Recurrent_Neural_Networks.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Recurrent_Neural_Networks.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RubenMcCarty/Machine-Learning-con-Python-001/blob/master/Recurrent_Neural_Networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24w4XWPbRS_u"
      },
      "source": [
        "# **Preprocesamiento de datos con RNN**\n",
        "##  Creado por [M.Sc. Ruben Quispe](https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/)\n",
        "### Curso [Machine Learning](https://eie.pe/curso-machine-learning/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hh1pskteQTRf",
        "outputId": "7b51b0f1-719a-44ad-b41f-697aab2273d1"
      },
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "from tensorflow.keras.preprocessing import sequence\n",
        "\n",
        "max_features = 10000  # Se usarán las 10,000 palabras más comunes de la base de datos\n",
        "maxlen = 500  # Usaremos solamente las primeras 500 palabras de cada crítica\n",
        "batch_size = 32\n",
        "\n",
        "print('Loading data...')\n",
        "(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)\n",
        "print(len(input_train), 'train sequences')\n",
        "print(len(input_test), 'test sequences')\n",
        "\n",
        "print('Pad sequences (samples x time)')\n",
        "input_train = sequence.pad_sequences(input_train, maxlen=maxlen)\n",
        "input_test = sequence.pad_sequences(input_test, maxlen=maxlen)\n",
        "print('input_train shape:', input_train.shape)\n",
        "print('input_test shape:', input_test.shape)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading data...\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 0s 0us/step\n",
            "25000 train sequences\n",
            "25000 test sequences\n",
            "Pad sequences (samples x time)\n",
            "input_train shape: (25000, 500)\n",
            "input_test shape: (25000, 500)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ksXnozMRV-e"
      },
      "source": [
        "# **Definimos el modelo**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ywwcdI9QqFO",
        "outputId": "93f85616-4a50-4764-a784-d678e5344438"
      },
      "source": [
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, SimpleRNN #RNN -> Recurrent Neural Network\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(max_features, 32))\n",
        "model.add(SimpleRNN(32))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\n",
        "history = model.fit(input_train, y_train,\n",
        "                    epochs=10,\n",
        "                    batch_size=128,\n",
        "                    validation_split=0.2)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "157/157 [==============================] - 21s 136ms/step - loss: 0.6745 - acc: 0.5678 - val_loss: 0.6037 - val_acc: 0.6982\n",
            "Epoch 2/10\n",
            "157/157 [==============================] - 20s 131ms/step - loss: 0.4443 - acc: 0.8046 - val_loss: 0.4547 - val_acc: 0.7946\n",
            "Epoch 3/10\n",
            "157/157 [==============================] - 20s 130ms/step - loss: 0.2985 - acc: 0.8759 - val_loss: 0.4166 - val_acc: 0.8330\n",
            "Epoch 4/10\n",
            "157/157 [==============================] - 20s 130ms/step - loss: 0.2140 - acc: 0.9166 - val_loss: 0.4551 - val_acc: 0.8050\n",
            "Epoch 5/10\n",
            "157/157 [==============================] - 21s 131ms/step - loss: 0.1485 - acc: 0.9463 - val_loss: 0.5128 - val_acc: 0.7756\n",
            "Epoch 6/10\n",
            "157/157 [==============================] - 21s 131ms/step - loss: 0.1159 - acc: 0.9596 - val_loss: 0.5233 - val_acc: 0.8004\n",
            "Epoch 7/10\n",
            "157/157 [==============================] - 20s 130ms/step - loss: 0.0605 - acc: 0.9817 - val_loss: 0.5814 - val_acc: 0.7960\n",
            "Epoch 8/10\n",
            "157/157 [==============================] - 21s 131ms/step - loss: 0.0541 - acc: 0.9815 - val_loss: 0.7022 - val_acc: 0.7614\n",
            "Epoch 9/10\n",
            "157/157 [==============================] - 20s 130ms/step - loss: 0.0308 - acc: 0.9905 - val_loss: 0.7615 - val_acc: 0.7666\n",
            "Epoch 10/10\n",
            "157/157 [==============================] - 21s 131ms/step - loss: 0.0178 - acc: 0.9945 - val_loss: 0.7286 - val_acc: 0.8024\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "wESm2N-fRfaw",
        "outputId": "a081ce5c-feab-4fad-efab-04d4b1313db0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "max_val_acc = max(val_acc)\n",
        "max_val_acc_epoch= val_acc.index(max(val_acc)) + 1\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "min_val_loss = min(val_loss)\n",
        "min_val_loss_epoch= val_loss.index(min(val_loss)) + 1\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'go', label='Precisión en el entrenamiento')\n",
        "plt.plot(epochs, val_acc, 'r', label='Precisión en la validación')\n",
        "plt.plot(max_val_acc_epoch, max_val_acc, 'bo', label='Maximum accuracy')\n",
        "plt.title('Precisión durante el entrenamiento y la validación')\n",
        "plt.legend()\n",
        "print(\"Best accuracy epoch : % d, Value : % .10f\" %(max_val_acc_epoch, max_val_acc)) \n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'go', label='Pérdida en el entrenamiento')\n",
        "plt.plot(epochs, val_loss, 'r', label='Pérdida en la validación')\n",
        "plt.plot(min_val_loss_epoch, min_val_loss, 'bo', label='Minimum loss')\n",
        "plt.title('Pérdida durante el entrenamiento y la validación')\n",
        "plt.legend()\n",
        "print(\"Best loss epoch : % d, Value : % .10f\" %(min_val_loss_epoch, min_val_loss))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best accuracy epoch :  3, Value :  0.8330000043\n",
            "Best loss epoch :  3, Value :  0.4165914655\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5oDDClxR-d9"
      },
      "source": [
        "# **Implementación de una LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hmK4GclSHHt",
        "outputId": "d5e5eb42-9d4e-43c9-d96d-76ad849cff99"
      },
      "source": [
        "from tensorflow.keras.layers import LSTM\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(max_features, 32))\n",
        "model.add(LSTM(32))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['acc'])\n",
        "history = model.fit(input_train, y_train,\n",
        "                    epochs=10,\n",
        "                    batch_size=128,\n",
        "                    validation_split=0.2)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "157/157 [==============================] - 47s 297ms/step - loss: 0.5067 - acc: 0.7560 - val_loss: 0.4016 - val_acc: 0.8282\n",
            "Epoch 2/10\n",
            "157/157 [==============================] - 50s 320ms/step - loss: 0.2997 - acc: 0.8823 - val_loss: 0.3300 - val_acc: 0.8558\n",
            "Epoch 3/10\n",
            "157/157 [==============================] - 50s 321ms/step - loss: 0.2395 - acc: 0.9089 - val_loss: 0.2885 - val_acc: 0.8866\n",
            "Epoch 4/10\n",
            "157/157 [==============================] - 50s 321ms/step - loss: 0.2012 - acc: 0.9255 - val_loss: 0.2933 - val_acc: 0.8826\n",
            "Epoch 5/10\n",
            "157/157 [==============================] - 50s 319ms/step - loss: 0.1798 - acc: 0.9341 - val_loss: 0.3430 - val_acc: 0.8768\n",
            "Epoch 6/10\n",
            "157/157 [==============================] - 54s 346ms/step - loss: 0.1658 - acc: 0.9418 - val_loss: 0.2908 - val_acc: 0.8876\n",
            "Epoch 7/10\n",
            "157/157 [==============================] - 50s 319ms/step - loss: 0.1454 - acc: 0.9484 - val_loss: 0.3333 - val_acc: 0.8886\n",
            "Epoch 8/10\n",
            "157/157 [==============================] - 50s 320ms/step - loss: 0.1348 - acc: 0.9526 - val_loss: 0.3106 - val_acc: 0.8842\n",
            "Epoch 9/10\n",
            "157/157 [==============================] - 50s 316ms/step - loss: 0.1209 - acc: 0.9587 - val_loss: 0.3466 - val_acc: 0.8816\n",
            "Epoch 10/10\n",
            "157/157 [==============================] - 51s 322ms/step - loss: 0.1111 - acc: 0.9618 - val_loss: 0.5019 - val_acc: 0.8204\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "Wd1fbHyhSNJW",
        "outputId": "9f3cf84b-6f39-4ca1-f6b0-74b902ff1fe1"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "max_val_acc = max(val_acc)\n",
        "max_val_acc_epoch= val_acc.index(max(val_acc)) + 1\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "min_val_loss = min(val_loss)\n",
        "min_val_loss_epoch= val_loss.index(min(val_loss)) + 1\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'go', label='Precisión en el entrenamiento')\n",
        "plt.plot(epochs, val_acc, 'r', label='Precisión en la validación')\n",
        "plt.plot(max_val_acc_epoch, max_val_acc, 'bo', label='Maximum accuracy')\n",
        "plt.title('Precisión durante el entrenamiento y la validación')\n",
        "plt.legend()\n",
        "print(\"Best accuracy epoch : % d, Value : % .10f\" %(max_val_acc_epoch, max_val_acc)) \n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'go', label='Pérdida en el entrenamiento')\n",
        "plt.plot(epochs, val_loss, 'r', label='Pérdida en la validación')\n",
        "plt.plot(min_val_loss_epoch, min_val_loss, 'bo', label='Minimum loss')\n",
        "plt.title('Pérdida durante el entrenamiento y la validación')\n",
        "plt.legend()\n",
        "print(\"Best loss epoch : % d, Value : % .10f\" %(min_val_loss_epoch, min_val_loss))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best accuracy epoch :  7, Value :  0.8885999918\n",
            "Best loss epoch :  3, Value :  0.2885023654\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rd66/Hjp1gpJoUVhV8/3DdW48NWD4SuCZHPMMxXXEuwx4E2jkjb8buD9gvgeAu/PbXvv27bUopi+brnFPxamMEo17Kk6nL5tepPWUZatWrQp63rin4pRRHPcX91RcsWKoWrWqqqqmpaVpv3799Nlnn9X77rtPX331VVVV3bNnjzZr1kwPHDig06ZN0wYNGuiuXbtUVfWJJ57Qa665RlVVly5dqtHR0bpgwQIXb1yc7tixQxcuXKitWrXSgwcPampqqjZt2lQfe+wxVVXduXNnVhwjR47UiRMnHhdffrE0btxY9+7dq4cPH9bY2FjdtGlTtmWPHj2qp59+um7fvl1VVWfOnJkV79VXX61vvPHGcdvr0aOH/u9//1NV1e+++067d++eNf8ll1yiGRkZunLlSm3atKmqqs6dO1fPO++8rOVzHqOixJ+5bHp6unbt2lWXLl2adUyfffZZVVW94447NCEhQfft26fbt2/XP/zhD6qqun79em3ZsqWqqk6ePFnHjBmjqqpHjhzR9u3b688//6xz587VE088UTdv3qwZGRl62mmn6fz587O9b6qqW7Zs0UaNGun27ds1LS1Nu3fvru+8885xx6ww53HEWLVKFVT//vcirwJYqHnk1VDddfM+MENVfxORG3D9S/YIdmERGQYMA4iNLVpxy+CEweXuDpviKKl6jcOHD5OYmAi4K/prr72WM844g9mzZ2eVpR85coRNm9x2evXqRe3atQH46quvGD58OACtW7emdevWx61//vz5XHTRRcTEuF9w/fr1y5q2YsUK7r//fvbu3cuBAwfo0+f4/sY/+eSTPGPp2bMnNWq4blhbtGjBxo0badSoUdaya9asYcWKFfTq1QtwV7j16tXL81gcOHCAb775hoEDB2aN++2337Je9+/fn6ioKFq0aMGvv/6a53oCj1FR4p81axZTpkwhPT2dbdu2sWrVqqxjm3n8EhISOHDgANWrV6d69epUrlz5uDqRTz75hGXLlmX9+khNTWXt2rVUqlSJjh070rBhQwASExPZsGEDZ555ZrblFyxYQLdu3ahb1zWwOHjwYL766iv69++f576XGxMnQuXKMGxYiaw+mES/BWgUMNzQG5dFVXcFDE4FHg1YtluOZefl3ICqTgGmACQlJVlPKKUgtkYsG1OPb+yuuPUamWX0gVSVt956i1NOyd7/9/fff0/VqlWLtb1AQ4cO5d1336VNmza8/PLLzJs377h58oulcuXf+xiPjo4+rkxcVWnZsiXffvttUPEcO3aMmjVrHnc8MgVuT/P5uR54jAob//r163n88cdZsGABtWrVYujQodnuU89cJioqKtvyUVFRue7/008/fdwX6Lx58wo8diYfu3fDK6/AkCFQN9dWhostmDL6BUAzEWksIpWAQcDswBlEJPCyph+w2nv9MdBbRGp5lbC9vXHGZ6VZr9GnTx+efvrprGS2ePHiXOc766yzsioiV6xYwbJly3Kd59133+Xw4cPs37+f999/P2va/v37qVevHmlpaSQn517XEGwsuTnllFPYsWNHVqJPS0tj5cqVec5/4okn0rhxY9544w3AJcqlS5fmu43q1auzf//+PKcXNv59+/ZRtWpVatSowa+//sq///3vfOfPT58+fXjuuedIS0sD4H//+x8HDx7Md5nA/enYsSNffvklO3fuJCMjgxkzZtC1a9cixxMxpk51lbC3315imygw0atqOnArLkGvBmap6koRGS0imb+bh4vIShFZCgzHldmjqruBMbgviwXAaG+c8dnghMFMuWAKcTXiEIS4GnFMuWBKiRR/PfDAA6SlpdG6dWtatmzJAw88kOt8N910EwcOHODUU0/lwQcfpH379sfN065dOy677DLatGnDOeecQ4cOHbKmjRkzhk6dOtG5c2eaN29erFhyU6lSJd58801GjBhBmzZtSExM5Jtvvsl3meTkZF588UXatGlDy5YtsypP89K6dWuio6Np06YNTz31VLHjb9OmDW3btqV58+ZcccUVdO7cueAdzcN1111HixYtaNeuHa1ateKGG24o8Mp92LBh9O3bl+7du1OvXj3GjRtH9+7dadOmDe3bt+fCC4O9ryNCpaXB009Djx6QkFBimwm7PmOTkpLUOh4pmtWrV3Pqqaf6HYYxxVKuzuNZs+Cyy+C99yCgvqkoRGSRqiblNi0inow1xpgyacIEaNoUzjuvRDdjid4YY/ywYAF88w0MHw7R0SW6KUv0xhjjhwkToHp1GDq0xDdlid4YY0rb1q3w+utw7bVw4oklvjlL9MYYU9qefRYyMuC220plc5boTamyZorDz8cff5znQ12mBBw+DJMnu7tsmjQplU1aojchFenNFAfGVBICmzu+7rrrWLVq1XHzvPzyy9x6661FWn/O4/jFF1/w8ccf06ZNm6IFbArvtddg5064445S22RE9DBlwkdgEwiDBw/m+eef56677gJcM8V5SU9Pp0KFvE/HDz/8MLSBlgFTp04N+TpzHscePXrQo0fQzVKZ4lJ1lbCtW0MpPhVsV/SmxERiM8WB8mr+N9OPP/5Ix44ds4Y3bNhAgvf04+jRo+nQoQOtWrVi2LBhubZ1E/jrYdq0aZx88sl07NiR//znP1nzvP/++3Tq1Im2bdty9tlnZzWOduDAAa655hoSEhJo3bo1b7311nHH8cknn6RVq1a0atWK8ePHZ8V46qmncv3119OyZUt69+6d7X0wxTR3Lixf7q7mS7HbRLuij1R33AGhLndNTAQvIRQkPT2df//73/Tt25exY8fSo0cPXnrpJfbu3UvHjh05++yzAfjhhx9YtmwZtWvX5sknnyQmJobVq1ezbNky2rVrd9x6Fy1axMyZM1myZAnp6em0a9cuq6mEAQMGcP311wNw//338+KLL3Jbjsqu/GJZsmQJixcvpnLlypxyyincdttt2VqvzGns2LHUrl2bjIwMevbsybJly7K1uNm8eXOOHj3K+vXrady4Ma+//jqXXXYZALfeemvWL5wrr7ySOXPmcMEFF+S6nW3btvHQQw+xaNEiatSoQffu3Wnbti0AZ555Jt999x0iwtSpU3n00Ud54oknGDNmDDVq1GD58uUAx9WLLFq0iGnTpvH999+jqnTq1ImuXbtSq1Yt1q5dy4wZM3jhhRe49NJLeeuttxgyZEiex8EUwvjxruGyyy8v1c3aFb0JqcxmipOSkoiNjeXaa6/lk08+Ydy4cSQmJtKtW7d8mynOTCjBNFN84oknHtdMcZcuXUhISCA5OTnXBsfyiyWzmd8qVapkNfObn1mzZtGuXTvatm3LypUrcy1Pv/TSS3n99dcBsiX6uXPn0qlTJxISEvjiiy/ybRzt+++/z2ret1KlSlnrAEhJSaFPnz4kJCTw2GOPZa3ns88+45ZbbsmaL2e9yNdff81FF11E1apVqVatGgMGDGD+/PkANG7cOKup6fbt27Nhw4Z8j4MJ0rp1MGcO3HgjVKlSqpu2K/pIFeSVd6hFcjPFgQpq/jfTZZddxsCBAxkwYAAiQrNmzThy5Ag333wzCxcupFGjRowaNSrXZYNx2223cdddd9GvXz/mzZvHqFGjirSeQDmPgxXdhMjTT0OFCnDTTaW+abuiNyUuUpopDhRs879NmzYlOjqaMWPGZF2JZyb1OnXqcODAgQL7ZO3UqRNffvklu3btIi0tLavZY3CdfzRo0ACAV155JWt8r169mDRpUtZwzqKbLl268O6773Lo0CEOHjzIO++8Q5cuXQpxBEyh7NsH06a5Bszy6aympFiiNyUuUpopDlSY5n8vu+wypk+fntUxes2aNbn++utp1aoVffr0ybYPualXrx6jRo3i9NNPp3Pnztladhw1ahQDBw6kffv21KlTJ2v8/fffz549e2jVqhVt2rRh7ty52dbZrl07hg4dSseOHenUqRPXXXddVrm/KQEvvQT795fqLZWBrJniCFKumnc1ESvizuOMDGjWDOrXh6+/LrHNWDPFxhjjlzlzYP16367mIchELyJ9RWSNiKwTkXvzme9iEVERSfKG40XksIgs8f6eD1XgxhhTJowfD7Gx4GMn6AXedSMi0cAkoBeQAiwQkdmquirHfNWB24Hvc6ziJ1VNDFG8xhhTdixdCvPmwaOPujtufBLMFX1HYJ2q/qyqR4GZQG4dPY4BHgGKdp+YMcZEmgkTICYGrrvO1zCCSfQNgM0BwyneuCwi0g5opKof5LJ8YxFZLCJfiojdv2WMKR+2b3cNmF19NQTZkF9JKfZvCRGJAp4EhuYyeRsQq6q7RKQ98K6ItFTVfTnWMQwYBhAbG1vckIwxxn+TJ8Nvv7muAn0WzBX9FiCwwY+G3rhM1YFWwDwR2QCcBswWkSRV/U1VdwGo6iLgJ+DknBtQ1SmqmqSqSXXr1i3anpiwICLZ2kVJT0+nbt26nH/++QDMnj2bcePG5buOrVu3cskll5RonPkp7aaPTQQ6etR1LtK3L+TxTEdpCibRLwCaiUhjEakEDAJmZ05U1VRVraOq8aoaD3wH9FPVhSJS16vMRUSaAM2An0O+F6ZIkpMhPh6iotz/PB4mLZSqVauyYsWKrMfmP/3006wnNwH69evHvffmeeMWAPXr1y/waVFjwtqsWfDLL77eUhmowESvqunArcDHwGpglqquFJHRItIv/6U5C1gmIkuAN4EbVXV3cYM2xZecDMOGwcaNronsjRvdcCiS/bnnnssHH7jqmhkzZnB5QEt9gZ1mDB06lOHDh3PGGWfQpEmTrOS+YcMGWrVqlTV///796dWrF/Hx8TzzzDM8+eSTtG3bltNOO43du93pFNik786dO4mPjy/U8nlZsmQJp512Gq1bt+aiiy7Kakpg4sSJtGjRgtatWzNo0CAAvvzySxITE0lMTKRt27bs37+/+AfTlD2q7pbK5s2hd2+/owGCvI9eVT9U1ZNVtamqjvXGPaiqs3OZt5uqLvRev6WqLVU1UVXbqer7Oec3/hg5EnI2137okBtfXIMGDWLmzJkcOXKEZcuW0alTpzzn3bZtG19//TVz5szJ80p/xYoVvP322yxYsICRI0cSExPD4sWLOf300/nXv/5VYDzFWf6qq67ikUceYdmyZSQkJPDwww8DMG7cOBYvXsyyZct4/nn3eMjjjz/OpEmTWLJkCfPnz+eEE04oMDYTgb75BhYtgttvL9U25/NjT8aWU17LvEGPL4zWrVuzYcMGZsyYwbnnnpvvvP379ycqKooWLVpkdZqRU/fu3alevTp169alRo0aWe22JyQkBNWEblGXT01NZe/evXT1egK6+uqr+eqrr7L2cfDgwUyfPj2rZ6zOnTtz1113MXHiRPbu3Ztvj1kmgo0fDzVrwpVX+h1JFkv05VReNzeF6qanfv36cffdd2crtslNYJO4ebW7FDhPVFRU1nBUVFRWU8IVKlTg2LFjAMc1+RvM8oX1wQcfcMstt/DDDz/QoUMH0tPTuffee5k6dSqHDx+mc+fO/Pjjj0VatynDNm6Et9925aAhbIK7uCzRl1Njx7rnOALFxLjxofCXv/yFhx56KKvrvJIWHx/PokWLAEJWkVujRg1q1aqV1SHHq6++SteuXTl27BibN2+me/fuPPLII6SmpnLgwAF++uknEhISGDFiBB06dLBEXx5NmuSKawI6fQkH9tuynBo82P0fOdIV18TGuiSfOb64GjZsyPBSvH/47rvv5tJLL2XKlCmcd955IVvvK6+8wo033sihQ4do0qQJ06ZNIyMjgyFDhpCamoqqMnz4cGrWrMkDDzzA3LlziYqKomXLlpxzzjkhi8OUAQcPwgsvwIABoftpHCLWTHEEibjmXU25VGbP4+eeg5tvhv/8B844o9Q3b80UG2NMSTp2zLVrk5QEp5/udzTHsaIbY4wpro8/hjVrYPr0sLmlMpBd0UeYcCuKM6Ywyuz5O2GC6wt24EC/I8mVJfoIUqVKFXbt2lV2PyymXFNVdu3aRZUqVfwOpXBWr3ZX9DffDJUq+R1NrqzoJoI0bNiQlJQUduzY4XcoxhRJlSpVaNiwod9hFM7EiVC5Mtxwg9+R5MkSfQSpWLEijRs39jsMY8qP3bvhlVfcfclh3KUm7PAAAB3NSURBVPKuFd0YY0xRvfACHD7s2rUJY5bojTGmKNLS4JlnoHt3aN3a72jyZUU3xhhTFO+8AykprtmDMGdX9MYYUxQTJkDTphDCJjdKiiV6Y4wprAULXLvzt90G0dF+R1OgoBK9iPQVkTUisk5E8uwHTkQuFhEVkaSAcfd5y60RkT6hCNoYY3w1YQJUrw7XXON3JEEpMNF7fb5OAs4BWgCXi0iLXOarDtwOfB8wrgWuj9mWQF/g2cw+ZEvEzp0ltmpjjAFg61Z4/XX4y1/gxBP9jiYowVzRdwTWqerPqnoUmAlcmMt8Y4BHgMBeHy4EZqrqb6q6HljnrS/01q6FU05xvbsYY0xJefZZyMhwxTZlRDCJvgGwOWA4xRuXRUTaAY1U9YPCLhsyjRtDt25w553w4oslsgljTDl3+DBMngwXXOAqYsuIYlfGikgU8CTw12KsY5iILBSRhUV+fL9CBXjtNejbF66/3v20MsaYUHrtNVdEfMcdfkdSKMEk+i1Ao4Dhht64TNWBVsA8EdkAnAbM9ipkC1oWAFWdoqpJqppUtziPEVeuDG+9BWeeCUOGwJw5RV+XMcYEUnWVsK1bu9KDMiSYRL8AaCYijUWkEq5ydXbmRFVNVdU6qhqvqvHAd0A/VV3ozTdIRCqLSGOgGfDfkO9FoJgYl+ATE+GSS2Du3BLdnDGmnJg7F5Yvd80dhGGb8/kpMNGrajpwK/AxsBqYpaorRWS0iPQrYNmVwCxgFfARcIuqZhQ/7AKceCJ89BH8+c+uLO2770p8k8aYCDd+PNSpA1dc4XckhRbZfcZu2wZdusCuXfDll2HfHoUxJkytWwcnnwwjR8KYMX5Hk6vy22dsvXrw2WdQrRr06uW6+jLGmMJ6+ml3w8dNN/kdSZFEdqIHiI93yV4Vzj4bNm70OyJjTFmybx9MmwaXXQb16/sdTZFEfqIH9yDVp5/CgQPQs6cr0jHGmGC89BLs3x/2bc7np3wkeoA2beDDD+GXX6B3b1dub4wx+cnIcF0Fdu4MSbkWf5cJ5SfRA5x+Osye7ZpL6NvX/SQzxpi8zJkD69eX6at5KG+JHqBHD3jjDViyxN16eeiQ3xEZY8LV+PHQqBFcdJHfkRRL+Uv04BL8q6/C/Plw8cVw9KjfERljws3SpTBvHtx6q7vjpgwrn4keYNAgmDLFPVh1xRWQnu53RMaYcDJhgnvS/rrr/I6k2Mpvogf3Bj75pGsf57rr4NgxvyMyxoSD7dshORmuvhpq1/Y7mmIr279HQuHOO12l7KhRrseYiRPLXDsWxpgQmzzZFekOH+53JCFhiR7gwQfdfbJPPOHayRk71u+IjDF+OXrUdS7Sty80b+53NCFhiR7cFfxjj7kr+3/8w13Z35tn17jGmEg2a5Z73qaM31IZyBJ9JhF47jn39Ox997lkf8stfkdljClNqu6WyubN3YOVEcISfaDoaHjlFTh40N1SVa2aq4wxxpQP33wDixa5opuoyLlXJXL2JFQqVnTdEPbs6Xp5f+stvyMyxpSW8eOhZk246iq/IwkpS/S5qVIF3n0XOnWCyy+Hjz/2OyJjTEnbuBHefhuGDYOqVf2OJqSCSvQi0ldE1ojIOhE5rpZSRG4UkeUiskREvhaRFt74eBE57I1fIiLPh3oHSky1aq4RtJYt3ePPX33ld0TGmJI0aZKrq4vAurkCE72IRAOTgHOAFsDlmYk8wGuqmqCqicCjwJMB035S1UTv78ZQBV4qatZ0V/OxsXD++RCqnq+MMeHl4EF44QUYMMB93iNMMFf0HYF1qvqzqh4FZgIXBs6gqoHNQFYFwqt/wuL4wx9cxyUnnQR9+sCKFX5HZIwJtX/9C/bujahbKgMFk+gbAJsDhlO8cdmIyC0i8hPuij7wcbLGIrJYRL4UkS7FitYvDRu6ZF+5suuScN06vyMyxoTKsWOuXZukJDjjDL+jKREhq4xV1Umq2hQYAdzvjd4GxKpqW+Au4DUROTHnsiIyTEQWisjCHTt2hCqk0Gra1PVSlZbmuiTcvLngZYwx4e/1111/0rffHrHNnwST6LcAjQKGG3rj8jIT6A+gqr+p6i7v9SLgJ+DknAuo6hRVTVLVpLp16wYbe+lr2dKV2e/e7ZL99u1+R2SMKarly6FfP9d67cknw6WX+h1RiQkm0S8AmolIYxGpBAwCZgfOICLNAgbPA9Z64+t6lbmISBOgGfBzKAL3Tfv28MEH7oq+d2/Ys8fviExBDh92TVo89pi1UGpcj1FXXeW6F/3yS9e21Q8/QKVKfkdWYgp8MlZV00XkVuBjIBp4SVVXishoYKGqzgZuFZGzgTRgD5D5OOlZwGgRSQOOATeq6u6S2JFS1aULvPOOuxo491xXpFOtmt9RmdysXu36Hli2zA3Pnes6nTnpJH/jMqVv+3aX1J97zj0Ff/fdMGJEuTgXRDW8bpBJSkrShWXlNsZ33oGBA6FrV3eVX6WK3xGZTKrw8suuKYuYGHdXxcaNrhy2Xj14880y3dmzKYR9+1zLtE88AUeOuCfeH3zQ3WQRQURkkarmelLbk7HFcdFFMG0afPGFS/hpaX5HZMA1OT1kiPtAd+rkuoQ75xy48Ub4+mv3JdC5s2tzPMwudEwIHTkCTz0FTZrA6NHu1/fKla5nuQhL8gWxRF9cV17pnqibM8eV+2Vk+B1R+bZoEbRrBzNnwpgxrlitfv3fp3fo4Mpje/RwiX/oUOsgPtJkZLhfc6ecAnfdBW3bwn//65ofPuUUv6PzhSX6ULj5Zhg3ziWXG2+0q0Q/ZDYve/rp7kpu3jy4/35XFpvTSSe5oraHH3bl9aedBmvXlnrIvlB1Ca9TJ9d95iefRM4vUVXXRlXr1nDNNe5hx08/dX8dOvgdnb9UNaz+2rdvr2XWyJGqoHrnnarHjvkdTfmxY4fq+ee7Y9+vn+rOncEv+9FHqrVrq554ourbb5dcjOFgwwbVc891x+nkk1WrV3evTzpJ9frrVT/9VDUtze8oi2bePNXTTnP7c8opqm++We4+g7ibY3LNq74n9px/ZTrRHzumettt7rD26KH6449+RxT5vvxStUED1UqVVCdOLNqHe+NG1Y4d3ft2991lN9nlJS1N9fHHVWNiVKtWVX3qKTfu8GHVd99VveIK1WrV3P7XqaN6ww2qn3+ump7ud+QF++EH1b59XewNGqi+8ELkvX9BskRfmjIyVJ99VrVGDZd87r9f9dAhv6OKPOnpqg8/rBoVpdqsmeqiRcVb35Ejqjff7D4SZ52lunVraOL024IFqm3buv264AL3pZabQ4fcL5pBg9yXAaj+4Q+qN92kOndu+CX9tWtdrKBaq5bqY4+V+8+ZJXo//PKL6pAh7hA3aaL64Yd+RxQ5UlJUu3Z1x3bIENV9+0K37unT3ZXvn/7kfi2UVfv2qd5+u/sirFevcEUZBw+6+S+91B0LUP3jH1VvucUdEz+T/tatqjfeqFqhgott5EjVPXv8iyeMWKL30+efuzJDUL34YtXNm/2OqGybM8cVL8TEqL78cslsY/lyV4YdHe2uFMtaWe9776k2bKgq4pLz3r1FX9eBA6qvv+7O3SpV3Hlcr54ropw/3/2CLQ179qjed5/qCSe4JH/zzarbtpXOtssIS/R+O3JE9e9/dx+UatVUn3ii3JYjFtlvv6nedZc7Zdu0Kfn6j9RUl9xAdcCA4iXL0pKS4mIF1YQE1W+/De369+9XnTFD9aKLVCtX1qxy8dtvV/3Pf0om6R88qPrII654Blx9wrp1od9OBLBEHy5++un3ux5at1b95hu/Iyob1q1TTUpyx+2WW1wlYmk4dkz1ySfdlf2f/6y6dGnpbLew0tNVn37a3UVTpYrquHGqR4+W7Db37VN97TXV/v1/T/oNG7o7zr79tvi/gtLSVCdPVq1f36373HNVFy8OTewRyhJ9ODl2TPWtt9yHAtxtbbt2+R1V+JoxwyWwmjX9u/3xq69cccUJJ6j+61/+xJCXpUtVO3Vy51KvXv5c7aamurqNfv3cDQigGhur+te/qn7/feGSfkaG6qxZrugMVE8/vWzXlZQiS/ThaP9+90GIjnZlztOmlb2y4JJ04IDqtde6U/SMM9w94H7atk21WzcXzw03lN6virwcPKh6zz3u/KlbVzU5OTzOn7173Zfh+eerVqzojld8vOrf/ubuAMovxk8+UW3f3i3TsqWrawiHfSojLNGHs6VLXSID1S5dVFes8Dsi/y1bpnrqqa4y8f/+L3zqM9LSVEeMcO9V+/aq69f7E8dHH6k2buziuPba8P1FuGePqzA/91xXgQou7hEj3O2wmUn8v/9V7dnTTY+LU33llfC7nbMMsEQf7jIyVKdOdU9oVqjgrtQOHPA7qtJ37Jjqc8+5cuY//tE9qRmO3n3XPSdRq1bp3jb7yy+ql1+uWU9/lqUijV27VF96yT3clJn0mzZV7dPHva5bV3XCBHfjgikSS/RlxY4dqtdco1llnO+953dEpWfPHtVLLnH73ru3S2rhbO1aV6EuovrAAyV7BZqR4Z74rFnTlYGPGlW2E+LOne7CpndvV/cxalRon4UopyzRlzXz56u2aqVZbbf4XT5d0r791v1kr1DB3UpXWvdmF9fBg6pDh2pWReiOHaHfxqpVrkgP3ENiq1eHfhsmIuSX6INqvVJE+orIGhFZJyL35jL9RhFZLiJLRORrEWkRMO0+b7k1ItKnmG2wlQ9nnuma0n30UfjsM2jRAh55JHJaGcx07Jjbxy5dXKfM8+fDPfdAVBlpVDUmBl56CV54Ab76yjWP/P33oVn3kSPw0EOuu7sVK+DFF13vWM2bh2b9pnzJ6xsg8w/XfeBPQBOgErAUaJFjnhMDXvcDPvJet/Dmrww09tYTnd/2yvIV/fRl0zXuqTiVUaJxT8Xp9GXTi7/SjRvdvcqg2qJF2SqXzc8vv7if7plPDJf1x9gXLnR3l1SsqPrMM8W7W+SLL36/vXDwYNVffw1dnCZiUcwr+o7AOlX9WVWPAjOBC3N8WewLGKwKZDbIfiEwU1V/U9X1wDpvfREneXkyw94fxsbUjSjKxtSNDHt/GMnLk4u34thY12Xh++/DwYOu28KhQ2HHjpDE7YvPPoPERHcV/Pzz8MYbULOm31EVT/v2rtOT3r1d94VDhrj3qzB27XLtqPfoAenprq346dNdu+rGFEMwib4BsDlgOMUbl42I3CIiPwGPAsMLs2wkGPn5SA6lZe+p6FDaIUZ+PjI0Gzj/fFi1Cu67D5KTXU85U6a44o+yIj0dRo50ybBWLdfrzw03uGKbSFC7NsyeDX//O8yYAR07wo8/FrycqusApXlzl9jvvReWL4devUo+ZlMuhKwwVFUnqWpTYARwf2GWFZFhIrJQRBbuKKNXqptSNxVqfJHExMA//uH6QG3d2iXJM890w+Fu0yb3a+Qf/3B9uS5YAAkJfkcVelFR7svsk09g+3bXs9Ebb+Q9/7p17ovvqqugWTNXN/PPf7r32pgQCSbRbwEaBQw39MblZSbQvzDLquoUVU1S1aS6desGEVL4ia0RW6jxxdKihauYe+UVlyjat3d9Y+7fH/pthcI777hKxeXL4bXXYOpUqFrV76hK1tlnw+LF0KoVXHop3Hln9sr0o0fdl15Cgvtl8+yzruPySPzyM74TV4afzwwiFYD/AT1xSXoBcIWqrgyYp5mqrvVeXwA8pKpJItISeA1XLl8f+Bxopqp59qCdlJSkCxcuLN5e+SCzjD6w+CamYgxTLpjC4ITBJbfhPXtccc6UKa4T7PHj4eKLS7Y45PBh2L27wL/kVYmMXHsNm9L+RGylXxn7T2HwXX8qubjC0dGjcPfd8PTT0LkzvP46bNwIw4bBypVwySUwYUL2DsyNKQIRWaSqSblNq1DQwqqaLiK3Ah/j7sB5SVVXishoXC3vbOBWETkbSAP2AFd7y64UkVnAKiAduCW/JF+WZSbzkZ+PZFPqJmJrxDK259iSTfLgyrqff95V4t14IwwcCH37wjPPQNOmeS+n6n4B5EzQe/YUnMSPHMl7vRUrQu3aJEdfybBf7uLQsRMA2Hi0HsMeAP4Ig0v4kISVSpVg4kQ44wzXGXfLlpCaCo0auQr288/3O0JTDhR4RV/ayuoVfVhIT4dJk+CBB1wxwXXXQXR07sl6zx43f15iYlzlYmH+atVyRTIixMe7C9ec4uJgw4aSOgBhbtUqV6/SsSM8/DBUq+Z3RCaC5HdFb4k+Em3Z4srs33zTJZOiJOwqVYoVQlSU+9GQk0jZulHImLKiWEU3pgxq0MCVBR875ttTprGxuV/Rx5ZA3bQxJn9l5FlzUyQ+NiUwduzxdwjGxLjxxpjSZYnelIjBg92NQHFxrrgmLs4Nl6uKWGPChBXdmBIzeLAldmPCgV3RG2NMhLNEb4wxEc4SvTHGRDhL9MYYE+Es0RtjTISzRG+MMRHOEr0xxkQ4S/TGGBPhLNEbY0yEs0RvjDERzhK9McZEuKASvYj0FZE1IrJORO7NZfpdIrJKRJaJyOciEhcwLUNElnh/s0MZvDHGmIIV2KiZiEQDk4BeQAqwQERmq+qqgNkWA0mqekhEbgIeBS7zph1W1cQQx22MMSZIwVzRdwTWqerPqnoUmAlcGDiDqs5V1cxesb8DGoY2TGOMMUUVTKJvAGwOGE7xxuXlWuDfAcNVRGShiHwnIv2LEKMxxphiCGl79CIyBEgCugaMjlPVLSLSBPhCRJar6k85lhsGDAOItb7mjDEmpIK5ot8CNAoYbuiNy0ZEzgZGAv1U9bfM8aq6xfv/MzAPaJtzWVWdoqpJqppUt27dQu2AMcaY/AWT6BcAzUSksYhUAgYB2e6eEZG2wGRckt8eML6WiFT2XtcBOgOBlbimBCQvTyZ+fDxRD0cRPz6e5OXJfodkjPFRgUU3qpouIrcCHwPRwEuqulJERgMLVXU28BhQDXhDRAA2qWo/4FRgsogcw32pjMtxt44JseTlyQx7fxiH0lzd+MbUjQx7fxgAgxOsXz9jyiNRVb9jyCYpKUkXLlzodxhlVvz4eDambjxufFyNODbcsaH0AzLGlAoRWaSqSblNsydjI8ym1E2FGm+MiXyW6CNMbI3c71rKa7wxJvJZoo8wY3uOJaZiTLZxMRVjGNtzrE8RGWP8Zok+wgxOGMyUC6YQVyMOQYirEceUC6ZYRawx5ZhVxhpjTASwylhjjCnHLNEbY0yEs0RvjDERzhK9McZEOEv0xhgT4SzRG2NMhLNEb4wxEc4SvTHGRDhL9MYYE+Es0RtjTISzRG+MMREuqEQvIn1FZI2IrBORe3OZfpeIrBKRZSLyuYjEBUy7WkTWen9XhzJ4E96sS0NjwkOBXQmKSDQwCegFpAALRGR2ji4BFwNJqnpIRG4CHgUuE5HawENAEqDAIm/ZPaHeERNerEtDY8JHMFf0HYF1qvqzqh4FZgIXBs6gqnNV9ZA3+B3Q0HvdB/hUVXd7yf1ToG9oQjfhbOTnI7OSfKZDaYcY+flInyIypvwKJtE3ADYHDKd44/JyLfDvIi5rIoR1aWhM+AhpZayIDMEV0zxWyOWGichCEVm4Y8eOUIZkfGJdGhoTPoJJ9FuARgHDDb1x2YjI2cBIoJ+q/laYZVV1iqomqWpS3bp1g43dhDHr0tCY8BFMol8ANBORxiJSCRgEzA6cQUTaApNxSX57wKSPgd4iUktEagG9vXEmwlmXhsaEjwLvulHVdBG5FZego4GXVHWliIwGFqrqbFxRTTXgDREB2KSq/VR1t4iMwX1ZAIxW1d0lsicm7AxOGGyJ3ZgwYH3GGmNMBLA+Y40xphyzRG+MMRHOEr2JeNYUgynvCqyMNaYss6YYjLErehPhrCkGYyzRmwhnTTEYY4neRDhrisEYS/QmwllTDMZYojcRzppiMMaejDWm1CQvT2bk5yPZlLqJ2BqxjO051r5wTMjk92Ss3V5pTCmw2zyNn6zoxphSYLd5Gj9ZojemFNhtnsZPluiNKQV2m6fxkyV6Y0qB3eZp/GSJ3phSYLd5Gj8FdXuliPQFJuB6mJqqquNyTD8LGA+0Bgap6psB0zKA5d7gJlXtl9+27PZKY4wpvGLdXiki0cAkoBeQAiwQkdmquipgtk3AUODuXFZxWFUTCx21McaYkAjmPvqOwDpV/RlARGYCFwJZiV5VN3jTjpVAjMYYY4ohmDL6BsDmgOEUb1ywqojIQhH5TkT65zaDiAzz5lm4Y8eOQqzaGFNY1hFL+VMaT8bGqeoWEWkCfCEiy1X1p8AZVHUKMAVcGX0pxGRMuWRP6JZPwVzRbwEaBQw39MYFRVW3eP9/BuYBbQsRnzEmhOwJ3fIpmES/AGgmIo1FpBIwCJgdzMpFpJaIVPZe1wE6E1C2b4wpXfaEbvlUYKJX1XTgVuBjYDUwS1VXishoEekHICIdRCQFGAhMFpGV3uKnAgtFZCkwFxiX424dY0wpsid0y6egHphS1Q9V9WRVbaqqY71xD6rqbO/1AlVtqKpVVfUkVW3pjf9GVRNUtY33/8WS2xVjTEHC5QldqxAuXfZkrDHlSDg8oZtZIbwxdSOKZlUIW7IvOdbxiDGmVMWPj2dj6sbjxsfViGPDHRtKP6AIkd+TsXZFb4wpVVYhXPos0RtjSpVVCJc+S/TGmFIVLhXC5YklemNMqQqHCuHyxipjjTHlVvLyZEZ+PpJNqZuIrRHL2J5jy+wXTrGaKTbGmEhUntr9saIbY0y5VJ7a/bFEb4wpl8rTbZ6W6I0x5VJ5us3TEr0xplwKp9s8S7rtH0v0xphyKVxu8yyNtn/s9kpjjPFRqNr+sbZujDEmTJVGpbAlemOM8VFpVAoHlehFpK+IrBGRdSJyby7TzxKRH0QkXUQuyTHtahFZ6/1dHarAjTEmEpRGpXCBiV5EooFJwDlAC+ByEWmRY7ZNwFDgtRzL1gYeAjoBHYGHRKRW8cM2xpjIUBqVwsE0gdARWKeqPwOIyEzgQgI6+VbVDd60YzmW7QN8qqq7vemfAn2BGcWO3BhjIsTghMElerdPMEU3DYDNAcMp3rhgBLWsiAwTkYUisnDHjh1BrtoYY0wwwqIyVlWnqGqSqibVrVvX73CMMSaiBJPotwCNAoYbeuOCUZxljTHGhEAwiX4B0ExEGotIJWAQMDvI9X8M9BaRWl4lbG9vnDHGmFJSYKJX1XTgVlyCXg3MUtWVIjJaRPoBiEgHEUkBBgKTRWSlt+xuYAzuy2IBMDqzYtYYY0zpCLsmEERkB3D888DBqwPsDFE4ZZ0di+zseGRnx+N3kXAs4lQ110rOsEv0xSUiC/Nq76G8sWORnR2P7Ox4/C7Sj0VY3HVjjDGm5FiiN8aYCBeJiX6K3wGEETsW2dnxyM6Ox+8i+lhEXBm9McaY7CLxit4YY0wAS/TGGBPhIibRF9RmfnkiIo1EZK6IrBKRlSJyu98x+U1EokVksYjM8TsWv4lITRF5U0R+FJHVInK63zH5SUTu9D4nK0RkhohU8TumUIuIRB9km/nlSTrwV1VtAZwG3FLOjwfA7bgnuw1MAD5S1eZAG8rxcRGRBsBwIElVWwHRuGZeIkpEJHoC2sxX1aNAZpv55ZKqblPVH7zX+3Ef5GCblo44ItIQOA+Y6ncsfhORGsBZwIsAqnpUVff6G5XvKgAniEgFIAbY6nM8IRcpib44beZHNBGJB9oC3/sbia/GA/cAOTvGKY8aAzuAaV5R1lQRqep3UH5R1S3A47he8rYBqar6ib9RhV6kJHqTCxGpBrwF3KGq+/yOxw8icj6wXVUX+R1LmKgAtAOeU9W2wEGg3NZpea3qXoj7AqwPVBWRIf5GFXqRkuit3fscRKQiLsknq+rbfsfjo85APxHZgCvS6yEi0/0NyVcpQIqqZv7CexOX+Murs4H1qrpDVdOAt4EzfI4p5CIl0RenzfyIIyKCK4NdrapP+h2Pn1T1PlVtqKrxuPPiC1WNuCu2YKnqL8BmETnFG9WTgP6fy6FNwGkiEuN9bnoSgZXTwXQOHvZUNV1EMtvMjwZeUtWVPoflp87AlcByEVnijfs/Vf3Qx5hM+LgNSPYuin4GrvE5Ht+o6vci8ibwA+5utcVEYHMI1gSCMcZEuEgpujHGGJMHS/TGGBPhLNEbY0yEs0RvjDERzhK9McZEOEv0xhgT4SzRG2NMhPt/R9fC3SXk+nEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}