StockPrediction_LSTM.ipynb

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  "metadata": {
    "colab": {
      "name": "StockPrediction_LSTM.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/ikyath/RNN_Introduction_Fundamentals/blob/master/StockPrediction_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8H94yZSakwa",
        "colab_type": "text"
      },
      "source": [
        "**Stock Time Series**\n",
        "\n",
        "This suite of APIs provide realtime and historical global equity data in 4 different temporal resolutions: (1) daily, (2) weekly, (3) monthly, and (4) intraday. Daily, weekly, and monthly time series contain 20+ years of historical data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Um31HmrtFU6y",
        "colab_type": "code",
        "outputId": "a025b8ee-af6c-4900-d591-3783c9d5f70b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "!pip install alpha-vantage"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting alpha-vantage\n",
            "  Downloading https://files.pythonhosted.org/packages/c9/72/c3a2a8371f94515a2f0b1203dc152cf8d82dc17baeea0c1af5a23d10eca7/alpha_vantage-2.1.3.tar.gz\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from alpha-vantage) (2.21.0)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->alpha-vantage) (2.8)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->alpha-vantage) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->alpha-vantage) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->alpha-vantage) (2020.4.5.1)\n",
            "Building wheels for collected packages: alpha-vantage\n",
            "  Building wheel for alpha-vantage (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alpha-vantage: filename=alpha_vantage-2.1.3-cp36-none-any.whl size=14870 sha256=50bb649633fa0a6040665935ce031ad837efb2e7a56085db27f65cf602acadcc\n",
            "  Stored in directory: /root/.cache/pip/wheels/d4/75/c2/483b0a988fd965b1128c1396a0ffadfb8903bd2bb569220048\n",
            "Successfully built alpha-vantage\n",
            "Installing collected packages: alpha-vantage\n",
            "Successfully installed alpha-vantage-2.1.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNszdLGpYgMQ",
        "colab_type": "text"
      },
      "source": [
        "Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdk2tg2OFNvi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,LSTM,Dropout,Bidirectional\n",
        "from alpha_vantage.timeseries import TimeSeries"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxIGdt9snoe0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def alpha_vanatage(symbol,outputsize):\n",
        "  ts = TimeSeries(key='5W7LO5BBKDO63VCC', output_format='pandas',indexing_type='integer')\n",
        "  dataset, meta_data = ts.get_daily(symbol=symbol, outputsize=outputsize)\n",
        "  return dataset\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dXlcoCtFQXE",
        "colab_type": "code",
        "outputId": "060b9f87-cf27-440e-ea6a-ad99a0e0b7f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset = alpha_vanatage('AAPl','full')\n",
        "print(dataset.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5032, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcY0D2CZsYhg",
        "colab_type": "text"
      },
      "source": [
        "## **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jShR1Gd4FgFi",
        "colab_type": "code",
        "outputId": "2cf2832b-e6f4-4aa2-c5ff-236a9fcfa1a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>1. open</th>\n",
              "      <th>2. high</th>\n",
              "      <th>3. low</th>\n",
              "      <th>4. close</th>\n",
              "      <th>5. volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-04-17</td>\n",
              "      <td>284.69</td>\n",
              "      <td>286.9450</td>\n",
              "      <td>276.8600</td>\n",
              "      <td>282.80</td>\n",
              "      <td>53451248.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-04-16</td>\n",
              "      <td>287.38</td>\n",
              "      <td>288.1975</td>\n",
              "      <td>282.3502</td>\n",
              "      <td>286.69</td>\n",
              "      <td>39281290.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-15</td>\n",
              "      <td>282.40</td>\n",
              "      <td>286.3300</td>\n",
              "      <td>280.6300</td>\n",
              "      <td>284.43</td>\n",
              "      <td>32788641.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-14</td>\n",
              "      <td>280.00</td>\n",
              "      <td>288.2500</td>\n",
              "      <td>278.0500</td>\n",
              "      <td>287.05</td>\n",
              "      <td>48748672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-13</td>\n",
              "      <td>268.31</td>\n",
              "      <td>273.7000</td>\n",
              "      <td>265.8300</td>\n",
              "      <td>273.25</td>\n",
              "      <td>32755731.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            index  1. open   2. high    3. low  4. close   5. volume\n",
              "index                                                               \n",
              "0      2020-04-17   284.69  286.9450  276.8600    282.80  53451248.0\n",
              "1      2020-04-16   287.38  288.1975  282.3502    286.69  39281290.0\n",
              "2      2020-04-15   282.40  286.3300  280.6300    284.43  32788641.0\n",
              "3      2020-04-14   280.00  288.2500  278.0500    287.05  48748672.0\n",
              "4      2020-04-13   268.31  273.7000  265.8300    273.25  32755731.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTlPEgArPKSU",
        "colab_type": "text"
      },
      "source": [
        "Lets sort the data in increasing order of date"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9zsGEzIPIx5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = dataset.iloc[::-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NM1hp2meP0kC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = dataset.set_index(pd.Series(range(dataset.shape[0])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQurKi6zPc_l",
        "colab_type": "code",
        "outputId": "5cd6c89d-275b-4c8d-e88f-2acfdffe309c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>1. open</th>\n",
              "      <th>2. high</th>\n",
              "      <th>3. low</th>\n",
              "      <th>4. close</th>\n",
              "      <th>5. volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000-04-17</td>\n",
              "      <td>109.50</td>\n",
              "      <td>123.94</td>\n",
              "      <td>109.06</td>\n",
              "      <td>123.87</td>\n",
              "      <td>3656800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000-04-18</td>\n",
              "      <td>123.50</td>\n",
              "      <td>126.87</td>\n",
              "      <td>119.37</td>\n",
              "      <td>126.87</td>\n",
              "      <td>3490400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000-04-19</td>\n",
              "      <td>126.19</td>\n",
              "      <td>130.25</td>\n",
              "      <td>119.75</td>\n",
              "      <td>121.12</td>\n",
              "      <td>4644200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-04-20</td>\n",
              "      <td>123.69</td>\n",
              "      <td>124.75</td>\n",
              "      <td>117.06</td>\n",
              "      <td>118.87</td>\n",
              "      <td>6447500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-04-24</td>\n",
              "      <td>115.00</td>\n",
              "      <td>120.50</td>\n",
              "      <td>114.75</td>\n",
              "      <td>120.50</td>\n",
              "      <td>3960900.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        index  1. open  2. high  3. low  4. close  5. volume\n",
              "0  2000-04-17   109.50   123.94  109.06    123.87  3656800.0\n",
              "1  2000-04-18   123.50   126.87  119.37    126.87  3490400.0\n",
              "2  2000-04-19   126.19   130.25  119.75    121.12  4644200.0\n",
              "3  2000-04-20   123.69   124.75  117.06    118.87  6447500.0\n",
              "4  2000-04-24   115.00   120.50  114.75    120.50  3960900.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqOtpnUnOe6a",
        "colab_type": "text"
      },
      "source": [
        "Lets us visualize the complete trend from 2000 to current day."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNe1PEyAN9rL",
        "colab_type": "code",
        "outputId": "7ae51900-2d92-41b8-f060-7c5d4314fea3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "plt.figure(figsize=(25,8))\n",
        "plt.plot(dataset['1. open'],color='green')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6122a9c358>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqiiW50zTLHk",
        "colab_type": "text"
      },
      "source": [
        "We are trying to predict the trend in last 60days, \n",
        "Please find below for the actual trend in last 60 days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlUv5oR-OqQh",
        "colab_type": "code",
        "outputId": "9f509eab-a1ee-4255-c1fb-1f582743a92d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "plt.figure(figsize=(25,8))\n",
        "plt.plot(dataset.iloc[-60:,1:2],color='red')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6122969cc0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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geXPYbjt4/PGsp8mdU06JLcKJE2GZZbKepup+/BFatYqQ+c03oW7drCfKrzSFvfeOB3xGjYru23yZNSseoCgriweWsggla6P99osHRz7+OLbUq8Njj8UGc/PmUTPTsmX1XLcinnkGunWLZ8r85z/x51uSJEmSJKq42ZwkyUpJkixb/nZj4K/Ah0mS9AR2AQ6aGzSXexLonoT2wORFBc1aAp9/DiefDGuuGa+bN4+w4qOP4gC2uUEzwLHHRv9vbdtuvueeCDJPPjnrSXKra9cIEZ99dvG3rQlOOSXqJfr1q/1BM0QX9Q03QIMGUe+yhA/iLZHbb4/t1yuuMGjOpcsui7+D1fVv5vXXw777RoXO4MGFFTQD7L579IJvuCHss088a2bmzKynkiRJkiQVuIrUaKwGvJokyXvAUODFNE2fBm4BVgHeTJJkZPnGM8AAYBzwCXA7cEzuxy4yb74ZW3ctW0ZAsccecRjZ669HSLmgMG+ppeDss+HVV+Pp9rVBWVnUFGyxBWy/fdbT5FaHDrDyyrWjSuPFF+Guu+D002MDt1issUYEwK+8Epug+TB5Mpx/PpSUwJ575ucaxaply3jQ7j//gZEj83edsjI49dS41l57wcsvw4or5u96VbHWWnEo6vHHw7//DTvsEIeySpIkSZK0EEtco5EP1mgswOzZsbV89dVx6Niyy8bG5HHHxWZzRcyYAeutF7cfNCi2L2uyZ5+Fzp3h3nvjqee1zZFHwgMPRJVGo0ZZT1M5U6dC69axcfvuuzX366issjLYcUd47z0YMyY6cHPpzDNjA7e2Hv6ZtUmTogJl003hpZdy/2/mjBlw6KHRZ37ssXDttTVn8/+hh6BnT2jYEO67D3bZJeuJJEmSJEkZqfIBgapGkyfHIVUtW8IBB0RlxA03xDbZZZdVPGiGCAXOPTc2o2tDPcPVV0eX6n77ZT1JfnTtClOmxKZjTdW7N3z2WVQ9FFvQDHHY3G23Rbf4CSfk9r6/+CI2+7t1M2jOl2WXjc3xV16JzuJc+vnnCGgfegguvzyepVJTgmaI/x4NGwarrQa77Zbf7W9JkiRJUo1l2Fwoxo2DE0+MMPlf/4K114Ynnohu1mOPhaZNK3e/hx0WBwiec05+e2Tz7b33YtPw+OOjF7c26tQperdrapXG8OHxgMCRR0LHjllPk50NNoDzzoNHHom/w7ly1lmxaXvJJbm7T/3Z0UfD+uvHv8OzZuXmPsePj0NNBw+OreDTTquZzzTZYIN4MCxN4zBFSZIkSZL+wLA5S2kKb7wRhy+ttx7ceCN06RKh3WuvRSdrVTff6tePbdMRI2puiAnRF7rUUhFk1lYNG8ahXE8+CXPmZD3Nkpk1K55iv8oqsbVZ7E49Nfqqjzkmnq1QVUOGwP33zzsYVPlTvz5ceSWMHRtb6lX17rvRyf7VV/D883DwwVW/zyytvDJsvDGUlmY9iSRJkiSpABk2Z+mdd+Kgu9dei8PUPv8c7rknDsDLpYMPhg03jG3LmhZiAnz7bWwDHnYYLL981tPkV5cu0dk8aFDWkyyZq6+Op9XfeGNUERS7+vWhX7/4s3vGGVW7rzSNLduVV676fali9tgjurfPPz96nCvrpZfi3/g6deKBxR13zN2MWSopiX+jcrX5LUmSJEmqNQybs7TFFnEg3Pjx0KcPrLFGfq5Tty5ceCG8/370hdY0N98coUavXllPkn+77RY1ITVpC/3jj2N7fu+9o3daYautohrnlltg4MDK38/jj8fnX3hh1Kwo/5IkuvN/+in+ba6Me+6Jv88tWkRvfuvWOR0xUyUlcRjoO+9kPYkkSZIkqcAkaQH0+LZt2zYdNmxY1mPUbmVlEW5PnQpjxkC9ellPVDHTpsFf/hJPQ3/yyaynqR677x4PDHz2WeH3uqZpdE2PGAEffBAHOGqeqVMjZKxfP+oUlvTQxJkzo7KgQYP4/Jry97a2OPzwqC/58MPo0a+INIVLL4Wzz46/G489Bsssk985q9u338ZBgZdfHv3TkiRJkqSikiTJ8DRN2y7oY242F4s6deCii+CTT+Duu7OepuLuuw9++CG6aotF167wxRcRLha6O+6IGpgrrzRoXpAmTeDWW+Gjj+Dii5f882++Of7OXnmlQXMWLr44ft8rWl8yezb8858RNB9yCDz7bO0LmgFWXTUOC7S3WZIkSZL0B242F5M0hfbtYyvto4/iQLpClqawySax1fnOO4W/5Zsr338fW4PnnAMXXJD1NAv37bfQqlUchPfqq/GAhhbssMPigZPhw+P3qyJ+/hnWXTeekfDCC8Xz57/Q9O4dfw8HDYJttln47aZOhQMPhKefhjPPhEsuqd3fs6OOggcfjKqRqh5kK0mSJEmqUdxsVkiS2NQbPx769896msV74YWoZjj55Nod2vzRyivDttsWfm/zCSdEzclttxk0L07fvrDcctCzZ8UP6bzkkgicr7qquP78F5pTT40Hf04+OR4AW5Dvv4/D/wYMgJtuip7n2v49KymBX36Jg0ElSZIkSSpnQlRsdt4ZOnaM0HnatKynWbSrr46Q54ADsp6k+nXtCqNGwaefZj3Jgj3xBDzyCJx3XjydXou2wgpw3XUwdGi8Xpxx4+D666MzeNNN8z+fFq5Jkwj+3357wQesfvxxbDyPHh39zP/8Z/XPmIWSknhtlYYkSZIkaT6GzcUmSaK7ecKE6IMtVKNHx2bzccdFjUax6do1XhfidvPkyXDMMXHw3amnZj1NzXHAAfB//xf1KJ99tujbnnFGdAVfdFH1zKZF694dNtssvi/Tp897/1tvRdA8eTK88grstVd2M1a3NdaAli0NmyVJkiRJv2PYXIw6doS//Q0uvRSmTMl6mgX797+hcePoBS1GLVpEuFWIYfOZZ0Zfc79+UL9+1tPUHEkSFQt16sDRRy+8kmHw4NgaP/VUD10sFHXrRhXKF1/AtdfG+554Ajp1igMABw+OPvxiU1ICAwdCWVnWk0iSJEmSCoRhc7G66CL44YeKPaW/un3/Pdx7Lxx6aNQPFKuuXeHNNyPYLRRvvBEb8b16Qbt2WU9T8zRvDpddFlv799zz54+nKZxyStTHuDVeWDp1gj32iEqNPn1g771ju3/wYFhvvayny0ZJSfSKjxqV9SSSJEmSpAJh2Fys2rWDPfeEK6+ESZOynub3br4ZZsyAE0/MepJsdekS4eMTT2Q9SZgxA444AtZaCy68MOtpaq5//jOqF046KR5Ymd8jj0Q1w8UXR1ewCsuVV0bX/dlnw+67R3XGyitnPVV27G2WJEmSJP2BYXMxu/DCCJr79s16knmmT4+qgd139+C51q1hnXUKp0rjrrvgww/hxhuhadOsp6m56tSB22+PCpv5H1CZMSM6gdu0ia1+FZ4NNoBrrone7cce8wGBtdaKF8NmSZIkSVI5w+ZitummsP/+0Y88cWLW04T7749tz5NOynqS7CVJVGm88kocQJal2bPh8stjI75z52xnqQ022ii2Yx94AJ55Jt53/fVxcGDfvtERrMJ03HFRQ1SvXtaTFIaSEnj99YV3kEuSJEmSiophc7Hr3Rt++w2uuCLrSSKsuOaa2Ozs1CnraQpD164waxYMGJDtHA8/DOPGwVlnRQiuqjvjDNh446jV+OyzqM7o3Bl23jnryaSKKymJ/v8PPsh6EkmSJElSATBsLnatWsHf/w433AATJmQ7y0svwejRcPLJBppzdegAq6ySbZVGWVkciLbxxnFAmnKjQQPo1w+++gq23hp+/bUwHvSRloS9zZIkSZKk+Rg2C847L2oS+vTJdo5rrolg9cADs52jkNSpA3vtBc8+G33WWXjqKXj//dhqruM/GTnVvj0cf3zU2BxxRAT6Uk2yzjqwxhpRpSFJkiRJKnomR4KWLeEf/4Bbb4UvvshmhjFjIlA97jho2DCbGQpV165xmNxLL1X/tdM0HoRYZ53o91bu9ekDV10Fl16a9STSkkuS2G4uLbW3WZIkSZJk2Kxy55wTocHFF2dz/X//Gxo1gqOPzub6haxTJ1h66WyqNF55BYYMgdNP90C0fGnSBE45BZZbLutJpMopKYFvv4WPP856EkmSJElSxgybFZo3j6D3P/+BTz6p3mv/8APcfTd07w4rrli9164JGjSA3XeHJ5+MupPq1KcPrLYaHHpo9V5XUs1hb7MkSZIkqZxhs+Y588wINi+4oHqve8st0Ud84onVe92apEuXCOUHDaq+a771Vmw2/+tfVptIWrj114++fcNmSZIkSSp6hs2aZ9VV47Cy++6DDz6onmvOmAE33AC77QatWlXPNWui3XaLwLc6qzT69IEVVoAjj6y+a0qqeextliRJkiSVM2zW7512GjRtCuefXz3Xu/12+O47OOmk6rleTdWsGey8c4TN1RHmvPcePPUU9OoVfx4kaVFKSuCrr+Czz7KeRJIkSZKUIcNm/d4KK0Tw++ijMGJEfq6RpvD887D99rFJ3a5dBKlatK5dYfx4GDky/9e67LIImY87Lv/XklTz2dssSZIkScKwWQty0kmw3HJw3nm5vd+yMnjiiQiXd90VPv8crr8eXnstnoatRdtzT6hTJ/9VGp98Ag89BMccE38OJGlxNtooDng1bJYkSZKkombYrD9bdlk49VR4+uk4JK6q5syBBx+ETTeNg+5+/jnqMz79NDZnGzeu+jWKwUorwXbb5T9svvxyqF/fahNJFZck8WwVw2ZJkiRJKmqGzVqw44+PcPPccyt/H7NmwZ13xsF/Bx0UofO998KHH0LPntCgQc7GLRpdu8Lo0bF9nA9ffQV33QU9esSBkZJUUSUl8YyV8eOznkSSJEmSlBHDZi1Y06Zw5pnw0ktRc7Ekpk+Hm2+G9daDww+P+3r00QhJDzkE6tXLy8hFoUuXeJ2v7ea+faNT+7TT8nP/kmove5slSZIkqegZNmvhjj4aVl89tpvTdPG3nzoVrrkG1lkn+n5XWw2eeQaGD4d99om+YVVNixaw+eb5CZsnToTbbosHBNZaK/f3L6l2a906apgMmyVJkiSpaJn+aeEaN4ZzzoE33oAXXlj47SZPhj59Igg9+WTYcEN4+WUYPBg6d/bwv1zr2hXefBMmTMjt/V53HUybBqefntv7lVQc6ta1t1mSJEmSipxhsxatR4/Ycj3nnD9vN//wQ2w9r7UWnH02tGsHgwbBK69Ap06GzPkyt0rjiSdyd5+TJ8P118Pee0fHtiRVRklJdMp/803Wk0iSJEmSMmDYrEVr0ADOPx+GDYMnn4z3ffstnHpqbDJffDHstFNUZTzzDGyzTabjFoVNNoGWLXNbpXHzzRE4n3lm7u5TUvGxt1mSJEmSipphsxavW7c47O+cc+D44yNkvvrq2LAdPRr++1/YYouspyweSRJVGq+8ApMmVf3+fvstvp+77AJbbln1+5NUvDbbDJo1M2yWJEmSpCJl2KzFq1cPLrggguVbb4W//x3GjoV774WNN856uuLUtSvMng0DBlT9vu64Iw4HPPvsqt+XpOJWrx5st51hsyRJkiQVqXpZD6Aa4oADoE4d6NAB/vKXrKdR+/aw6qpRpXHwwZW/n5kz4YorIhzafvvczSepeJWUwLPPwnffwSqrZD2NJEmSJKkaudmsiqlTJwJng+bCUKcO7LVXBDrTplX+fu67D778Es46K3ezSSpuc3ubX3892zkkSZIkSdXOsFmqqbp2halT4aWXKvf5c+bAZZdFx+quu+Z2NknFa8stoUkTqzQkSZIkqQgZNks11Y47wjLLRJVGZTz2GHz0UWw1J0luZ5NUvOrXh222MWyWJEmSpCJk2CzVVA0awO67w5NPxmGBSyJNoU8f2GAD2Hvv/MwnqXiVlMShsj/8kPUkkiRJkqRqZNgs1WRdu8KPP8IbbyzZ5z33HIwcCWecAXXr5mc2ScVrbm/zwIHZziFJkiRJqlaGzVJNtuuu0LDhklVppClcckkc9njIIfmbTVLx2moraNTIKg1JkiRJKjKGzVJN1rQp/PWv8PjjESJXxMCBMGgQnHpqdKtKUq41bAgdOhg2S5IkSVKRMWyWarquXWH8eBgxomK379MHVl4ZevTI71ySiltJCbz7Lvz8c9aTSJIkSaSi+b0AACAASURBVJKqiWGzVNPtsQfUqVOxKo3hw+H55+Gkk6Bx4/zPJql4lZTEMy6WtFNekiRJklRjGTZLNd1KK8H221csbL70UlhmGTjmmPzPJam4bb01NGhglYYkSZIkFRHDZqk26NoV3n8fPv544bcZMwYeewyOPx6WXrr6ZpNUnBo3jsDZsFmSJEmSioZhs1QbdOkSrxe13XzZZRH+9OpVPTNJUkkJvPMO/PJL1pNIkiRJkqqBYbNUG6y1FmyxxcLD5s8/h/vugyOPhBVXrNbRJBWxkhIoK4NBg7KeRJIkSZJUDQybpdqia1d46y345ps/f+zKK+MQwVNOqf65JBWvDh2gXj2rNCRJkiSpSBg2S7XF3CqNJ574/fu//Rb694dDD4U116z+uSQVryZNYKutDJslSZIkqUgYNku1xcYbw7rr/rlK45prYNYsOP30bOaSVNxKSmDYMJg6NetJJEmSJEl5Ztgs1RZJElUar74KkybF+376CW66CQ44IIJoSapuJSUweza8+WbWk0iSJEmS8sywWapNunaNUOeZZ+LXN9wAU6bAGWdkO5ek4rXttlC3rlUakiRJklQEDJul2mTrrWG11aJKY8oUuPZa2GMPaNMm68kkFatmzWCLLQybJUmSJKkIGDZLtUmdOrDXXvDssxE0//QTnHVW1lNJKnYlJfD22zBtWtaTSJIkSZLyyLBZqm26doXffoPzz4dOnaB9+6wnklTsSkpg5swInCVJkiRJtZZhs1Tb7LADLLMMzJnjVrOkwrDddnGIqVUakiRJklSr1ct6AEk51qAB/OMfMGZMbDZLUtaWXRY228ywWZIkSZJqOcNmqTa6+uqsJ5Ck3yspgVtugRkzoGHDrKeRJEmSJOWBNRqSJCn/Skpg+nQYOjTrSVSIjj8ejj0W0jTrSSRJkiRVgWGzJEnKv+23j9dWaeiPZs2CO+6Am26Cu+7KehpJkiRJVWDYLEmS8m+FFaB1a8Nm/dmwYfDbb7DiinDccTB2bNYTSZIkSaokw2ZJklQ9Skpg8ODYZJXmmvsAxAsvRJ/3QQdFt7ckSZKkGsewWZIkVY+SEpg6FYYPz3oSFZLSUthoI9h886jTGDECzjwz66kkSZIkVYJhsyRJqh4dO8ZrqzQ01+zZ8MYb8UAEwF57xUGB11wDAwZkO5skSZKkJWbYLEmSqsfKK0OrVobNmmfECJgyZd4DEQBXXRX93ocdBhMmZDaaJEmSpCVn2CxJkqpPx46xyTp7dtaTqBDMfeBh7mYzQKNG8OCDEUJ37w5lZdnMJkmSJGmJGTZLkqTqU1ICv/4KI0dmPYkKweuvw3rrwWqr/f79G20E114LL70Um86SJEmSagTDZkmSVH3mbrBapaE5c2DgwN9vNc+vZ0/Yd184+2wYMqR6Z5MkSZJUKYbNkiSp+qy+Oqy7rmGzYNQomDRp4WFzksBtt8WfmYMOgl9+qd75JEmSJC0xw2ZJklS9Skpio3XOnPxd44UXYO+94dtv83cNVc2C+pr/aLnl4P774fPP4eijIU2rZTRJkiRJlbPYsDlJkkZJkgxJkuTdJEneT5LkgvL3r50kydtJknySJMlDSZI0KH9/w/Jff1L+8Rb5/RIkSVKNUlISG62jRuX+vqdNg169YJdd4H//g379cn8N5UZpKay9NjRvvujbbbst9O4NDzwAd99dLaNJkiRJqpyKbDbPADqlabopsBmwa5Ik7YHLgWvSNF0X+BnoUX77HsDP5e+/pvx2kiRJIV+9zSNHQtu2cN11cPzxEVLec4/bsIWorCwOB1zUVvP8zjoLOnaEY4+Fjz7K72ySJEmSKm2xYXMappT/sn75Swp0Ah4tf/9dQJfyt/cq/zXlH98pSZIkZxNLkqSa7S9/gRYtchc2z5kDV14J7drBTz/Bc89F4Hz44RFMerhc4fngA/jxx4qHzXXrwn33QcOGcOCBMGNGfueTJEmSVCkV6mxOkqRukiQjge+BF4FPgUlpms4uv8lXwBrlb68BfAlQ/vHJwAq5HFqSJNVwJSWx2VpWVrX7GT8edtoJTjsN/u//oppjl13iY/vuC40axXazCsvcBxo6dqz456y5JvTvDyNGxKazJEmSpIJTobA5TdM5aZpuBqwJtAM2rOqFkyQ5MkmSYUmSDJs4cWJV706SJNUkJSWx2frBB5W/j/vvhzZtYPhwuOMO+O9/YcUV5318mWVgr73gwQdh5syqz6zcKS2N8HjttZfs87p0gWOOgauvhmefzc9skiRJkiqtQmHzXGmaTgJeBToAyyZJUq/8Q2sCX5e//TXQHKD848sAPy7gvm5L07RtmqZtV1pppUqOL0mSaqSq9Db//DMcfDAccghsvDG8+25UZiyotatbtwi1DSYLR5rO62uuTNPaVVdB69Zw6KHw7be5n0+SJElSpS02bE6SZKUkSZYtf7sx8FdgDBE671t+s0OBJ8rffrL815R//JU09WQeSZI0n7XXjs3WJQ2bX301tpkffhguuig+f511Fn77v/0NVl7ZKo1C8tFH8N13Fe9r/qPGjWNbfcoU6N696lUskiRJknKmIpvNqwGvJknyHjAUeDFN06eB04GTkyT5hOhk7l9++/7ACuXvPxk4I/djS5KkGi1JImwsLY1N18WZMQNOPTX6mRs3hsGD4ZxzoF69RX9e/fpw0EHw1FOxEa3szX2AobJhM8BGG8G//w0vvgh9++ZmLkmSJElVttiwOU3T99I03TxN0zZpmm6SpumF5e8fl6ZpuzRN103TdL80TWeUv396+a/XLf/4uHx/EZIkqQYqKYHvv4exYxd9u9GjoV27qE846qg4IK5du4pfp1u36Gx++OGqzavcKC2FVVeF9dar2v0ccQTss08cFjh0aG5mkyRJklQlS9TZLEmSlDOL620uK4vt1bZtYcIEePJJuPlmaNJkya6zxRbQqpVVGoUgTeP7Xdm+5vklCdx+O6y2Wmyv//JLbmaUJEmSVGmGzZIkKRvrrRcbrgsKm7/+GnbZBU46Cf76Vxg1CvbYo3LXSZLo9h00CMb5hKtMjRsX39uqVGjMb7nl4P774bPP4Nhjc3OfkiRJkirNsFmSJGVjYb3Njz4KrVtHL/Ott8ZG8yqrVO1ahxwS17v33qrdj6omF33Nf7TddnD++fG9dXtdkiRJypRhsyRJyk5JCXzzDXz6adQgHHoo7LcfrLtudDMfeWTV6xYAmjeHHXaAu++u2IGEyo/SUlhxxag1yaWzz4aOHeGYY+Djj3N735IkSZIqzLBZkiRlZ+6Ga9++sOmmsZ167rlRebH++rm9VvfuEWq/9VZu71cVV1oaoXAuHkCYX9268Wenfv3ob545M7f3L0mSJKlCDJslSVJ2WrWClVaCW26BOnVg4EC48MIIDXNtn32gcWOrFrLyxRfxkssKjfk1bw79+8Pw4XDWWfm5hiRJkqRFMmyWJEnZSZKoQOjVC0aOhG22yd+1mjWDLl3gwQdhxoz8XUcL9vrr8TpfYTNA167wz3/Gpvxzz+XvOpIkSZIWyLBZkiRlq1cv+Pe/IwzOt27d4OefYcCA/F9Lv1daCsstF4c/5lPfvrDJJtH//e23+b2WJEmSpN8xbJYkScXjr3+FVVaxSiMLpaWw/fZRl5JPjRvH9vovv8SDC6NG2eEsSZIkVRPDZkmSVDzq1YODD4ann4Yff8x6muLxzTfwySf5rdCY38Ybw3XXwUsvQZs20KRJbDsfdBD06QNPPQWffw5lZdUzjyRJklQk6mU9gCRJUrXq1g2uuQYefjj6fZV/paXxurrCZoAjjohN6hEjYrt51Ch4883Yep6radMIoVu3/v3LCitU35ySJElSLZKkaZr1DLRt2zYdNmxY1mNIkqRikKax7dqsGQwenPU0xeHoo+H++6Mvu27dbGf55RcYPTrC57mvR42Cn36ad5tVV/1zAN2qFSy1VHZzS5IkSQUiSZLhaZq2XdDH3GyWJEnFJUliu/n006PaYd11s56o9isthe22yz5oBlh6adhmm3iZK01hwoQ/B9A33QTTp8dt6tSBli0jeO7VCzp2zGZ+SZIkqYAZNkuSpOJz8MFwxhlxUOAFF2Q9Te32/ffw4Ydw2GFZT7JwSQKrrx4vu+wy7/1z5sQDEvOH0AMHwrBhMG5cYYTnkiRJUgHxgEBJklR81lwTdtoJ7r03tlqVP6+/Hq+rs685V+rWhQ02gH33hd694b//heuvh/Hj4eWXs55OkiRJKjiGzZIkqTh16xbbqfY251dpKTRpAltumfUkudGlCyy/PPTrl/UkkiRJUsExbJYkScVp773jwLe77856ktqttDT6kevXz3qS3GjYMB6oePxx+OGHrKeRJEmSCophsyRJKk5Nm0LXrvDww/MOgVNu/fhj9BzXxAqNRenRA2bNis5vSZIkSf+fYbMkSSpe3bvDpEnwzDNZT1I7DRwYr2tb2Ny6NbRrB/372/ktSZIkzcewWZIkFa+ddoLVVrNKI19KS6FRI9hqq6wnyb2ePeH99+Htt7OeRJIkSSoYhs2SJKl41a0LBx8MAwbYv5sPpaXQoUP0HNc2BxwQnd/9+2c9iSRJklQwDJslSVJx694dZs+Ghx7KepLaZdIkGDkSOnbMepL8WHrpCJwffBCmTMl6GkmSJKkgGDZLkqTi1qZNvHjYW24NGhR9xrWtr3l+PXpE0Pzww1lPIkmSJBUEw2ZJkqRu3aJ7d+zYrCepPUpLoUEDaN8+60nyZ5ttYMMNrdKQJEmSyhk2S5IkHXww1KkD996b9SS1R2kptGsHjRtnPUn+JElsNw8eDGPGZD2NJEmSlDnDZkmSpNVXh513jrC5rCzraWq+X3+F4cNrd4XGXN27Q716bjdLkiRJGDZLkiSFbt3g88/hjTeynqTmGzwY5swpjrB55ZVhzz3hrrtg5sysp5EkSZIyZdgsSZIE0LUrNGniQYG5UFoa277bbJP1JNWjZ0/44Qd46qmsJ5EkSZIyZdgsSZIEETTvsw888ghMm5b1NDVbaSm0bRu/p8Xgb3+DNdeEfv2ynkSSJEnKlGGzJEnSXN26weTJbqhWxW+/wdChxVGhMVfdunD44fD88/Dll1lPI0mSJGXGsFmSJGmuHXeMwwKt0qi8N9+EWbOgY8esJ6lehx8OaQr/+U/Wk0iSJEmZMWyWJEmaq25d+Pvf4bnnYOLErKepmV5/HerUge22y3qS6rX22rDzznDHHVBWlvU0kiRJUiYMmyVJkubXrRvMng0PPpj1JDVTaSlsvjksvXTWk1S/Hj3giy/g5ZeznkSSJEnKhGGzJEnS/DbZBDbbDO6+O+tJap7p0+Gtt4qrr3l+XbrA8stD//5ZTyJJkiRlwrBZkiTpj7p1g2HD4MMPs56kZhkyBGbMKN6wuVGjqGH53//gxx+znkaSJEmqdobNkiRJf3TwwdE77EGBS6a0FJIEtt8+60my06MHzJwJ996b9SSSJElStTNsliRJ+qNVV4W//S0CQw97q7jSUmjTBpZbLutJstOmDWy1FfTrB2ma9TSSJElStTJsliRJWpBu3WD8eHj99awnqRlmzoTBg4u3QmN+PXvC6NEwdGjWk0iSJEnVyrBZkiRpQbp0gaZNrdKoqGHDYNo0w2aAAw+EpZaK7WZJkiSpiBg2S5IkLchSS8G++8Ijj8Bvv2U9TeErLY3XxdzXPNfSS8P++8MDD8CUKVlPI0mSJFUbw2ZJkqSF6dYNfv0Vnnwy60kK3+uvw0YbwUorZT1JYejRI4LmRx7JehJJkiSp2hg2S5IkLcwOO0Dz5lZpLM7s2fDGG1ZozG/bbWGDDaB//6wnkSRJkqqNYbMkSdLC1KkDhxwCzz8P332X9TSFa8SI2OI1bJ4nSWK7edAgGDMm62kkSZKkamHYLEmStCjdusGcOdG/qwWb29ds2Px73btDvXpwxx1ZTyJJkiRVC8NmSZKkRdloI9hiC6s0FqW0FNZfH1ZdNetJCssqq8Aee8Bdd8HMmVlPI0mSJOWdYbMkSdLidO8O77wD77+f9SSFZ84cGDjQreaF6dkTJk6Ep5/OehJJkqTiM21aVL6ladaTFA3DZkmSpMU56CCoW9ft5gV57z2YPNmweWF22QXWWAP69ct6EkmSpOIyaRLstFM8S7GkZF71m/LKsFmSJGlxVl45QsP77oOysqynKSxzf2jv2DHbOQpV3bpw+OFxyOSXX2Y9jSRJUnGYOBF23BGGDYNTToFPP4UddoCdd4bBg7OerlYzbJYkSaqI7t3hq6/gtdeynqSwvP46rL02NG+e9SSF6/DD40GKO+/MehJJkqTa7+uvYxFi7Fh46im46ir45BO45hoYNQq23RZ22y2CaOWcYbMkSVJF7LknLL00XHednW9zlZVF2GyFxqKts048hfOOO9yMlyRJyqdx42D77SNwfu65eHYiQOPGcOKJ8fHLL4ehQ2GrrWCvveDdd7OduZYxbJYkSaqIxo3htNPgiSegVy8DZ4APPoAffzRsrogePeDzz+GVV7KeRJIkqXYaMyaC5smT42euBdW8NWkSP9OPGwcXXRSVcJttBvvvHz/bqsoMmyVJkirqrLPgpJPg+uvh9NMNnOf2NRs2L17XrrDcctC/f9aTSJIk1T4jRkS4XFYWP6O2bbvo2y+9NJxzDnz2Wbx+9lnYZBP4+9/h44+rZ+ZayrBZkiSpopIE+vaFf/4TrrwSevfOeqJslZZGV3OLFllPUvgaNYr/eXnssdgGlyRJUm4MGhSHAS61FAwcGKFxRS23XGw4f/YZnHoq/O9/0KoV/OMf8T4tMcNmSZKkJZEkcMMN8QPohRdCnz5ZT5SNNI2wuaQkfk+0eD16wMyZcN99WU8iSZJUO7z0Evztb7DKKvDGG7DuupW7nxVXjC7ncePg+OPh/vth/fXh6KPhyy9zO3MtZ9gsSZK0pOrUgdtug4MPhrPPjpOti83YsfD991ZoLIlNN42ndPbrZwWLJElSVT3xBOy+O7RsGYdWN29e9ftcZZX42f7TT+HII+OA53XXhRNOgAkTqn7/RcCwWZIkqTLq1oW77oJ99oGTT4abbsp6ouplX3Pl9OgBo0bBsGFZTyJJklRzPfBA/By+2Wbw2msREufSGmvAjTdGf3P37vGz/jrrwL/+BRMn5vZatYxhsyRJUmXVqxdPsdtjDzj22Nh8KBalpbDaapV/qmKxOuggaNzYgwIlSZIq6/bb4ZBDYPvto0Zj+eXzd6211orrjR0L++8fW89rrx0Hh//0U/6uW4MZNkuSJFVFgwbw8MPRFdezZ3H08aZpPFWxY0f7mpfUMsvAfvvFgxRTp2Y9jSRJUs1y9dVRb7HrrjBgADRrVj3XbdkyntX4/vuxaHLZZRE4608MmyVJkqqqUaM4ubqkBA49FB59NOuJ8mvcOPj6ays0KqtnT/j119r/50SSJClX0hQuuABOOQX23RcefzyeLVbdNtwwKjzefTfObtGfGDZLkiTlwlJLwVNPwdZbR1XCU09lPVH+2NdcNdttF6eb9+uX9SSSJEmFL02jK7l3bzjssAh7GzTIdqbWrXNzIGEtZNgsSZKUK02bxtP5Nt88Ni6efz7rifKjtBRWWglatcp6kpopSeKgwDfeiP4/SZIkLdicOXDUUVGfcfzxce5FvXpZT6VFMGyWJEnKpWWWgeeeiyC2Sxd49dWsJ8q90lL7mquqe3eoW9eDAiVJkhZm1qz4men226Mf+dproY5RZqHzOyRJkpRryy8PL74I66wTB4gMGpT1RLnzxRfxYoVG1ay6avzZuOuu+B8pSZIkzTN9+rxDlS+9FC65xEWHGsKwWZIkKR9WWglefhnWWAN22w2GDMl6otywrzl3evSA77+Hp5/OehJJkqTCMXVqPCj/xBNwww1wxhlZT6QlYNgsSZKUL6uuGoHziivCLrvAyJFZT1R1paWxub3JJllPUvPtuiusvrpVGpIkSXNNmgR/+xu88grceScce2zWE2kJGTZLkiTl05prxg/LzZrBX/8K77+f9URVU1oK229vX14u1KsXJ6o/+yx8/XXW00iSJGVr4kTo1AmGDoWHHoJDD816IlWC/5cgSZKUby1axIZz/fqw007w0UdZT1Q5X38Nn34ahwMqN/7xDygri80dSZKkYjR9Otx2G7RrB2PGRH3GvvtmPZUqybBZkiSpOqy3XgTOZWWxsTFuXNYTLbm5Bx0aNudOy5aw445RpVFWlvU0kiRJ1eenn+Dii2GtteCoo6Kq7aWX4rwT1ViGzZIkSdWlVav4AXratAicx4/PeqIlM2QINGwIm26a9SS1S48e8NlnMHhw1pNIkiTl32efwQknQPPmcO65sOWWUTs3bBhsu23W06mKDJslSZKqU5s28MILcfjJTjvBN99kPVHFDR0Km28edSDKnd13h7p1YcCArCeRJEnKn+HD4cADYd114ZZbYL/9YNSo+Bloxx0hSbKeUDlg2CxJklTdttwyDoX79tsInL//PuuJFm/27Ng2adcu60lqn2WXjS2eZ5/NehJJkqTcStP4GadTJ2jbNt4+5ZTYbr7zTthkk6wnVI4ZNkuSJGWhQwd45hn44gvYeWeYMiXriRZtzBj47TfD5nzp3BlGjoxDGCVJkmq6mTMjTG7dOn7O+egjuPJK+PJLuOIKWGONrCdUnhg2S5IkZaVjR7jvvnj64AsvZD3Nog0ZEq+32irbOWqruQfhPPdctnNIkiRVxeTJESavvTYcfjjUqQN33x2HY//rX7D00llPqDxbbNicJEnzJEleTZLkgyRJ3k+SpFf5+zdLkuStJElGJkkyLEmSduXvT5IkuS5Jkk+SJHkvSZIt8v1FSJIk1VidO0ODBvDmm1lPsmhDh0bdw7rrZj1J7dS6dWz4WKUhSZJqoq++ijC5eXM4/fQ4GPu55+Ddd6Fbt/h5V0WhXgVuMxs4JU3Td5IkaQYMT5LkReAK4II0TZ9NkqRz+a93AHYD1it/2Rq4ufy1JEmS/qhhw+hwfuutrCdZtCFDYqu5jk+My4skiQceHnwQZs3yEEZJklQzvPceXHUVPPBA9DPvv3+Ezlu4e1qsFvt/C2maTkjT9J3yt38FxgBrACkwd/d9GWDuUep7AXen4S1g2SRJVsv55JIkSbVF+/Zx+N7MmVlPsmDTpsX/SNjXnF+dO8Ovv8KgQVlPIkmStHBpCi+/DLvuCptuCo89BsceC598Avffb9Bc5JZoNSVJkhbA5sDbwInAlUmSfAlcBZxZfrM1gC/n+7Svyt8nSZKkBenQAaZPj0C3EI0YAXPm2NecbzvtFBvNAwZkPYkkSdLC3XRTHHA9ciRccgmMHw///je0aJH1ZCoAFQ6bkyRpCvwXODFN01+AfwInpWnaHDgJ6L8kF06S5MjyrudhEydOXJJPlSRJql3at4/XhdrbPPdwQDeb86tZM9h+e3ubJUlS4Zo1Cy67DLbbDj7/HM46C5ZfPuupVEAqFDYnSVKfCJrvS9P0sfJ3HwrMffsRYO7/fXwNNJ/v09csf9/vpGl6W5qmbdM0bbvSSitVZnZJkqTaoXnzOByuUHubhw6FNdeE1WxGy7vOnWH06NgQkiRJKjSPPBKHAZ52GjRqlPU0KkCLDZuTJEmIreUxaZpePd+HvgFKyt/uBHxc/vaTQPcktAcmp2k6IYczS5Ik1T7t2xdu2DxkiFvN1WW33eK1282SJKnQpCn07QsbbAC77571NCpQFdls3hboBnRKkmRk+Utn4Aigb5Ik7wJ9gCPLbz8AGAd8AtwOHJP7sSVJkmqZ9u1h3Dj4/vusJ/m9n36Kw14Mm6tHq1aw1lqGzZIkqfCUlsI778DJJ0OdJToGTkWk3uJukKbpG0CykA9vuYDbp8CxVZxLkiSpuHToEK/fegv23DPbWeY3dGi89nDA6pEkUaVx990wYwY0bJj1RJIkSaFvX1hpJejWLetJVMB8GEKSJKkQbLEF1KtXeIcEDh0aAeiWf9oxUL507gxTp8LAgVlPIkmSFD78EJ5+Go45Bho3znoaFTDDZkmSpELQuDFsvnnh9TYPGQIbbgjLLJP1JMVjxx2hQQMYMCDrSSRJksI118Qzro6xLVeLZtgsSZJUKNq3j3B39uysJwlp6uGAWWjSBHbYwd5mSZJUGCZOjIqv7t1h5ZWznkYFzrBZkiSpUHToAL/9BqNHZz1J+PJL+O47+5qz0LlzPF113LisJ5EkScXupptg+vQ4GFBajMUeEChJkqRq0r59vH7rLdhss2xngXmHA7rZXP06d4YTT4zt5mNrwdnbs2bBlCnw66/xev6Xue/bYQfYYIOsJ5UkSfObNg1uvBF23z2q1aTFMGyWJEkqFC1axFMT33wTjj4662miQqNBA2jTJutJis9660HLloUXNs+YAfffDz/99OeweGEh8pQpMHPm4u97xx3hlVfy/zVIkqSKu/feqNE45ZSsJ1ENYdgsSZJUKJIkqjQK5ZDAIUNiw7phw6wnKU6dO0O/frFRVCinvt96K/TqNe/XSy0FzZpB06bzXpZdFtZcM97+48fmvvzx/X37RhfkjBn+eZMkqVCUlcHVV8ch1jvskPU0qiEMmyVJkgpJ+/bwxBPw44+wwgrZzTFnDgwbBocemt0Mxa5zZ7j+eigthV13zXqaODDy9tuhbdvYQF5qKahbNzf3vdtucNttUd2y3Xa5uU9JklQ1zz4bZ0jce28sRUgV4AGBkiRJhaRDh3j99tvZzjF2bNQf2NecnZISaNQIBgzIepIwZEgcXnnEEbGZnKugGWD77eN1aWnu7lOSJFVN377xbKX99896EtUghs2SJEmFpG3bCPHefDPbOYYMideGzdlp3Bg6dYqtokJw++3QpAkcdFDu73uFFaB1a8NmSZIKxYgR8OqrcMIJUL9+1tOoBjFsliRJKiRNmsSBfFn3Ng8ZAksvDeuvn+0cxa5zZ/jkE/j442zn+PVXUatUYAAAIABJREFUePBBOOCA2GrOh44dYfBgmDUrP/cvSZIqrm/fOFfhiCOynkQ1jGGzJEn6f+zdd3RVVfrG8WcTehMQFASEARUUC2CQFCBSDJJxxPazjIKKbUZFEBTr2AuMoMhgG8ACttERrIxIgoIIGgIKooANpSgISFVqcn5/vMkiYiDtnHvuvfl+1so6cMvebzSHJO/Z59mINklJFqORmxteDdnZUqdOUiV+XAxVnz52DDtK4z//kX79Vbr88uDmSEuzORYsCG4OAABQvJUr7SLzFVfYxr9AKfDbAwAAQLRJSrKVpEuWhDP/jh3SwoXWbEa4WrWS2rQJP0pj3DipXTv72gxKt252JEoDAIBwjRljx0GDwq0DMYlmMwAAQLQp2CQwrCiNhQulPXvIa44WGRnSBx/Yqt8wLFpkK90vvzzYnegPPVRq21aaNSu4OQAAwIFt2SL9+9/SOedILVqEXQ1iEM1mAACAaHPEEbZhWlibBLI5YHTJyJB27rRNesIwfrxUtarUr1/wc3XrJn34YbgRMgAAVGQTJljDeejQsCtBjKLZDAAAEG2cs7iCsFY2Z2dLhx0mNW0azvz4va5dbePIMHKbt2+XJk2SzjrLLoAELS3NfsFduDD4uQAAwO/t2SM9+qj97EGcGsqIZjMAAEA0SkqSvvxS2rQp8nMXbA6I6FCtmtSzp+U2e15k55482b4GI7UTfVqaHcltBgAg8l57TfrhB1Y1o1xoNgMAAESjgtzmgkiLSNm0SfrqKyI0ok1GhvT999LSpZGdd/x426Tw5JMjM1/TplLr1uQ2AwAQaZ4njRolHXmk9Je/hF0NYhjNZgAAgGjUqZPFaUQ6SiMnx440m6NLnz52jGSUxtdf28aEl18uVYrgrw3dulmzOS8vcnMCAFDRzZ4tzZsnXX99ZL/vI+7w1QMAABCN6taV2rWL/CaBBSupExMjOy8O7PDD7evhf/+L3JwTJkgJCdIll0RuTsmiNH75Rfrii8jOCwBARTZqlO3PcPHFYVeCGEezGQAAIFolJ0uffBLZFZ7Z2dJRR0n16kVuTpRMRoat+N26Nfi5du+Wnn1WOu00qUmT4OcrrCC3mSgNAAAi4+uvpTfflP7+d6lmzbCrQYyj2QwAABCtkpKkjRstQzlS5s0jQiNaZWRYEzgrK/i53n5bWrvWIjQirUULqXlzNgkEACBSHnlEqlJFuuaasCtBHKDZDAAAEK0KNgmMVG7z6tXSjz/SbI5WqalSnTqRidIYN8426zv11ODn2pdztrp55kzbrAgAAARnwwa7m+mii6TGjcOuBnGAZjMAAEC0atPG4iwildtckNdMszk6VakinXKKbRIYZBN25Urp3XelSy+VKlcObp4DSUuTfv5ZWrYsnPkBAKgonnhC2r5dGjIk7EoQJ2g2AwAARKtKlaTOnSO3sjk725qLJ5wQmflQehkZ0qpV0uLFwc3xzDPWzB4wILg5ikNuMwAAwduxQxo71u5katcu7GoQJ2g2AwAARLOkJGssRmJTuHnzrNFcvXrwc6Fs+vSx49SpwYyfmytNmGArqP/0p2DmKIkjjrBbecltBgAgOC++aHs0DB0adiWIIzSbAQAAollSkpSXZ43gIBXMQYRGdDvsMLsgEFRuc2amtGJFOBsDFkZuMwAAwfI8adQo+7miZ8+wq0EcodkMAAAQzTp3tmPQURpffSVt2UKzORZkZEizZ0ubN/s/9rhxUsOGUt++/o9dWmlptmnl8uVhVwIAQPx5913pyy9tVbNzYVeDOEKzGQAAIJrVry+1bRv8JoFsDhg7MjIs7mL6dH/H/fln6Y03pP79pWrV/B27LLp1syNRGgAA+G/UKLtj6rzzwq4EcYZmMwAAQLRLTraVzUHGCWRnS7VrS23aBDcH/JGUJNWr53+UxnPPSXv2hB+hUeCYY2yVNc1mAAD89dlnUlaWdN11UtWqYVeDOEOzGQAAINolJUnr10vffhvcHPPmSYmJUkJCcHPAH5UrS+nptkmgXxcgPE8aP15KTZWOPtqfMcvLOVvdTLMZAAB/PfywVKuWdOWVYVeCOESzGQAAINolJ9sxqNzmnTtthQsRGrEjI0Nas8b+v/nhww8tt/uKK/wZzy9padL339umhQAAlNYvv0ivvMJms4WtXi299JJ02WUW1wb4jGYzAABAtDvmGIu4CKrZvGiRtGsXzeZYcuqpdpw61Z/xxo+X6taVzjnHn/H8UpDbPGtWuHUAAGLT6NGWSfz882FXEj3+9S8pL08aPDjsShCnaDYDAABEu4QEawQHtUlgweaAnToFMz78d+ihFnviR27zxo3Sq69KF15ot9RGk+OOs3xqojQAAGWRmWnHwYOltWvDrSUabNsmPfWUdNZZ0p/+FHY1iFM0mwEAAGJBcrK0cKH022/+jz1vnjUvmzf3f2wEp08fuwDxyy/lG+fFF6UdO6JnY8DCEhKkrl1Z2QwAKL0tW+yC+tlnW5N14MCwKwrf009LmzZJQ4eGXQniGM1mAACAWJCUJOXmSjk5/o+dnW0rp53zf2wEJyPDboN9772yj+F50rhxUseO9hGNunWzPOmffgq7EgBALJk50352uuYa6c477S6eKVPCrio8ubkWK5KSYj9XAgGh2QwAABALCn4p8Du3efNmaelS8ppjUadO0sEHly9KY/58WzEfjauaC6Sl2ZHVzQCA0sjMlGrUsLvDbrxRat9euvpqi4+qiKZMkZYvZ1UzAkezGQAAIBY0bCgdcYT/uc3z59vqVvKaY09CgtS7tzWb8/LKNsa4cfaL+F//6m9tfurQQapTh9xmAEDpZGVJXbpI1atLVapYhMS6ddKQIWFXFo5Ro6TWraW+fcOuBHGOZjMAAECsSE62lc2e59+Y8+bZkWZzbMrIsF+c588v/Xu3bbO85nPPlQ46yP/a/FK5spSayspmAEDJ/fST9MUXUq9eex/r0EEaNkx69llp2rTQSgvFnDn2M+TgwXaxGggQzWYAAIBYkZQkrVkjrVjh35jZ2bZiukED/8ZE5PTubVnbU6eW/r2vvmoN52iO0CjQrZs1DdavD7sSAEAsmDHDjj17/v7xO+6Q2raVrrxS2ro18nWFZdQoqX596dJLw64EFUDlsAsAAABACRXkNs+dK7Vo4c+Y2dnWyENsathQ6tzZojTuvLN07x03zn7hTk0NpjY/Fc5tPuuscGsBAES/zEy7kN6+/e8fr15dmjDB4jVuuUUaOzac+oryyivS3XdLNWvangwHH2zf5wv+XNRHrVrFb/D87beW13zLLfZ6IGA0mwEAAGLF8cdbvu7HH0vnn1/+8X76SVq1igiNWNenj3TXXRan0ahRyd7zxRd20WLkyOJ/SY0GiYn2tU+zGQBQHM+zvOYePYqOjEhJkQYOlMaMkc47T+raNfI17isrS7roIqlNG2swb9ggff21HTdv3v/7qlY9cDO6YCPhKlWka6+N3OeDCo1mMwAAQKyoXNkaw35tEliQ13zSSf6Mh3BkZNiq5mnT7BfVkhg/3n7x7N8/2Nr8UrWqZZazSSAAoDhffy2tXCndeuv+X3P//dKbb0qXXSYtXGgXNMPy+ed2IbVNG+nDD6V69X7//O7d0saN1ngu7mPZsr1/3r177xgDBkhNmkT280KFRbMZAAAgliQnSw8/LO3YYbeClkd2tq346dDBn9oQjo4dpUMOsZVLJWk279wpTZwonXFGyVdCR4O0NFvBvXGj5U4CAFCUzEw7Ft4ccF+1a1uc1Cmn2PeWESMiUtofrFpldyjVqWP7L+zbaJbs4vAhh9hHSXme7cuwYYN932zb1r+agWKwQSAAAEAsSUqylSoLFpR/rOzsvdEciF2VKkmnniq9+66Um1v866dMkX75RbriiuBr81Namv3yPHt22JUAAKJZVpZ0+OFS69YHfl2vXrayeeRIKScnMrUVtnmzNZq3bLFGc/Pm/o3tnDWwW7a0RQX8rIcIotkMAAAQSwo2Cfz44/KNk5dnMRpEaMSHjAxrIGdnF//a8ePtl8+ePQMvy1edO1ucxqxZYVcCAIhWubnSjBnWSC7JngQjR0qNG1vMxK5dwddXYNcu6cwzpaVL7SLw8cdHbm4gYDSbAQAAYknjxtYoLG+z+ZtvpE2b2BwwXqSn2wrnqVMP/Lpvv7UVX5ddZq+PJdWrW8OZ3GYAwP58+qn9fHOgCI3C6tWTnnjCcpOHDw+2tgKeZ83t99+Xnn469i7+AsWIsZ8wAQAAoKSk8m8SyOaA8aV+fSklxXKbD+Tpp63JfMklESnLd2lpFiGzdWvYlQAAolFBXnOPHiV/z+mnS+efL913n/TFF8HUVdhtt0kvvGCbFPbrF/x8QITRbAYAAIg1ycm2ocyqVWUfIztbqlVLOuYY/+pCuPr0kebPl9asKfr5PXukZ56x1zVrFtna/JKWZrdIz5kTdiUAgGiUmSkdd5x06KGle9+YMVLdunbnT0n2PyirJ5+UHnxQuuoq6ZZbgpsHCBHNZgAAgFjjR25zdrZ04olSQoI/NSF8GRl2fPfdop+fOlX66afY2xiwsORkqXJlojQAAH+0fbttIluWWIpGjazh/Mkn0qOP+l+bJL31lnTNNdJpp0ljx5YsUxqIQTSbAQAAYk379lK1amVvNu/aZZmG5DXHlxNOkJo02X+Uxrhxlvld0JSORbVqSYmJNJsBAH80Z460c2fJ85r3dcEF1gi+/Xbb28JP2dnSeedJHTtKL79sF06BOEWzGQAAINZUrWqrksua27x4sf0yRl5zfHHOGsnTpllkRmGrV9vK5ksvlapUCac+v6SlWeb4b7+FXQkAIJpkZVkTt1u3sr3fOYu5qFLF7gLKy/Onrm++sSZ2kybS22/bhVMgjtFsBgAAiEXJyZbPu2tX6d+bnW1Hms3xp08fafPmP16IeOYZ+6X5ssvCqctPaWnS7t3li5EBAMSfzEypc2epTp2yj9G0qTRypPTBB3ZHUHmtW2ffm/Py7M6j0mZJAzGIZjMAAEAsSkqy1ckLF5b+vdnZlk3YooX/dSFcvXrZqq7CURp5edKECVKPHlLr1uHV5pfUVKlSJaI0AAB7bdwo5eSUPUKjsMsvt++ZN94orVxZ9nF++006/XTb0PnNN6Wjjip/bUAMoNkMAAAQiwo2CSxLlEZ2tuU1szFN/DnoIKlLF4vMKDBjhvT99/bLczyoW1fq0IFmMwBgrw8+kDyvbJsD7ss5W9Wcmyv97W82bmnl5kp//attOPjii1JKSvnrAmIEzWYAAIBY1KyZfZQ2SmDrVunLL4nQiGd9+tiK99Wr7e/jxkkNGkhnnhluXX5KS7Ov/Z07Iz/3ypXShg2RnxcAsH+ZmZaF3LmzP+O1aiXdf79dvH3hhdK91/OkQYOkN96QHn00vr7/AiVAsxkAACBWJSWVfmXzggX2SxDN5viVkWHH//1PWr9emjJF6tdPql493Lr81K2bNZoL8scjZdMmKTHRNo4CAESPrCy7EFm1qn9jDhxoe2QMGiStXVvy940cKT32mHTDDTYGUMHQbAYAAIhVyckWj7BmTcnfU9Cc69QpkJIQBdq1k5o3t2bzxIm2mV68RGgU6NrVbnOOdJTGXXdJP/9s8+blRXZuAEDRVq2Sli3zJ0KjsIQE2/Ng27aSN41fekkaNkw67zxpxAh/6wFiBM1mAACAWFWQ21yaKI3sbLs1tGHDYGpC+Jyz1c3Tp0v//rd9nRx7bNhV+atBA+m44yLbbP7iC2nsWIuv+eUX6auvIjc3AGD/srLs6MfmgPs6+mjpjjukV1+1O4UO5P33pYsvtrtvnnvONrMFKiC+8gEAAGJVx45SlSqlazbPm8eq5oqgTx/L5162LH4jH9LSpDlzbOV20AryN+vWlV5+2R776KPg5wUAFC8zU2rUKLgLq8OGSSecIF19tbRxY9GvWbzYspmPOEJ6/XWpWrVgagFiAM1mAACAWFW9utShQ8mbzWvXSj/8QF5zRdCzp12IqF1bOvfcsKsJRrdu0m+/SfPnBz/X5Mm2cu6++yy+pkEDms0AEA08z5rNPXsGt5K4ShXp6aeldeukoUP/+Pzq1XZHUc2aFmFVv34wdQAxgmYzAABALEtKstXKe/YU/9p58+xIszn+1a5t+ZK33WZ/jkfdutkx6CiN336ThgyRjj9euvJKa2akpNiqagBAuJYssb0rgojQKKxjR+nGG6VnnpHee2/v41u2WKN540Zp6lSpRYtg6wBiAM1mAACAWJacbM2wzz8v/rXZ2bbZTYcOwdeF8I0aJd18c9hVBOeQQyxLc9asYOd56CFpxQppzBipcmV7LDXVIkrWrw92bgDAgWVm2tHvzQGLcuedUps2duFx2zZp1y7p7LOlL7+UXntNat8++BqAGECzGQAAIJYVbBI4d27xr83Oltq1k2rVCrYmIFK6dZNmz5Zyc4MZ//vvpeHDpfPOs4zoAikpdizJeQcACE5WltS6tdSyZfBzVa8uTZhgFyBvvtn2RMjMlMaNk9LTg58fiBE0mwEAAGJZixZS48bF5zZ7nsVoEKGBeJKWZrcwf/ZZMOPfcIPFZjz00O8f79TJMjzJbQaA8OzZI73/fmRWNRdITZWuvVZ67DFp4kTp7rulSy6J3PxADKDZDAAAEMucs9XNxa2w/O476ZdfaDYjvhSsNg4itzkry26LvvVWqXnz3z9Xo4bld9JsBoDwzJsnbd0afF7zvh54wCLJBg6U/vGPyM4NxACazQAAALEuOVn65psD58dmZ9uRZjPiyWGHSUcc4X9u8+7d0nXXSX/6kzR0aNGvSUmxRseuXf7ODQAomawsu+jevXtk561dW5o/37L8nYvs3EAMoNkMAAAQ6wpymz/5ZP+vyc621Zjt2kWmJiBSunWTPvxQysvzb8zHH7cNnx55xDI6i5KaKu3cKS1Y4N+8AICSy8y0TfkaNoz83DSZgf2i2QwAABDrTjxRSkg4cJTGvHl223/lypGrC4iEtDSLiFm82J/xfv5ZuvNOqXdv6fTT9/+6gk0CidIAgMj79Vf7uSfSERoAikWzGQAAINbVqiWdcML+NwncvdtWXxKhgXhUkNvsV5TGbbdZE2P06AOvXGvSxGI25szxZ14AQMnNnm0xRpHcHBBAidBsBgAAiAdJSRajkZv7x+e++ELavp1mM+JTixbS4Yf7s0lgTo40YYI0aJDUtm3xr09NtZXNnlf+uQEAJZeZKVWtKnXpEnYlAPZBsxkAACAeJCdL27ZZzuy+CjYH7NQpsjUBkZKWZiuby9P0zcuTBg6UDjlEuuOOkr0nJUVau1Zavrzs8wIASi8ry/4NrlUr7EoA7INmMwAAQDwo2CSwqNzmefOkBg2kVq0iWxMQKWlplrW8dGnZx3j+eYuiGTFCqlu3ZO9JTbUjuc0AEDnr10uffkqEBhClim02O+eaO+fed8596Zz7wjk3qNBzA51zS/Mf/2ehx29xzn3jnFvmnOsdVPEAAADI17q17cZeVG5zdrZFaLBzOuJVeXObt2yRhg2TOneW+vUr+fvatbPGNM1mAIic99+3I5sDAlGpJNuR75E01PO8Bc65OpLmO+emSzpUUl9JJ3iet9M5d4gkOeeOkXS+pHaSDpOU6Zw7yvO8IgIEAQAA4AvnbHXzvs3mX3+VFi+WzjgjnLqASGjd2jbsmzlTuuqq0r//3nttZfRbb0mVSnHzZ0KCnXdsEggAkZOZaRf6EhPDrgRAEYr9ScrzvJ88z1uQ/+etkpZIairp75KGe563M/+5n/Pf0lfSy57n7fQ8b7mkbySxGw0AAEDQkpKkJUukjRv3PrZggWXRsjkg4plztrp55szS5zYvXSqNHi0NGFC2XPPUVLugs2lT6d8LACi9rCzp5JOlyiVZPwkg0kqV2eycaympg6RPJB0lqatz7hPn3EznXMFPZk0lrSz0tlX5j+071pXOuRznXM66devKUjsAAAAKS062Y8GGgJLlNUtsDoj4l5Ym/fij9N13JX+P50mDB0s1a0oPPFC2eVNTbZyiImwAAP5avlz69lsiNIAoVuJms3OutqTXJA32PG+LLIKjgaQkSTdKesW5kgcBep73b8/zEj3PS2zUqFEpywYAAMAfdOpkEQCFNwnMzpZatJAOOSS8uoBI6NbNjjNnlvw9b70lTZsm3X132c+Rk06y844oDQAIXlaWHdkcEIhaJWo2O+eqyBrNL3ieNzn/4VWSJnsmW1KepIaSVktqXujtzfIfAwAAQJDq1JGOPfb3KywLNgcE4t3RR0uNGpW82bxjh3T99dIxx0jXXFP2eevUkU44gU0CASASsrIso//oo8OuBMB+FNtszl+tPEHSEs/zHi701OuSuue/5ihJVSWtl/SmpPOdc9Wcc3+SdKSkbAEAACB4BZsE5uVJ69bZ7aY0m1EROGerm0vabH74YYvcePRRqUqV8s2dmip98om0Z0/5xgEA7F9enjWbe/a0f/MBRKWSrGxOldRPUg/n3Gf5HxmSnpbUyjm3WNLLki7OX+X8haRXJH0p6V1J13ielxtQ/QAAACgsOVnavFlatoy8ZlQ8aWnSDz/Yx4GsXCndf7901ln+5H6mpEi//iotWlT+sQAARVu82C6kk9cMRLVit+70PG+2pP1dMrpoP++5X9L95agLAAAAZZGUZMePP5ZWrLAs2RNPDLcmIFIKcptnzZL69dv/64YNsxVyo0b5M29qqh0/+kjq2NGfMQEAv5eZaUfymoGoVuINAgEAABADjjpKqlfPNgnMzrY82tq1w64KiIzjjpPq1z9wlMasWdLLL1vDuWVLf+Zt3lxq2pRNAgEgSJmZUps2UrNmYVcC4ABoNgMAAMSTSpVsdXNBs5m8ZlQklSpJXbtaQ7koe/ZIAwdKhx8u3XSTf/M6Z6ub2SQQAIKxa5f9206EBhD1aDYDAADEm6QkyzVcv568ZlQ83bpJX38t/fTTH5/7978tV3nUKKlmTX/nTU21LOiVK/0dFwBgm7D++isRGkAMoNkMAAAQb5KT9/6Zlc2oaNLS7LhvlMaGDdLtt0vdu0tnn+3/vCkpdiRKAwD8l5Vld6+cfHLYlQAoBs1mAACAeFPQYK5WzTJsgYqkfXupTp0/Nptvv13askUaM8ZiL/x2wgm2WpooDQDwX2amlJhoufwAohrNZgAAgHhTr5507LHSiSdKVaqEXQ0QWZUrS126/D63+dNPpaeekq65xs6NIFSpInXuTLMZAPy2davFaBChAcQEms0AAADx6KWXpKefDrsKIBzduklffimtWyd5nnTdddLBB0t33RXsvCkp0sKF0rZtwc4DABXJrFm2wSubAwIxoXLYBQAAACAAQa3eBGJBQW7zrFnSzp3S7Nm2OWDQt1+npkq5uVJ2ttSjR7BzAUBFkZkpVa++NxsfQFRjZTMAAACA+HLiiZafPHWqdOON9vcBA4Kft2BzTqI0AMA/WVkWj1S9etiVACgBVjYDAAAAiC9Vq1rjtyBK5tVXpYSE4OetV09q106aMyf4uQCgIlizRvr8c+nCC8OuBEAJsbIZAAAAQPwpiNLo1y+yt16npkpz50p5eZGbEwDi1YwZdmRzQCBm0GwGAAAAEH/OO0869VRpxIjIzpuaKm3ebBsUAohPy5dLW7eGXUXFkJVlefsdOoRdCYASotkMAAAAIP4cdZT0v/9JTZpEdt6CVdTkNgPxafJkqW1bqW9fyfPCria+eZ5tDti9e2SikAD4gmYzAAAAAPildWvpkENoNgPxaPx46f/+T2rQQHr/fWnKlLArim/ffiutWCH16hV2JQBKgWYzAAAAAPjFOVvdzCaBQHz55z+lK66Q0tOlZcukY4+VbrhB2rEj7MriV2amHWk2AzGFZjMAAAAA+Ck11VbkrV0bdiUAysvzpJtuso/zzpPeeEOqW1caPdqymx95JOwK41dWltS8uXTEEWFXAqAUaDYDAAAAgJ9SU+1IlAYQ23JzpSuvtFXNf/ub9MILUtWq9lzPntIZZ0j33y/9+GO4dcaj3Fxpxgxb1exc2NUAKAWazQAAAADgp44dpWrViNIAYtnOndK551pO8+23S48//sdN6kaOlHbvlm65JZwa49lnn0m//GJNfQAxhWYzAAAAAPipWjUpMZGVzUCs2rpV+vOfpcmTLSbj3nuLXl3burU0ZIg0caL0ySeRrzOeZWXZkWYzEHNoNgMAAACA31JTpfnzpe3bw64EQGls2GANzg8+kJ57Tho8+MCvv/VWqXFjadAgKS8vIiVWCJmZtglj48ZhVwKglGg2AwAAAIDfUlLs9vr588OuBEBJrVolde0qLVpkq5r79y/+PXXqSMOH28rmF18MvsaKYMcOafZsVjUDMYpmMwAAAAD4LSXFjkRpALHhq6/sjoRVq6Rp06TTTy/5e/v1kzp1km66Sdq2LbgaK4q5c+2ukF69wq4EQBnQbAYAAAAAvzVqJB11FJsEArFgwQKpSxdrcH7wgZSWVrr3V6okPfqo9OOPtsoZ5ZOZaZsxdusWdiUAyoBmMwAAAAAEISXFms2eF3YlQHTzPGn0aPtYty6yc8+cKZ18slSjhkU3dOxYtnGSk6ULL5RGjpSWL/e1xAonK0vq3FmqWzfsSgCUAc1mAAAAAAhCaqq0fr3dng9g/x59VLr+evto2lQ6+2zpnXekPXuCnfett6RTT7U5P/rI7kYoj+HDbUXujTf6U19FtGmTNG8eec1ADKPZDAAAAABBSE21I1EawP69/bY0ZIh05pm2Md9110kffiiddpp0+OHSLbcEc8Fm4kSb87jjbL5mzco/ZrNmVu9rr1kcB0pv5kwpL4+8ZiCG0WwGAAAAgCC0aSPVr88mgcD+LFokXXCB1KGDNGmSNX5HjpRWr5amTJESE6WHHrJzqWtX6Zln/NmAb/Ro6eKLLT4jK0tq2LD8YxYYOlRq0UIaNEjKzfVv3IoiM1OqWVNKSgq7EgDeYQHEAAAgAElEQVRlRLMZAAAAAIJQqZLlNtNsBv5ozRrpL3+xXN4335Rq1dr7XJUq0hln2OMrV0ojRliW84ABUuPG0mWX2XlV2jx0z5P+8Q+L6zjrLIvqqFPH38+rRg1rkC9aJI0f7+/YFUFmpm0MWLVq2JUAKCOazQAAAAAQlJQUaelSacOGsCsBosf27dZMXr/ecpObNt3/a5s0kYYNk5YssQbzBRdIr7widekitW1rOck//lj8nHl50rXXSvfdZ83qV16RqlXz73Mq7JxzrGF6223Sxo3BzBGPVq+2fy+J0ABiGs1mAAAAAAhKQW7z3Lnh1gFEC8+TLr1U+uQT6fnnpY4dS/Y+5+zizbhxtir62WdtlfMtt0jNm1vG8+TJ0q5df3zvrl3ShRdKjz9ujetx42wjv6A4Z5se/vKLdM89wc0Tb7Ky7MjmgEBMo9kMAAAAAEHp1EmqXJkoDaDA3XdL//mPrUg+88yyjVGrlmUuz5xpmwfefLP06afS2WfbKukhQ6TFi+21v/4q9e0rvfyyxXGMGGHN4KC1by9dcYU0dqytykbxCvKzjz8+7EoAlIPzSptxFIDExEQvJycn7DIAAAAAwH8nnWQ5rjNnhl0JKpo5c2yTyqOPDrsS8+KLtsL4kkukp5/2t+mbmyu9956N+8Yb0u7dtsGgJC1YID31lHT55f7NVxLr1klHHiklJ0v/+19k5441W7ZIRxwhde9uFyMARDXn3HzP8xKLeo6VzQAAAAAQpNRUKTu76Nv7gaBs2yalp1tMxQsvhF2NRckMGGBZxk895f/q4oQEqU8f6dVXLcN59Gg75z7/3PKZI91olqRGjaQ77pDefVeaOjXy88eSm26yDO8hQ8KuBEA50WwGAAAAgCClpko7dkiffRZ2JahIpkyxCIk//Um66CJp6FBpz55wavn+e9sQsFkz6bXXpKpVg52vYUNp0CA75zZutHiNsFx7rXTUUdL113PBaX9mzpSefFIaPFjq3DnsagCUE81mAAAAAAhSSoodyW1GJE2aZI3mzz6TBg6UHn7YVv5u2BDZOrZskf7yF2nnTuntt60RHCnOWYRNmKpWlR55xLKlx44Nt5ZotH27rTpv1Uq6996wqwHgA5rNAAAAABCkww6TWrak2YzIWb1aysy0Fc1Vq0pjxliW8axZtmnlokWRqSM3V7rgAtsg77//ldq2jcy80SYjwxr9d98t/fxz2NVEl7vukr75Rho3zjZ+BBDzaDYDAAAAQNBSU63ZHAUbtKMCePFF+1rr12/vY5deas3mnTttw7pXXw2+jhtusKzisWOlXr2Cny+aPfyw9Ntv0u23h11J9MjJkUaOtJXNPXqEXQ0An9BsBgAAAICgpaRIa9ZYdi0QJM+TJk6UkpKkI4/8/XOdO0vz50vt20vnnivdequtPg7Ck0/aJn2DBkl/+1swc8SStm0tv3n8eOnTT8OuJny7d0uXXSYdeqj00ENhVwPARzSbAQAAACBoqal2JEoDQVu4UFq8WOrfv+jnGzeW3n9fuvJK6cEHLU950yZ/a8jMtMZqRoY0apS/Y8eyO+6QDj7YNsKr6Hc5/POfFufyxBNSvXphVwPARzSbAQAAACBoxx4r1akjzZkTdiWIdxMnSlWq2Mrl/alaVXrqKVt9nJkpnXSS9OWX/sy/dKl0zjnS0UdLL70kJST4M248qF9fuu8+izP573/DriY8S5ZI99xjX6N9+4ZdDQCf0WwGAAAAgKAlJFisASubEaQ9eyyv+bTTbAVtca66SpoxQ9qyxSI2Xn+9fPOvX29zV6smvfWWVLdu+caLR5dfLh1/vOVZb98edjWRl5tr8Rm1a9vGlQDiDs1mAAAAAIiE1FTp88+lzZvDrgTxavp0ae3a328MWJwuXWyjtqOPls48U7rrLikvr/Rz79wpnXWWtGqVNa1btiz9GBVBQoL06KPSihW2OV5F8/jj0ty5lud96KFhVwMgADSbAQAAACASUlMtp/WTT8KuBPFq0iSpQQPLSi6NZs0s2uHii6W777am85YtJX+/59kq6Q8/lJ55RkpOLt38Fc3JJ1vUyIMPSitXhl1N5Hz/vXTLLdKpp0oXXRR2NQACQrMZAAAAACKhc2epUiWiNBCMLVukKVOk886zGIvSql7dGsVjxkjvvGNfr199VbL3/vOf0nPPSXfeKV1wQennrogeeshWkN98c9iVREbBBQnnLCvcubArAhAQms0AAAAAEAl16lhWa6Q2CdyyxaINUDG89pq0Y4fUv3/Zx3BOGjjQNg1cv17q1MkazwcyebI1TM8/35rNKJmWLS23+cUXK8bGoRMnSu+9Jw0fLrVoEXY1AALkPM8LuwYlJiZ6OTk5YZcBAAAAAMG65hprumzcKFWuHNw8S5ZI3bpJbdtKH3xgObHR7NFHbcV3zZrl+6he3VaPV0Tdu0urV0vLlvmzavSHHyxO47PPpPvus/iDfcedP1/q2tUuorz/vlSjRvnnrUi2bZPatJEOO8zideL1a3fNGumYY6R27aSZM+P38wQqEOfcfM/zEot6LsCfbgAAAAAAv5Oaahtkff651KFDMHMsXy716mWrXGfPlh5+WLrxxmDm8sOUKdLgwVLz5tbM/O23vR9lUaPG3uZz06bSu+9KBx3kb83R5ocf7KLCPff4F0/QooV9/VxxhXTbbdKnn1rMRu3a9vzq1dLpp0uNGtmGgDSaS692bWnECNvQceJE6ZJLwq4oGNdea+fz+PE0moEKgGYzAAAAAERKaqod58wJptm8erXUs6e0fbvNceed0u2324Zx7dr5P195rV0rXXml1LGj9PHHUpUqe5/zPGuYF24+7/uxffv+n1u7Vvrvf6Vp06Rzzw3vc4yEF16wo9+brtWsKT3/vP3/GTZMWrrUGsuNG1ujecsWW5HeuLG/81Ykf/2r9NhjFkVy1llS3bphV+Sv116zjwcftFXcAOIeMRoAAAAAECmeJzVrJqWlWVarn9avt+iMlSulrCzppJOkn3+2JvPhh/+xmRs2z5P69rUc1wUL7DZ7P+3ZIzVsKJ1zjq2ojFeeZ//tGjWSZs0Kbp7p023zQcliMz78UHrzTenPfw5uzooiO9s2ZLzpJss0jhcbN0pHH703JiSa/v0BUC4HitHg/gUAAAAAiBTnbHXzRx/5O+7mzVLv3hah8dZb1miWpEMOkZ580pq5Dzzg75zl9fTTVuvw4f43miXLxO7Vy5rZUbDIKjDz59uK4379gp3nlFOknBy7WDJzpjRqFI1mv5x0km3s+Mgj1pSNF0OH2kWwCRNoNAMVCM1mAAAAAIik1FRpxQpp1Sp/xvv1V2v6LVpkt6uffPLvnz/7bLtV/777rOkcDZYvt5zm7t2l664Lbp70dFvpvXRpcHOEbeJEqVo16f/+L/i5WrWS5s61FdSDBgU/X0UyapQ18k8/Xfr++7CrKb/p0y3je9iw4PLpAUQlms0AAAAAEEkpKXacM6f8Y+3caTmvc+daLEdGRtGv+9e/LGahf397T5hyc6WLL7aNwp59NtgNw9LT7fjee8HNEabdu6WXXrIGZb16kZmzVi2pa1f/NiKEadhQeucdadcu6bTT7G6FWLVtm2WxH3WUdMcdYVcDIMJoNgMAAABAJLVvbxuvlTdKY88e6YILrJE6btyBV7Y2aGC5xV98YZsGhunhhy3v91//sizpILVsaQ2vadOCnScs775rMQX9+4ddCfzQtq3dnbBsmW1quWdP2BWVze232+rsCROk6tXDrgZAhNFsBgAAAIBIqlLFMlrLs7I5L08aMECaMkUaPdr+XJyMDOnyy6WHHvJnVXVZfP65NaLOPDP4jOEC6enSBx+Ev6I7CJMm2Yr13r3DrgR+6dHDctbfe08aODD28sbnzpXGjJGuuUbq0iXsagCEgGYzAAAAAERaSor06aeWt1xanmdNqEmTpHvvLV127qhRUvPm0iWXSL/9Vvq5y2PnTmsw168vPfVU5GIYeveWtm+XZs+OzHyRsmmT9Oab0vnns/lavLnsMummm6zpPHp02NWU3M6dVnuzZtKDD4ZdDYCQ0GwGAAAAgEhLTbXs4nnzSv/eW2+VHn9cuvFG6bbbSvfeunVt066vv5ZuuaX0c5fH3XdLCxda5EejRpGb9+STrRkbb7nNr75qzT0iNOLTAw/Y5p5Dh9pFhVhw//3SkiV2MalOnbCrARASms0AAAAAEGnJyXYsbW7zgw9Kw4dLV10ljRhRttXB3bvbyugxY6T33y/9+8tizhyr97LLpL/8JTJzFqhd25r78dZsnjjRMn5PPDHsShCESpXs/3FiomWzL1gQdkUHtmiR/fvUr5/Up0/Y1QAIEc1mAAAAAIi0+vWlY44pXbN57Fhb1fzXv0qPPVa+GIrhw6Ujj5QuvVTasqXs45TEtm3WgGrRQnrkkWDn2p/0dOmzz6S1a8OZ32/ffWexIP37Ry6OBJFXs6atam7Y0C7SrFoVdkVF27PHLiQ1aBDeOQ4gatBsBgAAAIAwpKbaZlp5ecW/9rnnbDXy6adLzz4rJSSUb+6aNW2clSvtNv0g3XCDtHy5fQ5h3VpfsIHe9OnhzO+355+344UXhlsHgte4sfT229LWrdZw3rYt7Ir+aPRoKSfHLogdfHDY1QAIGc1mAAAAAAhDSopt8rZkyYFf99pr0oABUs+e0n/+499mcCkplvs8frw0dao/Y+5r6lTLb73hBqlr12DmKIn27W116LRp4dXgF8+zzSG7d5cOPzzsahAJxx0nvfKKRVVccIHlvUeLb76R/vEP6YwzpHPOCbsaAFGAZjMAAAAAhCE11Y4HitKYNs2aS507S6+/LlWv7m8Nd98ttWsnXX659Msv/o69fr3dWn/ccdK99/o7dmlVqiSdcoqtbC7JSvJo9vHH1uDr1y/sShBJp54q/etftsr5hhvCrsbk5dm/HdWqlT/aB0DcoNkMAAAAAGE44gipUSPbPK8oH34onXmmNYOnTrWN7vxWrZptQrZunXTddf6N63nS3/8ubdhgq3CrVfNv7LLq3dsymz//POxKymfSJKlGDenss8OuBJF29dXS4MEWW/H442FXY3dFzJwpjRolHXZY2NUAiBI0mwEAAAAgDM5ZlEVRK5tzcqQ//9liEqZNk+rVC66Ojh2l22+XXnjBIjv88OKL0n//K91zj3TCCf6MWV6nnGLHWI7S2LlTevlliyyoWzfsahCGkSOl006zDPd33w2vjlWrbIV1jx4W8wMA+Wg2AwAAAEBYUlMtEmHt2r2PffGF3TLfoIGUmSkdckjwddx6qzWd//Y36eefyzfWypXSNdfY53bjjf7U54fDDrNIj/feC7uSsps6Vdq4UerfP+xKEJaEBOmll6Tjj5fOPTeclfqeZ/9W7NkjjRtHfAaA36HZDAAAAABhKchtnjvXjt9+aytwq1aVsrKkZs0iU0eVKhansWWLNZE8r2zj5OVJl15qTajnnrPGWDRJT7d4kl9/DbuSspk4UTr0UKlXr7ArQZhq15beekuqU8dWOa9ZE7m5ly6VLrlEeucd6f77pVatIjc3gJhAsxkAAAAAwtKxozWWP/rIbkvv1cuiEqZPl1q3jmwt7dpJ990nTZliMRhl8dhj1iR/+OHI118SvXtLu3ZJs2aFXUnpbdhgDb4LL5QqVw67GoStWTNrOK9fL51+uvTbb8HOl50tnXWWdMwx0quvSkOG+JvzDiBu0GwGAAAAgLBUry4lJlqO8CmnWENx2jRr/IZhyBDLkb72Wmn16tK9d+lSadgwy5q+4opg6iuvLl3sv3ks5ja/8oq0e7fUr1/YlSBadOxoF4ZycixaJS/P3/E9z2JnevSQOneWPvjA8t1/+ME2BYy2OxcARAWazQAAAAAQptRUy1394QdbuZqYGF4tCQnSs8/a6urLLy95nEZBE7RWLWn8+OjNcK1RQ+rWLTZzmydOtMzpaNlwEdGhb19r/L72mmWv+2HPHuk//5FOPNHuBvjqK7tbYcUK2/SzUSN/5gEQl2g2AwAAAECYzjjDNgGcPFnq2jXsaqQjj5T++U/p3XetcVwSDzxgqyuffFJq3DjY+sqrd29pyRLbyDBWfP219PHH1tCP1kY+wjN4sGWtjxghTZhQ9nF27LBzuG1b6fzzLZrj6ael776Trr/esqIBoBg0mwEAAAAgTCkptsHXqaeGXcleV19tt84PGSJ9//2BXztvnnTvvdJFF0nnnBOR8solPd2OsbS6edIkqVIly2sG9uWcNGaMfW3/7W+Wm14amzdLw4dLLVtKf/+7dPDBdvHryy9tw8+qVQMpG0B8otkMAAAAAGGLttWqlSrZikbnrNm0vyzY7dtttW2TJtK//hXZGsuqXTvpsMNip9mcl2fN5p49rW6gKFWqWK53mzbS2Wfb6v3i/PSTdPPN0uGHS7fcIrVvL82YYavozzzT/h0AgFLiXw4AAAAAwB+1aCGNHm2bgo0dW/Rrbr5ZWrbMcp7r1YtkdWXnnK0AnT5dys0Nu5riffSRrS7v3z/sShDtDjpIevttqVo16bTTpHXrin7dN99IV11lK5kfekjq00dasMCic7p3j76LXwBiCs1mAAAAAEDRLr1U+vOfpZtusqZyYZmZduv+ddfZqttY0ru3tHGjNH9+2JUUb9Ik23jxzDPDrgSxoGVL6c03pR9/tDz4HTv2PrdggXTeebb6+dln7fxetkx6+WWpQ4ewKgYQZ4ptNjvnmjvn3nfOfemc+8I5N2if54c65zznXMP8vzvn3Bjn3DfOuUXOuY5BFQ8AAAAACJBz0rhxUo0a0iWXSHv22OObNlmjqm1by3qNNb162ec2bVrYlRzYjh0WjXD22dZwBkqic2dp4kRpzhxpwACLxujdWzrxRFu9fOONtlr+ySelI44Iu1oAcaYkK5v3SBrqed4xkpIkXeOcO0ayRrSkdEkrCr2+j6Qj8z+ulPSErxUDAAAAACKnSRPpsccsx3XkSHts4EDLe5040RrRsaZhQ6ljx+jPbX7rLdu8rV+/sCtBrPm//5MeeEB66SW782DhQrswtGKFHZs0CbtCAHGqcnEv8DzvJ0k/5f95q3NuiaSmkr6U9IikYZLeKPSWvpImep7nSfrYOVfPOdckfxwAAAAAQKw5/3xp8mTpzjulXbuk55+X7rpL6tQp7MrKrndvacQIacsWqW7dsKsp2sSJUtOmlqMLlNbNN0tVq0q1a0sXXyxVrx52RQAqgFJlNjvnWkrqIOkT51xfSas9z1u4z8uaSlpZ6O+r8h/bd6wrnXM5zrmcdfsLrQcAAAAAhM856fHHbRPAO++0JvOtt4ZdVfmkp9sGgTNmhF1J0X7+2SIPLrxQSkgIuxrEIuekoUNtM0AazQAipMTNZudcbUmvSRosi9a4VdIdZZ3Y87x/e56X6HleYqNGjco6DAAAAAAgEho1kp55RmrXzlbcVqkSdkXlk5xsKz6jNUrj5ZctI5sIDQBADCk2RkOSnHNVZI3mFzzPm+ycO07SnyQtdM5JUjNJC5xzJ0laLal5obc3y38MAAAAABDLMjLsIx5UrWrxFNG6SeCkSVKHDtKxx4ZdCQAAJVbsymZn3eQJkpZ4nvewJHme97nneYd4ntfS87yWsqiMjp7nrZH0pqT+ziRJ2kxeMwAAAAAg6vTuLX33nfTtt2FX8ntLlkg5OVL//mFXAgBAqZQkRiNVUj9JPZxzn+V/HOhS9lRJ30n6RtI4SVeXv0wAAAAAAHyWnm7HaIvSmDTJcpovuCDsSgAAKBXneV7YNSgxMdHLyckJuwwAAAAAQEXieVKrVtIJJ0ivvx52NSYvT2rZUjruOOmdd8KuBgCAP3DOzfc8L7Go50q8QSAAAAAAAHHFOYvSmDFD2r077GrMzJnSypVsDAgAiEk0mwEAAAAAFVd6urR1q/Txx2FXYiZNkurWlfr2DbsSAABKjWYzAAAAAKDi6tHD8pGjIbf5t9+kV1+VzjlHqlEj7GoAACg1ms0AAAAAgIqrXj2pc2dp2rSwK5HeeEPatk3q3z/sSgAAKBOazQAAAACAiq13byknR9qwIdw6Jk6UDj9c6to13DoAACgjms0AAAAAgIotPV3yPCkrK7wa1qyxKI9+/aRK/KoOAIhNfAcDAAAAAFRsiYkWpxFmlMaLL0p5edZsBgAgRtFsBgAAAABUbJUrS7162cpizwunhkmTpJNOktq0CWd+AAB8QLMZAAAAAID0dGnVKmnJksjPvWiR9NlnrGoGAMQ8ms0AAAAAAKSn2/G99yI7r+dJw4ZJtWtL558f2bkBAPAZzWYAAAAAAFq0sAiLSOc2v/iizfngg1LDhpGdGwAAn9FsBgAAAABAknr3lmbOlHbsiMx8GzZIgwdLnTtLf/97ZOYEACBANJsBAAAAAJAsSmP7dumjjyIz39Ch0qZN0rhxUkJCZOYEACBANJsBAAAAAJCktDSpSpXIRGlkZUnPPWd5zccdF/x8AABEAM1mAAAAAAAk26SvS5fgNwncvl266irpyCOlf/wj2LkAAIggms0AAAAAABRIT5cWLpTWrAlujnvukb79VnrqKal69eDmAQAgwmg2AwAAAABQoHdvO06fHsz4CxdKDz0kDRggde8ezBwAAISEZjMAAAAAAAVOOEFq1CiYKI3cXOmKK6SDD7aGMwAAcaZy2AUAAAAAABA1KlWyKI333pPy8uzvfhk7Vpo3T3rpJalBA//GBQAgSrCyGQAAAACAwtLTpZ9/lhYt8m/MFSuk226T+vSRzjvPv3EBAIgiNJsBAAAAACjslFPsOG2aP+N5nnT11XZ84gnJOX/GBQAgytBsBgAAAACgsCZNpOOP9y+3+dVXpXfeke67T2rRwp8xAQCIQjSbAQAAAADYV3q6NHu29Ouv5Rtn40bpuuukxEQ7AgAQx2g2AwAAAACwr969pV27pJkzyzfOsGHS+vXSuHFSQoI/tQEAEKVoNgMAAAAAsK8uXaQaNcoXpTFzpjR+vDR0qNS+vX+1AQAQpWg2AwAAAACwr+rVpbS0sm8SuGOHdOWVUqtW0p13+lsbAABRimYzAAAAAABFSU+Xli6VVqwo/Xvvv1/66ivpySelmjX9rw0AgChEsxkAAAAAgKKkp9uxtFEaixdLw4dL/fpJp5zif10AAEQpms0AAAAAABTlmGOkpk1L12zOy7P4jIMOkh5+OLjaAACIQpXDLgAAAAAAgKjknK1ufv11KTdXSkgo/j1PPinNnStNnCg1bBh8jQAARBFWNgMAAAAAsD+9e0sbN0o5OcW/dtUq6eabLTrjoouCrw0AgChDsxkAAAAAgP3p1ctWOJckSmPgQGnPHlvd7FzwtQEAEGVoNgMAAAAAsD8HHywlJkrTph34dZMnW9zG3XdLrVpFpjYAAKIMzWYAAAAAAA4kPV36+GNp8+ain9+8Wbr2Wql9e+n66yNbGwAAUYRmMwAAAAAAB5KebhsEzphR9PM33yytXSuNGydVrhzZ2gAAiCI0mwEAAAAAOJDkZKl27aJzm2fPtozmQYMsbgMAgAqMZjMAAAAAAAdSpYrUo4flNnve3sd37pSuvFJq0UK6557w6gMAIErQbAYAAAAAoDi9e0vLl0vffrv3sREjpCVLpCeesJXPAABUcDSbAQAAAAAoTnq6HQuiNJYske6/X7rgAqlPn/DqAgAgitBsBgAAAACgOEccIbVqZVEaeXnSVVdJtWpJjzwSdmUAAEQNms0AAAAAAJREero0Y4ZtCPjhh9KoUdKhh4ZdFQAAUYNmMwAAAAAAJZGeLm3bJl13ndS9u3TJJWFXBABAVKHZDAAAAABASfToISUkSJUrS089JTkXdkUAAESVymEXAAAAAABATDjoIOmmm6TWraUjjwy7GgAAog7NZgAAAAAASur++8OuAACAqEWMBgAAAAAAAACg3Gg2AwAAAAAAAADKjWYzAAAAAAAAAKDcaDYDAAAAAAAAAMqNZjMAAAAAAAAAoNxoNgMAAAAAAAAAyo1mMwAAAAAAAACg3Gg2AwAAAAAAAADKjWYzAAAAAAAAAKDcaDYDAAAAAAAAAMqNZjMAAAAAAAAAoNxoNgMAAAAAAAAAyo1mMwAAAAAAAACg3Gg2AwAAAAAAAADKjWYzAAAAAAAAAKDcaDYDAAAAAAAAAMqNZjMAAAAAAAAAoNxoNgMAAAAAAAAAys15nhd2DXLOrZP0g8/DNpS03ucxAXBuAUHh3AL8x3kFBINzCwgG5xYQDM4t/7XwPK9RUU9ERbM5CM65HM/zEsOuA4g3nFtAMDi3AP9xXgHB4NwCgsG5BQSDcyuyiNEAAAAAAAAAAJQbzWYAAAAAAAAAQLnFc7P532EXAMQpzi0gGJxbgP84r4BgcG4BweDcAoLBuRVBcZvZDAAAAAAAAACInHhe2QwAAAAAAAAAiJCYajY75xKcc586597O/3sP59wC59xi59xzzrnK+Y/f6Jz7LP9jsXMu1znXIP+5U51zy5xz3zjnbg7z8wGiRSnOrYOcc2855xY6575wzl1aaIyLnXNf539cHNbnAkSTUpxb9Z1zU5xzi5xz2c65YwuNwfctYB/Oue+dc5/n/6yXk/9YA+fc9PzvQ9Odc/XzH3fOuTH559Ai51zHQuPwvQsopJTnVlvn3Fzn3E7n3A37jMP3LqCQUp5bF+Z/v/rcOTfHOXdCoXE4t4BCSnlu9c0/tz5zzuU457oUGoefCX0UUzEazrkhkhIl1ZV0uqQfJPX0PO8r59w9kn7wPG/CPu/5i6TrPc/r4ZxLkPSVpFMkrZI0T9IFnud9GcnPA4g2JT23nHO3SjrI87ybnHONJC2T1FhSbUk5+WN4kuZLOtHzvI0hfDpA1CjFufWQpG2e593tnGsr6THP83ryfQsomnPue0mJnuetL/TYPyX94nne8PxfwOvnf7/KkDRQUoakzpIe9Tyvs7OFCHzvAgop5bl1iKQWks6QtBKOj+cAAAT/SURBVNHzvJH5r+d7F7CPUp5bKZKWeJ630TnXR9Jd+d+3OLeAfZTy3Kot6VfP8zzn3PGSXvE8ry0/E/ovZlY2O+eaSfqzpPH5Dx0saZfneV/l/326pLOLeOsFkl7K//NJkr7xPO87z/N2SXpZUt/gqgaiXynPLU9SHeeckzWYf5G0R1JvSdM9z/sl/x/k6ZJOjdCnAESlUp5bx0iaIUme5y2V1NI5d6j4vgWURl9Jz+X/+TlZA6zg8Yme+VhSPedcE/G9CyipIs8tz/N+9jxvnqTd+7ye711Ayezv3JpTqMn1saRm+X/m3AJKZn/n1jZv74rbWrL+hsTPhL6LmWazpNGShknKy//7ekmVnXOJ+X8/R1Lzwm9wztWUfYG8lv9QU0krC71kVf5jQEVWmnNrrKSjJf0o6XNJgzzPyxPnFlCU0pxbCyWdJUnOuZNkK8WaiXML2B9P0nvOufnOuSvzHzvU87yf8v+8RtKh+X/e33nE+QX8UWnOrf3h3AL+qKzn1mWS/pf/Z84t4I9KdW455850zi2V9I6kAfkPc275rHLYBZTE/7d39yByVWEYx/+PglHEqCBEYRG/v0qxEIwaP1hRVBBBU0RFtLSwkZAmhdukExu1EFEQCwsXU4gmECRiFcWApgsGhUVcSMCAWhh9Le5Zd3Y2U9yZddhh/79mlztzhzswD++Zd+45J8njwHJVfZtkF0C77X038EaSbcAh4O+hU58Avq6qM1O9YGlGjJGtR4DjwIPAjcDhJF9N/8qlzW2MbB0A3kxynO6HnO9YX9MkrdpZVUttGv/h9qXhPy1vs7NWnLR5mC3p/9E7W0keoGs270TSKL2yVVWLwGKS+4AF4OHpXu7WMBPNZuAe4Mm25t7FwPYkH1bVHuBegCTzwC1D5+1mdQkNgCXW3v08145JW1XfbL0IHGhTT04mOQXcRpejXQOvOwd8OZV3IG1OvbJVVWfp8kVbpuYU8CNwCdYtaZ2qWmp/l5Ms0k0t/jXJNVX1S1smY7k9fdT4z9olDemZrVH8ziUN6Zuttp7su8CjVXW6HTZb0pBx61ZVHU1yQ5KrcEy44WZiGY2q2ldVc1V1HV0D+UhV7Wm/XNDuENsLvLNyTpLLgfuBTwde6hhwc5Lrk1zUXuvglN6GtOmMka2fgYfaYzuAW+kaYl8A80muTLfT63w7Jm1JfbOV5IpWlwBeBo62BrR1SxqS5NIkl638T1dzfqDLxsru4S+wOgY8CDyfzt3Ab21qpbVLGjBGtkaxdkkD+mYrybXAJ8BzA3t9gNmS1hgjWze1G3tIciewDTiNY8INNyt3No/yWpuqfAHwdlUdGXjsKeBQVf2+cqCqziV5he5DcyHwXlWdmOoVS7NhVLYWgPeTfA8E2Luy62uSBboBEMDrLl8jndeobN0OfNCmeJ2gmzJp3ZLObwfd9EfoxrIfVdXnSY4BHyd5CfgJeKY9/zPgMeAk8AdtFkFVnbF2SWv0ylaSq4FvgO3AP0leBe6oqrPWLmmNvnVrP93G0m+1c85V1V2OC6V1+mbrabobEP4C/gSebbO2HRNusKxuxChJkiRJkiRJ0nhmYhkNSZIkSZIkSdLmZrNZkiRJkiRJkjQxm82SJEmSJEmSpInZbJYkSZIkSZIkTcxmsyRJkiRJkiRpYjabJUmSJEmSJEkTs9ksSZIkSZIkSZqYzWZJkiRJkiRJ0sT+BSEJq5FXK01YAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1800x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcdPIVjrFmOR",
        "colab_type": "code",
        "outputId": "bae72a11-5eeb-47d0-c64e-e4ff1250bb37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>1. open</th>\n",
              "      <th>2. high</th>\n",
              "      <th>3. low</th>\n",
              "      <th>4. close</th>\n",
              "      <th>5. volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000-04-17</td>\n",
              "      <td>109.50</td>\n",
              "      <td>123.94</td>\n",
              "      <td>109.06</td>\n",
              "      <td>123.87</td>\n",
              "      <td>3656800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000-04-18</td>\n",
              "      <td>123.50</td>\n",
              "      <td>126.87</td>\n",
              "      <td>119.37</td>\n",
              "      <td>126.87</td>\n",
              "      <td>3490400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000-04-19</td>\n",
              "      <td>126.19</td>\n",
              "      <td>130.25</td>\n",
              "      <td>119.75</td>\n",
              "      <td>121.12</td>\n",
              "      <td>4644200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000-04-20</td>\n",
              "      <td>123.69</td>\n",
              "      <td>124.75</td>\n",
              "      <td>117.06</td>\n",
              "      <td>118.87</td>\n",
              "      <td>6447500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000-04-24</td>\n",
              "      <td>115.00</td>\n",
              "      <td>120.50</td>\n",
              "      <td>114.75</td>\n",
              "      <td>120.50</td>\n",
              "      <td>3960900.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        index  1. open  2. high  3. low  4. close  5. volume\n",
              "0  2000-04-17   109.50   123.94  109.06    123.87  3656800.0\n",
              "1  2000-04-18   123.50   126.87  119.37    126.87  3490400.0\n",
              "2  2000-04-19   126.19   130.25  119.75    121.12  4644200.0\n",
              "3  2000-04-20   123.69   124.75  117.06    118.87  6447500.0\n",
              "4  2000-04-24   115.00   120.50  114.75    120.50  3960900.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8C-sSVQPYoTD",
        "colab_type": "text"
      },
      "source": [
        "Creating the train and test data from the original dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awbmITtwFqMG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_train = dataset.iloc[:4971,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRaOIWLcFrWH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_test = dataset.iloc[-60:,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3t7Hk2XoFt5-",
        "colab_type": "code",
        "outputId": "c7dd7327-b294-42b7-89c8-e9c8cec6520b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(dataset_train.shape,dataset_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4971, 6) (60, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z6hwqtsFKfIq",
        "colab_type": "text"
      },
      "source": [
        "To create a numpy array of train values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgwxF8MuFydS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = dataset_train.iloc[:,1:2].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DVCoVxN1Kri3",
        "colab_type": "text"
      },
      "source": [
        "We have a numpy array of one column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGWsi-KtF2tQ",
        "colab_type": "code",
        "outputId": "944d12f8-6b4c-4945-fcef-d84bb31ed98d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "train[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[109.5 ],\n",
              "       [123.5 ],\n",
              "       [126.19],\n",
              "       [123.69],\n",
              "       [115.  ],\n",
              "       [122.12],\n",
              "       [126.62],\n",
              "       [117.19],\n",
              "       [127.12],\n",
              "       [124.87]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwGfMz5WLaWh",
        "colab_type": "text"
      },
      "source": [
        "## **Feature Scaling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ywf2pTYCF5oi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scale = MinMaxScaler(feature_range=(0,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CmVyQLjF6rF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_scaled = scale.fit_transform(train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7JYIEMYF9K3",
        "colab_type": "code",
        "outputId": "0eba98cc-b1a8-42de-ab7f-2b1dfbae6724",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "train_scaled[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.13998724],\n",
              "       [0.16029416],\n",
              "       [0.16419599],\n",
              "       [0.16056975],\n",
              "       [0.14796496],\n",
              "       [0.15829248],\n",
              "       [0.1648197 ],\n",
              "       [0.15114154],\n",
              "       [0.16554495],\n",
              "       [0.16228134]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDqrV-3SN_z2",
        "colab_type": "text"
      },
      "source": [
        "## **Creating a training data structure** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wFqk5BmpQmca",
        "colab_type": "text"
      },
      "source": [
        "This means that model is going to take 120 stock prices before time T in which RNNs are going to learn and understand some correlations and based on its understanding its going to predict the next output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2AaSSIMRT4R",
        "colab_type": "text"
      },
      "source": [
        "We can try the above by considering different timestamps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-BMrun2Sgqq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "time_len = 120"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVoppuNWF_Zt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = []\n",
        "y_train = []\n",
        "for i in range(time_len,dataset_train.shape[0]):\n",
        "    X_train.append(train_scaled[i-time_len:i,0])\n",
        "    y_train.append(train_scaled[i,0])\n",
        "X_train,y_train = np.array(X_train),np.array(y_train) #converting to numpy arrays so that they can be accepted in futute to our RNNs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ipjsIXx_Rsh0",
        "colab_type": "text"
      },
      "source": [
        "X_train will contain the 120 previous stock prices for current financial day.\n",
        "\n",
        "y_train will contain will contain stock price for current financial day"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k_PxRE2yTlKx",
        "colab_type": "code",
        "outputId": "79a7fafc-e530-4d55-affb-39cb08da8976",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "print(X_train[0],y_train[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.13998724 0.16029416 0.16419599 0.16056975 0.14796496 0.15829248\n",
            " 0.1648197  0.15114154 0.16554495 0.16228134 0.15993154 0.1536799\n",
            " 0.14813902 0.14188738 0.14374402 0.14116214 0.13209654 0.12819471\n",
            " 0.1349105  0.13789852 0.13276377 0.13145833 0.13055902 0.12511967\n",
            " 0.11714195 0.11242784 0.10617621 0.10952685 0.1088016  0.10825041\n",
            " 0.10716254 0.09973601 0.11714195 0.11650373 0.11456006 0.11695338\n",
            " 0.12275536 0.12149343 0.12094224 0.11342868 0.11850541 0.11351571\n",
            " 0.11677932 0.11251487 0.12403179 0.05440805 0.06202315 0.05916568\n",
            " 0.05730904 0.05916568 0.05848394 0.05812132 0.0577587  0.05677236\n",
            " 0.05839691 0.05730904 0.05743959 0.05961533 0.06383627 0.06547533\n",
            " 0.06601201 0.06402483 0.06564939 0.06601201 0.06121087 0.06093528\n",
            " 0.06000696 0.05739607 0.05413246 0.05345073 0.05368281 0.05698993\n",
            " 0.05246439 0.05413246 0.05223231 0.04724261 0.05291404 0.05060776\n",
            " 0.05069479 0.05097038 0.05078182 0.04909924 0.05018711 0.04969395\n",
            " 0.04915726 0.051333   0.05568449 0.05404543 0.05459662 0.05581503\n",
            " 0.06045662 0.06311102 0.06419889 0.0651127  0.06673726 0.06669374\n",
            " 0.0700879  0.07204607 0.07018943 0.06692582 0.07055206 0.0662876\n",
            " 0.06432944 0.06347364 0.06609904 0.06492414 0.0612979  0.06782513\n",
            " 0.06733196 0.06601201 0.05413246 0.05767167 0.05848394 0.05622117\n",
            " 0.05268196 0.02204752 0.01987178 0.01733341 0.01360564 0.0152447 ] 0.014069797801050159\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SDYKnF5TxUO",
        "colab_type": "text"
      },
      "source": [
        "We can see our dataframe with 120 previews of previous stock of the taken financial day"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lOpqqStPUlme",
        "colab_type": "text"
      },
      "source": [
        "We need to create the new dimensionality of the above mentioned new data structure, because that exactly is accepted by the future recurrent network. [Reference](https://keras.io/layers/recurrent/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtuNmunDGDzv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gt5GWmEXskWN",
        "colab_type": "text"
      },
      "source": [
        "## **Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EBnHLU9oV2yq",
        "colab_type": "text"
      },
      "source": [
        "We will build our model below :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uG3uiCMlY2lf",
        "colab_type": "text"
      },
      "source": [
        "Since capturing the trends of a stock price is complex, we need to have high number of neurons"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hc_Ds3HjGG9J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=50,return_sequences=True,input_shape = (X_train.shape[1],1)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units=50,return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units=50,return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units=50))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(units=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OV2JrBCKGUWq",
        "colab_type": "code",
        "outputId": "49d0e3e4-c211-4f17-fb88-c8d9b4e47abb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_5 (LSTM)                (None, 120, 50)           10400     \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 120, 50)           0         \n",
            "_________________________________________________________________\n",
            "lstm_6 (LSTM)                (None, 120, 50)           20200     \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 120, 50)           0         \n",
            "_________________________________________________________________\n",
            "lstm_7 (LSTM)                (None, 120, 50)           20200     \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 120, 50)           0         \n",
            "_________________________________________________________________\n",
            "lstm_8 (LSTM)                (None, 50)                20200     \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 71,051\n",
            "Trainable params: 71,051\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gfzd2Rx1GX_4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='RMSProp',loss='mean_squared_error')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhrWgws5apDV",
        "colab_type": "text"
      },
      "source": [
        "RMSProp optimizer is preferred for RNNs [Reference](https://keras.io/optimizers/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnQcwnvoGbZV",
        "colab_type": "code",
        "outputId": "9620ee98-c700-4bc0-e4a6-ac676f24eb94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "history = model.fit(X_train,y_train,epochs=20,batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "4851/4851 [==============================] - 44s 9ms/step - loss: 0.0085\n",
            "Epoch 2/20\n",
            "4851/4851 [==============================] - 46s 9ms/step - loss: 0.0040\n",
            "Epoch 3/20\n",
            "4851/4851 [==============================] - 43s 9ms/step - loss: 0.0032\n",
            "Epoch 4/20\n",
            "4851/4851 [==============================] - 40s 8ms/step - loss: 0.0028\n",
            "Epoch 5/20\n",
            "4851/4851 [==============================] - 40s 8ms/step - loss: 0.0025\n",
            "Epoch 6/20\n",
            "4851/4851 [==============================] - 41s 8ms/step - loss: 0.0021\n",
            "Epoch 7/20\n",
            "4851/4851 [==============================] - 40s 8ms/step - loss: 0.0020\n",
            "Epoch 8/20\n",
            "4851/4851 [==============================] - 41s 8ms/step - loss: 0.0018\n",
            "Epoch 9/20\n",
            "4851/4851 [==============================] - 41s 8ms/step - loss: 0.0018\n",
            "Epoch 10/20\n",
            "4851/4851 [==============================] - 41s 8ms/step - loss: 0.0016\n",
            "Epoch 11/20\n",
            "4851/4851 [==============================] - 41s 8ms/step - loss: 0.0016\n",
            "Epoch 12/20\n",
            "4851/4851 [==============================] - 40s 8ms/step - loss: 0.0014\n",
            "Epoch 13/20\n",
            "4851/4851 [==============================] - 42s 9ms/step - loss: 0.0014\n",
            "Epoch 14/20\n",
            "4851/4851 [==============================] - 42s 9ms/step - loss: 0.0014\n",
            "Epoch 15/20\n",
            "4851/4851 [==============================] - 41s 8ms/step - loss: 0.0014\n",
            "Epoch 16/20\n",
            "4851/4851 [==============================] - 41s 8ms/step - loss: 0.0013\n",
            "Epoch 17/20\n",
            "4851/4851 [==============================] - 41s 8ms/step - loss: 0.0012\n",
            "Epoch 18/20\n",
            "4851/4851 [==============================] - 42s 9ms/step - loss: 0.0012\n",
            "Epoch 19/20\n",
            "4851/4851 [==============================] - 41s 9ms/step - loss: 0.0012\n",
            "Epoch 20/20\n",
            "4851/4851 [==============================] - 43s 9ms/step - loss: 0.0012\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEi59_ClqnHP",
        "colab_type": "code",
        "outputId": "4fa7754c-7ec9-4f25-f33a-e2dcde2b1be0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h6f3Ap4Ffp22ZPvoDLALqI2JdRLQCy4Al3fosAW5Jl+8CzpKktH1ZRLRExHqgPn29Ea2uqpKtTS00t3XkuxQzs5zpb3B8CbgSuDsiVks6CnjoENvMAjZmPd+UtvXYJyLagd1A9SG2DeA+SSslXdzbm0u6WNIKSSsaGhoOUerQqKvuml7dex1mNnYV96dTRDwMPAyQDpJvj4gv5rKwPpwWEZslTQPul/RSRDzSvVNE3AjcCLBw4cJhuZy7NutajnnTJgzHW5qZDbv+nlX1U0kTJY0DXgDWSPrqITbbDNRmPZ+dtvXYR1IxMAlo7GvbiOh63AbczQg6hHXwIkBfy2FmY1h/D1UtiIgm4GPAb4C5JGdW9eVpYL6kuZJKSQa7l3frsxy4MF0+H3gwIiJtX5qedTUXmA88lZ4WPAGSU4SBD5EE2YhQPa6UipIMr+/wnQDNbOzq16EqoCS9buNjwPciok1Sn4d/IqJd0mXAvSQD6Ten4yNXASsiYjlwE3CrpHpgB0m4kPa7E1gDtAOXRkSHpOnA3cn4OcXATyPitwP90LkiyWdWmdmY19/g+D7wGvAs8IikI4GmQ20UEfcA93Rr+3rWcjPwiV62vRq4ulvbOuCEftacF7VVlR4cN7MxrV+HqiLiOxExKyI+HIkNwAdzXNuo1LXHkRxxMzMbe/o7OD5J0re7Tm+V9C1gXI5rG5XqqirY39pB477WfJdiZpYT/R0cvxnYA3wy/WkC/iVXRY1mXddyeJzDzMaq/o5xHB0Rf5L1/O8krcpFQaNd1ym5G3fs5z11U/JcjZnZ0OvvHscBSad1PZF0KuBzTnswe4qv5TCzsa2/exyXAD+WNCl9vpO3rr+wLOUlGaZPLPOhKjMbs/o75cizwAmSJqbPmyR9CXgul8WNVr6Ww8zGsgHdATAimtIryAG+koN6xoTaKb4vh5mNXYO5dayGrIoxpraqkjebmmlt78x3KWZmQ24wweEr3HpRV1VJBGze5fMHzGzs6XOMQ9Ieeg4IARU5qWgMyL6WY+5UXydpZmNLn8EREb6pxGGoq/JFgGY2dg3mUJX1omZ8GWXFRR4gN7MxycGRA0VForaq0hcBmtmY5ODIEV/LYWZjlYMjR+qqkms5PL26mY01Do4cmT2lgj0t7eza35bvUszMhpSDI0d8ZpWZjVU5DQ5JiyWtlVQv6Yoe1pdJuiNd/6SkOVnrrkzb10o6p9t2GUm/l/SrXNY/GF3Xcmz0bWTNbIzJWXBIygDXA+cCC4ALJC3o1u0iYGdEzAOuBa5Jt10ALAWOBxYDN6Sv1+Vy4MVc1T4UatPp1Tf4zCozG2NyucexCKiPiHUR0QosA5Z067MEuCVdvgs4S5LS9mUR0RIR64H69PWQNBv4CPDDHNY+aOPKipk3bTy3P/U6Tc0e5zCzsSOXwTEL2Jj1fFPa1mOfiGgHdgPVh9j2n4C/BvqcQVDSxV33SG9oaDjczzAo1/zJu3lzdzP//Zcv5OX9zcxyYVQNjkv6KLAtIlYeqm9E3BgRCyNiYU1NzTBU907vPXIKl581n39d9QZ3/35TXmowMxtquQyOzUBt1vPZaVuPfSQVA5OAxj62PRU4T9JrJIe+zpR0Wy6KHyqXfnAei+ZU8d9/uZoNjfvyXY6Z2aDlMjieBuZLmiuplGSwe3m3Pst56xa05wMPRnLF3HJgaXrW1VxgPvBURFwZEbMjYk76eg9GxJ/m8DMMWqZIXLv0RCS4fNkq2jp8jw4zG91yFhzpmMVlwL0kZ0DdGRGrJV0l6by0201AtaR6kjsKXpFuuxq4E1gD/Ba4NCI6clVrrs2aXME/fPxdrNq4i+seeCXf5ZiZDYoKYUqMhQsXxooVK/JdBn9917P8bOUmbv+Lkzn5qOp8l2Nm1itJKyNiYU/rRtXg+Gj3t//heOZUj+PLd6xit6ciMbNRysExjMaVFXPd0hNp2NPClXc/5wkQzWxUcnAMs3fPnsx/OedY7nl+C3eu2HjoDczMRhgHRx5cfPpRnHJ0Nd9YvoZXG/bmuxwzswFxcORBUZH49idPpLykiMuX/Z7Wdp+ia2ajh4MjT46YVM41f/JuXtjcxLfuW5vvcszM+s3BkUcfOv4IPv2+Or7/yDoee2V7vssxM+sXB0ee/c1HFjBv2ni+cucqGve25LscM7NDcnDkWUVphu8sPYld+9v42s99iq6ZjXwOjhFgwcyJXHHucTzw4jZue2JDvssxM+uTg2OE+Oypc/jAMTX8/a9f5OWte/JdjplZrxwcI4Qk/vETJzChvJgv3v57mttG7ZyOZjbGOThGkJoJZfzv80/gpS17+F+/eSnf5ZiZ9cjBMcJ88LhpfPbUOfzod6/x4Etb812Omdk7ODhGoK8tPo7jjpjAV3/2HNv2NOe7HDOzt3FwjEDlJRm+e8FJ7G1p51Pff4IXNu/Od0lmZgc5OEao+dMn8OPPLeJAawcfv+F33PTYel/jYWYjgoNjBHvfUdX85vLTOeOYqfyPX63holtW+OpyM8u7nAaHpMWS1kqql3RFD+vLJN2Rrn9S0pysdVem7WslnZO2lUt6StKzklZL+rtc1j8STBlXyg8+s5Bv/IcFPPbKds697lF+96rntTKz/MlZcEjKANcD5wILgAskLejW7SJgZ0TMA64Frkm3XQAsBY4HFgM3pK/XApwZEScAJwKLJZ2cq88wUkjiz0+dyy8vPZXx5cV8+odP8o/3rqW9w9Oxm9nwy+UexyKgPiLWRUQrsAxY0q3PEuCWdPku4CxJStuXRURLRKwH6oFFkei681FJ+lMwB/4XzJzIr75wGp9472y+91A9n7rxCTbt3J/vssyswOQyOGYB2fdG3ZS29dgnItqB3UB1X9tKykhaBWwD7o+IJ3t6c0kXS1ohaUVDQ8MQfJyRobK0mG+efwLXLT2RtVv28OHrHuU3z7+Z77LMrICMusHxiOiIiBOB2cAiSX/YS78bI2JhRCysqakZ3iKHwZITZ3HPF09nbs14Pv+TZ7jyF89zoNXTlJhZ7uUyODYDtVnPZ6dtPfaRVAxMAhr7s21E7AIeIhkDKUh11ZXcdcn7+csPHMXtT73OkusfY+0WT5BoZrmVy+B4Gpgvaa6kUpLB7uXd+iwHLkyXzwcejORiheXA0vSsq7nAfOApSTWSJgNIqgDOBgp6UqeSTBFXnvsH/Phzi9ixr43zvvcYtz2xwdd8mFnO5Cw40jGLy4B7gReBOyNitaSrJJ2XdrsJqJZUD3wFuCLddjVwJ7AG+C1waUR0ADOAhyQ9RxJM90fEr3L1GUaTM46p4TeXn877jqrmb375An/1k2fYvb8t32WZ2RikQvif6cKFC2PFihX5LmNYdHYGP3xsHd/87VqmTyznf378XZwxfyrJyWpmZv0jaWVELOxp3agbHLe+FRWJi884mp9//hSKM+LCm5/iU99/gifWNea7NDMbIxwcY9QJtZO578tncNWS43mtcR9Lb3yCT//wCVZu2Jnv0sxslPOhqgLQ3NbBbU9s4J///VUa97XywWNr+MrZx/Ku2ZPyXZqZjVB9HapycBSQfS3t3PL4a3z/4XXsPtDGOcdP58tnH8NxR0zMd2lmNsI4OBwcb9PU3MbNj63npkfXs7e1nY+8awZf+uNjmDdtfL5LM7MRwsHh4OjRrv2t/ODRdfzL/3uN5rYOPnbSLC4/az5HVo/Ld2lmlmcODgdHnxr3tvB/Hn6VHz++gY7O4Pz3zuYLZ81n1uSKfJdmZnni4HBw9Mu2pmZu+PdX+emTrwOwdFEtl505j2kTyvNcmZkNNweHg2NA3th1gO8+WM/PVmxkXFkxVy05nvNOmOmLCM0KiC8AtAGZObmCf/j4u7j3y2dwdM04Ll+2iktuW8l237bWzHBwWB+OrhnPzy45hSvPPY6H1jbwoWsf4dfP+d4fZoXOwWF9yhSJv/zA0fz6C6dRO6WCS3/6DJf99Bl27GvNd2lmlicODuuX+dMn8PPPn8JXzzmWe1dv4UPXPsy9q7fkuywzywMHh/VbcaaISz84j+WXnca0CeX85a0r+fIdqzx9u1mBcXDYgP3BjIn862WncvlZ8/m/z77B2dc+zIMvbc13WWY2TBwcdlhKMkV8+exj+OWlpzKlspTP/WgFX/3ZszQ1e+/DbKxzcNig/OGsSSz/wqlc+sGj+fkzmzjn2kd45OWGfJdlZjnk4LBBKyvO8NVzjuMXf3UqlaUZPnPzU/zXu59nb0t7vkszsxzIaXBIWixpraR6SVf0sL5M0h3p+iclzclad2XavlbSOWlbraSHJK2RtFrS5bms3wbmxNrJ/PqLp3PxGUdx+1Ovc861j3DjI6+y/Nk3eGr9Dl5v3E9zW0e+yzSzQcrZlCOSMsDLwNnAJuBp4IKIWJPV56+Ad0fEJZKWAv8xIj4laQFwO7AImAk8ABwDTANmRMQzkiYAK4GPZb9mTzzlyPBbuWEHf33Xc7zasO8d6yZXlnDExHKmTyxPHieVM31i2Vttk8qpqiylqMhTnJjlS19TjhTn8H0XAfURsS4tYhmwBMj+R34J8I10+S7ge0omRFoCLIuIFmC9pHpgUUQ8DrwJEBF7JL0IzOr2mjYCvPfIKh74ygdoam5na1MzW3Y3s6WpmW1NyeOW3S1sbWrmxTebaNjbQvf/v5RkxLQJ5Rx7xAROObqak4+qZsGMiQ4TsxEgl8ExC9iY9XwT8L7e+kREu6TdQHXa/kS3bWdlb5ge1joJeLKnN5d0MXAxQF1d3WF+BBsMSUyqKGFSRQnHTJ/Qa7/2jk4a9rawZXczW5uSQNmShs2zm3bx4EvbgGRP5X1zqzjl6Km8/+hq5k8b74kXzfIgl8GRM5LGAz8HvhQRTT31iYgbgRshOVQ1jOXZABVnipgxqYIZk3q+/8eW3c08sa6R3726ncfXNXLv6uSakanjSzn5qOqDQTKnutJBYjYMchkcm4HarOez07ae+mySVAxMAhr72lZSCUlo/CQifpGb0m0kOWJSOR87aRYfOynZ6dy4Yz+Pr2vk8VeTMPlVOvHijEnlvP+oat5/dPIze0plPss2G7NyGRxPA/MlzSX5R38p8J+69VkOXAg8DpwPPBgRIWk58FNJ3yYZHJ8PPJWOf9wEvBgR385h7TaC1VZVUltVyScX1hIRrN++j8fXNfK7Vxt5+OUGfvH75P8ndVWVLJgxkekTy5iWDrxPm1DG9InJYPykihLvoZgdhpwFRzpmcRlwL5ABbo6I1ZKuAlZExHKSELg1HfzeQRIupP3uJBn0bgcujYgOSacBfwY8L2lV+lb/NSLuydXnsJFNEkfVjOeomvF8+n1HEhG8vHUvj7+6nd+92si67Xt5fF0juw+884r20uIipk8sY/qEJFRqskKl67G2qpKy4kwePpnZyOU7AFpBaG7rYFtTC1v3NLO1KRmE39aULG/bkwzIb2tqYU+3ixbHlWb4wLE1nL1gOmceO51JlSV5+gRmwytfp+OajRjlJRnqqiupq+573GNfS/vBINmyu5kn1+/ggRe3cs/zW8gUiffNreLsBdM5e8F0j6FYwfIeh9khdHYGz27axX1rtnL/mq3Ub9sLwIIZEw+GyPEzJ3q8xMaUvvY4HBxmA7SuYS/3pyGy8vWdRMCsyRUHQ2TR3CpKMp4GzkY3B4eDw3KkYU8LD76UhMijr2ynpb2TieXFnHncNM5ecATHHjEBCDoDIqAz4uAjJI/JuuSxe9+ZkyoOeXjNLBccHA4OGwb7W9t55OXt3L9mKw++tJWdQ3RnxLqqSk6bP5XT503llKOneoDehoWDw8Fhw6y9o5OVG3aypamZIgmJ5JHkFOIiZT8myyLp09VfQH3DXh55eTtPrGtkb0s7RYJ3z57M6fOncvr8Gk6qm+zDYpYTDg4Hh41ybR2dPLtxF4+8sp3HXmlg1cZddEZyuvDJR1UneyTzazi6ZpwH6W1IODgcHDbG7D7QxuOvNvJYfQOPvrKdDY37gWTaldPmTeW0+VM5bd5UqseX5blSG60cHA4OG+M27tjPo69s59FXGvh/9dtpak4uZKyZUEZppojS4iJKMqLk4HIRpZm32kqKk+elmSJKinVwfWVpMVXjSphcWUrVuFImV5ZQNa6UKZWllP1LDv8AAArhSURBVJf4ivqxzMHh4LAC0tEZPL95N4++3MDmXQdo7eikrSNoa++kraMzfZ62dXTS2v72520dnbS0J+0t7Z29vk9FSeYdYTKlsoQpXcvjShlXmnnbGE9Rt/GdoqLs5+kyb/WfXFnCjEnlPvyWB75y3KyAZIrEibWTObF28qBfq72jk10H2ti5r5Ud+1rZub+NnfuT5V37W9mxL3m+c38rG3fsZ8e+1oN7O0NlQlkxxxwxgWOmT+CY6eM5dvoEjjliAlN9GC5vHBxm1qviTBFTx5cN6B/p7LDZ39pB0HX9SnKNSmfn269dSa5leev6loNtncH2fa28vGUPL2/dw29eeJPbn3rrFOfqcaXMzwqSY6dPYP70CUyq8OnKuebgMLMhdThh0x8RQcPeFl7espe1W/ckgbJtD3et3MS+1o6D/Y6YWJ4GyXjmTRvPnOpxzJk6jmkTynzIa4g4OMxsVJCS+9BPm1DOafOnHmyPCDbvOsDLW/ewdsteXt6a7KHcsq6R1qwxmoqSDEdWVzJ36jiOrB7H3KmV6aNDZaAcHGY2qkli9pRKZk+p5Mzjph9sb+/o5I1dzaxv3MeGxn2s376PDY37Wbt1Dw+8uJW2jrdODOoKlTnV4zhyaiVzq5NwqZlQRiYdwC+SDg7m9zTgn6x7+/oIaGnvpKW9g9asEw6yH1s7OmhpS05aaGnrpKWjk5a2Dlo7OolIaqsszVBRmqGiJHmsLM1QXpJJ1xUfbC/JaFgC0MFhZmNScaYoayr9mreta+/o5M3dzWmY7GP99v1saNzHy9v28G8vvT1URpNMkagsyVCehswRE8u585L3D/n7ODjMrOAUZ4oO3oK4e6h0dAZv7DrAa4372LGvNR2op8cB/IigozP7+dvXS1CaKaKsJENZpoiykqL0eRGlmQylxUWUFRd1e3yrHZKbkB1o62B/awcHWjto7lpuS553f+xa19zWQXlJbqajyWlwSFoMXEdy69gfRsT/6ra+DPgx8F6gEfhURLyWrrsSuAjoAL4YEfem7TcDHwW2RcQf5rJ+Mys8mSJlhUr+lZdkGPyJ1UMrZ7OjScoA1wPnAguACyQt6NbtImBnRMwDrgWuSbddQHL/8eOBxcAN6esB/ChtMzOzPMjltJqLgPqIWBcRrcAyYEm3PkuAW9Llu4CzlIzsLAGWRURLRKwH6tPXIyIeAXbksG4zM+tDLoNjFrAx6/mmtK3HPhHRDuwGqvu5rZmZ5cGYnchf0sWSVkha0dDQkO9yzMzGjFwGx2agNuv57LStxz6SioFJJIPk/dm2TxFxY0QsjIiFNTU1h97AzMz6JZfB8TQwX9JcSaUkg93Lu/VZDlyYLp8PPBjJdL3LgaWSyiTNBeYDT+WwVjMz66ecBUc6ZnEZcC/wInBnRKyWdJWk89JuNwHVkuqBrwBXpNuuBu4E1gC/BS6NiA4ASbcDjwPHStok6aJcfQYzM3sn34/DzMzeoeBv5CSpAdhwmJtPBbYPYTlDzfUNjusbHNc3OCO5viMjoscB4oIIjsGQtKK31B0JXN/guL7BcX2DM9Lr682YPR3XzMxyw8FhZmYD4uA4tBvzXcAhuL7BcX2D4/oGZ6TX1yOPcZiZ2YB4j8PMzAbEwWFmZgPi4EhJWixpraR6SVf0sL5M0h3p+iclzRnG2molPSRpjaTVki7voc8fSdotaVX68/Xhqi99/9ckPZ++9zuutlTiO+n395yk9wxjbcdmfS+rJDVJ+lK3PsP6/Um6WdI2SS9ktVVJul/SK+njlF62vTDt84qkC3vqk6P6/rekl9I/v7sl9Xh/oUP9LuSwvm9I2pz1Z/jhXrbt8+96Duu7I6u21ySt6mXbnH9/gxbp7Q8L+YfkDoWvAkcBpcCzwIJuff4K+D/p8lLgjmGsbwbwnnR5AvByD/X9EfCrPH6HrwFT+1j/YeA3gICTgSfz+Ge9heTiprx9f8AZwHuAF7LavglckS5fAVzTw3ZVwLr0cUq6PGWY6vsQUJwuX9NTff35Xchhfd8A/ks//vz7/Lueq/q6rf8W8PV8fX+D/fEeR2IwN53KuYh4MyKeSZf3kMz9NdruT7IE+HEkngAmS5qRhzrOAl6NiMOdSWBIRM83JMv+HbsF+FgPm54D3B8ROyJiJ3A/ObgjZk/1RcR9kcxBB/AEyazVedHL99cf/fm7Pmh91Zf+u/FJ4Pahft/h4uBIDOamU8MqPUR2EvBkD6vfL+lZSb+RdPywFgYB3CdppaSLe1g/Um7OtZTe/8Lm8/sDmB4Rb6bLW4DpPfQZKd/j50j2IHtyqN+FXLosPZR2cy+H+kbC93c6sDUiXullfT6/v35xcIwiksYDPwe+FBFN3VY/Q3L45QTgu8Avh7m80yLiPST3mL9U0hnD/P6HpGR6//OAn/WwOt/f39tEcsxiRJ4rL+m/Ae3AT3rpkq/fhX8GjgZOBN4kORw0El1A33sbI/7vkoMjMZibTg0LSSUkofGTiPhF9/UR0RQRe9Ple4ASSVOHq76I2Jw+bgPuJr1HfJZB35xrCJwLPBMRW7uvyPf3l9radfgufdzWQ5+8fo+S/hz4KPDpNNzeoR+/CzkREVsjoiMiOoEf9PK++f7+ioGPA3f01idf399AODgSg7npVM6lx0RvAl6MiG/30ueIrjEXSYtI/myHJdgkjZM0oWuZZBD1hW7dlgOfSc+uOhnYnXVYZrj0+j+9fH5/WbJ/xy4E/rWHPvcCH5I0JT0U86G0LeckLQb+GjgvIvb30qc/vwu5qi97zOw/9vK+/fm7nkt/DLwUEZt6WpnP729A8j06P1J+SM76eZnkjIv/lrZdRfKXBKCc5BBHPcndCI8axtpOIzls8RywKv35MHAJcEna5zJgNclZIk8ApwxjfUel7/tsWkPX95ddn4Dr0+/3eWDhMP/5jiMJgklZbXn7/kgC7E2gjeQ4+0UkY2b/BrwCPABUpX0XAj/M2vZz6e9hPfDZYayvnmR8oOt3sOssw5nAPX39LgxTfbemv1vPkYTBjO71pc/f8Xd9OOpL23/U9TuX1XfYv7/B/njKETMzGxAfqjIzswFxcJiZ2YA4OMzMbEAcHGZmNiAODjMzGxAHh9lhktTRbdbdIZtpVdKc7JlVzUaS4nwXYDaKHYiIE/NdhNlw8x6H2RBL76fwzfSeCk9Jmpe2z5H0YDoJ379Jqkvbp6f3t3g2/TklfamMpB8ouQfLfZIq0v5fVHJvluckLcvTx7QC5uAwO3wV3Q5VfSpr3e6IeBfwPeCf0rbvArdExLtJJgj8Ttr+HeDhSCZYfA/JFcMA84HrI+J4YBfwJ2n7FcBJ6etckqsPZ9YbXzludpgk7Y2I8T20vwacGRHr0skpt0REtaTtJNNgtKXtb0bEVEkNwOyIaMl6jTkk992Ynz7/GlASEX8v6bfAXpIZfH8Z6eSMZsPFexxmuRG9LA9ES9ZyB2+NSX6EZN6v9wBPpzOumg0bB4dZbnwq6/HxdPl3JLOxAnwaeDRd/jfg8wCSMpIm9faikoqA2oh4CPgayfT+79jrMcsl/0/F7PBVSFqV9fy3EdF1Su4USc+R7DVckLZ9AfgXSV8FGoDPpu2XAzdKuohkz+LzJDOr9iQD3JaGi4DvRMSuIftEZv3gMQ6zIZaOcSyMiO35rsUsF3yoyszMBsR7HGZmNiDe4zAzswFxcJiZ2YA4OMzMbEAcHGZmNiAODjMzG5D/D/8rZzw+4rb3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HPV4AZyQj0UQ",
        "colab_type": "text"
      },
      "source": [
        "Here we can visualize that the model's loss decreases in an exponential way as the epochs increase"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRokaL9IsvzV",
        "colab_type": "text"
      },
      "source": [
        "## **Creating a test data structure - LSTM**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XOVmDp-SndJQ",
        "colab_type": "text"
      },
      "source": [
        "Get the real stock price from testing dataset and convert it to numpy array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qV7kjnjYGcwB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "real_stock_price = dataset_test.iloc[:,1:2].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-TX30YWoEA6",
        "colab_type": "text"
      },
      "source": [
        "We trained our model in such a way that it can predict the stock price at time T, based on 120 previous stock prices.\n",
        "\n",
        "Therefore to predict stock prices for last 60days, we will need the 120 previous stock prices of the 60 previous financial days."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hl_HIw5MGgHn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_total = pd.concat((dataset_train['1. open'],dataset_test['1. open']),axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-G9thpc5Gjpw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inputs = dataset_total[len(dataset_total)-len(dataset_test)-120:].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jk9G1xQSpgab",
        "colab_type": "code",
        "outputId": "3d9e876e-c160-402d-b341-207381f1d54a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "inputs.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9vniwzYpmsy",
        "colab_type": "text"
      },
      "source": [
        "As mentioned above we have to change the input shape"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LgaKZ_5-GnTw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inputs = inputs.reshape(-1,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HL1AtRRBpkIs",
        "colab_type": "code",
        "outputId": "d3290661-d06d-4d66-efe3-c7b65496879d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "inputs.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FjGD6HEqmaN",
        "colab_type": "text"
      },
      "source": [
        "As done before we are doing feature scaling for the this data too."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8s-xaahGpAf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inputs = scale.transform(inputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2L1ayndqdcJ",
        "colab_type": "text"
      },
      "source": [
        "Creating X_test datastructure similar to X_train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zb0jlVZxGrAC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = []\n",
        "for i in range(120,120+60):\n",
        "    X_test.append(inputs[i-120:i,0])\n",
        "X_test = np.array(X_test)\n",
        "X_test = np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYA5bPlZs3r8",
        "colab_type": "text"
      },
      "source": [
        "## **Prediction-LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9NonPG6GttM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_stock_price = model.predict(X_test)\n",
        "predicted_stock_price = scale.inverse_transform(predicted_stock_price) #inversing the scaling"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PrNblCmq_Lo",
        "colab_type": "text"
      },
      "source": [
        "## **Visualizing the results-LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0Sk6puxGvUL",
        "colab_type": "code",
        "outputId": "ac4931b8-4dc1-408d-9506-4edb065626c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(15,8))\n",
        "plt.plot(real_stock_price,color='red',label='Actual Stock Price')\n",
        "plt.plot(predicted_stock_price,color='blue',label='Predicted Stock Price')\n",
        "plt.title('Apple Stock Price Prediction',color='blue')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('XYZ Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0KWPmypr9KZ",
        "colab_type": "text"
      },
      "source": [
        "The RNN we built was a regressor. Indeed, we were dealing with Regression because we were trying to predict a continuous outcome (the Apple Stock Price). For Regression, the way to evaluate the model performance is with a metric called RMSE (Root Mean Squared Error). It is calculated as the root of the mean of the squared differences between the predictions and the real values.\n",
        "\n",
        "However for our specific Stock Price Prediction problem, evaluating the model with the RMSE does not make much sense, since we are more interested in the directions taken by our predictions, rather than the closeness of their values to the real stock price. We want to check if our predictions follow the same directions as the real stock price and we don’t really care whether our predictions are close the real stock price. The predictions could indeed be close but often taking the opposite direction from the real stock price."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ApOw7NKI2kj",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9VawvYAkI3yY",
        "colab_type": "text"
      },
      "source": [
        "## **LICENSE**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6f1DmpWRI_51",
        "colab_type": "text"
      },
      "source": [
        "Copyright 2020 Ikyath Varma Dantuluri\n",
        "\n",
        "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
        "\n",
        "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
        "\n",
        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
      ]
    }
  ]
}