeynl063vOayRRUA/PHHHxbH0gb2\nbhw3bpzFUbfnnHOOxZyCBPyay2VQ0YiZy5Q4pc+/75tvvillPi7+GjVr1sRBBx0EIL12o20X2yzx\nehm9mDlFfPvtt7sx1i6nj6N2WV+85sY1nLUbyxu41Ie1G/cLXIqUTrvsMxrXXNbuf/7zHzeWSqVK\neayWB91xFEIIIYQQGaGNoxBCCCGEyAhtHIUQQgghREbkrMaxUqVKVovCOX/A92+MuXe2AuF+jdxz\nGvAWJ7EtHLdFa9CggcUxp//+++9bzJY+TzzxhJvHtQNHHXWUG2PbFa4n46PwgO+dueeee7oxPoqf\ntLoDSvdC5j7G3JoOKOmHrLZtuYG1y+8p4HuVs6UHALz88ssWs3a5HhfwtYCxZpZrdli7X331lZv3\n3nvvWcx9gqPlFNeRsX4Ab1+RTrvcaztaRLEdyoABAyx+6qmn3Dy+xlgf9+OPP6ptWw6oVKmS1VCl\nW3PZzgPw7f2OPPJIi6Nu+T2MbeEy1e27775rMfcI5vavgNdttKzi7wJ+nunW3H333deNcS0b1wVH\n2yu+xliHmdTSS7vlp3LlymY7k067hx12mBtj7fJ3c1yn+Pv96quvdmP8Hc62eV9++aWbx9rlswjc\n/hXw2mX9ALnRLtfPc73mkCFD3Dy+xljXOXXqVLUcFEIIIYQQfz/aOAohhBBCiIzIWap65cqVlk77\n/PPP3RinBrhTCuCd1ouLiy1md3/Apww55Qb41AYf0Y+pl4KCAov5dnO61Mi0adPcWO3atS1mm4t4\ne5lvo7O9AOBT65xC2Xbbbd087ioSadeuXSlXfZEdv/76q6UlCgsL3Rinq7hTCuBLJli7sRNROu2y\nTriDQtQTa+bhhx+2mNOJgC+L+OGHH9wYa3fhwoUWx64vnOZhK4h4HZxC+Svabd++fakOUuKv8+uv\nv+Ltt98GUFq3XB7A3SaA0uUSCbGbC+t2jz32cGNcmpFOt2y39uCDD1ocU3q8VqfTLZfnxK4vvI7H\nNbcsC6L27du7eel0u9122wEo6X62Zs2aMueJ/86KFSvMpi5ql8uyBg4c6Ma4ZILX3GiRk067nTt3\ntpg7HSV2ggm81vFniEuPAN+Ri61+4nXwmhu1y+t41G5ZFkTptMuvTTKXu+WVF91xFEIIIYQQGaGN\noxBCCCGEyIicpaqZeIryk08+sTimV5NUCwB07NjR4s8++8zN424C8ZYrdxbgFMKnn37q5nGakJ3a\np0yZ4uZxqj2mFtldnk/kxfQkP2anTp3cGLu/86lY7oADAB988IHF0Wm+SpUqpToniOxJXt94ev3j\njz+2OOpu7NixFu+www4Ws94Bn9KOj8EN7VetWmXx5MmT3TxOt5x33nkWx1OsrHk+PQjANbnnE3nx\nOXPqiFONgNcu6z+mF9nBgLuHACXajXoW5SOuP5MmTbI4rrmsW16b4nrZp08fi+fMmePGuCMG63bi\nxIluHpdOcDeweIKV9c6pOcBrix0wYnqSnzOnpgHg5JNPtphT61G36TrTVK5ceZP/L/46qVTKXsdY\nSsHvY9Qdd7Ji7fI6DQBXXnllmY/Ba+7q1astjtrlNfeCCy6wOGqXf47T24B3dnnssccsjif2+bs+\nrtu85nKqmruOAd55I2o01/sFfQKEEEIIIURGaOMohBBCCCEyQhtHIYQQQgiRETmrcWzQoIHVAXCN\nAuDrUtq1a+fG2rZta/GHH35ocayBYruPUaNGubG8vDyLr7/+eou5LgHwR/kXL15s8euvv+7mXXTR\nRRZH1/m+fftazDVjXFMBeGf4WEPJFjxs8cJdHABvGcRH/oGSeonouC+yo0GDBujevTuA0nUuXIOa\nTrtcoxJrSVi7I0eOdGPVqlWz+IYbbrCY6xgBry+2dXjllVfcvAsvvHCTPwMA1157rcWsXdY04LUb\nayjZgoettWKXGrYMil0dBg0aJO3mgIYNG9paNWHCBDfG9VTpdDt+/HiL45rLdh/pdMvdr84++2w3\nj61/2EqHdQT4tTraBV133XUWc1cj1jPg7UhiHRr/vkGDBlkc7azYBi7aVCV6l3bLT8OGDdGzZ08A\nwEcffeTG+P2JNl9bb721xVxHHdfcDh06WMw1rYDfL/Tv39/is846y81jHc6fP9/i+FngtTp2teM1\nnbvDsKaB7LTLnWIAb10U7X4eeuihnOpWdxyFEEIIIURGaOMohBBCCCEyIhUdxrN6kFSqsH79+gVJ\nN4B4C5Vv38Z0LN82/uWXXyzmo+WAt3zgdDTgUyVNmjSxuHXr1mVeBzdLjx1m2D6EUyMA0KhRI4t/\n++03iy+55BI3j+2EuHMO4NOhbHcSuxGwxQt32AGAHj16WCp7/vz58jbJklQqVdigQYOCRLMnnXSS\nG+/Vq5fFscMQp+tYu0kXmoTff//dYk5dAF67jRs3trhVq1Zu3mWXXWbxCy+8YHEsYeCUTdQuP346\n7bIdzy233OLGuJxk1113tTh2mGHt8msIAD179rRUtrSbHYluky4WbNkBwNKAQOkuLWXp9p133nHz\nWLesU8DrmNfErbbays3jNZdTbrF8gddcTukBZes26opt1OL1sm65M9j69evdPO4uxq8h//vhhx/G\n/PnzPy8uLvZeVSIjEu0mFkmnnnqqG+dSsahd3i8sX77cYrb1A7xO4hrGaWLWbn5+vpvHay5rN5Yw\nsHZjpznWLn+e4pr7xRdfWBy/I1i7vFfZuHGjm8fa7dGjhxvr1auXlWHMmzev3Guu7jgKIYQQQoiM\n0MZRCCGEEEJkhDaOQgghhBAiI3JW49isWbOCpEUV23QAvqYktlzjFk+JJQoANG3a1M3jmsHDDjvM\njXGd4EsvvWRxPJK+YcMGi7mGjO2CAN82iGsUAGDp0qUWs41PmzZt3LyKFStaHFsJHnjggRbffvvt\nFrPVAODrMLt06eLGqlWrltOahf9VEu0mljQPPvigG+e2U7F1Fdfhck3JFlts4eZxC8JY48t1Kc8+\n+6zFrBGgbO2yXRDgW61lqt1o18J1krEl2wEHHGAxtxaNj8F1mGeccYYbq1atmrUZmzt3rrSbBalU\nqrB58+YFiV1YbNvGtVCzZs1yY6xbruOLuuU2bkcffbQb43Zv3AaN9QH4GkJeIwcPHuzmcZtBrlMH\ngCVLllg8ZswYi7fbbjs3j7/LvvvuOzd20EEHWcy6jY8xevRoi6M9S1Ibet9992HevHmqccySRLuJ\nnVJsl8n1r7Nnz3ZjXMvIdYLZavfpp5+2ON2ay+cl2BIH8C0C02n3tddes5jXfcB/Tr755hs3dvDB\nB1vM2uUWzYDXbteuXd1YXl6etZb9+eefVeMohBBCCCH+HrRxFEIIIYQQGZGzzjGrVq0yG4+YNnnz\nzTctjkfeOY31xhtvWMwpQsA7vB966KFurEaNGhbzbe7o8M6pYE5rREd17pwRUy98LJ9T5rHrQrNm\nzSyO6Xm2mzjqqKMsjilzPr4fO3iMHz/eWQ6I7Fm1apWlk7mTD+BTui1btnRjnMriFFq0d+rXr5/F\nnTt3dmNsjbLvvvtaPGLECDePtcs2DFG73IEgpl44Fc7XwWUVALDllltaHNOcZWl3yJAhbh5rM2p3\n3Lhx0m4O+P3330233GUK8GmxaB9yzjnnbHJetHZi27NYHlSlShWLWbec+gOAbbbZxuJ0ay7rltPK\ngO8Udvjhh1tcuXJlN4/Lm2bOnOnG2CaFdfvkk0+6ebzmxg4eSVkVfz+I7OA1l7scAV6Tcc3lzkQ8\nL9o7cVehqN1KlSpZzJ+NYcOGuXlcfpNuzV2xYoXFnFYG/B7kiCOOsLhq1apuHqe4uasX4EvdOO0e\ntctrKtv7ACXlKbnUre44CiGEEEKIjNDGUQghhBBCZETOUtVr1661E1Dff/+9G+PbsgMGDHBj/O9D\nDjnE4th8nt3ZY0qFOx7wreHevXu7eXyakG9fV69e3c3jk7Vz5851Y3y6ik89x3Q0pwX5VC3gT8Vy\nmj2eAudOJfEk14MPPuhux4vsWbdunZ2YjqcxOZXM5RKAPxHPeuJUGOC1u+eee7qxsWPHWsxpvsSh\nIIE1zimPqN3kpD0ALFiwwI1xuvvll1+2OKajn3/+eYv5dCLgT8bWqVPH4pgW544PSZeYhEcffRTJ\nKXaRPevWrbM1N51ub7zxRjfGJzNZt5wGBrxu2RkD8OVH6XTLp2D5cxF1yydrFy9e7MbK0m1M6bGj\nxvp+i+AAABtgSURBVOTJk91Y27ZtLW7QoIHFMbXIXc9i2UpyEvyKK64olQoXf421a9fauvPtt9+6\nMdZu7KKSnAwGvF5j+RprlztcAb4k7qmnnrL4iiuucPNYu5wi5u9sABg4cKDF3M0G8KUar776qsXR\nreKVV16xeNKkSWU+Bjtl8H4JAGrVqmUxfw8AJWVw3MWpvOiOoxBCCCGEyAhtHIUQQgghREZo4yiE\nEEIIITIiZzWOxcXFWL16NQBf+wV4q5pYRzB//nyL+Xg916QAvt7k/PPPd2N8pJ7rs4YOHermTZky\nxWK2FWErCMDXQsZameuuu85idnuP9ZTDhw+3OC8vz43x8+R6x2hBtO2221ocay1vvvnmUjVsIjs2\nbtxoNhy33nqrG+MuKkmXjoR58+ZZzDUqsQMQd1JiKxQA2G+//Tb5GNGaiS1tWLurVq1y88aPH28x\nd1sCvL3KunXrLI7a5Zq1WItWVrekWLvZoUMHi6N2+/Xr5z73Ijs2btxo7//NN9/sxli3sRaadcs1\ng1xLBfi66thFheuxeT2L1iqsW7bEWbt2rZs3YcIEi6MNDttZrVmzxuJYs8X1anHN5bo2/m6J9fLc\nSSbWrSf1dlp3yw9r96abbnJjXJ/Iaxbg1xKuaY3a5c5EsXMVa5d1Ec8RsA5Zu9xRBgAmTpxocbQe\n4/pitsu58sor3TzWbqyh5Hpifp5Ru9tvv73Fcc299tprc7rm6o6jEEIIIYTICG0chRBCCCFERqTY\nzT/rB0mlClu2bFlw2223AQAuvPBCN863huNtYz42zvYKd999t5uXdKUBfBoG8LdsGzVqZDFbmAA+\nrff+++9bHNMmnIo4/fTT3Rhb8LCVQ+xiwI8Z3e/PO+88i/mYf3wvuDsDp/6AEvf6xDZo3rx55W5a\n/r9KKpUq3GqrrQruuOMOAEC3bt3cOHcOiuk67pDE2mXLCMBrN8JdZho2bGgx2/QAsDIQAPjggw8s\n5pQzABQVFVl82mmnuTHuesTa5U4KgO+MwOlowHduOOaYYyyO2mULLk7/JdecvHbSbnYkuk3WSV5T\nAN99pWvXrm7s3nvvtZh1y7YiAPDpp59azJ0zAF+akE63vOZ+9NFHFsfuG+l0+8wzz1jMz4vXR8Cn\nEGO5CH922eYswo/JqT++5vvuuw/z5s37vLi4eMcyH0iUSSqVKmzVqlVBokNeUwC/NsUxXlt/+ukn\ni6P9DGs3ppbZWor3C5wSBrx2OR3NJWpA+v0Cl/385z//sTiddrlEDfB7JrY5i59JtjHq2LGjG1u3\nbp29dnPnzi33mqs7jkIIIYQQIiO0cRRCCCGEEBmRs1Q1gILk33wKLhK7SNSvX99iTi9sueWWbh7f\n8i0oKHBj3GWGT91xKg3wLvQ1a9a0OHa6ueSSSyyOnW5Gjx5tMZ983W233dw8Pl0Y3eSXLFliMaeK\nuKMBADRr1szi2E2hW7du1slk9uzZSvdlSdRuPOHHcEchwKfo+KQmv28AMGzYMIt33NFnt1i7fOou\nptP4ND93CIgdCPiEdHwu7DjA2o2n+R9//HGL02mXT3fzawGk12737t3ttOGsWbOk3SyIur3lllvK\nnMslFYBfZ/iUZtTtk08+afFOO+3kxjhl9ssvv2zy/wHvRFC7dm2L+ZQq4HUbT9LyyW9eV+Op0mx0\nG9fc5s2bWxx1m5Rg3XDDDZg9e7ZS1VmSaDdxW4nfsQx3FAL8OsPa5fcNAIYMGWLxzjvv7Ma4O9DS\npUs3+f+Ad4CpW7euxawfALj00kstjs4bY8aMsZhPbbObBpD5foE/N39lze3Zs6d9h8ycOVOpaiGE\nEEII8fegjaMQQgghhMgIbRyFEEIIIURG5KxzTOvWra27S7R/6NGjh8X77LOPG+MaRz5OHm0d2E6B\nOxUAvv6A6x6uuuoqN+/kk0+2mK1JEiuWBLbWiXUP3GWGH+Obb75x8+rVq2dx+/bt3RhbAXGnEq7j\nBLzVSrSXGDFiBJYtWwZRftq0aWPdXc4880w31rNnT4u5QxFQtnbfeustN48tJbheC/A6YZuI2GHp\nlFNOsZh1d+edd7p5rN0TTzzRjSXdcQBf7xhrfNNpd9y4cRbz5yZql+1W2rVr58aefvppV1sksqNt\n27bWISNa2HCddqynKku33EUDAM4991yL2YYsPgbXd1922WVuHtuTjBo1yuJ0ay5rHfBWVHwdsdaM\ndRstoLgb2F133WUxd+wAvG6jLUoyV9otP23btrWa1FNPPdWN9erVy2K28gO87rg7UDrtxjWXH2PX\nXXe1OHbQYu2yJVQ67cbPIdvyvfrqqxbPmDGjzGuKNlDvvvuuxWxHxDXIgLcJiuv2k08+mVPd6o6j\nEEIIIYTICG0chRBCCCFERuTMjqdx48YFSTo5pm2rV69u8dtvv+3G+PYwpyRiw3G2cjjppJPKvJZJ\nkyZZfNRRR7kxblrPlg/s6A54Gx/u7AIABx54oMX82sXUIlu3xJQK20bwNXHaCPDPJaabLrvsMju2\n/8cff8jSJEsS7Sbp5K+//tqNs21T7CzAaTm2gYqWU3Xq1LGYyyX+7++3eMKECRYfdthhbh7rJLGy\nAXxnG8Db+0RLn7K027dvXzeP7VuibcrQoUM3eU1Ru9xpIWq3d+/e0m45SXSbpOSmTJnixtmyKabx\n+vTpYzGvudFuinUb04kM6/bQQw91Y3PmzLE4sQ8DgH333dfN45KgWGLBuuVuGdH6hK1b4prLKWm+\nJk53Av65xM/WxRdfDABYtmwZ1q9fLzueLEmlUoVNmjQpSLodRe3ymvv666+7sSuvvNJi7uwStcv7\nhdjNhddc7sJ1yCGHuHmzZs2ymC2Dog0U6zWu76xd7g4TLac4BT116lQ3xmVA6dZc1i7/XqCkXDAp\nbcvFmqs7jkIIIYQQIiO0cRRCCCGEEBmhjaMQQgghhMiInNU4br755gVJbUK0QuB2afPnz3djSZ0D\n4OsX4nW1aNHC4ho1arixESNGWMyWKV26dHHzZs+ebTG3gSsqKnLzuDaM6ygAbykxb948i7luAgAu\nuugii6PFC1sMdOrUyWK2ggC87RDXfQAl9XVJbVouWgj9rxK127FjRzfOtShRu2yzw/VWXIcFAPn5\n+RbH95Frr7gWMNblcL0N2zAsWLDAzeOWb1G7bOPD2o0ktVxA6c8Q186wdtk6CvCttuLntU+fPvZZ\nnzFjhrSbBYluk1rGqFuug43vNa9H1157rcXpdMs1k4Bv6Zapbvlnom65xjHqlq2j0umWW79FS5+D\nDjrIYm5ZG9dctkyJuk0026dPH8yYMUM1jlny37TLtbDx/eb1iNfmqN2WLVtaHN9HrtPmWtuo3Zkz\nZ1rMLVrj9wDXOHLNMODto+LPMVwvH2t8Dz74YIu5ZW3ULtsOxefct29f+x3Tp09XjaMQQgghhPh7\n0MZRCCGEEEJkRM46x1SsWBFNmjQBAHTu3NmNceqXUwaAd27nW9TRwoPTYrETxWeffWYx367lnwF8\n2qRx48YWX3jhhW4eO8HHFBynQ/h5sQ0K4G+jx7Q7Xz93O9hhhx3cvMMPP9zi2OEgLy8Pm2++OUT5\nqVixIpo2bQqgtA0OWzpF2ybumNGvXz+Lo40Hp8ZiB6BWrVpZzCm5qAW2ENlyyy0t5pIIAKhSpYrF\nUbuXX365xZwajNrl9GXULnfT4G4H8Xr5dePUEFBiz7XZZvqbtbywbnmtiP+Out1jjz0s5nUqWnhw\nWqxNmzZujNPYXL4TdTB37lyLudyIyyGA9Gsu65at2GJKj22lMtUtfzYB4Mgjj7R4U7oFIO3mANZu\ntM3j/UO0yNlzzz0t5nUqnXZ5jQWAZs2aWTxy5EiLo3Y5tbzVVltZzF2ZgMzXXLa0ipaC6cr0uAtM\nOu2ydSBb/gG5X3P1CRBCCCGEEBmhjaMQQgghhMiInJ2qrlOnTkFyuzh2KuD0dGwc//HHH1vMHVs4\nbQ3427zcUQXwt6wbNWpkcbx9fcstt1jMDcErVark5nG6mx3jAX8ylW8HxxNT7FxftWpVN/avf/3L\nYk5V77TTTm7ed999Z/H06dPdWPPmze02+6JFi3QyNUsS7SYn12KnAtZuhw4d3NjkyZMt5rIFPtEK\neO1ypwIAuOGGGyzm8omo3dtuu81i1m4sV+B09+23317mGJ9w5XQi4LUbuxNwaQlrd5dddnHzvv32\nW4tjF48WLVqYE8LChQul3SxIdJukofkUO+DT01G3vH7yKfzYzeK0006z+L333nNjrPEk5Qh4xwgA\nuOuuuyxm3UYXCk53888AZes2nrjlk99Rt1xWwrrltRhIr9vmzZsDAIYPH45FixbpVHWWpFKpwrp1\n6xYkaWg+yQ749HTULnek4lIFdm4B/Jobu9Xx3mKLLbawOGqXu7mwduOeiV1d+GcA4IUXXrCYSx/S\nrblxv8DabdCggcW8FgO+61nUbn5+vjnJFBUV6VS1EEIIIYT4e9DGUQghhBBCZIQ2jkIIIYQQIiNy\nVuPYqlWrgnvvvRdAabsCdjhn6xzA17PwsfP777/fzbv77rstjvVfXIf27LPPWvzYY4+5eVzPwpYU\nt956q5vHx9qjO/s333xjMdc9cI0C4O0rfvrpJzfGtWzjxo2zeMmSJW5ehQp/uiX17NnTjT3xxBNW\nV7l27VrViWVJKpUqbN26dcHAgQMBlK4ZZHuFTz/91I3dcccdFnO3pAceeMDN4zpZ7gIA+Fo0rp8d\nPHiwm8fa3WuvvSy+6aab3DzWbrSG+P777y3mTgtRu23btrV42rRpbiyx3AK8dhcvXuzm8esYrVce\nf/xxabecJLp98MEHAZTWLa9bXEcO+HrZ7bff3uKHHnrIzWOLqWiLwv/mOq5Bgwa5eVyHtffee1sc\na9JYt2vXrnVjrEH+voq6ZaurqFtec7leM51u2XoNAB599FEAJfXs69atU41jlqRSqcI2bdoUPPzw\nwwDSr7lRu7yWcseZ5LES+NxD1C5brnFnl0ceecTN49rCffbZx2LuEgZ4O6Go3fjdnxC1u80221g8\ndepUN1aWdhctWuTm8X6BO9EAJa9PLtdc3XEUQgghhBAZoY2jEEIIIYTIiJylqitVqlSQOLJ3797d\njf/www8Wx64abJGzYcMGi2PDcT6iHruosJ0O/y5OfwD+1i7bohxxxBFuHh/lj2n3zz//3GJ2bv/t\nt9/cPE61x84cX331lcUrVqywOLrJT5gwwWK2IQBKLDDeeecdAMDy5cuV7suSVCpVWLlyZdNu7CLE\neopdjzhNzKnfU045xc3jzgKxG0VeXt4mfxdbUwFeu+PHj7c4dmliC5VoecK6Y+un33//3c1L0vZA\nae1++eWXFrN2Tz75ZDfvo48+sph1DJRYYCQWGcuWLZN2syDqNpaycFlC1C2noHn9j+8hr6tPPvmk\nG6tRo8Ymf1fUy8KFCy1+//33LY7pQ17vo27ZZoRtn6Juk1IpwHdGAvy6/csvv1jMnxcA+PDDDy1m\nDQN/WreMHTsWy5cvV6o6S6J2YykLr4OxtIdLHFi78buzYsWKFsc1l9PErF22pgK8drksJ3Zi4v1C\nTLuzvROXbcT9Atv4xI5IZWk37pH4e4F1DJR0y3nzzTcB5GbN1R1HIYQQQgiREdo4CiGEEEKIjNDG\nUQghhBBCZESF/z4lM6pWrWrtBGPtycqVKy2OR9m5XRXXB0SbhKVLl1rMNTqArzHg2oHYkmfNmjUW\n77jjn+Up3HYI8HUJF1xwgRurU6eOxVxvE20ouG4j1gdxfRlb8HAtHAC89NJLFsej9+3bt3fH70X2\nVKlSxbS7atUqN/brr79afNVVV7kxtqbh9yJql//NNb2A1y5/TqJ2+bo6deq0yWsAvF1UrDVu2LCh\nxax/bp0JeO0+8cQTboxtgfh5Re2yRUvUbocOHUrVAom/TpUqVawlW7o194orrnBjvOaybrmmC/Dv\nW7Qs4zredLpdvXq1xTvssIPF3BoW8HWMPXr0cGOscdZ+rF3r1auXxVG3vFazbmN7N7bEirpNLLek\n3fJTtWpVez3TrbmXX365GytLu/G9KioqsvjOO+90Y1zLm067vF9g26qoXV5zY63xlltuaTHbB8Vz\nGmz9lM5GMJ12uTUnP//kd+dyv6A7jkIIIYQQIiO0cRRCCCGEEBmRs3uX1apVw8477wygtJ3Cv//9\nb4ujTQJ3hGHH+Jj6YsuceJuXb0Vzypgd1wGfMma7nBtvvNHN43R3tCsaO3bsJq+pf//+bt7y5cst\njmkk7kDCHR44lQgAhx9+uMWJ63vCwoULS3W1EdmRl5dXpnb3339/i6PlA3eO4Y4BbGMC+M4CMYV2\n++23W8ydCxo0aODmDRkyxOKkWwhQumyD0zyRt956a5PXFB8jnXa58xN/XqN2+fHnzZvnxqTd3JCX\nl2dprKjb/fbbz+Jo78G65TRhXHPZziyW4rBuWY9169Z187gMgvUdO8dwyjCS2IgAXlfXX3+9m8e6\njXYn3IGEdRvTjmyD9fPPP7uxJP3HHcNEduTl5VlqOGqXLfuitRmXTLB2K1eu7Obx+xi7ynDnJNZu\n7ObC3btY/9dcc42bF7XGsHaPPfZYi7mzDZAb7fLnNa658+fPz+maqzuOQgghhBAiI7RxFEIIIYQQ\nGZHTU9XJCb94EplTI/FUKZ/04S4Yu+++u5vHDcfZSR3wqXBOEx544IFuHp80GjVqlMUxzcbu7Hwa\nD/ApRD6dFTvM8FitWrXcGDc+b968ucVnnnmmm3fRRRdZHG9L33XXXaXSiCI7qlataqfmzjvvPDfG\npRTcKQbw2uWyhXg6j//NZQqATylyt4PY7WPBggUWjxw50uKoXU5VR+3yXD7NHeFuGjF98+OPP1rc\nokULi2MXAz6ZHa/xzjvvlHZzAK+55557rhvjLiqZ6jbqhXXLJQqAX3P58WKnDy6xGT58uMVxPWM9\nxOvgkqN0XToy1W1+fr7FsVsOn8yO15ikONOlJkVm8Jp71llnuTEuI4vlCLxGHnnkkRYnpUYJ6dZc\n1i5rKHaEKUu7sQRu1qxZFkftsiNAuu5IXbp0sThqd9q0aRazdmMJCp/Mjtd466235lS3uuMohBBC\nCCEyQhtHIYQQQgiREdo4CiGEEEKIjEhFu5msHiSVKqxVq1bBXnvtBaC0hQ3XnsRuFpzb5zrBcePG\nuXl8vJ67BwD+6Pkhhxxi8Q033ODmsb0Nu8LHWpn33nvP4hEjRrgxtpvgTgv82AAwceJEix966CE3\nNnXqVIvHjx9vMXfHAXy9RJs2bdzYTz/9ZLVuCxcu9AUTImMS7e6zzz4ASmuX61K6devmxs444wyL\nWUOsH8DbJBQUFLix2bNnW3zooYdaHGt7uJ6HrX+45gcA3n33XYuffvppN1avXj2Lud6XHxsAPvro\nI4vZQgXw9Tb8GV22bJmbx9pt27atG/vxxx+tZkjazY5Et/vuuy+A0rplbcb6R64pY92+8847bh7b\nh8Q1d+bMmRZzbXq0KuHH4DU32qewbqPdWlm6ZWsewOuWrVQA4IcffrCYdZtuzd16663dWFInOWzY\nMCxatOjz4uLiHSH+Mol2k1rDqF3WZ9euXd3Y2WefbTHvF9JplzsWAcD06dMtPuywwyzu27evm8fW\ngazd2LGFtRu7xPF+gbXL3wkA8OGHH1ocLdu+//57i/m7JWqX6zqjdqdNm2afq6KionKvubrjKIQQ\nQgghMkIbRyGEEEIIkRE5s+OpUKGCWdXwcXcAmDNnjsVs4RHHuIF37GLA3S1is24+vs52JDHdzanA\nt99+u8zHe/XVVy3mpvcAsHr1aovZtmfMmDFuHt8qZysVAHjggQcsHjZsmMXsHg/42+/xFvjKlSux\nYsUKiPLD2mV7HMDrk62TAG/DwGmD2DmGSyai1urUqWMxa4tTF4BPy7Eu4uO98sorFr/00ktujDst\nsHZZ7wBw/PHHW8zpFQAYOHCgxVzGEVPV/PkaOnSoG1uxYoW0mwMqVqxoVkfc4QjwJRBs4RHH0q25\n3N0llkTwmsvdLLhEB/DpPk6zRd2yVqMey9Itax3wuo1rLtsTsRVb1C13BuNuTQBMs9Ju+alYsaLZ\nHUXt8rrasmXLMsfSaZc7s6TT7saNGy3+5JNP3DzuWsN7ifh4L7zwgsVxH1CWduPazNY6sUvcPffc\nY/Ho0aMtjtrlLjXcsSn53fz7y4vuOAohhBBCiIzQxlEIIYQQQmSENo5CCCGEECIjcmbH06hRo4Kk\nZR7XbQG+HRDXKAC+3oSPw8dagfr161u8zTbbuDFub8Y2D9Fmh1sQvvXWW5t8bMAf+Y+tgbhejdtf\ncS0cAHzwwQcWx3ZsrVu3tvj555+3OLa743ZgsYbjkksusRqcBQsWyNIkS1KpVGHjxo0LEgsItk8A\n0muXW/+xDclrr73m5nGbynbt2rkxbivJtZFRu9yC8I033rA4tvM755xzLI7a5boftsHimjfAW0Sx\nrQvgtcv1NrHNKNcTR+327t3banCk3exIdJu833HNZVuZqFtun8a2T7FmkHW77bbbujG2VWPdxtpF\nbkHIa3ps58e6jdrn7wVuyxp1y3VosY0d25k9++yzm3we8Xqjbi+77DIAJVY/RUVFsuPJklQqVdik\nSZOCxCYqrrnptMtnAvjcw8svv+zmsXbbt2/vxthWjesVo3bZ2o+1G9fc888/3+LYeviqq66ymLXL\ndlaA1y7vPwCvXd4v9ejRw8074IADLI7a7dOnDwYNGgQAmD9/vux4hBBCCCHE30Ou7jgurVChQt3E\nqDXu3KtVq2Yxn8AD/MkgbggeT67xY8bd9M8//8zXsskYAGrWrLnJx4/XG+9AMnxaj/9Sis+LG4rH\nv6j4riifdIq/l41K43Np1KgRlixZggoVKmD16tW6a5MliXaT1z7e7cjLy7OYjbcBfwqem8r/ndqN\nJ/zSaZdP67GpcnxerF2eB3jt8vPnv/CBEoP6BGk390Td/t1rLmdY/indxue1cuXKTc4DcqdbAFiy\nZAnWr1+/rLi42P8SkRGJdpPX/q+suazdpk2bWhxPDLN2o2E336lOp91atWpZnG6/EDXEcHOSTLUb\nPwuZajcxqAdKP5fGjRtj8eLFOVtzc7VxnAmgJoBZ5X4w8VfIB/BrcXFxy/82UWwaafcfIx/SbtZI\nt/8o+ZB2s0ba/cfIR450m5ONoxBCCCGE+P8f1TgKIYQQQoiM0MZRCCGEEEJkhDaOQgghhBAiI7Rx\nFEIIIYQQGaGNoxBCCCGEyAhtHIUQQgghREZo4yiEEEIIITJCG0chhBBCCJER2jgKIYQQQoiM0MZR\nCCGEEEJkhDaOQgghhBAiI7RxFEIIIYQQGaGNoxBC/J9260AAAAAAQJC/9QoDFEUALOIIAMAijgAA\nLOIIAMASV2felK+psvAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 235,
              "width": 327
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "fc0091ec-ced4-158e-0a10-c3f76b679e46",
        "_uuid": "55bb69f1303c53d00b6890c6fc36db4c0705f70d",
        "id": "dffDL-RNiRyt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can see that as we move away from the digit, the images we encounter become less and less distinguishable. At first we can still see the eight shape but before we know it we're back in static land.\n",
        "\n",
        "Perhaps a good analogy here is that of a solar system: the surface of our planets are the manifolds we're interested in, one for each digit. Now say you're on the surface of the earth which is a 2-manifold and you start moving in a random direction (let's assume gravity doesn't exist and you can go through solid objects). If you don't understand the structure of earth you'll quickly find yourself in space or inside the earth. But if you instead move within the local earth (say spherical) coordinates you will stay on the surface and get to see all the cool stuff. \n",
        "\n",
        "There are however some differences: first of all we're in a much higher dimensional space and we're not sure how many dimensions we need to capture the structure of the digit subspaces. Secondly these subspaces could be really crazy looking - think for example two donuts entangled in some weird way. You could in fact get from one manifold to another without going into static space as all."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "47a34c03-db94-ce74-3080-5e4cab28faaf",
        "_uuid": "512e4bc736b6a4c0c0bd6e5cbdcc501e36f26ba0",
        "id": "yQuOoHu0iRyv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Our digits' best friends aka Nearest Neighbors:\n",
        "\n",
        "Another thing to do to understand better the structure of the image space is to look at what images are closest to the \"eight\" image using some metric. In this case I'll use the sklearn knn wrap with l_2 distance as the metric on the flattened images. "
      ]
    },
    {
      "metadata": {
        "_cell_guid": "a7aa66a8-6d89-23b4-bf89-70715aad36c5",
        "_uuid": "0e9c821d65ed0f3f676f21e22300d9dbb067b74a",
        "id": "skUngIrUiRyw",
        "colab_type": "code",
        "colab": {},
        "outputId": "b16f87e1-928a-4e64-9043-c8fd04e85e0f"
      },
      "cell_type": "code",
      "source": [
        "X_flat = X_train.reshape(X_train.shape[0], X_train.shape[1]*X_train.shape[2])\n",
        "\n",
        "knn = NearestNeighbors(5000)\n",
        "\n",
        "knn.fit(X_flat[:5000])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "         metric_params=None, n_jobs=None, n_neighbors=5000, p=2,\n",
              "         radius=1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "963d393b-f844-368c-11b5-9872df2b8869",
        "_uuid": "a91381552c308d8d8b5b6120c9821d85dcbfd510",
        "collapsed": true,
        "id": "JsujELLSiRyz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "distances, neighbors = knn.kneighbors(img.flatten().reshape(1, -1))\n",
        "neighbors = neighbors[0]\n",
        "distances = distances[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "bccda10a-a75e-5032-bb39-3e4d52bfdb1d",
        "_uuid": "bd7a3fffb8a1701daebea8bd0cc38e3e5aa18511",
        "id": "q8-mBF_QiRy2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Histogram of L_2 distances from the \"eight\" digit:"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "1661677c-c0e7-f014-d5ca-a830689f245c",
        "_uuid": "6a97ca162b879743b6c3d39a771398f553d09027",
        "id": "7dKoWmvziRy2",
        "colab_type": "code",
        "colab": {},
        "outputId": "00a1d851-4611-4fff-fc5e-c7f3a50f3f8e"
      },
      "cell_type": "code",
      "source": [
        "plt.hist(distances[1:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([   8.,   28.,  120.,  408.,  852., 1346., 1257.,  711.,  218.,\n",
              "          51.]),\n",
              " array([ 6.93865358,  7.66124204,  8.38383049,  9.10641895,  9.8290074 ,\n",
              "        10.55159586, 11.27418432, 11.99677277, 12.71936123, 13.44194968,\n",
              "        14.16453814]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv8AAAH0CAYAAACq1EJ8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCeEfAAA6IfwDAEAn/n8klwo47L1ELQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 250,
              "width": 383
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "e9d04607-8708-9ceb-7f84-7314dd195979",
        "_uuid": "e89a224a6ab6ee5b8635e37b9abcf04f49273c66",
        "id": "tuQY-FrhiRy7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The distances of the first 5000 images from the \"eight\" image is roughly normally distributed - in fact it's much more well behaved than I expected. At first I though I'd see multiple modes and a higher variance given that we have different classes. "
      ]
    },
    {
      "metadata": {
        "_cell_guid": "99749f29-0d04-559e-275c-aa02baba7d28",
        "_uuid": "46e1292f90b79811860aed194ed034a6f04ea501",
        "id": "YV7FwcEaiRzB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 32 Nearest Neighbors for our \"eight\" image:"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "26ea39ae-3152-0fd5-bce3-5470a39dbe58",
        "_uuid": "bc281817426dd0109557be4e1a09e58764e940ef",
        "id": "RLxE003ciRzC",
        "colab_type": "code",
        "colab": {},
        "outputId": "9e23c5a6-7bb6-4e7e-9c9e-b894937e2447"
      },
      "cell_type": "code",
      "source": [
        "for digit_num, num in enumerate(neighbors[:36]):\n",
        "    plt.subplot(6,6,digit_num+1)\n",
        "    grid_data = X_train[num]  # reshape from 1d to 2d pixel array\n",
        "    show(grid_data)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAAHWCAYAAAAxV+IxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzs3XmgTPX/x/HnbRMhpZD1tkikCBFC\nSklJJZUWaRFKG6VUJCktslQIadNCUcqSSiUkUURKi5Yr1S9KqLTS/P7wfZ9zZrlz5945c2bu3Nfj\nH6cznzn3o48z98z78/m83zmhUAgRERERkSDsku4OiIiIiEjJoYdPEREREQmMHj5FREREJDB6+BQR\nERGRwOjhU0REREQCo4dPEREREQmMHj5FREREJDB6+BQRERGRwOjhU0REREQCo4dPEREREQmMHj5F\nREREJDB6+BQRERGRwOjhU0REREQCo4dPEREREQmMHj5FREREJDC7+XGRnJycb4DyQJ4f1ytGcoFf\nQ6HQgenuiF80ltkxliV4HEFjmS1yyaJxBI2lxjIr5OLDWOaEQqGke5KTk7MJ2DfpCxVToVAoJ919\n8IvGMjvGsqSPI/BLKBSqmO5O+KGkj2W23JOgsdRYZo9kx9Kvafc8n64j6ZeX7g6IL/LS3YE0y0t3\nB3yUl+4OiG/y0t0B8U1eujtQnGnNp4iIiIgERg+fIiIiIhIYPXyKiIiISGD08CkiIiIigdHDp4iI\niIgERg+fIiIiIhIYPXyKiIiISGD08CkiIiIigfGlvKZIKjRr1gyA+vXrAzBp0qSoNm+99RYAU6dO\nBeCRRx4JqHdSWLvttvPj5thjjwXgrLPOcl7r3bs3AH379gXccfz777+D7KJIVtl9990BOP/88wE4\n/vjjndcuuuiiAt8/ZswYwP2cfemllwDwozKilGyKfIqIiIhIYPyq7b4caJR8dxJTqlQpAPbZZ5+o\n1zp06ADEjpJF2mWXnc/es2fPBmDQoEHOaytXrky4P1lWrzaQsbT/9/Xq1QOgc+fOAHTp0sVpYxHP\nRGzfvh2Azz77DIArr7zSee2dd95J+DrZMpZB35PxWKRz4MCBAJx00kkFvufWW28F4O677y7qj10R\nCoUaF/XNmSRdY2lRs65duzrn9tprLwD+/fdfAHJzcwu8zurVqwGYNm2ac64wv3ey5Z6E1I7lnnvu\n6RxXrVoVgLlz5wJwyCGH+PIzxo0bB8BNN93knPvjjz8Sfr/GsugqVaoEQIMGDQD3MxKgdevWQGL3\n1ZYtWwB49tlnAVi6dKnz2tNPP51wfzKltruIiIiISIH08CkiIiIigSlW0+41a9YE3Cl17+JpT1+A\nxMLPkW3/7//+z3mtRYsWAKxfv77A62gqITH77befc/zQQw8BcO655xb4vg8++ACAV155BYCOHTs6\nrzVqFLurq1atco4vuOACANasWVPgz8qWsUzXVG3ZsmWB8CUs1157LQB77LFHvu/777//AHc5xu+/\n/w64GxwAZs2aBcD06dOBAu9xTbsXUuXKlQHo168fAJ06dQKgTp06vlx/woQJzrHdy/ZvwpY+xdpg\nli33JKRmLKtVqwbA66+/7pw77LDD/PwRUex3McD333+f8Ps0lonx/l5r3749AD169ACgVq1asfoC\nFO2556effnJeO+CAAxLuo6bdRURERKTYyPjI56GHHuoc9+/fH4BLLrkkXl+A6G8A3qjmVVddBcCo\nUaOA8G9x5q677gJg8ODBBfZR3+bia9u2LQDdu3d3ziWS5uO+++4DYOzYsQDUqFEDgDZt2jhtDjzw\nQADOO+88wN0QYf8OABYtWgS4kbMnn3wSgI0bN0b9zGwZy6Ajn/btfMaMGUD45gezY8cOwE3b8uKL\nLzqv1a5dG3CjbvFYVOeLL76I10yRzwRUqVLFOZ43bx4Ahx9+eKGvYxuQAH777TfAjWpaNNwr8nN6\n+fLlANxzzz1OmxdeeMHaZMU9Cf6OZWTEM9lo519//QXAL7/84pyzaPiuu+4a1nb06NHO8fXXX5/w\nz9BYxmb3SsuWLYHw+6Bx450fY3av/Pzzz4CbXvB/fQHcmcTHH38ccMcU4OijjwbglFNOCbuezTIB\nnHPOOUB4FD0/inyKiIiISLGRsUnmzz77bMBNcgtQsWLFIl/PG/l84403APjkk0+A2JHPwqSPkNhs\njaeldLBv0fEsWbLEObbxXrduXaF/tvfb3NatWwF3nGNFPKVoTj75ZMD9Fh4r4mnrpm2mwRs1Md5v\n+pJ6ZcqUAeDll192zuUX8dy8ebNz/NVXXwFuZMV8++23zvGcOXMAd113nz598u3Htm3bADc1kH3u\ngxv5lHAWhbQ0ZH6t71ywYAEQnqbOZilirTMU/9i/e5uV887cffnllwAMGDAAcGeXYrnuuuvyfe3g\ngw8GYMOGDQBcfPHFQPh99uGHHxa260WmyKeIiIiIBCbjIp/27dvK65UrV855LZn1qXXr1nWObY2K\nJW2NRd/0kmdrvywh8RNPPOG8Zuv/bKdku3btgPAIio23rVs57bTTAChfvrzTpkKFCmE/888//wTg\nqKOOcs7ZN0fxn60R8o4JhO9Sv+WWWwC3AICxJObgrmuK5L3nhw8fDsDXX3+dRI8F3F3ln3/+uXPO\n1oQZmxnyZpdIZBbCoqrxPl8tGm5rv99+++0Eei3g/k70josfbN22RbfjmTlzpq8/u6QqXbo04JYV\nNgsXLnSObf9JsveIjaut7bbIp32Ggztb4d0BnyqKfIqIiIhIYPTwKSIiIiKByZhpd6vXbhsXbGrB\nkk6Dm4g6kjdEbAvYbYrWEov37t3baWOpe+zadl1vPfdEUixJfDa1Z9PvXk899RQAl156aYHXufrq\nq8P+tPEDuOKKK8Lajh8/HtBUeyrZvQpu3XZjdYM7d+6c7/st9c7kyZOdcyeccELMtt6NSN5axpIc\n+3y1z8lYrI53IlPtDRs2dI5tY5k3JRqEp9bq1q0b4C6TkcTZPWabVBJJi2NibaS1ZRKJsDRM3t+V\n4h/baGSbgsDdIJ2sgQMHAjB06FDAfe555plnnDZBjqsinyIiIiISmIyJfO67776AGxWxjQbeaGfk\nhiNLMu2NvngT5AIcdNBBgFviL9a1bZOLNyVIEAtuSzJv1Ksg++yzD+AuiO/Vq1dUm6VLlwJuOgpJ\nHW8akMjk01b6MpZmzZoBMHHiRACOOOKIqDaLFy8G3M1FVnZR/BUZPYPoCJql3Hn44Yedc7aJyDZw\n2syDpUoC9/PYZh/ef/99AHr27Om0UcQzeVZCeNOmTUBiqQhj/X9PJPL58ccfAzBlyhTATV8n/rJn\nk6IUevBq0qQJ4BZq8Z6z5x4rU24zikFT5FNEREREApMxkU9LAn/nnXcC7pZ/7/qySJbCxxvttPa2\n3sjSFFj5Pi9LB2PlNr2J6MU/a9eujTpnZRQTSR9hJcNsDZqXpWqytCPeMn+SGt6SbZaY2sqcWglU\nK3MKbiJzi3zaa//884/TZsSIEYC7/sjWaktqvfPOO87x7bffHvanFWXwrtW0hOZvvvkmEJ6mxVhU\nbNiwYUB4ijXxj5VZPP/88wF47bXXCnxPItFRi6SC+zlticgVsfaX/f+88MILAff3YZ06dZw2luj/\n1FNPDXuPl63htoip3XutW7d22lixCIte23NPun5nKvIpIiIiIoHJmMinefTRRwG3zOLq1avzbWsl\n3ry7YJs3bw643ySMNzH1gw8+CISX7pTUseS23rVjhxxySMy23m/mFun0rkuD8B15FnnxfluX4Pz4\n449h/3388ccD4fdtbm5uWJtFixYB7swFwHvvvZeiHko83ii2RUAsomblcc8444yo93nL4IK7rhPc\ntaLLly/3t7MSk62Ttmg05J89Ih4b965duzrn5s+fn2TvJBFWgMPW8VrRFXD3rbRo0QJwx7latWpO\nGxsnaxuLzQZnyqySIp8iIiIiEhg9fIqIiIhIYDJu2t1YaNi7ySQyobjV9fYmHbc0MBs3bgTgjjvu\nAMITqf76668p6LHkxxZIe6fdbaOKbQizRLrepOKRtaZXrFgBhKfW8k4bSvAil8VYvfbIqXaACRMm\nAG6yYy2VyCy2AWz79u0FtrUiApdddhkAH374ofOaEpAXT7YcRlPt6XPeeecB4akeq1evDsArr7wC\nuCkm7fkH3FRnlqrJlsHYxiNwl79lCkU+RURERCQwGRv5NBYZA3chezxWMvPVV18F4LHHHgPcUo+S\nPpaoGNz0SZbY+uabb873fZZOyd6jaGd6WbJ/gBtvvLHA9hbxtA1GmnlIvz322ANw07cA3HDDDQBU\nrly5wPfPmDEDcDd9SvD23HNPwN04W5RNRpJZLG3kWWed5ZyzUtRWDKBevXpR77NZC3tGKg73pSKf\nIiIiIhKYjI18WrJU7zdzW8/w+++/A25pv9KlSzttrHTUySefDLhJr63Um6SPNw1ErBKZke6++27A\nTY21YcOG1HRM4rKk8F26dAHC1+5a9MVYoYYDDjjAOWffwhXxTD8rwvH0008D4RGWRNgshHdGSoJl\nUWvbD9G9e/eoNnav2ThbAnqLbgO0atUq7D2WJu3ggw92zmXaOsGSwsYC3Kim9zkHwsfmggsuAOCD\nDz4IoHf+UORTRERERAKTMZFPSy4+evRowP1G7i2vaWWmbO3YUUcdBbilOL3t999/f8At+6fIZ/qc\neeaZgFuqDaBly5YFvu/II48E3H8binymh80+xFtHZPftsmXLAHj22Wed12ytrr0mwbNCDba22pug\nOtK2bdsA2LJlS1RbO+7WrRvgluKU4PTv3x+IHfE0tufh6quvDjv/22+/Ocf2+9TsvffeQPRshqSe\nRbN79+4NQJ8+fZzXbMY3ku1wh+IV8TSKfIqIiIhIYPTwKSIiIiKByZhpd1v8fOKJJwJuGNoSiwMM\nHjw47Jz96a0THpn6pUmTJgDMmzcvFd2WGCzRuG0UslrBNq3jZcn/bZrWW+/bpnutxnTz5s1T1GMx\n9v8a3I1FkbW9N2/e7Bxb2iVLMl6+fPmoa5500kmAu2Deig5IcGx6vG7duvm2sen2iy66CHCn8ryb\nVGwa1/tvQILlvUe9Zs2a5Rz37Nmz0Ne135FaohY8uz9jpa5bsmQJ4CaOv+aaa4DwDUi2HMY2BBYH\ninyKiIiISGDSGvm0dErgRr4s4mnfur2Jc+2beaR4ZfqK40Lc4mjfffd1ji3psUU8jXfDkLWZOHEi\n4JYT8yaQt5KbFr2uWbMmAN9++62vfReXtwytzUKYn3/+GYCOHTs65yLvL/sGbmVuwb3PbTOgIp/B\n86a+8nrzzTed48svvxxwE11bxDtWtNRSaknm8JZktA23tsHIZihs9jAWS2GogizBadq0KeDODpmX\nX37ZObZIp6Vcsv/2svRYinyKiIiIiMSQ1sind32fRUUWLVoEuOv98ot2erVp08Y5tvKalmxeguFN\nz9G2bduw11atWgXAFVdc4Zx77733Yl5n/vz5UW1sraelGIlMHyLJy83NBaBFixZRr9n6vk6dOgGx\nUybZuuvZs2cD4elBbC2aN82LBMtK2x577LGAG93yJou3mQVLWD106NCo62zcuDHsepI5Lr30UufY\nUhXaTFKFChWA8NSFkn722WjreO2z9rbbbnPafPfddwBUqlQJCJ9VMgsXLkxpP1NBkU8RERERCUxa\nIp+2G9q+jYEbKZk7dy7gRjytLUC9evXCrmOJjo877jjnnEU880vMKqnhHcuyZcuGvdagQQPAXd8J\n7vpBM3PmTMCNroEbiTHFaT1LcWMRESul6fXKK68AbiTaOy62lmzQoEEAVK9eHXDXJ4G7zmzHjh1+\nd1sSVKZMmbD/trX1U6ZMcc5ZpoLIMn7eSMvXX38NwJo1a1LST/GHZRaJlWEkP7abWlLL+7xi95w9\nr9g+iVgzC1akxdqOGDEild1MOUU+RURERCQwevgUERERkcCkZdrdNgXFqiF71VVXAe6mFe8CaUtE\nnwhLGxEvDZP457PPPnOOLUWSTS/YlHz9+vXzfb+19U7x2fSCTdF7f4b4y5a5eO+XihUrAnD66acD\n7uL4du3aOW0iNzBYTelhw4Y551auXJmCHkth2OehsfuscuXK+b7H/i2cdtppzjlNt6ef1WRv3bo1\nAA0bNkzqepbg/P7770/qOpIYb+Ec+9y135H2WfvRRx9Fvc82lP37778A/PHHHyntZ6op8ikiIiIi\ngcnxY2NOTk7OcqBRou1tU4M3zYelhohMhhwrEhaPJUq2lE1BlAoLhULRuQ+KqcKOZTyWfsdSLHXu\n3Nl5rVatWmFtJ0+eDIRvVFm8eDEACxYsACAvL8+PbsWVLWNZ1HHs06ePc2zRy3LlyoW12b59u3M8\nfvx4ANauXQu4JTm9bdJkRSgUapzuTvjBj3vSNhh16NABcGeWYiWsnjNnDgBDhgwB0l+oI1vuSfD3\n89WiZRa5jJeCburUqYAbNQV385h9vgaRnlBj6W7KBPfz0gqpWHGAeM89t956KwD33ntvYX+0r5Id\nS0U+RURERCQwaYl8xmLrVs477zzAjZZ5U79YgmOLkhn79gDBRMci6dtc9siWsSzp44ginwVdEwhP\nZWcsap0phTqy5Z4E3Zcay9gsGmqRaSuXCW7kc9y4cQAMHDgQgF9//dWPH11kinyKiIiISLGRMZHP\n4kzf5rJHtoxlSR9HFPnMGtlyT4LGUmOZPRT5FBEREZFiQw+fIiIiIhIYPXyKiIiISGD8evjM9ek6\nkn656e6A+CI33R1Is9x0d8BHuenugPgmN90dEN/kprsDxZlf5TVtz3+eT9crLnJx/+7ZQmOZHUrq\nOILGMlvkkl3jCBrLbKKxTIIvu91FRERERBKhNZ8iIiIiEhg9fIqIiIhIYPTwKSIiIiKB0cOniIiI\niARGD58iIiIiEhg9fIqIiIhIYPTwKSIiIiKB0cOniIiIiARGD58iIiIiEhg9fIqIiIhIYHyp7Z6T\nk/MNUJ4SWuM0FAodmO6O+EVjmR1jWYLHETSW2SKXLBpH0FhqLLNCLj6MpS+13XNycjYB+yZ9oWIq\nFArlpLsPftFYZsdYlvRxBH4JhUIV090JP5T0scyWexI0lhrL7JHsWPo17Z7n03Uk/fLS3QHxRV66\nO5BmeenugI/y0t0B8U1eujsgvslLdweKM635FBEREZHA6OFTRERERAKjh08RERERCYwePkVEREQk\nMHr4FBEREZHA6OFTRERERAKjh08RERERCYwePkVEREQkML6U10y3GjVqOMcnnHACAA0aNADg8MMP\nDzsP8OqrrwLwxRdfAPDvv/8C8OCDDzptvvvuuxT2WBJx0UUXAdCpUycADjroIAAaNmwY1XbLli0A\nDB48GIAxY8Y4r/lRxUuKLjc3F4BevXoBsN9++wGw++67O226d+8OwE033QTA9OnTATj11FOdNkce\neSQAffr0AeCff/5JYa9FRDLXrrvu6hy3bNkSgOeffx6ASpUqAZCT4xYhmjJlCgA///wzAHPnzgXg\nrbfectr8/fffKexxOEU+RURERCQwftV2Xw40Sr47ialVqxYADzzwABAe1SxTpgxQtGhXXl6ec9y0\naVMAfvnllwLfl2X1agMZy9122xl0b9WqFQDXXHNN2Hlwo15FGcsbb7zROR4xYkTC78uWsQz6ntxz\nzz0BaN68OQA333yz89pRRx0FwL777mt9A2KPq71m9529x6tRo51/rZUrV8br0opQKNS4EH+FjBXU\nWFokZf/99wegS5cuANSsWTOqbf/+/QHYsGEDAAsXLnReO+uss8La/vHHHwB06NDBOffOO+8k3K9s\nuSch+PvSlCtXDoAKFSo45+zz9dxzzw1r642WLV++HIC1a9cCMHXqVMCdaSosjWXyLMo5YMAA59wp\np5xS5OtNnDjRObZZpf/++6/A92VKbXcRERERkQLp4VNEREREAlOspt1to8ltt90GuBtRvJYuXQq4\nm4pmzJgBwFVXXeW06dGjR4E/68QTTwRg/vz5BbbVVELhzZo1C4g/XRA5PfvDDz8A7ph62VThOeec\nA4QvoraxTES2jGVQ49i+fXsAHnvsMQCqVKliP99pE/kZk8i0u73mvY7Za6+9APjzzz/jdU3T7nHs\ns88+AHTu3Nk5Z2PpPRenT0BiS2Ks7ebNm51zxx13HAAff/xxge/PlnsSgp+qPfPMMwEYNGgQ4G7E\nhfzHLt69a9Pvq1evds6NHDkSgPfee6/A/mgsC8+Ww9xzzz2AOzVeqlQp33+WLZtatmxZgW017S4i\nIiIixUbGp1qyjQwAc+bMAaBy5cqAm2pl9OjRThtLtWPpk4ylcAFYt24dAM2aNQOgY8eOfndbPPbY\nYw8Ann76aeecd5NYoq644goAZs+eHfXaJZdcAriRT/GfbeYbPny4c+7yyy8HwjeKQfiGhLfffhuA\nl19+GYA6deoA4amWbGbCvs1b9GXHjh1OmyFDhgDBpgPJNr179wbce8lS0UFi0Uz77LT0WRa59KY2\nszZNmjQB4M477wTCN7scc8wxYe8Xf1nE8/HHHwegbNmyvly3du3aABx66KHOOUvvI/4pX768c/zi\niy8C0LZt25T/3Lp16wKJRT6TpciniIiIiAQm4yOf48ePd44t4rl+/XoAunXrBiSWtmPr1q3O8bRp\n0wDo27dvWJv333/fOV6zZk0ReyyRWrduDUSnYPH6/fffgfBE/7bG0yIo9t9elnjeG3kBmDlzZhI9\nllgOO+wwwI2eeX3//fcATJgwAYCHH37YeW3Tpk0xr+eNfNosxLHHHgu40Tdv+jOLoEnRWZoWb8TT\nrFixAnAj1U8++WRUG5sl6tevH+BGY7zrOY3dr7HGbdu2bYXtuhRC165dgeiIp33Ogpti57nnngPg\nxx9/jHqPra+3iKdZsGCBc2zrQCV5FvG0MYHURzwtZSXAU089ldKf5aXIp4iIiIgEJuMjn961Jca+\nsRUmUbF37ajtGrME1qtWrQLg9NNPd9ps3Lix8J2VMLZ+zxvNjGRRbNtp+/nnnxd4XW95TVsHaOM7\ncOBAIDzyJv544okn8n3toYceAuC+++4r8DoVK1YE3F2y4EbkjCUm/+CDD5xzVatWBWJHwCUxlhUi\nFtuBHi8qaWNnkdNYEc+zzz4bgEceeSTsvHc26YUXXkisw5Iwb5lpKxBgbL9ErAwx8a5j64DtT4t4\nHn/88cl1VsLYjvZE1ndapNm7V8Uiprau3j4r47HPbO9+mESSy/tFkU8RERERCYwePkVEREQkMBk/\n7R5LrKmeglhNWnDD1TYNZDVSNdXuL5tKt9Q6XvPmzQPgoosuAuL/v7c0PhdffDEQPo1vU/u24WXc\nuHEAbN++PZmuSwyWaimWDz/8sMD32zhaTfZYU0OffPIJALfccgvgFiMQf9hyhljJ+61eu23ytM9Z\nb2orm3a1acL69esDbuEPiN5YaPe2bYIBN02e+Me7se/dd98F3KThNu0ej6VNu/76651zhxxyCAA/\n//wz4Carl9TYZZeC44G2+cs+I8FdrmZpDeOx35GjRo0CotNSBkWRTxEREREJTMZHPmOlabFUL940\nTJEsXYRFOU8++WTnNSvLN2zYMMCNwom/rr322rD/9m4wufnmm4H4EUqLrlj6Hot8etnmiJ49ewLh\nKbXEX0uWLAHgwAMPLNT7LDp27733AlCtWjUgPJm5pfiwqEt+6ZkkObYhzErOli5d2nnNolq2ac9K\n1F522WVOG4uCWmqzWKU4bVxtZskinkpfl1oW1QY3XZZFPm1M7bPUyzbwXnjhhUB42UaLeNomssWL\nF/vcawG3mIZFqNu0aVPge7p3715gGxs/gLFjxwJw1113hf3MdFHkU0REREQCkxOvlFrCF8nJWQ40\nSr470SxBOcCrr74KuGsULDmqd72RsTQCVkbOy9Z43n///b70MRQKRS+gKqb8HMtFixYB0KJFCyA8\nsfVnn31W4PvvvvtuAG688caw85b4GODSSy8F4Ndff02us/+TLWOZinvS1vJZkQYvW5tpEZKaNWs6\nr82fPx9wI57Gu07U1gd7v6knaUUoFGrs18XSKRVjaeUtbZ0nwBlnnAFEl9e0iDfAfvvtB7gp8Kyt\nJagHdy2ZpVNKdn1nttyTkNrflV4WvbR0hEcddRQQPt6nnnoq4H4+23pB73pgi3rHS7NWGBrL+KyQ\njvfZxGYXvOkiC2KRb28aJW/aOj8kO5aKfIqIiIhIYDJ+zefChQudY4t02rc3K49pa1a8bU466aSw\n61gpTtAO2nTxrlGxknuRCa2HDh3qHEeue7E1Y95yfX5FPKVgFSpUAKIjY+CurX7zzTcByM3NdV6z\nXe32vq+++gpw1x0C/PLLL/53WPL13nvvAeE70y0aauuxLTJm6wa9/vrrL8CdeXjllVec13777bcU\n9FgKw9bm2hpfy/YyYsQIp43dj7ZW1D5LrfiKBG/Dhg1A+PPKTz/9BETvoYjHZpv8jnb6SZFPERER\nEQmMHj5FREREJDAZP+3uZRuLbDq2WbNmAKxbt85pY0laly1bBsCtt94KwBtvvBFYP2Unm9qzut3e\nxc+nnHIK4C6MtvRZu+++u9PGFr7bJhSrJ2zTEBIsqxvsTdcSmazcNi94z9v0nqXVsuUymmrPLHa/\nzpw5E3Cn3WOxsVy9ejWgqfZMF2upjJ2z9D6xNu5KeniXJCWSUqk4UuRTRERERAJTrCKf//33H+Am\noI71bW7Lli2AuxDeyvVJ8AYPHgy4KVwOOugg5zVLPG5/Gu+YWjqmevXqpbSfkhiLQHu/lVs6HduM\nFMvSpUsB6NevH+BG2CSzXHLJJUD4Bk4IT/i/zz77AFC+fHkAnnvuOQCaNGnitPGW45T0sM1jV155\nZcLv+eKLL1LVHUlQ48Y7s8N5y6FasRXz8ccfA+ElqW1WsDhR5FNEREREApPxkc+DDz7YOZ4yZQoA\njRqF53X1RjctknbaaadFvSbBshQeVvrSm5zcIijxlCtXDoA6deoA8Pnnn/vdRSkEW0/tXXNr5yJn\nIbxrPidPngwo4pmJvOvJJk2aBLhjaelazjnnHKeNJb+2Urd169YFwouBqFxxenhT01mi/yOPPDJd\n3ZFCsM9RK4MaGe30srFt2rS+kqm0AAAgAElEQVSpc06RTxERERGRODIu8mnfACzJqq0bBLdkX15e\nHuDuxuzQoYPTxpLo2jeBe+65J7UdlgLZmk0r+eZlyaq3bt0KQNmyZZ3XrByj7a4+7rjjUtlNKYBF\nUZYvX15gW28k1O7F8ePHp6ZjUmRWwtbr66+/BqBr164AbN682Xnt008/DaZjkjCLeFrmEHD3R5gF\nCxYAbhEWgBdffDH1nZOE7LbbzkexTp065dvmjjvuAGD69OkA3HvvvanvWAop8ikiIiIigdHDp4iI\niIgEJmOm3StVqgTAgw8+CECXLl2i2tj0q02t2/SQLcCVzNKnTx/AXfpQpkwZ57UZM2YAcPvttwOw\nfv16wF1wDXD99dcDcMQRR6S8r5K/du3aAfD8888D4ZuJduzYAbjjaAUFTj75ZKeNTSX16tULCE9S\nL+nRsWNHILyOty2B6d+/PxCeYkkyj6VTst9/3ql2W/ZiU+t33XUXEF7YIVaqQkkPK7ISixXIsTE8\n/PDDgfDfp5G2bdvmY+9SQ5FPEREREQlMWiOfVapUcY6tdJ8lWTVjxoxxju0b+b///hvW5tBDD01V\nF6UIbGOQRTwtEbl385dFrf/555+w99oYgxv5tJKb+++/P6DymkGxiOXYsWMBN5G8N2JiJfls44pt\nGFuyZInTxjacPfTQQwDMnTsXgG+//TZlfZfYcnNzARg5ciTgbnQAeP311wH3szgWbzJ5cO/Fb775\nxs9uSgJq1KgBxE6nNGjQIMD9/WnlT+09klnipVayDZ6HHHIIAEOGDAHiRz4ff/xxH3uXGop8ioiI\niEhg0hr59K7VtIinpdyxFEve9WEW8bSUPQMGDADcFEzgRmW0rix9rIyifTMbPXo04JbLjMWSztua\nXy8bd0U8g2URFUt5ZbzJ/h9++GHALQZh0dG9997baWNrRC3KZms/b7311lR0W+KwWSJv8Q6zcOHC\nmO/p27evc2zp7exz9oknngDgyy+/9LObUgQ//PCDc2yfuX/++WdYm3HjxgXaJ0meFYJo1aoVAC1a\ntMi37dq1a4HoGcVMpMiniIiIiAQmrZHP0qVLR5376KOPADdhrq1dAXeN6LHHHgvEXuv56KOPAu46\nQwmeRTxtx52tE7vsssui2jZs2BBwy/TFKru5cePGlPRT4vOWVfTyRlMs4XEi5d22b98OuGs/JX0S\n2enctm1bIDxCbZ/Zdk/efPPNKeidJMIi0jazYOt4ITriaaWKLXIN8PfffwNw3333pbSfkhx77vHu\nkYlkY2mFIay0dSZT5FNEREREAqOHTxEREREJTFqn3S3djpctql25cmWB79+wYQMAjzzyiHPOpt0l\nfSZNmgS4U+lF3fxlSZMvueQSfzomhTJr1iwAqlevDribiY466qhCXcemAC+99FIAfvzxR7+6KIVk\naVssNdKBBx7ovFa7dm0ALrjgAsDdEGrjDrBu3TpAG1cygW0wsiUUZ555pvOapTqrU6cOANdee21Y\nW4DZs2cDsGLFitR3VuKyz9rrrrsOcD9z47FlTOAWdEnkuSlTKPIpIiIiIoHJ8aPEVk5OznKgUWHf\n591wdMIJJwBu2bcePXpEtX///fcBmDNnDuBGPC0Cmi6hUCin4FbFQ1HHMuIagLv42dLx2PgBHHbY\nYYD7Tc3SSZx00klOm5deeglwy/4FIVvG0o9xNJbcuGrVqoCbXgegVq1aALz66qsAfPfdd0B46h1L\nKv/xxx/70Z1ErQiFQo0Lbpb5/BxLY5tVhg8f7v05QPRmJO8MlRUVmDp1qp/diStb7knwdyytvKYV\ngbDNm5D/hrJ3333XObZIaZBlVDWW8dnvRe99ecopp4S1sXRKlmweYMqUKX52IyHJjqUinyIiIiIS\nmLRGPrOFvs1lj2wZy5I+jijyGZfNOnnLYlr5WvudYOs7vbMRX331lZ/dSEi23JOQmrG0NEpWmAWi\niwEMHToUcNd5gltyM0gay+yhyKeIiIiIFBuKfPpA3+ayR7aMZUkfRxT5zBrZck+CxlJjmT0U+RQR\nERGRYkMPnyIiIiISGD18ioiIiEhg/Hr4zPXpOpJ+uenugPgiN90dSLPcdHfAR7np7oD4JjfdHRDf\n5Ka7A8WZX+U1f/3fn3k+Xa+4yMX9u2cLjWV2KKnjCBrLbJFLdo0jaCyzicYyCb7sdhcRERERSYTW\nfIqIiIhIYPTwKSIiIiKB0cOniIiIiARGD58iIiIiEhg9fIqIiIhIYPTwKSIiIiKB0cOniIiIiARG\nD58iIiIiEhg9fIqIiIhIYHwpr5mTk/MNUJ4SWmYqFAodmO6O+EVjmR1jWYLHETSW2SKXLBpH0Fhq\nLLNCLj6MpS/lNXNycjYB+yZ9oWIqFArlpLsPftFYZsdYlvRxBH4JhUIV090JP5T0scyWexI0lhrL\n7JHsWPo17Z7n03Uk/fLS3QHxRV66O5BmeenugI/y0t0B8U1eujsgvslLdweKM635FBEREZHA6OFT\nRERERAKjh08RERERCYwePkVEREQkMHr4FBEREZHA6OFTRERERAKjh08RERERCYwePkVEREQkML6U\n1xQRkeKncePGznHr1q1jtunUqZNzfNxxxwHw33//hbXZsmWLc3znnXcCMGrUKAAuv/xyAGbPnu20\n+b//+78kei3pUr58eQDmzJnjnNttt52PEddddx0AS5cuDb5jJciuu+4KQMuWLZ1zZ599NgC9evUC\n3DFo1apVwL1LnCKfIiIiIhKYrIt83n777QC0adMm7PyCBQuc47fffjvsT8kcVatWdY7PPffcAttb\nBGXq1Kn5tvn2228BGDt2LACNGjUCYMmSJU6b0aNHF76zUiR77LEHAHvttZdz7p577gHcb/AVKlQA\n4JlnnnHaXHHFFQD8/vvvgfQzmzVs2BCAefPmOecsqhWPRTxDoVDY+b333ts5Hj58OAB9+/YFoHLl\nygDccccdTpsePXoA4RE0Sa+cnJ2lusuUKZNvG3utVKlSzrmjjz4acKNsinym1m233Rb2Zyz169cH\nwn+vXX/99QDs2LEjhb1LnCKfIiIiIhIYPXyKiIiISGCK9bT7/PnzAXcRfDzeNoMHDw57zabfvVPz\nNn0vyTvhhBMAuPbaa51zDRo0ANypHpvGsylZgP333z/sOpFtAf755x8A7r333phtwZ32u+qqqwCY\nPn06AJMmTSrS30eixZqOM1deeSUAxxxzDOBO00WOr5eN8fnnn++cs38zLVq0ADT9XhRDhw4F4OKL\nLwbCp8sjp9KTVa1atbDrese7dOnSvv4sKbxmzZoB7ufy7rvvDkCXLl3S1ieJZvfNSy+9BLifo17b\nt28H3HvN7uvq1as7bU488UQAxo8fD8App5wCwKeffuq08fszIB5FPkVEREQkMBkb+YwXeYyMXCbL\noqLe6Kg2JfnHNhrYN20vi1B+8MEHQHjkLF5kzFik1KIskdcF99vcpk2bADcVjCJnRXPsscc6x7aI\n/aijjgKgRo0a+b4vVuS6MJYtWwZEp/mRxB166KEAHHDAAfm22bhxIwB///131Gu77LIzXmFjYPdr\npUqVCtUPb8RV/GfjYrM99jl52WWXOW3sXvXONiXjr7/+8uU6Es5+T0VuBLNUZuBu2LR7t3fv3gA8\n/PDDThvbXFirVi0APvnkEwDat2/vtHn99dd97Xs8inyKiIiISGAyLvJp0Ue/optDhgzx9XpSeN4E\n1JF+++03AE4//XQA/v33X+e1tm3bhrV97rnnEv6Z3333nXNsURqL+jz22GNhP1PisyTS9m162LBh\nzmvedEnJ+OGHHwBYuXIlAO+99x4Ab775ptNmxYoVgLvOVxKXm5sLwJFHHplvG/t/f9pppwGJJYK3\nCKoltwYYOHBgge+zNo8++miBbSUxAwYMcI7POussAJo0aZJve5uB8Cb/j3xPlSpVCvy569atA8LT\nool/brnlFsBNj2a/T/v165fvez7++GMA5s6d65zzpjH08v4eVORTRERERLJSxkY+E2FRzUR2psdr\nE2/NpyTPdth615YY+9b1448/Rr1mu9ITiVDat0FbzxkraXzXrl0B/6J12c7KuFkmASuflwjvelpL\n8j9jxgzAjZQsX77cabN27VoAtm3blkSPJT95eXkArF69GnBnAWwNJ8CIESOAwpW+tLbeCOagQYPC\nrh1rja7350pynnjiCQC6devmnLP1lzbe06ZNA8LHycbFPnst4m27omOxGSXvmNo60s2bNxf9LyFh\nvOtwO3ToEPbaxIkTC3y/RTkPP/xw59zJJ58cs61lgwmaPgFEREREJDB6+BQRERGRwGTctLtNdwe5\nQUhplVLLpnW+/PJL51zt2rWB8MT+ELu2e6dOnYDY03hW0/2+++4DYNWqVfn2I179d4lmmxbiTbev\nWbMGgLFjxwLuPeSdPl+/fn2KeiiFZZtM7E/vvWTFAGzTgaUm8zrnnHPC3m8saX2sa0emOgPo2bNn\n0f8SAsCee+4JuMUXvEsZRo4cCbhLIGKx9o888gjgFnSIVSjCrmOfs9r0l1re++vPP/8Me61ixYr5\nvs9+f/bo0QOAmjVr5tvW7kdLXh80RT5FREREJDAZG/lMhEVHve9R9DLz2GaHJ5980jlnm5BsM4ol\nordvbBCdJuSzzz4D3IgcuNFUfRP3X7ly5WKe//DDD53jM888E1B0Mxs0b94cgLfeeguIXYTBSvsV\nplCAXa9v377OOUtwLcnbunVr1Dm7d23T4I4dOwCoX7++02b48OGAW/7Yymt6x90+l20DpzcVnqSO\n9/+z/Y6z4h72+8/7u9I2iVlS+YMPPjjfa1siettgZsU7gqbIp4iIiIgEJsePQvI5OTnLgUbJd8dl\naY/mz59fqPdZYvIgI6ChUCin4FbFQyrG0nhL8FmaHVujEu/foUVO7Ru6pepJhWwZSz/G0Uogzpo1\nC4CWLVsCsH37dqeNfSu3tFi2fsyb5D9NVoRCocbp7oQf/LwnLd3KlClTAChfvrzzWiK/C/IrkWqR\nNYA//vgDcP/djBs3DnALBxRWttyTkJrPVysc4P3/W7p0acBNv2Tr+7ypliJLrNraPyvCAe4Y+kVj\nWXgWrba12Jb437uGukKFCoBbEMRYEReARYsWAe76XSvaUVTJjqUinyIiIiISmIyNfMZiieLbtGkD\nxE5IbxHPyNKMqaRvc4nxfiuz3dG2bsX+Hf79999OG0s83qVLFyB8t3yqZMtY+jmO9erVA9zISLz1\nRBb18kZhLCo6YcIEP7qTKEU+47DPUFuPCclFPu1+BnjxxReB6EwWRZUt9ySkZixt17qtowe3JKNl\nHIiX1N9KMF5wwQVAapPFaywTY2t1wf3c7dixY4Hvs1kpK4H8wAMPOK/98ssvfnZRkU8RERERKT70\n8CkiIiIigSlW0+4mkc1IQU6/ayohMX369HGObTogchpvyZIlTptWrVqlohtxZctYpmIcmzZtCkD/\n/v2dczYlf9hhhxX4/qeffhqAm266CXCLD6SIpt1jsBQ8ttzFkpFD7BrskWz61tKe2QYmbQJMTCo/\nX7334Lx58wCoXr16vu2HDBkCuMvZgqCxjM+Swj/11FPOudatWyf8/quvvhqAMWPG+NmtmDTtLiIi\nIiLFRrGMfEaK93ewyFqKf76+zcVhiakXL14c9VqskpnGNjFcc801fnYnrmwZy6DvSUv3YgmQDzro\nIOc1O2fpXywC6o2gpiAKWuIjnzVq1HCO7R60Mpjt27e3azttCrPh6PnnnwfgvPPOK2y3Ci1b7klI\n7X1pac4gPAG51xdffOEcN2688/aIVUwgVTSWsdlYLFy4EIAyZcrk29bKbdrnqdc+++wDwJYtW/zo\nVlyKfIqIiIhIsZFx5TWLwruus7BJ6SX1GjZsCMSOrNjasccffxxwU0QA9OrVC4CPPvoIgEmTJqW0\nn1J0Nkb2p5dFOKdNmwbA+eefD7gRUEj5+s8SyaKd4CaVj2SFAyD6/rzzzjuBYNPWSeKaNWsGuCUw\nrQiH119//QW4Y1uxYkXnNUtWHkQKO4mtZ8+egLsO3iKe3plAuw/XrFkDuLNMlk6ruFLkU0REREQC\nkxWRz3ilNG1nfJDlNmWnSy65BIB77rkn6jUrmWm7ZW3dkTcSc+qppwLujmopniyquddee4Wdt/EF\neO211wLtU0lna8LilbyMl5TaIjSlSpUCwotDSGpZ5GvUqFEANG/ePKqNlS+2Nb5WRvWdd95x2kyc\nOBGA448/PmV9lfgOPPBAIHyNPMANN9zgHNs4GyvesMcee0S179q1KwDjx4/3v7M+U+RTRERERAKj\nh08RERERCUxWTLsHmSRXCmaJrG0hfNmyZQHYsWOH0+buu+8GopNT23lwp2Ut1dKqVasAePLJJ1PR\nbUmRyy+/HIjeeGZ14CU1OnXqFHXOpttPOumkAt8/c+ZMwE2V5WX3po3p0qVLi9xPKZg3bZYV4ohM\nx/P11187xzb9apuJbMnLJ5984rQ5+uijAXcs58yZ43e3pQBnn3122H8PGDAACK/JHsmWMU2fPt05\nZ9Pup59+OqBpdxERERGRMFkR+Rw8eHC+r2mjUfAswfEBBxwQdt77bezRRx8t9HWPOOKI5Domgala\ntapzbBHwSNpklFpt2rRxji05/CuvvALAihUrCnz/M888A0CjRm4e7X79+gFuKhjb0KnIZ2p5N51E\nRjwtAtatWzfnnKVYMtu2bYu6pkVDI9tKau23337Osc0K2oZb+0xMpNRtrNKplStX9qOLgVDkU0RE\nREQCU6wjn1rrmZlGjhwJuN/e3n33XQCuvfbaQl0nsjTqokWLfOidJMq+oVsand9++63A99jaNO96\npEqVKoW1efXVVwFYtmyZL/2U2LxJ4+14w4YNABx66KFAeLnFSJakvnbt2s45u6fteh07dgTg3nvv\n9avb4nHBBRcAcMYZZ0S9ZlEya/PPP/9EtbF0PFdccQUA9evXd16zsbR/ExIM76yQRZ933313wF1f\nvXbtWqdNrKg1hN+Xxvu+TKfIp4iIiIgEplhFPi3SaWuZbL2RZJbI6EgibM1YzZo1nXP2/pUrVwLw\n8ssv+9VFScCxxx4LQJ8+fQDo3LkzEDsCasmsraDA/vvvH9XG/l0MHToUyP8bvfjDCjkAVKtWDYC+\nffsC7lh6o152n1WoUAGAdu3aAbDvvvsm9DPEf1999RUAu+66a9RrdevWBeD6668HoFWrVs5rpUuX\nBtz1oU2bNo16v2Uf+fjjj33ssRTEW4J48uTJAFx55ZUADBw4EICbb77ZafPGG28AbnlN0759+6hr\nf/rpp/52NoUU+RQRERGRwOjhU0REREQCk1OYqdF8L5KTsxxoVGDDBPg1tW4pltq2betDr+ILhUI5\nBbcqHvwYS0t2bFPoNk0bOW3wv58HuMmqvfVqbaPLpZdeCsBzzz2XTLcSki1j6cc4Wt3uefPmAe54\nehe116tXD3Cn2WNNDxqbWpowYUIy3UrUilAo1DiIH5RqRR1L2wwE8PzzzwPh99f/ru0cJ/K7IDJl\nky232LRpU2G7l7BsuSeh8GNZpUoVIDw1VmQKu3jsM9Q2li1evNh5bdiwYQCsX78+4eslqySPZSy7\n7LIz/terVy8Axo0bl1SfunfvDrjT+amU7Fgq8ikiIiIigcm4yGey/RkyZAgQbBomfZsLZ+UUx4wZ\nA8SPhlkkxTafeL/hW/qWuXPnJtOdQsmWsfTzntx7770BmDVrFgAtW7Ys8D0bN250jgcNGgTApEmT\n/OhOokp85NPLyi32798fgAYNGti1nTaJfPYuXLgQcEt3WnLsVMqWexKKPpY2MwTuBsBzzz0XgB9+\n+AGAOnXqOG2++eYbwL33rGBAumksY7NZpuuuuw6A3Nxc57XevXvHfI93s2CXLl0At/Sqt5R1qijy\nKSIiIiLFRsZFPi1iaWs2vaUzI9d/BrmuMx59m4tt3bp1gJvmJRaLpAwfPhwINsoZS7aMpZ/jaMqX\nLw+4qV3ALRxgKXceeOABwF1jCGlLqaTIZ5bIlnsSNJYay+yhyKeIiIiIFBsZF/ksjvRtLntky1iW\n9HFEkc+skS33JGgsNZbZQ5FPERERESk29PApIiIiIoHRw6eIiIiIBMavh89cn64j6Zeb7g6IL3LT\n3YE0y013B3yUm+4OiG9y090B8U1uujtQnO3m03V+/d+feT5dr7jIxf27ZwuNZXYoqeMIGstskUt2\njSNoLLOJxjIJvux2FxERERFJhNZ8ioiIiEhg9PApIiIiIoHRw6eIiIiIBEYPnyIiIiISGD18ioiI\niEhg9PApIiIiIoHRw6eIiIiIBEYPnyIiIiISGD18ioiIiEhg9PApIiIiIoHxpbZ7Tk7ON0B5SmiN\n01AodGC6O+IXjWV2jGUJHkfQWGaLXLJoHEFjqbHMCrn4MJa+1HbPycnZBOyb9IWKqVAolJPuPvhF\nY5kdY1nSxxH4JRQKVUx3J/xQ0scyW+5J0FhqLLNHsmPp17R7nk/XkfTLS3cHxBd56e5AmuWluwM+\nykt3B8Q3eenugPgmL90dKM605lNEREREAqOHTxEREREJjB4+RURERCQwevgUERERkcDo4VNERERE\nAqOHTxEREREJjC9J5kVEREQkfbp16wbAoEGDAKhdu7bz2jnnnAPAtGnTgu9YDIp8ioiIiEhgsj7y\n2axZMwAaNWrknOvSpQsAxx9/fFjbZ5991jm+4IILAuidSMnRuHFjAEaOHAlAq1atnNes0tqwYcMA\nuPvuuwH4448/guyiJKh8+fIAHHDAAVGvde3aFYCBAwcC8OOPPwJw5513Om0mTpwIuOMuwbPfjTk5\nOwvV1KlTJ6rN559/DsD3338PwPr16/O93uOPPw7A7NmzAXjhhRf866xEGTp0qHPcv39/AHbbbecj\nnY3pf//957S57bbbAEU+RURERKQE8qu2+3KgUYENU6Rs2bLOsa15sOimRVfsG0E83m8Jbdq0AWDx\n4sUFvi/L6tWmdSzjqV+/PgBvvvmmc26//fYD4LvvvgPgxBNPBOCLL74o0s/IlrFM9zjuv//+zvHN\nN98MuLMJFSvuLLlu387BjYDZuaOPPhqAFStWFLULK0KhUOOivjmTBD2We++9NwCtW7cGoEmTJs5r\nxxxzDOBGPO2eLKxSpUoB8O+//xbYNlvuSQh+LO333mmnnQbAHXfc4bxWmLFbt24dAKNGjQLcyDe4\nawktsjZjxox8r6OxTN6SJUuA8Ptyl10KjiNaFLtevXqA+9zkneWdMGFCwv3IlNruIiIiIiIF0sOn\niIiIiASmWG44sqmfXr16AdChQwfntUqVKhX5ut7QdZ8+fQDYvHkzAGvWrCnydaVgtszBOxVkm09s\nSnbXXXcFoHTp0k4be61atWoA1K1bFyj6tLskx6bbFyxY4JyzjQw2pW5j1rZtW6fNk08+CUBubm4Q\n3ZQY7HPVNgZFbsiU4uehhx4CoHfv3vm2sftxw4YNAFSpUiWqTa1atQAYPXp0vtf56KOPitxPCXfY\nYYc5x/Y5ec011wBw8MEHA4lNtXstW7Ys7L9tec3YsWOdc/ZvwTYEppIinyIiIiISmIyPfNqid4je\nTGRP7l62gP31118HYPny5QBMnTo1qq19w+/cuXPUa7aI+pFHHgEU+fSb/f+9//77AXcs99prL6dN\nZKRMMpd9U587dy4ANWvWdF6LHD/774ULFzrnfv7556j3Seq1a9fOObaNIt570E8vvfSSc7xjx46U\n/IySbI899nCOO3XqBED37t3D2mzdutU5tt+n27dvB9zfleXKlXPa2IZdS6MUi20KrFy5MgBfffVV\n0f4C4nyOvvbaa8656tWr+3LtP//8M+Z578bPU089FVDkU0RERESyTMZFPm29yeDBgwE477zznNe8\n6R0APvnkEwCmT5/unLNv16tWrcr3Z9h1vFFVgG3btjnHtuZz/vz5hfsLSL722Wcf5/i6664DoGrV\nqunqjvjA1njeddddgBu59H6btoiaRTcvv/xyIDzCVqZMGaDw65gkObfccotznF/E05sO6dprrwXc\ntC02e9S8efN8f4aNu70XwtPaiT+GDx/uHNv6QGMplh588EHn3KZNm2Jexxv5rFGjBgB///03APPm\nzQPg6aefdtpY6jsbZyk6W9+ZSLTzhx9+cI4fffRRAJ544gnA/f169dVXO21snCzF0pgxY6Ku6U1j\nmGr6pBcRERGRwGRc5LNnz56Au5Pd65tvvgHcXdCTJ08G4icqtgiMd23TpEmTADdBuX0D9O6a/+CD\nD4r2F5B8WVlFgKZNmxbY3r6F2U72WOXfzK+//grATz/9lEwXpZDOPPNMAM444wzAXc/pjYL069cP\ncKNs1sa7o9PGVhGxYNj/+xYtWuTbZvXq1YA7xuAmG7dIt+2Qj8U+l+3zNl5pRim6gw46CICTTz7Z\nOWdFNwYMGADAlClTgPj3lxV/uPfee51ztgbQItzeEqniv8iIdSy2n8VmhyF6J7tlOXjrrbecc/ZM\nY89Nti44XRT5FBEREZHA6OFTRERERAKTcdPu8VgqiVi1ofNra1N+NlXv9csvvwDudLum2lOjUaOd\n5W87duyYbxubznvqqaecc7fffjvgbv564IEH8n3/G2+8AcC7776bVF+lcGzDUeS9aGlXvCxBtdUS\n9m52sfdb2pZvv/3W/86Kw2qze9PzGEvJYml6vv76a+e1W2+9FYAbb7yxwJ9hic3jpemR5NkmIJt+\nB7fO+jPPPFPg+9u3bw+44/TXX385r1nxB023p5Yljo+3Adem220ZjHecItmzjd3n4E7B165dO7nO\n+kSRTxEREREJTMZFPi26ZU/nFiUBd+PJPffcA7jpBLwpJixicsMNNwDQrFmzqJ9hG4xsk4N9SxB/\nWcoOGwtvqiVjCY4HDhwIuAvjvSy6FouVdLPoqAQrcqNRvIIAn332GeDOQnijKfY+m4VQ2pbUOvfc\nc/N9zcosfv/994C7udFEUecAACAASURBVAigR48eBV770ksvBcJnMSR1bNbIG/lMxGWXXQa496N9\nXg8ZMsRp490kKqlzxBFHAFCqVKl82xx99NGAm4bpyy+/jGpjG3ltVum0004r8Ge///77zvFzzz2X\nYI+Tp8iniIiIiAQm4yKfVv7LElF71/lZWU1L/3D44YcDMGLEiHyvZxEV71pAi9Yo4plalt7KSml6\nWcTTotaxIp72Tbxv3775/gwr5aYUS+mxaNEiABo3bpxvG0vrE7nW848//nDaRCail9Sy8nkWpQTY\nbbedvw5yc3MBmD17NgD16tVz2kQmot+8eTPgzlyAm4BcJTSDcfrppwNw1VVXOecOPPDAsDYVKlQA\noH///s45KyttM0u2ntebakmCYcVxfvvtNwD23XffqDY2c2iRaW954gsvvBCA+vXrA9EFebxsreiS\nJUuA8Gcsm/UIgiKfIiIiIhKYnHhrtBK+SE7OcqBR8t1JTOnSpQE3WhYvWaolOo63liJZoVAo/233\nxYyfY2m78qZNmxb12qeffgq4a1323HNPIHz37dy5c4HodbsWNQU3Sbbtkk5WtoxlUPekFYV4+OGH\nAXemwVvGz/4dWOlNazNo0CCnzd133+1311aEQqH8w7HFSCrH0puE+rjjjkv4fZGFOVKZKSRb7kkI\n7r60ktNHHnkk4JaObtiwYVTbbt26AeElM1NFYxmfzfJ5s/PEykhRFFYS1yKdEyZMSOp6yY6lIp8i\nIiIiEhg9fIqIiIhIYDJuw1E8ttjdUn/YxqN4dtll5/O1t460pXyR9KlUqRLgjuWJJ54IuAnpwU08\nHrk0xFv/1q/pdklOZJJ5S4MG7vjZWNnU0jvvvBNQ7yQ/3iUxhZl2tyUxKsyROex3Hbj3Y7yE4i+8\n8AIAL774Ymo7JgmzogADBgxwzu23335Fvp43HdPVV18NwJtvvlnk6/lJkU8RERERCUzGRz69qT1s\noaw3PQi4i9/BLStmpd1sQ4tteoCUbG6QOGKVQbVvc95veJHsm/x///0Xdn7BggU+9k4Ky5v0/9hj\njwXiJ5m36LZtQlI6pcxhqV0Ky5LUW9TMUsVI8HbffXcgfAOJpVGKZNFOgLFjxwLhKc8kPewz1e6j\nokY7bSzffvttwE1ZCfDjjz8m0UP/KfIpIiIiIoHJ2MinJZAfPXq0c+6EE04Ia2MJUS+66CLn3Lx5\n8wA3ImORzyZNmqSusxJXUdN5WcTT3m+ptdauXetPxyQhNvtgUWpLRg3u2ERGt73/bd/mFfHMPJGz\nSImyaNtjjz0GwOLFi53XVPAhWK+//joQf83u77//DoQnH7fSxJJ+VoAjVjnwwrD9LF27dgXcFFuZ\nSJFPEREREQlMxkU+LUL56quvArHLTM2aNQuA66+/Hgjf0WW7pU866STAjZ7FSnQuqWVjaN/IvSU0\ny5QpA7iRaiudGoslpLeIW+QaUEmtZcuWAVCnTh0gPJJt6zktgfGTTz4Z9X5bb63MBJnDCnPEmhGy\nssMWGYsXUbOyjYmsNxR/7LrrroCbHD7WGFrZ4TFjxgAwc+ZMAJYvX+60sUTmVnLz119/TVGPxQ/f\nffcd4BbOqVatmvOaJaK35x+b+X3ttdeC7GKhKPIpIiIiIoHRw6eIiIiIBCZjpt2bNm0KwCuvvAK4\n0+07duxw2ljagKeeeirsterVqzttxo8fD0DFihUBN1Q9derUlPVdYvvzzz8BN5m41WEHd+rIphDi\nTbtbSpBvv/02Jf0Ulze12eTJkwGoW7cu4C5/OOuss5w2tsC9ceOdZdRto5F3w1EySZLFXzY9Zyl3\n7D70WrNmDeAuXTrjjDOc155//vmY1/Wm35LUat68OQCPP/542Pn777/fOb7zzjsB2Lp1K+Auj7AN\nYgD9+vUD3N+nKvqQmVatWgW4y1lso/W7777rtDnyyCOD71iSFPkUERERkcCkNfLpTSswe/ZswI14\nWkTsyiuvdNo88cQTgPstrmXLlgCMGzfOaVOjRg3A3YSkFEuZY/369c6xRdgs8Xg8VnJMUs9bjOH0\n008H3NQ5HTp0AGJHoC0qalGzevXqpbSfkpxYEU9jETWbPbr55pud1/IreWszVpIa3vLQ3s1dAP/8\n8w8A06dPd85ZxNNs2bIFCE+vZGN4yy23AHDKKaf42GMpjI4dO+b7WoMGDQB3c66lGowV7bSNZvY5\nnMkU+RQRERGRwKQ18jly5Ejn2L5lf/PNNwDccMMNAMyfP99p0759ewAGDhwIuJFPr3Xr1gFuKhGl\nj8hMFqHu3r17vm2GDx8OuGtHJfVatWrlHFuUy8rXxksSb+tCLeLpXfOp5PLFyx133BH2pzelS2TE\n09IyeWefxH8nnniicxw5q2AJ/6+77jrnnP3+/P7778PaelOh9erVC3A/iyV9jj766ALbHHTQQWF/\nxmKzUt5ZxkylyKeIiIiIBCYtkc/WrVsD7g53L1uzYOtPRo0a5bxWs2bNsLa2jmXOnDnOuZtuugmA\nH374wccei9+snFg8Vs5RgmNrN8GNclly+aVLlwLu2iMvm5WIVUp1xowZvvdTUscb6cyPle2zLBWR\nawzFX2XLls33NZtlsJKKAMcccwzgzhzaOtFDDjnEaWMRz8qVK/vbWSm0uXPnAnD22WcX6f22R2bo\n0KG+9SnVFPkUERERkcDo4VNEREREApOWaXdLs7PLLtHPvu3atcv3fRZattQ7tvFIU+zFQ8OGDZ3j\nCy+8MGabadOmBdUdicGb1sqWufTt2xdwNzocfvjhThubZo9MwXPbbbc5bVTTPXNs374dgIkTJwLQ\ns2fPQr1/9erVANx4440AvP322/51TvLlTShv6QMt6Xgsubm5AFxyySUAXHTRRUDsFFsvvfSSX92U\nIrIlE++//z4AgwYNcl4rV65czPdYyiVwn5usqE5xoMiniIiIiAQmJ9YGgUJfJCdnOdCosO+bOXOm\ncxyZZNXK9o0YMcI5t3LlSgCWL19elG6mTCgUyim4VfFQ1LFMxKxZs5xjS1hubMOCN6VIOiJm2TKW\nfo5jo0Y7L2MJ6L0RF9uMZOmYhg0bBsADDzzgx49OxopQKNQ43Z3wQyruyVKlSgGwYMEC51ysDaAA\nvXv3do6tTHGQKeyy5Z4Ef8aySpUqANSqVQuAPffcE3CTxYNbGjUeu2ft/g6ifLHGMnskO5aKfIqI\niIhIYNIa+cwW+jaXmKpVqzrHTz31FADly5cH4LHHHgPg4YcfTsWPTli2jGVJvydR5DNrZMs9CRpL\njWX2UORTRERERIqNtJbXlJLFm5XghBNOSGNPREREJF0U+RQRERGRwOjhU0REREQCo4dPEREREQmM\nXw+fuT5dR9IvN90dEF/kprsDaZab7g74KDfdHRDf5Ka7A+Kb3HR3oDjza8ORZRzO8+l6xUUu7t89\nW2gss0NJHUfQWGaLXLJrHEFjmU00lknwJc+niIiIiEgitOZTRERERAKjh08RERERCYwePkVEREQk\nMHr4FBEREZHA6OFTRERERAKjh08RERERCYwePkVEREQkMHr4FBEREZHA6OFTRERERALjS3nNnJyc\nb4DylNAyU6FQ6MB0d8QvGsvsGMsSPI6gscwWuWTROILGUmOZFXLxYSx9Ka+Zk5OzCdg36QsVU6FQ\nKCfdffCLxjI7xrKkjyPwSygUqpjuTvihpI9lttyToLHUWGaPZMfSr2n3PJ+uI+mXl+4OiC/y0t2B\nNMtLdwd8lJfuDohv8tLdAfFNXro7UJxpzaeIiIiIBEYPnyIiIiISGD18ioiIiEhg9PApIiIiIoHR\nw6eIiIiIBEYPnyIiIiISGD18ioiIiEhg9PApIiIiIoHRw6eIiIiIBMaX2u6ZqFq1agBcdtllANSu\nXdt57cILLwTgzz//BGDYsGEADB8+3Gnz999/B9JPERERkcKoWNGtHnz55ZcDULduXQDatWsHQNWq\nVZ02H3zwAQDPPvssAKNGjQqkn/lR5FNEREREAlOsI5+77LLz2blfv37OuTp16gBw6qmnAlC5cuWo\n9/33338AlCpVCoAhQ4YAsHz5cqfN3LlzU9BjKYiNBcCgQYPCXsvJyQHgp59+cs499NBDAMyaNQuA\nlStXprqLJZb9/3/++eedc5988gkAmzZtitkWIBQKAbB161YAJk+eDECDBg2cNq1btw57f+fOnQE4\n7rjjnHP2b+Prr78Ou46IhNttt52/2nv06AG4n6VVqlRx2tjvT/t9uHbtWgDmzJkTdb3Zs2cD8O67\n7wKaGUyHmjVrAjBy5EgA2rZt67xWoUKFmO+xz16ARo0aAdCwYcOwNumKgCryKSIiIiKB0cOniIiI\niAQmxxuWLfJFcnKWA42S705iLHz88ssvA+GLas3PP/8MwP+3d59hUlRpG8f/s66KmMFwYWJ2ccGE\nrgQRLzFhwrwioq4BzAlzDhjAgC6XigkDqGtADHgpKiiroGtCBRUVMaOIGVRQVMS33w/uXae6p2em\nZ6a6uqe5f18oqk/3HDlWT9Vzznme77//HoBhw4ZFr912221AmLb7xz/+AYRwNsDpp59ecH8ymUxV\n/a2ah7THct111wXgySefBMLUwv/6UvDnfPfdd0BYaN3Y6fdKGctijOM+++wDwL333lvIz4+O9R3z\n22+/AfDpp58C2VNF8cXz9X2Opge1uL4WUzOZTOd6O9oMpH1NLr/88gAcf/zxNV7T5kxN1dZl8ODB\nAFxwwQVN6k+lXJNQ3LGM/x686aabAOjVq1ddfQGyp2brazNx4kQA5s6dG7XRtK2uy9wlOHEey4bT\n8iQtfdA45/tu1L/9okWLAPjiiy+iNu3btwegZcuWQFhmGF9GpXujusYw9jObNJaOfJqZmZlZaprV\nhqONN94YCAuiV1tttRptZs6cCYSn9ro2Ds2fPz/hHlqhDjnkECBERdq2bdukz1t55ZUB2HzzzQFv\nPErCsssuC0CbNm2AEKVurCWXXBKAdu3aAfmf3AuhJ/899tgDgEceeaRJ/bI/6JrULJHGP04Rz0LG\n66yzzgLCxpbcDYSWrDFjxkTHXbt2BRp2XRUivslFNCPSt29fAB544IFEf+biqHPnMGlz//33A/ln\neOWcc84B4IYbbgDCvc2ZZ54Ztbnwwgvz/gx9v0PYQJrGhmtHPs3MzMwsNWUf+YynBdDduNaKjR8/\nHshen7lgwQIgREDzadGiBQA777wzAAsXLgRgxIgRCfXa8rniiiui45NOOgmAJZZYotb2imh9/vnn\nWefjawX322+/rNcUbRk+fHjTOmtR+iOlsSoXhVzjVpj+/ftHx4p4LrPMMol8tq7tvfbaC3Dks1h2\n2203ADbddNN6206dOjU6VjqzXNpTAeE7oK4I6jXXXAMUtk7QCnP55ZdHx9XV1VmvKRKqSHM+imoe\neeSR0TmllpTZs2cDsPXWW0fnlMYuDY58mpmZmVlqyj7yGV+noDWezz77LBASyReiS5cu0fF1112X\n9XmnnXYaADNmzGhSXy0/7bLr169fdC434qldlAcddFB0TjsqldC4VatWQHjyy0c7+azprr766lJ3\ngTfeeCM6fuaZZ4Cwk9fXa+Ppu++EE06IzuVGPLVjVrtsASZMmADADjvsAMBOO+2U972WHo2BEstD\nWJurqHO+xPG1+eyzz6Jjr6dOl/Yu9OzZMzqnqLPKYqpkeD7K3DN06FAgO2qam3Vk4MCBQLrRzjhH\nPs3MzMwsNb75NDMzM7PUlP20ez4NSeWgDUvxaYdVVlkFCFO9mkqy4njwwQeB/InE9W+vNC9fffVV\njTZ63+jRo4Hset+WrIMPPjg6jif8b4pvvvkGgFdffbXWNu+88w4QptRF9eAhFI6wplNKlo4dO9Z4\n7cUXXwTgxBNPBLI3qYjGSQUDPO1eOtogFN8UpE2aDZlut/KhlEcAG2ywAQDXXnstEDZIx5177rlA\nSLmkTdX56NpVQvlSceTTzMzMzFLTLCOfSmp7/fXX19pGm1uUskAl4yCU1Tz22GMB+Pnnn4vRTfuf\neGok0aJ2Panli3gqQj1q1Cggf4LjXEr7YY2z4oorRsdKCl8XpTtSGTeVVIwnkFfkU+XcrHS0oaFD\nhw61trn55puB/BFP0UaW2sqixo0cObIhXbQG0oxQt27donPaeLnWWmsB2ZuIrHypTLRmCyFEPm+8\n8UYgpFHS3yHc7yy11FK1frau6/POOy+5DjeBI59mZmZmlpqyj3xOmzYtOt59990BWHPNNYEQGVNK\nHgiJV5XQXIlvBwwYELWpK1WPJUelM/OVQdV6k9zoypZbbhkdKxXEdtttV+vPUMJxlVONp4Wx4lOa\nlw8//LDEPbFC9OnTB4D111+/xmuKjk2aNKnW9+s7V8UcchNXx73wwgsA3HvvvY3qqxVGsw5xSkun\ncsMuedm8KEoJsO+++wKhiIDSaBXi6KOPzvuZ5cCRTzMzMzNLTVVdZbMK/pCqqilAp3obNpGiZdqR\nO336dCA76qLoqCKmJ598MlD303xTZTKZqvpbNQ9JjOX+++8PwB133AGEhPKPP/541EYRmF9++QWA\nLbbYAgglUwGWXXbZen/Wxx9/DMC6667blC5HKmUskxjH9957D4B27drV21brOhUJjSeHL5GpmUym\nc6k7kYQkxlJrAF977TUg/5hqvfSpp54KwJ/+9Edsok2bNlGbhx9+GMgue5xLu+V1jX/55ZdN6XrF\nXJNQnN+V+neOR5i15lolFHv16gXAW2+9leSPbjCPZcNpLa9mEmI/PzrWfZyyTyjZ/Ouvv160fjV1\nLB35NDMzM7PU+ObTzMzMzFJT9huO4i655BIgTLsrBYH+hDAtpDRKTZ3ysYZbffXVgZr12+NpILp3\n7w7AeuutB4TxKmSqXZuMIPw/YckbNGgQUFgyYm1EeeKJJ4DsNGj6HCudPffcE4C//OUvtbbR96qW\nKO26664AHH744VEbTfXlLtdSHXgIyen93ZsOLXNQWkEIG8K0ZEIbMY844oiojYurlK/4Rj5dT4VQ\nsZZiTrcnxZFPMzMzM0tN2Uc+l1tuueh42LBh9bZXJMxP3eVn++23z3vcULNmzYqOS10irJJp85cS\nGKuE29/+9rda36MIaP/+/aNzK6ywAhCioUpMb+lRoYauXbsCcMIJJ9RoowT0Dz30UIM/P57irK7k\n9JY8pcjS9QmhbOouu+wChGTz48aNi9qoUIDT05Wf+Pen0kcWQgV4nn322cT7lDRHPs3MzMwsNWUb\n+VRqkHvuuSc6pzQuSuuidRGKrFh5UKnMTz75BIC2bdsW/N4ZM2ZExyomEC+NCnDnnXc2tYtWAF1n\nd911FwB33303AMcdd1zURqXaFPGU+Jgr3Zme5vfee2+geTydV5oRI0YAoaSw0qJBYdep0i/lJrr2\nWJaXq666CoB3330XgP322w/ITps1ZswYIKTleeyxx9LsouVx0EEHAXDDDTfU2kZFcq699tro3Nix\nY4FQbEV/L+d1vY58mpmZmVlqyi7yqWTjeipbddVVo9e0a/bKK68EwhpQrUmDUE5sypQpRe+r5af1\nZb///jsQxkvrjuIeeeQRIKzdfO6556LX9IS3zTbbZL3n/fffT7bDVhDtcL7uuuuiczrW7lqtuVaE\nDEKUTGsKJ06cCNTMhmDF9/bbbwNhfaAKQUCIaCvjxAEHHABkJybv3PmPvP36f+Gdd94BYPTo0cXs\ntjWQZo0mT54MwC233ALAf/7zn6iNvo+HDx8OhNLGmrGy9KyzzjoAnH322TVe07WmWeDDDjsMgIUL\nF0ZtVGJVs8CdOv2R+96RTzMzMzMzfPNpZmZmZikqm2l3TRMoSXyrVq0AeOCBB6I2Q4YMAcJi+Xz1\no+uqOWzpuu+++wB4+eWXAejSpUuNNpoW+OGHHwDYaKONotc23HDDrLaqW/vSSy8l31lrkqFDhwJh\nqYTqwkPNhORWPuLjpGTWWg6hpOWvvvpqre9XahdtMrTyoDRKJ510EhDSMcVT3GnJhDYhaTp34MCB\nqfXT/qACDx06dKjxmu5zjjzySCB7ul2ULkvv7927NxDumcqRI59mZmZmlpqyiXzefPPNQIh4Pvjg\ng0AoFwXwyy+/ZL1Hi2qtvCmpeF3JxTfZZBMgbE6CsNns119/BcIGMz3FWzo0NpdeeimQXe5NmxNU\ndlF/WvOljYJff/01kF3oQz766CMgpOOy0ounzRowYAAQNqJIPNKdq3379sXpmNVLkUqZPXt2dKyN\nf5rxLYQ2d5YzRz7NzMzMLDVlE/ncaqutsv5+7LHHAjWjnRDWAmpdS9yTTz5ZhN5ZsWmtSs+ePWu8\n9uabbwIhcbKlY4MNNgBC2jMlIY8XAlDCf0U8DzzwwDS7aEWg79ejjz661jYqlTp37txU+mT1UylN\nCBHO3MjnjTfeWOv7XRa1dHJnjqZPnx69Fv++jWvdunV0rPW6er9mjsuZI59mZmZmlpqyiXxOmzYN\nCEniu3btCmQnSe3WrRsAgwcPBkLZxfj6CO+Ebl60nlDrOfNZsGBBWt2xGEU6q6ura22jXZq58iWZ\nF5diLE/LLLMMEHa59+rVq0YbJbpW5NNKr0+fPkBYGwiw9dZbAyF5uRLIx9soSqYiAi5bXDoqitOx\nY0cge521fke2aNECCOtDFe0EWGmllYCQWeSpp54qco+bzpFPMzMzM0uNbz7NzMzMLDVlM+2uNC6q\n9a2kqZMmTYraaCpetYeVEmS77baL2syaNavofbXkHHPMMUBIqxSn8dUyCyuNxiSJj0+16/3jxo0D\nvCmpXGmZRb7pdlGhh0WLFqXSJytc/DpVqp0zzzwTgFNPPRUIvzshpLBTIvrczUmWnn//+99AWHbY\nvXv36LXaNoLF09pp7JWGUCnwypkjn2ZmZmaWmrKJfL7yyisADB8+POt8/Clc6SNUgvOhhx4C4IMP\nPkiji1YEimbn8/zzzwPNY/F0JdJGLz1FKzJWiO+//z46fuaZZ4BQHk6lVK28rLjiivW2UflUKx+a\n7YsnIW/ZsiUAl1xySa3vUyL6iRMnFrF3Vgh9R6r8qTb9QShhqw1Hot+PEMqQjxw5EoD58+cXr7MJ\nceTTzMzMzFJT1Zj1XDU+pKpqCrDY1rrMZDIVU1Mw7bFUcnk93R1xxBHRa/379wfSfTKvlLFMchyV\nxkNrNXv06BG9llsWTuLrsEuUWmlqJpPpXIofnLS0rsl+/foBcOutt9ba5s9/Tn+yrFKuSSjuWO66\n667RsfZO6Pe7Zg0HDRoUtRk1alQxulEnj2XlaOpYOvJpZmZmZqlx5DMBfpqrHJUylov7OOLIZ4Op\nXN/48eOBUF714osvjtoMGTKk2N2ooVKuSfB16bGsHI58mpmZmVmz4ZtPMzMzM0tN2aRaMjOz0pkz\nZw5Qd/ozM7MkJBX5rE7oc6z0qkvdAUtEdak7UGLVpe5AgqpL3QFLTHWpO2CJqS51B5qzpCKf8/73\n58yEPq+5qCb8t1cKj2VlWFzHETyWlaKayhpH8FhWEo9lEySy293MzMzMrBDecGRmZmZmqfHNp5mZ\nmZmlxjefZmZmZpYa33yamZmZWWp882lmZmZmqfHNp5mZmZmlxjefZmZmZpYa33yamZmZWWp882lm\nZmZmqUmkvGZVVdXHwAospmWmMpnMX0rdkaR4LCtjLBfjcQSPZaWopoLGETyWHsuKUE0CY5lIec2q\nqqo5QKsmf1Azlclkqkrdh6R4LCtjLBf3cQTmZjKZ1qXuRBIW97GslGsSPJYey8rR1LFMatp9ZkKf\nY6U3s9QdsETMLHUHSmxmqTuQoJml7oAlZmapO2CJmVnqDjRnXvNpZmZmZqnxzaeZmZmZpcY3n2Zm\nZmaWGt98mpmZmVlqfPNpZmZmZqnxzaeZmZmZpcY3n2ZmZmaWGt98mpmZmVlqEimvaWZWn+233x6A\nPffcE4Cddtopeq1du3YA3HDDDQDcddddAEyePDnNLpqZlbXjjjsOgH322Sc6t/XWWwOQW7FywYIF\n0fGtt94KwOjRowF47733AJg7d27xOlsHRz7NzMzMLDVJ1XafAnRqenca58QTT4yOL7roIgBWXHFF\nAN58800A3n333VrfP3LkSADmz58fnXv++eeBmk8S+VRYvdqSjmU+G2ywAQDHH388kP3E9/bbbwPw\n4IMPAjB+/HgAPvjgg0b9rEoZy1KPYzyqqWtvypQpQLg286mq+uOf/4UXXgBCtBTg119/bUgXpmYy\nmc4NeUO5KvVYLrfcctHxxRdfDIRrUGO79957R23i36NJqJRrEko/lmuvvXZ0PGvWLAD22msvAKqr\nqwHYdtttoza6jk8++WQAxowZA8BXX33VqJ/vscxvqaWWAqBHjx4AtG/fPnot/l0K0L17dwBat24d\n7wtQ2P2KaHapX79+De8w5VPb3czMzMysXr75NDMzM7PUNMsNR3/+8x/dXnLJJQFYuHBh9Nrvv/8O\nhPDzRhttlPVnPr17965xTu2nT5+eQI+tUPvvv390PGTIEABWXnllAJZddtka7bXQWn9qUfWRRx5Z\n1H7aH1q0aAHAbrvtBsB1110HhDED+PHHH4Ew3T5nzhwAxo0bF7XRFNIuu+wChKmlFVZYIWrzzTff\nJP8fYDUsscQSQBgDTc8BtG3bNqutpnEvvfTS6NyAAQOK3UXLIz4Ne8899wDw9ddfA7D77rsD8N13\n30VttLTll19+AcJGwFatWtX47Ouvvx6AVVZZBYBBgwYl2vfF1cCBAwHo27cvAB06dGjS502YMAGA\nTz/9FIBlllkmeq1NmzZAuLc54IADAPjwww+jNmmOqyOfZmZmZpaaso98/ulP4f544403BuCaa64B\nwuLcJ598Mmqj1AKKkmkhbyEWLVoUHcejqZY8Ra21oeT8888HYJNNNona6Kntt99+A+Cnn34C4NFH\nH43azJs3D4AjjjgCgC233BIIT/4QNqHVtenMGufoo48G4F//+letbXQNjhgxAoATTjgByN5AdMwx\nxwAh8mnpU1TrJIbMBQAADqtJREFU7LPPBuCUU04p+L3HHntsdPz6668DYbytuBTJOvfcc6Nzm222\nGRBmG15++eWs85C9+QhC1Cu+meiSSy4BwkzGOuusk2jfF3eKRuZGPOPfjT/88AMQZnx1T6S/Ayy/\n/PJASFU3duxYIGxEgjCj8cQTTwBhtjA+8+vIp5mZmZlVpLKPfPbp0yc6HjVqVN4255xzTnQ8depU\nANZdd10AJk6cCMCaa65Z431Kx3PqqacC2VHWxqbqsdoplQfAQw89BGRHOgHeeOON6FjryF555RUA\nZs6cWeMzhw4dmvX39dZbL+tPgMMOO6zxnbY67bHHHnnPv/XWW9HxBRdcAMDDDz+c1SZ+TW611VZZ\nr2kd0s8//5xIPy0/RTsBnnrqKSDMMDVE/LtTaZgc+UzH008/DWSv1fzss8+AmmunlSoLQrRM369K\nVxf/3aeE5vE13Jac2bNn5z2vGSWAO++8E4CVVloJgKWXXjrr7xDSJen+R+KplzSzq5lDRT5LxZFP\nMzMzM0tN2UY+9VSmXXYA//d//wfAAw88AIQdXe+//37URusg+vfvD4SnA60bBBg2bBgA5513HtDg\n5NVWIEVDLr/8ciCsy4SwJlfrw7QOMP7kFi8NFhfPXHD44YdnvTZt2jQADj744OicdnNa8rSmKL62\nCLKjnLkRT12jykwAsOOOOwIhu0THjh2T76xFlDj+rLPOis7VFvGMJ43X7FO3bt2AmjMXAH/9618B\nWHXVVQFnKSi23LWAALfffjsQSijqd+euu+5a4/1aW6//J7TmF2DDDTfMaqvd85aMfNcPhHGL+/77\n77P+Hl+bGx+z+sRnk0vJkU8zMzMzS41vPs3MzMwsNWU77a6phPgiaqUcUJ3ZL774IqsthA0oSnSs\n8HV8eva5554rVrct5sADDwTgtNNOA7JTWZ155pkAXHXVVfV+jtIyaXOLppTirymViOoSe6NKOpRU\nXtOwSqtU11KHwYMHA7DDDjtE57QwPp7w2IpH34/abBmnNHNXX301ENK3AHzyyScAdO7cGQiJyuMp\n7VSXWhv9tOzG0nPzzTcDYbpdVIQl3zmly4ovjxKluRs+fHii/VzcqZCKigDoXqZr165Rm8mTJzf6\n8+PXpQqvdOqUSDn6JnPk08zMzMxSU7aRTy1ynzJlSnROT9tjxowBQjoCJSgH2HvvvQGYMWMGENIJ\neNF7+uLpIiB7A1G8zFt9Ro4cCcA///lPIDs6poXW2oRm6VJ6FiWZV+JqleoDeP7554Ew/vGE5HLb\nbbcBYeOZFVd8Q14upefR5r999903ek0zFtqk2ZAiHlYcKlcbT72jiJpm/PJtqtVGo0mTJgHZ0bZc\n2pw7a9aspnfYIq+++ioQZnH1/Rn/HrzjjjuA7I1/9dGM8WWXXRady005mLuBO22OfJqZmZlZaso2\n8qn1gfG1mrlpPl577bUa79OaJK39c8SzdLRm7N577wVComMICaiVSuvuu+8GsgsJ/P3vfwdCxFOJ\ny3feeeeozeeff16UvlvDKLG4Ehh36dIlek2plhR9admyJRDSKkEoh+q1uulQqb24b7/9FshO4QKh\nRCOEMqh1UTEIXdNWXFovqGTxEL4zVVJY66zjxo0bB9SMeMbTEmq97k033ZRgjy2Xxkf/zkpXBqEg\niyKXuseJU+rCnj17AqFsdV0FVlQ6Nd//G2lw5NPMzMzMUlMVL7/U6A+pqpoCFH0L1WqrrQbAE088\nAeRP0Krdm4Xsok5KJpOpqr9V85DkWCrpsdbdnnHGGdFril7H1ylB9lO3dt0qItOjRw8glI4rhkoZ\ny7SuyVwDBw7M+vN/fQHCjnbtntY6MoB58+Yl3ZWpmUymc9IfWgrFGMsJEyYAIUISpwwGWgsYX6O7\n3Xbb5f28+C5qZbnQjEdTVco1CcW9LrWbGcIYal2fImrxkorrr78+EDKGjB07FoCLLrooapNbrrGp\nPJb5aSZC+1fi342itb0qpBJff6uIZ74y4qJI92OPPQbALbfcAuTPgFCIpo6lI59mZmZmlhrffJqZ\nmZlZaprVtLtS91x55ZVAmC7ITaQLsMUWWwChdngxeSqh4dZZZx0gTBdoWiheMECUTknpQ4qpUsYy\n7Wl3bQ677777AGjXrl30mpZfqDaxFtM3JN1WI3javQ66pi699NJEPk9TeJA9/ZuESrkmIb3rcvbs\n2QC0adOm1jZaVqHfkeeccw5Qd4GIpvJYFia+bElT8foezUev6V5IaZlGjx4dtTnqqKMS7aOn3c3M\nzMys2SjbVEuyxhprRMdKcKyIpxJZ77bbblGb4447DghP4ltuuSWQP8mulc6nn34KwMSJE4GwGSVO\nG1X69u0LhKc4pXKx0oin3tFmFD2p69qMj6eexhVRSWK2xZpGKc0233zz6FyvXr2AMIYNoWIDVjoq\nPwyw6qqr1ttekc6XXnqpaH2yxrn44oujY6U3q2tM9R37xhtvANC/f/+sv5cjRz7NzMzMLDVlH/nU\n2iSAtm3bAtC7d28gpFxS9AxC6hCV4lQ6ppdffrn4nbUGO+igg4Cw1lPrkAC22WYbIKwndCm/0lLJ\nNpW3hTCzoGjmXXfdBWQn/9eMhZ7cGxNZs2Rp9iBeBlXX2Y477gjA+++/D2QnvFYZVXnhhRcAf7+W\nkspkqkQt1Fw7r8IB8SIOLVq0SKF31hinn356dKzv3UJorf1PP/2UeJ+S5sinmZmZmaWmbCOfWld2\n6KGHRudmzJgBhGSpomTkALfffjsAl112GQAnnngiEMqNWXlo3bo1ACeddBIQ1qycddZZURuVFdOO\nTf353nvvpdZPC5EwrbmNl0lVxFMZKLSOrJAyjFZetOs5N0NIfGYp18iRI4GiZy6wOmgmYaeddorO\nKXKmaOiXX34JwOqrrx61yVdi1UpjueWWA0JJ6oMPPjh6TTvZtYNdZTFVrCP+mspsasY4Xp683Nba\nO/JpZmZmZqnxzaeZmZmZpaZsp9379esHZC+c1hR8XUlwcxfaanrXyoumFVZeeWUAXnzxRQA++uij\nqE3uxhSny0qXNjIoEfnyyy9fo83QoUOB7KTIAB07dixy76zY2rdvD0D37t1rvKYNS0nVb7fG03Wq\nqXUI0+v//e9/gbBZc9GiRVEb/R7V9+xvv/1W/M5altVWWw2Aww47DAj3PXHavKk0kmPHjq3RZvz4\n8UBYenHIIYcAMGDAgKjNggULEup1Mhz5NDMzM7PUlF3kU+kfTj75ZCB7c0ltyXBbtmwZHWsDi1xz\nzTVJd9EaaauttoqOlURXJRe1eDq+mUVpQX788UcglIyzdKgE6qabbpp1XqVQIaRRkm7dugGw7777\nRudyS79ZeVOU7M477wRg6aWXrtFm2LBhQPNI6VKpNE5XXHEFEMpPA/Tp0wcI46M/77nnnqiNUhWq\nIIsjn+lTYYBBgwbV2maXXXYB4K233so636FDh+g49zv6kUceAcp7ttCRTzMzMzNLTdlFPhU5UdTl\nqKOOqvc98UiMEiJ/9tlnQFhLaKWnp3EIKSGUnFppfOIpsdZee20grCucNWtWKv20P+y3335ASNHx\n4YcfAiHVR1yXLl0AePjhhwFYaaWVotc+/vhjIJR8U7TbytNaa60FwGabbVbjNUVSHn/88VT7ZDUp\nrZmKcaigCtScJdTaeqUihFAgwtHr0omvwY2Lr+v85JNPgJCOqWfPngDccccdURutx9fnPfbYYwD8\n/vvvCfc4OY58mpmZmVlqyi7yqaTwEt/9nEulNOPJdUW7vBxlKb011lgDCE/acc888wwQntq1sxrC\njtp4ZNvSowTV559/PhASiZ9xxhlRm759+wJh3XV8/bUoYq2xtvIWL2mc6/777wfg3XffTas7Vgtd\nT8o0ccopp0SvxddcQyjReOutt0bnJk+eXOwuWj0UiVaZ29133z3rT2hYAYcLLrgAgBEjRiTUw+Jx\n5NPMzMzMUuObTzMzMzNLTdlNu8+ZM6feNppuV9qIVVZZJXpNNabzJWK10lAi3fXWW6/Ga19//TUA\nkyZNArKLCmgK/oMPPihyDy2fefPmAfDNN98AYVNR165doza59YKnT58OZNcDz11KY+WpXbt2QPbG\nwFza/Gelp2tM36HaiAJhqZOmbDt16gRkb0B59NFHU+mn1U9jEZ9ub4jTTjsNCOnRmgNHPs3MzMws\nNVW5kYtGfUhV1RSgU9O7AxtuuCEAb775JgALFy6MXlOSaiXXVfLqeJRT5akaski3qTKZTFVqP6zI\nkhxLUcRzypQp0TmVhBOlcDnmmGOic/G0IGmplLFMchxV+u3yyy8HQrk3CFFppWEaPHgwEKKmJTQ1\nk8l0LnUnklCMazKfbbfdFoCnn34667yi2RCSWce/l4utUq5JKM5YKtXShAkTonNLLLGEfh4QZiji\nadKUnH7+/PlJdqdOHsv8NF5KL6iNnADnnXceEH5nKnVd7969ozbTpk1LohsN0tSxdOTTzMzMzFJT\ndpFPPaldeOGFQEjzAjB+/HgA3nnnHSBEYr799tuoTRL/PQ3lp7nC3H333dHx/vvvD4R0Sueeey4A\no0aNKsaPLliljGVa0bIy5shnA6nEbXz2AeDQQw+NjpV+K02Vck1CcccyvvehR48eQEhTp7W6Q4YM\nKcaPLpjHsnI48mlmZmZmzUbZRT6bIz/NVY5KGcvFfRxx5LNiVMo1CR5Lj2XlcOTTzMzMzJoN33ya\nmZmZWWp882lmZmZmqUnq5rM6oc+x0qsudQcsEdWl7kCJVZe6AwmqLnUHLDHVpe6AJaa61B1ozpIq\nr6mM0jMT+rzmoprw314pPJaVYXEdR/BYVopqKmscwWNZSTyWTZDIbnczMzMzs0J4zaeZmZmZpcY3\nn2ZmZmaWGt98mpmZmVlqfPNpZmZmZqnxzaeZmZmZpcY3n2ZmZmaWGt98mpmZmVlqfPNpZmZmZqnx\nzaeZmZmZpcY3n2ZmZmaWGt98mpmZmVlqfPNpZmZmZqnxzaeZmZmZpcY3n2ZmZmaWGt98mpmZmVlq\nfPNpZmZmZqn5f5Lb4NNnjnI5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 36 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 235,
              "width": 335
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "87d42bad-134e-29c7-baac-632b4484b6bf",
        "_uuid": "452c986c7af14441e62c6a9b248804adef0869a2",
        "id": "ORTqlAgViRzG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Interesting stuff - most of the neighbors are also eight but not all - we see some five and some nines as well. However all in all it looks like KNN would be a decent way to attack this problem - maybe with 5 or 10 neighbors."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "515b7b57-4abf-a9c4-c9af-7f1e7d1adda8",
        "_uuid": "bf0aaa35ac0bf47f27c547743d43023dc200e9bd",
        "id": "HmdPO-QViRzH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Learning the manifold with an autoencoder:\n",
        "\n",
        "Ok so how do we figure out what (combinations of) dimensions in the image space are important? One option would be to hand engineer features - for examples the mean of all pixels is probably a good feature to have. Other worthwhile features would be the slant, and the vertical or horizontal symmetry. \n",
        "\n",
        "But we want do to machine learning not hand-craft features because we're lazy and machines tend to better capture important features in messy datasets. There are many ways to try to reduce the dimensionality - hereI am going to use an autoencoder. I like autoencoders because they have a nice intuitive appeal and you can train them relatively fast. \n",
        "\n",
        "An **autoencoder ** is a feed- forward neural network who tries to learn a lower dimensional representation of our data. It does that by decreasing the number of layers in the middle of the network and then increasing it back to the dimension of the original image. \n",
        "\n",
        "Since the autoencoder is forced to reconstruct the images from a smaller representation it discards any variation that it doesn't find useful. Here is a great description form the [Deep Learning](http://www.deeplearningbook.org/contents/autoencoders.html) book:\n",
        "\n",
        "\"The important principle is that the autoencoder can aﬀord to represent only the variations that are needed to reconstruct training examples. If the data generating distribution concentrates near a low-dimensional manifold, this yields representations that implicitly capture a local coordinate system for this manifold: only the variations tangent to the manifold around x need to correspond to changes in h=f(x).\""
      ]
    },
    {
      "metadata": {
        "_cell_guid": "80f17775-979d-3830-220d-7550d9058506",
        "_uuid": "b2bec03fe26b9cd5182e29aa330ff07122883c69",
        "id": "HLCzgibiiRzI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's build an autoencoder in keras - It will have 3 hidden layers with 64, 2, and 64 units respectively. Our model will compress the image to a 2-dimensional vector and then try to reconstruct it . Note that we don't use the target y at all, instead we use X_flat for both the input and the target i.e. we're doing unsupervised learning."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "cd280c3a-144c-9d4d-5167-e4a829b48ec0",
        "_uuid": "77670a5d9fa78c1b36e69ee6cb684a4fd61ebb56",
        "id": "4zweFT3-iRzI",
        "colab_type": "code",
        "colab": {},
        "outputId": "793680c9-66a6-4e38-a223-21ff2a1eb439"
      },
      "cell_type": "code",
      "source": [
        "input_img = Input(shape=(784,))\n",
        "encoded = Dense(64, activation='relu')(input_img)\n",
        "\n",
        "encoded = Dense(2)(encoded) #keep it linear here.\n",
        "\n",
        "decoded = Dense(64, activation='relu')(encoded)\n",
        "decoded = Dense(784, activation = 'sigmoid')(decoded)\n",
        "\n",
        "autoencoder = Model(input=input_img, output=decoded)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/obabilonia/.conda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:9: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "2f9c5ac4-b590-5292-2bfe-8f59b2bda551",
        "_uuid": "246de9e60e4d338b29bedd50d0aee8938c245157",
        "id": "wJUfsYiiiRzN",
        "colab_type": "code",
        "colab": {},
        "outputId": "7116eab6-27f5-4a14-8192-c62a4a127f78"
      },
      "cell_type": "code",
      "source": [
        "autoencoder.compile(optimizer = \"adam\", loss = \"mse\")\n",
        "autoencoder.fit(X_flat, X_flat, batch_size = 128,\n",
        "                nb_epoch = 10, verbose = 3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/obabilonia/.conda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:3: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
            "  app.launch_new_instance()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "Epoch 2/10\n",
            "Epoch 3/10\n",
            "Epoch 4/10\n",
            "Epoch 5/10\n",
            "Epoch 6/10\n",
            "Epoch 7/10\n",
            "Epoch 8/10\n",
            "Epoch 9/10\n",
            "Epoch 10/10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe259dae2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "4f920618-7361-a8f3-57e4-c8bbfaedff3f",
        "_uuid": "f021e626e1c5eb3ec273d5cd2a936a6b4d758666",
        "id": "D0AEoOeJiRzU",
        "colab_type": "code",
        "colab": {},
        "outputId": "ad699204-14d7-406c-e725-1a404661a329"
      },
      "cell_type": "code",
      "source": [
        "encoder = Model(input = input_img, output = encoded)\n",
        "\n",
        "#building the decoder:\n",
        "encoded_input = Input(shape=(2,))\n",
        "encoded_layer_1 = autoencoder.layers[-2]\n",
        "encoded_layer_2 = autoencoder.layers[-1]\n",
        "\n",
        "\n",
        "decoder = encoded_layer_1(encoded_input)\n",
        "decoder = encoded_layer_2(decoder)\n",
        "decoder = Model(input=encoded_input, output=decoder)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/obabilonia/.conda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  if __name__ == '__main__':\n",
            "/home/obabilonia/.conda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:11: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "6d39ac90-e759-c4ba-5fc2-e97414942091",
        "_uuid": "24ca7794f954bbacac34dcd9ba9ef98f969f8d0b",
        "id": "f04tuqPwiRzb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2D - representation learned by the autoencoder:"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "18294c61-7ee2-4ac1-5ab0-a4e9035c7a30",
        "_uuid": "fde6d4c8694272391db41983762aa398f1e884d2",
        "id": "Ott-ARCBiRzc",
        "colab_type": "code",
        "colab": {},
        "outputId": "77607081-500f-4097-919c-ae334d382b1e"
      },
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "X_proj = encoder.predict(X_flat[:10000])\n",
        "X_proj.shape\n",
        "\n",
        "proj = pd.DataFrame(X_proj)\n",
        "proj.columns = [\"comp_1\", \"comp_2\"]\n",
        "proj[\"labels\"] = y_train[:10000]\n",
        "sns.lmplot(\"comp_1\", \"comp_2\",hue = \"labels\", data = proj, fit_reg=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe238517a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxIAAAK6CAYAAACt/Gr8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 349,
              "width": 393
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "28aa31bf-a848-32ce-16fe-160c97695063",
        "_uuid": "5d0dee96ab950325c33f03f82e7e13be67943ae8",
        "id": "QtDBpbcXiRzh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can see the autoencoder does a decent job of separating certain classes like 1, 0 and 4. It does better than PCA but is not as good as TSNE. The autoencoder learns a better representation that simpler methods like PCA because it can detect nonlinearities in the data due to its relu activations. In fact if we used linear activation functions and only one hidden layer we would have recovered the PCA case.\n",
        "\n",
        "\n",
        "Can we recover the images from their 2-dimensional represetation?"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "f85301be-7057-2760-d75d-59102a87fff0",
        "_uuid": "cd41ecae450858e31dd41769583b96cdb5663206",
        "id": "85j4vrloiRzi",
        "colab_type": "code",
        "colab": {},
        "outputId": "cb662c87-a9cf-477a-b483-c80a0a55add2"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "#how well does the autoencoder decode:w1\n",
        "plt.subplot(2,2,1)\n",
        "show(X_train[160])\n",
        "plt.subplot(2,2,2)\n",
        "show(autoencoder.predict(np.expand_dims(X_train[160].flatten(), 0)).reshape(28, 28))\n",
        "plt.subplot(2,2,3)\n",
        "show(X_train[150])\n",
        "plt.subplot(2,2,4)\n",
        "show(autoencoder.predict(np.expand_dims(X_train[150].flatten(), 0)).reshape(28, 28))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnoAAAH0CAYAAACq+IA5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACQqP8FzNFVvcYld2MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 250,
              "width": 317
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "30ca9950-d90d-93e9-4117-be04c340a219",
        "_uuid": "39f0bfe2a85c7c7b5b92de8ab57c792d13f6f2c1",
        "id": "mrYe_WsWiRzl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Not really - the encoding decoding process is quite lossy - but that makes sense since we're converting a 784 dimensional vector into 2 dimensions."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "15ee9850-abfb-d609-980f-2457a1f6e211",
        "_uuid": "1bf66d2c608a55193d4084f88a103535b7165464",
        "id": "tDBXe1E_iRzm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Generating new digits by moving in the latent 2D - space:"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "26f3f20c-6525-9b7b-87d8-ac38886068d5",
        "_uuid": "cacf36c2ec4ad439dd30e405edbb45b20ed9ab0b",
        "id": "A0uD004CiRzn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now the hope is that the new 2-D representation of the data is a good coordinate system for the subspace of the data that is actually meaningful i.e. the digits. One (hand-wavy) way to check this is to see what happens if we sample points in the 2-D representation space and move in various directions - do we get some meaningful change in the decoded image of the path or just noise as we did in the original space?"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "7700fd66-b715-3efb-4675-7184c015cc99",
        "_uuid": "4c359f06edf9aa1ea25197872e58cd8cf298ce8e",
        "id": "47zzsmhYiRzn",
        "colab_type": "code",
        "colab": {},
        "outputId": "6b16cd02-37dd-4333-81b1-afbf19144f70"
      },
      "cell_type": "code",
      "source": [
        "#moving along the x axis:\n",
        "for i in range(64):\n",
        "    plt.subplot(8,8,i+1)\n",
        "    pt = np.array([[i/3,0]])\n",
        "    show(decoder.predict(pt).reshape((28, 28)))\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqgAAAHWCAYAAAC7eHtwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 64 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 235,
              "width": 340
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "0eaf5d23-9e08-0af7-e7c2-73d6a6fcadad",
        "_uuid": "84306582ec67d75d7698ac4797e1e58df5df5ec2",
        "id": "8behVXDsiRzq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Pretty neat! We see that moving in a given direction in the 2D representation corresponds to staying on the digits manifolds in the original space. We never end up in static space. Sure not all the images are exactly digits but they are all digit-like. You can also clearly see the transition from one class to another.\n",
        "\n",
        "Note that the way we're generating images here doesn't have very good statistical properties since we have to look at the 2-d plot first. Using a variational autoencoder makes the generative process more rigorous but we'll settle for this."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "9464bce6-2d0d-d947-49b0-3a6c1f101241",
        "_uuid": "4c9f543559fc9b1f27ff73b12fa77a127f12d901",
        "id": "NRaYLgrRiRzq",
        "colab_type": "code",
        "colab": {},
        "outputId": "56a18b21-b5e5-4f6a-cb6b-41e6103d97f7"
      },
      "cell_type": "code",
      "source": [
        "#moving along the y axis:\n",
        "for i in range(64):\n",
        "    plt.subplot(8,8,i+1)\n",
        "    pt = np.array([[10,i/3]])\n",
        "    show(decoder.predict(pt).reshape((28, 28)))\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqgAAAHWCAYAAAC7eHtwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCFEpPgXNpA3W0BTKjgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 64 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "height": 235,
              "width": 340
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "8a08f815-9a1a-0b5d-8cce-a55472edfa1b",
        "_uuid": "08bde3ae3c37bacce770c383bb59bb3107addc18",
        "id": "NuvLJBA2iRzv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### References:\n",
        "\n",
        "-  [Autoencoder in Keras](https://blog.keras.io/building-autoencoders-in-keras.html) by Francois Chollet\n",
        "\n",
        "- [Deep Learning Book Ch 14](http://www.deeplearningbook.org/contents/autoencoders.html) by Ian Goodfellow and Yoshua Bengio and Aaron Courville."
      ]
    },
    {
      "metadata": {
        "id": "1YCezwLSiRzx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}