ts_forecasting_tensorflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "rnn_lstm_models_tensorflow.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hiydavid/time-series/blob/main/rnn_lstm_models_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FIi8t8NUTEJ"
      },
      "source": [
        "# Time Series Prediction with TensorFlow\n",
        "\n",
        "Link: /~https://github.com/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9DqRvEBU4aL"
      },
      "source": [
        "---\n",
        "# Load Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lA_En8ZAUSKG"
      },
      "source": [
        "# load libraries\n",
        "try:\n",
        "    # %tensorflow_version only exists in Colab.\n",
        "    %tensorflow_version 2.x\n",
        "    IS_COLAB = True\n",
        "except Exception:\n",
        "    IS_COLAB = False\n",
        "\n",
        "# TensorFlow ≥2.0 is required\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "assert tf.__version__ >= \"2.0\"\n",
        "\n",
        "# Common imports\n",
        "import numpy as np\n",
        "import os\n",
        "from pathlib import Path\n",
        "\n",
        "# To plot pretty figures\n",
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSItPJipBaZ5"
      },
      "source": [
        "---\n",
        "# Create time series generation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wb-Z7wNKUJko"
      },
      "source": [
        "# create function\n",
        "def generate_time_series(batch_size, n_steps):\n",
        "    freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)\n",
        "    time = np.linspace(0, 1, n_steps)\n",
        "    series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10))  #   wave 1\n",
        "    series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20)) # + wave 2\n",
        "    series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5)   # + noise\n",
        "    return series[..., np.newaxis].astype(np.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxtQg198furX",
        "outputId": "ffed6635-ff81-42aa-cb45-a78de617aca5"
      },
      "source": [
        "# create time series\n",
        "np.random.seed(42)\n",
        "\n",
        "n_steps = 50\n",
        "series = generate_time_series(10000, n_steps + 1)\n",
        "X_train, y_train = series[:7000, :n_steps], series[:7000, -1]\n",
        "X_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]\n",
        "X_test, y_test = series[9000:, :n_steps], series[9000:, -1]\n",
        "\n",
        "X_train.shape, y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7000, 50, 1), (7000, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "hYsz5Dr2fyBG",
        "outputId": "4865af37-2f0f-41a1-8747-d27924355051"
      },
      "source": [
        "def plot_series(series, y=None, y_pred=None, x_label=\"$t$\", y_label=\"$x(t)$\"):\n",
        "    plt.plot(series, \".-\")\n",
        "    if y is not None:\n",
        "        plt.plot(n_steps, y, \"bx\", markersize=10)\n",
        "    if y_pred is not None:\n",
        "        plt.plot(n_steps, y_pred, \"ro\")\n",
        "    plt.grid(True)\n",
        "    if x_label:\n",
        "        plt.xlabel(x_label, fontsize=16)\n",
        "    if y_label:\n",
        "        plt.ylabel(y_label, fontsize=16, rotation=0)\n",
        "    plt.hlines(0, 0, 100, linewidth=1)\n",
        "    plt.axis([0, n_steps + 1, -1, 1])\n",
        "\n",
        "fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(12, 4))\n",
        "for col in range(3):\n",
        "    plt.sca(axes[col])\n",
        "    plot_series(X_valid[col, :, 0], \n",
        "                y_valid[col, 0],\n",
        "                y_label=(\"$x(t)$\" if col==0 else None))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3nEBxy-gGiW"
      },
      "source": [
        "---\n",
        "# Baseline forecast"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtSqxFK0fyDr",
        "outputId": "2106a77c-2048-4763-94af-8bd7d48234d7"
      },
      "source": [
        "# naive forecast MSE\n",
        "y_pred = X_valid[:, -1]\n",
        "np.mean(keras.losses.mean_squared_error(y_valid, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020211367"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "mTcTAfPlfyGT",
        "outputId": "6f403181-4369-4906-b818-4379af979443"
      },
      "source": [
        "# show naive pred\n",
        "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "SglD0YKqjRA6"
      },
      "source": [
        "---\n",
        "# One-step ahead forecast"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T1Zv45UegseB"
      },
      "source": [
        "## Linear model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzG_hs3cfyI3",
        "outputId": "44c276aa-1a0a-4551-dbc3-47df7a8825aa"
      },
      "source": [
        "# linear model (flatten)\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "    keras.layers.Flatten(input_shape=[50, 1]),\n",
        "    keras.layers.Dense(1)\n",
        "    ])\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
        "history = model.fit(\n",
        "    X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "219/219 [==============================] - 4s 5ms/step - loss: 0.1398 - val_loss: 0.0545\n",
            "Epoch 2/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0443 - val_loss: 0.0266\n",
            "Epoch 3/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0237 - val_loss: 0.0157\n",
            "Epoch 4/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0142 - val_loss: 0.0116\n",
            "Epoch 5/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0110 - val_loss: 0.0098\n",
            "Epoch 6/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0093 - val_loss: 0.0087\n",
            "Epoch 7/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0083 - val_loss: 0.0079\n",
            "Epoch 8/20\n",
            "219/219 [==============================] - 1s 2ms/step - loss: 0.0074 - val_loss: 0.0071\n",
            "Epoch 9/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0064 - val_loss: 0.0066\n",
            "Epoch 10/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0063 - val_loss: 0.0062\n",
            "Epoch 11/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0059 - val_loss: 0.0057\n",
            "Epoch 12/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0054 - val_loss: 0.0055\n",
            "Epoch 13/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0052 - val_loss: 0.0052\n",
            "Epoch 14/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0050 - val_loss: 0.0049\n",
            "Epoch 15/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0048 - val_loss: 0.0048\n",
            "Epoch 16/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0048\n",
            "Epoch 17/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0046 - val_loss: 0.0045\n",
            "Epoch 18/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0043 - val_loss: 0.0044\n",
            "Epoch 19/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0042 - val_loss: 0.0043\n",
            "Epoch 20/20\n",
            "219/219 [==============================] - 1s 3ms/step - loss: 0.0043 - val_loss: 0.0042\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Niprx2IffyL1",
        "outputId": "26a91ff3-fb58-41a9-da73-cf75d49454bf"
      },
      "source": [
        "# linear model eval\n",
        "model.evaluate(X_valid, y_valid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 [==============================] - 0s 2ms/step - loss: 0.0042\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.004168086219578981"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "QgPoRCKxfyOz",
        "outputId": "3f79196a-de37-4a8c-b2e4-1894521b84bc"
      },
      "source": [
        "# function to plot learning curves\n",
        "def plot_learning_curves(loss, val_loss):\n",
        "    plt.plot(np.arange(len(loss)) + 0.5, loss, \"b.-\", label=\"Training loss\")\n",
        "    plt.plot(np.arange(len(val_loss)) + 1, val_loss, \"r.-\", label=\"Validation loss\")\n",
        "    plt.gca().xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n",
        "    plt.axis([1, 20, 0, 0.05])\n",
        "    plt.legend(fontsize=14)\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.grid(True)\n",
        "\n",
        "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "code",
      "metadata": {
        "id": "zkGgUUZ14V-W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "b049a5ec-4c48-435f-b13a-49a58df7d142"
      },
      "source": [
        "# plot linear model\n",
        "y_pred = model.predict(X_valid)\n",
        "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G4IuLTYXA1KYbQK1ZzXTNq6v3sqesmrHpgV3Dyd/s2XDHHfD88yO4446OJxuwhGN6sGN1DSzbVsLMMf2594KsgK7d3h4TBvUlJkpYbTMOmC7IKyzj+/9cB8Bfl+0MWrPw4sXw2GNw8807eeyx1gcStMW1w6KNCbaFGw5SXl3HN88exayxoRuh1ig+JooJg/vZjAOmS5YXlFDvdRqG6xucZuFA/3Dy77MR2cnXvta5ZjW7wjE91oKVuxmS1IszRge2k7Ujpg5LYv2ecuobvGGLwUS2U0c693EJwWkWbmmAQFuj19piCcf0SHsPV7N0WzFXZw8N2D0LnTF1eBLVdQ1sPmjzyZrO6R3rrN10ycmDAt4s3NZotM4kHUs4pkd6OW8PqnBN9tCwxjF1mPPlYM1qprMa+2weuHhcwJvSVqxou9msMemsWNG+81kfjulxvF5lwcrdzByTyrCU3ic+IIiGpfQiJSGWNbsPc9OMjLDGYiLTyp1lpPd1biQOtPZMVzN7tt34aUyrlheUsKesmmunDQt3KIgIU4clsXqXDY02nZNXWMa0jJSIWD3WEo7pcRas3E2f+GgunOiOBaqmDEti+6FKyqvrwh2KiTD7y6vZe7g6pMP5u8ISjulRyqvrePuzA1wxZTDxMVHhDgegaWbfh9/aENKpdUzkW7nT+bxMG2EJxxjXeWPtPmrqvVw3bXi4Q2mivsl1/pm3hzl/WW5Jx7RbXmEZvWKiGD+ob7hDaRdLOKZH+cfK3Ywb2IeThrjnD3TdnnLAWRs91PO5mci2srCUKcOSiAnROk5dFRlRGhMAG/cfYd2ecq6dNsxVHawzRqUS7bsXKDoqtPO5mchVUVPPhn1HmB4hzWng4oQjIiki8qqIVIpIoYjc2Mp+94vIZyJyVER2iMj9zbbvFJFqEanwPd4LzTswbrNg5W5iooQrpw4JdyjHyc5I5vfXTgbg5tMzIqYD2ITXml2H8SpkR9CKsa5NOMCjQC2QDswBHhORiS3sJ8AtQDJwEXC3iFzfbJ/LVDXR97ggmEGbwMkrLOPRxdsC0qexvKCY+Z/uYnpGCikJoVtorb0unzKEUWkJbNpvMw6Y9llZWIrI57OORwJXJhwRSQCuBn6sqhWquhR4A7i5+b6q+htVXaWq9aq6GXgdmBnaiE2gfVJQwnVPfMzv3tvc5Y70t9fv58anPuFYnZeVhaWu7ZS/YMJAlheUUF5lw6PNieUVlpGV3oe+8THhDqXdRFVPvFeIichUYJmq9vYruw84W1Uva+M4AVYBT6jq476ynUAvnOS6GrhfVde2cOxcYC5AWlpa9oIFCwL3hiJURUUFiYmBWV9jW1kDm0obGJcSxZjktocj13uVHy+rZn+l89n0AFeNjeFLozt2ZVJRq7y5vZaFhfU0To3ZmXMFsh7asu1wAw8vP8bcSXGcMdhdk4CEqg7czi314FXlzverOGNwNLdMjAvpa7dVB7Nnz85T1WmtHeuuT/XnEoEjzcrKgT4nOO5BnO+Uv/qVzcFJQgJ8G3hXRMap6nGTV6nqk8CTAFlZWZqTk9PZ2LuN3NxcAlEPn+4o4VfvfYJXldjohjYnGKxr8HLXvFXsr6wiyiM0eBUR4YbzprerbyOvsIylWw9RdLSGN9buo7KmnpxxaSzbVkJ9g5eYaE+7z9UoUPVwImd5lSfy/80eTSYnJzvor9cRoaoDt3NLPeTvK+fYu0u5/IyTyAlxn2RX6sCtCacCaD5utS/QagO3iNyN05czS1VrGstVdZnfbr8UkVuBWcCbgQvXtOXppTua1uuorfeyvKC4xS/8ugYv33pxNe9tOMiDl03g5KFJ/M+7m1heUMq+dtxNnVdYxvVPfkxdg/Na0zKS+cVVJ5OZ3oe8wjKWF5QwY1SqazvlPR7h/AnpvLZ6L8fqGlxzY6pxn8ZmYbd+llvjyj4cYAsQLSJj/comA/kt7SwiXwceAM5V1T0nOLfiXO2YEGjwKuv2lDdVuFdh2bYSDlfVHrdfXYOXe+av5p38A/zkSxP46syRZGck8/w3TuOU4Un85yvr2VVS1erreL3Kr9/Z2JRsPAKzxw0gM925KM7OSOau2WNc/wd6wYR0qmob+Gh7cbhDMS4WzAk7g8mVCUdVK4FXgIdEJEFEZgJXAM8331dE5gC/AM5X1YJm24aLyEwRiRWReN+Q6f7AsubnMcGxcMMB9pcf47vnZ3LfBZnceNpwPt1RyoWPfMgHWw4BziqF33lpDW9/doAfXTqer585sun4mCgP/3f9VBD41kurqWthobLaei/3LljDpzvKiPIIUQKxQViIKhROH51KYlw07+UfDHcoxsUiacJOf25tUgO4E3gGKAJKgDtUNV9EZgFvq2pjr9XDQCqwwq/yX1DVb+L0+TwGjAaOAWuAi1XVbuUOAVXlsQ8KGJ7Sm7tmj2la6OzGU4fz3b+v4dZnPuXikwayo7iSTQeO8qNLx3PbrFFfOM+wlN78+upJ3DlvFb99bzM/vHh807ajx+r45gt5LNtWwv0XZjFjZArLd5S6uumsLXHRUeRkpfH+xoM0eDWsi8MZd9p32Jmw87ZZI0+8s8u4NuGoailwZQvlS3AGFTQ+b7XWVTUfmBSUAM0JfbKjlLW7D/PzK0867ovzpCH9ePNbZ3LfP9by1rr9AER7pGkSy5ZccvIgbjh1OE98UMDM0f05KzONg0eOceszn7KtqILfXTOZq32LqUXSjXAtOX9COm+t28+a3WVkZ0T2ezGBt9LXfzMtAj8brmxSM93D4x9sp39ibIurasb7JhxszEOqesI5xH7ypQlkpifyrfmr+MHLa7n0D0vYXVrFM1+d3pRsuoPZ4wYQEyXWrGZalLezlN6xUYwfdKJBu+5jCccExcb9R8jdfIivnjGi1dFWM0alEhvtIUogph19Lr1io7gzZwzl1fX8fcUeiitqefDyiZyVmRaMtxA2feNjmDEqlXfzD+DG++RMeK0sLGPKsCSiI2TCTn+RF7GJCE9+WEDv2Kg2l03Ozkhm3m0zuPeCrDbvzfG393B104g3j0DR0Zo2949UF0wcyM6SKrYVVYQ7FOMiFTX1bNx/hGkR2D8JlnBMEOwpq+KNtfu44dThJPVu+47+jg5XnjEqlbgYT0SPRGuP88enA/DeBmtWM5+LxAk7/bl20ICJXE8v3YEA3zgz8KNoGq+K3H4TZ1cN7BfP5GFJvJd/gLtmjwl3OMYlInHCTn8Bv8IRkT+IyFstlPcVkQdFZLxf2XdEZL2I2JVWN1FWWctLn+7m8imDGZwUnJvSIuUmzq66YEI6a/eUc6D8WLhDMS6xeFMR/RNi2XowMptaA/pFLyKjgW/izGnW3DTgp4D/1KZPAGnArYGMw4TP88sLqa5r4D/OGh3uUCLehROdZrWFG61ZzcCKnaWs3VPOoYraiF2KPNBXFt8B1qrqyha2TQVqgA2NBapaDTwH3BfgOEwYfLy9mMdyt5OdkUzWwMgbsuk2o9MSGdwvnqeXFETkl4sJrNdW7236d6QuRd6uhCMiY0SkTkQealb+mG+lzWkiEgfcBLzYwvEbgd8CcUCdiKiIvOzb/BIwQUTO6NI7MWGVV1jGzU9/SnVdA+v3lNsXZACs2nWYg0dr2FlSxY1PReYvWhM4VbUNAO2+jcCN2pVwVHUb8BfgOyKSCiAiPwG+DnzZd0UzA0gClrRwiluAApwZmk/3Pe71bVuDMwv0RZ1/GybclheUNM0I3eCNzF9fbrO8oKTpPpyaei/v5R8Ic0QmnNbvLefkIX07dBuB23SkSe0hIAp4QERuw+mPuVlV3/dtn4EzE/O6Fo5dCwwFFqnqct+jEEBVvb7tMzr5HowLNN71LETury+3abwxtnE2hjfX7qOkonved2TatqO4km1FFVx1ytCIHjDT7mHRqrpfRB4Bvuc77h5V9V8WczBwRFVrWzh8IhCLsxBaSw4Bme2NxbjP0WP1ANw0I4Mrpw6J2D8IN/EfAt6vVww/f2sDX3t2BS/ePoPEOLujoSd533c/1nm++7MiVUc/tVtx+mGWquqjzbbF4wwKaMkpOFc/a1rZXo2zDLSJUEu2FpPUO4YHL59oMxwHUHZGclPyHpwUz+3P5TH3uZX89WvTiYu2Bdp6ioUbDzJuYB+GpfQOdyhd0u4mNRE5F2cY88fATBFpPgtzCU4fTkumAttVtfmy0Y1SAFtxKkKpKku3FjNzdH9LNkF0zrh0/ucrk/hoewnfeWkNDV6bZ60nKK2sZeXOUi6YENlXN9D+UWqnAK/iDBzIAXYBv2y22yYgVkRamrZ3An7DoVswEtjcnliM+2wrquDAkWPMGts/3KF0e1edMpQfXTqetz87wDdfyOPRxVtt9Fo3t2hTEV6F8ycMDHcoXXbChCMiY4C3gfeAb/n6aH4GXCIiZ/nt+qHvv6e2cJrDwGQRuVBEZjSOdPOdPwmn/+bDFo4zEWDJVufi9ExLOCFx26xRfHnqEBZuOMj/vLuF6574mPmf7qK23lkNNa+wjEcXb7NE1E28v+EgA/vGc9KQvuEOpcva7MMRkYE4iWYjMMc3ogycmzW/D/wKOANAVXeKyKfAZTjLQ/v7CfA08BpOX88sYKlv26VALc4VlIlAS7YeYmT/BIYmR3b7ciQZMyABwekYrfcqP3xlPT97M5/RaQlsPlBBg1eJifLwoy+N9607JHgEth48ymf7jjB1WDInD+1HTJQQ5RFiojxs2H+E/L3lnD66/3GDPvIKy3hrey19RpbZYJAQO1bXwIdbD3HVKUMibjnplrSZcFT1APCFNX9VtQEY/8UjeAz4PxG5S1Wr/Pb/DDitlZe5CfhH82WfRSQFJ0ldgNO/80NVbemmUsFJfLf5iv4CPKC+GxhEZIrvPONxEuc3VLW1wQumg2rqG1heUMo107rPAmiRYMao/sTFbKOu3kt0lIdvnzuG4oo6/t/6fU33Q9U2ePnJ6/ktHv/cx4VtnH0LY9ISGJWWiEekabnrt3Yuj9j7PyLVR9uLqapt6BbNaRD42aJfAH4A3Ikzs0CbfMngHJxh0809inPlkw5MAf4lImt9y0b7m4uzFPVknB98C4EdwOMiEgu8DjwC/Bn4D+B1ERnbyvBt00GrCg9TXdfAmWOsOS2UWps1+9JJg5jz1HJqG7xEezz856XjGJPWhwZV3lizl1dW7UVx1hK65ORBzM4aQINXeX/jQRZuOEjjMIR6r1JYUkVhaWVTAmucTsUSTugs3FBEYlw0M0ZF5nIEzQU04ahqvYh8DWcYdHsMBL7qm8mgiYgkAFcDJ6lqBbBURN4AbgYeaHaOW4Hfqeoe37G/A24HHscZ4BANPOK74vmDiNyHk+Te6cRbNM0s3XaIKI9w+mi70TPU/IdM+5fNu73l5RsS46L51/r91NV7iYn28LWZI5u2jx6QyIdbDzVt+921U8jOSCavsIwbnlpObb0Xj4jd0BtCXt8PgbMz07rPEHhVdd0DZxh1VbOy+4A3W9i3HDjN7/k04Kjv398F3m62/1vA91o4z1xgJbCyd+/einO1ZI8TPAbe/HtNn/PrsMdhj/Y9YgeP074zrtHYwePavS128DgdctfzOujrj4Y9/p70iB2UqRk/eEsTJuSEPZYOPFa29d3u1nVoEoHm9+yUAy1NQZzo2+a/X6Kvb6f5tlbPo6pPquo0VZ02dOjQsCddNzwWL17c5vbSihrih2TywFevDHus4ayHSHrU7N1I+ccLqNm7sd3bavZu5KbsdGLTMthy4EjY30NP+Sw8/NfXifIIe1a8G/b33d46OBG3JpwKoPkYwL44k3yeaN++QIU6774j5zEd9NH2ElRh1ti0cIdiguyMwdFEe4R/5u0Jdyg9xsINBzl1RMoJl2mPJG5NOFuAaBEZ61c2GWhpyE2+b1tL++UDk+T48YSTWjmP6aAlWw/RJz6ayUP7hTsUE2R944TZ4wbwyuq91Dd4T3yA6ZLCkkq2HKzgvG4wu4A/VyYcVa3EuZfnIRFJEJGZwBXA8y3s/hxwr4gMEZHBOJOLPuvblgs0APeISJyI3O0rXxTM+HsCVWXJ1mLOGJ1KdJQrP0YmwK7JHsqhozV8sOVQuEPp9hb6JuvsDtPZ+HPzN8WdOBN6FgHzgTtUNXSFKVMAACAASURBVF9EZomI/4LeT+Css7Me+Az4l68MdYY+X4mzHs9hnPV7rlQbEt1lO4or2Xu4mjOtOa3HmD1uAKkJsfxjpTWrBdv73WSyzuZcO8e5qpbiJIvm5UtwBgM0PlecWQ++38p5VgPZQQqzx1q6zZnOZpbdf9NjxER5uHLqEJ77eCellbWkJHSfvgU3OVxVy4qdZdxx9uhwhxJwbr7CMS724ZZihqX0IiO1e/0CM227ZtpQ6hqU11bvDXco3dYzS3fQ4FWGpXS/FVss4ZgOq2tw7jg/c0xat5jfybTfuIF9OXlIPxutFiR5hWX8abFzH/xP38jvdhOwWsIxHbZ292Eqauo5y2aH7pGumTbUmehzX/Nb3ExXLd16iMZljhqnEupOLOGYDvtwazEegTNGW8LpiS6fPJjYKI8NHgiC+BhnChuPQEy0p9tNJWQJx3TY0q2HOHloEv16x4Q7FBMGSb1jOX9COq+v2du0Bo8JjIJDlfSOieI752V2y5m5LeGYDvlwyyFW7zrM2AEJ4Q7FhNFXpg2lrKqOf288GO5Quo3GWbvPm5DOPeeO7XbJBizhmA7IKyzjG39bgQJvrNnf7To0TfudNTaN9L5x/KObDR5oa7XUxoXogvW5X7WrjJLKWi6Y2L1u9vTn2vtwjPssLyihvsHp0Wzw2tooPVmUR7jqlKE8nrud37yziXPHp0f8ZyGvsIw5Ty2npt5LdJTw7XPHMrK/c8tfwaEK/rBoK/UNwVuI7t3PDhAb5SEna0BAz+smlnBMu80YlUrjusbdsUPTdMyEQX1Q4LHc7TyzbEfE9zksLyjmmK9Pqq5B+e17W1rcLxgL0akq7204yMwxqSTGdd+v5e77zkzATRjUFw8wbWQK379oXER/uZiu21VaDTiLoHSH1UCPVNcDzm+q2GgPv7pqEhMG90UENuw7wv3/XEtdgyJBWIhu88Gj7Cqt4o6c7je7gD9LOKbd1uw+TIPCf5w9KqK/WExgzBiVSpRHaPAqMVGRfcVbWFLJ88sLOXlIXy6cOJDTR/c/7jOeme7Ma3bvC8vZddSLk2YD5738g4jAueO7b3Ma2KAB0wF5haUAnDLcko1xlrP+8ZfGA/Dt8yJ3VFWDV7nvH2uJEuGJm6dx9zktv5fsjGQeOC2eoSm9+PZLazh6rC5gMbybf4BThiczoE98wM7pRpZwTLut2FlGZnpit1oQynTNjadmkBAbxZ6y6nCH0mlPLy1gxc4yHrx8IoOT2p6/rFe08Mh1U9lffoyfvh6YZbX2lFWRv+8IF3bj0WmNLOGYdmnwKqsKy5g2IiXcoRgXiY32cMaY/uRuPtSuJYbdZvOBo/z23S1cMCGdq04Z0q5jsjOS+dY5Y3hl9V5eX9P1SUwb1745f8LALp/L7SzhmHbZfOAoR2vqmT4iMptNTPDkZKWx93A12w9VnHhnF6mt93LvgjX0iY/mF1ed3KGJaO+ePYZThifxo9c+Y09ZVZfieC//IJnpiYzs3/1vpraEY9plpa//ZlqGXeGY4zXeN5K7ObJWAv3Toq3k7zvCf3/5ZPonxnXo2OgoD49cNxVVuPfva2nwdu7qrqyylk93lnJBD7i6AUs4pp1W7ixjYN94hiZ3vzU6TNcMSerF2AGJEbX09N9X7OKPi7dxdmZ/Ljqpc1/2w1N789AVE/l0Zylf/eunnZqB4N+bimjwareeXcCf6xKOiKSIyKsiUikihSJyYxv73i8in4nIURHZISL3N9u+U0SqRaTC93gv+O+ge1q5s5TsEcm2/o1p0dmZaXxSUEpVbX24QzmhT3eU8MDL61GF5QWlXZqqJiOlNx6BJVuLue6Jj/mkg8sJvJd/gEH94jl5SL9OxxBJXJdwgEeBWiAdmAM8JiITW9lXgFuAZOAi4G4Rub7ZPpepaqLvcUGwgu7O9h6uZl/5MaZH6LBXE3w5WQOobfDy8Xb3r9/y8qo9TXfR1Dd0bc2Z5TtKm/5d71XuenEV6/Ycbtex1bUNfLj1EBdMSO8xP+RclXBEJAG4Gvixqlao6lLgDeDmlvZX1d+o6ipVrVfVzcDrwMzQRdwzrNzp67+xEWqmFdNHJtM7Nioi+nEqjjlXYVEBWHNmxqhUYqM9zrminJtgv/znj/jNO5s4VtfQ5rEfbj3EsTovF0zsGf03AOKmoYwiMhVYpqq9/cruA85W1ctOcKwAq4AnVPVxX9lOoBdOYl0N3K+qa1s5fi4wFyAtLS17wYIFXX9DbdhW1sCm0gbGpUQxJjkqqK/VWRUVFSQmJvJcfg0f7avn0XN7E+XpGb/E/DXWQ0/Wnjp4JO8Yeyu8/OasXq79xa6q3P9hNUlxwpS0qA7//bVUD/5/y4MSPby0qZYle+sZnCBcOCKGo7Xa4us8ta6G1UX1/OGc3kRH0N9VW5+F2bNn56nqtNaOddvUNonAkWZl5UCfdhz7IE5i+atf2RycJCTAt4F3RWScqn7hmldVnwSeBMjKytKcnJyOxt6iDzcf4l+f7WNQv14kxkVTdLSGTfuPsHRbMV6F2OgG5t/uzkkPc3NzycnJ4VdrPmT6qDjOPee0cIcUFo310JO1pw52xxfy49c+I+Ok6YxKc2eC3rj/CMXvLuHei07ixtOGd/j4luohp9k+l54PuZuL+N6Ctfw1vxaAKKnnssmDOXloP9L6xNE/MZa1i1cyIq0vyaNPcuXff2u68vcQ0oQjIrnA2a1sXgZ8C+jbrLwvcPQE570bpy9nlqrWNJar6jK/3X4pIrcCs4A3OxZ55/y/dfu588VVx5XFRXuIj/E0rVteW+/l7yt2ufYDV15dx+aDR7nk5EHhDsW4XE5mGuAMj3Zrwnnfd5PleUGesywnawA3zRjOH/69DQUaVHlj7V5ea3aj6Mb9R5jzl+Asd+BGIe3DUdUcVZVWHmcCW4BoERnrd9hkoNU5JETk68ADwLmqeqLVoBTnaifoSitr+a/X1jc99wjcc84YNv38Ip756qnExzjtvgK8smovb6/fH4qwOmzVrjJUYZrd8GlOYFhKb0alJZDr4uHRCzceZMqwJAb0Df6cZWdlDiDO93ceH+PhH/9xOmt+cj4Lv3sW100b2rjSR9NM2z2BqwYNqGol8ArwkIgkiMhM4Arg+Zb2F5E5wC+A81W1oNm24SIyU0RiRSTeN2S6P86VVFBV1zbw9WdXUFFTT2yU84GLjfZwdtYARITsjGTm3TaDey/I4m9fO5XJw5K468VVzP90V7BD67CVO0uJ8ghThiWFOxQTAXIyB7C8oITq2rY7zMPhQPkx1u0p5/wJobnnxf/vfN5tM8gekUJS71jGpvfh2unDm5JRT1pbym19OAB3As8ARUAJcIeq5gOIyCzgbVVtvF5/GEgFVvh1Ur6gqt/E6fd5DBgNHAPWABeralB/StQ3ePnW/NWs3XOYx+Zkk9YnjuUFJcwYlXrcJXN2RnLT82kjk7lz3ip++Mp6yqpquePs0a7pdF2xs4yTBveld6wbPyrGbXKy0nhm2Q6W7yhhtstWrly40WlOuyBECQeO/ztvXj7vthktfjd0Z677FlHVUuDKVrYtwRlY0Ph8ZBvnyQcmBTzANqgqP349n/c3HuShKyY23cF8og9T79honrplGvf9Yy2/eWczm/YfIWtgH2aM6h/WD2K9V1m7+zA3zcgIWwwmspw6MoX4GA8fbD7kvoSz4SAZqb0ZM8Ad/UutJaPuzHUJJ5L9adE25n+6iztzRnPL6SM6dGxMlIf/vXYKtfVe3li7H1m7n7iYbWHtTNx5xEtNvdcm7DTtFh8TxemjUsndXAS0dr926B09VsfH24u59fQRrmk96Ilc1YcTqfIKy7jjhTx+t3ALV00dwv0XZnXqPB6PcNIQZ5CeGzoTt5Y567tn24SdpgNysgaws6SKncWV4Q6lyYdbiqlr0JD135iW2RVOF+UVlnHDk8upbfDiEbhu+rAu/YKaMao/cdHbqKn3BmXt9I7YUtbAyP4JpPXp2Ey6pmfLyXKGR3+w5RAjXDLl/sINB0juHdPjmrDcxq5wumjxpiJqG5wrAQFWdmEiQHDadV+8fQYZKb1J6hUTttFhqsrWsgb7AzUdlpGawKB+8fzto51dmhgzUOoavCzaVMQ549KJjrKvvHCy2u8CVWXFTqfJyxPA4Y3ZGcn88JJxFFfWsmhTUZfP1xnbD1VSUYf135gOyysso+hoDQXFlcx5annYk86KHaUcOVbP+RPcNYihJ7KE04LyGm3XH8mClbv5ZEcZt5yewfcax9oH6IrgvPHppPeN44XlhQE5X0flFdqEnaZzlheUNC03XeOCmxoXbjxIbLSHWWPTwhqHsYTTorIa5cYT/DIrOFTBg29s4IzRqTx42UTumj0moM1P0VEerp8+nA+3HmJXSdeWsO2Mdz47QKwHDlfWhvy1TWRrnEG5UTivklWVhRsOcuaY/iTEWZd1uFnCaUVNvZfXVu9tcVtdg5fv/H0NsdEefn/tFDxBmun1hlOH4xFh3qehvcrJ21lK7uZD1HphztOfhL1JxESWxpsar5wyGAWKK8L3o2XTgaPsKau20WkuYQmnFQLMX7GLBSt2f2HbI+9vYd2ecn511ckM7Be8OZkG9ovnvPED+MfKPdTUh26qkDfX7W9aoCrcQ7NNZMrOSOZ3104hI7U3Ty0pOPEBQbLQN1nnuUGerNO0jyWcFiTHCc98bTozRqby/ZfX8V+vrqe23hmJ9klBCX/O3c5104ZxcQhmUL5pRgallbW8vf5A0F+rUeMywR561jxPJrCiPMI3zhzJ6l2Hm/oEQ+39xsk6+wR/sk5zYpZwWtAvTpidNYBnvzad/zh7FPM+2cUNTy3njTV7uf25laT3ieMnl00ISSwzR/dnRGrvkA4eWLennPGD+nDV2JgeM226CY6vZA+lX68Y/rJkR8hfe2H+QdbtKWfC4OYrnphwsYTThugoDz+8eDyP3ngKn+0t556X1nDkWD2lVXVsOtDmEj0B4/EIc07LYGVhGZsONF+bLvB2FFey6cBRrskexpdGx1qyMV3SOzaaOacN5938AyEd/JJXWMYd8/IAeDlvj/VDuoQlnHa4dNIgbjz189UBGxpC26/xleyhxEZ7QnKV826+03R34Uk9Z511E1y3njGCKI/wzLLQXeUsLyih3rfKYX2I/15N6yzhtNOXJg9uWjQt1P0ayQmxfGnSIF5dtZeKmvqgvtY7nx1g0tB+DEnqFdTXMT1Het94Lps8mAUrd1NeVReS12ycoUOwfkg3sYTTTl9YTCnETU03zcigsrah1aHagbC/vJo1uw9z4US7ujGBdduZo6iqbeDFEC0y2PjD7NppQ60f0kXsTqgOCOf6FVOHJTFhUF+e+rCA8uraoKyV8+5nTnPaxdacZgJswuC+zByTyrMf7eAbZ4487sbQYFi0sYg+cdH8/MqTg/5apv3s/0SEEBHOGtufwtIqfvfeFub8JfBzVL2Tf4DM9ERGpbljgSrTvdw2axQHj9Twr/X7gvo6Xq+yaHMRZ2WlWbJxGdf93xCRFBF5VUQqRaRQRG5sY98HRaRORCr8HqP8tk8RkTwRqfL9d0po3kVwxMdGAeDVwN+QWVJRw6c7SrnImtNMkJw9No0xAxL5v/e38ujirUEbObZ+bzmHjtZw7ji72dNtXJdwgEeBWiAdmAM8JiJtLR34d1VN9HsUAIhILPA68AKQDPwNeN1XHpFmjU0jJsqZRsfjCexaOe9vPIhXbXSaCR6PRzh/vLM4W7Cu0gH+vakIjzgLwRl3cVXCEZEE4Grgx6paoapLgTeAmztxuhycPqpHVLVGVf+AM2jlnEDFG2rOwIXTSEmIpX9iHJOG9gvYud/57ADDUnoxYZDdJGeCJz4meFfpjRZtOsgpw5NJSYjY35bdltsGDWQC9aq6xa9sLXB2G8dcJiKlwH7gT6r6mK98IrBOG+dJd6zzlb/T/CQiMheYC5CWlkZubm6n30Sw3ZIlPLLqGD974d+clxHT5fNV1Skfbqni/IxoPvjgg6byiooKV9dDqFg9BK4OEo424BEn4XgE4g4Xkpu7p+sB+pQd8/LZ3mq+khkTlP9n9lnoWh24LeEkAs1vpy8H+rSy/wLgSeAgcBrwsogcVtX5vnOVt/dcqvqk71xkZWVpTk5OZ+IPibNV+bT8E/5VeITvXzeTvvFdSzqvr9lLg67h9otPPW7kW25uLm6uh1CxeghcHeQAA0bu5Z6X1nD9qRncduVJXT6nvxc/2QWsZ+6lp5OZ3trXRufZZ6FrdRDSJjURyRURbeWxFKgAmrfp9AVanEdGVTeo6j5VbVDVj4D/A77i29yhc0USEeE/LxnP4eo6/rx4e5fP985nBxjQJ46pYVrO2vQsl08ZwvQRySzbVszxDRBd9++NBxma3IuxA2ykpRuFNOGoao6qSiuPM4EtQLSIjPU7bDKQ396XwOmnwXfMJBHxX6xmUgfO5WonDenHl6cO4ZllO9hT1vk5qqprG8jdfIgLJw4M2ro+xjR37bRhFBRXsmJn4AYNVNc2sHRbMeeNT+f4P3vjFq4aNKCqlcArwEMikiAiM4ErgOdb2l9ErhCRZHGcCtyDMzINIBdoAO4RkTgRudtXviiobyKE7rsgCwF+++7mTp/jw62HqK5r4CIbnWZC6NJJg0iMi+alFYGbeeDjgmJq6r2cY8OhXctVCcfnTqAXUATMB+5Q1XwAEZklIhV++14PbMNpJnsO+LWq/g1AVWuBK4FbgMPA14ErfeXdwuCkXtw2aySvrdnHuj2HO3WOdz47QFLvGE4dmRLg6IxpXe/YaC6fMpj/t34/R44FZn619zcWkRAbxWmj7LPsVq5LOKpaqqpXqmqCqg5X1Rf9ti1R1US/5zeoaqrv/ptxvqHP/udararZqtpLVU9R1dWhfC+h8M2zR5OaEMt//2tjh9vDPyko4V/r93PKsCRiolz3UTDd3HXThnGszssba7o+84CqsmhjEbPGphEXHRWA6Eww2LdMhOsTH8N3zs/kkx2l3LtgbbtvpMsrLOOmpz+htt7Lkm3Ftl6ICblJQ/sxbmAf/t7CMu4dlb/vCAeOHOMcW0ra1SzhdANZ6YkI8OrqvVz3xMd8tK24zf037DvC9xasoa7BuSLyetXWCzEhJyJcN30Y6/eWk7+v+R0MHbNoUxEiMNtmF3A1SzjdwIqdZTQOyqn3Kl97dgW/eWcTRUeOHbff/vJq7vvHWi794xIOHa0h2iNhWd/HmEZfnjqE2GgPC7p4lfPvTUVMHppEWp+4AEVmgsFtN36aTpgxKpXYaA919V6iojxMGZbEYx9s56klBVw+eQjTRiTz+pq9rCosA4TbZ43irpwxbDtUwfKCEmaMSrX1QkxYJPWO5aKJA3l19V5+eMn4pqlvOqLo6DHW7j7M987PDEKEJpAs4XQDjYvD+SePwpJKnlm6g/mf7ublVc7UIR6BR2+cysUnD2o6zhKNCbfrpg/jjbX7eDf/AFdMGdLh43M3HQLg3PHpgQ7NBJglnG6iefLISE3gZ1ecRN9eMfxp0bamO2ILiivDFqMxLTl9VCrDUnrx0qe7O5Vw/pm3mz5x0VTXBnf5ddN11ofTzeVkDSAuxmN9Nca1PB7h2uxhfFxQQmFJx34Qfby9mE93lnG0pp45T39ioy1dzhJON9fY3HbvBVm2trtxra9MG4oAP3h5XYeSxm/f+3xi+WAtd2ACxxJOD5Cdkcxds8dYsjGute/wMURgeUFpuxdme/GTXeQVlhFloy0jhvXhGGPCbnlBCY0TZRyr87Jk66E2fyB9tL2Yn7z+GWdnpnHX7NGs2Flmoy0jgCUcY0zYzRiVSlyMh5o6Lwq8tW4/N8/IIDXxi/fV7Ciu5I4XVjGifwJ/vHEqfeNjOHWkXdlEAmtSM8aEXWNf430XZvGDi7LYXVrFVx7/mF0lxy+9UV5dxzf+tgKPwNO3Tuvy4oMmtCzhGGNcobGv8Y6cMbx4+2mUVdVy1WPLWL/HmfamvsHL3S+uYndpFY/flE1GakKYIzYdZQnHGOM62Rkp/PObZxAXHcX1T37M00sLuPaJj1mytZj/vvJkTrPBARHJEo4xxpXGDEjklTvPoH9iHD9/ayOrdh0myiOMtuWjI5YlHGOMa6X3jeeKqYM/L1Cb2TySWcIxxrja2ZkDiLfZMroF1yUcEUkRkVdFpFJECkXkxjb2fVtEKvwetSKy3m/7ThGp9tv+XmjehTEmUGy2jO7DjffhPArUAunAFOBfIrJWVfOb76iqF/s/F5FcYFGz3S5T1feDFKsxJgRsZvPuwVVXOCKSAFwN/FhVK1R1KfAGcHM7jh0BzAKeC2aMxhhjOsdVCQfIBOpVdYtf2VpgYjuOvQVYoqo7m5XPE5FDIvKeiEwOUJzGGGM6yG1NaonAkWZl5UCfdhx7C/Bws7I5wCqcpWC+DbwrIuNU9XDzg0VkLjAXIC0tjdzc3I5F3g1VVFRYPWD1AFYHjaweulgHqhqyB5ALaCuPpcBUoKrZMd8D3jzBec8EKoDEE+y3CadPp804MzMz1aguXrw43CG4gtWD1UEjq4e26wBYqW18t4b0CkdVc9ra7uvDiRaRsaq61Vc8GfjCgIFmbgVeUdWKE4WAc7VjjDEmxFzVh6OqlcArwEMikiAiM4ErgOdbO0ZEegHXAs82Kx8uIjNFJFZE4kXkfqA/sCxob8AYY0yrXJVwfO4EegFFwHzgDvUNiRaRWSLS/CrmSuAwsLhZeR/gMaAM2AtcBFysqnabsjHGhIHbBg2gqqU4SaSlbUtwBhb4l83HSUzN980HJgUjRmOMMR3nxiscY4wx3ZAlHGOMMSFhCccYY0xIWMIxxhgTEpZwjDHGhIQlHGOMMSFhCccYY0xIWMIxxhgTEpZwjDHGhIQlHGOMMSFhCccYY0xIWMIxxhgTEpZwjDHGhIQlHGOMMSFhCccYY0xIWMIxxhgTEpZwjDHGhIQlHGOMMSHhuoQjIneLyEoRqRGRZ9ux/3dF5ICIHBGRZ0Qkzm/bCBFZLCJVIrJJRM4LavDGGGNa5bqEA+wDHgaeOdGOInIh8ABwLpABjAJ+5rfLfGA1kAr8F/BPEUkLdMDGGGNOzHUJR1VfUdXXgJJ27H4r8LSq5qtqGfBz4KsAIpIJnAL8VFWrVfVlYD1wdXAiN8YY05bocAfQRROB1/2erwXSRSTVt61AVY822z6xpROJyFxgru9pjYh8FoR4I01/oDjcQbiA1YPVQSOrh7brIKOtAyM94SQC5X7PG//dp4VtjduHtHQiVX0SeBJARFaq6rTAhhp5rB4cVg9WB42sHrpWByFtUhORXBHRVh5LO3HKCqCv3/PGfx9tYVvj9qMYY4wJuZAmHFXNUVVp5XFmJ06ZD0z2ez4ZOKiqJb5to0SkT7Pt+Z1/B8YYYzrLdYMGRCRaROKBKCBKROJFpLWmv+eAb4jIBBFJAn4EPAugqluANcBPfef4MjAJeLkdYTzZ1ffRTVg9OKwerA4aWT10oQ5EVQMZSJeJyIPAT5sV/0xVHxSR4cAGYIKq7vLtfy/wA6AXTjL5pqrW+LaNwElApwG7gLtU9f3gvwtjjDHNuS7hGGOM6Z5c16RmjDGme7KEY4wxJiQs4fgRkRQReVVEKkWkUERuDHdModDW/HUicq5vHroq37x0bd7YFalEJE5Envb9fz8qImtE5GK/7T2lHl4Qkf2+uQm3iMhtftt6RB34E5GxInJMRF7wK7vR9zmpFJHXRCQlnDEGk+9WlmMiUuF7bPbb1uF6sIRzvEeBWiAdmAM8JiItzkzQzbQ4f52I9AdeAX4MpAArgb+HPLrQiAZ2A2cD/XBGPC7wTQDbk+rhl8AIVe0LXA48LCLZPawO/D0KrGh84vs+eAK4Ged7ogr4c3hCC5m7VTXR98iCzteDDRrwEZEEoAw4yTekGhF5Htirqg+ENbgQEZGHgaGq+lXf87nAV1X1DN/zBJwpLaaq6qawBRoiIrIOZzLYVHpgPYhIFpALfBtIoofVgYhcD1yFMzJ2jKreJCK/wEnIN/r2GQ1sBFKbTaPVLYhILvCCqv6lWXmn6sGucD6XCdQ3JhufVude6yEm4tQBAKpaCWynB9SJiKTjfCby6WH1ICJ/FpEqYBOwH/h/9Lw66As8BNzbbFPzetiO0yqSGbroQu6XIlIsIstEJMdX1ql6sITzuUTgSLOycpx52Xqq1uaj69Z1IiIxwDzgb75f7z2qHlT1Tpz3NgunGa2GHlYHODPPP62qe5qV97R6+AHOsi9DcG74fNN3NdOperCE8zmbe+2LelydiIgHeB7n19rdvuIeVw+q2qCqS4GhwB30oDoQkSnAecD/trC5x9QDgKp+oqpHVbVGVf8GLAMuoZP1EOmzRQfSFiBaRMaq6lZfWU+fey0fZ80hoKndfjTdtE5ERICncTpBL1HVOt+mHlUPzUTz+XvtKXWQA4wAdjkfCRJxptmaALyD3/yNIjIKiMP5/ugJFBCazWPZ7npQVXv4HsBLOKuEJgAzcS4RJ4Y7rhC872ggHmeE0vO+f0cDab46uNpX9mtgebjjDWI9PA4sBxKblfeIegAGANfj+4IFLgQqcUar9Yg68NVDb2Cg3+O3wD99dTARp+l9lu974gXgpXDHHKR6SPJ9Bhq/D+b4Pg+Zna2HsL8pNz1whnu+5qvUXcCN4Y4pRO/7Qd8vF//Hg75t5+F0HlfjjFgaEe54g1QHGb73fQynuaDxMaen1IPvC/UD4LDvy2Q9cLvf9m5fB63Uy4M4I7Uan9/o+36oxFkAMiXcMQbx87ACp5nssO/H2PldqQcbFm2MMSYkbNCAMcaYkLCEY4wxJiQs4RhjjAkJSzjGGGNCwhKOMcaYkLCEY4wxJiQs4RhjjAkJSzjGRAgR6SsiD4rI+HDHYkxnWMIxJnJMA34KxIQ7EGM6wxKOMZFjKs5SARvCHYgxnWFT2xgTpsabHwAAAOpJREFUAURkIzCuWfErqnp1OOIxpjMs4RgTAURkOs5s5vnAL3zF+1W1MHxRGdMxth6OMZFhLc5iaH9U1eXhDsaYzrA+HGMiw0QgFlgV7kCM6SxLOMZEhlNw1utZE+5AjOksSzjGRIapwHZVPRLuQIzpLEs4xkSGCdhwaBPhbNCAMZHhMHCKiFwIlANbVbUkzDEZ0yE2LNqYCCAiJwFPA5OAeGCWqi4Nb1TGdIwlHGOMMSFhfTjGGGNCwhKOMcaYkLCEY4wxJiQs4RhjjAkJSzjGGGNCwhKOMcaYkLCEY4wxJiQs4RhjjAmJ/w95bw5F2hh5tAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FnpwLEvJheI-"
      },
      "source": [
        "## Simple RNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INHQB1_ChU4q",
        "outputId": "c3a6fa42-f85a-4c3d-d3f1-7e5eb76a09c7"
      },
      "source": [
        "# simple rnn with one layer\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "    keras.layers.SimpleRNN(1, input_shape=[None, 1])\n",
        "])\n",
        "\n",
        "optimizer = keras.optimizers.Adam(lr=0.005)\n",
        "model.compile(loss=\"mse\", optimizer=optimizer)\n",
        "history = model.fit(\n",
        "    X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "219/219 [==============================] - 11s 46ms/step - loss: 0.1554 - val_loss: 0.0489\n",
            "Epoch 2/20\n",
            "219/219 [==============================] - 10s 44ms/step - loss: 0.0409 - val_loss: 0.0296\n",
            "Epoch 3/20\n",
            "219/219 [==============================] - 10s 44ms/step - loss: 0.0277 - val_loss: 0.0218\n",
            "Epoch 4/20\n",
            "219/219 [==============================] - 10s 44ms/step - loss: 0.0208 - val_loss: 0.0177\n",
            "Epoch 5/20\n",
            "219/219 [==============================] - 10s 44ms/step - loss: 0.0174 - val_loss: 0.0151\n",
            "Epoch 6/20\n",
            "219/219 [==============================] - 10s 45ms/step - loss: 0.0146 - val_loss: 0.0134\n",
            "Epoch 7/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0138 - val_loss: 0.0123\n",
            "Epoch 8/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0128 - val_loss: 0.0116\n",
            "Epoch 9/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0118 - val_loss: 0.0112\n",
            "Epoch 10/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0117 - val_loss: 0.0110\n",
            "Epoch 11/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0112 - val_loss: 0.0109\n",
            "Epoch 12/20\n",
            "219/219 [==============================] - 9s 42ms/step - loss: 0.0115 - val_loss: 0.0109\n",
            "Epoch 13/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0114 - val_loss: 0.0109\n",
            "Epoch 14/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0114 - val_loss: 0.0109\n",
            "Epoch 15/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0113 - val_loss: 0.0109\n",
            "Epoch 16/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0114 - val_loss: 0.0109\n",
            "Epoch 17/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0114 - val_loss: 0.0109\n",
            "Epoch 18/20\n",
            "219/219 [==============================] - 10s 43ms/step - loss: 0.0115 - val_loss: 0.0109\n",
            "Epoch 19/20\n",
            "219/219 [==============================] - 9s 43ms/step - loss: 0.0115 - val_loss: 0.0109\n",
            "Epoch 20/20\n",
            "219/219 [==============================] - 9s 42ms/step - loss: 0.0116 - val_loss: 0.0109\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxU3DCp1hU7c",
        "outputId": "b3687311-43c7-4c3a-afd8-4eed6b0fb764"
      },
      "source": [
        "# simple rnn eval\n",
        "model.evaluate(X_valid, y_valid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 [==============================] - 0s 6ms/step - loss: 0.0109\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.010881561785936356"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "_Iu4eaNphU-K",
        "outputId": "451e6935-138f-408d-dc48-852a23d12359"
      },
      "source": [
        "# plot simple rnn learning curve\n",
        "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "RE8J071YhVAv",
        "outputId": "d0a13ac0-c6cb-4036-8460-688da7537982"
      },
      "source": [
        "# plot simple rnn\n",
        "y_pred = model.predict(X_valid)\n",
        "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "Da_aAzHUitde"
      },
      "source": [
        "## Deep RNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRKFKcm9hVDX",
        "outputId": "0f144c75-c311-45b9-92c4-67d2a69d0fd8"
      },
      "source": [
        "# rnn model\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "model = keras.models.Sequential([\n",
        "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
        "    keras.layers.SimpleRNN(20, return_sequences=True),\n",
        "    keras.layers.SimpleRNN(1)\n",
        "    ])\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
        "history = model.fit(\n",
        "    X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "219/219 [==============================] - 31s 131ms/step - loss: 0.1324 - val_loss: 0.0090\n",
            "Epoch 2/20\n",
            "219/219 [==============================] - 29s 133ms/step - loss: 0.0078 - val_loss: 0.0065\n",
            "Epoch 3/20\n",
            "219/219 [==============================] - 28s 129ms/step - loss: 0.0057 - val_loss: 0.0045\n",
            "Epoch 4/20\n",
            "219/219 [==============================] - 28s 128ms/step - loss: 0.0045 - val_loss: 0.0040\n",
            "Epoch 5/20\n",
            "219/219 [==============================] - 28s 127ms/step - loss: 0.0044 - val_loss: 0.0040\n",
            "Epoch 6/20\n",
            "219/219 [==============================] - 28s 128ms/step - loss: 0.0038 - val_loss: 0.0036\n",
            "Epoch 7/20\n",
            "219/219 [==============================] - 28s 127ms/step - loss: 0.0036 - val_loss: 0.0040\n",
            "Epoch 8/20\n",
            "219/219 [==============================] - 28s 126ms/step - loss: 0.0038 - val_loss: 0.0033\n",
            "Epoch 9/20\n",
            "219/219 [==============================] - 28s 127ms/step - loss: 0.0037 - val_loss: 0.0032\n",
            "Epoch 10/20\n",
            "219/219 [==============================] - 28s 128ms/step - loss: 0.0035 - val_loss: 0.0031\n",
            "Epoch 11/20\n",
            "219/219 [==============================] - 28s 129ms/step - loss: 0.0034 - val_loss: 0.0030\n",
            "Epoch 12/20\n",
            "219/219 [==============================] - 29s 131ms/step - loss: 0.0033 - val_loss: 0.0031\n",
            "Epoch 13/20\n",
            "219/219 [==============================] - 28s 128ms/step - loss: 0.0034 - val_loss: 0.0031\n",
            "Epoch 14/20\n",
            "219/219 [==============================] - 28s 127ms/step - loss: 0.0034 - val_loss: 0.0032\n",
            "Epoch 15/20\n",
            "219/219 [==============================] - 28s 127ms/step - loss: 0.0034 - val_loss: 0.0033\n",
            "Epoch 16/20\n",
            "219/219 [==============================] - 28s 126ms/step - loss: 0.0037 - val_loss: 0.0030\n",
            "Epoch 17/20\n",
            "219/219 [==============================] - 28s 126ms/step - loss: 0.0034 - val_loss: 0.0029\n",
            "Epoch 18/20\n",
            "219/219 [==============================] - 28s 126ms/step - loss: 0.0031 - val_loss: 0.0030\n",
            "Epoch 19/20\n",
            "219/219 [==============================] - 28s 126ms/step - loss: 0.0032 - val_loss: 0.0029\n",
            "Epoch 20/20\n",
            "219/219 [==============================] - 28s 126ms/step - loss: 0.0033 - val_loss: 0.0029\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ezjs3nb8i2gb",
        "outputId": "7bb61f55-9ff7-4bfa-ca31-a3d468042506"
      },
      "source": [
        "# deep rnn eval\n",
        "model.evaluate(X_valid, y_valid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63/63 [==============================] - 1s 13ms/step - loss: 0.0029\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0029105613939464092"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "1GrzxTJ3i6Dv",
        "outputId": "a2976441-0d1f-4878-e343-663106e074d3"
      },
      "source": [
        "# plot deep rnn learning curve\n",
        "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "02l4iUZgi9aJ",
        "outputId": "7cc5654a-89a1-4fca-935b-5ac78139b2a6"
      },
      "source": [
        "# plot deep rnn learning\n",
        "y_pred = model.predict(X_valid)\n",
        "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5AdSa1UzPvLZ2H3vLa5mQ4d81nHzNnw933AHPPz+aO+7oerIBSzimD6trdLNyRylzxw/i3vMn+nXt9s6YMnQAMVHCWptxwPRAXlE53//nBgD+urIwYM3Cy5bBY4/BTTcV8thjX+zT6YywHRZtTKAt3nSIytpGvnnWWOZNCN4ItWbxMVFMGTbQZhwwPZJbUEqTx2kYbnI7zcL+/uHk22cjUsjXvta9ZjW7wjF91qLVexie3I/Tx/m3k7UrZoxMZuPeSprcnpDFYCLbKWOc+7iEwDQLtzVAoKPRax2xhGP6pH0VtazYUcJVM0f47Z6F7pgxKpnaRjdbD9l8sqZ7EmKdtZsuPnGo35uFOxqN1p2kYwnH9Emv5O1FFa6eOSKkccwY6Xw5WLOa6a7mPpsHLprk96a0Vas6bjZrTjqrVnXufNaHY/ocj0dZtHoPc8enMTI14fgHBNDI1H6kJsaybk8FN87JDGksJjKtLiwnY4BzI7G/dWa6mvnz7cZPY9qVW1DK3vJarpk1MtShICLMGJnM2t02NNp0T15RObMyUyNi9VhLOKbPWbR6D/3jo7lgqh8XqOqB6SOT2Xm4msraxlCHYiLMgcpa9lXUBnU4f09YwjF9SmVtI+98dpDLpw8jPiYq1OEAtMzs+/Dbm4I6tY6JfKsLnc/LrNGWcIwJO2+u3099k4drZ3Vt4ahAUu/kOv/M28uCv+Ra0jGdlldUTr+YKCYPHRDqUDrFEo7pU/6xeg+ThvTnhOHh8we6YW8l4KyNHuz53ExkW11UxvSRycQEaR2nnoqMKI3xg80HjrBhbyXXzBoZVh2sc8amEe29Fyg6KrjzuZnIVVXfxKb9R5gdIc1pEMYJR0RSReQ1EakWkSIRuaGd/e4Xkc9E5KiI7BKR+1ttLxSRWhGp8j7eD847MOFm0eo9xEQJV8wYHupQjjEzM4U/XDMNgJtOy4yYDmATWut2V+BRmBlBK8aGbcIBHgUagAxgAfCYiExtYz8BbgZSgAuBu0Xkulb7XKqqSd7H+YEM2vhPXlE5jy7b4Zc+jdyCEhZ+upvZmamkJgZvobXOumz6cMamJ7LlgM04YDpndVEZIp/POh4JwjLhiEgicBXwE1WtUtUVwJvATa33VdXfquoaVW1S1a3AG8Dc4EZs/O2TglKufeJjfv/+1h53pL+z8QA3PPUJdY0eVheVhW2n/PlThpBbUEpljQ2PNseXV1TOxIz+DIiPCXUonSaqevy9gkxEZgArVTXBp+w+4CxVvbSD4wRYAzyhqo97ywqBfjjJdS1wv6qub+PY24HbAdLT02cuWrTIf28oQlVVVZGU5J/1NXaUu9lS5mZSahTjUzoejtzkUX6yspYD1c5n0wVcOSGGL43r2pVJVYPy1s4GFhc10Tw1ZnfO5c966MiOCjcP59Zx+0lxnD4svCYBCVYdhLtwqQePKncuqeH0YdHcPDUuqK/dUR3Mnz8/T1VntXdseH2qP5cEHGlVVgn0P85xD+J8p/zVp2wBThIS4NvAeyIySVWPmbxKVZ8EngSYOHGiZmdndzf2XiMnJwd/1MOnu0r59fuf4FElNtrd4QSDjW4Pd724hgPVNUS5BLdHERGuP3d2p/o28orKWbH9MMVH63lz/X6q65vInpTOyh2lNLk9xES7On2uZv6qh+M506M8kf9v9moK2dkzA/56XRGsOgh34VIP+fsrqXtvBZedfgLZQe6T7EkdhGvCqQJaj1sdALTbwC0id+P05cxT1frmclVd6bPbr0TkFmAe8Jb/wjUdeXrFrpb1OhqaPOQWlLT5hd/o9vCtl9by/qZDPHjpFE4ckcx/v7eF3IIy9nfibuq8onKue/JjGt3Oa83KTOGXV55IVkZ/8orKyS0oZc7YtLDtlHe5hPOmZPD62n3UNbrD5sZUE36am4XD9bPcnrDswwG2AdEiMsGnbBqQ39bOIvJ14AHgHFXde5xzK87VjgkCt0fZsLeypcI9Cit3lFJR03DMfo1uD/csXMu7+Qf56Zem8NW5Y5iZmcLz3ziVk0cl85+vbmR3aU27r+PxKL95d3NLsnEJzJ80mKwM56J4ZmYKd80fH/Z/oOdPyaCmwc1HO0tCHYoJY4GcsDOQwjLhqGo18CrwkIgkishc4HLg+db7isgC4JfAeapa0GrbKBGZKyKxIhLvHTI9CFjZ+jwmMBZvOsiByjq+e14W952fxQ2njuLTXWVc8MiHfLDtMOCsUvidl9fxzmcH+fElk/n6GWNajo+JcvG/180AgW+9vJbGNhYqa2jycO+idXy6q5wolxAlEBuAhaiC4bRxaSTFRfN+/qFQh2LCWCRN2OkrXJvUAO4EngGKgVLgDlXNF5F5wDuq2txr9TCQBqzyqfwXVPWbOH0+jwHjgDpgHXCRqtqt3EGgqjz2QQGjUhO4a/74loXObjhlFN/9+zpueeZTLjphCLtKqtly8Cg/vmQyt84b+4XzjExN4DdXncSdL67hd+9v5YcXTW7ZdrSukW++kMfKHaXcf8FE5oxJJXdXWVg3nXUkLjqK7InpLNl8CLdHQ7o4nAlP+yucCTtvnTfm+DuHmbBNOKpaBlzRRvlynEEFzc/brXVVzQdOCkiA5rg+2VXG+j0V/OKKE4754jxh+EDe+tYZ3PeP9by94QAA0S5pmcSyLRefOJTrTxnFEx8UMHfcIM7MSufQkTpueeZTdhRX8furp3GVdzG1SLoRri3nTcng7Q0HWLennJmZkf1ejP+t9vbfzIrAz0ZYNqmZ3uHxD3YyKCm2zVU1470TDjbnIVU97hxiP/3SFLIykvjWwjX84JX1XPLH5ewpq+GZr85uSTa9wfxJg4mJEmtWM23KKywjITaKyUOPN2g3/FjCMQGx+cARcrYe5qunj253tNWcsWnERruIEojpRJ9Lv9go7sweT2VtE39ftZeSqgYevGwqZ2alB+IthMyA+BjmjE3jvfyDhON9cia0VheVM31kMtERMmGnr8iL2ESEJz8sICE2qsNlk2dmpvDirXO49/yJHd6b42tfRW3LiDeXQPHR+g73j1TnTx1CYWkNO4qrQh2KCSNV9U1sPnCEWRHYPwmWcEwA7C2v4c31+7n+lFEkJ3R8R39XhyvPGZtGXIwrokeidcZ5kzMAeH+TNauZz0XihJ2+wnbQgIlcT6/YhQDfOMP/o2iar4rC/SbOnhoyMJ5pI5N5P/8gd80fH+pwTJiIxAk7ffn9CkdE/igib7dRPkBEHhSRyT5l3xGRjSJiV1q9RHl1Ay9/uofLpg9jWHJgbkqLlJs4e+r8KRms31vJwcq6UIdiwsSyLcUMSoxl+6HIbGr16xe9iIwDvokzp1lrs4CfAb5Tmz4BpAO3+DMOEzrP5xZR2+jmP84cF+pQIt4FU51mtcWbrVnNwKrCMtbvreRwVUPELkXu7yuL7wDrVXV1G9tmAPXApuYCVa0FngPu83McJgQ+3lnCYzk7mZmZwsQhkTdkM9yMS09i2MB4nl5eEJFfLsa/Xl+7r+XfkboUeacSjoiMF5FGEXmoVflj3pU2Z4lIHHAj8FIbx28GfgfEAY0ioiLyinfzy8AUETm9R+/EhFReUTk3Pf0ptY1uNu6ttC9IP1izu4JDR+spLK3hhqci8xet8Z+aBjdAp28jCEedSjiqugP4C/AdEUkDEJGfAl8Hvuy9opkDJAPL2zjFzUABzgzNp3kf93q3rcOZBfrC7r8NE2q5BaUtM0K7PZH56yvc5BaUttyHU9/k4f38gyGOyITSxn2VnDh8QJduIwg3XWlSewiIAh4QkVtx+mNuUtUl3u1zcGZi3tDGseuBEcBSVc31PooAVNXj3T6nm+/BhIHmu56FyP31FW6ab4xtno3hrfX7Ka3qnfcdmY7tKqlmR3EVV548IqIHzHR6WLSqHhCRR4DveY+7R1V9l8UcBhxR1YY2Dp8KxOIshNaWw0BWZ2Mx4edoXRMAN87J5IoZwyP2DyKc+A4BH9gvhl+8vYmvPbuKl26bQ1Kc3dHQlyzx3o91rvf+rEjV1U/tdpx+mBWq+mirbfE4gwLacjLO1c+6drbX4iwDbSLU8u0lJCfE8OBlU22GYz+amZnSkryHJcdz23N53P7cav76tdnERdsCbX3F4s2HmDSkPyNTE0IdSo90uklNRM7BGcb8MTBXRFrPwlyK04fTlhnATlVtvWx0s1TAVpyKUKrKiu0lzB03yJJNAJ09KYP//spJfLSzlO+8vA63x+ZZ6wvKqhtYXVjG+VMi++oGOj9K7WTgNZyBA9nAbuBXrXbbAsSKSFvT9k7BZzh0G8YAWzsTiwk/O4qrOHikjnkTBoU6lF7vypNH8ONLJvPOZwf55gt5PLpsu41e6+WWbinGo3DelCGhDqXHjptwRGQ88A7wPvAtbx/Nz4GLReRMn10/9P73lDZOUwFME5ELRGRO80g37/mTcfpvPmzjOBMBlm93Lk7PsIQTFLfOG8uXZwxn8aZD/Pd727j2iY9Z+OluGpqc1VDzisp5dNkOS0S9xJJNhxgyIJ4Thg8IdSg91mEfjogMwUk0m4EF3hFl4Nys+X3g18DpAKpaKCKfApfiLA/t66fA08DrOH0984AV3m2XAA04V1AmAi3ffpgxgxIZkRLZ7cuRZPzgRASnY7TJo/zw1Y38/K18xqUnsvVgFW6PEhPl4sdfmuxdd0hwCWw/dJTP9h9hxsgUThwxkJgoIcolxES52HTgCPn7Kjlt3KBjBn3kFZXz9s4G+o8pt8EgQVbX6ObD7Ye58uThEbecdFs6TDiqehD4wpq/quoGJn/xCB4D/ldE7lLVGp/9PwNObedlbgT+0XrZZxFJxUlS5+P07/xQVdu6qVRwEt+t3qK/AA+o9wYGEZnuPc9knMT5DVVtb/CC6aL6Jje5BWVcPav3LIAWCeaMHURczA4amzxER7n49jnjKalq5P9t3N9yP1SD28NP38hv8/jnPi7q4OzbGJ+eyNj0JFwiLctdv12YG7H3f0Sqj3aWUNPg7hXNaeD/2aJfAH4A3Ikzs0CHvMngbJxh0609inPlkwFMB/4lIuu9y0b7uh1nKeppOD/4FgO7gMdFJBZ4A3gE+DPwH8AbIjKhneHbpovWFFVQ2+jmjPHWnBZM7c2afclJQ1nwVC4Nbg/RLhf/eckkxqf3x63Km+v28eqafSjOWkIXnziU+RMH4/YoSzYfYvGmQzQPQ2jyKEWlNRSVVbcksObpVCzhBM/iTcUkxUUzZ2xkLkfQml8Tjqo2icjXcIZBd8YQ4KvemQxaiEgicBVwgqpWAStE5E3gJuCBVue4Bfi9qu71Hvt74DbgcZwBDtHAI94rnj+KyH04Se7dbrxF08qKHYeJcgmnjbMbPYPNd8i0b9mLt7W9fENSXDT/2niAxiYPMdEuvjZ3TMv2cYOT+HD74ZZtv79mOjMzU8grKuf6p3JpaPLgErEbeoPI4/0hcFZWeu8ZAq+qYffAGUZd06rsPuCtNvatBE71eT4LOOr993eBd1rt/zbwvTbOczuwGlidkJCgOFdL9jjOY8hNf9CMBb8JeRz26NwjdtgkHTDnao0dNqnT22KHTdLhdz2vQ7/+aMjj70uP2KFZmvmDtzVxSnbIY+nCY3VH3+3hug5NEtD6np1KoK0piJO823z3S/L27bTe1u55VPVJVZ2lqrNGjBgR8qQbDo9ly5Z1uL2sqp744Vk88NUrQh5rKOshkh71+zZT+fEi6vdt7vS2+n2buXFmBrHpmWw7eCTk76GvfBYe/usbRLmEvaveC/n77mwdHE+4JpwqoPUYwAE4k3web98BQJU6774r5zFd9NHOUlRh3oT0UIdiAuz0YdFEu4R/5u0NdSh9xuJNhzhldOpxl2mPJOGacLYB0SIywadsGtDWkJt877a29ssHTpJjxxOe1M55TBct336Y/vHRTBsxMNShmAAbECfMnzSYV9fuo8ntOf4BpkeKSqvZdqiKc3vB7AK+wjLhqGo1zr08D4lIoojMBS4Hnm9j9+eAe0VkuIgMw5lc9FnvthzADdwjInEicre3fGkg4+8LVJXl20s4fVwa0VFh+TEyfnb1zBEcPlrPB9sOhzqUXm+xd7LO3jCdja9w/qa4E2dCz2JgIXCHquaLyDwR8V3Q+1YbvHIAACAASURBVAmcdXY2Ap8B//KWoc7Q5ytw1uOpwFm/5wq1IdE9tqukmn0VtZxhzWl9xvxJg0lLjOUfq61ZLdCW9JLJOlsL2znOVbUMJ1m0Ll+OMxig+bnizHrw/XbOsxaYGaAw+6wVO5zpbObZ/Td9RkyUiytmDOe5jwspq24gNbH39C2Ek4qaBlYVlnPHWeNCHYrfhfMVjgljH24rYWRqPzLTetcvMNOxq2eNoNGtvL52X6hD6bWeWbELt0cZmdr7VmyxhGO6rNHt3HF+xvj0XjG/k+m8SUMGcOLwgTZaLUDyisr5v2XOffA/ezO/103AagnHdNn6PRVU1Tdxps0O3SddPWuEM9Hn/ta3uJmeWrH9MM3LHDVPJdSbWMIxXfbh9hJcAqePs4TTF102bRixUS4bPBAA8THOFDYugZhoV6+bSsgSjumyFdsPc+KIZAYmxIQ6FBMCyQmxnDclgzfW7WtZg8f4R8HhahJiovjOuVm9cmZuSzimSz7cdpi1uyuYMDgx1KGYEPrKrBGU1zTy782HQh1Kr9E8a/e5UzK455wJvS7ZgCUc0wV5ReV842+rUODNdQd6XYem6bwzJ6STMSCOf/SywQMdrZbavBBdoD73a3aXU1rdwPlTe9fNnr7C9j4cE35yC0ppcjs9mm6PrY3Sl0W5hCtPHsHjOTv57btbOGdyRsR/FvKKylnwVC71TR6io4RvnzOBMYOcW/4KDlfxx6XbaXIHbiG69z47SGyUi+yJg/163nBiCcd02pyxaTSva9wbOzRN10wZ2h8FHsvZyTMrd0V8n0NuQQl13j6pRrfyu/e3tblfIBaiU1Xe33SIuePTSIrrvV/LvfedGb+bMnQALmDWmFS+f+GkiP5yMT23u6wWcBZB6Q2rgR6pbQKc31Sx0S5+feVJTBk2ABHYtP8I9/9zPY1uRQKwEN3WQ0fZXVbDHdm9b3YBX5ZwTKet21OBW+E/zhob0V8sxj/mjE0jyiW4PUpMVGRf8RaVVvN8bhEnDh/ABVOHcNq4Qcd8xrMynHnN7n0hl91HPThp1n/ezz+ECJwzufc2p4ENGjBdkFdUBsDJoyzZGGc56598aTIA3z43ckdVuT3Kff9YT5QIT9w0i7vPbvu9zMxM4YFT4xmR2o9vv7yOo3WNfovhvfyDnDwqhcH94/12znBkCcd02qrCcrIyknrVglCmZ244JZPE2Cj2lteGOpRue3pFAasKy3nwsqkMS+54/rJ+0cIj187gQGUdP3vDP8tq7S2vIX//ES7oxaPTmlnCMZ3i9ihrisqZNTo11KGYMBIb7eL08YPI2Xq4U0sMh5utB4/yu/e2cf6UDK48eXinjpmZmcK3zh7Pq2v38ca6nk9i2rz2zXlThvT4XOHOEo7plK0Hj3K0vonZoyOz2cQETvbEdPZV1LLzcNXxdw4jDU0e7l20jv7x0fzyyhO7NBHt3fPHc/KoZH78+mfsLa/pURzv5x8iKyOJMYN6/83UlnBMp6z29t/MyrQrHHOs5vtGcrZG1kqg/7d0O/n7j/BfXz6RQUlxXTo2OsrFI9fOQBXu/ft63J7uXd2VVzfwaWEZ5/eBqxuwhGM6aXVhOUMGxDMipfet0WF6ZnhyPyYMToqopaf/vmo3f1q2g7OyBnHhCd37sh+VlsBDl0/l08IyvvrXT7s1A8G/txTj9mivnl3AV9glHBFJFZHXRKRaRIpE5IYO9r1fRD4TkaMisktE7m+1vVBEakWkyvt4P/DvoHdaXVjGzNEptv6NadNZWel8UlBGTUNTqEM5rk93lfLAKxtRhdyCsh5NVZOZmoBLYPn2Eq594mM+6eJyAu/nH2TowHhOHD6w2zFEkrBLOMCjQAOQASwAHhORqe3sK8DNQApwIXC3iFzXap9LVTXJ+zg/UEH3ZvsqatlfWcfsCB32agIve+JgGtwePt4Z/uu3vLJmb8tdNE3unq05k7urrOXfTR7lrpfWsGFvRaeOrW1w8+H2w5w/JaPP/JALq4QjIonAVcBPVLVKVVcAbwI3tbW/qv5WVdeoapOqbgXeAOYGL+K+YXWht//GRqiZdswek0JCbFRE9ONU1TlXYVF+WHNmztg0YqNdzrminJtgv/znj/jtu1uoa3R3eOyH2w9T1+jh/Kl9o/8GQMJpKKOIzABWqmqCT9l9wFmqeulxjhVgDfCEqj7uLSsE+uEk1rXA/aq6vp3jbwduB0hPT5+5aNGinr+hDuwod7OlzM2k1CjGp0QF9LW6q6qqiqSkJJ7Lr+ej/U08ek4CUa6+8UvMV3M99GWdqYNH8urYV+Xht2f2C9tf7KrK/R/WkhwnTE+P6vLfX1v14Pu3PDTJxctbGli+r4lhicIFo2M42qBtvs5TG+pZW9zEH89OIDqC/q46+izMnz8/T1VntXdsuE1tkwQcaVVWCfTvxLEP4iSWv/qULcBJQgJ8G3hPRCap6heueVX1SeBJgIkTJ2p2dnZXY2/Th1sP86/P9jN0YD+S4qIpPlrPlgNHWLGjBI9CbLSbhbeF56SHOTk5ZGdn8+t1HzJ7bBznnH1qqEMKieZ66Ms6Uwd74ov4yeufkXnCbMamh2eC3nzgCCXvLefeC0/ghlNHdfn4tuohu9U+l5wHOVuL+d6i9fw1vwGAKGni0mnDOHHEQNL7xzEoKZb1y1YzOn0AKeNOCMu///b05O8hqAlHRHKAs9rZvBL4FjCgVfkA4Ohxzns3Tl/OPFWtby5X1ZU+u/1KRG4B5gFvdS3y7vl/Gw5w50trjimLi3YRH+NqWbe8ocnD31ftDtsPXGVtI1sPHeXiE4eGOhQT5rKz0gFneHS4Jpwl3psszw3wnGXZEwdz45xR/PHfO1DArcqb6/fxeqsbRTcfOMKCvwRmuYNwFNQ+HFXNVlVp53EGsA2IFpEJPodNA9qdQ0JEvg48AJyjqsdbDUpxrnYCrqy6gR+9vrHluUvgnrPHs+UXF/LMV08hPsZp9xXg1TX7eGfjgWCE1WVrdpejCrPshk9zHCNTExibnkhOGA+PXrz5ENNHJjN4QODnLDszazBx3r/z+BgX//iP01j30/NY/N0zuXbWiOaVPlpm2u4LwmrQgKpWA68CD4lIoojMBS4Hnm9rfxFZAPwSOE9VC1ptGyUic0UkVkTivUOmB+FcSQVUbYObrz+7iqr6JmKjnA9cbLSLsyYORkSYmZnCi7fO4d7zJ/K3r53CtJHJ3PXSGhZ+ujvQoXXZ6sIyolzC9JHJoQ7FRIDsrMHkFpRS29Bxh3koHKysY8PeSs6bEpx7Xnz/zl+8dQ4zR6eSnBDLhIz+XDN7VEsy6ktrS4VbHw7AncAzQDFQCtyhqvkAIjIPeEdVm6/XHwbSgFU+nZQvqOo3cfp9HgPGAXXAOuAiVQ3oT4kmt4dvLVzL+r0VPLZgJun948gtKGXO2LRjLplnZqa0PJ81JoU7X1zDD1/dSHlNA3ecNS5sOl1XFZZzwrABJMSG40fFhJvsiek8s3IXubtKmR9mK1cu3uw0p50fpIQDx/6dty5/8dY5bX439GZh9y2iqmXAFe1sW44zsKD5+ZgOzpMPnOT3ADugqvzkjXyWbD7EQ5dPbbmD+XgfpoTYaJ66eRb3/WM9v313K1sOHGHikP7MGTsopB/EJo+yfk8FN87JDFkMJrKcMiaV+BgXH2w9HH4JZ9MhMtMSGD84PPqX2ktGvVnYJZxI9n9Ld7Dw093cmT2Om08b3aVjY6Jc/M8102lo8vDm+gPI+gPExewIaWdi4REP9U0em7DTdFp8TBSnjU0jZ2sx0N792sF3tK6Rj3eWcMtpo8Om9aAvCqs+nEiVV1TOHS/k8fvF27hyxnDuv2Bit87jcgknDHcG6YVDZ+L2cmd995k2YafpguyJgyksraGwpDrUobT4cFsJjW4NWv+NaZtd4fRQXlE51z+ZS4Pbg0vg2tkje/QLas7YQcRF76C+yROQtdO7Ylu5mzGDEknv37WZdE3flj3RGR79wbbDjA6TKfcXbzpISkJMn2vCCjd2hdNDy7YU0+B2rgQEWN2DiQDBadd96bY5ZKYmkNwvJmSjw1SV7eVu+wM1XZaZlsjQgfH87aPCHk2M6S+Nbg9LtxRz9qQMoqPsKy+UrPZ7QFVZVeg0ebn8OLxxZmYKP7x4EiXVDSzdUtzj83XHzsPVVDVi/Temy/KKyik+Wk9BSTULnsoNedJZtauMI3VNnDclvAYx9EWWcNpQWa+d+iNZtHoPn+wq5+bTMvle81h7P10RnDs5g4wBcbyQW+SX83VVXpFN2Gm6J7egtGW56fowuKlx8eZDxEa7mDchPaRxGEs4bSqvV244zi+zgsNVPPjmJk4fl8aDl07lrvnj/dr8FB3l4rrZo/hw+2F2l/ZsCdvuePezg8S6oKK6IeivbSJb8wzKzUJ5layqLN50iDPGDyIxzrqsQ80STjvqmzy8vnZfm9sa3R6+8/d1xEa7+MM103EFaKbX608ZhUuEFz8N7lVOXmEZOVsP0+CBBU9/EvImERNZmm9qvGL6MBQoqQrdj5YtB4+yt7zWRqeFCUs47RBg4ardLFq15wvbHlmyjQ17K/n1lScyZGDg5mQaMjCecycP5h+r91LfFLypQt7acKBlgapQD802kWlmZgq/v2Y6mWkJPLW84PgHBMhi72Sd5wR4sk7TOZZw2pASJzzztdnMGZPG91/ZwI9e20hDkzMS7ZOCUv6cs5NrZ43koiDMoHzjnEzKqht4Z+PBgL9Ws+Zlgl30rXmejH9FuYRvnDGGtbsrWvoEg21J82Sd/QM/Wac5Pks4bRgYJ8yfOJhnvzab/zhrLC9+spvrn8rlzXX7uO251WT0j+Onl04JSixzxw1idFpCUAcPbNhbyeSh/blyQkyfmTbdBMZXZo5gYL8Y/rJ8V9Bfe3H+ITbsrWTKsNYrnphQsYTTgegoFz+8aDKP3nAyn+2r5J6X13Gkromymka2HOxwiR6/cbmEBadmsrqonC0HW69N53+7SqrZcvAoV88cyZfGxVqyMT2SEBvNglNH8V7+waAOfskrKueOF/MAeCVvr/VDhglLOJ1wyUlDueGUz1cHdLuD26/xlZkjiI12BeUq5718p+nughP6zjrrJrBuOX00US7hmZXBu8rJLSilybvKYVOQ/15N+yzhdNKXpg1rWTQt2P0aKYmxfOmkoby2Zh9V9U0Bfa13PzvISSMGMjy5X0Bfx/QdGQPiuXTaMBat3kNlTWNQXrN5hg7B+iHDiSWcTvrCYkpBbmq6cU4m1Q3udodq+8OBylrW7anggql2dWP869YzxlLT4OalIC0y2PzD7JpZI6wfMozYnVBdEMr1K2aMTGbK0AE89WEBlbUNAVkr573PnOa0i6w5zfjZlGEDmDs+jWc/2sU3zhhzzI2hgbB0czH946L5xRUnBvy1TOfZ/4kIISKcOWEQRWU1/P79bSz4i//nqHo3/yBZGUmMTQ+PBapM73LrvLEcOlLPvzbuD+jreDzK0q3FnDkx3ZJNmAm7/xsikioir4lItYgUicgNHez7oIg0ikiVz2Osz/bpIpInIjXe/04PzrsIjPjYKAA86v8bMkur6vl0VxkXWnOaCZCzJqQzfnAS/7tkO48u2x6wkWMb91Vy+Gg950yymz3DTdglHOBRoAHIABYAj4lIR0sH/l1Vk3weBQAiEgu8AbwApAB/A97wlkekeRPSiYlyptFxufy7Vs6SzYfwqI1OM4HjcgnnTXYWZwvUVTrAv7cU4xJnITgTXsIq4YhIInAV8BNVrVLVFcCbwE3dOF02Th/VI6par6p/xBm0cra/4g02Z+DCqaQmxjIoKY6TRgz027nf/ewgI1P7MWWo3SRnAic+JnBX6c2WbjnEyaNSSE2M2N+WvVa4DRrIAppUdZtP2XrgrA6OuVREyoADwP+p6mPe8qnABm2eJ92xwVv+buuTiMjtwO0A6enp5OTkdPtNBNrNE4VH1tTx8xf+zbmZMT0+X02j8uG2Gs7LjOaDDz5oKa+qqgrreggWqwf/1UHiUTcucRKOSyCuooicnL09D9CrvM7DZ/tq+UpWTED+n9lnoWd1EG4JJwlofTt9JdC/nf0XAU8Ch4BTgVdEpEJVF3rPVdnZc6nqk95zMXHiRM3Ozu5O/EFxliqfVn7Cv4qO8P1r5zIgvmdJ5411+3DrOm676JRjRr7l5OQQzvUQLFYP/quDbGDwmH3c8/I6rjslk1uvOKHH5/T10ie7gY3cfslpZGW097XRffZZ6FkdBLVJTURyRETbeawAqoDWbToDgDbnkVHVTaq6X1XdqvoR8L/AV7ybu3SuSCIi/OfFk6mobeTPy3b2+HzvfnaQwf3jmBGi5axN33LZ9OHMHp3Cyh0lHNsA0XP/3nyIESn9mDDYRlqGo6AmHFXNVlVp53EGsA2IFpEJPodNA/I7+xI4/TR4jzlJRHwXqzmpC+cKaycMH8iXZwznmZW72Fve/Tmqahvc5Gw9zAVThwRsXR9jWrtm1kgKSqpZVei/QQO1DW5W7Cjh3MkZHPtnb8JFWA0aUNVq4FXgIRFJFJG5wOXA823tLyKXi0iKOE4B7sEZmQaQA7iBe0QkTkTu9pYvDeibCKL7zp+IAL97b2u3z/Hh9sPUNrq50EanmSC65KShJMVF8/Iq/8088HFBCfVNHs624dBhK6wSjtedQD+gGFgI3KGq+QAiMk9Eqnz2vQ7YgdNM9hzwG1X9G4CqNgBXADcDFcDXgSu85b3CsOR+3DpvDK+v28+GvRXdOse7nx0kOSGGU8ak+jk6Y9qXEBvNZdOH8f82HuBInX/mV1uyuZjE2ChOHWuf5XAVdglHVctU9QpVTVTVUar6ks+25aqa5PP8elVN895/M8k79Nn3XGtVdaaq9lPVk1V1bTDfSzB886xxpCXG8l//2tzl9vBPCkr518YDnDwymZiosPsomF7u2lkjqWv08Oa6ns88oKos3VzMvAnpxEVH+SE6Ewj2LRPh+sfH8J3zsvhkVxn3Llrf6Rvp8orKufHpT2ho8rB8R4mtF2KC7qQRA5k0pD9/b2MZ967K33+Eg0fqONuWkg5rlnB6gYkZSQjw2tp9XPvEx3y0o6TD/TftP8L3Fq2j0e1cEXk8auuFmKATEa6dPZKN+yrJ39/6DoauWbqlGBGYb7MLhDVLOL3AqsJymgflNHmUrz27it++u4XiI3XH7Hegspb7/rGeS/60nMNH64l2SUjW9zGm2ZdnDCc22sWiHl7l/HtLMdNGJJPeP85PkZlACLcbP003zBmbRmy0i8YmD1FRLqaPTOaxD3by1PICLps2nFmjU3hj3T7WFJUDwm3zxnJX9nh2HK4it6CUOWPTbL0QExLJCbFcOHUIr63dxw8vntwy9U1XFB+tY/2eCr53XlYAIjT+ZAmnF2heHM43eRSVVvPMil0s/HQPr6xxpg5xCTx6wwwuOnFoy3GWaEyoXTt7JG+u3897+Qe5fPrwLh+fs+UwAOdMzvB3aMbPLOH0Eq2TR2ZaIj+//AQG9Ivh/5buaLkjtqCkOmQxGtOW08amMTK1Hy9/uqdbCeefeXvoHxdNbUNgl183PWd9OL1c9sTBxMW4rK/GhC2XS7hm5kg+LiilqLRrP4g+3lnCp4XlHK1vYsHTn9hoyzBnCaeXa25uu/f8iba2uwlbX5k1AgF+8MqGLiWN373/+cTygVruwPiPJZw+YGZmCnfNH2/JxoSt/RV1iEBuQVmnF2Z76ZPd5BWVE2WjLSOG9eEYY0Iut6CU5oky6ho9LN9+uMMfSB/tLOGnb3zGWVnp3DV/HKsKy220ZQSwhGOMCbk5Y9OIi3FR3+hBgbc3HOCmOZmkJX3xvppdJdXc8cIaRg9K5E83zGBAfAynjLErm0hgTWrGmJBr7mu874KJ/ODCiewpq+Erj3/M7tJjl96orG3kG39bhUvg6Vtm9XjxQRNclnCMMWGhua/xjuzxvHTbqZTXNHDlYyvZuNeZ9qbJ7eHul9awp6yGx2+cSWZaYogjNl1lCccYE3ZmZqbyz2+eTlx0FNc9+TFPryjgmic+Zvn2Ev7rihM51QYHRCRLOMaYsDR+cBKv3nk6g5Li+MXbm1mzu4IolzDOlo+OWJZwjDFhK2NAPJfPGPZ5gdrM5pHMEo4xJqydlTWYeJsto1cIu4QjIqki8pqIVItIkYjc0MG+74hIlc+jQUQ2+mwvFJFan+3vB+ddGGP8xWbL6D3C8T6cR4EGIAOYDvxLRNaran7rHVX1It/nIpIDLG2126WquiRAsRpjgsBmNu8dwuoKR0QSgauAn6hqlaquAN4EburEsaOBecBzgYzRGGNM94RVwgGygCZV3eZTth6Y2oljbwaWq2phq/IXReSwiLwvItP8FKcxxpguCrcmtSTgSKuySqB/J469GXi4VdkCYA3OUjDfBt4TkUmqWtH6YBG5HbgdID09nZycnK5F3gtVVVVZPWD1AFYHzaweelgHqhq0B5ADaDuPFcAMoKbVMd8D3jrOec8AqoCk4+y3BadPp8M4s7Ky1KguW7Ys1CGEBasHq4NmVg8d1wGwWjv4bg3qFY6qZne03duHEy0iE1R1u7d4GvCFAQOt3AK8qqpVxwsB52rHGGNMkIVVH46qVgOvAg+JSKKIzAUuB55v7xgR6QdcAzzbqnyUiMwVkVgRiReR+4FBwMqAvQFjjDHtCquE43Un0A8oBhYCd6h3SLSIzBOR1lcxVwAVwLJW5f2Bx4ByYB9wIXCRqtptysYYEwLhNmgAVS3DSSJtbVuOM7DAt2whTmJqvW8+cFIgYjTGGNN14XiFY4wxpheyhGOMMSYoLOEYY4wJCks4xhhjgsISjjHGmKCwhGOMMSYoLOEYY4wJCks4xhhjgsISjjHGmKCwhGOMMSYoLOEYY4wJCks4xhhjgsISjjHGmKCwhGOMMSYoLOEYY4wJCks4xhhjgsISjjHGmKCwhGOMMSYowi7hiMjdIrJaROpF5NlO7P9dETkoIkdE5BkRifPZNlpElolIjYhsEZFzAxq8McaYdoVdwgH2Aw8DzxxvRxG5AHgAOAfIBMYCP/fZZSGwFkgDfgT8U0TS/R2wMcaY4wu7hKOqr6rq60BpJ3a/BXhaVfNVtRz4BfBVABHJAk4Gfqaqtar6CrARuCowkRtjjOlIdKgD6KGpwBs+z9cDGSKS5t1WoKpHW22f2taJROR24Hbv03oR+SwA8UaaQUBJqIMIA1YPVgfNrB46roPMjg6M9ISTBFT6PG/+d/82tjVvH97WiVT1SeBJABFZraqz/Btq5LF6cFg9WB00s3roWR0EtUlNRHJERNt5rOjGKauAAT7Pm/99tI1tzduPYowxJuiCmnBUNVtVpZ3HGd04ZT4wzef5NOCQqpZ6t40Vkf6ttud3/x0YY4zprrAbNCAi0SISD0QBUSISLyLtNf09B3xDRKaISDLwY+BZAFXdBqwDfuY9x5eBk4BXOhHGkz19H72E1YPD6sHqoJnVQw/qQFTVn4H0mIg8CPysVfHPVfVBERkFbAKmqOpu7/73Aj8A+uEkk2+qar1322icBHQqsBu4S1WXBP5dGGOMaS3sEo4xxpjeKeya1IwxxvROlnCMMcYEhSUcHyKSKiKviUi1iBSJyA2hjikYOpq/TkTO8c5DV+Odl67DG7silYjEicjT3v/vR0VknYhc5LO9r9TDCyJywDs34TYRudVnW5+oA18iMkFE6kTkBZ+yG7yfk2oReV1EUkMZYyB5b2WpE5Eq72Orz7Yu14MlnGM9CjQAGcAC4DERaXNmgl6mzfnrRGQQ8CrwEyAVWA38PejRBUc0sAc4CxiIM+JxkXcC2L5UD78CRqvqAOAy4GERmdnH6sDXo8Cq5ife74MngJtwvidqgD+HJrSguVtVk7yPidD9erBBA14ikgiUAyd4h1QjIs8D+1T1gZAGFyQi8jAwQlW/6n1+O/BVVT3d+zwRZ0qLGaq6JWSBBomIbMCZDDaNPlgPIjIRyAG+DSTTx+pARK4DrsQZGTteVW8UkV/iJOQbvPuMAzYDaa2m0eoVRCQHeEFV/9KqvFv1YFc4n8sCmpqTjVe7c6/1EVNx6gAAVa0GdtIH6kREMnA+E/n0sXoQkT+LSA2wBTgA/D/6Xh0MAB4C7m21qXU97MRpFckKXnRB9ysRKRGRlSKS7S3rVj1YwvlcEnCkVVklzrxsfVV789H16joRkRjgReBv3l/vfaoeVPVOnPc2D6cZrZ4+Vgc4M88/rap7W5X3tXr4Ac6yL8Nxbvh8y3s10616sITzOZt77Yv6XJ2IiAt4HufX2t3e4j5XD6rqVtUVwAjgDvpQHYjIdOBc4H/a2Nxn6gFAVT9R1aOqWq+qfwNWAhfTzXqI9Nmi/WkbEC0iE1R1u7esr8+9lo+z5hDQ0m4/jl5aJyIiwNM4naAXq2qjd1OfqodWovn8vfaVOsgGRgO7nY8ESTjTbE0B3sVn/kYRGQvE4Xx/9AUKCK3msex0PaiqPbwP4GWcVUITgbk4l4hTQx1XEN53NBCPM0Lpee+/o4F0bx1c5S37DZAb6ngDWA+PA7lAUqvyPlEPwGDgOrxfsMAFQDXOaLU+UQfeekgAhvg8fgf801sHU3Ga3ud5vydeAF4OdcwBqodk72eg+ftggffzkNXdegj5mwqnB85wz9e9lbobuCHUMQXpfT/o/eXi+3jQu+1cnM7jWpwRS6NDHW+A6iDT+77rcJoLmh8L+ko9eL9QPwAqvF8mG4HbfLb3+jpop14exBmp1fz8Bu/3QzXOApCpoY4xgJ+HVTjNZBXeH2Pn9aQebFi0McaYoLBBA8YYY4LCEo4xxpigsIRjjDEmKCzhGGOMCQpLOMYYY4LCEo4xxpigsIRjjDEmKCzhGBMhRGSAiDwoIpNDHYsx3WEJx5jIMQv4GRAT6kCM6Q5LOMZEjhk4SwVsCnUgxnSHTW1jTAQQkc3ApFbFPOd6cwAAAOJJREFUr6rqVaGIx5jusIRjTAQQkdk4s5nnA7/0Fh9Q1aLQRWVM19h6OMZEhvU4i6H9SVVzQx2MMd1hfTjGRIapQCywJtSBGNNdlnCMiQwn46zXsy7UgRjTXZZwjIkMM4Cdqnok1IEY012WcIyJDFOw4dAmwtmgAWMiQwVwsohcAFQC21W1NMQxGdMlNizamAggIicATwMnAfHAPFVdEdqojOkaSzjGGGOCwvpwjDHGBIUlHGOMMUFhCccYY0xQWMIxxhgTFJZwjDHGBIUlHGOMMUFhCccYY0xQWMIxxhgTFP8fNzUEombBA5UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uk-Mf63GjGa8"
      },
      "source": [
        "---\n",
        "# Forecasting several steps ahead"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPD-XQ8njBFE"
      },
      "source": [
        "np.random.seed(43) # not 42, as it would give the first series in the train set\n",
        "\n",
        "series = generate_time_series(1, n_steps + 10)\n",
        "X_new, Y_new = series[:, :n_steps], series[:, n_steps:]\n",
        "X = X_new\n",
        "for step_ahead in range(10):\n",
        "    y_pred_one = model.predict(X[:, step_ahead:])[:, np.newaxis, :]\n",
        "    X = np.concatenate([X, y_pred_one], axis=1)\n",
        "\n",
        "Y_pred = X[:, n_steps:]\n",
        "\n",
        "Y_pred.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "tZKe8_oSjdIF",
        "outputId": "5a6d1fc7-0b8a-4fff-d670-a44bf583fc24"
      },
      "source": [
        "def plot_multiple_forecasts(X, Y, Y_pred):\n",
        "    n_steps = X.shape[1]\n",
        "    ahead = Y.shape[1]\n",
        "    plot_series(X[0, :, 0])\n",
        "    plt.plot(np.arange(n_steps, n_steps + ahead), Y[0, :, 0], \"ro-\", label=\"Actual\")\n",
        "    plt.plot(np.arange(n_steps, n_steps + ahead), Y_pred[0, :, 0], \"bx-\", label=\"Forecast\", markersize=10)\n",
        "    plt.axis([0, n_steps + ahead, -1, 1])\n",
        "    plt.legend(fontsize=14)\n",
        "\n",
        "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "code",
      "metadata": {
        "id": "_OdioAC5jg_D"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}