Stock_price_analysis_of_nabil_bank.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gautammilan/Stock-price-predictoin-Nabil-Bank-/blob/main/Stock_price_analysis_of_nabil_bank.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc31e8c4",
      "metadata": {
        "id": "dc31e8c4"
      },
      "source": [
        "# Loading the data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4828322b",
      "metadata": {
        "id": "4828322b"
      },
      "source": [
        "As the dataset for the stock price is in excel format. It needs to be converted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1d2c8ce7",
      "metadata": {
        "id": "1d2c8ce7"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm import tqdm\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras import Model,layers\n",
        "from tensorflow.keras import Input\n",
        "from tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint\n",
        "from tensorflow.python.ops.math_ops import reduce_prod\n",
        "import pickle\n",
        "import re\n",
        "!pip install transformers\n",
        "from  transformers import AdamWeightDecay,WarmUp"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4338c969",
      "metadata": {
        "id": "4338c969"
      },
      "source": [
        "## Downloading and understanding the data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --header=\"Host: storage.googleapis.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\" --header=\"Accept-Language: en-US,en;q=0.9\" --header=\"Referer: https://www.kaggle.com/\" \"https://storage.googleapis.com/kaggle-data-sets/2076307/3447108/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20220425%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20220425T113937Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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\" -c -O 'archive.zip'"
      ],
      "metadata": {
        "id": "CLu7-tZTnJD1"
      },
      "id": "CLu7-tZTnJD1",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/archive.zip"
      ],
      "metadata": {
        "id": "00Jm7fypng7Y"
      },
      "id": "00Jm7fypng7Y",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data= pd.read_csv('/content/nepsealpha_export_price_NEPSE_2000-01-01_2022-01-01 (2).csv')\n",
        "data= data[['Open','High','Low','Close']]\n",
        "data.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "cBLwiuYznnCZ",
        "outputId": "35be2b0a-9fe5-4678-e499-65afc4881b55"
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      "id": "cBLwiuYznnCZ",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Open     High      Low    Close\n",
              "0  2520.55  2538.78  2512.41  2524.50\n",
              "1  2523.87  2533.63  2486.05  2518.99\n",
              "2  2600.21  2616.27  2518.16  2521.65\n",
              "3  2531.43  2596.43  2531.15  2591.42\n",
              "4  2522.51  2542.29  2485.38  2520.23"
            ],
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              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
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              "      <th>0</th>\n",
              "      <td>2520.55</td>\n",
              "      <td>2538.78</td>\n",
              "      <td>2512.41</td>\n",
              "      <td>2524.50</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2523.87</td>\n",
              "      <td>2533.63</td>\n",
              "      <td>2486.05</td>\n",
              "      <td>2518.99</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2600.21</td>\n",
              "      <td>2616.27</td>\n",
              "      <td>2518.16</td>\n",
              "      <td>2521.65</td>\n",
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              "      <th>3</th>\n",
              "      <td>2531.43</td>\n",
              "      <td>2596.43</td>\n",
              "      <td>2531.15</td>\n",
              "      <td>2591.42</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2522.51</td>\n",
              "      <td>2542.29</td>\n",
              "      <td>2485.38</td>\n",
              "      <td>2520.23</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "Therefore will be working on open,close,low and high value of stock price and by using this we will try to predict the future of any of these values"
      ],
      "metadata": {
        "id": "vaCIYAq4oCJi"
      },
      "id": "vaCIYAq4oCJi"
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    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "48fe1255",
      "metadata": {
        "id": "48fe1255"
      },
      "outputs": [],
      "source": [
        "#Slitting the data into train and test set\n",
        "train= data[:int(data.shape[0]*0.8)]\n",
        "validate= data[int(data.shape[0]*0.8):int(data.shape[0]*0.9)]\n",
        "test = data[int(data.shape[0]*0.9):]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "210b4a26",
      "metadata": {
        "id": "210b4a26"
      },
      "source": [
        "# Sliding window\n",
        " Time series analysis especially on stock dataset, can be done in vaireties of format:\n",
        "\n",
        "1. A model that makes a prediction one hour into the future, given six hours of history, would need a window like this:\n",
        "\n",
        "  ![Screenshot 2022-04-22 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wkG5566qnhhBNOWLvx/1/96lex/+GYLJNbJqWltamSG3HHCGGp+ygqRcqBQYHOuiwQLyUtUnTsfKfk99iPlZONwagyliyOwQqSFUQlVkOEIEkCxAQiCLFmELeIGCKJhkGP/cSHMQhijUUwlQbl4hjqgO+WFvvEfhKg0sGuslDvxa0rJUFQllcv3CdWJAZg1vorbyMebty4cfH4suA3wwrEfZBMRZ0UX+6FCQP7iHVlsCaWlLqRug3PGZMw2jrPQGmTJ9oFG22Z9laZdl8c2gGWcYQk8cp4PLgmyXVjx46NWdusk0pmdOo14XOeVcpDX1N8w7vCfjwLTCizvEGMOM7S2lTJjWQerKxS91FUiuQZOnMGjtQ9lYIIYhBCEDEAlRRaJUUS52AQKHme6oA1jYxzLKgk5NDBYy3FwkIyAZZKBheSgrCyMVggohBQWCFLg/JzHwx63FNpFhjuoaruvnxCfVd2K0+kIo4Z2BmMuefSJhrspz45D4N6Pn43qd3wPPAs8PzwnJcmGHkWmGjQXniOypsslUWa0ENyzv/7f/8vJt2wZizLCJEgh6BEYLJKBGVIQ3Rwf5PoVtqGpfOwww6L4T3VhWe8ZDsqayutbqTuoagUyTN0nripSro/sSIQC4gFC7GWCrJUhLK/eMfLIEWMHoNSVhj4sFZilcQSiajEbYdbjRgrYhiJfaLMvJMYSyWZ2biuy4KBi+9yH+lSKMXhXnCtcQ/rWmBxv8R0/vGPf4zWyvI2lkk677zzyrTIAoM6dYHFiUGc37HkIElMKvXAb4qIr2yMphQuTLyYqPGc44JOLYUpPCO0CyZrtBOOpd1UBQQZzxshKfQVxGV/5zvfibG7LDPEm3eI2URQEuPMNXn+ELC0T5byYtWKdCP2k6V6iKPGe1EybKUqcL7S2lTJ7Te/+U046KCDct+SuoyiUqQGYOkgguCLw1prrGNHZz906NC1HT+DDVY+4pOKu0wZoBB3aWJNVrgOAxJikvMigLBkYG1BLDEAIWSxeiAEsWAiOsuCsmM9wcLJupycszjEbRJHxWC3rkUlg2+aqENMW3kbYrqiRB1EKsfh3ibulPstGYvGfRKbidBApKfJFVJ34fnCHc3zxlqwxNMWhzaM5R+vAM8a4SS0m6pAKAVLEvEyAERkmljD5IUYTpICmfBgIU/bGZNH2jV9DssLFZ8A0S6Jc0RcnnPOOXESWV2whpZsT6VtJurUHxSVIjUACSK8F5r1G1kLjrUcyRImYB1hwvqViA5cywg3BgAC7seNGxcTR+j0GUCY5WOlKGndQAAxqCBwWOCYgHyOKw+sbSSYICAZqHDJEVPFtRkUsZ4inIir5Hok6PD/suAYBlXW12OgvOeee+LbfxBW3Oe1114bB8OS7j6uh8DlmghR7pls+HzD+bl2ZTeOLws+Qywed9xxcYDk9/zDH/4QM3GZKNx0001x/UyEOpYhBHppLnKpWyCYiE3EDc0zcd1116196w1tAAsdLx1A9OFupt2W95yVBhMe2iLtjTZ25513hsmTJ8fnDsskKxAwyaEtppneWAWZ2ND3XHjhhbEf4lhWLqBN/vnPf46TVQQpFn2gXdInpQk+fCddp7YsqtLGqnrfUpj4Rh2p9yBsmM0TM4foSoUUgwJWAcQW7qLinSJuYsQfs34WKKYzJwAeQUUAPWIR8ZZ2/IgmrH/ELxHPRGeO6GCgwLqAhZIOHLGHZYPvcW0EKNYOBhYGiXSRcAQhljGWKcE6wrlJtuG4sqBjx7JAvCSuOmIsKQuDEZ9Rfq7PeRnE+Ix7576xgvA5gw1lxjrDPfM3A1HqqmcwxTrC4IWVkoEKEcY5WIA5tXxyPIvHI2ARxpyfe8NVSP1h0WFBd9yFKVgGiQdloMRCShnW5buRqaO0fsmM53fiXnl+mEQwSFNm3llM+XV/1334jZlsYK3n2eWZ57mgH6Cd8azimib+kT4E7wTgLSBZjueI16wiTpmYIeho+7RDniMmJ1yDSRhtifZC+8QTQggL1+B6XINXiPK6Ro5Nwy/oO2gzPKe0K9olZaMtjxo1Kr6kgDZOO6ZNshQW/RB9D30bMcL0QVx7fYHbn+RCJsXEgHJ/UntRVEq9B4HIwICQwm2Udvzsx2WDixiLXnFRSZwTHTODAe/FTtesTEUl1gtczWn8HQMGoikdXNKOkXPScTMo0LFjFcAVzvF05gwslIF4P8qRxjiyj+/yHTpbyjxkyJAyk2oAUYTQJfkHIUn5KGdqNeRzBiKEJ0IZ4ZYu64Poo2zUCZYN6onBCBDjiC3Og/BDXHE+rDdphilCHSHN4EuZKQdQdu6Fc1E/DF7pd3G/F3eZIb4Z+LgO4o03b/C7rStSUc5vRbn4nagPJgSIW54DBmruk3uUug/PKs8yfQf/0kbSDGvaDPtp87wxp3goCXHSHEu7Kd4u0gQvJlN8h+eKa/DMMwmkfdAGaGNMLDkHsZFYJvfZZ5/YFjme/oO+iXbHc4tA5Dnl3BzD8lf0LfQraV/EcWn/QbvkbybBnH99ikrqkwk4ZWHNTydrtZsGyYOffb0SEYnB+LxKDZc1a8GdccYZUXzgEuVfOuqKxAadPwMLHX0qbgsFBjgGOu4Bscf91uUBAFHJvTLIY61FVDAwS/0lnWyy0YaZhOR7gkEb4/xpP1HRNWiXHI84QxzSNtflZEzqF/aAIjVEajHAWoCorMzggpDESpFaCAsJBBXCCiso5a/rFgWsJ1ia+H0R0ApKoY3z7NMGsDLmW1ACfQrnruw1eC7xhqQeBZ5VkZrCXlCkBkFY0qnzb2Wo6vG1jUIvf1XhXuvT/UrF1HQbSM9flWtU5zsi1UFRKZInsMxhtUrXfKwJK4WIiEhtxZhKkTxBIDxLe5BpSZIJ4jINghcREanrKCpFREREJDO6v0VEREQkM4pKEREREcmMolJEREREMqOoFBEREZHMKCpFREREJDOKShERERHJjKJSRERERDKjqBQRERGRzCgqRURERCQzikoRERERyYyiUkREREQyo6gUERERkcwoKkVEREQkM4pKERGRekRRUVFYs2ZNeP3118P777+f2yuSHUWliJQLA9Dq1avDBx98EL766qvcXhEpVL755puwfPnycPPNN4cXX3wxt1ckO4pKESmXr7/+OixatCj89re/DfPmzcvtFZFChTa9dOnSMHny5PD222/n9opkR1EpIuWyatWq8O6774YHHnggiksGJBEpXL788sswc+bM8Nlnn4WPPvoobiL5QFEpIuXyxRdfhNmzZ4fFixeHd955Jw5EIlK44PrGSklYy5IlS7RWSt5QVIpIuRBL+fLLL4fWrVuHN998MyxcuDD3iYgUGitWrIiTQ9r01ltvHa2Wr7zySoydFsmKolJEyoTEHFzfM2bMCLvsskuYM2dOtFpi4RCRwoLQFQTllClTwgYbbBBOOOGE0LFjx/Dqq6+Gjz/+WGEpmVFUikiZzJ07N1oxGIBI1GHQQWBqrRQpPBCOkyZNCuPHjw9nn3122HPPPcMWW2wRPv/88zBu3Li4zJBIFhr9IiH3t4jIWsgOveOOO6LL+5hjjgkDBgwIDRs2jGvbzZ8/P2y//fahWbNmoUGDBrlviEht5Y033gg33nhjTNA56KCDwn777RfatGkTOnToEJYtWxYefvjh8OGHH4ZNNtkkhrrYrqU6KCpF5D/A5U3g/p133hkWLFgQtttuu3DAAQfEAah9+/YxUQcXOFbMjTbaKGy44YahcePGuW+LSG0AqyOWSSaBjz76aHjkkUeieNxxxx3DiBEjQteuXeMksWXLlqFFixZh5cqV4emnn46JOyTnsZZlo0aNQvPmzRWYUmkaFBlEIVLvIdYKFxjWyTSIH8sG1snhw4eHvn375o4M4a233oqDFAPQDjvsELbddtvQu3fv0KVLl2jhYKASkcqD+GN1BSyFZGbTHrMMzQhKJoeIQxLtCFdBIA4ZMiRu3bp1yx35bziWCeQNN9wQy4HQ7NSpU2jXrl1s10waswhLrk0IDefr3r17nKA2adIk96nUJeqlqOSWaXQ0XjLfaFCuvSf1FSwSWCkYVIifRFAyyJ100klhr732itbIkvBqN5Ykufbaa+Pggyu8f//+oUePHmtd4lo3pD6CgGratGm08GEBLEuQMQ6R8IaQpM3xZhsmbIhA9meZnJHhzTnatm0bttxyyzBw4MCwzz77ROsk5SsNykNf8OSTT8a4y2nTpkWBmd5LdcvDOfkuE85NN900DB48OE5EEbbUj9Qt6qWoZDFnBkXcATRkXHk0bJH6Bh0+LjEmWRtvvHF0ddPp4x7DqsCAUtpgwveYjCE+J0yYEJ566qkoSBGmWCTYyhq8ROoyhIiQ/LLTTjuFYcOGRfFEOyoJbY7VFC688MLYbrD677777mHzzTeP4i9LG0qHdcQs7Rdhy1YZYUi50q34eaoLxhvuk1hOsszxcuCCP/HEE6PQtZ+oW9QrUUkjQUT+61//Ci+99FLo2bNn6NWrVzTvM6Nj1mTigdQ3Ulcbzz7WBNoC1kcGoPLaAt/hu7yNA3GJqw3LP99zoJD6BtZ+LISffvppNFqwFBdiESv+/vvvH62FqajDijh16tQYt8zfBx98cOjXr19MmiFGmbZYUfsrFOgjqBcmr8RjE69Npjl9BMlChx56qP1FHaJeiEpukYaLmHz++efjzAkxudlmm8X4DgZQYkhoyFljR0REpP6B5Z4N8URsMq80JS4ZYYnBYueddw4HHnhgFFAYNyZOnBjfpX/EEUfE2GXiDOv62IOHg4knno3HHnss/n/MmDFRcDP2SuFTL0Ql7u50Vkhw8L777hvdErgXFJAiIlITkPxGfCIiCmvdt771rdCnT5+4VBfxk8QsH3bYYZVyS9clEJOIajbG4AsuuMBljOoIdf5JxuXNrPGqq66K7gkyWUeNGhVdDD7AIiJSUyCUcG2PHj06esyuvPLKaOBAUPLZyJEj652gBO550KBBMTQAwf3ee+9F448UPnX+aSbei2w2Ylwwsw8dOjT3iYiISM2CgCJp5/jjj49u79tvvz1mU/PaU/6trxByxlJlZIKzkgRjtRQ+dV5UIiaJ3dhjjz1iDGV9bsQiIrLuIV6fN9VgtWQdWDxlZHnXZ/AUdu7cOYrK6dOnxyQeKXzqvKgkE4+lDHhweXF+fXQ1iIjI+gMBhbubd20ThsUKCySJ1neoB2JMZ82aFROcpPCp8wqLxBzWDWNWiLldRERkXYOXjIXIsc6x4ggis75DsizreDJG+4adukGdF5Us6MySDYhKXA4iIiLrGpbMYU1krJWskSz/VyeHH354XPBdCp86v6QQSxew+KrrT4qIyPqE4Za1LFmr0nUZ/w11wiotjtF1g3r5mkYRERERyS9mrYiIiIhIZhSVIiIiIpIZRaWIiIiIZEZRKSIiIiKZUVSKiIiISGYUlSIiIiKSmTq/pNAHH3wQpk2bFlatWhXXw5J/Q124Nth/Y72UjvVSOmm9sO6gr4D9T6gX6sR6+U+sl/+EemjTpk3o379/fG2jFDZ1XlQ+/fTT4ayzzgotmjYNDR0M17Js+fIw9513Qr8+fULzZs1ye4X38r719tth8003DRtusEFur6z66qswK6mXXt27h1YtWuT2CgJh1rx5oVvXrqFNq1a5vcJLJ2Yn9dK5Y8fQLhEM8n/MmT8/1kmHdu1ye+o3DRJR2bxly/CrX/86bL/99rm9UqjUeVE5adKkcO7ZZ4cbf/nL0Ll9+9xeef6VV8KJ//u/4f6rrgp9e/TI7ZXX58wJo773vTD+T38K/fv1y+2VdxYtCmN+8INwxU9+Enaz41/LR598Eg5PJq0/Of30sM9uu+X2yperVoUjf/SjcOqhh4aDhw7N7RU44fzzw0F77RUO33//3J76zcIlS2KdXHXttWHwkCG5vVKo1BtReecll4QuHTrk9sqzM2aEo885J0xIGvJmvod2LTNnzw7DE4HwwJVXhh223DK3V+a/91444IwzwjUXXhgG77BDbq98mIjKkWeeGX7x3e+G/QYNyu2VlYmoHJ1Mzr5z5JHhkH32ye0VoN8dtffe4agDDsjtqd8seP/9ODG7+h//CIN33z23VwoVgzpEREREJDOKShERERHJjKJSRERERDKjqBQRERGRzCgqRURERCQzikoRERERyYyiUkREREQyo6gUERERkcwoKkVEREQkM4pKEREREcmMolJEREREMqOoFBEREZHMKCpFREREJDOKShERERHJjKJSRERERDKjqBQRERGRzCgqRURERCQzikoRERERyYyiUkREREQyo6gUERERkcwoKkVEREQkM4pKEREREcmMolJEREREMqOoFBEREZHMKCozsObrr8PX33yT+192ON+cd98N9z3+ePj7uHHh5gceCM/MmBE++Pjj3BGFAffBVhMUFRWFiVOnhonPPBPmLliQ21sY5LtePv7sszDttdfCHQ8/HJ+Xm+6/P0x+4YXw3tKluSMKA9pQPutl6Ucfhadffjm2H+rl3sceC7Pmz6+xZ7KmoF5W12CZp7/5ZvjXlCnhhZkzc3tqP98k7Z/fcfWaNbk92Xh38eIwNXlWJjz9dJnb58uW5Y6uvVAvbPnks+S+X3rjjXB70r9ce9dd4c5//Su8mPQ3K1aujP2wSGk0+kVC7u86yfxkMJn4yCPh8GHDQssNN8ztzcaapEN7ZdasOHB9+dVXYePOnXOfVA8a6JerVoUHn3gi3PnPf0Zh8M6iRWHm7NnhpddfD4s++CA0b9Ys83WKs3DJknDXhAnhuJEjQ4e2bXN7s/FNMgjSQb+cdERfrV4dNurUKfdJfljx5ZfhzbffjkLhw08+CV07dgw9N94492l+WJoI+FsefDAcc+CBeSk/vy3i4InkN30jKTvPTucOHXKfVp8XXn01/n73JIJpGs9IIiSnv/VWfF6YmDDw9urWLTRsmJ9546dffBFufeihMHLo0NBjo41ye6sP9bIy+T0fe/bZMDspL+XMx3P4bDIJu2vixPDA5MnhtTlzwoL33w8vJ3XyatKWuIduXbqE5k2bhgYNGuS+kQ2eSQbdvQYODH26d8/trT7UC4P5hGTi9PbChbHPat2yZe7T/EA75dy3Js/5U9OmhYZJXQzcdtvcp/mB54++bMA224QtNt00t7f6UC9LkskCInheUvaNk7ZJn5gVzjcuGR8mPf98eD5pU6Vtu++0U2jfpk3uG9mh3W7Ru3fYZrPNcnuywW/5xIsvhveSPj0fzyC8moxvTMaomxnJ5GPee++FV5L+hb6d6/TeZJNY/43y0L8g2rnOQaNGhR49e+b2SqGipbIarEoE08NPPhkemDQpvDVvXm5v9fk66YARA+OThsW/bVq1Ctttvnno16tX+PjTT+Ns+f7HH49CimNrK4inex59NNbN3EQo5JPPEkFAh5Za47BSLl+xIvdp7QXrwbKknPcmvx+/4zuLF+c+qR4M1ghfzvdQUs+LkwnHpslAsl2/fvF5QahNTgZIhM7sd96Jk5XaCM8x94FV/vFEWC758MPcJ9WD8zHY3Z+0SSzZn37+eayP/kk76tS+fXh97twwNhE5iPtVyUSwtkIbYkLJgM7vyH3kEwQlzyOiG0H14syZ4d1EeNd2EJX0tfQvTyYCisl8VnhmZiRCaer06XHi3rhRo1K3fE1AagomUtTLc6+8ktuTDSZiPBuMb0xW+/ToEbZN2tFGySSez+hbaGOf5PnZlLqBorIaYIVjZotAyOqGobNckQz8YxNBiYtu5223DT/9znfCT884I/zmrLPCt484IrRs0SK6exGc+ehMawoGrCenTQvzk0GRwTEfYNn7IBHTWAzuSETBbQ8+GD757LPcp7Wfb5KBi5k4biOsw0UZ6wWRyHPwSCJQmzVpEk4aMyb87pxzwnmnnhp+88Mfhu8ee2y0UD6VDLwcg4u8NrImqYf3EyGJ2MMClRWE4tOJOKBd0l6+ffjh4Vc/+EE477TTwi/OPDPss+uucUDEOvdFIqpod7URhA6WOKzNHyUTynyDZRWLORPYhUl95MuSXdPwa81KJkn0LflyRzMpZWLTYoMNogWePre0Ld8el3xD38LYgVs6HzCZeTQZb5gQn5a0I/oVxqQLku2w/faL12G8yke7lbqHonI9g+UJayQWlI06dw57DBgQ+iYzQ8C1cNCee4ahAweGz5cvj5apQrDO5RME5Y333ht+9pe/hLsnTowz5ubNm+c+rX8sTzp0LDVYbvfYeedw4B57hKaNG8fPmiT/7pXs23/w4CgecG3i8q0PMNlCSNNOBu+wQzggaTepa65Lhw7R8k/7wq1He8tXTF6hgRX0jzfcEEU1Lt0N63FbWpRMarDaEkaza//+YYcttyx1q291lFohGXeOHDEi9iuAuN4tqac9kz5mVTK5Xb16dd7jOKXwaZB0LnX6qZg0aVI49+yzw52XXBIHl6wQS/nLv/41uhqwOvXs1i1aF/dOGuCu228f408qkyhBR9avd+/QpmXL6H754W9/G0YMGRKOGzUq7LzNNrmj/m3JJDbpL7fdFq12Y//4x7zEEeIyOfqcc8KEa68Nm+UhjgUrAsIPcdykSZPQKykjHfWJhxwS7xHrSGXqpXvXrmHzXr3CJl26xHtHQN2QiEosbghuOvkfXnxxjEk6efToMHz33XPfzA/EsQ4//fTwwJVXxmtlAUFHPN+VyW/3TPK8NGvaNNYL5z3n5JPDp8k9EauEcK4IYphw5zLAET9FvOCg5HnbLdlw0aVg6SI+6Zzf/z5sv8UW4eLk2c9H7Nb8pJwHnHFGuObCC6NoywJuaiwhxD6SCLBhs2ahV3J//Ka//sEPojuW8InKuNeYgG3Vp09o1aJFWLB4cVicCAXaVu+kXRZ3W3Ktv95xRzzm4auvju22eL1VF0JSRp55ZvjFd78b9hs0KLe3emBJjeEjTz0VZib9DPGU3Bv9xDknnRQt3dQLltaKoA3x+29QLO6QkBHc6sQk/+jEE2M86zvJ77pn0nddkjwn+WRlIjpGf+974TtHHhkO2Wef3N7qwX1fd9ddYVLSt8yaNy/GmO601VZhq759w6mHHhrrjbjcisIaiKWlXmh/9C3/Sur5z7fcEsU1Fskt8xD7WRnod0ftvXc46oADcnuqB6Evl15/fZj03HPh/Y8+Cp3atYuxsTsmdTN6331jEhL9bkW0TtoO7YjnjLo+7Wc/C+1atw4nJH33wXvtlTvq3zCpJRGO35dxCCtvVvj9Dj/rrHD1P/4RBue5P5d1j5bKKtI0EUyd27ePwqlxstHBdWzbNmyQDPbEszHLu+7uuyvc/pl0aPMXLozuzHcXLYqWk47JeWnMxWFgbJVcg4aPu/CL5cujdbO2gbUM0d44+ZeZLXGhJF5QX7jxSDworR5Kbo8lYgO3XEqHpKMcmXTApx9xROzgEFe4fQsBXIsEs3dM7qFJImAod9ukXvh/4+Sz2YkQJ7attHoouSHWcTchEhhQGTS2TgbVksIIUcVgwrVJqMlHMkO+ocwIpnbJYM6z0iwpY/vkuWdQpNwIe8RPafVQcpvy0ksx7pBz8mwM2HrreN+0G2bLtBUEOLGDuE13Sj5HgOYjwSDfcA/0J0zCqBd+OwQP7ahR8hkTlHETJpRaDyU3Euboj1JI/mHyyjZkxx3DHjvtFNtrbY8XBOqFZ6VV8szQv6Rtir6SeiF7nbCY0uqh+HbbQw/F0JGUhckkF0slk78p06aFXyQTyXMvvTRccPnl4e9jx0bDASE9tRXqhXpg7OFvBB5jE88Q9UL5S6uHkhvPFLGljEGEXjDGkEzImEMSIJP6P998c7hm/Pi4GsmGyXUQ4PkQlFL3UFRWEawgzLwRknT4DGIH7LFHzHDEitQ9GdBocBVtDHyIRRoyVjg6LxpxaRnqnJeBhpk4M0UslrUNBr5D99svlhNxsMt224XhQ4bEJAli3Hp07VpqPZTcNkmOS+uAAY96wp27f3IurEuFBAKQ2fyI5PlomzwruI8GJwP6iGQ2jrBpn9QZ8Y+l1UPJrVvnzvE5YFDlPJv37PlfGak8G88nAwnxp/wOuw8YEEVsbYNBb9t+/cKeSfm4h+5dusTs6QOTSQPCu0Oyb9NEIJZWDyU3soCxAKcwiWGARShQF8ThEj5BnTAA84xS97VRTDERw9LERr3w7Oy7225hv+T557dn0tm3e/dS66HkRj9FPQBWOcQUQpP+5tiDDgo9knNTV4UAzzJhHf2TZwYhSZvBQ8G+FklfgQDCAllaPRTf8MjQH6VgtWaiQTIK1v802xsLJgL0tocfDlOSOkN41kZ4julLmEzRzjfv3Tsckkw26XsR4EwaSquHkhvPFP034wrWT3IGCLPCK3dHUg/EZmMswdp/8/33xzAkJq4cJ1IS3d/VALfcEck5aVQnjR4dXbzVBbfidffcE65PZowEQh8xfPh/WSufmT49ziixauH+xtqSdZaYb/c3IHp3P+64ODjiliIeJ5+Qdc8AcMRZZ8WOtLa7v4G4I6yHR//4x/H5o8wHlXApZYUmzLPIuoO4NnlecIFd9j//E8UJVous5NP9DcRATn/jjfA/f/hDnIhhiWbplnxBOMZN990XPQJYMhFlQ5Lzn3XCCaFncj0EVT6EZT7d30Abosx/vPHG6JI8OylvlvAFYt4o4yVJO8cVSjwcrnQstYRIPP3SSzE2tza7v4HEP/rA65O+cpu+fcNFybkRztWFNvP93/wmxh0zKaGt79K/f6yX1+fMiVY+wjDYf1Hy2yJaU5GelXy5v1PO/t3v4oRh7112iUk11QXjxu2JiPzrnXfG9oIAJymH8C48USxdhNWfZ5TnEg9Slt8gRfd33UJLZQHA4FcoWZqy7sH6RMeMcMCigFv8rOOPz5ugLESwbpFUQD2cPGZM6JIMfg8/8UT4n0svjdYYYk/rA0xq7nrkkWiBw5ty/MiRtdL1vz4gXpVJ6Y8Skf3n//3fcEJSN9866KBw0fe/H36YPDcsoTNhypS4lFkhrThRXZiAsBIJXjNivek/rv75z2Oc84WJsP7TeeeFM446Kk4IL7vppjjRNFFHSmLvkieY+TKrIwEBd1tFGy4FlscoDgNdaYMdFrpCzfqmy8FSUtl6efy552KweH2AmMqHEqFTWj2U3EhYKm0JD9y8WFvOvuSS8NrcuTHkgI4fAdGwQAUl1hDWmyytHkpuvICgtIQeBkQsqogGvAkstURCCoug475jXcJCgyRAyl5aPZTcsFbT7rBes/zLlomAIhGQEIC6RvpWqdLqofjGMQhEYKI+ZtiwcObRR4d9d901WuKYuLMRz0rSGGEHiCYsvB/WwPJONQ0TidLqoeR296OPRlc3dZJa8EnaGZS0H0KOqA8mp7Qpkr/ICseqSfxlvtdRlcJHUZknEJXEtMWElHvuqXBjgXASdXCpYFWhM+P7uI1K8lUyM1yWfEZjJ3amUBJVIkm90CGz8Hdp9VByQ3xidasP4KKNiTql1EPJjZivkqKSuCYWKCarmXUND9xzz+hSI5GH8Ijan4JROjMTURkTdUqph5IbCRalDWwMhG1bt46xz7yJCqslqxGQsELyQSEOhmTKk9lfWj2U3EjIIcuedTuxOpE1zhu7sGb/6uqr40aCC88Usae/veaaONEttFfCAvfB255Kq4fiW8lEHcJRWG0CsUTfmsJfxBhullvajbjL2voSgfKIiTql1EPJjTVLSdTBgt0uGV8Yk4hVJq6ZjPkU4rmpF/aTAMfyXRVl3Ev9Q1GZJ+iUaHTEa+FWqWgj2JyEBTIZSU6hIZMlzfqDJSFQnI6NWEuSe7hOIUHCCokBpdVDyS0mMJWSrFQXoQOngy6tHkpuLLFEnFMKghIRgJWBpWKwyB174IFh1+22izGthQyJOrxurrR6KLkhGImJY3AjE5zlVXDLlQThQIITrj2sLIWYZEBmb9+ePUuth5Ib94qXABFNHVE/WGkfT0RmurHKAn0LkzhWF2ABbSavhQbicItevUqth+Ib1jfiBPEG0c8ST4qVs9S3OSX9eSo0EVuFGH5EvGNp9VBy45nqlIhFJmK8xpT2FK22xYR2SmrJBQwpOr+lJCbqVAOsHEf+6Edx9nri6NHRvVZd0vdZn/i//xtftcfaYGR8ps2Za7CkwzXjxkUhevlPfhIzgbNSo4k6ifA95dBD8xaInlLwiTrJgMYbcEqu/VYVcMfxTNz+4IMxs/mjRCCRAYp7F6tc2uHnm9qeqMOE7PzLLovPx+HDh4ejk2eveHYzbRbr54+T6+Hu5E07ZBNnpTYn6lA2hDYWcbwdJeEtXWRAk2WOq5fkLp55xGtWanOiDnWMFffCq66KK03wgon9kzZUXERRZ+P+9a9wcdI/np88K0fsv38UX/kg34k6P7700hgCwwoKFyd9Y3Whr2KZJdaphGOSSerxo0b9R4ISLw/4x113RavvZeedF8NtsF5mwUSduoWWymrADBb3IgN81uV9sD4xeGCtfHPevPDG3Ln/sTYabk1izLCskL1a3FpVW8E1UhvX0lzf8LxkfU0jAyIxXrwDnXVLeb3c+aeeGromE6aaEpSFAG2S+6cNEXtIMk5xmCzwClFi53ZMhFNtXGop3xA/yfPBQufnf/vb/7WxHBrL8ZDde+4pp4RhiSguvuROXYXnhGXPiKt97LnnwnOvvhrfEJNC/zt7/vyY9Eao0YBttolvY6qtIIyxDWV10acvrWCSweQMKy4W3dTuRL0Q704cM142jBwlVyoRafSLhNzfdZL5dA6PPBIOHzas1DUgqwMCb/ILL8REC6wBny5bFhscHVV1l51glvxaMvC9nggGxALnI/aLdcEIuGaW/N2jj47XyEdGL53DXRMmhONGjsw800yhzHFR90WL4lticNkTk0MHlA+XPdYKFuYlrozODKtKvqwHKbwL+JYHH4yzdFyIWUHsNEruHavH2wsWxPPjfsVqyaLFVf0tiXm77MYbY3wdIhUL1MuJiCJm9ZEpU9ZuuIHfTJ591oBkPbuskBBE3BoihRCFrCCusWbhwudtKAxiuCFZMw/3W+p6rCy45Vj3k4QD3LxYVpnYLE2eQ5K/xib1TzwdCUxnHnNMtI5Wt60WB0/D7Q8/HK1EuOyzQhuiLlgihlhI+gKsangTiq/HWVmoR/qW0jZEE5ZKxAFWVo6tWq2XDXXPm8AQZNR5VhA2TLhJPCL0Y0nSDmgLTMaLvzWoUiT3yW+/Mmk7tEneP885gWVz7nnssdjH8DySDY4Vlz6yqs9kWdDvkgiUjzddAeIPwwPPDRZ72iqTgyrXSwL3yPeoD8aft5I+hN+SZzAuaZe0IyZseKIwcuRjcsY4QX0fNGpU6JHn/lzWPVoqqwFuNdYE2zQZRN5JGh8DOoMZ7tnqwMyZQQk3EZ0XAy1LNuDyJl6OdSnp3OJAmAdxVlMwsGPtYKZLuRE3uJGqWy8loUsnvomYQYQSM+vaToOkvFiX99511xjb9ta8eTGZi+cGq2NV4TsMgtQDG9aW52bMiNm+xbdnX3klvuqP5K/aCGIa0c5rJhmYaD+8NpBQgerEO/Is8H7vE5KBCTcu9fy3sWOjAEfcMNjiXj/jyCOjiKqOQFsXUC/E/u2Q3AP9AskW9AdYjBCc+YQ3ozDhKwTvByKY8CB+YyaXxIDyvFRnYfLYjyT1vH/SVx2+//7xeUDEX37LLXFjYozoHLPvvuGoZHJJ2FRtjqnktb7bJvXC6iOUnVhRhFp1Ybw5asSI2Dax/PN2IeqFRC9sloSX4L6viysJSHa0VFYDhALir2XSKZNUQHAzi+P22WSTagkdZofEIdJIyVhFtOKm4xoMkMSt8Go1BoF8zZZrwlJJ2Sh/vJfknFgRtu7TJ9ZP8fi26kKHxoboIMCc2X6+O7a8WyqTjQERIcyr5rA0MzlgcKTsVXVZr16zJnycCCTun/UoebMOFrKSGyEV1A+DTT6e+3xbKnlWsGIjaGhDDNxMRliyhOenqlZEzodQJKEJKw3vE+cctFHEPG+p4c1MvJ6wOpbQssi3pZJnhQkT7YXkolgvSX1T/pjRn6dyAxMOzk+MYr7iqlPybankvhHA/HZ4KYin5P3u6UoH1YHz4P6n/8M6h9DkGvyOTEB4ixftLN8T+XxbKmkvtCP6XsJgaPv0DdVt95yra9L3Uce0UdoRzyRJhYxDB+y+e5z45KNPBy2VdQsTdeopNZGoUxfId6JOXSHfiTp1hXwn6tQV8p2oU5fId6JOoWOiTt1C97eIiIiIZEZRKSIiIiKZUVSKiIiISGYUlSIiIiKSGUWliIiIiGRGUSkiIiIimVFUioiIiEhmFJUiIiIikhlFpYiIiIhkRlEpIiIiIplRVIqIiIhIZhSVIiIiIpIZRaWIiIiIZEZRKSIiIiKZUVSKiIiISGYUlSIiIiKSGUWliIiIiGRGUSkiIiIimVFUioiIiEhmFJUiIiIikhlFpYiIiIhkRlEpIiIiIplRVIqIiIhIZhoUJeT+rpM8/vjj4XtnnhlOHzMmtG7RIrdX5rz7brjq9tvDuaecErp27JjbKwuXLAmXXn99OOv440PPjTfO7ZUPP/kk/PHGG8NJo0eHzXr2zO2VZStWhD/ddFM4ZN99wzZ9++b2yuo1a8Jfbr017DVwYNhxyy1zewX+NnZs2CGpk4HbbpvbU7/59Isvwo333ReuuuaasNvgwbm9UqjUeVH5wgsvhJ///OehYXKbDXL7JIQ1Sae/bOXK0GrDDUOjRo1ye+Xrr7+O9dJygw2sl2J88803YcWXX4bmzZqFxtbLWug+V65aFZo2aWK9FIN6WbV6dawT6+U/QXA3bNgwNEo2CbEu2rRqFf7nggvC1ttsk9srhUqdF5UiIiIiUvM4VRIRERGRzCgqRURERCQzikoRERERyYyiUkREREQyo6gUERERkcwoKkVEREQkM4pKEREREcmMolJERGQd8dZbb4UPPvgg97/6TXypwooV4bXXXgvLli3L7ZVCRlEpIiKyjrjvvvvCG2+8kftf/YY3mC1dujSMHz8+vP/++7m9UsgoKkVERNYRd999d5g5c2buf/Wbr776KixatCiKyiVLluT2SiGjqBQREVlHbLTRRmH16tXhww8/zO2pv+D6RkxusskmoXnz5rm9UsgoKkVERNYRm222Wfjiiy/C22+/ndtTf/noo4/C66+/HrbYYovQqlWr3F4pZBSVIiIi64iddtopfPbZZ+HVV18NRUVFub31jzVr1oT33nsvTJ8+Pey6666hXbt2uU+kkFFUioiIrCN22WWXKCZJ1qnPySlkwM+ZMyd8/PHHUVS2bds294kUMopKERGRdUSvXr3C5ptvHj755JNw1113xQzo+gaievLkyeGFF14Ie++9d+jcuXNo0qRJ7lMpZBSVIiIi65ARI0aEHXfcMTz44IPh97//fViwYEG9cYUjoseNGxcef/zx0KFDh3D88ceHZs2a5T6VQqdB8iDX36AOERGRdQzZ37NmzQqPPPJIePTRR8P2228fRWa/fv1idnjHjh1zR9YNuF+ScmbPnh2mTp0aF4Dv06dP2GeffcLOO+8cGjVqlDtSCh1FpYiIyDoGoYWF8o477ogJK7h/yYBm69SpU2jcuHFo2LD6zkS+26JFiyhQN9544yhWK2MRJNZx4cKFYfHixTFLnYSaLHCfq1atCsuXL48JSpy/e/fu0e2NkNbtXbdQVIqIiKwHGH55VeHTTz8dpkyZEl555ZW4biNCi3UbqysqcTHz3ZYtW0ZBiVUQa2jv3r1D+/bt/0vIUQ6EH2JyxowZcXH2+fPnx7hPytegQYO4VYeVK1dGYdq6detoiUVMYp0027tuoqgUERGpQ/Ae7dTd/Mwzz8TYTYTkkUceGcaMGROFZnGRiDVx7ty54YILLojW0wEDBoR99903bLXVVvHYDTfcUBe1VApFpYiISB0C6yLWwdTt/Pnnn4cnnngibrjDTz/99Gg1xJqJoMRSeuONN4amTZuGQw89NC5G3qZNm2gtTd3w1bVUSv1CUSkiIlLHeffdd6N4ZBkfROfZZ58dunbtGhcfnzBhQnR9H3PMMaF///7RVa2IlOrgkkIiIiJ1nB49eoRhw4aFgQMHxkXXH3jggfDOO++EZ599NgrK4cOHh0GDBkULpYJSqouiUkREpB6A63vw4MExXnLs2LFh4sSJ4c0334xCElGJq1skC4pKERGRekK6nA8xl1dffXWMt+Q1icRPimRFUSkiIlKPYC1M3urDcj8sMUTSjkg+UFSKiIjUI1giaLfddovWSd67jfVSJB8oKkVEROoRLB3Ut2/fKCa7dOkSLZci+UBRKSIiUo9gIfMOHTqEoUOHxrftiOQL16kUERERkcxoqRQRERGRzCgqRURERCQzikoRERERyYyiUkREREQyo6gUERERkcwoKkVEREQkM4pKEREREcmMolJEREREMqOoFBEREZHMKCpFREREJDOKShERERHJjKJSRERERDKjqBQRERGRzCgqRURERCQzikoRERERyYyiUkREREQyo6gUERERkcwoKkVEREQkM4pKEREREclMg6KE3N8iIiIFB8PYN998E/9t2LBh3GoCzv/111+HBg0axGvwb02wrq6TUrz+GjVqVKnrpd+BypRxXd9TPkjvMS2zVEyjXyTk/hYRESk4Fi1aFCZNmhTuuOOO0KZNm9CqVavQrFmz3Kf5Y/HixeH3v/99eP/990Pr1q1Du3btcp/kj6+++iosXLgw3HLLLeG9994LLVu2DG3bts19WjMsWbIkPPnkk+H2228PnTp1inXYuHHj3KelQx3885//DFOnTg0bbrhhaN++/VrhtXr16vDll1+GJk2arBWPs2bNCrfeemt4/fXXw0YbbRR/o9rO22+/HcaOHRuFJeVt3rx57hMpC6W3iIgUNB999FF44YUXwp133hmFAIKmJvj444/jNRBgS5cuze3NL2vWrInnRrA9++yz4cMPP8x9UnN89tlnYdq0aVFAvfvuu1EUVsSnn34ann766TBx4sQogrHqwdy5c8O4cePCFVdcEVatWhX3AcL/oYceCo8//nj45JNPcntrL2+99Va46667ohBGEK9cuTL3iZSHolJERAoaxAtCDEHz+eefR2FWE3CdOXPmRCtdTYkMrGKce8GCBfGeakogFwcLXLdu3cJ2220XraKVcfVSF1g4saYuX758rajEEvnAAw9E8Vj8d+AYBCvHFxebtQ3q/9VXX42CEnH8xhtvRAFdU89UXUNRKSIi9RoEEaIBscNWWQFR/HvEC6bCqjw4Hhc3WxqTuL7p3Llz2H///cP5558fttxyy1JDB7i/tNzlgWhE3Fd0XPG6wzJambqrSSgLFtQZM2bE0IPx48dHQSlVQ1EpIiL1klTYYBnEzYx1cP78+dFCmIrL0sQO+xBZWN84lu8gSLAqsr8k6fEILVz1uIuJz8TtnFVQpUKPshYHwcq5S7sP/k4/o1yIyK5du4YtttgixoqSrJOSnof7o9xs6X2WLDfHsfEdNo5L/18cvkudUxdYL7F4UpelnbM0OIZj0+uVt5X1G5YECzfxod/5znfC3//+91h2BLZUDbO/RUSkoCGeEiFw/fXXh7/97W/hgAMOiMkgFYFAwk173333Rbct4ochEXdwz549w8EHHxzGjBkTOnbsGI8n7nDAgAHx/AgOhCjXRryQUDN48OAwatSoMHTo0P8QZgiyl156KVx77bUxVg8BhYu5Q4cOYY899ggnnXRS6N27d0yOWbZsWbzOmWeeGXbcccdw+umnx/OWhHJyXUQQgmjkyJHh+OOPz30awssvvxxduE888UQ44ogjwmGHHba2Tj744INYT0899VT41re+FXbaaadwzz33RHfvxRdfHIYMGRLvB9E5e/bsWK9pfCfJN5tssknYddddY2wp4vDcc88NBx10UPjJT34S7r333liv3F+PHj3C6NGjw+GHHx73nX322bEM++yzT3jnnXfiffJ9knm22mqrcPTRR4fhw4dXmMSDJZR6nzlzZm5P2fA7cs6NN974P36TkhDWQKIScZ/8vptvvnmsQ36zn//85/E54L6lfLRUiohIvQMhhoi66aaborVx2LBhUQAhJPv27RuFy1//+tfwzDPPxASd4hBzh6BCACJAjj322BiTiPC6+eabYyZ6aq/hu48++mi47LLLwiuvvBK23nrrcOihh0YRhnWQ+MMrr7wyis6SFr2KQCSxIVQpU/Hvk7D0/PPPx2sinLGKplBuEmy++OKLKGSxdBIniruXeuE8qaD89a9/HY8l1nLEiBFrRR/7XnvttdwZ/w1CDBGHIMXiucsuu8S6TEUi10G8kYTEtRGv1EOfPn1i+fk9qLuKQKQjKB977LEKN4Qr916R/YxM/r322iv89Kc/DUcddVTYdttt4z1I1VBUiohIvQPRSPYygoN4wlNOOSWcfPLJcUMk7rzzzmH69OlR7JXMwEaAEYeIyMLKyHbqqadGQcV3sNYhoBAyiCWsX4ggBNQJJ5yw9jqIl0033TSKPqyGuIErC9Y9BCXJNRtssEG0BPJ9rsmGJRDXMhZD4gRJNkEs4tal/GQ0b7bZZqF79+6lJuZgzUQ4IwCxomJtpI6412OOOSaKTFzQxcFCO3DgwLgsEZbBE088Mey5556hS5cu8XOOR8BjraTOORfnxBqLKKeuEOYVgYV3++23j/Vf0YZFlbJWtC4myyhhsU2tlCyTJFVHUSkiIvUKRBdiEncmQg8XcP/+/aOYIK4QC1vqcib2EatacRApBx54YHQr47bFzYvbG6GEwCM2D/GEe/q5556L1jKE2VlnnRVFFtY73Of77bdfFFQIUI7B4lhVdthhh2jx5HpYGrk33OuIyhUrVoRevXqtFZy4mhGXb775ZvwXa1xZLt00NAAheuSRR0b3OsdjacWiy/8RxMXB4ohIbdGiRbROItI4BsslIOyI3+R8iFQ+p/44F38T/8h1K4K6xJ3/s5/9rMINYcs9luf6Biy2CMmKxKeUj6JSRETqFQgHXJ24pC+44IJoVcPdixUP1yqCbN68ebmj/xsEKFa+krF/CC7EIoIViyWuZJJ4cDcjyDge4YS4Y0PoYNlDdHEcW1VJhSHlTl3glB1rJAKPWEDuF1c21kfEJ7GCXBtrHxbC0iCJBpHKublXFjcvDhZAxF1VQLhRJiy6JV3LCGMEOb9BRSCcqUfEeEVbZRN1JD8oKkVEpF6CyJowYUJMxDjuuOOiu5QkEqxbxFqWBe5crHElIS6PDTcvlkDOj9WQZXZuu+22KCBJ9Ek3XOy77757TBLi+JKxm5UBQYo1kuSiVFTiakfQInD33XffeAyiEkHJxvUQxriRy3pzDuWm/NwPyTkl4bupBbKyIGQRp6WdryoQL8qSPxdeeGGFG0lGZPWXdNVLzaCoFBGRegfChIxxrJXEMyLGEGd77713dIfjni0LLH9luUnT/YgYrGT8S7weFkVc5IcccsjaDTcylkTc4sRkIjSrCkINFzMiD8siYhBRSTlwRxP72a9fvxhfyYY7n39JkkEwlnUfKYjO0o5hf2UWSS8O5ynrfFWhqok6CGytlesGRaWIiNQrEBhkGT/44IPRzYt1EhGJmMRKSawkcZVlgXu7NDct4oXPUosc7m4siMTqEUfJudPEHjb+zzJAWBN32223KruTU3BPI4hxeWOFxPWOKxmxybVZmojPSNghQQdXP6ISsVsWxD7iosZ6SmxoSRCvuJfXBwhoYkmJa61oo14rk6gj+UFRKSIi9QpEJZnNuKWxDrJ+IskjiEvcwghBXKZlgaUP13BxUYVFkphIEk0QYwg9sqBZ4xJ3L+5t/kY4IjDZiC3kWiTocM7KxBOWBuchyYjykK3NUj9YIRGViEvuERE4ZcqUuMQQooz7LM99jRBDqPLObqybfL84WHqpg9KgfmvSMkgdsiwTy/9UtJFtX5lEHckPikoREakzYFVDnKXJMMU39qdWN6x10LRp0yj6cFWnyR9kR7O2JOAWLwmiDXGGKOV8fA+XLMvhsA4jbmdczsRdEtdIDCbJMWSFp5Y/NpYqYvmh3/3ud3Hx8fKEbHkgAEmAwSrJKwbJ9Ob/iEpEK5nVWE3TBcPJuMYtXl5sI8v+DBo0KNbb5MmTo/BNy03dsU4l9VQaCErqOq3v0upQ6iaKShERqRMgXi6//PJonWKZmpIb7uarrroqukJJmsFFTdwdGeC8VYfFt0na4RxkUGPdIrGF7O3i8D3eVsMbZHjjCguef/e7343nQMixBmXqbmXtRtywnAOL6C9/+ctwww03hOuuuy7+/Yc//CG6mjkO8VddEIFYJBGUWEr5P2KTc2PFxGpK/WCd5DoVWe5YJom4T1znCN/f/va34Yorroii9fvf/34U3aUlFlE3XB8Retppp4U///nP0SUv9YNGv0jI/S0iIlJwIG5wHyMAEVG4orE4YlErviGkWEKHjGvED8KPOEhcucQakuiC1RHXMCKP76QiDJcr1khiMYmBxL3N51gsybrmM+Iwic3k/Oni2bif08XAOQZ3MtfCyof7GNd1GleJRZOy4yrnnAhUYgcRiBWBYOSeuReEIOfDxc49khxD9jefYzklQQiXcJpoQxwoyyhRf6zbSZkoN9ZN/qZOEKsIRSyxuMJ5vSSiFTd7urwQ58P6iaWS+2RDYCJqKQPiknpgOSfqpDicm2twPd7as75hjU9CGbAes7Yov3dVs93rI777W0REChrEEC7piixiuKMRP4gERBgiElGIIEVw4QpH1GyzzTbRPUzcJS5cxCKiAhE4duzYuB4l4hXRhMjiu4hU1n1EtPF3cRBluLp55SPiDVGG2EOkIByxmhLniOjFXcx1eGc3wotrVUZUck7OTZkRzghjrI0pqWseEYhbu3jyCslKaf0hRvkedYE8oDy4zakr6hnhiPhlOSTc/1hgEb7EX/IZQpu3FeHqRyRS39Qf98YbjBDbJAmVFJWEByAsKR+LyK9vmGwgxEl64l75nRSVFaOoFBEREZHMGFMpIiIiIplRVIqIiIhIZhSVIiIiIpIZRaWIiIiIZEZRKSIiIiKZUVSKiIiISGYUlSIiIiKSGUWliIiIiGRGUSkiIiIimVFUioiIiEhmFJUiIiIikhlFpYiIiIhkRlEpIiIiIplRVIqIiIhIZhSVIiIiIpIZRaWIiIiIZEZRKSIiIiKZUVSKiIiISGYUlSIiIiKSGUWliIiIiGRGUSkiIiIimVFUioiIiEhmFJUiIiIikhlFpYiIiIhkRlEpIiIiIplRVIqIiIhIZhSVIiIiIpIZRaWIiIiIZKbOi8pPP/00TJs2LXz11Ve5PSIiIrK++eabb8KyZcvC9OnTwxdffJHbK4VMnReVCxYsCLfddlt8YIuKinJ7RUREZH2yZs2asHTp0jB+/Pjw/vvv5/ZKIVPnReWiRYvCPffcEz7++OPw9ddf5/aKiIjI+gQPImP0Aw88EMWlFD51XlS2bNky9O7dO8yePTt8/vnnub0iIiKyPlmxYkUUk5tssknYYIMNcnulkKnzorJdu3Zhu+22C1OnTnUmJCIiUkv48MMPw8yZM8OWW24ZWrVqldsrhUydF5WdOnUKQ4YMCTNmzAhz5syJQcEiIiKy/iCe8r333otJOoMGDYoGICl86ryobN++fdhll12iaf3ll18Ob775ZnyYRUREZP2wePHiMGvWrLB8+fIwYMCA0KZNm9wnUsjUeVHZqFGjaK087bTT4ozowQcfDEuWLDFpR0REZD2watWqMHny5DgmDxs2LHTu3Dk0adIk96kUMnVeVELTpk3D4MGDw5gxY8K8efPCL3/5yzhDWr16de4IERERqWkw6IwbNy489thjoWPHjuG4446LY7TUDRoU1aPFG1m64JlnnlmbtLP99tuHnXbaKfTr1y9aMxs3bpw7UkRERPIBSweRlEP42RNPPBGNO1tvvXW0UjION2xYL+xb9YJ6JSoBMUnSzlNPPRVjOYi1ZGvWrFncNMGLrF+Y3BFftdFGG4Xu3bvH5UYIY6kI3s5B4D8vPCBe65NPPolWEV96ILL+wCOIu/vLL78MK1eujONur169wh577BFXZtGYU7eod6ISGGh4feOUKVPWZoUzAKXCskGDBrkjRWRdQhId7Y/1ZYmz6tGjRxx4+vTpE7NDS5v0MWh99tlnYe7cuXF5kvnz58fJI/s4F1YQ27TI+gExSbtmyaC+fftGMbnDDjuEtm3b5o6QukS9FJUiUvtI3wOcuskIU8GjgCXj5JNPDnvuuWe0Xha3WjJBREBy7JVXXhkFJCEtDFysfUfMVosWLSpl6RQRkWwoKkWk1oCwRCgSg4WFA4E5ceLE8PDDD4eBAwfGoH6slim8Kev+++8Pd911Vzj22GPjmrTdunWLIS0E/yMmjdcSEVk3KCpFpNaC22zhwoUxuP+FF14IHTp0CGeccUZ0jbN//Pjx0eW9++67R0vmxhtvHJo3b577toiIrEucwotIrQXXN0H9w4cPj9miJOCw1izB/o8++mjMIt1mm23CyJEj43EKShGR9YeiUkRqPV26dAl777132GKLLcLdd98drZaTJk2KlksEJf/q5hYRWb/YC4tIQcB6siThsFLDJZdcElasWBEzw8koFRGR9Y+iUkQKhq5du8bMbt7jv/nmm4fNNtss94mIiKxvFJUiUjC0b98+rnHH2pSIShZGFxGR2oGiUkQKBtacZEmhnj17RkHZunXr3CciIrK+UVSKSMHA2pOsQzlq1Kj4CkcXNRcRqT24TqWIiIiIZEZLpYiIiIhkRlEpIiIiIplRVIqIiIhIZhSVIiIiIpIZRaWIiIiIZEZRKSIiIiKZUVSKiIiISGYUlSIiIiKSGUWliIiIiGRGUSkiIiIimVFUioiIiEhmFJUiIiIikhlFpYiIiIhkRlEpIiIiIplRVIqIiIhIZhSVIiIiIpIZRaWIiIiIZEZRKSIiIiKZaVCUkPtbRNYzH3/8cVi1alX45ptvcnvKp3HjxmGDDTYIrVq1Cg0aNMjtXbd8+OGHgW5kww03DC1atMjtrRm4zurVq8NHH30UmjdvHq/ZrFmz3Kdls2zZsrBixYpYX23btg0NG5Y9n16zZk1Yvnx5PL5NmzbxOuUdvy75+uuv4/Px5Zdf5vaUDfdK/TRq1Gi9PRsiUr9QVIrUIv75z3+GBQsWhK+++iq3p3xat24dNttsszBw4MAoHioL50c8IZYQZVlEx3333RdFzpZbbhm222673N6agTIjYh988MHQo0ePsPXWW4du3brlPi2b6dOnh9dffz20b98+7LXXXlEo0vVxPspOHSDCqI9PP/00zJw5Mx4/ZMiQ0KdPn0oJ13XB559/Hp+P2bNn5/aUDeJ52223jf9W5dkQEakuikqRWsRJJ50UJk+eHL744ovcnvLp1atXOOSQQ8K5554bmjZtmttbPli7pk6dGl555ZUosg499NBKf7c0+D5C7Nhjjw0nn3xybm/NsHLlyij4uNbee+8dTjjhhLDbbrvlPi2bq6++Otx0001hiy22CH/4wx9Cu3bt4rlmzJgRRfF+++0Xtt9++7h/7ty58dgbb7wx/PKXvwwjR46MFsvawBtvvBHGjRsXrrzyytyeskHg/+pXvwr9+/ePIlpEpKYxplKkFnHggQeGM844I/zwhz9cu+2xxx7RjYngOfzww8P3v//9tZ+deOKJYfDgwVWyRGHteuyxx8JDDz0UPvnkk2ixywLn++yzzyrlks0KlkQE3p577hm22WabaIWrDJSNMiLW09CC9957Lzz55JPh4Ycfjp+l+xHduL6pG1zNtWnezb0jEo888shSNwQwoRDcL89Mly5dtFKKyDpDUSlSi0BAIg6wxKXbrrvuGjp27Bg22mijMHz48P/4bNSoUdHCVpWYP4QSgmrhwoWVjt2sLeCiRigdf/zx0VJJnVQX4iwXL14c6wIhWQgwsRgwYEC0aJfceB66du0arc5YKQ8++ODQuXPnWGciIuuCRr9IyP0tIusZEl2wviEe0m3WrFnRTctniMitttoquq35DMsV8X5Y1ubPnx9j7ebMmRPefffdsGTJkigamzRpEjeEE9a31157LTz++ONRTCFCiMvk3GncIFaud955J7qB2ebNmxfPt3Tp0mi1S8+XcvPNN8fv7LDDDmHnnXfO7f1vuB7nRcxR9jSOkzKSeEO5SFSiLKk7ns84nnvCIsp1sbxhceQ4rHLF4x0p49tvvx3rgXK///778TovvfRSdPd36tQpHHTQQfFc/P/555+P9Uudcm6SnhDdHD9t2rQoXDt06BDjGN96661Yx8R0Ui5cyhVZAalzkn74DmUub+M3JMaTey8rxpUy8nshpotvPA+U+5ZbbonXGzFiRBSZxetZRKSmUVSK1HJIMkHgIJ4QORtvvPFayyTiBkHy6quvro0DJObukUceiXGTZEqn4pTkHM5z/fXXx88QcgjPl19+OfTr1y9aADkekYU4YRs/fny4//77o4v42WefjaISqynnS8tQWVF57733hltvvTUKs9Rlj+Dhu1OmTAl0RYhnkm8oC1CeN998M/zlL3+JwhbLG/d7/vnnR4HM/xHGlAtRxn3fcMMNcSOZh/Mi0hCabByPqHz66afD3XffHf/lPNQDQhRxxjGpqCTRhetSF9TtAw88ED9DLHJd6qE8EHicd8KECTEesrwNwc1vxHmLi/aK4N6xuHI//PaEBhx22GExcUpEZF2i+1ukgPnggw+i4CNBBtGG5ZK4zH333TcKmj/96U/hsssuiyISEYdo2nzzzaMwxCrXvXv3KAYRR4gTRNaZZ54ZhWLLli3DsGHDYiIQLleslhdffHG4/fbbo4ipKpwP8fbiiy9GoZW6nDkXwva5556LcZ645RGTgOCk7C+88ELch+DDcon4xBLJ38C/1113Xbj00kuj6CMjHvdvz549w7XXXhvFcXoscB6SnBCvuIc33XTTaK1kf3GuueaaeL9wwAEHhJ122ilaTanXK664okK3OQlM1CmCuaLtj3/8Y/wtSSCqCghRrKjcJ3GU+++/f9hxxx1zn4qIrDu0VIrUcsqyVGKlRGxhxcP6R4LP0UcfHeMyEYrEYuK2xQqG8MCKmFoZESEkpyBA+E7v3r2jmEPUPPHEE9HSxX6W30GgYLHDgobLGHGKCOM7UFlLJXAvWAz5LpnYCDrOSeIQ5cTaSLkRfLi2cVMjlvh39913j+IWFzfXZKkfyoYQRKT+9re/jcJ49OjRMcZwl112iSKQulq0aFG0SCIysVTyHe4D9zhimeMR0FhsKUNqqUR0jxkzJsZwkmXO0k2IW6yX3POgQYNiOctyg7MfdzX3itAvb+O3pcyUsSpxkPyWWJMnTZoUfzN+U1ziur1FZF2jpVKkQMEKhhBjiR3EHEk8iBwEDFY3BCHZwMQeYgnkOKyFrO+YxmJiqUNIIXywcvHdo446Km6IKFyoWDZJ/ECsEu+JwEOgVRUEGoIJ6x6ijfhBSOM1uQZxiog8xB6fc4+Izk022aRMsYU4RqwirkhaYnkgxCf3hejE0krGdPFkJoQ1IhnLLcKPtS4RuiXd2YhIxCZij7rgfKxdyfHUAaK9PGsldUo5jjjiiAo3xC7nLx4jWhFcm/U0cfPzmyIoqSsFpYisDxSVIgUKrm8EGda5oUOHRutU8Vg8RBQCCKsiiSKIs/IEEBZQhOnpp58ez4VbOk1mwYWLMCWekeuxVRUELRZIBCznxdJHcgnCDOsgsYCINayuWBZx3+PiJsEHYYuoLI1UeGJBRJRhwSwOAhthx/WrCt8teV3qhuQdrse1sRiXBfXEcdxLRRt1UNUljNKJBfGYCGriUalfEZH1gaJSpEDBYoiVDiGJ9bG0Ba4RilgXidNLs7fLAosdSS2IJNzRJPQQo/jTn/40nH322eG8886LVsQsICqxWHIeEoWwSCIauSZWQVzo/J8NIYx7nzKzJiXfKw3EWHoO3PslRRXWTSyQfFZVsDSWtBwiTqlrrodgLK9OKRuJTyTRVLTxNiUsrgjLysK5sdASH0sM6fp8XaeIiKJSpMBBRCAGi7t3UxCKlRUZCFSym4855pgoIHnTDGINFzmC5aKLLvovK2BVQVRiNUTk4gJHNCIssfxhZSQGEmsoVlJiPLGOIpgRlJWxNFIHpdUDwrsqbuV8gSWRRKPiCTllbSTqICyrkqhD1j+JQ9Qf1mpEsIjI+kJRKVKgYJ1CRODSJquaZJySYA3EtYroxIJXnsAkK5uED0Tet7/97fiKvwsvvDC+uQehSZwilrAs4DrGlU25EYy41rHMIRqxJBIbSgwoLnHKQ7wgQhPRVJpYBO4NSyT3Rpxj8SzvFPYh8NY1lItYVJKIKtpYJokY2KoIQ1zfTAb69u0bQweqkuAjIpJvFJUiBQoiDJGGC/aZZ56JoqmkKxZLFvGJCEqsjGUJM8AlzQLkWARJHGEBbWIysSBiLcR9jgDE7VtebGZ5IIRxyROniKjEUkmZKBufEcPIfeHSZYF27olscBJqyoIElTSTHBGKIC0OsadYXFlYfV2DQKyJRB1+ZyYMWHOxwiIqccnr+haR9YmiUqRAwXqH1Q+RRvYvwpLlerDWIaCwYv3rX/+KlkeSdciuxh3OhpDDzYrgTN9Wg2BELPIZ/5JIg4UPUYbYZLkh4hyxiFYl7q8kiEaSSogHpIyISdaVpFy42sleplxYKvkMSyXCsSywBnIM32MZoKeeeiomrnBP3PvkyZPXJgYVh/tM75WEJ5KCqpPVXh6cH7FHXGtFG/fI/VZGGFJmxDPl5XtlJTGJiKxLFJUiBQoWKpa5YV1GLHpXX311XL+RTG3Wmrz88stjjCTuZDKrseYhcrCEYUHjOxMnTozrQPLWGtZuRHxiAfvHP/4RxRmJIyTt3HTTTVGg4mol+YR/qwvXISmHc2D9RBQWj9XEskeZEb1YSFnqpzyXMJZVEnlYnBwxeuedd8YFy7GCIoT//ve/x/jNkolM/J/vsnQR8aPUHYuqFwJYi7lXfgtEJcsjiYisbxSVIgUMgouFu88999zoEkVAnXLKKXEh9HvuuSfGKPIZi2KnIKRYdxHLJRZI3kSDEGU5mhNPPDFaP2+77bZw6qmnxkXQiatEgPIGmX322ScKGZb9qS5Y5VhMPX1vN5ZWxGMK18etj0WTtTKJmawI3PvnnHNOLD+WVLLWeff1BRdcEC26xDWWtOZh2aR+ENKITrLdEdJZrLDrCn5rrMZYX6lH6kpEZH3TIOmcqr7gnIisM3DN4s4lZhBrI6KsuIuUmEpiBjkO6xXZ0xxLHCLCiYxr/i7+HbKrcfdyPN9nKR8SPRApWC1xrWIpxCWNJYxEGqyJxF3iVu7UqVMUhpC+Yzy1KlYEXQ7JQ3wPAYiIJCYQKyqQdJS+gjG9bpqAgtsXCyevdMRVjlBENALWO8QuG25vyo/gokzcB/9HoLIQOv9yPFZZhDXXpH6I6eQ+uH/OgwUU0Vt8/U/un3qgHLjtsRJy/nUJZSeeknAH7pH6Ky9EQERkXaCoFKlDIJywtCHQcCHzb1kxejR9XL8INURWKurYz3kQiuzjs/WxHE914X4Qq2y4uBGE6b2VBvfLsRyTxpuKiEjVUVSKiIiISGackouIiIhIZhSVIiIiIpIZRaWIiIiIZEZRKSIiIiKZUVSKiIiISGYUlSIiIiKSGUWliIiIiGRGUSkiIiIimVFUioiIiEhmFJUiIiIikhlFpYiIiIhkRlEpIiIiIplRVIqIiIhIZhSVIiIiIpIZRaWIiIiIZEZRKSIiIiKZUVSKiIiISGYUlSIiIiKSGUWliIiIiGRGUSkiIiIimVFUioiIiEhmFJUiIiIikhlFpYiIiIhkRlEpIiIiIplRVIqIiIhIZhSVIiIiIpKREP4/Uc3EKjjqflEAAAAASUVORK5CYII=)\n",
        "\n",
        "\n",
        "2. For example, to make a single prediction 24 hours into the future, given 24 hours of history, you might define a window like this:\n",
        "\n",
        "  ![Screenshot 2022-04-22 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)\n",
        " \n",
        "  Imagesource(https://Tensorflow/tutorials)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "** There are few terms that we need to understand here:**\n",
        "\n",
        "Input width= The number of days the model is going to look back in the past for making the prediction.\n",
        "\n",
        "Offset= The number of days that the model is going to jump in order to make the prediction for a particular day in future.\n",
        "\n",
        "Label width= The number of days that the model is going to make predictoin for.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4KRn7VmHw6aj"
      },
      "id": "4KRn7VmHw6aj"
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "496232b2",
      "metadata": {
        "id": "496232b2"
      },
      "outputs": [],
      "source": [
        "class WindowGenerator():\n",
        "    \n",
        "  def __init__(self, input_width, label_width, shift,\n",
        "               train=train, val=validate, test=test,\n",
        "               label_columns=None):\n",
        "    # Store the raw data.\n",
        "    self.train = train\n",
        "    self.val = val\n",
        "    self.test = test\n",
        "\n",
        "    # Work out the label column indices.\n",
        "    self.label_columns = label_columns\n",
        "    if label_columns is not None:\n",
        "      self.label_columns_indices = {name: i for i, name in\n",
        "                                    enumerate(label_columns)}\n",
        "    self.column_indices = {name: i for i, name in\n",
        "                           enumerate(train.columns)}\n",
        "\n",
        "    # Work out the window parameters.\n",
        "    self.input_width = input_width\n",
        "    self.label_width = label_width\n",
        "    self.shift = shift\n",
        "\n",
        "    self.total_window_size = input_width + shift\n",
        "\n",
        "    self.input_slice = slice(0, input_width) #0,width,none\n",
        "    self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n",
        "\n",
        "    self.label_start = self.total_window_size - self.label_width\n",
        "    self.labels_slice = slice(self.label_start, None)\n",
        "    self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n",
        "    \n",
        "    #parameters needed for generating dataset\n",
        "    self.end_input= input_width\n",
        "    self.start_label= input_width+shift\n",
        "    self.end_label= self.start_label+self.label_width\n",
        "  \n",
        "\n",
        "  def __repr__(self):\n",
        "    return '\\n'.join([\n",
        "        f'Total window size: {self.total_window_size}',\n",
        "        f'Input indices: {self.input_indices}',\n",
        "        f'Label indices: {self.label_indices}',\n",
        "        f'Label column name(s): {self.label_columns}'])\n",
        "   \n",
        "  \n",
        "\n",
        "  def generate_dataset(self,features,label,label_norm=False,lstm=False):\n",
        "\n",
        "    #Normalizing the features but not the labels    \n",
        "    train_feat= self.train[features]\n",
        "    train_lab=self.train[label] \n",
        "    print(train_feat.min())\n",
        "    \n",
        "  \n",
        "    val_feat=(val_feat-train_feat.min())/(train_feat.max()-train_feat.min())\n",
        "    \n",
        "    val_lab=self.val[label]\n",
        "    val_lab= (val_lab-train_lab.min())/(train_lab.max()-train_lab.min()) if label_norm== True else val_lab\n",
        "    \n",
        "    \n",
        "    test_feat= self.test[features]\n",
        "    test_feat= (test_feat-train_feat.min())/(train_feat.max()-train_feat.min())\n",
        "    \n",
        "    test_lab=self.test[label] \n",
        "    test_lab= (test_lab-train_lab.min())/(train_lab.max()-train_lab.min()) if label_norm== True else test_lab\n",
        "    \n",
        "    \n",
        "   #doing the normalization on training data at last because it values are uses on val and test data to there normalization\n",
        "    train_feat= (train_feat-train_feat.min())/(train_feat.max()-train_feat.min())\n",
        "    train_lab= (train_lab-train_lab.min())/(train_lab.max()-train_lab.min()) if label_norm== True else train_lab\n",
        " \n",
        "    \n",
        "    X_train,y_train= self.generate_features_labels(train_feat.values,train_lab.values,lstm)\n",
        "    X_val,y_val= self.generate_features_labels(val_feat.values,val_lab.values,lstm)\n",
        "    X_test,y_test=self.generate_features_labels(test_feat.values,test_lab.values,lstm)\n",
        "    \n",
        "    return X_train,y_train,X_val,y_val,X_test,y_test  \n",
        "\n",
        "  def generate_features_labels(self,features,labels,lstm):     \n",
        "      X=[]\n",
        "      y=[]\n",
        "  #   \n",
        "      labels=labels.reshape(-1,1) if labels.shape==(labels.shape[0],) else labels\n",
        "      \n",
        "  #     print(labels.shape)\n",
        "  #     print('The shape of the feature',features.shape)\n",
        "      for i in range(features.shape[0]): #features is basically an array containing all the features if train_df=[1337,5]\n",
        "        if lstm!= True:\n",
        "          if i+self.end_label-1<=features.shape[0]: \n",
        "              X.append(features[i:self.end_input+i,:])\n",
        "              y.append(labels[self.start_label-1+i:self.end_label-1+i,:])\n",
        "        else:\n",
        "          if self.end_input+i+1<=features.shape[0]:\n",
        "              X.append(features[i:self.end_input+i,:])\n",
        "              y.append(labels[i+1:self.end_input+i+1,:])\n",
        "\n",
        "      return np.array(X),np.array(y)        \n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*The important thing to note here is that the features values have been normalized between 0-1, so the problem which arrises if the features are in different scales are eliminated using by this technique. But when normalizing validate and test data don't use the validate.max()/text.max() and validate.min()/text.min() for there respective normalization use train.max and train.min for both of them. Because we can't look at the validate or test dataset they are unknown to us."
      ],
      "metadata": {
        "id": "YkYrtRUO2RjL"
      },
      "id": "YkYrtRUO2RjL"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Types of Models:\n",
        "\n",
        "1) Single step Model\n",
        "\n",
        "2) Multi step Model"
      ],
      "metadata": {
        "id": "Trs4dXzd0O-o"
      },
      "id": "Trs4dXzd0O-o"
    },
    {
      "cell_type": "markdown",
      "id": "6bcf3b82",
      "metadata": {
        "id": "6bcf3b82"
      },
      "source": [
        "### 1) Single Step Model:\n",
        "\n",
        "In single step model, model will look one step into the future. For example given all the past one month of stock data model will predict what will be the stock value tomorrow. For this task we will be using models like:\n",
        "\n",
        "1.1) Dense Model\n",
        "\n",
        "1.2) RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aaa07857",
      "metadata": {
        "id": "aaa07857"
      },
      "source": [
        "## 1.1) Dense Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "b95d761a",
      "metadata": {
        "id": "b95d761a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c35e2617-5b9d-479a-d106-b51bf99d29e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 3, 1)]            0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 3)                 0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 556)               2224      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 556)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 228)               126996    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 228)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               29312     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 166,853\n",
            "Trainable params: 166,853\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def dense_func(input_shape):\n",
        "  input= tf.keras.Input(shape= tf.constant(input_shape))\n",
        "  x= tf.keras.layers.Flatten()(input)\n",
        "\n",
        "  #Basically there are four dense layer each followed by an dropout layer\n",
        "  x= tf.keras.layers.Dense(units=556, activation='relu')(x)\n",
        "  x= tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "  x= tf.keras.layers.Dense(units=228, activation='relu')(x)\n",
        "  x= tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "  x= tf.keras.layers.Dense(units=128, activation='relu')(x)\n",
        "  x= tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "  x= tf.keras.layers.Dense(units=64, activation='relu')(x)\n",
        "  x= tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "  output= tf.keras.layers.Dense(units=1)(x)\n",
        "  model= tf.keras.Model(inputs= input,outputs= output)  \n",
        "  return model\n",
        "dense_func((3,1)).summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Optimization**\n",
        "\n",
        "Selection of an good optmizer and learning rate is a crucial part of training. Learning rate decay helps to minimize the overshotting of the global minima and to obtain an stable stable training. Similarly, if the model weights gets update with an large extend during early stages of training, the model will need to unlearn those parameter which ultimately increases the number of epochs needed to training the model. So, to totally remove this phenomena warnup training is introduced where model doesn't change its weight significatly during early training which totally eliminates this problem"
      ],
      "metadata": {
        "id": "l3sV-LflW1gi"
      },
      "id": "l3sV-LflW1gi"
    },
    {
      "cell_type": "code",
      "source": [
        "def optimizer_func(epoch,batch,X_train,lr_rate= 1e-4):\n",
        "  \n",
        "  beta_1= 0.9 \n",
        "  beta_2= 0.999  \n",
        "  initial_lr= lr_rate\n",
        "  epochs= epoch\n",
        "  batch= batch\n",
        "  num_of_steps= epochs*X_train.shape[0]//batch #Total number of training steps\n",
        "  num_of_warmup= num_of_steps*0.1#The learning rate is very small here\n",
        "\n",
        "  #Scheduler and warmups\n",
        "  scheduler= tf.keras.optimizers.schedules.PolynomialDecay(initial_learning_rate=initial_lr , decay_steps=num_of_steps, end_learning_rate=0, power=1.0,\n",
        "          cycle=False, name=None)\n",
        "  warm= WarmUp(initial_lr,decay_schedule_fn= scheduler,warmup_steps= num_of_warmup)\n",
        "\n",
        "\n",
        "  optimizer= AdamWeightDecay(learning_rate= warm, beta_1=beta_1,beta_2=beta_2, epsilon=1e-07,weight_decay_rate=0.01,name='Adam')\n",
        "  return optimizer"
      ],
      "metadata": {
        "id": "B2vf9JhWeRY_"
      },
      "id": "B2vf9JhWeRY_",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " We will be training multiple models so instead of compiling and training every single time just define a function which will reduce eventual redundency"
      ],
      "metadata": {
        "id": "CLko07DIerVS"
      },
      "id": "CLko07DIerVS"
    },
    {
      "cell_type": "code",
      "source": [
        "#Parameters\n",
        "epoch= 100\n",
        "batch=5\n",
        "patience=10\n",
        "\n",
        "def Compile(model,epoch,patience,X_train,y_train,X_val,y_val,batch):\n",
        "    es= EarlyStopping(monitor= 'val_loss',patience= patience,mode='min')\n",
        "    # checkpoint= ModelCheckpoint(checkpoint_path+path,save_weights_only=True)\n",
        "    model.compile(optimizer= optimizer_func(epoch,batch,X_train),loss= 'MSE',metrics=tf.keras.metrics.MeanAbsoluteError())\n",
        "    history= model.fit(x= X_train,y= y_train,batch_size= batch,epochs= epoch\\\n",
        "                       ,callbacks=[es],validation_data=(X_val,y_val)).history\n",
        "    \n",
        "    return history\n"
      ],
      "metadata": {
        "id": "c2E2cFGyW0fw"
      },
      "id": "c2E2cFGyW0fw",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hyperparameter Tuning**\n",
        "\n",
        "On of the most important hyperparameter for stock price prediction is the number of days that the model sees to make future prediction ie input_width. This hyperparameter value is calculated by training the model on different number of days and the model which produces lowest loss it is selected."
      ],
      "metadata": {
        "id": "PVkslWphgfhT"
      },
      "id": "PVkslWphgfhT"
    },
    {
      "cell_type": "code",
      "source": [
        "class Trainer:\n",
        "  def __init__(self,model_function,widths,label_width,shift,features,label,lstm=False,autoregressive=False):\n",
        "    self.model_function= model_function\n",
        "    self.widths= widths\n",
        "    self.label_width= label_width\n",
        "    self.shift= shift\n",
        "    self.features= features\n",
        "    self.label= label\n",
        "    self.lstm= lstm\n",
        "    self.autoregressive= autoregressive\n",
        "  #Training an single model\n",
        "  def train_single_model(self,input_width,model_name):\n",
        "      \n",
        "      print('Getting the data for input_width ',input_width)\n",
        "      window= WindowGenerator(input_width,label_width,shift)\n",
        "\n",
        "      if self.lstm:\n",
        "        X_train,y_train,X_val,y_val,X_test,y_test= window.generate_dataset(features,Label,lstm=True)\n",
        "      else:\n",
        "        X_train,y_train,X_val,y_val,X_test,y_test= window.generate_dataset(features,Label)\n",
        "      # print(X_train.shape)\n",
        "      # print(y_train.shape)\n",
        "      # print(X_val.shape)\n",
        "      # print(y_val.shape)\n",
        "      # print(X_test.shape)\n",
        "      # print(y_test.shape)\n",
        "\n",
        "      #For autoregressive model we don't need to input the shape to create the model but we need to send the actual input value to create the network\n",
        "      if self.autoregressive:\n",
        "        model_class= self.model_function()\n",
        "        model= model_class(units= 128,output_steps= self.label_width)\n",
        "        val= model(X_train[0:3])\n",
        "        # model.summary()\n",
        "\n",
        "      #For all the other model except autoregressive we need to send the input shape\n",
        "      else:\n",
        "        #Creating the model\n",
        "        model= self.model_function(input_shape= (X_train.shape[1:]))\n",
        "\n",
        "      #Compiling the model with that \n",
        "      print('Compiling the model:',model_name)   \n",
        "      print('...')\n",
        "   \n",
        "      history= Compile(model,epoch,patience,X_train,y_train,X_val,y_val,batch)\n",
        "\n",
        "      train_metric= min(history['mean_absolute_error'])\n",
        "      val_metric= min(history['val_mean_absolute_error'])\n",
        "      _,test_metric= model.evaluate(X_test,y_test,batch_size=batch)\n",
        "      y_test_pred= model.predict(X_val)\n",
        "      y_test_data= y_val\n",
        "      \n",
        "      if self.autoregressive:\n",
        "        memory= {'train_metric':train_metric,'val_metric':val_metric,'test_metric':test_metric,'y_test_pred':y_test_pred\\\n",
        "                ,'y_test_data':y_test_data,'inputs':X_val[0]}\n",
        "      else:\n",
        "        memory= {'train_metric':train_metric,'val_metric':val_metric,'test_metric':test_metric,'y_test_pred':y_test_pred\\\n",
        "                ,'y_test_data':y_test_data}\n",
        "\n",
        "      return memory #Returning all the important output of a model\n",
        "      \n",
        "  #method responsible for training an single model on different input_\n",
        "  def training_multiple_models(self,name='LSTM'):\n",
        "      #if width=[5,6,7,8]\n",
        "      #Than of the window of size 7 6 different models will be created having timestamp 2 upto 7 inorder to check the \n",
        "      #relationship of the window and timestamp\n",
        "      models_output= {}\n",
        "      \n",
        "      for width in self.widths:\n",
        "\n",
        "          #Getting the name of the model\n",
        "          model_name= name+'_'+str(width)\n",
        "          memory= self.train_single_model(width,model_name) #getting all the important outputs of that model\n",
        "          models_output[model_name]= memory\n",
        "              \n",
        "      return models_output         "
      ],
      "metadata": {
        "id": "4v71qAPzXPJ4"
      },
      "id": "4v71qAPzXPJ4",
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Plotter:\n",
        "\n",
        "  def __init__(self,model_outputs):\n",
        "     self.models_output= model_outputs\n",
        "     return \n",
        "\n",
        "\n",
        "  #Method responsible for plotting an individual model metrices on respective input_width\n",
        "  def analysing_input_width(self):\n",
        "    \n",
        "    train_metrices= [self.models_output[model]['train_metric'] for model in list(self.models_output.keys())]\n",
        "    val_metrices=[self.models_output[model]['val_metric'] for model in list(self.models_output.keys())]\n",
        "    test_metrices= [self.models_output[model]['test_metric'] for model in list(self.models_output.keys())]\n",
        "\n",
        "\n",
        "    x = np.arange(len(list(self.models_output.keys())))\n",
        "    plt.figure(figsize=[22,8])\n",
        "    plt.bar(x-0.4,train_metrices,width=0.2,label=\"Train\")\n",
        "    plt.bar(x-0.2,val_metrices,width=0.2,label=\"Test\")\n",
        "    # plt.bar(x,test_metrices,width=0.2,label=\"Test\")\n",
        "\n",
        "    plt.xticks(ticks=x,labels=self.models_output.keys(),rotation=45)\n",
        "    \n",
        "    plt.ylabel('Mean Square Error')\n",
        "    _ = plt.legend()\n",
        "    return \n",
        "    \n",
        "  #Plotting stock value of true and prediction\n",
        "  def true_pred(self,best_width,num_points=50):\n",
        "\n",
        "    for model in list(self.models_output.keys()):\n",
        "      \n",
        "      if model.split('_')[-1]==str(best_width):\n",
        "        y_pred= self.models_output[model]['y_test_pred'] \n",
        "        y_true= self.models_output[model]['y_test_data']\n",
        "\n",
        "        if 'inputs' in self.models_output[model].keys():\n",
        "          y_true= y_true.reshape(y_true.shape[0]*y_true.shape[1])\n",
        "          y_pred= y_pred.reshape(y_pred.shape[0]*y_pred.shape[1])\n",
        "\n",
        "        else:\n",
        "          y_true= np.array(y_true).reshape(y_true.shape[0])\n",
        "          y_pred= np.array(y_pred).reshape(y_true.shape[0])\n",
        "        \n",
        "        break\n",
        "\n",
        "    plt.figure(figsize=(10,5))\n",
        "    x=[i+1 for i in range(num_points)]\n",
        "    plt.plot(x,y_true[:num_points],'ro-', label='Label', linewidth=2)\n",
        "    plt.plot(x,y_pred[:num_points],'bo', label='Prediction')\n",
        "    plt.legend()\n",
        "    plt.title('Comparision between truth value and predicted value')\n",
        "    plt.xlabel('Points')\n",
        "    plt.ylabel('Close value of the stock')\n",
        "    return \n",
        "  \n",
        "  #Plotting for multiwindow model\n",
        "  def multiplot(self,inputs, y_true,y_pred):\n",
        "    plt.figure(figsize=[10,8])\n",
        "     #only selecting the close value\n",
        "    y_true= y_true[0]\n",
        "    y_pred= y_pred[0]\n",
        "\n",
        "    inputs= inputs.reshape(inputs.shape[0])\n",
        "    y_true= y_true.reshape(y_true.shape[0])\n",
        "    y_pred= y_pred.reshape(y_true.shape[0])\n",
        "\n",
        "    total_days= [i+1 for i in range(inputs.shape[0]+y_true.shape[0])]\n",
        "    plt.plot(total_days[:inputs.shape[0]],inputs,'go-',label='inputs')\n",
        "    plt.plot(total_days[inputs.shape[0]:],y_true,'bo',label='label')\n",
        "    plt.plot(total_days[inputs.shape[0]:],y_pred,'ro',label='prediction')\n",
        "    plt.legend()\n",
        " \n",
        "    return "
      ],
      "metadata": {
        "id": "odMv5tFUuBsa"
      },
      "id": "odMv5tFUuBsa",
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# inputs= tf.random.normal(shape= [5,1])\n",
        "# y_true= tf.random.normal(shape=[20,3,1])\n",
        "# y_pred= tf.random.normal(shape=[20,3,1])\n",
        "\n",
        "# p= Plotter('a')\n",
        "# p.multiplot(inputs.numpy(),y_true.numpy(),y_pred.numpy())"
      ],
      "metadata": {
        "id": "VPXXXe7T6qg-"
      },
      "id": "VPXXXe7T6qg-",
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Trainig the model on multiple input width\n",
        "\n",
        "widths =[3,5,8,11,15,18,22,25,28,31] #it will take time to evaluate on all possible input_width so selecting an list of input_widths\n",
        "\n",
        "#As it is an single step model the label width will be 1\n",
        "label_width=1\n",
        "shift=1\n",
        "\n",
        "#Features that will be used \n",
        "features= ['Open','High','Low','Close']\n",
        "batcg= 6\n",
        "#Model will try to make the prediction on the closing value of the stock\n",
        "Label= 'Close'\n",
        "model_function= dense_func\n",
        "model_trainer= Trainer(model_function,widths=widths,label_width=label_width,shift=shift,features=features,label=Label)\n",
        "models_output= model_trainer.training_multiple_models(name='Dense')"
      ],
      "metadata": {
        "id": "6MYFcVY9d8cw"
      },
      "id": "6MYFcVY9d8cw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Analysing the model at different input_width\n",
        "plotting_stock_value = Plotter(models_output)\n",
        "plotting_stock_value.analysing_input_width()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "s5XwT6dWy3jC",
        "outputId": "6ea799bb-e7b2-4204-e517-d628751046c8"
      },
      "id": "s5XwT6dWy3jC",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x576 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The test loss is minimum for the input_width=3, so it will be our hyperparameter."
      ],
      "metadata": {
        "id": "bV66fIQ52gbM"
      },
      "id": "bV66fIQ52gbM"
    },
    {
      "cell_type": "code",
      "source": [
        "plotting_stock_value = Plotter(models_output)\n",
        "plotting_stock_value.true_pred(best_width= 3)"
      ],
      "metadata": {
        "id": "an_-1OoYyFGh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "6a519c02-d62f-4a96-b22b-b5d64a26319c"
      },
      "id": "an_-1OoYyFGh",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "h6Nj_FYGkKvh"
      },
      "id": "h6Nj_FYGkKvh"
    },
    {
      "cell_type": "markdown",
      "id": "8ae6ead3",
      "metadata": {
        "id": "8ae6ead3"
      },
      "source": [
        "## 1.4) Recurrent neural network\n",
        "\n",
        "A Recurrent Neural Network (RNN) is a type of neural network well-suited to time series data. RNNs process a time series step-by-step, maintaining an internal state from time-step to time-step.\n",
        "\n",
        "For more details, read the [text generation tutorial](https://www.tensorflow.org/tutorials/text/text_generation) or the [RNN guide](https://www.tensorflow.org/guide/keras/rnn). \n",
        "\n",
        "In this tutorial, you will use an RNN layer called Long Short Term Memory ([LSTM](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/LSTM))."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "faacd61a",
      "metadata": {
        "id": "faacd61a"
      },
      "source": [
        "An important constructor argument for all keras RNN layers is the `return_sequences` argument. This setting can configure the layer in one of two ways.\n",
        "\n",
        "1. If `False`, the default, the layer only returns the output of the final timestep, giving the model time to warm up its internal state before making a single prediction: \n",
        "\n",
        "![An lstm warming up and making a single prediction](images/lstm_1_window.png)\n",
        "\n",
        "2. If `True` the layer returns an output for each input. This is useful for:\n",
        "  * Stacking RNN layers. \n",
        "  * Training a model on multiple timesteps simultaneously.\n",
        "\n",
        "![An lstm making a prediction after every timestep](images/lstm_many_window.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "8a07cf6c",
      "metadata": {
        "id": "8a07cf6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9aa32bc8-5507-4279-abc4-10fe1dfde0a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_28\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_29 (InputLayer)       [(None, 3, 1)]            0         \n",
            "                                                                 \n",
            " GRU (LSTM)                  (None, 128)               66560     \n",
            "                                                                 \n",
            " Dense1 (Dense)              (None, 256)               33024     \n",
            "                                                                 \n",
            " Dense2 (Dense)              (None, 64)                16448     \n",
            "                                                                 \n",
            " Dense3 (Dense)              (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_176 (Dense)           (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 118,145\n",
            "Trainable params: 118,145\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def lstm_model(input_shape):\n",
        "    \n",
        "    inp= Input(shape=input_shape) #BATCH,TIMESTAMP,FEATURES\n",
        "    x= tf.keras.layers.LSTM(128,return_sequences=False,name= 'GRU')(inp)#batch,timestamp,32\n",
        "    x= Dense(units=256, activation='relu',name= 'Dense1')(x)\n",
        "#     x= layers.Dropout(0.2)(x)\n",
        "    x=Dense(units=64, activation='relu',name= 'Dense2')(x)\n",
        "#     x= layers.Dropout(0.2)(x)\n",
        "    x=Dense(units=32, activation='relu',name= 'Dense3')(x)\n",
        "#     x= layers.Dropout(0.2)(x)\n",
        "\n",
        "    out= Dense(units=1)(x)\n",
        "#     out= tf.keras.layers.TimeDistributed(dense)(x)\n",
        "    model= Model(inp,out)\n",
        "    return model\n",
        "model= lstm_model([3,1])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Trainig the model on multiple input width\n",
        "\n",
        "widths =[3,5,8,11,15,18,22,25,28,31] #it will take time to evaluate on all possible input_width so selecting an list of input_widths\n",
        "\n",
        "#As it is an single step model the label width will be 1\n",
        "label_width=1\n",
        "shift=1\n",
        "\n",
        "#Features that will be used \n",
        "features= ['Open','High','Low','Close']\n",
        "\n",
        "#Model will try to make the prediction on the closing value of the stock\n",
        "Label= 'Close'\n",
        "model_function= lstm_model\n",
        "batch =16\n",
        "patience= 30\n",
        "epoch= 100\n",
        "model_trainer= Trainer(model_function,widths=widths,label_width=label_width,shift=shift,features=features,label=Label,lstm=True)\n",
        "models_output= model_trainer.training_multiple_models(name='LSTM')"
      ],
      "metadata": {
        "id": "nWj1dpAco0lZ"
      },
      "id": "nWj1dpAco0lZ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multi step Model\n",
        "\n",
        "Both the single-output and multiple-output models in the previous sections made single time step predictions, one hour into the future.\n",
        "\n",
        "This section looks at how to expand these models to make multiple time step predictions.\n",
        "\n",
        "In a multi-step prediction, the model needs to learn to predict a range of future values. Thus, unlike a single step model, where only a single future point is predicted, a multi-step model predicts a sequence of the future values.\n",
        "\n",
        "There are two rough approaches to this:\n",
        "\n",
        "Single shot predictions where the entire time series is predicted at once.\n",
        "Autoregressive predictions where the model only makes single step predictions and its output is fed back as its input.\n",
        "In this section all the models will predict all the features across all output time steps.\n",
        "\n",
        "For the multi-step model, the training data again consists of hourly samples. However, here, the models will learn to predict 24 hours into the future, given 24 hours of the past.\n",
        "\n",
        "Here is a Window object that generates these slices from the dataset:"
      ],
      "metadata": {
        "id": "bY7aQBFXsuT7"
      },
      "id": "bY7aQBFXsuT7"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) Auto regressive model\n",
        "\n",
        "The above models all predict the entire output sequence in a single step.\n",
        "\n",
        "In some cases it may be helpful for the model to decompose this prediction into individual time steps. Then, each model's output can be fed back into itself at each step and predictions can be made conditioned on the previous one, like in the classic Generating Sequences With Recurrent Neural Networks.\n",
        "\n",
        "One clear advantage to this style of model is that it can be set up to produce output with a varying length.\n",
        "\n",
        "You could take any of the single-step multi-output models trained in the first half of this tutorial and run in an autoregressive feedback loop, but here you'll focus on building a model that's been explicitly trained to do that.\n",
        "\n",
        "![autoregressive.png](data:image/png;base64,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)\n",
        "\n",
        "Imagesource(https://www.tensorflow.org/tutorials/)"
      ],
      "metadata": {
        "id": "ekR2a175tHXv"
      },
      "id": "ekR2a175tHXv"
    },
    {
      "cell_type": "code",
      "source": [
        "class denseLayers(tf.keras.layers.Layer):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.dense1= layers.Dense(256)\n",
        "        self.dense2= layers.Dense(128)\n",
        "        self.dense3= layers.Dense(32)\n",
        "        self.dense4= layers.Dense(1)\n",
        "    \n",
        "    def call(self,inputs):\n",
        "        x= self.dense1(inputs)\n",
        "        x= layers.Dropout(0.1)(x)\n",
        "        \n",
        "        x= self.dense2(x)\n",
        "        x= layers.Dropout(0.1)(x)\n",
        "        \n",
        "        x= self.dense3(x)\n",
        "        x= layers.Dropout(0.1)(x)\n",
        "        \n",
        "        x= self.dense4(x)\n",
        "        return x\n"
      ],
      "metadata": {
        "id": "Ep_hjhaesvk-"
      },
      "id": "Ep_hjhaesvk-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def AutoRegressive_func():\n",
        "\n",
        "  class AutoRegressive(tf.keras.Model):\n",
        "      def __init__(self, units,output_steps):\n",
        "          super().__init__()\n",
        "          self.unit= units\n",
        "          self.out_step= output_steps\n",
        "          self.lstm_cell= layers.LSTMCell(self.unit)\n",
        "\n",
        "          self.lstm_layer= layers.LSTM(128,return_state=True)\n",
        "\n",
        "          self.dense_layer= denseLayers()\n",
        "\n",
        "          \n",
        "      def call(self,inputs,training= True):\n",
        "          '''\n",
        "          input= [batch,timestamp,features]\n",
        "          '''\n",
        "          predictions= []\n",
        "          \n",
        "\n",
        "\n",
        "          output,state_h,state_c= self.lstm_layer(inputs) \n",
        "          #output=[batch,units], similarly output= state_h\n",
        "\n",
        "          state= [state_h,state_c] # The state\n",
        "      \n",
        "          prediction= self.dense_layer(output) \n",
        "          predictions.append(prediction)\n",
        "          \n",
        "          #Now iterating through the every step\n",
        "          for i in range(self.out_step-1):\n",
        "\n",
        "              output,state= self.lstm_cell(output,state,training)\n",
        "              prediction= self.dense_layer(output) #Prediction= [batch,1] As we are outputting \"Close\" value at every time stamp\n",
        "              predictions.append(prediction)\n",
        "              \n",
        "\n",
        "          # predictions.shape => (time, batch, features)\n",
        "          predictions = tf.stack(predictions)\n",
        "          \n",
        "          # predictions.shape => (batch, time, features)\n",
        "          predictions = tf.transpose(predictions, [1, 0, 2])\n",
        "\n",
        "          return predictions\n",
        "  return AutoRegressive"
      ],
      "metadata": {
        "id": "1HBGhfG9ur5j"
      },
      "id": "1HBGhfG9ur5j",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Trainig the model on multiple input width\n",
        "\n",
        "widths =[3,8,15,22,28,31] #it will take time to evaluate on all possible input_width so selecting an list of input_widths\n",
        "\n",
        "#As it is an single step model the label width will be 3 days in the future\n",
        "label_width=3\n",
        "shift=1\n",
        "\n",
        "#Features that will be used \n",
        "features= ['Open','High','Low','Close']\n",
        "\n",
        "#Model will try to make the prediction on the closing value of the stock\n",
        "Label= 'Close'\n",
        "model_function= AutoRegressive_func\n",
        "patience= 30\n",
        "epoch= 100\n",
        "batch= 32\n",
        "\n",
        "model_trainer= Trainer(model_function,widths=widths,label_width=label_width,shift=shift,features=features,label=Label,lstm=False,autoregressive=True)\n",
        "memory= model_trainer.training_multiple_models(name= 'Autoregressive')"
      ],
      "metadata": {
        "id": "SChni70Gutah"
      },
      "id": "SChni70Gutah",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Analysing the model at different input_width\n",
        "plotting_stock_value = Plotter(memory)\n",
        "plotting_stock_value.analysing_input_width()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "Ix7IOAfF2ILe",
        "outputId": "3c7bb08d-064b-4ad5-e91f-2f213d327d09"
      },
      "id": "Ix7IOAfF2ILe",
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1584x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For autoregressive model it is clear that if input width is greater than the model will perform well which is quite opposite to that of single step model where best result where obtain for smaller size of input width."
      ],
      "metadata": {
        "id": "tyLGIPofAuEK"
      },
      "id": "tyLGIPofAuEK"
    },
    {
      "cell_type": "code",
      "source": [
        "#As the input has been normalized in order to see the trend we need the actual input value for validation dataset\n",
        "val= data[int(data.shape[0]*0.8):int(data.shape[0]*0.9)]\n",
        "\n",
        "#plotting the plot for multistep\n",
        "plotting_stock_value = Plotter(memory)\n",
        "\n",
        "#Plotting the plot for the best model ie for input width 31\n",
        "plotting_stock_value.multiplot(val['Close'].values[:31],memory['Autoregressive_31']['y_test_data'],memory['Autoregressive_31']['y_test_pred'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "5hEBm6apCfp5",
        "outputId": "5035b524-a333-4b29-988b-83caa30ec2bd"
      },
      "id": "5hEBm6apCfp5",
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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Z215WPpzAnl+az5iRY4iLiOvw2sL0hZxoPMHrO1732fNFAkJ2NkREtG+LiGhpH6CSkpK2A5ifffZZzj///Havz549m/fff5/CwkIAjh07xvbt20lLS6OoqIidO3cCdAheHvPnz2fFihVAy0T4yspKIiMjqa6u7vT6Cy64gJzWuWPbt2+npKSEadN884sfKFiJSDdW569mVOgoLp18aa+ujx0RS9yIOJ9OYM8vze/QW+VxfuL5jBk5RsOBIj3JyoKVKyEpCYxp+bxyZUv7AE2bNo1HH32U9PR0ysvL24btPBISEnjqqae44YYbmDFjBnPmzKGgoIDw8HBWrlzJFVdcwdlnn82YMWM6vf9//Md/sG7dOjIyMjjnnHPIz88nLi6OuXPncuaZZ3L33Xe3u/6OO+6gubmZjIwMrrvuOp566ql2PVXeZqy1Prt5b2VmZtq8vDynyxCRkzQ1N3H6b09n3qR5PL/4+V6/b84TcxgRPIK3b37b6zVZa4n5VQxZGVk8esWjnV7z7bXf5tnPn6X07lLCg8O9XoOIv3K73aSnp/d8oQ8VFRXxta99jc8//9zROrypsz9XY8wGa21mZ9erx0pEOvVeyXuU1pb2ehjQY2rcVJ8NBR6oOUBlXWWXPVbQMhxYU1/Dmzvf9EkNIiLdUbASkU6tzl9NeHA4X039ap/eNzV2Knur9nKs/pjXa2o7yiah69/KL5p0EdHh0RoOFHFAcnJyQPVW9YeClYh00GybeangJS6bclmfV9d5VgYWHi30el1dbbVwstCgUK6cdiVrtq2hoanB6zWIiHRHwUpEOvh478fsr97f52FA8O3KwPzSfGLCYzht5GndXrcwbSHlJ8p5p+gdr9cg4s/8Yd50IOnPn6eClYh0sNq9mhBXCF+b+rU+v3dK7BQAn6wMdJe5mZ4wvcM+Oae6dPKljAwZGXDDgTlbckh+JBnXL1wkP5JMzpbBOdtNhobw8HCOHDmicOUl1lqOHDlCeHjfFsFog1ARacday2r3ai5OuZjo8Og+v39U6CjGRY7zSbDKL83nmrRrerxuRMgILk+9nFcKXuHRyx8lyBXk9VoGW86WHJauXUptQ8sxJMWVxSxduxSArIyBL5GXoW/ChAns3buX0tJSp0sJGOHh4UyYMKFP71GwEpF2Pj34KUUVRfz0gp/2+x6+WBlYeqyUstqybudXnWxR+iJezH+RD/Z8wAVJF3i1Ficsf2t5W6jyqG2oZflbyxWsBICQkBAmTZrkdBnDnoYCRaSd1fmrCTJBXJV2Vb/vkRqb6vUeq95MXD/Z5amXExYUFjDDgSWVnZ/h1lW7iDhDwUpE2lhryXXncmHyhcRHxPf7PlPjplJWW0b58XKv1dbXYBUZFsmlky/lJfdLATHnJDGq8zPcumoXEWcoWIlIm62lW9l+ZHu/VgOezBcrA/NL84kMjWR85Phev2dR+iL2VO0hb//QP9mhs6HZiJAIsucP/Gw3EfEeBSsRabM6fzUGwzXpPU8Q705qbMteVt4cDswvyyc9Ib3HFYEnWzBtAcGu4IAYDqyoqwBo22oiKiyKlQtWan6ViJ9RsBKRNqvdq5mbOJexo8YO6D4pMSm4jIsdR7zbY9XbYUCP2BGxXJR8Eavdq4f0cGB9Uz2PfPQI8ybN4+BdB0mOTuaKqVcoVIn4IQUrEQFgx5EdbDm8ZcDDgABhwWEkRyez/ah3eqzKj5dzsOYg0+P7FqygZTiw8Gghnx8eusds/Hnrn9lXvY+75twFQFp8GgVlBQ5XJSKdUbASEYC24bKF6Qu9cj9vrgx0l7mB3k9cP9lVaVdhMEN2ONBay0MfPMT0hOlcNuUyANLiWoJVs212uDoROZWClYgALcHqC+O+4LVVZlPjprLjyA6vDMH1dUXgycaOGsv5iefzkvulAdfhhLd2v8WmQ5u4a85dbfPL0uLTqG2oZV/VPoerE5FTKViJCMUVxeTtz/PKMKBHamwq1fXVHDp2aMD3yi/NZ0TwCJKik/r1/kXpi9hyeItX53wNloc+eIixo8byjYxvtLWlxacBaDhQxA8pWIkMczlbcjjr8bMA+M9//qfXzp/zbLngjeHA/NKWFYEu07+/sjyrHIfacODmQ5v5686/8r1zv0dYcFhbu4KViP9SsBIZxjznz5WfaNnIc3/1fpauXeqVcNW2l5UXeon6syLwZIlRiXxh3BeG3HDgbz/8LSNDRvLtzG+3ax8zcgzR4dEKViJ+SMFKZBjr7vy5gUqMSiQ0KHTAPVZVdVXsqdrTrxWBJ1uUvohP9n8yZI6A2Vu1l2e3PMttZ91G7IjYdq8ZY1pWBh5RsBLxNz0GK2NMuDHmn8aYTcaYrcaYX7S2TzLGfGyMKTTGvGCMCT3pPdcaY/Jbr3/Wlz+AiPSfL8+fC3IFMTlm8oC3XPD0yqQnpA/oPp7VjkOl1+r3H/+eZtvMnbPv7PR1bbkg4p9602NVB8yz1s4EZgGXGWNmA78CfmetnQKUA7cBGGNSgR8Dc621ZwCd/60gIo7z9flznpWBAzGQFYEnS41LJWNMxpAIVlV1VTy24TEWT1/MpJhJnV6TFpfG/ur9VNVVDXJ1ItKdHoOVbVHT+m1I64cF5gG5re2rgKtbv14CPGqtLW99/2GvViwiXpM9P5sgE9SuzZvnz6XGplJ4tHBA+y25S92EBoWSEpMy4HoWpS9ifcl6DtYcHPC9fOmJjU9QVVfF3efd3eU1ngns28q2DVZZItILvZpjZYwJMsZ8BhwG3gR2AhXW2sbWS/YCnpNRpwJTjTHvG2M+MsZc5u2iRcQ7sjKySIpKIjwoHIMhKSrJq+fPTY2bSl1THXsq9/T7Hvll+UyLm0awK3jA9SxMX4jF8mrBqwO+l680NDXwyMeP8OWkL5M5LrPL67QyUMQ/9SpYWWubrLWzgAnAuUBaN5cHA6nAhcANwB+NMdGnXmSMWWqMyTPG5JWWlva5cBEZOGstZcfLuO3s22j+eTNFdxZ59fy51LiBH8Y80BWBJztzzJmkxqb69bYLufm5lFSWcNd5d3V7XUpMCsGuYAUrET/Tp1WB1toKYB0wB4g2xnh+hZwAeLYA3gussdY2WGt3A9tpCVqn3multTbTWpuZkJDQ7x9ARPrvQM0BquqqSI8f2MTwrgx0L6vahlp2l+/2WrAyxrAwfSHritZx9PhRr9zTm6y1PPThQ6TFp3F56uXdXhsSFMKU2ClaGSjiZ3qzKjDB0+NkjBkBXAK4aQlYi1svuxnw9K2/QktvFcaYeFqGBnd5tWoR8QpPb4dnWMnbTh91OiNDRrLjaP8msG8r24bFei1YQcs8q8bmRtZuW+u1e3rLO0XvsPHARn4050e92gxVKwNF/E9veqxOB9YZYzYDnwBvWmtfA+4FfmiMKQTigCdar/8rcMQYk09L+LrbWnvE+6WLyEC5S1sON/ZVsDLGkBrX/8OYvbUi8GSZ4zKZOHqiXw4HPvThQ4wZOYYbZ9zYq+vT4tLYcWQHjc2NPV8sIoOix9mg1trNwFmdtO+iZb7Vqe0W+GHrh4j4sYKyAiJDIxkXOc5nz5gaN5UN+zf06735pfkEu4KZEjvFa/UYY5ieMJ2129fi+oWLxKhEsudne3VuWX9sPbyV/93xv/zyol8SHhzeq/ekxafR0NzA7vLdbfPZRMRZ2nldZBgrOFJAWnwaxhifPWNq7FSKKoqob6rv83vzy/KZEjuF0KDQni/upZwtObxT9A4AFktxZbHXjvEZiIc/fJgRwSNYlrms1+/RykAR/6NgJTKMuUvdA97RvCepcak02SZ2l+/u83vdpW6vDgNCyzE+dU117dq8dYxPfx2oPsCftvyJW8+6lbiIuF6/b1r8NEDBSsSfKFiJDFPVddXsq95HWpxv5ld59HdlYF1jHYVHCwd8RuCpfHmMT3/91z//i4amBn4w+wd9el90eDRjR41VsBLxIwpWIsOUt87g60lqbMvcn76uDNxxdAdNtsnrPVa+Psanr2rqa1iRt4KF6QuZHDu5z+/XYcwi/kXBSmSY8vVWCx5xEXHEjojtc4+VL1YEQssxPhEhER3auzrs2Nee/PRJyk+U97ghaFfS4tJwl7ppWTckIk5TsBIZptxlboJdwUyO6XsvSV9NjZvar2DlMq62oURvycrIYuWClSRFJWEwjIscx4igEazatIrahlqvPqsnjc2N/O6j3zF34lxmT5jdr3ukxadRfqKc0lqdYCHiDxSsRIapgrICpsROISQoxOfPSo1N7fNQYH5pPikxKYwIGeH1erIysii6s4jmnzez74f7yL0ul00HN/Fva/5tUHt+XnK/RFFFUbeHLfdEKwNF/IuClcgwVVBW4PNhQI+pcVPZW7W3Tz1C3jwjsCeXp17O/fPu57nPn+PhDx8elGdaa3nog4dIjU1lwbQF/b6PgpWIf1GwEhmGGpoa2HF0h8/OCDyVZziv8Ghhr65vaGpg+5HtXl8R2J0fn/9jFqUv4p6/38ObO9/0+fPeK3mPT/Z/0uvja7oyMWoiI4JHKFiJ+AkFK5FhaFf5LhqbGwetx8qzMrC386x2lu+kobnB5ysWT2aM4amrn2J6wnSuX309u8p9e8TpQx88RHxEPN+c+c0B3cdlXEyLn6ZgJeInFKxEhiF3WcsZgYPVY+U5bqW3wcpzhuFgDQV6jAodxSvXvUKzbebq56/mWP0xrz8jZ0sO4387nrXb19LQ1MBLBS8N+J46jFnEfyhYiQxDnn+EPTt3+9qo0FGMixzX6wnsnq0WBqtH7WSTYyfz/KLn2Vq6lVvX3OrVyew5W3JYunYp+2v2A1BZV+mV43TS4tIoqijieMNxb5QpIgOgYCUyDLnL3IyPHM/osNGD9szU2NRe91jll+WTFJXEqNBRPq6qc1+Z8hUemPcAf976Z379/q+9dt9737y3wwR+bxynkxafhsX2eeWliHifgpXIMDSYKwI9+rKX1WCuCOzKPXPv4dozruXHb/2Yvxb+dUD3qqqrYvlby9lXva/T1wd6nI5WBor4DwUrkWHGWou71D3owSo1NpWy2jLKj5d3e11TcxMFZQWOBytjDE9e+SQZp2Vw/erre72i8WSNzY2s+GQFU/5zCg+sf4CRISM7vW6gx+mkxqViMApWIn5AwUpkmDlQc4Dq+upBm7ju4dlyoafhqqKKIk40nnA8WAGMDB3Jy9e9jMu4uPr5q6mpr+nV+6y1rN22lowVGdzxv3cwPWE6nyz5hMcXPN7hOJ2IkAiy52cPqM6IkAiSopMUrET8gIKVyDAzWGcEnsoTrHoaDvTVGYH9lRKTwguLX8Bd5uZbr3yrx8nsGw9sZP7T87ny+Stpts28ev2rrLt5HZnjMjscp5MUlcTKBSvJysgacJ1aGSjiH4KdLkBEBpdnK4PB3CMKWgKKy7jYcaT7HitPsBrsHrXuXJxyMb+++Nfc9eZd/Pv6f+cnF/ykwzV7Kvew/O3lPLP5GeIj4vmvr/4XS89Z2uHIoKyMLK8EqVOlxaXxbvG7NNvmAW04KiIDo2AlMswUlBUQGRrJ6aNOH9TnhgWHkRSVxPaj3fdYucvcjIscR1R41CBV1js/nPNDNh7cyPK3l/PIR49QVltGYlQiP/3ST9ldvpuHP3oYay33zb2P+86/b9DrT4tPo7ahlr1Vewc8Z0tE+k/BSmSYcZe5SU9Ixxgz6M/uzcpAf1gR2BljDBdPupjntjxHaW0pAMWVxSxZuwSAG2fcyP0X3U9SdJIj9Z28MlDBSsQ56i8WGWac2GrBIzU2lR1HdnQ5T8la2xKsBvGMwL74xT9+gaVj7WNHjeWZa55xLFSBtlwQ8RcKViLDSFVdFfuq9zk2f2lq3FSq66s5dOxQp6/vqdrDsYZjftljBV3vN3WopvOfZzCNGTmG6PBoBSsRhylYiQwj28q2Ac4cFQP/OjOwqwns/rYi8FRdDbH5w9CbMUYrA0X8gIKVyDDi1FYLHj1tueDvwSp7frZP9qHyFgUrEecpWIkMI+4yN8GuYCbHTHbk+UlRSYS4QroNVmNGjiEuIm6QK+sdX+5D5Q1pcWkcqDlA5YlKp0sRGba0KlBkGCkoK2BK7JQOeysNliBXEJNjJ3e5+7q/rgg8ma/2ofIGT0/ktsa+y30AACAASURBVCPbOHf8uQ5XIzI8qcdKZBhxl7kd33izqy0XrLV+Ud9QppWBIs5TsBIZJhqaGig8WujY/CqPqbFTKTxaSLNtbtd+sOYgFScq/L7Hyp+lxKQQ7ApWsBJxkIKVyDCxs3wnjc2NjvcIpcalUtdUx57KPe3a/X3i+lAQEhTClNgpClYiDlKwEhkmnF4R6NHVykAFK+/QykARZylYiQwT/hKsUmNb9rLqLFjFhMdw2sjTnCgrYKTFpVF4tJCGpganSxEZlhSsRIYJd5mb8ZHjiQyLdLSOcZHjiAiJ6LAyML+sZUWgE2cYBpK0+DQamhvYXbHb6VJEhiUFK5FhwskzAk9mjOl0ZeBQ2GphKNDKQBFnKViJDAPWWtyl/rOVQWpsartgVXqslLLaMgUrL5gWPw1QsBJxioKVyDBwoOYA1fXVftFjBS0T2Isqiqhvqgc0cd2bosOjGTtqrIKViEMUrESGAXepG4D0BP/osZoaN5Um28Tu8pZ5QO6y1vr8pEdtqEuPT1ewEnGIgpXIMOAvKwI9PCsDPRPY80vzGRU6igmjJzhZVsDwbLlgrXW6FJFhR8FKZBgoKCtgdNhoTh91utOlAB33svJMXNeKQO9Ii0+j/EQ5pbWlTpciMuwoWIkMA+4yN2nxaX4TXOIi4ogJj+kQrMQ7tDJQxDkKViLDgL9stXCyqXFT2XF0B+XHyzlQc4Dp8QpW3qJgJeIcBSuRAFdVV8W+6n1+NzHcs5eVZ+K6eqy8Z8LoCUSERChYiThAwUokwG0r2wb4z8R1j9TYVPZW7SVvfx6gYOVNLuNiWtw0BSsRByhYiQQ4f93KwDOBfc22NYwIHkFSdJLDFQUWHcYs4oweg5UxJtwY809jzCZjzFZjzC9a2ycZYz42xhQaY14wxoSe8r5FxhhrjMn0VfEi0rOCsgKCXcGkxKQ4XUo7nmD1j+J/kJ6Qjsvo9zxvSotPo6iiiOMNx50uRWRY6c3fZHXAPGvtTGAWcJkxZjbwK+B31topQDlwm+cNxphI4PvAx94vWUT6oqCsgNTYVEKCQpwupZ0psVMAaGxu9LvetECQFp+GxXY47FpEfKvHYGVb1LR+G9L6YYF5QG5r+yrg6pPe9ktagtcJ75UqIv3h2WrB36zZvqatl+ov2/9CzpYchysKLFoZKOKMXvW9G2OCjDGfAYeBN4GdQIW1trH1kr3A+NZrzwYmWmv/4oN6RaQPGpoaKDxa6Hc9Qjlbcli6dinNthmAiroKlq5dqnDlRamxqRiMgpXIIOtVsLLWNllrZwETgHOBTn/9Nca4gIeBH/V0T2PMUmNMnjEmr7RUuwOL+MLO8p00Njf6XY/V8reWU9tQ266ttqGW5W8td6iiwDMiZATJ0ckKViKDrE+zRa21FcA6YA4QbYwJbn1pArAPiATOBN4xxhQBs4E1nU1gt9autNZmWmszExISBvAjiEhX/O2MQI+SypI+tUv/aGWgyODrzarABGNMdOvXI4BLADctAWtx62U3A69aayuttfHW2mRrbTLwEXCltTbPJ9WLSLfcpS1bLfhbsEqMSuxTu/RPWnwa245saxtyFRHf602P1enAOmPMZuAT4E1r7WvAvcAPjTGFQBzwhO/KFJH+KDhSwPjI8USGRTpdSjvZ87OJCIlo1xYREkH2/GyHKgpMafFp1DbUsrdqr9OliAwbwT1dYK3dDJzVSfsuWuZbdffeC/tdmYgMWEFZAekJ/jVxHSArIwtomWtVUllCYlQi2fOz29rFO05eGajeQJHB0WOwEpGhyVqLu9TNzTNvdrqUTmVlZClI+djJwerSyZc6XI3I8KCtjkUC1IGaA1TXV/tlj5UMjoSIBGLCYzSBXWQQKViJBCh/nbgug8cYo5WBIoNMwUokQHn+MfW3zUFlcClYiQwuBSuRAOUuczM6bDRjR411uhRxUFp8GgdqDlB5otLpUkSGBQUrkQBVUFZAWnwaxhinSxEHeYaCtx3Z5nAlIsODgpVIgCooK9AwoOgwZpFBpmAlEoCq6qrYV71PE9eFSdGTCHGFtC1mEBHfUrASCUDbylqGfdRjJSFBIUyJnULBEfVYiQwGBSuRAOQu01YL8i9aGSgyeBSsRAJQQVkBIa4QUmJSnC5F/EBafBqFRwtpaGpwuhSRgKdgJRKA3GVupsROISQoxOlSxA+kxafR2NzIrvJdTpciEvAUrEQCkGerBRHQykCRwaRgJRJgGpoaKDxaqInr0mZa3DRAwUpkMChYiQSYneU7aWxuVI+VtIkKj+L0UadrZaDIIFCwEgkwbWcEJqjHSv5FKwNFBoeClUiA8WwE6Rn+EYF/BStrrdOliAQ0BSuRAFNwpIAJoycQGRbpdCniR9Li06g4UcHhY4edLkUkoClYiQQYd6lb86ukA60MFBkcClYiAcRaq8OXpVMKViKDQ8FKJIAcqDlAdX21eqykgwmjJxAREqFgJeJjClYiAcQzcV3BSk7lMi6mxU3TlgsiPqZgJRJA2rZa0FCgnCJnSw7bjmzjjcI3SH4kmZwtOU6XJBKQFKxEAoi7zM3osNGMHTXW6VLEj+RsyWHp2qXUNtQCUFxZzNK1SxWuRHxAwUokgHgmrhtjnC5F/Mjyt5a3hSqP2oZalr+13KGKRAKXgpVIAHGXaasF6aiksqRP7SLSfwpWIgGiqq6K/dX7Nb9KOkiMSuxTu4j0n4KVSIDYVrYN0IpA6Sh7fjYRIRHt2iJCIsien+1QRSKBS8FKJEC4y1q2WtDhy3KqrIwsVi5YSVJUEgDhweGsXLCSrIwshysTCTwKViIBoqCsgBBXCJOiJzldivihrIwsiu4s4tozrmXC6AkKVSI+omAlEiDcZW6mxE4hJCjE6VLEj6VEp1BUUURTc5PTpYgEJAUrkQBRUFagYUDpUUpMCo3Njeyt2ut0KSIBScFKJAA0NDVQeLSQtDhNXJfupcSkALCrfJfDlYgEJgUrkSEuZ0sOyY8k09jcyOMbHtdu2tItT7DaXbHb4UpEAlOw0wWISP+delTJkeNHWLp2KYAmJ0unJkZNJMgEqcdKxEfUYyUyhOmoEumrYFcwSdFJClYiPqJgJTKE6agS6Y+UmBQFKxEfUbASGcJ0VIn0R0q0gpWIryhYiV/xTMR2/cJF8iPJmojdg+z52YQGhbZr01El0pOUmBRKa0uprqt2uhSRgKNgJX7DMxG7uLIYi6W4spila5cqXHUjKyOLWafNwmVcGAxJUUk6qkR6pJWBIr6jYCV+o6uJ2Hf/7W6HKvJ/x+qP8Xnp59x+zu00/7yZojuLFKqkR9rLSsR3FKzEb3Q14fpAzQHOevwsHvrgIe0WfYo3Ct+gtqGWxdMXO12KDCEKViK+o2AlfqOrCdcx4TGEuEK4+827SfxdIhetuoj/3vjflB8vH+QK/U+uO5eEiAQuSLrA6VJkCIkZEUN0eLSClYgP9BisjDHhxph/GmM2GWO2GmN+0do+yRjzsTGm0BjzgjEmtLX9h8aYfGPMZmPMW8aYJF//EBIYsudn4zLt/5OMCIng95f/nn8u+Sfbv7Odn3/55+yr2seStUsY+9uxXPPCNby49UWONxwHhtfk9+MNx3lt+2tck3YNwS7t9St9oy0XRHyjN38b1wHzrLU1xpgQYL0x5nXgh8DvrLXPG2MeA24DVgCfApnW2lpjzDLg18B1PqpfAshZY8+i2TYTHR5N5YlKEqMSyZ6f3TZnKDUulZ9f+HN+9uWfseHABp7d8izPf/48rxS8QmRoJLNOm8U/9/+TuqY6gLbJ7xCYu5D/beffqKmv0TCg9EtKTApbDm1xugyRgNNjj5VtUdP6bUjrhwXmAbmt7auAq1uvX2et9cxA/giY4NWKJWA9lvcYoUGhbP/O9m4nYhtjyByXycNfeZg9P9jD32/6O4unL2b9nvVtocojkHchz3XnEjsilguTL3S6FBmCJkVPYnfFbppts9OliASUXs2xMsYEGWM+Aw4DbwI7gQprbWPrJXuB8Z289Tbg9S7uudQYk2eMySstLe175RJQjtUfY9WmVSyevpiEkQm9fl+QK4j5KfN58qonu7wmEHchr2usY822NVw97WpCgkKcLkeGoJSYFOqb6tlfvd/pUkQCSq+ClbW2yVo7i5bep3OBtJ7eY4y5EcgEftPFPVdaazOttZkJCb3/h1QC0/OfP09VXRXLMpf1+x7DaRfyv+/6O1V1VRoGlH7TykAR3+jTqkBrbQWwDpgDRBtjPHO0JgD7PNcZYy4GlgNXWmvrOtxI5BQr8lZwRsIZzJ04t9/3yJ6fTURIRLu2QN2FPNedS1RYFPNT5jtdigxRClYivtGbVYEJxpjo1q9HAJcAbloClufX5ZuBV1uvOQt4nJZQddgXRUtg+WTfJ2w4sIFlmcswxvT7PlkZWaxcsJKkqJaFqC7j4g9X/CHgJq7XN9XzSsErXJV2VYfjbER6KzEqEZdxsbtcu6+LeFNveqxOB9YZYzYDnwBvWmtfA+4FfmiMKQTigCdar/8NMAp40RjzmTFmjQ/q7pPhtAR/KFqRt4KRISO5aeZNA75XVkYWRXcW8XrW6zTbZsKCwrxQoX9Zt3sdFScqWJyuYUDpv9CgUCaOnsiuCvVYiXhTj9stWGs3A2d10r6LlvlWp7Zf7J3SvMNz/pznqJRAX4I/1JQfL+f5z5/nphk3MTpstNfue+nkS5kUPYkVeSu4/szrvXZff5Cbn0tkaCSXTL7E6VJkiNNeViLeF/A7r3d1/txQWII/HHraVm1axfHG4yz7Qv8nrXfGZVx8+5xv827xu2w9vNWr93ZSY3MjLxe8zIJpCwgPDne6HBniFKxEvC/gg1VXS+39fQm+p6etuLIYi23raQukcGWt5bG8x5g9YTazxs7y+v1vPetWQoNCeXzD416/t1P+UfQPjhw/omFA8YqUmBQO1hzs8MuniPRfwAeroboEfyj3tPXWuqJ1bDuybUBbLHQnYWQCi6cvZtWmVRyrP+aTZwy23PxcRoaM5LIplzldigQAz8pATWAX8Z6AD1ZDdQn+UO1p64vH8h4jJjyGr0//us+esSxzGVV1VTz3+XM+e8ZgaWpu4qWCl7hi6hWMCBnhdDkSALTlgoj3BXywOnkJvqFlKf83zvyG309cjw6P7rR9YtTEQa7ENw5UH+Dlgpe5ZdYtPg0JcyfO5cwxZ7IibwXWWp89ZzCsL1nP4WOHNQwoXqNgJeJ9AR+s4F9L8Jt+1kTmuEzeLnqbxubGnt/okNd3vE75iXKCTFCH18ZFjqO+qd6BqrzriU+foLG5kdszb/fpc4wxLMtcxsYDG/lk/yc+fZav5ebnMiJ4BF9N/arTpUiAiBsRR2RopIKViBcNi2DlYYzhpxf8lF3lu3hui38ODW09vJXrcq9j1thZ7XraEqMSue6M6/ho70d87dmvUV1X7XSp/dbU3MTKDSu5OOViUuNSff68G2fcyMiQkazIW+HzZ/lKs21mtXs1X039KqNCRzldjgQIY0zLykDtZSXiNcMqWAFcOe1KZpw2gwfWP0BTc5PT5bRTVlvGgucWEBESwZrr13DrWbdSdGcRzT9vpvjOYp5f/DxPXvkkb+9+m3lPz6P02NA8vPovO/7Cnqo9Ppu0fqrRYaPJysji+c+f5+jxo4PyTG/7cM+HHKg5oGFA8TptuSDiXcMuWHl6rQrKCljtXu10OW3qm+pZ+MJC9lfv59XrX+1yLtUtZ93Cy9e9zOeHP2fuk3Mpqiga3EK9YEXeCsZFjuPKaVcO2jOXfWEZJxpPsOqzVYP2TG/Kzc8lLCiMK6Ze4XQpEmA8wWqoz0EU8RfDLlgBLExfSFp8Gve/ez/NttnpcrDWcvtrt/NeyXs8dfVTfHHCF7u9fsG0Bfz9pr9TWlvKeU+cx+ZDmwep0oHbVb6Lvxb+lSVnLyHY1ePG/14za+wsZk+YzWMbHhty/4A022Zy3bl8ZcpXvLo7vQjApOhJnGg8wcGag06XIhIQhmWwCnIFsfyC5Ww5vIW129Y6XQ6//fC3/M9n/8PPvvSzXh+/MjdxLutvWY/LuPjS/3yJ94rf83GV3rFyw0pcxsWSs5cM+rOXZS5j+5HtrCtaN+jPHohP9n3C3qq9GgYUn9DKQBHvGpbBCuD6M69ncsxkfvnuLx3twVi7bS33vHkPX5/+dX5+4c/79N4zxpzBB7d9wNhRY7nkmUt4teBVH1XpHXWNdTzx6RMsmLaA8aPHD/rzrz3jWmJHxA65Sey5+bmEuEJYMG2B06VIAFKwEvGuYRusgl3B/Pj8H7PhwAb+uvOvjtSw+dBmvvHSNzj79LN56uqncJm+/8+RGJXI+lvXM3PsTBb+eSH/vfG/vVKbL84pXO1eTVlt2aBNWj9VeHA4t8y6hVcKXuFA9QFHaugray257lwumXxJl3ubiQxEUnTLymMFKxHvGLbBCuCmmTeRGJXoSK/VoZpDLHhuAaPDRvPq9a922B2+L+Ij4nn7m29zScolLFm7hAfee2BAP4+vzilckbeCyTGTuTjl4gHdZyC+fc63aWxu9FoA9bWNBzZSVFGkYUDxmfDgcMaPHs/uCh1rI+INwzpYhQaFcu/ce/lgzweDOu/mROMJrnnhGkqPlfLq9a96ZVhsZOhI1tywhqyMLJa/vZyv/umrJD2S1Osep8bmRkoqS1hfsp47X7/T6+cUbjm0hfUl67k98/Z+9cx5S2pcKhenXMzKjSv9epNYj9z8XIJdwVyVdpXTpUgA05YLIt4zeMuy/NStZ93K/e/ez/3v3s+8SfN8/jxrLUvWLuHDvR/y4tdfJHNcptfuHRoUytPXPM2R2iO8sfONtvbiymKWrFnCnso9ZIzJoKSyhOLKYkoqS9o+9lXv63GF5EDOKXws7zHCgsK4ZdYt/b6HtyzLXMaiPy/iL9v/4teBxTMMOG/SPGJHxDpdjgSwlJgU3tz5ptNliASEYR+swoPDuWfuPfzgrz/g/ZL3mZs416fPe3D9g/xp85/45UW/ZPF07w/vuIyL/LL8Du3HG4/z47d+3PZ9iCuEiVETSYxK5KJJF5E4OpHEqJaPW1+9lf01+zvco789azX1NTyz+RmuPeNa4iLi+nUPb7py2pWMixzHYxse8+tgtfnQZgqPFnLPefc4XYoEuJToFPZV7+NE4wnCg8OdLkdkSBv2wQpg6TlLeeC9B7j/vft5Pet1nz3nJfdL/OTtn3DDmTew/IL+D6v1ZE/lnk7bDYYPbvuAxKhExo4a2+WQ3K8v/TVL1y7tMBxYU1fDW7veYn7K/D7Vk7M5h+r6ascmrZ8q2BXMkrOX8P/+8f/YVb6rbVWUv8nNz8VlXFyddrXTpUiA8/x/oKiiiLT4NIerERnahvUcK4+IkAh+NOdHvFH4Bp/s895BvSevrBv323Fcn3s9Xxz/RZ648gmMMV57zqkSoxK7bJ89YTbjIsd1O88pKyOr3TmFSVFJ3H/R/YyNHMvFz1zMXX+7i7rGul7VYq1lRd4KZpw2g9kTZvfr5/GFJWcvwWVcPJ73uNOldMpay4v5L3Jh8oUkjExwuhwJcNpyQcR7FKxa3fGFO4gJj+H+9+73yv1OXVl3oOYAjc2NfHPmNxkRMsIrz+hK9vzsDqsMI0IiyJ6f3et7ZGVktZ1TWHRnEcu/tJwNSzdwR+Yd/PbD3/LF//4iWw9v7fE+H+/7mE2HNrEsc5lPw2RfjR89niunXcmTnz3Z65A4mPJL89l2ZJtWA8qgULAS8R4Fq1aRYZHcOftO1mxbw6aDmwZ8v+VvLe8wlGax/Pr9Xw/43j3prMdp5YKVZGVkDei+ESERPHrFo7x2w2scqDlA5h8z+f3Hv+92a4cVeSsYFTpqwM/2hWWZyyirLSM3P9fpUjp4Mf9FDIZr0q9xuhQZBsaMHENESISClYgXKFid5Htf/B6jw0aT/V7ve3a60tUKuoGsrOuLU3ucvBlsrph6BZtv38z8SfP53hvf46s5X+10w80jtUd44fMXuGnGTUSGRXrt+d4yP2U+k2Mm++VO7Ln5uVyQdAFjR411uhQZBowx2nJBxEsUrE4SHR7Nd77wHXLzc3GXuvt9n6bmpi4Py+1q/tNQc9qo01h7w1r+cPkfeLf4XWY8NqPDkTpPffYUdU11fjNp/VQu4+L2zNt5f8/7bDm0xely2rhL3Wwt3aphQBlUClYi3qFgdYofzPkBI0JG8MD6B/r1/qKKIi5cdSGVdZUEmaB2r/V1npO/M8aw7AvL2LB0AxNHT+TqF65m6dqlPPnpkyQ9ksRdb95FWFAYmw9vdrrULt0y6xbCgsJ4LO8xp0tps9q9GoCF6QsdrkSGk5TolmDl5NmpIoFAweoU8RHxLMtcxrNbnqXwaGGf3puzOYeZj81k08FNPH3106y6epXX5zn5o/SEdD76t4+4d+69/HHjH/m3Nf/WNuRZ11TnleNwfCUuIo5rz7iWZzY/Q019jdPlAC3DgOdNPM+Rg6pl+EqJSeFYwzFKa0udLkVkSFOw6sRd591FaFAoD65/sFfXV5yo4Burv8GNL99IxpgMNt2+iZtm3kTWDN/Nc/I3oUGhPHjxg5w28jQs7X/jHehxOL62LHMZ1fXV5Gx2PvztOLKDTYc2aRhQBp1WBop4h4JVJ8aOGsuSs5ewatMqiiuKu732H0X/YOZjM/nz1j/zy4t+yTvfeodJMZMGqVL/c/jY4U7bB2vSfn/MnjCbmafNZEXeCseHQTzDgIumL3K0Dhl+PH9vKViJDIyCVRfuPu9uDIZfvf+rTl+vb6rnx3//MRetuojQoFA+uO0DfvqlnxLsGt6b2Xe3Oam/MsZwe+btbDq0iY/2ftSve5y8GWxvDr3uSm5+LueOP9ev/7wkMCVHJwMKViIDpWDVhYlRE7ll1i088ekT7K9uf25eQVkBc56Yw4PvP8htZ93Gp9/+lHPHn+tQpf7FG5uTOiErI4uwoDAueeaSPoejUzeDLa4s7vO8spwtOYx/eDwbDmxg+5HtfjsnTQJXREgEp486XcFKZICGd/dKD+47/z7+uPGPpP1XGjX1NUyMmsj8SfN5/vPniQiJ4KVrX9IGjqfwzCNb/tZySipLSIxKJHt+tt/PL1uzfQ1Ntom6hpZd2D3hCP71M1XXVVNcWUxJZUm7j9z8XOqa2u/eXttQy62v3sqLW19kXOS4Tj/iRsRhjGkLZp4NZStOVHR4tshgSIlJYXfFbqfLEBnSjNNzSgAyMzNtXl6e02V0kLMlh5tfvpkm29Su/cwxZ/LXG//KuMhxDlUm3pb8SDLFlR3n040IHkFqXCollSVUnKho91qwK5iJoyd2+w9RxpgM9lfv58jxIx1eC3GFcHrk6RysOUh9U32H15Oikii6s6jvP4xIP33z5W/yj+J/UHxn93NLRYY7Y8wGa21mZ6+px6oby99a3iFUAVTVVSlUBZiuJtcfbzxOUlQSX0r8EolRie0+xo4aS5ArqMtQlhSVxOZlLXt4nWg8wcGag+yv3s+B6gPsr97f8lGzn6c3Pd2nmkR8JSUmhT9t/hP1TfWEBoU6XY7IkKRg1Y2u/mHbU7lnkCsRX0uMSuwyHK25YU23782en91uKA86zisLDw4nOTq5bYLwyf5R9I9On60J7DLYUmJSWuYJVhSTGpfqdDkiQ5Imr3djKK5wk/4ZyKT7gR56PVQn/Evg0V5WIgOnHqtu9KYnQgLDQCfdZ2Vk9Xui+VCd8C+BR8FKZOAUrLqhf/CGl4GEo6H8bBGPsaPGEh4crmAlMgAKVj3QP3giMly4jItJ0ZPYVaFgJdJfmmMlIiJtUmJS1GMlMgAKViIi0sYTrPxhj0ORoUjBSkRE2qTEpFBVV8XR40edLkVkSFKwEhGRNloZKDIwClYiItJGwUpkYHoMVsaYcGPMP40xm4wxW40xv2htn2SM+dgYU2iMecEYE9raHtb6fWHr68m+/RFERMRbPKcDKFiJ9E9veqzqgHnW2pnALOAyY8xs4FfA76y1U4By4LbW628Dylvbf9d6nYiIDAGjQkcxZuQYBSuRfuoxWNkWNa3fhrR+WGAekNvavgq4uvXrq1q/p/X1+cYY47WKRUTEp1JiUthdsdvpMkSGpF7NsTLGBBljPgMOA28CO4EKa21j6yV7gfGtX48H9gC0vl4JxHmzaBER8R3tZSXSf70KVtbaJmvtLGACcC6QNtAHG2OWGmPyjDF5paWlA72diIh4SUp0CiWVJTQ0NThdisiQ06dVgdbaCmAdMAeINsZ4jsSZAOxr/XofMBGg9fUo4Egn91pprc201mYmJCT0s3wREfG2lJgUmmwTe6r2OF2KyJDTm1WBCcaY6NavRwCXAG5aAtbi1stuBl5t/XpN6/e0vv621Ra+IiJDhrZcEOm/3hzCfDqwyhgTREsQ+7O19jVjTD7wvDHmfuBT4InW658AnjHGFAJHget9ULeIiPiIgpVI//UYrKy1m4GzOmnfRct8q1PbTwBf90p1IiIy6MZFjiM0KFTBSqQftPO6iIi0E+QKIjk6WcFKpB8UrEREpANtuSDSPwpWIiLSQUq0gpVIfyhYiYhIBykxKZSfKKf8eLnTpYgMKQpWIiLSgWdloI62EekbBSsREelAWy6I9I+ClYiIdDApZhKgYCXSVwpWIiLSweiw0cRHxCtYifSRgpWIiHRqUvQkBSuRPlKwEhGRTqXEpGjyukgfKViJiEinUmJSKKoooqm5yelSRIYMBSsREelUSkwKjc2N7K3a63QpIkOGgpWIiHRKWy6I9J2ClYiIdErBSqTvFKxERKRTE0ZPINgVrGAl0gcKViIi0qlgVzBJUUnsqlCwkl7KyYHkZHC5Wj7n5Dhd0aALdroAERHxXykxKeqxkt7JyYGlS6G2tuX74uKW7wGyspyra5Cpx0pERLqkYCW9tnz5v0KVR21tS/swomAlIiJdSolJoay2jKq6KqdLEX9XUtK39gClJlfTsgAAHMZJREFUYCUiIl3yrAzcXa4d2KUHiYl9aw9QClYiItIlbbkgvZadDRER7dsiIlrahxEFKxER6ZKClfRaVhasXAlJSWBMy+eVK4fVxHXQqkAREelGdHg0MeExClbSO1lZwy5InUo9ViIi0q2UmBTtZSXSSwpWIiLSrUkxk9RjJdJLClYiItKtlOgUiiqKaLbNTpci4vcUrEREpFspMSnUN9Wzv3q/06WI+D0FKxER6ZZWBor0noKViIh0S8FKpPcUrEREpFuJUYm4jEvBSqQXFKxERKRbIUEhJEYlKliJ9IKClYiI9CglJkXBSqQXFKxERKRHKdEKViK9oWAlIiI9SolJ4dCxQxyrP+Z0KSJ+TcFKRER65FkZuLtit8OViPg3BSsREenR9iPbAZixYgbJjySTsyXH4YpE/JOClYiIdCtnSw7/vv7fAbBYiiuLWbp2qcKVSCcUrEREpFvL31rO8cbj7dpqG2pZ/tZyhyoS8V8KViIi0q2SypI+tYsMZwpWIiLSrcSoxD61iwxnClYiItKt7PnZRIREtGsLCwoje362QxWJ+C8FKxER6VZWRhYrF6wkKSoJgyHIBBEfEc+10691ujQRv6NgJSIiPcrKyKLoziKaf95M7rW57Kvexx8++YPTZYn4nR6DlTFmojFmnTEm3xiz1Rjz/db2mcaYD40xW4wxa40xo1vbQ4wxq1rb3caYH/v6hxARkcFz1bSruHTypfz8nZ9z+Nhhp8sR8Su96bFqBH5krZ0OzAb+jzFmOvDfwH3W2gzgZeDu1uu/DoS1tp8DfNsYk+ztwkVExBnGGP7jsv/gWMMxfvLWT5wuR8Sv9BisrLUHrLUbW7+uBtzAeGAq8G7rZW8CizxvAUYaY4KBEUA9UOXlukVExEFp8Wl8/4vf58lPn+STfZ84XY6I3+jTHKvWnqezgI+BrcBVrS99HZjY+nUucAw4AJQAD1lrj3qhVhER8SM/+/LPGDNyDN99/bs022anyxHxC70OVsaYUcBq4E5rbRVwK3CHMWYDEElLzxTAuUATMA6YBPzIGJPSyf2WGmPyjDF5paWlA/wxRERksI0OG82vLv4VH+/7mGc2PeN0OSJ+oVfByhgTQkuoyrHWvgRgrS2w1l5qrT0HeA7Y2Xr5N4A3rLUN1trDwPtA5qn3tNautNZmWmszExISvPGziIjIILtp5k3MnjCbe/9+L5UnKp0uR8RxvVkVaIAnALe19uGT2se0fnYBPwUea32pBJjX+tpIWia8F3i3bBER8Qcu4+L3X/09h48d5pfv/tLpckQc15seq7nATcA8Y8xnrR+XAzcYY7bTEpr2A//Tev2j/P/27j0+rrrO//jrk95oChSEghRIomBZLgUKgYKyKK2UmwiKLJeIKMXAA1hBhR+68YZLdr0CXQExUlEhC+oCtkUuBeqKoL8uKS2U0hYFmmyh0IJQqOHW5rt/zKC9JE2mneTMJK8njz5m8j1nznzm+zht3pzzmXNgy4hYADwM3JBSeqwXapcklYDa0bVMHjeZKbOnsHDFwqzLkTIVKaWsa6C2tja1tLRkXYYkaRMt/+tyxvxgDON3Gc/ddXeTO9kh9U8RMSeltEGbE3jldUlSEewwYge+ecQ3mfnUTKYtnpZ1OVJmDFaSpKI476Dz2HvU3nz+ns/z+tuvZ12OlAmDlSSpKAZXDOYHx/yAJa8s4Xt/+F7W5UiZMFhJkormiPccwcl7ncy/P/jvtL7SmnU5Up8zWEmSiup7k3JHqy6595Ju1pT6H4OVJKmoqkZW8eXDvsyvnvgVs56ZlXU5Up8yWEmSiu7i919MzTY1fO6uz7G6Y3XW5Uh9xmAlSSq64UOGc+VRV7JgxQKuffjarMuR+ozBSpLUK07Y4wQm7TaJr/32a6z464qsy5H6hMFKktQrIoIpR0/h1Tdf5b1T3kvFZRXUXFVD8/zmrEuTes3grAuQJPVfc5bNYVDFIFa9vQqA1pWt1M+oB6BubF2WpUm9wiNWkqRe03B/wwbN6+1vt9Nwf0NGFUm9y2AlSeo1bSvbChqXyp3BSpLUa6pGVnU6PrhiML9v/X0fV1M+muc3U3NVjX1pZchgJUnqNY0TG6kcUrnO2LBBw9h62NYc/tPDOfPXZ/LCqhcyqq40Nc9vpn5GPa0rW0mkv/WlGa7Kg8FKktRr6sbW0XR8E9UjqwmC6pHVTD1hKm2fb+NfDvsXbp5/M3tcvQfX/M81rOlYk3W5JaHh/gba325fZ8y+tPIRKaWsa6C2tja1tLRkXYYkqY8tfnExF9x1Afc9fR8H7HQA1x57LeN3GZ91WZlIKfH48sfZ97p9O10eBB1f7+jjqtSZiJiTUqrtbJlHrCRJmdlj+z2Y+cmZ3HLSLTy/6nkOnXoo58w4h5faX8q6tD7RkTqYvXQ2l957KWOuHtNlqIKu+9VUWgxWkqRMRQSn7HMKi85fxOcP+TxT505lj6v3YOojU+lIHWXbyN1V3as7VjPrmVlccOcFVF1ZxSFTD+GK/38Fu227Gz/6yI+45thrOu1La5zYmMXHUIE8FShJKinzX5jPeXeex4NtD7L7truz9LWlvLH6jb8trxxSSdPxTSV9gdF3GtDX7pUaNmgY43cez4IVC3jp9ZcYPng4R+9+NB/f8+Mc977j2Hb4tuu8vuH+BtpWtlERFey05U48feHTDBk0JIuPo/Vs7FSgwUqSVHJSStz42I18Ztpn6Egb9hVVj6xmyUVL+r6wHqq5qobWla0bjAfB6WNP5+N7fpyjdjuKEUNHdLutaYumceIvTuS7R36Xi99/cW+UqwIZrCRJZanisgoSG/6eKvVG7mLX/dGbP8qsZ2ax8PyF7Dpy12KUqM1g87okqSx11bBd6uFimy226XR8UxvQ/+OY/6AjdXDh3RduTlnqAwYrSVLJ6uwCowBbD92aZa8ty6Ci7v14zo95+Y2XGRSD1hmvHFK5yQ3oNdvU8NXDv8rti27nN0/+phhlqpcYrCRJJWv9C4xWjaxi8v6Teerlp9jvuv248093Zl3iOn4272ecc8c5HL370Uz96NR1Loy6uQ33X3z/F9lz+z3557v+eYMLiKp02GMlSSo7C1cs5NRbT+WxFx7jwvEX8u0Pf5thg4dlWtPN82/mk7d/kgnvmcD0U6czfMjwor/Hfy/5b4742RE0/GMDl0+4vOjbV8/YYyVJ6lf2HLUns8+ezecO/hxTZk9h/PXjWfTioszqufWJWznj9jM4rOowpp06rVdCFcCHaj7EGfuewXce+k6mn1ddM1hJksrSFoO3YMoxU5hx2gyefe1ZDmw6kOsfuZ6+PhMzffF0Tr31VMbvMp47Truj056wYvrukd9lxNARnH/n+X3+WdU9g5Ukqax9ZMxHePTcRzl0l0P57IzPcsp/ncIrb7zSJ+9995/v5uRfncy4d4/jztPvZKthW/X6e+645Y7824R/Y9Yzs7j58Zt7/f1UGIOVJKnsjd5qNDPPmMm3Jn6L2xfdzn7X7cdDbQ/16nve//T9nHjLiew9am/u+eQ9jNxiZK++39rqD6znoNEH8YV7vtBnIVI9Y7CSJPULFVHBpYddykNnPcTgisEc/tPDOekXJ1F9VXXR7zP4uyW/4/ibj2fMdmOYecbMdW5H0xcGVQzih8f9kBXtK/jKrK/06Xtr4wxWkqR+5eCdD2buOXM5dJdDuW3RbbStbCORaF3ZSv2M+s0OV3/43z9w3H8eR802Ndz3qfvYvnL7IlVemANHH8h5tedx7cPXMue5OZnUoA0ZrCRJ/c7Ww7Zm6atLNxhvf7udi2de3On9B3vi4Wcf5pjmYxi91Wju/9T97DBih80tdbNcPuFydtxyR879zbms6ViTaS3KMVhJkvqltpVtnY4/v+p5dr5iZz47/bNMXzy9xxfbnLtsLpNumsR2w7dj1pmz2GmrnYpZ7iYZucVIrph0BS3PtfCjOT/KuhzhBUIlSf1UzVU1tK5s3WB8u+HbMfG9E7nrT3fx2luvscXgLfjwez/MR8d8lI+M+cjfAlPz/GYa7m+gbWUb797y3bz65qtsV7kdv/v076jZpqaPP03XUkoceeORtDzXwqILFvHuLd+ddUn93sYuEDq4r4uRJKkvNE5spH5G/TpHpCqHVDLlmCnUja3jrTVv8UDrA0xfPJ3pi6dzx5N3AHDQ6IOoHlnNHX+6gzdWvwHAslXLCIJ/Hf+vJRWqACKCa469hn2v25dL7r2EGz92Y9YlDWgesZIk9VtrH3WqGllF48TGTu/Xl1Li8eWPM33xdGY8OYPZz87udHvVI6tZctGSXq5603x11le5/PeXM+tTszjiPUdkXU6/trEjVgYrSZLWU3FZBYkNfz8GQcfXN63xvbe9/vbr7PPDfRg6aCiPnvsoQwcNzbqkfst7BUqSVICqkVUFjZeC4UOGc/UxV7PoxUV8/w/fz7qcActgJUnSehonNm5wz7/KIZU0TmzMqKKeOeZ9x3DQ6INomNVQ9IuiqmcMVpIkradubB1NxzdRPbKaIKgeWU3T8U2d9meVkub5zTy+/HFS/r9iXRRVPWePlSRJ/URXl5go5ab7cmSPlSRJA0BXF0XtalzF122wiohdI+K3EfFERCyIiAvz4/tFxB8jYn5EzIiIrdd6zb75ZQvyy7fozQ8hSZLKs+m+v+nJEavVwBdTSnsBhwDnR8RewPXAl1JKY4HbgUsAImIwcBNwbkppb+BDwNu9ULskSVpLZ033QyqGlHzTfX/SbbBKKS1LKT2Sf/4asBDYGRgDPJBf7V7gpPzzScBjKaVH8695KaXknSElSepl6zfdD6kYwugtR5d8031/UlCPVUTUAOOA2cAC4IT8opOBXfPPxwApIu6JiEci4v8Vp1RJktSdurF1LLloCR1f7+DKo66k9dVW5i6bm3VZA0aPg1VEbAncClyUUnoVOAs4LyLmAFsBb+VXHQwcBtTlHz8WERM72V59RLRERMuKFSs282NIkqT1nT72dIYNGsbUuVOzLmXA6FGwiogh5EJVc0rpNoCU0qKU0qSU0oHAzcBT+dWXAg+klF5MKbUDdwIHrL/NlFJTSqk2pVQ7atSoYnwWSZK0lm2Hb8sn9voENz12E6+//XrW5QwIPflWYABTgYUppSvWGt8h/1gBfAW4Lr/oHmBsRFTmG9k/CDxR7MIlSVL3Jo+bzMo3V3LbwtuyLmVA6MkRqw8AZwATImJe/s+xwGkR8SSwCHgOuAEgpfQycAXwMDAPeCSl9JteqV6SJG3UB2s+yG7b7ubpwD4yuLsVUkoPAtHF4ildvOYmcpdckCRJGaqICs4adxYNsxp46i9Psdu7dsu6pH7NK69LktTPnbnfmVREBT+Z+5OsS+n3DFaSJPVzO2+9M8e+71humHcDqztWZ11Ov2awkiRpAJg8bjLLVi3j7j/fnXUp/ZrBSpKkAeC49x3HjiN2tIm9lxmsJEkaAIYMGsKZ+53JjMUzeH7V81mX07nmZqipgYqK3GNzc9YVFcxgJUnSAHHWuLNYk9bw80d/nnUpG2puhvp6aG2FlHKP9fVlF64MVpIkDRB7bL8Hh1UdxtS5U0kpZV3OuhoaoL193bH29tx4GTFYSZI0gJw97myefOlJHmx7MOtS1tXWVth4iTJYSZI0gHxir0+w1dCtSq+JvaqqsPESZbCSJGkAGTF0BKePPZ1fLvglK99YmXU5f9fYCJWV645VVubGy4jBSpKkAWbyuMm8vvp1bnn8lqxL+bu6OmhqgupqiMg9NjXlxstIlELzWm1tbWppacm6DEmSBoSUEvtdtx/DBg/j4c8+nHU5ZSci5qSUajtb5hErSZIGmIjg7APOpuW5Fh574bGsy+lXDFaSJA1AdWPrGDpoKFMfKbEm9jJnsJIkaQDarnI7PvYPH+PGx27kjdVvZF1Ov2GwkiRpgDr7gLN5+Y2X+fWiX2ddSr9hsJIkaYCa8J4J1GxTU3rXtCpjBitJkgaoiqjgrP3P4r6n7+OZl5/Jupx+wWAlSdIA9un9P00Q3DDvhqxL6RcMVpIkDWC7jtyVo3Y/ihvm3cCajjVZl1P2DFaSJA1wZ487m6WvLmXmUzM3azvNzVBTAxUVucfm5qKUV1YMVpIkDXDH73E8oypHbVYTe3Mz1NdDayuklHusrx944cpgJUnSADd00FDO2PcMpi2exvK/Lt+kbTQ0QHv7umPt7bnxgcRgJUmSmHzAZFZ3rObGR2/cpNe3tRU23l8ZrCRJEnuN2otDdzmU6+deT0qp4NdXVRU23l8ZrCRJEgCTx01m0YuL+OPSPxb82sZGqKxcd6yyMjc+kBisJEkSAKfscwrDBg1j0o2TqLisgpqramie37Pu87o6aGqC6mqIyD02NeXGB5LBWRcgSZJKw7TF01jdsZo317wJQOvKVupn1ANQN7b7hFRXN/CC1Po8YiVJkgBouL+BNWndi4S2v91Ow/0D7Kt9m8FgJUmSAGhb2flX+Loa14YMVpIkCYCqkZ1/ha+rcW3IYCVJkgBonNhI5ZB1v9pXOaSSxokD7Kt9m8FgJUmSgFyDetPxTVSPrCYIqkdW03R8U48a15UTm3IRsGKrra1NLS0tWZchSZLUrYiYk1Kq7WyZR6wkSZKKxGAlSZJUJAYrSZKkIjFYSZIkFYnBSpIkqUgMVpIkSUVisJIkSSWhuRlqaqCiIvfY3Jx1RYUbnHUBkiRJzc1QXw/t7bmfW1tzPwPUldH1ST1iJUmSMtfQ8PdQ9Y729tx4Oek2WEXErhHx24h4IiIWRMSF+fH9IuKPETE/ImZExNbrva4qIlZFxMW9VbwkSeof2toKGy9VPTlitRr4YkppL+AQ4PyI2Au4HvhSSmkscDtwyXqvuwK4q5jFSpKk/qmqqrDxUtVtsEopLUspPZJ//hqwENgZGAM8kF/tXuCkd14TEScCzwALil2wJEnqfxobobJy3bHKytx4OSmoxyoiaoBxwGxyoemE/KKTgV3z62wJXApcVqwiJUlS/1ZXB01NUF0NEbnHpqbyalyHAoJVPjDdClyUUnoVOAs4LyLmAFsBb+VX/QZwZUppVTfbq4+IlohoWbFixSYVL0mS+o+6OliyBDo6co/lFqoAIqXU/UoRQ4A7gHtSSld0snwMcFNK6eCI+D35o1fANkAH8LWU0tVdbb+2tja1tLRsSv2SJEl9KiLmpJRqO1vW7XWsIiKAqcDCtUNVROyQUloeERXAV4DrAFJK/7jWOt8AVm0sVEmSJPUXPTkV+AHgDGBCRMzL/zkWOC0ingQWAc8BN/RinZIkSSWv2yNWKaUHgehi8ZRuXvuNTahJkiSpLHnldUmSVPZK5T6D3itQkiSVtVK6z6BHrCRJUlkrpfsMGqwkSVJZK6X7DBqsJElSWSul+wwarCRJUlkrpfsMGqwkSVJZK6X7DPqtQEmSVPbq6krj3oIesZIkSSoSg5UkSVKRGKwkSZKKxGAlSZJUJAYrSZKkIjFYSZIkFYnBSpIkqUgMVpIkSUVisJIkSSoSg5UkSVKRGKwkSZKKxGAlSZJUJAYrSZKkIjFYSZIkFYnBSpIkqUgMVpIkSUVisJIkSSoSg5UkSSp/zc1QUwMVFbnH5uZMyhicybtKkiQVS3Mz1NdDe3vu59bW3M8AdXV9WopHrCRJUnlraPh7qHpHe3tuvI8ZrCRJUnlraytsvBcZrCRJUnmrqipsvBcZrCRJUnlrbITKynXHKitz433MYCVJkspbXR00NUF1NUTkHpua+rxxHfxWoCRJ6g/q6jIJUuvziJUkSVKRGKwkSZKKxGAlSZJUJAYrSZKkIjFYSZIkFYnBSpIkqUgMVpIkSUVisJIkSSoSg5UkSVKRGKwkSZKKpNtgFRG7RsRvI+KJiFgQERfmx/eLiD9GxPyImBERW+fHj4yIOfnxORExobc/hCRJUinoyRGr1cAXU0p7AYcA50fEXsD1wJdSSmOB24FL8uu/CByfHz8TuLH4ZUuSJJWeboNVSmlZSumR/PPXgIXAzsAY4IH8avcCJ+XXmZtSei4/vgAYHhHDil24JElSqSmoxyoiaoBxwGxyoemE/KKTgV07eclJwCMppTc72VZ9RLRERMuKFSsKKUOSJKkk9ThYRcSWwK3ARSmlV4GzgPMiYg6wFfDWeuvvDXwbOKez7aWUmlJKtSml2lGjRm1q/ZIkSSUjUkrdrxQxBLgDuCeldEUny8cAN6WUDs7/vAswC/hMSumhHmx/BdDag3q3J9fDpZ5zzgrnnBXOOSucc1Y456wwzlfhejpn1SmlTo8KDe7ulRERwFRg4dqhKiJ2SCktj4gK4CvAdfnxbYDfkGts7zZUAXRVXCe1tKSUanuyrnKcs8I5Z4VzzgrnnBXOOSuM81W4YsxZT04FfgA4A5gQEfPyf44FTouIJ4FFwHPADfn1LwB2B7621vo7bE6RkiRJ5aDbI1YppQeB6GLxlE7Wvxy4fDPrkiRJKjvlduX1pqwLKEPOWeGcs8I5Z4VzzgrnnBXG+SrcZs9Zj5rXJUmS1L1yO2IlSZJUssomWEXE0RGxOCL+HBFfyrqechARS/L3bJwXES1Z11OKIuInEbE8Ih5fa+xdEXFvRPwp/7htljWWmi7m7BsR8ex6X3ARG73fqvtZFzYyZ+5nXYiILSLifyLi0fycXZYff09EzM7/7vxFRAzNutZSsZE5+2lEPLPWfrZ/Qdsth1OBETEIeBI4ElgKPAycllJ6ItPCSlxELAFqU0pex6QLEXE4sAr4eUppn/zYd4C/pJS+lQ/x26aULs2yzlLSxZx9A1iVUvpelrWVoojYCdgppfRIRGwFzAFOBD6N+1mnNjJn/4T7Wafyl0YakVJalb/25IPAhcAXgNtSSrdExHXAoymlH2ZZa6nYyJydC9yRUvqvTdluuRyxOhj4c0rp6ZTSW8At/P12OtImSyk9APxlveETgJ/ln/+M3D/oyutiztSFjdxv1f2sCxuZM3Uh5azK/zgk/ycBE4B3AoL72Vo2MmebpVyC1c7A/67181L8S9YTCZgZEXMioj7rYsrIjimlZfnnzwM7ZllMGbkgIh7Lnyr0tFYn1rvfqvtZD6w3Z+B+1qWIGBQR84DlwL3AU8ArKaXV+VX83bme9ecspfTOftaY38+ujIhhhWyzXIKVNs1hKaUDgGOA8/OncFSAlDtXXvrny7P3Q2A3YH9gGfD9bMspPbHh/Vb/xv2sc53MmfvZRqSU1qSU9gd2IXem5x8yLqnkrT9nEbEP8GVyc3cQ8C6goFP05RKsngV2XevnXfJj2oiU0rP5x+XA7eT+oql7L+R7PN7p9ViecT0lL6X0Qv4fqA7gx7ivrSPfv3Er0JxSui0/7H62EZ3NmftZz6SUXgF+CxwKbBMR71wM3N+dXVhrzo7On4pOKaU3yd1VpqD9rFyC1cPA+/LfbhgKnApMz7imkhYRI/JNn0TECGAS8PjGX6W86cCZ+ednAtMyrKUsvBMQ8j6G+9rf5BtkN7jfKu5nXepqztzPuhYRoyJ3r14iYji5L3stJBcWPpFfzf1sLV3M2aK1/ocnyPWkFbSflcW3AgHyX6u9ChgE/CSl1JhxSSUtIt5L7igV5G5d9J/O2YYi4mbgQ+TuaP4C8HXg18AvgSqgFfinlJLN2nldzNmHyJ2eScAS4Jy1+ocGtIg4DPg9MB/oyA//C7meIfezTmxkzk7D/axTEbEvueb0QeQOmvwypfTN/O+CW8id0poLfDJ/JGbA28iczQJGkbud3zzg3LWa3LvfbrkEK0mSpFJXLqcCJUmSSp7BSpIkqUgMVpIkSUVisJIkSSoSg5UkSVKRGKwkSZKKxGAlSZJUJAYrSZKkIvk/8A217by4H3AAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see the differences between the predicted values ie Red points and actual values is not much it around 2 rupees."
      ],
      "metadata": {
        "id": "upZOyJMaGS7i"
      },
      "id": "upZOyJMaGS7i"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "newGPU",
      "language": "python",
      "name": "newgpu"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.10"
    },
    "colab": {
      "name": "Stock price analysis of nabil bank.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "6bcf3b82"
      ],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}