LSTM.ipynb

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    "language_info": {
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Importing Libraries**"
      ],
      "metadata": {
        "id": "RVHWb2ru1frg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from pandas.plotting import register_matplotlib_converters\n",
        "from torch import nn, optim\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "MiY3atkTvDLq"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Load Dataset**"
      ],
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        "id": "HFGwekLh1nhw"
      }
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      "cell_type": "code",
      "execution_count": 90,
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        "outputId": "f4797731-af23-4fcc-daee-c3e7749d930e"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       Sales\n",
              "Month       \n",
              "1-01   266.0\n",
              "1-02   145.9\n",
              "1-03   183.1\n",
              "1-04   119.3\n",
              "1-05   180.3\n",
              "1-06   168.5\n",
              "1-07   231.8\n",
              "1-08   224.5\n",
              "1-09   192.8\n",
              "1-10   122.9\n",
              "1-11   336.5\n",
              "1-12   185.9\n",
              "2-01   194.3\n",
              "2-02   149.5\n",
              "2-03   210.1\n",
              "2-04   273.3\n",
              "2-05   191.4\n",
              "2-06   287.0\n",
              "2-07   226.0\n",
              "2-08   303.6\n",
              "2-09   289.9\n",
              "2-10   421.6\n",
              "2-11   264.5\n",
              "2-12   342.3\n",
              "3-01   339.7\n",
              "3-02   440.4\n",
              "3-03   315.9\n",
              "3-04   439.3\n",
              "3-05   401.3\n",
              "3-06   437.4\n",
              "3-07   575.5\n",
              "3-08   407.6\n",
              "3-09   682.0\n",
              "3-10   475.3\n",
              "3-11   581.3\n",
              "3-12   646.9"
            ],
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              "      <th>Sales</th>\n",
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              "    <tr>\n",
              "      <th>1-01</th>\n",
              "      <td>266.0</td>\n",
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              "    <tr>\n",
              "      <th>1-02</th>\n",
              "      <td>145.9</td>\n",
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              "    <tr>\n",
              "      <th>1-03</th>\n",
              "      <td>183.1</td>\n",
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              "    <tr>\n",
              "      <th>1-04</th>\n",
              "      <td>119.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-05</th>\n",
              "      <td>180.3</td>\n",
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              "    <tr>\n",
              "      <th>1-06</th>\n",
              "      <td>168.5</td>\n",
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              "      <th>1-07</th>\n",
              "      <td>231.8</td>\n",
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              "      <th>1-08</th>\n",
              "      <td>224.5</td>\n",
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              "      <th>1-09</th>\n",
              "      <td>192.8</td>\n",
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              "    <tr>\n",
              "      <th>1-10</th>\n",
              "      <td>122.9</td>\n",
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              "    <tr>\n",
              "      <th>1-11</th>\n",
              "      <td>336.5</td>\n",
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              "    <tr>\n",
              "      <th>1-12</th>\n",
              "      <td>185.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-01</th>\n",
              "      <td>194.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-02</th>\n",
              "      <td>149.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-03</th>\n",
              "      <td>210.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-04</th>\n",
              "      <td>273.3</td>\n",
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              "    <tr>\n",
              "      <th>2-05</th>\n",
              "      <td>191.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-06</th>\n",
              "      <td>287.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-07</th>\n",
              "      <td>226.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-08</th>\n",
              "      <td>303.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-09</th>\n",
              "      <td>289.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-10</th>\n",
              "      <td>421.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-11</th>\n",
              "      <td>264.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-12</th>\n",
              "      <td>342.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-01</th>\n",
              "      <td>339.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-02</th>\n",
              "      <td>440.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-03</th>\n",
              "      <td>315.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-04</th>\n",
              "      <td>439.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-05</th>\n",
              "      <td>401.3</td>\n",
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              "    <tr>\n",
              "      <th>3-06</th>\n",
              "      <td>437.4</td>\n",
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              "      <th>3-07</th>\n",
              "      <td>575.5</td>\n",
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              "      <th>3-08</th>\n",
              "      <td>407.6</td>\n",
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              "    <tr>\n",
              "      <th>3-09</th>\n",
              "      <td>682.0</td>\n",
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              "      <th>3-10</th>\n",
              "      <td>475.3</td>\n",
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              "    <tr>\n",
              "      <th>3-11</th>\n",
              "      <td>581.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-12</th>\n",
              "      <td>646.9</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 148.93716412347476,\n        \"min\": 119.3,\n        \"max\": 682.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          646.9,\n          149.5,\n          315.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "df = pd.read_csv(\"shampoo_sales.csv\", index_col=\"Month\")\n",
        "month = np.array(list(df.index))\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Visualisation**"
      ],
      "metadata": {
        "id": "1FVlkO0c1sxQ"
      }
    },
    {
      "source": [
        "df['Sales'].plot(kind='line', figsize=(8, 4), title='Sales')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
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        "id": "g_-RsI-hvTNc",
        "outputId": "c7cbc7f0-62c0-4f06-b88b-a1516b0caeb0"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "df['Sales'].plot(kind='hist', bins=20, title='Sales')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "s8vJOvH-vR49",
        "outputId": "2218cc5f-7996-47ef-91a9-b2a9cf7d9195"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MinMax Scaling**"
      ],
      "metadata": {
        "id": "qLDbS4UW1xPp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "scaler.fit(df)\n",
        "df['Scaled_Sales'] = scaler.transform(df)\n",
        "df"
      ],
      "metadata": {
        "id": "mjyOHCv3z77q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bd5485cd-5e64-43c7-e8e8-2c450e7ac8e5"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Sales  Scaled_Sales\n",
              "Month                     \n",
              "1-01   266.0      0.260707\n",
              "1-02   145.9      0.047272\n",
              "1-03   183.1      0.113382\n",
              "1-04   119.3      0.000000\n",
              "1-05   180.3      0.108406\n",
              "1-06   168.5      0.087436\n",
              "1-07   231.8      0.199929\n",
              "1-08   224.5      0.186956\n",
              "1-09   192.8      0.130620\n",
              "1-10   122.9      0.006398\n",
              "1-11   336.5      0.385996\n",
              "1-12   185.9      0.118358\n",
              "2-01   194.3      0.133286\n",
              "2-02   149.5      0.053670\n",
              "2-03   210.1      0.161365\n",
              "2-04   273.3      0.273680\n",
              "2-05   191.4      0.128132\n",
              "2-06   287.0      0.298027\n",
              "2-07   226.0      0.189621\n",
              "2-08   303.6      0.327528\n",
              "2-09   289.9      0.303181\n",
              "2-10   421.6      0.537231\n",
              "2-11   264.5      0.258042\n",
              "2-12   342.3      0.396304\n",
              "3-01   339.7      0.391683\n",
              "3-02   440.4      0.570642\n",
              "3-03   315.9      0.349387\n",
              "3-04   439.3      0.568687\n",
              "3-05   401.3      0.501155\n",
              "3-06   437.4      0.565310\n",
              "3-07   575.5      0.810734\n",
              "3-08   407.6      0.512351\n",
              "3-09   682.0      1.000000\n",
              "3-10   475.3      0.632664\n",
              "3-11   581.3      0.821041\n",
              "3-12   646.9      0.937622"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84a34f64-adaa-4037-8412-3a8247bbe4bd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sales</th>\n",
              "      <th>Scaled_Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1-01</th>\n",
              "      <td>266.0</td>\n",
              "      <td>0.260707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-02</th>\n",
              "      <td>145.9</td>\n",
              "      <td>0.047272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-03</th>\n",
              "      <td>183.1</td>\n",
              "      <td>0.113382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-04</th>\n",
              "      <td>119.3</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-05</th>\n",
              "      <td>180.3</td>\n",
              "      <td>0.108406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-06</th>\n",
              "      <td>168.5</td>\n",
              "      <td>0.087436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-07</th>\n",
              "      <td>231.8</td>\n",
              "      <td>0.199929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-08</th>\n",
              "      <td>224.5</td>\n",
              "      <td>0.186956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-09</th>\n",
              "      <td>192.8</td>\n",
              "      <td>0.130620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-10</th>\n",
              "      <td>122.9</td>\n",
              "      <td>0.006398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-11</th>\n",
              "      <td>336.5</td>\n",
              "      <td>0.385996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1-12</th>\n",
              "      <td>185.9</td>\n",
              "      <td>0.118358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-01</th>\n",
              "      <td>194.3</td>\n",
              "      <td>0.133286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-02</th>\n",
              "      <td>149.5</td>\n",
              "      <td>0.053670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-03</th>\n",
              "      <td>210.1</td>\n",
              "      <td>0.161365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-04</th>\n",
              "      <td>273.3</td>\n",
              "      <td>0.273680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-05</th>\n",
              "      <td>191.4</td>\n",
              "      <td>0.128132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-06</th>\n",
              "      <td>287.0</td>\n",
              "      <td>0.298027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-07</th>\n",
              "      <td>226.0</td>\n",
              "      <td>0.189621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-08</th>\n",
              "      <td>303.6</td>\n",
              "      <td>0.327528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-09</th>\n",
              "      <td>289.9</td>\n",
              "      <td>0.303181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-10</th>\n",
              "      <td>421.6</td>\n",
              "      <td>0.537231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-11</th>\n",
              "      <td>264.5</td>\n",
              "      <td>0.258042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-12</th>\n",
              "      <td>342.3</td>\n",
              "      <td>0.396304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-01</th>\n",
              "      <td>339.7</td>\n",
              "      <td>0.391683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-02</th>\n",
              "      <td>440.4</td>\n",
              "      <td>0.570642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-03</th>\n",
              "      <td>315.9</td>\n",
              "      <td>0.349387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-04</th>\n",
              "      <td>439.3</td>\n",
              "      <td>0.568687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-05</th>\n",
              "      <td>401.3</td>\n",
              "      <td>0.501155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-06</th>\n",
              "      <td>437.4</td>\n",
              "      <td>0.565310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-07</th>\n",
              "      <td>575.5</td>\n",
              "      <td>0.810734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-08</th>\n",
              "      <td>407.6</td>\n",
              "      <td>0.512351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-09</th>\n",
              "      <td>682.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-10</th>\n",
              "      <td>475.3</td>\n",
              "      <td>0.632664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-11</th>\n",
              "      <td>581.3</td>\n",
              "      <td>0.821041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3-12</th>\n",
              "      <td>646.9</td>\n",
              "      <td>0.937622</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 36,\n  \"fields\": [\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 148.93716412347476,\n        \"min\": 119.3,\n        \"max\": 682.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          646.9,\n          149.5,\n          315.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Scaled_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2646830711275542,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          0.9376221787808778,\n          0.05366980629109647,\n          0.34938688466323076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Create the past sequence and its next output**"
      ],
      "metadata": {
        "id": "o0-2JZur2BIA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Convert DataFrame to numpy array\n",
        "data = df['Scaled_Sales'].values\n",
        "\n",
        "# Define sequence length\n",
        "sequence_length = 4  # We will use the previous 4 data points to predict the next one\n",
        "\n",
        "# Initialize lists to store input sequences (X) and output sequences (y)\n",
        "X = []\n",
        "y = []\n",
        "\n",
        "# Create input-output pairs\n",
        "for i in range(len(data) - sequence_length):\n",
        "    X.append(data[i:i+sequence_length])  # Input sequence contains previous 4 data points\n",
        "    y.append(data[i+sequence_length])   # Output is the next data point after the input sequence\n",
        "\n",
        "# Convert lists to numpy arrays\n",
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "\n",
        "# Reshape input sequences to match LSTM input shape (samples, time steps, features)\n",
        "X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
        "\n",
        "print(X)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvmDpHELw-R4",
        "outputId": "3cc419e1-dd61-4a46-a56a-c793778e1f62"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " [[0.08743558]\n",
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            "\n",
            " [[0.38599609]\n",
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            "\n",
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            "\n",
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            "\n",
            " [[0.05366981]\n",
            "  [0.16136485]\n",
            "  [0.27368047]\n",
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            "\n",
            " [[0.16136485]\n",
            "  [0.27368047]\n",
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            "  [0.29802737]]\n",
            "\n",
            " [[0.27368047]\n",
            "  [0.12813222]\n",
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            "  [0.18962147]]\n",
            "\n",
            " [[0.12813222]\n",
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            "\n",
            " [[0.29802737]\n",
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            "  [0.30318109]]\n",
            "\n",
            " [[0.18962147]\n",
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            "  [0.53723121]]\n",
            "\n",
            " [[0.32752799]\n",
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            "\n",
            " [[0.30318109]\n",
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            "\n",
            " [[0.53723121]\n",
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            "\n",
            " [[0.25804159]\n",
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            "\n",
            " [[0.39630354]\n",
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            "\n",
            " [[0.39168296]\n",
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            "\n",
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            "\n",
            " [[0.34938688]\n",
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            "\n",
            " [[0.56868669]\n",
            "  [0.50115514]\n",
            "  [0.56531011]\n",
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            "\n",
            " [[0.50115514]\n",
            "  [0.56531011]\n",
            "  [0.81073396]\n",
            "  [0.51235116]]\n",
            "\n",
            " [[0.56531011]\n",
            "  [0.81073396]\n",
            "  [0.51235116]\n",
            "  [1.        ]]\n",
            "\n",
            " [[0.81073396]\n",
            "  [0.51235116]\n",
            "  [1.        ]\n",
            "  [0.63266394]]\n",
            "\n",
            " [[0.51235116]\n",
            "  [1.        ]\n",
            "  [0.63266394]\n",
            "  [0.82104141]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Convert numpy arrays to DataFrame\n",
        "X_df = pd.DataFrame(X.reshape(X.shape[0], -1), columns=[f\"X_{i+1}\" for i in range(sequence_length)])\n",
        "y_df = pd.DataFrame(y, columns=['Output'])\n",
        "\n",
        "# Concatenate X and y DataFrames\n",
        "result_df = pd.concat([X_df, y_df], axis=1)\n",
        "\n",
        "# Display the DataFrame\n",
        "result_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GrxDXp5e2KLp",
        "outputId": "f7e16b17-46dd-4f26-e42a-807a73f14efa"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         X_1       X_2       X_3       X_4    Output\n",
              "0   0.260707  0.047272  0.113382  0.000000  0.108406\n",
              "1   0.047272  0.113382  0.000000  0.108406  0.087436\n",
              "2   0.113382  0.000000  0.108406  0.087436  0.199929\n",
              "3   0.000000  0.108406  0.087436  0.199929  0.186956\n",
              "4   0.108406  0.087436  0.199929  0.186956  0.130620\n",
              "5   0.087436  0.199929  0.186956  0.130620  0.006398\n",
              "6   0.199929  0.186956  0.130620  0.006398  0.385996\n",
              "7   0.186956  0.130620  0.006398  0.385996  0.118358\n",
              "8   0.130620  0.006398  0.385996  0.118358  0.133286\n",
              "9   0.006398  0.385996  0.118358  0.133286  0.053670\n",
              "10  0.385996  0.118358  0.133286  0.053670  0.161365\n",
              "11  0.118358  0.133286  0.053670  0.161365  0.273680\n",
              "12  0.133286  0.053670  0.161365  0.273680  0.128132\n",
              "13  0.053670  0.161365  0.273680  0.128132  0.298027\n",
              "14  0.161365  0.273680  0.128132  0.298027  0.189621\n",
              "15  0.273680  0.128132  0.298027  0.189621  0.327528\n",
              "16  0.128132  0.298027  0.189621  0.327528  0.303181\n",
              "17  0.298027  0.189621  0.327528  0.303181  0.537231\n",
              "18  0.189621  0.327528  0.303181  0.537231  0.258042\n",
              "19  0.327528  0.303181  0.537231  0.258042  0.396304\n",
              "20  0.303181  0.537231  0.258042  0.396304  0.391683\n",
              "21  0.537231  0.258042  0.396304  0.391683  0.570642\n",
              "22  0.258042  0.396304  0.391683  0.570642  0.349387\n",
              "23  0.396304  0.391683  0.570642  0.349387  0.568687\n",
              "24  0.391683  0.570642  0.349387  0.568687  0.501155\n",
              "25  0.570642  0.349387  0.568687  0.501155  0.565310\n",
              "26  0.349387  0.568687  0.501155  0.565310  0.810734\n",
              "27  0.568687  0.501155  0.565310  0.810734  0.512351\n",
              "28  0.501155  0.565310  0.810734  0.512351  1.000000\n",
              "29  0.565310  0.810734  0.512351  1.000000  0.632664\n",
              "30  0.810734  0.512351  1.000000  0.632664  0.821041\n",
              "31  0.512351  1.000000  0.632664  0.821041  0.937622"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02baa179-778b-414c-ba84-744fa3e21fd3\" class=\"colab-df-container\">\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X_1</th>\n",
              "      <th>X_2</th>\n",
              "      <th>X_3</th>\n",
              "      <th>X_4</th>\n",
              "      <th>Output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.260707</td>\n",
              "      <td>0.047272</td>\n",
              "      <td>0.113382</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.108406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.047272</td>\n",
              "      <td>0.113382</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.108406</td>\n",
              "      <td>0.087436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.113382</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.108406</td>\n",
              "      <td>0.087436</td>\n",
              "      <td>0.199929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.108406</td>\n",
              "      <td>0.087436</td>\n",
              "      <td>0.199929</td>\n",
              "      <td>0.186956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.108406</td>\n",
              "      <td>0.087436</td>\n",
              "      <td>0.199929</td>\n",
              "      <td>0.186956</td>\n",
              "      <td>0.130620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.087436</td>\n",
              "      <td>0.199929</td>\n",
              "      <td>0.186956</td>\n",
              "      <td>0.130620</td>\n",
              "      <td>0.006398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.199929</td>\n",
              "      <td>0.186956</td>\n",
              "      <td>0.130620</td>\n",
              "      <td>0.006398</td>\n",
              "      <td>0.385996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.186956</td>\n",
              "      <td>0.130620</td>\n",
              "      <td>0.006398</td>\n",
              "      <td>0.385996</td>\n",
              "      <td>0.118358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.130620</td>\n",
              "      <td>0.006398</td>\n",
              "      <td>0.385996</td>\n",
              "      <td>0.118358</td>\n",
              "      <td>0.133286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.006398</td>\n",
              "      <td>0.385996</td>\n",
              "      <td>0.118358</td>\n",
              "      <td>0.133286</td>\n",
              "      <td>0.053670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.385996</td>\n",
              "      <td>0.118358</td>\n",
              "      <td>0.133286</td>\n",
              "      <td>0.053670</td>\n",
              "      <td>0.161365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.118358</td>\n",
              "      <td>0.133286</td>\n",
              "      <td>0.053670</td>\n",
              "      <td>0.161365</td>\n",
              "      <td>0.273680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.133286</td>\n",
              "      <td>0.053670</td>\n",
              "      <td>0.161365</td>\n",
              "      <td>0.273680</td>\n",
              "      <td>0.128132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.053670</td>\n",
              "      <td>0.161365</td>\n",
              "      <td>0.273680</td>\n",
              "      <td>0.128132</td>\n",
              "      <td>0.298027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.161365</td>\n",
              "      <td>0.273680</td>\n",
              "      <td>0.128132</td>\n",
              "      <td>0.298027</td>\n",
              "      <td>0.189621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.273680</td>\n",
              "      <td>0.128132</td>\n",
              "      <td>0.298027</td>\n",
              "      <td>0.189621</td>\n",
              "      <td>0.327528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.128132</td>\n",
              "      <td>0.298027</td>\n",
              "      <td>0.189621</td>\n",
              "      <td>0.327528</td>\n",
              "      <td>0.303181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.298027</td>\n",
              "      <td>0.189621</td>\n",
              "      <td>0.327528</td>\n",
              "      <td>0.303181</td>\n",
              "      <td>0.537231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.189621</td>\n",
              "      <td>0.327528</td>\n",
              "      <td>0.303181</td>\n",
              "      <td>0.537231</td>\n",
              "      <td>0.258042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.327528</td>\n",
              "      <td>0.303181</td>\n",
              "      <td>0.537231</td>\n",
              "      <td>0.258042</td>\n",
              "      <td>0.396304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.303181</td>\n",
              "      <td>0.537231</td>\n",
              "      <td>0.258042</td>\n",
              "      <td>0.396304</td>\n",
              "      <td>0.391683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.537231</td>\n",
              "      <td>0.258042</td>\n",
              "      <td>0.396304</td>\n",
              "      <td>0.391683</td>\n",
              "      <td>0.570642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.258042</td>\n",
              "      <td>0.396304</td>\n",
              "      <td>0.391683</td>\n",
              "      <td>0.570642</td>\n",
              "      <td>0.349387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.396304</td>\n",
              "      <td>0.391683</td>\n",
              "      <td>0.570642</td>\n",
              "      <td>0.349387</td>\n",
              "      <td>0.568687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.391683</td>\n",
              "      <td>0.570642</td>\n",
              "      <td>0.349387</td>\n",
              "      <td>0.568687</td>\n",
              "      <td>0.501155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.570642</td>\n",
              "      <td>0.349387</td>\n",
              "      <td>0.568687</td>\n",
              "      <td>0.501155</td>\n",
              "      <td>0.565310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.349387</td>\n",
              "      <td>0.568687</td>\n",
              "      <td>0.501155</td>\n",
              "      <td>0.565310</td>\n",
              "      <td>0.810734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.568687</td>\n",
              "      <td>0.501155</td>\n",
              "      <td>0.565310</td>\n",
              "      <td>0.810734</td>\n",
              "      <td>0.512351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.501155</td>\n",
              "      <td>0.565310</td>\n",
              "      <td>0.810734</td>\n",
              "      <td>0.512351</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.565310</td>\n",
              "      <td>0.810734</td>\n",
              "      <td>0.512351</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.632664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.810734</td>\n",
              "      <td>0.512351</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.632664</td>\n",
              "      <td>0.821041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.512351</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.632664</td>\n",
              "      <td>0.821041</td>\n",
              "      <td>0.937622</td>\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7bd7a192-9a54-4cd1-8f64-f527e7cb5834 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result_df",
              "summary": "{\n  \"name\": \"result_df\",\n  \"rows\": 32,\n  \"fields\": [\n    {\n      \"column\": \"X_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19913444952218234,\n        \"min\": 0.0,\n        \"max\": 0.8107339612582192,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          0.5653101119601919,\n          0.27368046916651856,\n          0.3916829571707837\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2362013448877739,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          0.8107339612582192,\n          0.1281322196552337,\n          0.5706415496712279\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23836874050400814,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          0.5123511640305669,\n          0.29802736804691665,\n          0.34938688466323076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25089842261837003,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          1.0,\n          0.18962146792251644,\n          0.5686866891771815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Output\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.263844985345017,\n        \"min\": 0.006397725253243308,\n        \"max\": 1.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          0.6326639417096144,\n          0.32752799004798294,\n          0.5011551448373911\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Visualisation of Result_df**"
      ],
      "metadata": {
        "id": "DHWr7xXk2IdF"
      }
    },
    {
      "source": [
        "result_df['X_1'].plot(kind='line', figsize=(8, 4), title='X_1')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "OV0XIByQ3HAp",
        "outputId": "3cf741b1-5ec0-4e11-f6d6-53f5010fe23f"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "result_df['X_2'].plot(kind='line', figsize=(8, 4), title='X_2')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "a9_3Tx_Z2w3h",
        "outputId": "753dd132-d0d9-4936-fade-1eb8a9273f3a"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "result_df['X_3'].plot(kind='line', figsize=(8, 4), title='X_3')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "mYFHv6VA2Rrr",
        "outputId": "961ff9ec-2777-4984-a189-3ab2d7e656c4"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "result_df['X_4'].plot(kind='line', figsize=(8, 4), title='X_4')\n",
        "plt.gca().spines[['top', 'right']].set_visible(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "c2EW0UGH3RXi",
        "outputId": "04eac81f-863c-42cd-e88d-fb61e1808455"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Train-Test Split**"
      ],
      "metadata": {
        "id": "Ie0KQKPv33ek"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into train and test sets (70% train, 20% test)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=False)\n",
        "\n",
        "# Further split the test set into test and validation sets (50% test, 50% validation)\n",
        "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.5, shuffle=False)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gAF3Gzg33M8",
        "outputId": "1698596c-052b-4304-e1f6-e0384704aba7"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5, 4, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Convert to Tensor**"
      ],
      "metadata": {
        "id": "jZxoS3PH7NNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = torch.from_numpy(X_train).float()\n",
        "y_train = torch.from_numpy(y_train).float()\n",
        "X_val = torch.from_numpy(X_val).float()\n",
        "y_val = torch.from_numpy(y_val).float()\n",
        "X_test = torch.from_numpy(X_test).float()\n",
        "y_test = torch.from_numpy(y_test).float()"
      ],
      "metadata": {
        "id": "JnP7vIGe5r9W"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **LSTM**"
      ],
      "metadata": {
        "id": "Okizvlzl7pmY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LSTM(nn.Module):\n",
        "    def __init__(self, input_size=1, hidden_layer_size=512, output_size=1, p=0.1, num_layers=2):\n",
        "        super().__init__()\n",
        "\n",
        "        # Define the size of the hidden layer\n",
        "        self.hidden_layer_size = hidden_layer_size\n",
        "\n",
        "        # Define the LSTM layer with specified input size, hidden layer size, and dropout rate\n",
        "        self.lstm = nn.LSTM(input_size, hidden_layer_size, dropout=p)\n",
        "\n",
        "        # Define the linear layer for output\n",
        "        self.linear = nn.Linear(hidden_layer_size, output_size)\n",
        "\n",
        "        # Initialize the hidden cell state\n",
        "        self.hidden_cell = (torch.zeros(1, 1, self.hidden_layer_size),\n",
        "                            torch.zeros(1, 1, self.hidden_layer_size))\n",
        "\n",
        "    def forward(self, input_seq):\n",
        "        # Pass the input sequence through the LSTM layer\n",
        "        lstm_out, self.hidden_cell = self.lstm(input_seq.view(len(input_seq), 1, -1), self.hidden_cell)\n",
        "\n",
        "        # Pass the LSTM output through the linear layer for final prediction\n",
        "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
        "\n",
        "        # Return the last prediction in the sequence\n",
        "        return predictions[-1]"
      ],
      "metadata": {
        "id": "Ek9FwY7WAQbG"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LSTM()\n",
        "loss_function = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jd7kgeMtA4DF",
        "outputId": "0d69447d-be18-4db8-ec02-40c206281a24"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py:82: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader=[]\n",
        "val_loader= []\n",
        "test_loader= []\n",
        "\n",
        "for i in range(len(X_train)):\n",
        "  train_loader.append((X_train[i], y_train[i]))\n",
        "# print(train_loader)\n",
        "\n",
        "for i in range(len(X_val)):\n",
        "  val_loader.append((X_val[i], y_val[i]))\n",
        "\n",
        "for i in range(len(X_test)):\n",
        "  test_loader.append((X_test[i], y_test[i]))\n",
        "\n",
        "print(test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIu3RG1oIALs",
        "outputId": "ca8709a2-3287-4f12-fb45-9f05da24fc1a"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(tensor([[0.2580],\n",
            "        [0.3963],\n",
            "        [0.3917],\n",
            "        [0.5706]]), tensor(0.3494)), (tensor([[0.3963],\n",
            "        [0.3917],\n",
            "        [0.5706],\n",
            "        [0.3494]]), tensor(0.5687)), (tensor([[0.3917],\n",
            "        [0.5706],\n",
            "        [0.3494],\n",
            "        [0.5687]]), tensor(0.5012)), (tensor([[0.5706],\n",
            "        [0.3494],\n",
            "        [0.5687],\n",
            "        [0.5012]]), tensor(0.5653)), (tensor([[0.3494],\n",
            "        [0.5687],\n",
            "        [0.5012],\n",
            "        [0.5653]]), tensor(0.8107))]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Train and Validate the Model**"
      ],
      "metadata": {
        "id": "ZY9XlPRY2QrL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train_val(epochs, train_loader, val_loader, loss_function, optimizer, save_path=\"/content/\"):\n",
        "    # Lists to store training and validation losses\n",
        "    train_losses = []\n",
        "    val_losses = []\n",
        "\n",
        "    # Training loop\n",
        "    for epoch in range(1, epochs + 1):\n",
        "        model.train()  # Set the model to training mode\n",
        "        train_loss = 0.0\n",
        "\n",
        "        # Iterate over training data\n",
        "        for seq, labels in train_loader:\n",
        "            optimizer.zero_grad()\n",
        "            model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
        "                                  torch.zeros(1, 1, model.hidden_layer_size))\n",
        "\n",
        "            y_pred = model(seq)\n",
        "            loss = loss_function(y_pred, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            train_loss += loss.item()\n",
        "\n",
        "        # Calculate average training loss\n",
        "        train_loss /= len(train_loader)\n",
        "        train_losses.append(train_loss)\n",
        "\n",
        "        # Validation loop\n",
        "        model.eval()  # Set the model to evaluation mode\n",
        "        val_loss = 0.0\n",
        "\n",
        "        with torch.no_grad():\n",
        "            # Iterate over validation data\n",
        "            for seq_val, labels_val in val_loader:\n",
        "                model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
        "                                      torch.zeros(1, 1, model.hidden_layer_size))\n",
        "\n",
        "                y_pred_val = model(seq_val)\n",
        "                loss_val = loss_function(y_pred_val, labels_val)\n",
        "                val_loss += loss_val.item()\n",
        "\n",
        "        # Calculate average validation loss\n",
        "        val_loss /= len(val_loader)\n",
        "        val_losses.append(val_loss)\n",
        "\n",
        "        # Print training and validation losses for each epoch\n",
        "        if epoch % 25 == 1:\n",
        "            print(f'Epoch [{epoch}/{epochs}], Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}')\n",
        "\n",
        "        # Save the model checkpoint\n",
        "        torch.save(model.state_dict(), f'{save_path}/model_epoch_{epoch}.pt')\n",
        "\n",
        "    return train_losses, val_losses\n"
      ],
      "metadata": {
        "id": "ig6vw9yHBUPx"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 400\n",
        "train_losses, val_losses = train_val(epochs, train_loader, val_loader, loss_function, optimizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQnq0hR6geVs",
        "outputId": "c8062eb4-8a00-43ac-fb4d-a1dd9c397e2d"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py:535: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/400], Train Loss: 0.0204, Val Loss: 0.1799\n",
            "Epoch [26/400], Train Loss: 0.0116, Val Loss: 0.1226\n",
            "Epoch [51/400], Train Loss: 0.0106, Val Loss: 0.0527\n",
            "Epoch [76/400], Train Loss: 0.0101, Val Loss: 0.0421\n",
            "Epoch [101/400], Train Loss: 0.0096, Val Loss: 0.0644\n",
            "Epoch [126/400], Train Loss: 0.0087, Val Loss: 0.1109\n",
            "Epoch [151/400], Train Loss: 0.0070, Val Loss: 0.1072\n",
            "Epoch [176/400], Train Loss: 0.0070, Val Loss: 0.1631\n",
            "Epoch [201/400], Train Loss: 0.0063, Val Loss: 0.1394\n",
            "Epoch [226/400], Train Loss: 0.0060, Val Loss: 0.2592\n",
            "Epoch [251/400], Train Loss: 0.0046, Val Loss: 0.2043\n",
            "Epoch [276/400], Train Loss: 0.0066, Val Loss: 0.1714\n",
            "Epoch [301/400], Train Loss: 0.0035, Val Loss: 0.1730\n",
            "Epoch [326/400], Train Loss: 0.0008, Val Loss: 0.1192\n",
            "Epoch [351/400], Train Loss: 0.0071, Val Loss: 0.0682\n",
            "Epoch [376/400], Train Loss: 0.0037, Val Loss: 0.0760\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Plot the Train and Validation Loss Vs Epochs**"
      ],
      "metadata": {
        "id": "pnOvJUg42Wgm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot(epochs, train_losses, val_losses):\n",
        "  # Plotting train and validation losses vs. epoch\n",
        "  plt.plot(range(1, epochs + 1), train_losses, label='Train Loss')\n",
        "  plt.plot(range(1, epochs + 1), val_losses, label='Val Loss')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.title('Training and Validation Losses vs. Epoch')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "VcVoPR3tCzpL"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot(epochs, train_losses, val_losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YZNCxhwwp5eX",
        "outputId": "a8adba4c-e015-44b3-837f-0c1420836de9"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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yEB4eDrVaLVo40FwxQY6TJ0/i33//xZo1azBp0iT9enNuDUvExsYiLS0NxcXFAivQ+fPnbTrOww8/jG3btuG3337DunXrEBgYiJEjR+q3b9iwAU2bNsXGjRsFd8+c5cDWOQNsMGrTpk3163Nyckzuhjds2IB77rkHn3/+uWB9fn4+wsLC9K9tqewdGxuLXbt2oaioSGAF4lys3PxcQWxsLE6cOAGdTiewAonNRalUYuTIkRg5ciR0Oh1mzpyJjz/+GPPmzdMLl9DQUEydOhVTp05FcXEx+vbti4ULF+Lxxx/X/629vb2RkpJicW7mjmWO8ePH47XXXsNPP/2EiIgIFBYWYsKECSbjoqKiMHPmTMycORPZ2dno2LEj/ve//1ntTrYXzhLG/8z8+++/AKDPFoyNjRX9Dhn/XxISErB//35UVlZSIHMtgSxAhEfC3SHx74g0Gg0++ugjd01JgEKhQEpKCjZt2iSIXbh48aJVd65i749hGEEqs60MGzYMVVVVWLVqlX6dVqvFihUrbDrO6NGj4evri48++gi//fYbxowZA7VabXbu+/fvx759+2yec0pKCry9vbFixQrB8ZYvX24yVqFQmNwh//jjj/pYFQ4/Pz8AsCr9f9iwYdBqtfjwww8F69977z3IZDKn/wAbzyUzM1PgMqqqqsKKFSvg7++vd4/m5eUJ9pPL5frilBUVFaJj/P390axZM/328PBw9O/fHx9//DFu375tMpecnBz9c0vHMkfr1q2RmJiI9evXY/369YiKihLc0Gi1WhP3ZXh4OKKjowXHz83Nxblz51BaWmrxnLZw69Yt/Pzzz/rXhYWFWLt2LZKTkxEZGQmA/b8cOHBA8PkuKSnBJ598gri4OH1c0dixY5Gbm2vyWQLIsuOpkAWI8Eh69uyJkJAQTJ48Wd+m4euvv/aoC8nChQuxY8cO9OrVCzNmzND/kLZr185iG4ZWrVohISEBc+bMwc2bNxEYGIiffvqpRjEAI0eORK9evfDSSy/h6tWr+pomtsbH+Pv7Y/To0fo4IL77CwBGjBiBjRs34v7778fw4cNx5coVrF69Gm3atEFxcbFN5+LqGS1ZsgQjRozAsGHDcPToUfz2228Cqw533kWLFmHq1Kno2bMnTp48iW+//VZgOQLYO/Hg4GCsXr0aAQEB8PPzQ7du3RAfH29y/pEjR+Kee+7BK6+8gqtXryIpKQk7duzAL7/8gtmzZwsCnh1BWloaysvLTdaPHj0a06dPx8cff4wpU6bg8OHDiIuLw4YNG/DXX39h+fLlegvV448/jjt37uDee+9F48aNce3aNaxYsQLJycn6uJQ2bdqgf//+6NSpE0JDQ3Ho0CF9qjnHypUr0bt3byQmJuKJJ55A06ZNkZWVhX379uHGjRv6+krWHMsc48ePx/z586FWqzFt2jSBdauoqAiNGzfGuHHjkJSUBH9/f+zatQsHDx7EO++8ox/34Ycf4rXXXsPu3bvRv39/i+c8cuQIvvnmG5P1CQkJ6NGjh/51ixYtMG3aNBw8eBARERH44osvkJWVhS+//FI/5qWXXsJ3332HoUOH4plnnkFoaCjWrFmDK1eu4KefftK/n0mTJmHt2rVITU3FgQMH0KdPH5SUlGDXrl2YOXMmRo0aZdXfi3Ahrk88I+orUmnwbdu2FR3/119/Md27d2d8fHyY6Oho5oUXXmC2b9/OAGB2796tHyeVBi+WcgyjtGypNHixtNzY2FhBWjbDMExaWhrToUMHRqlUMgkJCcxnn33GPPfcc4xarZb4Kxg4c+YMk5KSwvj7+zNhYWHME088oU+r5qdwT548mfHz8zPZX2zueXl5zKOPPsoEBgYyQUFBzKOPPsocPXrU6jR4ji1btjAAmKioKJPUc51Ox7zxxhtMbGwso1KpmA4dOjC//vqryf+BYSynwTMMw2i1Wua1115joqKiGB8fH6Z///7MqVOnTP7e5eXlzHPPPacf16tXL2bfvn1Mv379mH79+gnO+8svvzBt2rTRlyTg3rvYHIuKiphnn32WiY6OZry9vZnmzZszS5cuFaTlc+/F2s+FMZbSs7/++muGYRgmKyuLmTp1KhMWFsYolUomMTHR5P+2YcMGZtCgQUx4eDijVCqZJk2aMP/5z3+Y27dv68e8/vrrTNeuXZng4GDGx8eHadWqFfO///2P0Wg0gmNdunSJmTRpEhMZGcl4e3szjRo1YkaMGMFs2LDB5mNJceHCBf373Lt3r2BbRUUF8/zzzzNJSUlMQEAA4+fnxyQlJTEfffSRYBz3Wed/7+35O/P/T7Gxsczw4cOZ7du3M+3bt2dUKhXTqlUr5scffzQ57qVLl5hx48YxwcHBjFqtZrp27cr8+uuvJuNKS0uZV155hYmPj2e8vb2ZyMhIZty4ccylS5cE87Pm2kQ4HxnDeNAtNUHUAUaPHm1X2jBBEK4jLi4O7dq101ekJuofFANEEDXAuG3FhQsXsHXrVqvM9ARBEIT7oBgggqgBTZs2xZQpU9C0aVNcu3YNq1atglKpxAsvvODuqREEQRBmIAFEEDVgyJAh+O6775CZmQmVSoUePXrgjTfeMCnsRxAEQXgWFANEEARBEES9g2KACIIgCIKod5AAIgiCIAii3kExQCLodDrcunULAQEBNpXVJwiCIAjCfTAMg6KiIkRHR5s0FjaGBJAIt27dQkxMjLunQRAEQRCEHVy/fh2NGzc2O4YEkAhcyfnr168jMDDQzbMhCIIgCMIaCgsLERMTI2huLAUJIBE4t1dgYCAJIIIgCIKoZVgTvkJB0ARBEARB1DtIABEEQRAEUe8gAUQQBEEQRL2DYoAIgiCIOo9Wq0VlZaW7p0HUEG9vbygUCocciwQQQRAEUWdhGAaZmZnIz89391QIBxEcHIzIyMga1+kjAUQQBEHUWTjxEx4eDl9fXypuW4thGAalpaXIzs4GAERFRdXoeCSACIIgiDqJVqvVi58GDRq4ezqEA/Dx8QEAZGdnIzw8vEbuMI8Igl65ciXi4uKgVqvRrVs3HDhwQHLsxo0b0blzZwQHB8PPzw/Jycn4+uuvBWOmTJkCmUwmeAwZMsTZb4MgCILwILiYH19fXzfPhHAk3P+zpjFdbrcArV+/HqmpqVi9ejW6deuG5cuXY/DgwTh//jzCw8NNxoeGhuKVV15Bq1atoFQq8euvv2Lq1KkIDw/H4MGD9eOGDBmCL7/8Uv9apVK55P0QBEEQngW5veoWjvp/ut0C9O677+KJJ57A1KlT0aZNG6xevRq+vr744osvRMf3798f999/P1q3bo2EhAT897//Rfv27bF3717BOJVKhcjISP0jJCTEFW+HIAiCIIhagFsFkEajweHDh5GSkqJfJ5fLkZKSgn379lncn2EYpKWl4fz58+jbt69gW3p6OsLDw9GyZUvMmDEDeXl5ksepqKhAYWGh4EEQBEEQdYW4uDgsX77c3dPwKNwqgHJzc6HVahERESFYHxERgczMTMn9CgoK4O/vD6VSieHDh2PFihUYOHCgfvuQIUOwdu1apKWl4a233sKePXswdOhQaLVa0eMtWbIEQUFB+gd1gicIgiDcgXH8qvFj4cKFdh334MGDmD59eo3m1r9/f8yePbtGx/Ak3B4DZA8BAQE4duwYiouLkZaWhtTUVDRt2hT9+/cHAEyYMEE/NjExEe3bt0dCQgLS09MxYMAAk+PNnTsXqamp+tdcN1mnoSkFlBSURxAEQQi5ffu2/vn69esxf/58nD9/Xr/O399f/5xhGGi1Wnh5Wf4pb9iwoWMnWgdwqwUoLCwMCoUCWVlZgvVZWVmIjIyU3E8ul6NZs2ZITk7Gc889h3HjxmHJkiWS45s2bYqwsDBcvHhRdLtKpdJ3fnd6B/jbx4E3ooBfn3XeOQiCIIhaCT92NSgoCDKZTP/63LlzCAgIwG+//YZOnTpBpVJh7969uHTpEkaNGoWIiAj4+/ujS5cu2LVrl+C4xi4wmUyGzz77DPfffz98fX3RvHlzbN68uUZz/+mnn9C2bVuoVCrExcXhnXfeEWz/6KOP0Lx5c6jVakRERGDcuHH6bRs2bEBiYiJ8fHzQoEEDpKSkoKSkpEbzsYRbBZBSqUSnTp2QlpamX6fT6ZCWloYePXpYfRydToeKigrJ7Tdu3EBeXl6NiyY5hD1vs8tD4kHeBEEQhPNgGAalmiqXPxiGcdh7eOmll/Dmm2/i7NmzaN++PYqLizFs2DCkpaXh6NGjGDJkCEaOHImMjAyzx3nttdfw4IMP4sSJExg2bBgefvhh3Llzx645HT58GA8++CAmTJiAkydPYuHChZg3bx6++uorAMChQ4fwzDPPYNGiRTh//jy2bdumj929ffs2Jk6ciMceewxnz55Feno6xowZ49C/mRhud4GlpqZi8uTJ6Ny5M7p27Yrly5ejpKQEU6dOBQBMmjQJjRo10lt4lixZgs6dOyMhIQEVFRXYunUrvv76a6xatQoAUFxcjNdeew1jx45FZGQkLl26hBdeeAHNmjUTpMm7DUrHJAiCcBtllVq0mb/d5ec9s2gwfJWO+cldtGiRIO41NDQUSUlJ+teLFy/Gzz//jM2bN2PWrFmSx5kyZQomTpwIAHjjjTfwwQcf4MCBA3bVzXv33XcxYMAAzJs3DwDQokULnDlzBkuXLsWUKVOQkZEBPz8/jBgxAgEBAYiNjUWHDh0AsAKoqqoKY8aMQWxsLAA2fMXZuF0AjR8/Hjk5OZg/fz4yMzORnJyMbdu26QOjMzIyIJcbDFUlJSWYOXMmbty4AR8fH7Rq1QrffPMNxo8fDwBQKBQ4ceIE1qxZg/z8fERHR2PQoEFYvHixh9QCIgFEEARB2E/nzp0Fr4uLi7Fw4UJs2bJFLybKysosWoDat2+vf+7n54fAwEB9mwlbOXv2LEaNGiVY16tXLyxfvhxarRYDBw5EbGwsmjZtiiFDhmDIkCF691tSUhIGDBiAxMREDB48GIMGDcK4ceOcXr7G7QIIAGbNmiWpUtPT0wWvX3/9dbz++uuSx/Lx8cH27a5X91ZDFiCCIAi34eOtwJlFrvcG+Hg7poM5wIoVPnPmzMHOnTuxbNkyNGvWDD4+Phg3bhw0Go3Z43h7ewtey2Qy6HQ6h82TT0BAAI4cOYL09HTs2LED8+fPx8KFC3Hw4EEEBwdj586d+Pvvv7Fjxw6sWLECr7zyCvbv34/4+HinzAfwgEKI9Q8SQARBEO5CJpPBV+nl8oczq1H/9ddfmDJlCu6//34kJiYiMjISV69eddr5xGjdujX++usvk3m1aNFC36/Ly8sLKSkpePvtt3HixAlcvXoVv//+OwD2/9KrVy+89tprOHr0KJRKJX7++WenztkjLED1ChlpToIgCMJxNG/eHBs3bsTIkSMhk8kwb948p1lycnJycOzYMcG6qKgoPPfcc+jSpQsWL16M8ePHY9++ffjwww/x0UcfAQB+/fVXXL58GX379kVISAi2bt0KnU6Hli1bYv/+/UhLS8OgQYMQHh6O/fv3IycnB61bt3bKe+AgAeRqSAARBEEQDuTdd9/FY489hp49eyIsLAwvvvii0zoarFu3DuvWrROsW7x4MV599VX88MMPmD9/PhYvXoyoqCgsWrQIU6ZMAQAEBwdj48aNWLhwIcrLy9G8eXN89913aNu2Lc6ePYs//vgDy5cvR2FhIWJjY/HOO+9g6NChTnkPHDLG2XlmtZDCwkIEBQWhoKDA8TWBfnocOPkj+3xhgWOPTRAEQegpLy/HlStXEB8fD7Va7e7pEA7C3P/Vlt9vMke4HIoBIgiCIAh3QwLI1ZALjCAIgiDcDv0auxpKgycIgiAIt0MCyOWQACIIgiAId0MCyNWQBYggCIIg3A4JIJdDAoggCIIg3A0JIFdDFiCCIAiCcDskgFwNZYERBEEQhNuhX2NXQxYggiAIgnA7JIBcDgkggiAIwrn0798fs2fPdvc0PBoSQK6GLEAEQRCEBCNHjsSQIUNEt/3555+QyWQ4ceJEjc/z1VdfITg4uMbHqc2QAHI1FANEEARBSDBt2jTs3LkTN27cMNn25ZdfonPnzmjfvr0bZlb3oF9jl0MWIIIgCEKcESNGoGHDhvjqq68E64uLi/Hjjz9i2rRpyMvLw8SJE9GoUSP4+voiMTER3333nUPnkZGRgVGjRsHf3x+BgYF48MEHkZWVpd9+/Phx3HPPPQgICEBgYCA6deqEQ4cOAQCuXbuGkSNHIiQkBH5+fmjbti22bt3q0Pk5Ai93T6DewXeB6XSAnDQoQRCEy2AYoLLU9ef19rUqBMLLywuTJk3CV199hVdeeQWy6n1+/PFHaLVaTJw4EcXFxejUqRNefPFFBAYGYsuWLXj00UeRkJCArl271niqOp1OL3727NmDqqoqPPXUUxg/fjzS09MBAA8//DA6dOiAVatWQaFQ4NixY/D29gYAPPXUU9BoNPjjjz/g5+eHM2fOwN/fv8bzcjQkgFwO7wvAaEFGOIIgCBdSWQq8Ee368758C1D6WTX0sccew9KlS7Fnzx70798fAOv+Gjt2LIKCghAUFIQ5c+boxz/99NPYvn07fvjhB4cIoLS0NJw8eRJXrlxBTEwMAGDt2rVo27YtDh48iC5duiAjIwPPP/88WrVqBQBo3ry5fv+MjAyMHTsWiYmJAICmTZvWeE7OgH59XQ0/Bkindd88CIIgCI+kVatW6NmzJ7744gsAwMWLF/Hnn39i2rRpAACtVovFixcjMTERoaGh8Pf3x/bt25GRkeGQ8589exYxMTF68QMAbdq0QXBwMM6ePQsASE1NxeOPP46UlBS8+eabuHTpkn7sM888g9dffx29evXCggULHBK07QzIAuRq+AKIIQFEEAThUrx9WWuMO85rA9OmTcPTTz+NlStX4ssvv0RCQgL69esHAFi6dCnef/99LF++HImJifDz88Ps2bOh0WicMXNRFi5ciIceeghbtmzBb7/9hgULFuD777/H/fffj8cffxyDBw/Gli1bsGPHDixZsgTvvPMOnn76aZfNzxrIAuRqBDFAJIAIgiBcikzGuqJc/bCxBMqDDz4IuVyOdevWYe3atXjsscf08UB//fUXRo0ahUceeQRJSUlo2rQp/v33X4f9iVq3bo3r16/j+vXr+nVnzpxBfn4+2rRpo1/XokULPPvss9ixYwfGjBmDL7/8Ur8tJiYGTz75JDZu3IjnnnsOn376qcPm5yjIAuROyAJEEARBiODv74/x48dj7ty5KCwsxJQpU/Tbmjdvjg0bNuDvv/9GSEgI3n33XWRlZQnEiTVotVocO3ZMsE6lUiElJQWJiYl4+OGHsXz5clRVVWHmzJno168fOnfujLKyMjz//PMYN24c4uPjcePGDRw8eBBjx44FAMyePRtDhw5FixYtcPfuXezevRutW7eu6Z/E4ZAAcjVkASIIgiCsYNq0afj8888xbNgwREcbArdfffVVXL58GYMHD4avry+mT5+O0aNHo6CgwKbjFxcXo0OHDoJ1CQkJuHjxIn755Rc8/fTT6Nu3L+RyOYYMGYIVK1YAABQKBfLy8jBp0iRkZWUhLCwMY8aMwWuvvQaAFVZPPfUUbty4gcDAQAwZMgTvvfdeDf8ajkfGMAzj7kl4GoWFhQgKCkJBQQECAwMde/AdrwJ/sx8iPPcvEBDh2OMTBEEQAIDy8nJcuXIF8fHxUKvV7p4O4SDM/V9t+f2mGCBXw9eb5AIjCIIgCLdAAsjV8AUQucAIgiAIwi2QAHI1jI73nAQQQRAEQbgDEkCuhi+AyAJEEARBEG6BBJCrEViAdNLjCIIgCIdAuT51C0f9P0kAuRyKASIIgnAFXHPO0lI3ND8lnAb3/+T+v/ZCdYBcDcUAEQRBuASFQoHg4GBkZ2cDAHx9ffXVlInaB8MwKC0tRXZ2NoKDg6FQKGp0PBJArkYQA1TlvnkQBEHUAyIjIwFAL4KI2k9wcLD+/1oTSAC5GgqCJgiCcBkymQxRUVEIDw9HZWWlu6dD1BBvb+8aW344SAC5GkEhRAqCJgiCcAUKhcJhP5xE3YCCoF0NFUIkCIIgCLdDAsjVUBA0QRAEQbgdjxBAK1euRFxcHNRqNbp164YDBw5Ijt24cSM6d+6M4OBg+Pn5ITk5GV9//bVgDMMwmD9/PqKiouDj44OUlBRcuHDB2W/DOigGiCAIgiDcjtsF0Pr165GamooFCxbgyJEjSEpKwuDBgyUj9kNDQ/HKK69g3759OHHiBKZOnYqpU6di+/bt+jFvv/02PvjgA6xevRr79++Hn58fBg8ejPLycle9LWnIAkQQBEEQbkfGuLlEZrdu3dClSxd8+OGHAACdToeYmBg8/fTTeOmll6w6RseOHTF8+HAsXrwYDMMgOjoazz33HObMmQMAKCgoQEREBL766itMmDDB4vEKCwsRFBSEgoICBAYG2v/mxPjpceDkj+zzRzYCzQY49vgEQRAEUU+x5ffbrRYgjUaDw4cPIyUlRb9OLpcjJSUF+/bts7g/wzBIS0vD+fPn0bdvXwDAlStXkJmZKThmUFAQunXrJnnMiooKFBYWCh5Og1phEARBEITbcasAys3NhVarRUREhGB9REQEMjMzJfcrKCiAv78/lEolhg8fjhUrVmDgwIEAoN/PlmMuWbIEQUFB+kdMTExN3pZ5KAaIIAiCINyO22OA7CEgIADHjh3DwYMH8b///Q+pqalIT0+3+3hz585FQUGB/nH9+nXHTdYYqgRNEARBEG7HrYUQw8LCoFAokJWVJViflZVltsy1XC5Hs2bNAADJyck4e/YslixZgv79++v3y8rKQlRUlOCYycnJosdTqVRQqVQ1fDdWQkHQBEEQBOF23GoBUiqV6NSpE9LS0vTrdDod0tLS0KNHD6uPo9PpUFFRAQCIj49HZGSk4JiFhYXYv3+/Tcd0GlQIkSAIgiDcjttbYaSmpmLy5Mno3LkzunbtiuXLl6OkpARTp04FAEyaNAmNGjXCkiVLALDxOp07d0ZCQgIqKiqwdetWfP3111i1ahUAtu/L7Nmz8frrr6N58+aIj4/HvHnzEB0djdGjR7vrbRqgVhgEQRAE4XbcLoDGjx+PnJwczJ8/H5mZmUhOTsa2bdv0QcwZGRmQyw2GqpKSEsycORM3btyAj48PWrVqhW+++Qbjx4/Xj3nhhRdQUlKC6dOnIz8/H71798a2bdugVqtd/v5MoCBogiAIgnA7bq8D5Ik4tQ7Qtw8CF6qLNo5eBSQ/5NjjEwRBEEQ9pdbUAaqfUAwQQRAEQbgbEkCuhrLACIIgCMLtkAByNRQDRBAEQRBuhwSQqyEBRBBEbaS8ENj/MVB4290zIQiHQALI1ZALjCCI2siWVOC3F4A1I909E4JwCCSAXA0VQiQIojZyfhu7zLvg3nkQhIMgAeRq+ALownbgyp/umwtBEIS1yGTungFBOBQSQK6G7wK78gewZgSgo4rQBEF4OiSAiLoFCSBXI9b+grrCEwTh6ZD+IeoYJIBcjkjhbV2l66dBEARhE6SAiLoFCSBXI2YB0pIAIgjCw5HRzwVRt6BPtKshFxhBELURCoIm6hgkgFwNWYAIgqiVkAAi6hYkgFyNqAWIBBBBEB4OWYCIOgYJIFfDiARBkwWIIAh7uPIH8PkgIPOUC05GAoioW5AAcjViAohigAiCsIc1I4Hr+4HvH3L+uSgImqhj0Cfa1VAMEEEQjqYkx3HHqtKI36iRACLqGPSJdjUUA0QQhKORezvmOIW3gLdigc1Pm26jGCCijkECyNWIWoDIBUYQRA2QK8xvzzoDrOgEnNwgbt3hOPAJUFkKHP1aZCMJIKJuQQLI5VAlaIIgHIwlAXQ5Hci7CJz4AfioO7Bppvi4qgrpY5AFiKhjkAByNRQDRBCEo5F7md+urRY2F7YDOeeAY9+Kj6sqlz4GXwCZsyIRRC2BBJCroRgggiAcjUUBZOU1xpwFiO8Co8xVog5AAsjVUAwQQRCORiYHLu0GSvLEt5sVNvxxVlqAtBrr50YQHgoJIFcjWgeILEAEQYhQegf4ewVQlGl+XP414OvRwJZU8e1aawWQlRYgEkBEHYAEkKuhStAEQVjLkbXAjleBfz6ybnxRJlB2FygvFK4Xu8boRKzR5ixA/ASOKhJARO2HBJCroW7wBEFYS3l+9bLAuvGVJcCHXYCP+whvtsQsO2KWZ3MWIL6rnixARB2ABJCroSwwgiCshbs2WHuNKM5hq0LfvSq8sRITLGLHNGcB4gsmEkBEHcBC6gDhcCgLjCAIa5ESQJoSoDjLdHxlmXBfRXWFaDHLDl/EXPkD2PQUUJBheS5i8yGIWggJIJdDMUAEQVigvADIvWgQKcYWl9W9gTuXTferLDU8t2Sx4VuI1o4GGK35OVmyKBFELYMEkKuhGCCCICyxujeQnwH4hrGvja8RYuIHMBI9NrjAxMSPTgfI5eLjSQARdQASQK6GYoAIgrBEfrUrqjSXXfKvEXw3lzn4okkqCDrrNHDlT+n95UrheA4SQEQdgASQq6EYIIKof1Rp2Hgce/tp8QWHpZpAHDoLMTvaKuDzQYCmWGL/KgDVAohhyAVG1DkoC8zVUCVogqhflN4B3mwCrBtv/zH44sNaASRwWUlYgKTEj/E5jV1wZLUm6gAkgFyNWA9BsgARRN3lzC9AVRnbiNReBBag29btY8kFZsmKI7D4GF2jyAJE1AFIALkaigEiiPpDZbn4d95W+NcIsfR3S/uIBkFXQdDewhgdLzDa+CbN2t5iBOHBUAyQq6EsMIKoH1zYCXw7DghNsH6fKo3lOEF7LECiafCVgLcvWz3a4v7kAiPqHiSAXA1ZgAiifrDxCXZ555J143U64L02lq8R9gRBi/Xu0mqEdYNM9q8WPTn/AsfXme5LELUcEkAuh7rBE0S9QGehsKAx5flsGwsxtHZYgAR1gERcVhVF0F+P/nsc+LifofcYYBBAK7uIHJsEEFH78YgYoJUrVyIuLg5qtRrdunXDgQMHJMd++umn6NOnD0JCQhASEoKUlBST8VOmTIFMJhM8hgwZ4uy3YR1kASKIuo9OZ7tr25w1RiCArIwBslS3pyzf8DyoCeClNtrfjICjaxZRB3C7AFq/fj1SU1OxYMECHDlyBElJSRg8eDCys7NFx6enp2PixInYvXs39u3bh5iYGAwaNAg3b94UjBsyZAhu376tf3z33XeueDuWoRgggqjb7H0PeDvOvKARo8JcSrodLjCtBRcY12Hey4et+OxtLIDMXJc4ixLDUBkPotbidgH07rvv4oknnsDUqVPRpk0brF69Gr6+vvjiiy9Ex3/77beYOXMmkpOT0apVK3z22WfQ6XRIS0sTjFOpVIiMjNQ/QkJCXPF2LEMWIIKoPdw8ApTk2bbPxTSDuLAFczV5uGtERRFQYeWx+RYcMRcYN0elL7s0sQCZE0DVgurbccCKjpQVRtRK3CqANBoNDh8+jJSUFP06uVyOlJQU7Nu3z6pjlJaWorKyEqGhoYL16enpCA8PR8uWLTFjxgzk5UlfxCoqKlBYWCh4OA1OAMl4f3qKASIIz+PQF8Cn9wCbnrRtP3OWH0asEFg1FUXS2zgBlH/d+nlw1xVtlfiNFxfv4+3HLm0SQNXHvvoXkH8NKLwpPZYgPBS3CqDc3FxotVpEREQI1kdERCAz0zoz74svvojo6GiBiBoyZAjWrl2LtLQ0vPXWW9izZw+GDh0KrVbcp71kyRIEBQXpHzExMfa/KXPwL36pZ4ER77HPyYRMEJ5FSS7w67Ps8ws7gIIbwN1r1u2rMSeAzNQEMluVuVpwFNxgl8FNLM+DEylSAct6C1C1ALrnFaNzasVdZ/xjcu/HnLAjCA+lVmeBvfnmm/j++++Rnp4Otdpw9zJhwgT988TERLRv3x4JCQlIT0/HgAEDTI4zd+5cpKam6l8XFhY6RwTxLxJyb/YBkAWIIDyN47yYQYUSeK8t+/zl2waXkRQaibo6gMGqIlcI1zOMBQtQteAoqG6SGt7G0DBV8lycAJJwTxm7wFoMYm/MvhjCWnV0VdJz4sQV10Xe1ow3gvAA3CqAwsLCoFAokJUlzGrIyspCZGSk2X2XLVuGN998E7t27UL79u3Njm3atCnCwsJw8eJFUQGkUqmgUqlsfwO2wr/7k8nY5ogAxQARhKdxjeeC51tQyvMtCyCpwoIA8N1Eti7QzP2GoOMbh4EfJgEaMwJIV8WKJM4FFhxrfg6AwbIsZcXhssC8ee8nMJoVfNw5peKNTCxADqh2TRAuxq0uMKVSiU6dOgkCmLmA5h49ekju9/bbb2Px4sXYtm0bOnfubPE8N27cQF5eHqKiohwyb7sxFkDyav1JWWAE4TkwDJAhEYNozXfVnAvsUhpw9ypweTf7uvA28Nm9QOENy4HTuiqeC8wKCzU3V2tdYBz865KUBYgLetYLILIAEbUPt2eBpaam4tNPP8WaNWtw9uxZzJgxAyUlJZg6dSoAYNKkSZg7d65+/FtvvYV58+bhiy++QFxcHDIzM5GZmYniYtZ/XlxcjOeffx7//PMPrl69irS0NIwaNQrNmjXD4MGD3fIe9QgEkJwsQAThieT+C5TdYdPDlQHCbZaynXQ6tvGpJTh3+NnN1s9LqwEKqi1AQdYIICtjgLyNLFp8AVQukRCirRS69MkCRNRC3B4DNH78eOTk5GD+/PnIzMxEcnIytm3bpg+MzsjIgFxu0GmrVq2CRqPBuHHjBMdZsGABFi5cCIVCgRMnTmDNmjXIz89HdHQ0Bg0ahMWLF7vGzWUW3gVDJqcYIILwRK79xS4bdwYKbwF3eFaQqnLz+1pd+6f6WiBV+VkMbaXBBWaNAOJurKREG5cFZmIBqo5P0mmBKqkYII0w7ocEEFELcbsAAoBZs2Zh1qxZotvS09MFr69evWr2WD4+Pti+fbuDZuZgyAJEEJ5NeQHw57vs86b9gH+3C3t5WbIA2Vr8sPSO9WMrSw1tMIJjgMhEIPOk9Hi9C0xizlzWmVkXmJQFyKhpKwVBE7UQt7vA6hXGAohigAjCs/jzXdbNFBIHdJsB+Ajri1m0AJlLZefDuY/KbBBANw8DYNi6PX4Ngam/AdN2mbrpOLSVwO0TwG8vmT+uOReYuSww/vWM0uCJWohHWIDqDQIzMWWBEYTHkXmCXfZOBVT+gK+tAshGF5gtFqBD1dXxW49gkyhUAUBMF8N1xBhdFfBxH8vHNc5qE8QASWWBVQgDnykImqiFkAXIlTAUA0QQHk1pdcX4gOqMURMLkINcYPZYgC79zi47ThKul0vcx1prWfY2EwMkZQGqKDayAFEMEFH7IAHkSkxigKovXFQJmiA8A84i49ugemnUQ9CiBchMDSABnAXoLrsMbGTdboGNgNhewnVSFiBrLcvmLEDGMUDqYHZZkkMxQESthwSQKyELEEF4NiW57NKvWgDZYgHKPMnW+bEGTjxwFqCw5tbt5x/Bur/4SFmAuCwvSyj9xY8nlgavDmKXJTmUBUbUeigGyJVQJWiC8Fw0pYYaPnoLkA0CaHVv68/FMEBlmcFlFtYCuJxueT/jjC1A2gJ096p1c7ElCNonmG2ToSkWWrtIABG1ELIAuRKTStCcBYhcYAThdkqrrT8KpcEqYq0FSGejANBpDe42mQIIibduPzEBJK+hADJxgXExQCIuMIWSLRAJAMW8FkYUBE3UQkgAuZRqF5is+s+ujwEiCxBBuB0uANo3zOBm4ixBHFIxQLa6sXWVBveXbyibcWYNxtYawHAdMcZqC5BUHSCRIGithk3BB4CiTMN6sgARtRASQK6Eu0hwAohigAjCcyjhBBBP9DRsCTTtb3gtZQGSajchhbbSYAHyCQW81NbtZ4sFyNo5mSuEaJwGr60E/KsFUDFPANlqASMID4AEkCsxFkAKnguMCokRhHvhLEB+PAGk8AYm/QJ0n8m+rigE7l4z3Veq47oUxhYgLrvKErbEAFmLOlD4mhNAjNYQFM5RVQH4hbPPyQJE1HJIALkS/UWi2rzOz96gOCCCcC9cDJCx2wsAvKr7CO77EHi/PXD1L+F2my1AVUILUMK9QKsRlvcTc4FJWYCshcvs0h+vOgaoosi0sSvfAsS15QBIABG1EhJAroQxjgHiXbgoDogg3As/BsgYYxfVsXXC11L9tqQQWIBC2DieCd8C3Z8yv59xwDJgECz2IJNLp8FzFh6F0rBNy7cAURA0UbshAeRKpGKAANvvIAmCcCwlVliAOLx9hK9tvYHRVgI559nn/hGG9VIBzRzGYgWwzQUW1hKI7mh4rQqQritUnM0u+YJQqwH8yQVG1A1IALkSqRggAHgr1nDBIQjC9YjFAHEYW4CMLTG23sCU5gFn/4993nK4YT3f2iKGPS6wJj3YZe9UYNYBoM9zhm3G7i+AJ4CqBQ5fEKoCDFlggiBosgARtQ8qhOhKjF1gxqbrjH+ANve5dk4EQbAYt8HgY2IBMgpGttQjzJgT69mU+oatgUY8i4wlMSMaBG3hMn7fh0DZXSC6Q/V43jlUYgKo+rrEubj8GgAPbwB2LgBGf2ToeF+SY9iHLEBELYQEkCvRW4AkttfEl08QRM3gCvtxFg4+xhYghTdwdS/QqDPgrbbdBcaJh/YPCF1QlsSMPRYgdRAQ1ox3Dt544wwwgGcBqv57+IYBzQeyDwC4edh0H8piJWoh5AJzKUYWIGNsvYskCMIxMIwhpoXrBM/H2AJ0aiPw1XDgr/fZ17YGQXP4Rwpf22UBsrSPcaVnvgAy4wLjrlfGFjGZyI0aBUETtRASQK7EOAYIAB7ZaHhOgdAE4R4qCoHK6t5WYgJIYSSACm+wy6Jb7JL/3Q1qAsR0s+68xsLKopixoRAiAEBmaF0hdg6VGQsQh59RVpyYpZpcYEQthASQKxETQM0GAC2GsM/JAkQQ7oGz/qiDxFPNjV1gXOFDLviXe92oMzD7BBAYbd15jQWQVGd3DltaYXDj5UaXeastQNUYN4QVs2BTEDRRCyEB5ErEBBBgyPwgCxBBuAeuqJ+Y9QcwFSqcy4v74ee+uwolG9MjJlTEMLYs2WUBMiOAxMQcXzCJxgAZWXiM6yKJusDIAkTUPkgAuRLjStAc3MWVLEAE4R4KOQEUKb7d2ALEVW7nllwQtFf1zUxQY+vO62WU9m4pDd5WF5iloGmHucDIAkTUPkgAuRLjNHgO7i7Q3kBKgiBqht4CJOG6MrYAcegFUPV3lxMwvWaz8X2NOps/r7GwshQEbasLTDRomieyrHKBGQdBi/xskAWIqIWQAHIlUi4w7i7Q1oaKBEE4Bn0GmJUWIA5GxAUGsK6nZgPEBQgfY4uPxTR4H9N1tlqALLrAjObgYxQDJGoBojR4ovZBAsiVkAWIIDwTLpvL2hggDuMgaBNBY8GiY6sFyLhthaVziPYOs+QCMxI4xlYiCoIm6ggkgFyJVCFEsgARhHux1wKkqwIyTxraQhgLIEuCxtY0eDH05xARR8YVq43PYckFplCyhR75UBA0UUegStAuhSxABOGRcAJIKn1dygJ08wiwujdvnI0uLRPBZMclmZtzcBMg/5pwm6WsMbHmqvzt1liIgJoFQR//HqgoAro+Yf8xCMIOSAC5EosxQCSACMLlaKuAwmoXmKQAkrAAlRg1MDZxgVnI6hJrsWEr7R9k512cBfz8H+E2URcY77JvbN0x3i4WI2SPBUhbxfYQ8wkWrtfpgM1PszFUieMAnxDzxyEIB0IuMFciWQeIswCRC4wgnArDAAU3hesKb7AWDIXKtDUFh5QFyBjjuj4WXWA2CibRc3oDCfeIu7PEXGD8wGixmCe+hcdqC5AFAbTxcWBZcyDzlHC9rtJw3assM38MgnAwJIBciaQFiOoAEYRL2P8x8F4b4MQPhnV3q91GwU1MqyZziAUfi2FswbHkArM1CNocYvtKFUJ84Qr7EBN2Fi1ANgZB3z4OnP6ZFTpH1gi38ZvI0g0g4WJIALkSqUKI+krQNnaUJgjCNnLOscvcfw3r7l5llyGxNT++rUHQZtPgq68TvVMBdTAwcLGFY4mILamK1L6hpi0uOCzFAInWATKTBr93ueH5tb+F23R8AUTXP8K1UAyQK5FKg/eiIGiCcAncDy5XwBAwBA4HO0AAmbi0zAgghcrUssQXTAPmAY27AE16AvfOk7ZO6Y9XfW6ZwhCULBW7ZA6+i0s0S8yGIOjiHODsZsPrrFOsCzKoEftay/s/kAAiXAxZgFyJpV5g5AIjCOfCuWr4AohzgVmyALUbZ/n4tgRBi7mfjFPU4/uylh1L4gcwiCe+1ceemCKLFiAbYoBO/sj+raM7Ao27susu/W7YriMXGOE+SAC5EksxQHQBIAjnwlkZ+DErehdYnPl9x34G3POK+TEmQdBmjOxi4oQ/3taU+JA41uITmWhYZ2yRsgaBBagGQdAMAxxbxz5PfghoXN0WhHNDAkYxQGQBIlwLCSBXoneBGa1XUBA0QbiEmrjAZDLLFhWTIGgzLjAx9xR/vK0B0f4NgdSzwCM/8Y5nZfYaH3ssQGJB0Me/B7JOsu+z3VggtCm7/s5lwxgKgibcCMUAuRJLdYDoAkAQzoX7oeZ+eCvLgJIc9nlwE8v7W7LK2BIELWad4e9vT1FE31BhQLJdlaX5AijAdLtYRpyxBaiiCNj2Evu834vsvBoksK/zLhnGkQuMcCNkAXIpFipBkwWIIJyL1sgCVFFk2GZNET4x9w8fk9YWZkSMmAVI0IbCzvtTvkCpaQyQaBq8zPQaZhwEnfMvUJ4P+IYBPZ9m14VWC6C7V0yFKCC0yhGECyAB5ErIAkQQ7kVnFANUVc4uvdTW1fqxaAEydoGZESBi2wQuMAcY6O0SQBYKIQKmbjDjNPiyO+wyMMrwnoIas/PRaoCCG+w6sgARbsQjBNDKlSsRFxcHtVqNbt264cCBA5JjP/30U/Tp0wchISEICQlBSkqKyXiGYTB//nxERUXBx8cHKSkpuHDhgrPfhmUsVYImCxBBOBfOysAtKzkBZGWsjFgNHD62VIIWLUJYgxggMaKSbN9HYAESSYMHTC1hxjFApdUCyLeBcB8u0JyLAxKkwZMAIlyL2wXQ+vXrkZqaigULFuDIkSNISkrC4MGDkZ2dLTo+PT0dEydOxO7du7Fv3z7ExMRg0KBBuHnTUN7+7bffxgcffIDVq1dj//798PPzw+DBg1FeXu6qtyWOVCFEqgNEEK5BaySA9BYgH+v2t+QCM1vY0AhLafD2xO9wzLkAzDrEWmBsxVIQNCBiATKKASrNY5c+RsUWOTfYneo4ICqESLgRtwugd999F0888QSmTp2KNm3aYPXq1fD19cUXX3whOv7bb7/FzJkzkZycjFatWuGzzz6DTqdDWloaANb6s3z5crz66qsYNWoU2rdvj7Vr1+LWrVvYtGmTC9+ZCFKFEPV1gOgOiCCcirEFqMpGC5Alt5RxYLM5K45YhpZcAf0NkiWxZQ7/cCCsuX378q9PYjFAgOncjAUQ5wIzrjbNZYLlXQb2LAWu/GHYRhYgwsW4NQtMo9Hg8OHDmDt3rn6dXC5HSkoK9u3bZ9UxSktLUVlZidBQ9ot25coVZGZmIiUlRT8mKCgI3bp1w759+zBhwgSTY1RUVKCiwmB9KSwstPctmcdiHSCyABGEUzj0BXBhF6ApYV8bxwB5W2kBEksB52NLIUSpbQpvVgw4wgVmD1U8S7mkBcjIim0cBM25wIwtQFyg+YFPhNYfgCxAhMtxqwUoNzcXWq0WERERgvURERHIzMy06hgvvvgioqOj9YKH28+WYy5ZsgRBQUH6R0xMjK1vxTosVYJmdEKfOEEQjmHfR8D5LUD2afa1vTFANgdBmxExUtWdueuBI4Kg7YInbqSEoSUXmJQFiGvOaix+AFYAUUd4woW43QVWE9588018//33+Pnnn6FW29Hzppq5c+eioKBA/7h+/boDZ8lDL4AkYoAAsgIRhDMw/l7ZHQNkYxB0UGPpsVIB1ZzwqUkMUE2ISgKSHgL6vyydGWdPEDQg3ZwVAH57HvhfJHD7hG3zJQg7casACgsLg0KhQFZWlmB9VlYWIiMjze67bNkyvPnmm9ixYwfat2+vX8/tZ8sxVSoVAgMDBQ+noI8BMu4Gz7toUiYYQTgeY8uqsQDytvIGylYXWEw34LEd4i00pAQQJ3xqEgNUE2Qy4P5VQP8XzYyxkAYv5QJT+lk+/+43LI8hCAfgVgGkVCrRqVMnfQAzAH1Ac48ePST3e/vtt7F48WJs27YNnTt3FmyLj49HZGSk4JiFhYXYv3+/2WO6BqkgaC/DOgoEJAjHY+xy4SwWnMvF2q7ptgZBy2RAk27iRRalxFRUMqAKstybzJ1YHQRt9L7NWYD0x5LoLE8QDsbtrTBSU1MxefJkdO7cGV27dsXy5ctRUlKCqVOnAgAmTZqERo0aYcmSJQCAt956C/Pnz8e6desQFxenj+vx9/eHv78/ZDIZZs+ejddffx3NmzdHfHw85s2bh+joaIwePdpdb5NFKgYIYK1AVWVkASIIZ2AcYKu3AFV/36wWQDZagPT7iVxqpSxAD61nhZnK37o5uQNLlaAlLUBWCCCxvmIE4QTcLoDGjx+PnJwczJ8/H5mZmUhOTsa2bdv0QcwZGRmQ8/zuq1atgkajwbhx4wTHWbBgARYuXAgAeOGFF1BSUoLp06cjPz8fvXv3xrZt22oUJ+QQzAogJSuAyAJEEI7HuM2CXgA52AIkKYBEhJOUmJIrPFv8ACICiGcBqiwz/F2Ng6C9rXCBkQWIcBFuF0AAMGvWLMyaNUt0W3p6uuD11atXLR5PJpNh0aJFWLRokQNm50CkCiECrOm8AmQBIghnYMkCZHUMkKUgaAkBJObusnQsT8ZcEDRn/ZF7mabRkwWI8CBq8TewFmLJBQZQFhhBOAOHxQA50gVmRe8xT8VcGjy/CrTxe7QqBoixPIYgHAAJIFciVQkaMARPUjVognAsOp1pkK7dMUAOdIFZyijzZMwFQUvVAAKsywIjFxjhIkgAuRKyABGE6xErumccA+SoStBSdYLEvvO12QVmzgJUXN3H0a+h6X7WWIDIBUa4CI+IAao3SBVCBMgCRBDOQqzFgqMrQfuFA/e8bNt+7qrz4wjMBUEX3GCXYkUgyQJEeBC1+BakFmJOAJEFiCCcg6gFyKgXWE0rQd8zF+g81cx+dc0FZvR34FttzAkgucKyu5EsQISLIAHkDkRjgKoFEGWBEYRt3DoGnNwgvV2sv5693eClRIul2KC6lgVmzgVmTgABlt1gVAqEcBHkAnMlluoAAfTlJwhb+aQfuwxsBMSKVHsX+06ZtMKw1gIkccm0JIDEtjfubLqutmASBM2z2hTeZJeBEgJI6WcIlBZDU1KzuRGEldglgK5fvw6ZTIbGjdkP+IEDB7Bu3Tq0adMG06dPd+gE6xTmBBBnFuYuyARB2EbOOVMBtHM+oCk1HWt3DJCdFiC+y2j4u6zgajfWunN6ImYtQNXNpO21AJkTQAwDlOQC/iIB1gRhI3bZYB966CHs3r0bAJCZmYmBAwfiwIEDeOWVVzyv+KAnYU4AcQXCuLokBEHYhvH3KusM8Nf7wMFPTcdWFAJbnwcy/mZfWx0DZKcFiC8YItoCyQ/V8jpAEkHQ5YVAeQH7PKiR+L6WiiFWighWjp3zgWXNgYx/rJsnQZjBLgF06tQpdO3aFQDwww8/oF27dvj777/x7bff4quvvnLk/OoW5ipBc3dFZP4lCPsw/lEuyZYeq6sCDnxieF3TStC2uMBqc/Azh0kl6OprG+f+UgcDqgDxfS21w6gsNRzPmKzTABgg+4y1MyUISewSQJWVlVCpWJPxrl27cN999wEAWrVqhdu3bztudnUNc4UQufRQEkAEYR/GFhUx15cUNa0EbVEAKcSf11akLEAF1QJIyv0FWNcOQ8oKxMVzUbkQwgHYJYDatm2L1atX488//8TOnTsxZMgQAMCtW7fQoEEDh06wTmHWBVYtgMyZfwmCEMJvm2D8vbLlu1TTStC2uMAsja0NSAVBc/E/gRLuL8C6YohSN4JcliyVCyEcgF0C6K233sLHH3+M/v37Y+LEiUhKSgIAbN68We8aI0QwJ4DIBUa4CoapO7Fm/C7vxt+rikLrj2OtAJJyXylssQDVAQEkFQTN9QEzF6RsTTFETbH4er0FiAQQUXPs+ib2798fubm5KCwsREhIiH799OnT4etrhbqvr+gFkMg2pT+7JAFEOJtvHwAupwOpZ2t/Ng2/yrOxACq7a/1xrI0BsjsNvo4JIKleYFwAtDpYel9rLEBX9wLBTQCFt3A9CSDCgdhlASorK0NFRYVe/Fy7dg3Lly/H+fPnER4e7tAJ1il6pwLz8oAR75tu02eBkQuMcDIXd7LVkc9scvdMag6/xk9NBJCzY4BkdTwGiKveXJ7PLs0JIGtigP7vGeD3xabruf83ucAIB2CXABo1ahTWrl0LAMjPz0e3bt3wzjvvYPTo0Vi1apVDJ1inkMtZU7mYuZxcYISrseZO3NMR6/PF4QwB5IgssLpgAZJMg6+2APkES+9rKQuM4y+RG8UqCoImHIddAujIkSPo06cPAGDDhg2IiIjAtWvXsHbtWnzwwQcOnWC9gVxghCvg91mytvqxJ8O3ABn3kCrLt/44Tq8EXU9cYNzfXB0kva81FiBA/O+kpSBownHYJYBKS0sREMDWeNixYwfGjBkDuVyO7t2749q1aw6dYL2BXGCEK6goMjyvcwLIqOeXLRYgrhWNJex2gfEutXXCBWYpBsiMAFIFWneO4Cam6ygGiHAgdgmgZs2aYdOmTbh+/Tq2b9+OQYMGAQCys7MRGGjlh5sQQi4wwhXwBVBdKMjHd4Hxu77fPg7kX7f+ONZWZSYLEEtNgqBbjQCSHwaiksyfo1KkLRDn+qooAk7/DJSa6SlGEBawSwDNnz8fc+bMQVxcHLp27YoePdj+Ozt27ECHDh0cOsF6A7nACFfAF0DGFpPaiJgF6PQm4OO+QEGG489nbzd4QQxQHRCeFoOgzViA/BoAoz8C4vuZP0fZHWGdJ8Dw/z73K/DjFODr0VZOmCBMsetWZNy4cejduzdu376trwEEAAMGDMD999/vsMnVK8gFRriCuiaA+FYf7kd4w1TnnU8uFQRtQdTUtUKIxkIw7wLw9RiD29FcEDSHWAPa8LZsLaHiTLYxdGWpoW6QTif8fwOspY8g7MQuCxAAREZGokOHDrh16xZu3LgBAOjatStatWrlsMnVKzgXmFZjPrOFIGpCXRNAAhdYFXDjkLAzuSXC2wCPbbftnGICxrhejck+dUwAiQnBS2mG5+YsQBxicVetRwDPnQPk1X9PvotLS5lfhGOxSwDpdDosWrQIQUFBiI2NRWxsLIKDg7F48WLopJrYEebhXGAAucEI58GvjmycNVUbMXaBXdxl2/6dHwOadLdtHzE3WH1rhmruPShU1gXYiwkgmYKNx/KtbqnEVZYGSAARDseuW5FXXnkFn3/+Od5880306tULALB3714sXLgQ5eXl+N///ufQSdYLvJTsRVJXxQoga0zIBGErdc4CZCSAbEl9B6zP/uIj9zJNw7YkgHxCgJbDWEuvlx3n9DTMufyssf4A4i4wzrLkG8q6wcqqLUA3DgG5/9o2R4KwgF0CaM2aNfjss8/0XeABoH379mjUqBFmzpxJAshevP2AigKKAyKcB18AMXXBAmQUA8RlIbUcDpzfYnl/uwSQHRYgmQyY+J3t5/JUpApCAtYLICkLEAD4hLJLzgX2/UNAcZb18yMIK7DLBXbnzh3RWJ9WrVrhzh1KS7QbLtiPXGCEs6gtFqDSO9bVejG2AHEuvmYDgCd2Awn3mt/fHmuMPQKormHOBaYKsO4Yohag6uP68gQQw5D4IZyCXQIoKSkJH374ocn6Dz/8EO3bt6/xpOotSqoFRDiZ2iCAijKBt+OBFZ2F63POs6nPWWcM64wFEL8QX6OOQGAj8+dSiPwIW8KeGKC6hjkXmLUWbLG/vYznAgNYF5gjih7u/xg4Z4VFkKhX2PWtffvttzF8+HDs2rVLXwNo3759uH79OrZu3erQCdYrvCkVnnAytSEI+tJudmlcx+frMUDhDeDyHuD5i8CPk4FbvDRovgWIqzYsZmXgY2m7GGQBMu8Cs7Y4oZj1TcwFVtPrYeZJ4LcX2OcLC2p2LKJOYZcFqF+/fvj3339x//33Iz8/H/n5+RgzZgxOnz6Nr7/+2tFzrD9QMUTC2XiiBej8b8DX9wOFt6tXMOLjCtlyGyi7A1zdC5z9P6FI4scAcXEolmJ8LKWviyEmduqbADJnASqzUgCJ/c3CWrBLfhZYlUhFaFsg9xkhgd3f2ujoaJNg5+PHj+Pzzz/HJ598UuOJ1UvIBUY4G02x4bmnCKDvJrDL7XOBB76ybh8xq4CuCiivtgCpqy1AFgWQA1xgMrl0gcS6irkYoNYjrTwG72/20A/A3Wts7BbAiwHKAyrL7Juj4USGpwxjfdsTos5Tz25bPBwuCJpcYISzcJUF6OQG9lydbajKXJLLLo3bH4ghVuxQW2mHC8wBQdD1zfoDSLvAujwO3POK7ceI6wO0GGx47deQXZbm1lwA8QWPrso+qx9RJ6mH31wPxpuywAgnIxBATooB0umAn6axz1sMAQKjrNtP/4NopwCqKDSIOqtdYA6IAaqPAkjKBdZrtsF6Ywm+ADL+P3ECqDjHsgDSVgEKc/8DngDSakgAEXrqmd3WwyEXGOFsXGEB4mdm2fJZlisATal1FiCxH0Uu+FamMFhTLVmA7KkDZOz+qY8CSMoFZsvfk2+ZMRZUnAAqyQEqLXyGSnLMtw/in+faPuDoN9bPkajT2PTNHTNmjNnt+fn5NZkL4aVml1U19XkThASCLDBnCSBe2rItnc+v/Q28GWMIhDUHP5aJgwu+VQcafvQsWXjscoF5mX9dH5CKebLl78m3ABnH5XACiNHyguMleLcVEJUE/OcP8e18Qb3xcbZha+OuQEMrPmdEncamb25QkPkKn0FBQZg0aVKNJlSv0Qsg6nlDOAGGcY0FiP/5NZcubbJfdbZPNq/Oj04n/mNbISKAuL5RXPwPYPkH2S4XmNF86qMAkrQA2fD3VAdLb/NSstvL84H8DOlxHOa6wvM/51y3emsz1Yg6jU3f3C+//NJZ8yAAg7neuM8QQTgCrUYYO+OsGCD+57em7TZ0VYBcRMSIudZKq3/c1DwBZNECZI8AIguQpLC15e8Z3QHo/SwQ3ER8u19D6wWQOcTcYzVNrSfqBBQD5ElwFw9HVD4lCGOMswudZgHifX51IsHKtiCYI89NIuYC01Rbt/iWBYsWIDsCYikGSNy1KZPb5vKUyYCUhUDnx8S3+4ezy/xr1h1PK/F51okJIAdcY+9eBfatpJjNWgwJIE9CQQKIcCLGgcNOiwHiNyit4Tn4FiT+j6uYAOJQ2WABckgWmA0/+nUFV7QD8Qtjl9ZagKSCpUUtQA64xu55G9j+MnB6U82PRbgFtwuglStXIi4uDmq1Gt26dcOBAwckx54+fRpjx45FXFwcZDIZli9fbjJm4cKFkMlkgodY41aPhLtbJQFEOAOXCSAbXGCWMr4kLUBm7rr5LjCLWWAOqARdH9OqRduBOPjv4FdtASq8ad1448+3phTY9xGQd9F0rCOusWX57LKc2mvUVtwqgNavX4/U1FQsWLAAR44cQVJSEgYPHozs7GzR8aWlpWjatCnefPNNREZGSh63bdu2uH37tv6xd+9eZ70Fx8IFQVMMEOEMXOYCM2pQag6thYB/fpwSP+6EH8xtjJqXrGFJnNhTFdg4/qU+usDEYoAc/XfgXGDWfk6NRfHu/7HVxdOXmI51xDWWc62JudiIWoFbBdC7776LJ554AlOnTkWbNm2wevVq+Pr64osvvhAd36VLFyxduhQTJkyASiV9Z+fl5YXIyEj9IywszFlvwbEoyAJEOBETC5ALgqAtncNSMKpOwgXGZXyJYYsLzB5MgqDroQtM7D2bLUZoB342Xre3zgHW3GdweV3ZIz3WEUHQ3HnM1SCqjVhTh6uO4DYBpNFocPjwYaSkpBgmI5cjJSUF+/btq9GxL1y4gOjoaDRt2hQPP/wwMjLM+5ArKipQWFgoeLgFfRo8CSDCAvaIF7cEQZuZZ94lIOMf88fiz5FvdSjJkd5H5W94bk+dH0twP/6cECILEIvDY4DCbRt/6XdW9FxMY1+b+yF3RKkR7rPpKT31HMG5rcDb8cCFne6eiUtwmwDKzc2FVqtFRESEYH1ERAQyMzPtPm63bt3w1VdfYdu2bVi1ahWuXLmCPn36oKhI2mS+ZMkSBAUF6R8xMTF2n79G2JIGTyKp/rJ3OfBmE+DWUdv2c1kMEO/HxVwM0IqOwLoHzR+Lvz9/vlzfMDG8fAzPnWEB4gKAuWM7OvalNuCKIGh/GwUQB/c5MSuAyAIkypU/2FpJVySKStYx3B4E7WiGDh2KBx54AO3bt8fgwYOxdetW5Ofn44cffpDcZ+7cuSgoKNA/rl+/7sIZ87DWBXbzCLAkBtiz1PlzIjyPXQvYLKjdb9i2n6ssQHwBJGUBstbMzv8x438vzGWB8QOf+c9je5vvYm4t3A89Z12qjxYg0SBoN7vAjOch1i+Ow1LsmTXUxRggvVXLSe5xD8NtAigsLAwKhQJZWVmC9VlZWWYDnG0lODgYLVq0wMWLIpkA1ahUKgQGBgoebsFaF9iOV1kr0e7XnT8nwnPxbWDbeFfFAFkTBG2tBZObo7YSVjVJBQzfI0DYm2rAfOCVTCC8jXXHkYKrBM2dpz7GALkkDb6mFiAzAsihFqA65ALTC6A6JOrM4DYBpFQq0alTJ6SlpenX6XQ6pKWloUePHg47T3FxMS5duoSoKCs7UrsT7o7S0t2JPQ0cCc8i9yKwPBE4JB7wLwlfxAQ1tn9fwL1p8Nb2u9MLIBtcvlIWIIUX+x2rqfDjfugVZAESrnPw30HlD3j72r4flw1mVgA5MgaoDomFuhjXZAa3usBSU1Px6aefYs2aNTh79ixmzJiBkpISTJ06FQAwadIkzJ07Vz9eo9Hg2LFjOHbsGDQaDW7evIljx44JrDtz5szBnj17cPXqVfz999+4//77oVAoMHHiRJe/P5vRW4As3J14+5jfTng+W59jC7z9+qxt+/GLwqkCbNtX7wKrTv12ZxB0pZV34NwcbfnBkrIAcYG75n4YrYGzfnBNW0Ob1ux4tRGx8gHOEIL2uMH0BREpBshmBBbXuo9bb13Gjx+PnJwczJ8/H5mZmUhOTsa2bdv0gdEZGRmQ8xoP3rp1Cx06dNC/XrZsGZYtW4Z+/fohPT0dAHDjxg1MnDgReXl5aNiwIXr37o1//vkHDRs2dOl7swt9DJCFiz3/Aq+tcnz6KeF8rBUAxtzltQWwVcBwFiBVIFBR4N4YIKstQNVzdIQFiBM+Ne1Pxh2zaT9g2FIgyE1JE+5EzAXmjOuQX7jtvcA01ULfbAwQ1QESpZ7FALn9l3PWrFmYNWuW6DZO1HDExcWBsRA8+f333ztqaq7HWgsQ/662NA8IiJAeS3godtbauHvV8NzWi5ReAAVUCyBn1QGyIgbIWgHIiRVb7tgFFiC+AKr+m9f0fXeawgZhtxkl3cizruOKStCAfZlgeheYme/YlT+B7x4CBv8PCI23b25c7A/FANVa3C6ACB7cnaWukm0iKZfwUPKzeUpySADVRuwtNpbvCAtQgH37W0uVFWnwtsYA2eQCM4r70c+FES7tJaYrELO2Zseo7bgiCBoQd4El3AsUZ7PbLqebbrfGBZZ/jX006gj0nWPf3Oq0BagOiToz1Lk0+FoN37IjFghdWQZcPwBU8Ao1Hv0GyNjv/LkRDsYBFiBb/fSccNYLIFdUgq6hBciuIGi18LV/dVZpRHX2V01dYIRrgqAB8Uyw2F7AjL+ARp3F97HGBcaRfQZY1Qs4YoegrcsxQPVEAJEFyJPgX7irygFvowv5T48D534Vrtu/ihVBz1+g4OjahL1WiLxLhuceawHiCyCRHyGGsT0GyF4LEADMPsneUCj9qo9JAqjGiFqAnFAOQMwFpv+RlhAenNC35jt26id2uflpoOMk2+ZWF60lujro1jMDWYA8CX7jRjELkLH44dAUAZfN9L0hPA97MpE0JUDuecPrmsQA2bO/tZiLASrLZ9P/fxGP+zNBL4BELEaRieL7GN8IeCmF7TF6Vp+71Qjr5kCYIpYFZqnxrD2IucA4S57Uj7SmmBU/Nc32s4QzLUDXDwA3Dzv+uJaoi6LODGQB8iRkMtYKVFVue5rm+a1AyyHOmRfhBOywAN0+Lryo22wB4lxg/vbtby3mWmFkngQKbKi0zphxgTXqxB7PGGMXmDHdnwLiete8IGJ9xlVB0HwX2JQtrAWUE65S9dLO/AIsa26+X5wjEIsBqihiA+9r0oOuvAD4fCD7fF6ea7N861kQNFmAPA0ua8XWQl3/bhd3NxCeiT0uMOM7QlsvUvw0eMA1QdDGViYuQ8dazAVBS8WAGLvAjJHLgegOlscR0rjDBRbTDeg02WB9Mvf5t0f82HL9ZBhTd1HZXWBJY2B1b9vPzYff587VQkTvXqwfbmISQJ6Gvhq0DUGfMgVQnAnctrE5JuFG7BFAR9ilNxfLYq8FyMkxQOaCoM318BLDXB2gRp3E93FGA1RCiKuCoIMaA6ogIKiJc1xsfMrzrR/L/1xzIuXqX+yS76a2B774cLUrSv99IwsQ4Q6srQXEp9UwdvnX+8A/q+tNAFutxh4L0K1qARTThV16agwQPwjaOA7DZgHEWYBEBFDDlmwtnsj2hnUyBRUGdQWuSoNX+gFP/QNM3226rc8cwK8hEN/PMecqvWP9WL5A0Iq4wmpijWc8QADVkxggEkCehrXVoAHANwzo9qTBJ37mF2Dbi8CZTU6bHuEo7BBARZnskmvB4KlZYOaCoCvstACJCSC5AnhwLTB5s2Gds60EBItM5KfDWX/7wGjxYOjgGGDOBWCQg5pCl+ZZP1YgdjgBxBMuNWm1IbAuudgVVc9igOhWydOQsgAZ31EoVMDzF1l/uPGdS8Y+IHGc8+ZI1Bxb9Q/DGESAsjqIucZ1gNzQCsNWCxBnQeKO6R/Bxnf0eMowhm95qI+NSd2BWJFWZ8QAWUImM5Q3qCm2CCC+lV0r4jaqLAOUdjRyBYQ3v652RVEMEOFWpDrCG/9wyL0MwYC+ocIaFrdPOG9+hIOwUQFpKw372JvF5ZYgaAfFAHHiL74f8OJVYOBiwxiBAHLDj3B9RNQF5ibrm6Pqn9XUAsT/bPOr9dsK/+aXXGBOhQSQp6HPAjOyABn/cBgLpPtWALOqs4QyT1AckKdjawwQ//PAWYBsuUvT6QzFB11ZCdo4Dd5uF1j1+/dSAuogYR0asgC5HjEXmLv+9pbKHliLTRYgkRggfoX+SisLfYrhCQKIgqAJt+AlkQZfUSR8LeajDW3K3t1XlQOf3kOWII/GVgHEExXcHa8tF0f+RdWllaAdlAbPCX6xDC++NYIEkGsQK4Torr+9byjQ7yWg7ws1O05pruUxHGJxbuU8AWRtpXOLx3ZXDFD9uIEmAeRpcALIOO3XmjtnudyQEZN5Akh/07FzIxyHvRYghcrgarAlUFHUglQLYoCMXWBid/v8eBQSQO7Dndl398wF7n2lZsewJQuM/93RW4B4N6kOswC5KwaIBBDhDrykXGBFpmPFaDva8PzKH+QK81jstAB5qQ3ZNrZcpDjLi9zb8BlzRyVoY0umJYyDoC1V2KUYIPfhE+LuGdQMYxfYyQ3Am7GmbYau7QOOrTO81om5wGoSA2RFM2FnUc8sQHS75GlIVYK29oej6xNAh0eAZS2BigK2fUJjiYJxhOvRlAC3jtruY+csgl4qw4+8JfN44W1gz1ts64nmg9h1qgCDlYTRspYoMXdGTTAbBG2rC8woBshSkUOx4FzCCfA+M4P+B1z6nS3JUZvhCyCdDvhpGvt8z9tAU16toS+NWg5pRVxgNbIAeYAAqicxQCSAPA2pNHhbgke9fYD4Pmzz1CvpJIA8ie8fBi6LFHWzhD4IWG0QMJYujoe/ZB8AcOMQu1QHCq0kOq3jXRdaczFARp9jv3D2vfHvnvkYd4O3aAGiS5pL4IvmLtMMDWZrM4W3Dc8v/W54rg4yPBcrcKhztAvMzPfH2egtQJQGT7gDa9PgYy30m+Gqo578qd58mGsF9ogfgOcCUxp+5C3dpXGFEwFDmX++BQhwzh2mLb3ARq8CXrgsfSx9EHT1+7dkASIB5HrEMsJqE6FN2WXhDeDTe4FVvYEDnxi288W5WAybWBZY1ilT15m18G9+3VYHqH5YgGr5J7cOoneBGQdBV3+5Wg4HhrwJPPCV+eO0G8veuWSfBo6scfg0CSthGODbB4G1o+1rf8EhsABZGQNUJhLUqQpyvgCyJQ3euzqmScp1xc1PUx1TYam4HAkgF8GzAHmS2/HBr4HEB2zbJygGaNCcfX7zMJB1Eriw3bC9OMvwvLzAdH/Olcx3ge19D1h7H3DHjLiXwlwvPWdTz2KASAB5Gl5SAqj6hyM4Bug+A/BvaP44fg2Ae6qzItLfrDc+XY+jspS9mF7ebVuzRWOq7IgBEstqUQcKf7CcIoAkYoAYxjSY36s6pV+qjQL3Hrn4DJ9Q8+emIGjX40kWoDb3AWM/s20fdSDQqKP0dksCCGA/52Ju3PwM2+YCeEYMEAkgwi1IpcFzpleuhos1dJrKxlgUZwHntzpmfoRt8C9mUv3drGmcyM8CszYGSEwAqQKNLEAOdo9qq4QNUI37Ixk3R/WujnmTqiLMWZA4a5ZvA/PnJwuQa/APNzx3dBC9q1EHAdEiAqhRZ3ZZXgBUlhuei3H8O/EbHFtS6zk8oRAio6tZQ9daAgkgT0MqDZ6zAHE1XKw6lhLo+Cj7/NAXNZ8bYTsCf75IQ0/AuoucwALECSALVj0xF5g6sLpujsz6c9uC8Xs8+jXw6QCgKEs8kJ8L+pcKxObmx1mAfC1ZgEgAuQS/MODRn4HHttdeARQcy8ZKJj8CRHcw3d5quCEkoTgLKMkTWoP4bH5afH3ZXdvnZS6L0pkwjHs70bsBulp4GpJp8NXmVZUNAggAOk4G/nwHuJzOZjkERtV4ioQN8AWQlAXIOE7G3HEUKuvqADGMeGl/zoIo92IFlKMvcsauW10VcPMQcPVPcTcDV9VaygL05zts3RW9ACILkMeQcK+7Z1AzopOBB9eyz6s0QJMerIu1opBtKN1iCHDoS6Aggy0n8uNkUwumJcRuQizhLguQsTU4PwMIiZV2T9cByALkaVhygSltcIEB7Ae4cVf2+blfazY3wnb4gkDSAmSNALIxBqiiyHDx5IsLrhGqtW40WzHOXuSoKhevAcRZgMwJl4y/Dc8pBoioKR0eBfwaAikLDeu8lMBj24CJ64DxXwMz9wMRbQyuvv2rbRc/AFBqjwXITTFAxuf6sBPw839cd343QALI09CnOEsUkFP62X7M1iPZ5dn/s39ehH0ILEDl4mOscoHZWAeIu/P08gECeFY/tRMFUFk+sLqP+LaqCnEXGGcBsqYWkTKA6gARNaf/XGDOBUP6uzE+IUBYM/Z5QCS7zDln37lK84D867btw79RcmUlf7FrQc55153fDZAA8jQUEn2ezPVCskTrEezy6l42FoNwHfy7Oal4AGvuLLViMUBmLo78mBl+ITe9BcjKTDJbuL4fKMkW31ZVwRPxPDeul4UgaD6+VrRaIAFESMFlqyl9rY9b4ixAtnSK53Pie2B5O+DURstjy/KB9Y8Ap382rHOnBQioWUHHWgAJIE9DIVEI0dpeSGKENgViurGxJvtX1Wx+hG3wrT5l+eJjbHKB8eoAmbs75EzvxgKIe+4MCxD3IxHWAhi2TLhNW2FIgY9sz96FD33b8ENkTZyBpfgfgAQQwTLpFyCoCRvozDH4DeDeV23rWeYfYf1YLzUbcynG9pct738pzdRK784YIEDaal1HIAHkaUi5wDgBpLBDAAFAr9ns8uDn0qmchOPhW4CkUmJtcoEpedYbK1xgPlIWIF4/MEfBvb+oJFMhUqUxfO7UgUD/l4BuvPgCayxAluJ/AGFneKL+0rQ/8OxJoPlAw7ruM4C+z9t2HLHsMCkG/8/Qc88Ya0IXxG6QyALkVOhq4WlwAsfYBcYVMrQ3Ir/FELbaaUUhcH6b/fMjbENgAZIQQFZlgdlYB4ifNSUQQLwsMEvHsBX+OU0EULn5TC5rgpfNWYCiktllh0ctH4eoR9QwRb9ZivVjlf6GulYm26oFUNldacut2I2pSwWQSFkNsgARLoUTOMYuMO4H0FIvJCnkckMs0MVd9h2DsB2rLEAWBJCm1HAnZnUMEFc40NgF5sQYIL7VyVjQaDXCORlTUxfYY9uAmf8ALQZbN1eiflDTGkVyBdD7WevGevuyD9F5KICCm8DbCcA3Y8THiFWS9gQLUE1a+Hg45DD3NJzlAgOAZgPZHjWX0tgqn+QucD7WWIDMXeRK8oD32xvKIHipDWKB60EkdpHnixF+qwJnpsHzA69NLEC8IGgxV5Y1LjBzVdC9fYDw1tbNk6hHOKBI4z2vsGnzd64ABz+VHqf0M2Q1GlOaywY3M1rgyh7x763bLUBiN0MM+9vjZeeNt4dDv4CehmQQdA1dYAAQ05X9ASzNA24ftf84hGV0Ojbe6hbv72xPFtjpjcIO1F5qoXVF6gJ56xi7DIjkWYBkhgwspwggXuC1cX8obYV5F5g1afBSdZQIQgpH9ClTeAM9npKO7+FQ+ktbgIpzhJZYsWtBuYgFKPMkcPgr11hhpK4FdTgOiCxAnoZUGrw+C6wGSlzhzQYHnt0MnNkMNOpk/7EI81z7C9iSKlxnjwvM2JKiUJp2c+eL4v2fAIe/BLLPsFaVViMMLk9VoMHq52gBVFkmtDoZ90WqsiCArLEAObpvGVH3cWSj1gALGWFKX2kLUFWZ0AJ894qpK1jMAnRiPfuADAiMBuJ6S5+jpkhdC+pwHBBZgDwNfQwQTwDpdAZBVBMXGAAkPsAuT/xAPyjORKxnkD0uMGMzOT8IWmzf319nxQ/AdsYOiDDcefJdSOYyyS6nA8fXS8/JmFMbgTeiDecVDYKuMN/Q1FL6uioQ6Drd+jkRBODYPmWWUuKVftIWIADIvWB4fveq6XaxGCCO/3sG+HYc8NsL5udQE+qhBYgEkKchFxNAvOc17cvSYjCgDgaKbgFX/qjZsQhp+G4rDqmy+OaywCqKhK+9VEJrCf9zwjBABe8usscsdhkSK1wCPAuQ0bmrNMDaUcDP04G716TnxefnJ4VuPN9QNuiTj1ZjvqGpuc91dEfghcvC+ROENQQ78DMTEAkMXAy0GSW+3dtPWKg2NEG4Pe+i4bmYALKmPMmRtZbH2AtZgAi3I+YC48cD2ZsFxuGlAtpVZyGc+aVmxyKkEWv7UCFxgTNniTOuDWISA8Tbt7LU8Dz1nKH5aGQi8OgmYMwnhu1SLrDbxwzPpTpfG2NcndxHJAhaU2y4wNtqAfL2qdMNGQkn0nkq0H0m8PAGxxyv1zPSWWGcBUgVCEAGTE8Hnj4CNOrMbhezADGM4Xvh7vpsUtehOmwBohggT0MfBM0TQPwu4o74IWgxBDj0BZsNJpVFRNQMY8uNOcwFQRvH0nip2P+XTMFajvgChguilCkMPYw4Eu4RvpYSQBn7DM9LcixOHQDg10Ao7pS+phmG+hYsMtYCaYy5z3UdzUAhXIDCGxiyxLHHlHJzeVd/7p87x36nVQFs2Ql9O41cw1hOAO2cB+xbCTy2QzwI2pWQBcj1rFy5EnFxcVCr1ejWrRsOHDggOfb06dMYO3Ys4uLiIJPJsHz58hof0+PQp8GLWIBkCsd0u47rzQqt/Awg71LNj0eYIuYCk8JsRed84WtODIgJGC6GQBVgWdRynyOtUbB9xj+G58USfb2MqRLJzjK26BTdZpc+weIZX+aCoO3pf0cQzkLq88iJfqWfMN7Or6HpWE4A/b2CFUu/zAQqSxw6TZuhGCDXsn79eqSmpmLBggU4cuQIkpKSMHjwYGRni194S0tL0bRpU7z55puIjIwUHWPrMT0OsTR4R2SA8VH6AU16sM+pKKJzMBfQaIw5F5iYBQgQd5Vyd5BcsUNzcJkk/Ls7nc7IApQLi+h04q4y4xgg7u8h1c6CE0ViQogEEOFJmAt0FkNMABXcEAqL3H+tP56m1PIYeyALkGt599138cQTT2Dq1Klo06YNVq9eDV9fX3zxxRei47t06YKlS5diwoQJUKnExYCtx/Q4jIvcAbwiiA6Mg2g2gF1eSnPcMQkDYjFAUpgLgjauF6Lvni5SyZlzQ6mCYBHuDpXvqsu/JjyfVGd3PqV54hdOqZgeqWrO3HixHxcSQIQnIdXuQoqgxqbrGB2Qddq+8xdnOqcukNSNGAkgx6PRaHD48GGkpBh6rcjlcqSkpGDfvn1m9nT8MSsqKlBYWCh4uA2FSIaPI6pAG5NQLYCu7gUq6+4H3G04zQXGCSARF5gtFiAxAZRzXjjGmhig4kzDc7k3220bkHbVSgkgLlunQVPTbbb+4BCEM/GysQ6PcfYiZxG6aOfN553LwMpuwIZp9u0vhaQLrO7+PrhNAOXm5kKr1SIiQlhbISIiApmZmRJ7OeeYS5YsQVBQkP4RExNj1/kdgiDFWSNc1jQDjE9EW8A/ks0cyrBPcBJmsCUIWmdjEDRgIQbICgHEVYTmu+pyzgnHFFsQQDcOA7+9xD6PTARevmnoti0pgCRcYD1mAdP3AJ1FLupkASI8CWuqlvMxTsXnwg8u7LDv/BfTgNzzwJlN5q8dtiLpAqMYoDrN3LlzUVBQoH9cv37dfZPhW4C4+I4qJ7jAZDJygzkTR7jAGMbUAsSJYLF6UTZZgKrH8OfJxSHE9WGXlixAX98PXNvLPvePFMaoGccAcagl3HMKLyA6WdzKSVlgRG3G2AUW25Nd3jxk3/FuHGSXuirr3NTWQhYg1xEWFgaFQoGsLGEAZVZWlmSAs7OOqVKpEBgYKHi4DX7shDNdYACQcC+7vPi7Y49LABpbLEASFx5Nsak40luAxGKAbLAAibrAqi1Acb3ZpaWLKz/13bhNgJQFyNLcxO6ubXU5EIQnYSzgjVsQcTccABAUA4z5FEh6SPp4N3jC6eYRtl+YI5CMASILkMNRKpXo1KkT0tIM1gedToe0tDT06NHDY47pcmQy07t7fRaYMwSQDMg+DRTeduyx6zs2ucAkLjzG1h/AvAvMJgsQ5wKrnifDADlGFqCyu6Zp8hz82lRic5UKgjbX0R2QCIImCxBRy+FbPsNbC7c17ix83f5BIKaLmYPxAqC/nwis7i1eWdpWyALkWlJTU/Hpp59izZo1OHv2LGbMmIGSkhJMnToVADBp0iTMnTtXP16j0eDYsWM4duwYNBoNbt68iWPHjuHixYtWH7NWYJwK7ywLkG+ooVowucEciyNcYFz8j08oENEOaNLT8BnQp8HbGQPECREuWLvwFmu1kinYO1TOhSWVCl9sFFOXNFH4WtIFZmFu8X2BjpOBfi8a1lEMEFHb4QsgVQAQ38/wOrARMHM/Gxt03wp2nVg5CKWZmwdHWIHqYRq8WytBjx8/Hjk5OZg/fz4yMzORnJyMbdu26YOYMzIyIOdVlL116xY6dOigf71s2TIsW7YM/fr1Q3p6ulXHrBUovIBKGD6QzhJAAJsNdvMwG1jX4RHHH78+UqUBtCLFAaWwZAHyCwP+8yf7nCtwqLcA2RsDxLnAqvfJz2CXwTFs1pVfGFvfpyQHCIwy3b/wFrsMbARMWAdEJQm3S7rALFiAlH7AfR+wAdZ73mLXURYY4Wmog4StK6I7SI/lxvPp9wJwZQ/73D8cCG8FPLbNsF3Mgtq0H3DuV/HjMwzbu8/efnlVGiBfIva17C5be0hpY/2jWoDbW2HMmjULs2bNEt3GiRqOuLg4MFbUPzB3zFqBiQvMQZ3gxWg2APjjbeDybvaH2BGVpus75lLgvf1MK75KCqDqmjzqYNPWEjWOAeKCoIuE5+LS1P0aVgsgiTigwpvsMjiWDV42xl4BpN+f937JAkR4Gk8fBe5cAkLi2AalHR41P77VSNZKwwmhuN5A65FsQ+omIuEZYgIooh1w7S/T2mAA8OtstibX/Z8ASeNtfTfAmhHA9f3i2459y87zmaN1rief2wUQIYKxC4xrNeAMAdSoM1s4r+wucOuoqT+asB1z8T/qQFMBJOUCy6tunhjcxHQbPwaoqgL4cpghq0Qq04qP0igGiHO3cX26uFolUi4wLmZMzDrEn58x1hRpNN6fYoAIT8OvAfsAgL5zLI/v/SzbBqaZoUYdHljDWm7EAv/F1vmHA2EtxIVKaR673Pq8fQJISvxwFFxn6w81bGn7sT0YSoP3RBRGAa7OdIEpvFjTKmB/YS5CiLmuzmLiRMoCxPn1IxNNt8l5MUBZp4UptTZlgVVbqzh3m08wu+QEkFQ/ML0LLFp8u1QMkNUWIL4AoiwwopbjpQS6/QdokGBYJ1dI1xQS3EDIgK7/AdreL11IlMOZ/cT43eyLMoFvHwD+3e6887kAEkCeiEkQNOcCc5L5keoBOY7Tm4CP+0hvFxMnUsGHmafYpagA4jXNNRZQ1ogMboy2grUgcWZ1nxB2yXWwlnKBFVULoAAJASRlAbImPgkQCiiyABH1Df73J6YrMOxtNmmlQTPz+5mrKl9T8i4A1w+wFagPfsYWcty/2nnncwHkAvNETGKAql1gzvoh4Npi3DjI/hByP4KE7Wy1YA4XEwBiLjBNCZBXnd0oKoB4MUDGWRq2BEEDrBXIxAUWxi4lXWAWLEA1jgHi7e9NFiCiniHlAu79LCtC2owCts813c8erO0rlnsR2LVQuM6adjkeDFmAPBG9C8y4EKKTLEDBMUBYS7ZB3+U9zjlHfcFS/I2oBUiknH3WGQAM4B9hsMbwEcQAGQkgawSsXGGouaMpEnGBVZ/TXheYlADy9rM8N4BigIj6Df/zz2+B5BsKTNsO9JgJ/Pc40PcF031tqUEGGGJMLZF3wXRdSZ5t5/IwSAB5InoXmAuywDjIDeYYKi1UTRWzzoiZrbPMxP8AwjpAfAE0YR2bSm4N/GrQxi4wfRC0yB1eeSEbFAkAoSLNSwHpGCDjbDYp+AKKssCI+oY1NwAhcYY6bnyk0tml0FgZNyQVfO2MzvQuggSQJ2LsAtNngTnxTpgTQBd/r9UfaLdSVWGwjABASLzpGDELiJgLLLu6LUV4G/Fz8esAcZVa4/sBrYZbP1++ADJ2gfmbEUCZJ9hlYGODq0xqfvYi+AEgAUTUMwQWIDM3vtz3lU9+Bnsjpq1iE1tOb2ITJf5YKl7V2VzZDktoK2q2v5uhGCBPhLu7N6kE7cQaDLG92B+awhtA1ilpywMhTcENAAybtfTkXjYb7LN7hWPE7ubEssC4vlwNW4mfSywGyFahwE+Fl8oCK8lhBTFXgBEAbh1jl2L1f4znZy8UBE3UZ6y9ARBzd1/ZA2x8gg2evrhLuE2nBfq/JFxnjQUoOBbIvya+rSTX+tg+D4MsQJ6IcZsDV7jAvH2A5oPY58e+c9556jLcBSK4CRDWDGjcCRj7OTD1N8MYsYuZmAss5zy7lBRAIjFAtlZMtsYFpqsyLbx2+xi7jEqWPraUC8wenPm5JwhPhJ8eb64HJHfDwuefj9iiqMbiBwAu7TZdV1kqfXy5F+vm7m8m4Prr+4Edr0pv92BIAHkiUr3AnH0nnPwwuzyxXroJJiHN3WoBxC9HnzgOiO1peC32P2SMgqDL8g29thq2ED+XXCQGyFYLkL4adKGpC8xLZShaaJwJdvs4uzRrAarhpUVBLjCiHiMVBG0M3wVmjduZ+y3hY86FNecCMPMftkFrg+biY+5eAf5eIZ0wIYaHhFmQAPJE+DVeANe4wAA2DsgvHCjNBc5udu656iL6flpm+vGIWoCMXGC51V3ZAxtJm5b5FqBKewVQ9bGLsgxWKP4dpT4OiHdhqywzFESLbG/b+WzBJwQY/AYw9G1D53qCqC/IrbQA8a2+zQeLN1HlI2btMXaBca05+r3IZp15qViX9uM7gce2G7JHjfliCPDVCOsaQR/8DPiwK/CPe+sIkQDyRExcYE6sBG183s6Psc//eEc8PZuQ5s5ldinWuoJDNAbIyAWmj/8xU3aei7HR1sACxKXs373CLhVK4cVNLBW+8BYAhh0nlp7vSHo8xVbPJYj6hj1JAHIFMHoV+1wqhvPuNdPrurEAiukGvHwbuOdl4XqfEKBJd+nv/Z1LwNU/gQtWVIfO2Afknjf0L3QTJIA8ESkXmCtiIbo/CSgDgOzTwPktzj9fXUGnA67uZZ+LpaZyhLc2XWecBZZtIQAacEwMEFdWnxNu6mBhsHNIHLvkXF4AWwIfAAIihWOtwdLdKUEQLNa6wIz3af8A8MRuYOo2YNgyQ5FbjqoyoKi6j1/Wafb7bCyAKorMd373jzQ/j9sn2OapbzQCjq1jK9oXZQnHZFSn1Md0s/y+nAhlgXkiJmnwLhRAPiHsXfefy4A9bwGtRtj+Q1cfyTzOug6V/kDjrqbbH9sO5F1i76CMMXaBZexjl+aCjPlWQq72kK0WIN9Qdpl3iV0aB1Q2GwAcXwdc2AkMfI1dx108AySaoJojxIxrkCAIA9a6wAC23MbdK0C7sexr7gas6xNs6wzj2m55F9kM1VU92e9xj1nC7ZbS2gMizG+/dYQNxNZqgE0z2HXBTdhM4/O/sdbjoltsooSbm2+TAPJEFEYCyJUWIIB1Pfyzim3Gef43oNUw15y3NsM1ko3vK37BatLdIH58w1ixxMEXQOUFhiyreDM9xbhih+X5hjpR9gqgsjvs0jilNuFeQCZnrYH519mK4XwLkLUk3MvGKY14z7b5EUR9xRYL0ONpQPYZIK636TYxd/za+4C46mtL0W1DJ3kOSzE8/iICaPi7bAzkX8uB6wdNg63zMwwxklzCRWSi9UVbnQS5wDwR/d29kQCydCfgKHxD2bsHAEhbxMaZEOa5sINdJtxrfhwAPPoz0KQne0cECF1g1/axWWGhCdJtJgAgqDG7LLjBmrUBOwSQUWdpX6Oihr6hQOMu7POLO9mlPRagpvcAj/0GhJtx6REEYYCf8GLpuu/XgL1ZErPUBzYyPOcXZr36p+F51inhPpbicuL7sl6KMF6GaufHgHvnsTXQuOuRJcTCAVwMCSBPxKQZqostQADQezZrEcg5Cxxd67rz1kayTrNl4mUKoKUV1rKo9qwg4AQQ3wLEXZjE7ub4BFXf2RXcMFiAbI0B8gkVvg4UETXcPG5XV3/WW4BsEEA1LYpIEPUN/nemJlXVvdUGi82wZWxdMmP4MX6AoQaYFK1HAi/fBB76gX3dpCcrvhRe5uMfAWDIW0DHyexzruyKGyEB5Im42wUGsOKHK371+//Y/k+EOAc/Y5ethgNBjcyP5cNd5PgWoJtH2CW/dpAYnAUo/zovBsjGrunGFiAxUcPd5XGd6fUWIBtcYI4sikgQ9QG+6Knp96f7DNblFduDjRMytvQWVwcoN+3P3sAN/p/lY3qpgNB44LnzwKRfDOuHLAFaDmevG2EiNcxaDQNGvg/MuWjexe8iSAB5IiYuMBdUghaj82NsEF1pLrCX4jdE0emAkz+xzzm3obVwFzZ+Gvzdq+xSqugYR3AMu6woMNTpsbVQpq+xBUhEvDVoxi65QGlbXGBcSn3CPbbNiyDqO3wBVFMLau9ngSm/svE2Mpnh5smY1iOBid+Zd70bExApdNFFJQET1wGzDgIdJ7HrgmOBfi8BAxezMUkymaHGmJshAeSJGKfB65uhulgAKbzZDy0A7FtpCGIjDNy5xIoQLx/WFGwL+n5e1XU5KsvZ7AjAcsaU0s/gwuLEibeNFiBvH2HdHzEXGNftvegWGxxpSxB06llg1iHz9YwIgjCFXzLC0RbUFkPE14s1aq4JrUeyrvrOjwH3zAV6PePY4zsAEkCeiL4StIsLIYrRcihrPtVWALtec/35PR2uMWhkO2H7BmswdoEVXGeXSn9T95QY3J0cV93VnlYp/PMEiNz5+YYahNbtY4ZzWWMB8gkGwixYsgiCMEUQA+Tgn+k+z7EWmfs+FK53dEZWSBzw7Ek2ntRDIQHkiZh0g692gbkqC4yPTFbtE5YBpzYANw65fg6ejDWNQaUwdoFxvcSCY62rvWSc4mprDBAgFE1iFiDA4Aa79Du7VAeZL5RGEETN4H//HW0B8lKyFpnEB4QWYDenpLsDEkCeCGfp4WKAuEq/7uqKHZUEJD/EPt/+ssc0snMr5QXAis7Avuq7KHONQaXQV3OutgBxLSmsLRho7Mu3xwJUxavXIdV3jBNAXLA3lxpPEITzkTnpZ9pbLXSHKetfzz0SQJ6IcTNUfasDO+7wHcW9r7J3C9f3A2c2uW8enkLGP0DeBcNreyxAxi6wfK6bfJx1+wfFCF/b8/nQVlge0yCBXZYXsMvmg2w/D0EQ9lGTNHhLcNWjAdvLaNQBSAB5Ivog6ErW2mJvmrMjCYwGelYHse1cYAjMrq9knzE8D4iyL9CXu7PTW4Cusktz3eT5GLus7LIAlVse02a08DUJIIJwHc4sGNh8oOG5Pe1tajkkgDwRfhq8VgOg2uXkboXe6xm2EV7+NWD/x+6di7vJPssuuzwB/OdPYeVWazFxgdloATJuSmiPQG7UiV2aywAJa8ZmcgBsoHRovPRYgiAcw9NHgGm7nPt981IBTx0Epu2U7vJehyEB5InwCyFyWTeAey1AABskN2A++/yPZUBJnvnxdRlOACXcY39NC0kXmJUWIONUdHssQPd9yFZmfXyn+XGDlwD3vAo8SFXBCcIlNEgAYlwQb9ewBRAj0sC5HkACyBPht8KorHZRyOT2WRkcTdJEtoldRQGw5013z8b13DgEvNMKyKxuDVET8zQ/C6zsriHGRqyBoRjGAsieGKCgRsB9HwARbc2P81YD/Z53zQWZIAjCBZAA8kS4O/mqckNjOW9f61KjnY1cDgyqLpV+8HMg51/3zsfV/P2BoRoyAATH2X8svguMc3/5hVufjmo8ztZmqARBEPUYEkCeiDqIXZYXGCxAnvTj1rQf2zOG0QI757t7Nq6DYYCbRw2vQ5vWrEgZty+jMwRAW+v+4uDX8fAEgUwQBFFLIAHkiaiD2WXZXZ4FyM3xP8YMXMRaMP79Dbic7u7ZuIa7V4CC6nYgHR5hm/rVBL0LTGt7CjwH91khCIIgbIIEkCfiE8wuywsADdfmwIMsQADb4qDzNPb59lcNmUx1mfO/scvYXsColUB835odT+8CqxJWgbYF7rNCEARB2AQJIE9Ef1fPGDp9uzsFXoz+L7HuuqyTwPHv3D0b57L/Y2D7K+xzR9XB4WeB6V1gcbYdgyxABEEQdkECyBPxVhtS3gurA269PbD3km8o0Pd59nnaYrZbeF0k82S1+GGAjpOA7jMcc1xRFxhZgAiCIFwBCSBPhfth4zKOPM0FxtF1Omu1KM5kM6TqGtpKYNMMtihlqxHAyA/sq7cjBhcEXVUB5FfHFtnqAjNuh0EQBEFYBQkgT8UnhF1yAsjTgqA5vFRsQDQA/PUBUHjLvfNxJNoqIG0RawHyCQGGv+vYTCsuBijnLFvxWxVku6Dp9yIQ3QEY+rbj5kUQBFEPIAHkqXCxHYUebgECgNb3AU16sBlraYvdPRvHUFEEfNLPYNUatgwIiHDsOYz/p9FJtqfV+zUApqcD3f7jsGkRBEHUB0gAeSrGLjBPtQABrFWEK454/Dvg1jG3TqfGZJ4EtjwHZJ1ihejgN4Rdkx1FVJIwtiu6o+PPQRAEQYjiEQJo5cqViIuLg1qtRrdu3XDgwAGz43/88Ue0atUKarUaiYmJ2Lp1q2D7lClTIJPJBI8hQ4Y48y04Hs4CVJTJLj1ZAAFA405A4oMAGDZgmGHcPSP7OLwGWN0bOLGefT16FdDjKecUGfT2ARLuNbyO7uD4cxAEQRCiuF0ArV+/HqmpqViwYAGOHDmCpKQkDB48GNnZ2aLj//77b0ycOBHTpk3D0aNHMXr0aIwePRqnTp0SjBsyZAhu376tf3z3XS1L0+ZigLhCiO5uhGoNA+azbp1re4Fj37p7NrZx+wTw0+PA/z1jWNdmFNByqHPP2yzF8LwRWYAIgiBchdsF0LvvvosnnngCU6dORZs2bbB69Wr4+vriiy++EB3//vvvY8iQIXj++efRunVrLF68GB07dsSHH34oGKdSqRAZGal/hISEuOLtOA7j9GZPrANkTHAM0H8u+3zbXKDghnvnYy0Z+4EvhwEnf2RfN+kJzMtlO587u71Em1GAX0MgIpEyugiCIFyIWwWQRqPB4cOHkZJiuAuWy+VISUnBvn37RPfZt2+fYDwADB482GR8eno6wsPD0bJlS8yYMQN5eXmS86ioqEBhYaHg4XaMC9zVBgsQAPR8GmjcBagoBH6Z5dmuMJ0WOPQFsGYkoCkCwloCAxYAD64BFN6umYNvKDDrEDBtO/XyIgiCcCFuFUC5ubnQarWIiBBm10RERCAzM1N0n8zMTIvjhwwZgrVr1yItLQ1vvfUW9uzZg6FDh0KrFW/XsGTJEgQFBekfMTEecCfuY2Sxqg0WIICtbjx6FesKu7wb+Ocjd89InNI7wOcDgV+fBbQVQIuhwPTdQJ9UwD/ctXPxCba+AzxBEAThELzcPQFnMGHCBP3zxMREtG/fHgkJCUhPT8eAAQNMxs+dOxepqan614WFhe4XQSYusFpiAQLYPmGDXge2zgF2zGOzneJ6u3tWLOe3AVufNzQ1VQcBfV8Aus+sWWd3giAIolbh1it+WFgYFAoFsrKyBOuzsrIQGRkpuk9kZKRN4wGgadOmCAsLw8WLF0W3q1QqBAYGCh5up7a6wDi6PA60H8/2ufpxClBw031z0emAC7uAbx8EvhtvED8+ocBj24Ges0j8EARB1DPcetVXKpXo1KkT0tLS9Ot0Oh3S0tLQo0cP0X169OghGA8AO3fulBwPADdu3EBeXh6ioqIcM3FX4BcmfF1bXGAcMhkwYjkb3FuSA3wzFijJde0cGAa4nA58NgD4dixwYTu7vsvjwLSdwMx9QHhr186JIAiC8AjcftubmpqKTz/9FGvWrMHZs2cxY8YMlJSUYOrUqQCASZMmYe7cufrx//3vf7Ft2za88847OHfuHBYuXIhDhw5h1qxZAIDi4mI8//zz+Oeff3D16lWkpaVh1KhRaNasGQYPHuyW98inoLQST359GDtOi8c46QlqbGiWCdQ+CxAAKH2BCd8AAVFsu4e1o9nYG2dTXgjs/wRY2RVYOwq4dQRQ+gPdZgBPHwGGvwPEdAUCpK2GBEEQRN3G7TFA48ePR05ODubPn4/MzEwkJydj27Zt+kDnjIwMyHnuiZ49e2LdunV49dVX8fLLL6N58+bYtGkT2rVrBwBQKBQ4ceIE1qxZg/z8fERHR2PQoEFYvHgxVCoHNbGsAXsu5GDb6UzcLdVgUFszP8AKb1YEcV3Ca5sFiCMkDpi0GfhqOJB1EvhqBCuKQps69jw6XXX9oXXAmV+AylJ2vdIfSJrIdq13dCsLgiAIotYiYxhPzlN2D4WFhQgKCkJBQYHD44G+3X8Nr/x8Cm2iArH1v33MD147inXhAGysSpPuDp2LS8k+C6y5DyjJZpt+3r8KaDW8ZsdkGCDnPHB6I3DsO0NsD8CmtHd9go1DUntATBdBEAThdGz5/Xa7Bai+UVrBpuKXaKosDw6JMzz35Gao1hDeGvjPHjYg+vp+4PuH2CaqvZ+1rQJyZTlw8xDw73bg3BbgziXDNlUg0G4MkPwwW4uI6uoQBEEQEpAAcjGc8CmpsFEA1aY0eCkCo4EpW4C014B9K4Gzm9lHo85AiyFAk27se1YHAzI5W0yxOBvIuwhknQYy/mHFj1ZjOKZCCcT3A5ImsBaluvB3IgiCIJwOCSAXwwmfYlsFUG23AHEovNkaQUkTgb/eB05tZEXNzUPWH8M/ghU9rYaxvbRUAc6bL0EQBFEnIQHkYko0rAusvFKHKq0OXgoziXjBsYbndc2yEdEWGPMJK4ZObwKu/wPcOAgUZRosPDI5W6snrDn7aNwFiO3FBlCTe4sgCIKoASSAXEwpz/JTUqFFkK+1AsjXibNyI/7hQLfp7ANgA5srywBGx7aHIKFDEARBOAESQC6muMLQj6xYU4UgXzNNN/0aACPeY5t2qvxdMDsPQCZj6wcRBEEQhBMhAeRiSjV8C5AVcUCdH3PibAiCIAiifuL2StD1DS4GCLAyEJogCIIgCIdDAsjFlFTYaAEiCIIgCMLhkAByMaUkgAiCIAjC7ZAAcjFCF5jWzEiCIAiCIJwFCSAXwjCMwOpTXF7pxtkQBEEQRP2FBJAL0Wh1qNIZes/yrUEEQRAEQbgOEkAupNTI5bV0+3ks3X7OTbMhCIIgiPoLCSAXIpb2vnL3JeSXakRGEwRBEAThLEgAuZBSCZfXxexiF8+EIAiCIOo3JIBcSIlGPO39QnYxKrU6F8+GIAiCIOovJIBciHEMEMfcjSfRcdFOXMktcfGMCIIgCKJ+QgLIhZhrfVFUUYXvD2S4cDYEQRAEUX8hAeRCSiVcYBwf/3EZ976Tjh2nM100I4IgCIKon5AAciFc3Z+Ehn6SYy7nlGDWd0dx6maBq6ZFEARBEPUOEkAuhKsCnRwTgvXTu+PPF+5Bu0aBCPb1xsSuTaCQywAAmiodRqzYi6e+PYIfDl1355QJgiAIok7i5e4J1CfCA1ToGh+KZuH+6Na0AQDg++k9oKnSIdRPidfua4syjRajP/oLV3JLsOXkbWw7nYk+zcMQFeTj5tkTBEEQRN1BxjAMY3lY/aKwsBBBQUEoKChAYGCgy89/MbsYs9cfxambhQCAJ/sl4KWhrVw+D4IgCIKoTdjy+00uMA+kWbg/fn26Dz55tBMA4Nv915CRV+rmWREEQRBE3YEEkAczoHUEkhoHoai8Co+tOWgxi4wgCIIgCOsgAeTBKOQyfPxoZ0QEqnAxuxgrfr/o7ikRBEEQRJ2ABJCHExmkxuJR7QAAn/15Geczi9w8I8KTuHG3FEOW/4H3d11w91QIgiBqFSSAagED20QgpXU4KrUM/vv9UZy9XYjnfzyO9PPZ0OkYaHUUx17buJVfhkc/34/Nx29h4ebTWL3nkl3H+SDtAs5lFmHF7xdw/Q7FiREEQVgLZYGJ4O4sMDGyi8oxdPmfyCvRmGwLUHkhLsxPX2coJtQXucUV+tR5hRzwU3rBR6mAt0IOlRf7CPTxRoivEiF+3vBVekHpJYdSIRcsvRX85zIo5DLIZDKXvve6REZeKTYfv4nD1+5i9/kcwba9L96DxiG+Vh/rfGYRhn/wJ6qqBfBD3ZrgjfsTrd7/dkEZfjl2Cw92jkGon9Lq/QiCIDwVW36/SQCJ4IkCCAD2X87Ds+uP4VZBOYJ9vZFfWunyOchk0Isib4WMfe7FvZbD24tdx41RyGV64eQll8NL/1wGL4UcXnJZ9Ri5Yb3xOLkMCoUc3tVjvRTVY8T25T3XaHWQAZDLZcgsKIePtwI/H72JznEhyCosR1SQD+5LjkZ+SSV8VQpkFpTj93PZ6NuiIc7cKkSLCH8kNPSHlmEQ5q/S/w0qtTr88W8O2kYHoai8El4KOeLDpKt7c+h0DEat/AsnJap8Pz+4JZ66p5lV/4fd57Lxn28OQ1OlQ0yoD67fKYNMBnwxuQvuaRVu1TGe+vYItpy8jSahvtg8qxeCfU1FEMMwuJpXirgGvjYJ31+O3URJhRYPdWti9T5E7SGrsBxv/nYOQT7emDeijb6IK0G4GxJANcRTBRAAlFdqcfx6PpJigvHridsID1AhxFeJnOJy+Ku8UVGlxfU7ZQjzVyKzsBwKuQwMw1ahLqvUQlOlg6ZKh/IqLQrKqpBfqsHdUg3KNFpotDr99kotwz7X6tz9lt2KQi6DjmHQPNwfd0oq4a9SoFSjRXZRBdTecpRX6qBUyNG3RRhyijUY06ER8oorkNwkGBl5pYgN88Oe8zn6Y8z75bTJ8TkXZtMwPzzYJQaHrt7FgpFtEBWkhpeC9VIzDIPX/u8MbuaXYfn4ZNz34V5cyilBr2YN8O6DyXg/7QLW7c+Al1yG0R0aYc6glogMUpu8n1M3C1BSUYW2jYLQbsF2/frhiVFY+XBHk/GvbjqJb/7JwP0dGuGdB5Igt+KH7lpeCfotTQcAfPt4N/RqFmb13xsACsoqMWvdEbSJDsTcoa0lx+UUVUDlLUeg2tum4xM1I7uwHMM++BO5xaw1+ok+8XhleBs3z4ogWEgA1RBPFkCuhmEYVOkYVGp1qKxioNHqUKnlRJKu+jW33fCa216lY6DVseu0OvZYVfr1wufsGF31mOqx1a+1Wt7z6vkYjsfoz2N4zsBLIUOVlkFFlQ4RgSrczC9Dr2ZhuJRdjAb+Spy4XoCiiiq9kFHIZWgbHYgTNwoQHqBCdlGF5N9FqZDbLQ67Nw1FcUUVXhzSCnEN/OCtkOPed9JRWt0rjs/9HRrhzK1CnM8yBL9z1r9AtRf+euleBKi9oanS4envjmD76SwArKUu1FeJyCA1BraJQOfYUOw8k4m1/1wDwwCNgn1wM79MP5ZhgO+nd0f36grlAPB/x2/h6e+O6l8/3K0JXh/dzqIlaPGvZ/D53iv6Y88b3gYD20QgJtS8e6+iSouDV+7i872X9e7BtOf6IaGhv8nYzIJyDHx3DyKC1Nj23z56oSjGirQLyLhTimcHtkB0cM0qqq/cfRE/HbmBzyd3scrqVxd5Y+tZfPLHZcE6488OQbgLEkA1hARQ/aC8Uoui8io0DFCxweQMA2+FHJkF5QjzV+J2QTm0OgY6hsGV3BJEBKpRVqlFRaUOyU2C8ce/OYgMUuOfy3m4fqcUgWpvHLx6B+EBahy6dgeNQ3xx6mYBGvgrodUBeSUVmNAlBgvvawuVl0IwlwNX7mD+L6dwzoYsv1n3NMOcwS0F645m3MX/tpzFoWt3Jffji7dnBjRHdmE5vj94HQq5DEPaRmJkUjTulGiwcPNpaLQ69Gkehr0Xc8EwwIQuMXhleGsEVFtdGIYVmFU6Bn/8m4Pi8ios/vUMiiqENavkMuCtse3xQOcYk/kUlldiy4nb+OzPy7iUUyLYNqVnHBbe19Zkn5W7L2Lp9vMAgM8mdUZKmwjR93rgyh08+PE+AGys3AtDWqJzXChaRQbY5NKr0upwu6AcA97ZA41Wh1HJ0Xh/Qger968r5BVXoN/SdBRXVOHLKV2w82wW1u3PQLtGgdjwZE+ovRWWD0JYxfU7pSgoq0S7RkHunkqtggRQDSEBRDiKOyUa+CoVqNTqkF9aadYKwjCs5erkzQJ8+ddVtIwMwKd/Xka3+FDkFFUg2FeJEF8ldp3Nwv0dGuGloa0kf3ByiyuQXViBc5mF+PXEbVzOKUbThv6Y0jMOraMC9WLjzbGJUMhkmL3+GPb8m2NynMFtI7DyoY7YcPgGXtp4Ur8+MlANX6UCtwrK9NYzfjZiYqMgpA5qgZ1nsnAxqxgHrt6B0kuOOYNaQC6ToVLLIMTXG7nFFfjyr6v64P4AtRdKKlhRmlVYAaWXHGM7NkabqACM6dgYPt4KnM8qwn0f7kWl1nC+pmF+mNA1BlN7xcO72hpUpdVh/Cf/4PC1uwhQeQlEWfvGQXh9dDu0bxxs8X+47dRtLNh8GlmFBougXAYsvK8tFHIZ7m0VLtqrr7C8EpVVOvx05AZUXgqE+avQKipA1KLlaTAMg5W7L6JUo8Xzg1tCJpPh+p1SPPzZfmTcKUWryAD89t8+uFOiQf+l6SiqqEJUkBrfPt4NTWvB+3MEReWVOHmjAN2aNnB4DFSVVoc+b+/G7YJyrJjYASOToh16/LoMCaAaQgKI8BQYhnFZ1t3Z24VYu+8aztwqgEIuQ+9mYZh5TzO9yPrzQg5e3XQK1yTassQ28EWonxJd40Px9L3N4a9iey3rdAymf30Yu85mSZ67aZgfxnZqjEe6xcJXpYBCJsOUrw7iD54oiwhUoUrL6MWS0ksOTZXQDdk1LhQJ4f7w8Vbg1K0CHLhyB2pvOX5/rj+2nryNjUdu4nJuMcor2f3iw/zQvnEQBreNhNpbjnbR7N22r8oLh6/dxf7LefgoXViioEmoLzJ4JQdUXnL0aR6G+DA/PNI9FjlFFfjjQi4+SDOtzaSQy9CneRj8lF54dmALNAv3x90SDW4XlGPl7os4cTMfWi2DqGAfdGwSjHaNguCn9EJCuD+Ky6vQJNQXQb6s9a2iSovTtwpxK78MJ28UoKJKh67xoSjVaOGnVCCnuALHMvIxukMj9G4Wpo/fOn49H1mF5fBXeSE62AdxRq68wvJKfL3vmt7CtvqRjhjSLgqPrzmIXWez0TjEB59P7oKWkQEAgD3/5uDljSdxM78MKa3D8dnkLpL/Z4CtXTVv0ymM7xKDIe2izI71ZFLXH8PGozcxPDEK741PhtLLcVVl/r6Ui4c+3Q+AFds7nu2LZuEBDju+FAWlldhy8jbuS47Wf39rGySAaggJIIIQh2EY5JdW4kpeCcortWgU7INgHyUKyirROMRHMki6TKPF1/9cxcGrd6H2VkCpkCOvpAJKhRwprSNwf8dGessNR0WVFgs3n8G/WUW4ebcMmYXlAAB/lRca+CvxaPdYBKq9cTO/DA0DVHhj61mTOCqVlxwfPtQRA3kusrziCsz58bhJGQJzPNK9CQa0joBWy6Br01B8+sdlHMm4i78u5lnct01UIKKD1cgqrBBkAPp4KxDbwNcmt6efUoEu8aFQeclx8kYBbhWUW7VfXANfdG/aADIZ8N2B6/r1XIzWI91jofSS45/LeXh8zSEU86xl/iovTO/bFO/u/BcKuQw7nu1rYsW6mF2MQe/tgY5hsxkndm0iWVrhsa8O4vdz2QCApMZB6Na0Aab2ihO1ojEMg/1X7iBQ7Y020YZrcX6pBgFqb6stL+WVWn22qCMor9Si1bxt+tfTesdj3og2KCithMpbbrcrUKdjsOXkbazcfVHwuXigU2MsfSCpxvM2h6ZKh4c+/QeHrt3FuE6NsczJ53MWtU4ArVy5EkuXLkVmZiaSkpKwYsUKdO3aVXL8jz/+iHnz5uHq1ato3rw53nrrLQwbNky/nWEYLFiwAJ9++iny8/PRq1cvrFq1Cs2bN7dqPiSACMKzKCirxMErdxDqr0S76CDRu+3zmUXYePQGfLwVKKvUIkDlhUFtI9EiwvTOuUqrw+7zOSiv1OK3U7dxK78cd0o0yLhTCrkM0DFAdJAaRRVVGNQmEkvHtRcVd6dvFWDyFwfRKFiN81lFAnfgo91jMaN/AiID1fp9d5/Pxr+ZRfj9XDb2X7mjP45CLkPf5mF4om9T+Cq9cDmnGIev3cW/WUUoqdDiQnYRfLwVKCwXxlYF+3ojtoEf2jcKglwGbD+dhUAfL+QWa1BeqcXgtpHYdSbLJCYrPEAFH6VCb83zVsgQ7KtEUXklyivZpIEhbSOx8ehNFPHOaa7W1IsbTmD9IVZcBfl4o2VkABoF+yA8kC0hoZDJ8OPhG8gRSS5QKuQY07ERRiU3QqNgH9wt1WDxr2eQW1yBq3mlkMmA6X2aIiJQjQNX7mDb6UwE+3rjuUEt8Wj3WP1xfjl2E3vO52B8lxh0jQ+FTCbDnxdyMGvdUfgqFVh4X1sMbhtpcv5KrQ5zN55EkI83Xh3e2qLVNe1sFqatOST4/338SCek/nAMMpkMLw9rhQc7x9hkvS0sr8R/1h7GvssGUf3Mvc3wwe8XoVTIkf58/xoH8fPJyCvFvsu5CPVToUOTYMzbdAq/ncrUb5/QJQYPd4tFYmPnxCBdyilGfAM/qzJLbaFWCaD169dj0qRJWL16Nbp164bly5fjxx9/xPnz5xEeblrP5O+//0bfvn2xZMkSjBgxAuvWrcNbb72FI0eOoF07tmXEW2+9hSVLlmDNmjWIj4/HvHnzcPLkSZw5cwZqtWlqsDEkgAiifsIFvecVaxARqLLqB0ynYyCXy3AtrwRF5VUIVHvjzO0CDGwTKWlx0OkYnLpVgIw7pegaF4rwQPPXJe4yvfdiLm4XlKO8Ugu1lwL3JUeLWhsqqrSo0jLwU7ExVXv+zcGZW4W4mleCgW0iMCq5ERiGwcd/XMYnf1zGHV6B1c6xIfjm8W5Qeyvwz+U8bDh8A/mllejQJBjTesdLWjcqqrT49p8M/HDoukWr1oj2UfCSyxDo443zmUUCMWgrI5OiUVmlw/msIlzJNQTRd4oNQXyYHzYeuQF+sfzezcLQtlEgxnVsjOYRAbhbosEHv1/Al39dBQAseyAJ4zo11o83dkMzDINZ645iy8nbmNwjFrcLyrHjjKl7d1ynxujetAEqtToMbRcpWmeLz9yNJ/Ddgevw8VagSqdDVJAPdqb2xUOf7sfha3cR5q/Cc4NaYGzHxvobgKu5JUg7l41KrQ6jkxshMkht1jp27Ho+lm4/hxBfJbaevC34uwCAl1ymL6zK0TycrYfWq1kDhPmr0DY6CDGhPsgsLMepm4U4c6sQEYEq3JccDV+lebdZRl4pDl27g4NX72L9wQwseyAJYzo2NruPrdQqAdStWzd06dIFH374IQBAp9MhJiYGTz/9NF566SWT8ePHj0dJSQl+/fVX/bru3bsjOTkZq1evBsMwiI6OxnPPPYc5c+YAAAoKChAREYGvvvoKEyZMsDgnEkAEQdQXGIbB7YJy3C3VoKJKh6TGwTVyFVVpdfj9XDbKKrU4c7sQpRVaFFdUIa9Eg1FJ0Qj29Ubv5mGCTMgDV+7guwMZOHDlDnKKK6Cp0qFDk2A80acpWkcFYv4vp3A5pwStowKg1TF46p5m2HcpD+/s/Nfk/F3jQ3EsI19QpmJcp8bwV3nhq7+vCsaKlbtQe8sxvnMMQvyU2HT0Jq7fLUPzcH8MS4xCckww1h+6ji0nbgMAfprRA1FBPhi18i+9ZWt0cjQ2HbslOGaonxJjOjRCYuMg+HgroGMYXMsrxZ1SDZqHByC7qBxvb2Njrr6f3h1togMhAxCg9sblnGI8+c1h/JtVDICNlxuRFA0ZgE/+uIyyStbtq5DLEBGgwq2CcgT5eGNw2whUVOlw8Mod5JZoEKDyMukk0LFJMC7nliC/tBKtIgPwxphEBPt446WfTuLAVWlRqvKSo8Io/i5Q7YXxXWIwMikaKi+FvgyKpkqHc5mF2H/lDrafyhQIrEk9YrGoutelo6g1Akij0cDX1xcbNmzA6NGj9esnT56M/Px8/PLLLyb7NGnSBKmpqZg9e7Z+3YIFC7Bp0yYcP34cly9fRkJCAo4ePYrk5GT9mH79+iE5ORnvv/++xXmRACIIgnAP5ZVanM8sQtvoQLP1nQDWorH52C34q70QFcRmJt6XFI2cogpsPn4L1++U4p5W4ejfMhwMw+DLv67icMZdVFTqkHYuC9yvn5dchpFJ0cgtrsCfF3ItzlEuAxaNaodHqt1v1/JKsHDzaQxoHYFHusdiz7852HT0JrIKy5FZWI7LRuUdpHikexO8PtrUxVheqcU3/1zD6j2XkVssFGzJMcHwVshw8Kp06Qs+QT7e6NeiIQa1jcCI9tHQVOlQVqlFkI+woGhxRRXm/3IKam8FGvgpcfZ2EbIKy3EusxCVWgYKuQzNw/3RKjIAR6/nSyZHGJMcE4zoYDWm9IxH1/hQq/axBVt+v90a5p2bmwutVouICGENj4iICJw7d050n8zMTNHxmZmZ+u3cOqkxxlRUVKCiwvChKiwstO2NEARBEA5B7a1AUkywVWOTY4KRLDI2PFCNx/s0FayTyWR4rHc8HkM8ADaQ+nxmEVpEBCCkOmCbYRj8cSEXm4/dQmZhGcZ2bIxOsSE4dPUufj56E9lF5Wga5o8Z/RMEc4xt4IcvpxriVvu1aIh+LRoCYOOLdp3Jwp5/c3AltwQarQ4KmQwN/JVoGKDC1dxSVFRpMbZjY9E6Wdzf5PE+TfFglxh8+08GbuaXori8Cq2iAvF473h4Vdcvu5xbjJYRAbiQXYzNx29B5SXHoDaRaBzCFj69fqcUw9tHCVxVSi+5aEydv8oL7z6YbLK+okqLG3fL0CjYR+8O1eoY/H4uG98dyMCRjLtQyAwti7wVMsSE+qJTbAj6tmiIjk1CRN+jO6ideW4OZsmSJXjttdfcPQ2CIAjCRQT7KtHNqHq1TCYTiBeO2AZsmQZ78FbIMTQxCkMTa57yH6j2xoz+CaLbIoPU+vY3DfxVJpW5Y0J9HVKtW+WlMMkCVMhlGNgmQpBtWRtwXOECOwgLC4NCoUBWljCALCsrC5GRppH6ABAZGWl2PLe05Zhz585FQUGB/nH9+nXRcQRBEARB1A3cKoCUSiU6deqEtLQ0/TqdToe0tDT06NFDdJ8ePXoIxgPAzp079ePj4+MRGRkpGFNYWIj9+/dLHlOlUiEwMFDwIAiCIAji/9u795gq6z8O4O+DwBFQbh6BgwqCEF6hFKWjphVMQVdqttRYoTUZig6XWt7RVsOtZbc1NlfqPyZLJ+hSKUWhdHiBQMALiaOw9IjKkAOKt/P5/eF4fj0Cgiac2/u1ne3wfB9On/c+j/HZc57nHPtl8bfAPvjgAyQlJSE6OhpjxozBl19+iaamJsybNw8A8O6776Jfv37IyMgAAKSlpWHixIn4/PPPMXXqVGRlZaGoqAibN28G8PAU5pIlS/DJJ58gPDxcuQ0+MDBQdaE1EREROS6LD0CzZs3CtWvXsG7dOhiNRjz//PPIzc1VLmKuqamBk9P/T1SNHTsWP/zwA9asWYNVq1YhPDwcOTk5ymcAAcCHH36IpqYmJCcno76+HuPHj0dubm6nPgOIiIiI7J/FPwfIGvE2eCIiItvzJH+/LXoNEBEREZElcAAiIiIih8MBiIiIiBwOByAiIiJyOByAiIiIyOFwACIiIiKHwwGIiIiIHA4HICIiInI4HICIiIjI4Vj8qzCsUcuHYzc0NFi4EiIiIuqslr/bnfmSCw5AbTCZTACAAQMGWLgSIiIielImkwleXl6P3YffBdYGs9mMy5cvo3fv3tBoNM/kNRsaGjBgwABcunTJbr9fzN4z2ns+wP4z2ns+wP4z2ns+wP4zdmU+EYHJZEJgYKDqi9TbwjNAbXByckL//v275LU9PT3t8oD+N3vPaO/5APvPaO/5APvPaO/5APvP2FX5Ojrz04IXQRMREZHD4QBEREREDocDUDfRarVIT0+HVqu1dCldxt4z2ns+wP4z2ns+wP4z2ns+wP4zWks+XgRNREREDodngIiIiMjhcAAiIiIih8MBiIiIiBwOByAiIiJyOByAusm3336LgQMHomfPnoiJicHJkyctXdJTWb9+PTQajeoxePBgZb25uRmpqano06cPevXqhZkzZ+Lq1asWrLhjv/76K1577TUEBgZCo9EgJydHtS4iWLduHfR6Pdzc3BAXF4cLFy6o9qmrq0NiYiI8PT3h7e2N999/H42Njd2Yon0d5Zs7d26rnsbHx6v2seZ8GRkZGD16NHr37g0/Pz9Mnz4dlZWVqn06c1zW1NRg6tSpcHd3h5+fH5YvX4779+93Z5R2dSbjyy+/3KqPKSkpqn2sNWNmZiYiIyOVD8YzGAw4cOCAsm7r/QM6zmjL/WvLxo0bodFosGTJEmWb1fVRqMtlZWWJq6urbNmyRc6cOSPz588Xb29vuXr1qqVLe2Lp6ekybNgwuXLlivK4du2asp6SkiIDBgyQvLw8KSoqkhdffFHGjh1rwYo7tn//flm9erXs3r1bAEh2drZqfePGjeLl5SU5OTly+vRpef311yUkJERu376t7BMfHy9RUVFy/Phx+e233yQsLEzmzJnTzUna1lG+pKQkiY+PV/W0rq5OtY8155s8ebJs3bpVKioqpLS0VKZMmSJBQUHS2Nio7NPRcXn//n0ZPny4xMXFSUlJiezfv190Op2sXLnSEpFa6UzGiRMnyvz581V9vHnzprJuzRn37t0r+/btkz/++EMqKytl1apV4uLiIhUVFSJi+/0T6TijLffvUSdPnpSBAwdKZGSkpKWlKdutrY8cgLrBmDFjJDU1Vfn5wYMHEhgYKBkZGRas6umkp6dLVFRUm2v19fXi4uIiO3fuVLadO3dOAEhhYWE3VfjfPDogmM1mCQgIkM8++0zZVl9fL1qtVnbs2CEiImfPnhUAcurUKWWfAwcOiEajkX/++afbau+M9gagadOmtfs7tpRPRKS2tlYASEFBgYh07rjcv3+/ODk5idFoVPbJzMwUT09PuXPnTvcG6IRHM4o8/AP67z82j7K1jD4+PvLdd9/ZZf9atGQUsZ/+mUwmCQ8Pl4MHD6oyWWMf+RZYF7t79y6Ki4sRFxenbHNyckJcXBwKCwstWNnTu3DhAgIDAxEaGorExETU1NQAAIqLi3Hv3j1V1sGDByMoKMhms1ZXV8NoNKoyeXl5ISYmRslUWFgIb29vREdHK/vExcXByckJJ06c6Paan0Z+fj78/PwQERGBBQsW4MaNG8qareW7efMmAMDX1xdA547LwsJCjBgxAv7+/so+kydPRkNDA86cOdON1XfOoxlbbN++HTqdDsOHD8fKlStx69YtZc1WMj548ABZWVloamqCwWCwy/49mrGFPfQvNTUVU6dOVfULsM5/h/wy1C52/fp1PHjwQNVQAPD398f58+ctVNXTi4mJwbZt2xAREYErV65gw4YNeOmll1BRUQGj0QhXV1d4e3urfsff3x9Go9EyBf9HLXW31b+WNaPRCD8/P9W6s7MzfH19bSJ3fHw83njjDYSEhODixYtYtWoVEhISUFhYiB49ethUPrPZjCVLlmDcuHEYPnw4AHTquDQajW32uGXNmrSVEQDefvttBAcHIzAwEGVlZfjoo49QWVmJ3bt3A7D+jOXl5TAYDGhubkavXr2QnZ2NoUOHorS01G76115GwPb7BwBZWVn4/fffcerUqVZr1vjvkAMQPZGEhATleWRkJGJiYhAcHIwff/wRbm5uFqyMntbs2bOV5yNGjEBkZCQGDRqE/Px8xMbGWrCyJ5eamoqKigocPXrU0qV0mfYyJicnK89HjBgBvV6P2NhYXLx4EYMGDeruMp9YREQESktLcfPmTezatQtJSUkoKCiwdFnPVHsZhw4davP9u3TpEtLS0nDw4EH07NnT0uV0Ct8C62I6nQ49evRodaX71atXERAQYKGqnh1vb28899xzqKqqQkBAAO7evYv6+nrVPractaXux/UvICAAtbW1qvX79++jrq7OJnOHhoZCp9OhqqoKgO3kW7RoEX766SccOXIE/fv3V7Z35rgMCAhos8cta9aivYxtiYmJAQBVH605o6urK8LCwjBq1ChkZGQgKioKX331lV31r72MbbG1/hUXF6O2thYjR46Es7MznJ2dUVBQgK+//hrOzs7w9/e3uj5yAOpirq6uGDVqFPLy8pRtZrMZeXl5qvd+bVVjYyMuXrwIvV6PUaNGwcXFRZW1srISNTU1Nps1JCQEAQEBqkwNDQ04ceKEkslgMKC+vh7FxcXKPocPH4bZbFb+J2ZL/v77b9y4cQN6vR6A9ecTESxatAjZ2dk4fPgwQkJCVOudOS4NBgPKy8tVg97Bgwfh6empvEVhSR1lbEtpaSkAqPpozRkfZTabcefOHbvoX3taMrbF1voXGxuL8vJylJaWKo/o6GgkJiYqz62uj8/8smpqJSsrS7RarWzbtk3Onj0rycnJ4u3trbrS3VYsXbpU8vPzpbq6Wo4dOyZxcXGi0+mktrZWRB7e5hgUFCSHDx+WoqIiMRgMYjAYLFz145lMJikpKZGSkhIBIJs2bZKSkhL566+/ROThbfDe3t6yZ88eKSsrk2nTprV5G/wLL7wgJ06ckKNHj0p4eLjV3Cb+uHwmk0mWLVsmhYWFUl1dLYcOHZKRI0dKeHi4NDc3K69hzfkWLFggXl5ekp+fr7qF+NatW8o+HR2XLbffTpo0SUpLSyU3N1f69u1rNbcYd5SxqqpKPv74YykqKpLq6mrZs2ePhIaGyoQJE5TXsOaMK1askIKCAqmurpaysjJZsWKFaDQa+eWXX0TE9vsn8viMtt6/9jx6Z5u19ZEDUDf55ptvJCgoSFxdXWXMmDFy/PhxS5f0VGbNmiV6vV5cXV2lX79+MmvWLKmqqlLWb9++LQsXLhQfHx9xd3eXGTNmyJUrVyxYcceOHDkiAFo9kpKSROThrfBr164Vf39/0Wq1EhsbK5WVlarXuHHjhsyZM0d69eolnp6eMm/ePDGZTBZI09rj8t26dUsmTZokffv2FRcXFwkODpb58+e3Gs6tOV9b2QDI1q1blX06c1z++eefkpCQIG5ubqLT6WTp0qVy7969bk7Tto4y1tTUyIQJE8TX11e0Wq2EhYXJ8uXLVZ8jI2K9Gd977z0JDg4WV1dX6du3r8TGxirDj4jt90/k8RltvX/teXQAsrY+akREnv15JSIiIiLrxWuAiIiIyOFwACIiIiKHwwGIiIiIHA4HICIiInI4HICIiIjI4XAAIiIiIofDAYiIiIgcDgcgIqJO0Gg0yMnJsXQZRPSMcAAiIqs3d+5caDSaVo/4+HhLl0ZENsrZ0gUQEXVGfHw8tm7dqtqm1WotVA0R2TqeASIim6DVahEQEKB6+Pj4AHj49lRmZiYSEhLg5uaG0NBQ7Nq1S/X75eXlePXVV+Hm5oY+ffogOTkZjY2Nqn22bNmCYcOGQavVQq/XY9GiRar169evY8aMGXB3d0d4eDj27t3btaGJqMtwACIiu7B27VrMnDkTp0+fRmJiImbPno1z584BAJqamjB58mT4+Pjg1KlT2LlzJw4dOqQacDIzM5Gamork5GSUl5dj7969CAsLU/03NmzYgLfeegtlZWWYMmUKEhMTUVdX1605iegZ6ZKvWCUieoaSkpKkR48e4uHhoXp8+umnIvLw29JTUlJUvxMTEyMLFiwQEZHNmzeLj4+PNDY2Kuv79u0TJycn5ZvvAwMDZfXq1e3WAEDWrFmj/NzY2CgA5MCBA88sJxF1H14DREQ24ZVXXkFmZqZqm6+vr/LcYDCo1gwGA0pLSwEA586dQ1RUFDw8PJT1cePGwWw2o7KyEhqNBpcvX0ZsbOxja4iMjFSee3h4wNPTE7W1tU8biYgsiAMQEdkEDw+PVm9JPStubm6d2s/FxUX1s0ajgdls7oqSiKiL8RogIrILx48fb/XzkCFDAABDhgzB6dOn0dTUpKwfO3YMTk5OiIiIQO/evTFw4EDk5eV1a81EZDk8A0RENuHOnTswGo2qbc7OztDpdACAnTt3Ijo6GuPHj8f27dtx8uRJfP/99wCAxMREpKenIykpCevXr8e1a9ewePFivPPOO/D39wcArF+/HikpKfDz80NCQgJMJhOOHTuGxYsXd29QIuoWHICIyCbk5uZCr9ertkVEROD8+fMAHt6hlZWVhYULF0Kv12PHjh0YOnQoAMDd3R0///wz0tLSMHr0aLi7u2PmzJnYtGmT8lpJSUlobm7GF198gWXLlkGn0+HNN9/svoBE1K00IiKWLoKI6L/QaDTIzs7G9OnTLV0KEdkIXgNEREREDocDEBERETkcXgNERDaP7+QT0ZPiGSAiIiJyOByAiIiIyOFwACIiIiKHwwGIiIiIHA4HICIiInI4HICIiIjI4XAAIiIiIofDAYiIiIgcDgcgIiIicjj/A6FBPsB3PMcMAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Evaluate the Model And Plot the Actual Vs Predicted wrt to month**"
      ],
      "metadata": {
        "id": "MIX8Z3XQ2e4d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate(test_loader):\n",
        "    # Set the model to evaluation mode\n",
        "    model.eval()\n",
        "\n",
        "    # Lists to store predicted and true values\n",
        "    y_pred = []\n",
        "    y_true = []\n",
        "\n",
        "    # Evaluate the model on test data\n",
        "    with torch.no_grad():\n",
        "        for seq, labels in test_loader:\n",
        "            # Get the model predictions\n",
        "            test_outputs = model(seq)\n",
        "\n",
        "            # Append the predicted values to the list\n",
        "            y_pred.extend(test_outputs.numpy())\n",
        "\n",
        "            # Append the true labels to the list\n",
        "            y_true.append(labels.item())\n",
        "\n",
        "    # Calculate the Mean Squared Error (MSE) between predicted and true values\n",
        "    test_mse = mse(y_true, y_pred)\n",
        "    print(f'Test MSE: {test_mse:.4f}')\n",
        "\n",
        "    # Inverse transform the scaled predicted and true values\n",
        "    y_true = scaler.inverse_transform(np.array(y_true).reshape(1, -1))\n",
        "    y_pred = scaler.inverse_transform(np.array(y_pred).reshape(1, -1))\n",
        "\n",
        "    # Plot the actual vs predicted sale prices\n",
        "    plt.plot(month[22:27], y_true.T, label='Actual')\n",
        "    plt.plot(month[22:27], y_pred.T, label='Predicted')\n",
        "    plt.xlabel('Month')\n",
        "    plt.ylabel('Sale Prices')\n",
        "    plt.title('Actual Vs Predicted')\n",
        "    plt.legend()\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "AAthFGSoWIIP"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "cA6UmXfiqG_y",
        "outputId": "4050ccf8-fdcd-412a-9ae5-0fa2d59e9334"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test MSE: 0.0821\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Hyperparameter Tuning**"
      ],
      "metadata": {
        "id": "2ZNwccuoa-5o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "\n",
        "epochs = 20\n",
        "\n",
        "# Define ranges of hyperparameters\n",
        "hidden_layer_sizes = [32, 64, 128]\n",
        "dropout_values = [0.1, 0.2, 0.3]\n",
        "num_layers_values = [1, 2, 3]\n",
        "learning_rate = [0.1, 0.01, 0.001, 0.0001]\n",
        "\n",
        "best_loss = float('inf')\n",
        "best_hyperparameters = {\"hidden_layer_sizes\": hidden_layer_sizes[0] , \"dropout_values\": dropout_values[0], \"num_layers_values\": num_layers_values[0], \"learning_rate\": learning_rate[0]}\n",
        "\n",
        "# Iterate through all combinations of hyperparameters\n",
        "for hidden_layer_size, dropout, num_layers, lr in itertools.product(hidden_layer_sizes, dropout_values, num_layers_values, learning_rate):\n",
        "\n",
        "    # Create the LSTM model with current hyperparameters\n",
        "    model = LSTM(hidden_layer_size=hidden_layer_size, p=dropout, num_layers=num_layers)\n",
        "\n",
        "    loss_function = nn.MSELoss()\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "\n",
        "    # Training loop\n",
        "    for epoch in range(1, epochs + 1):\n",
        "      model.train()  # Set the model to training mode\n",
        "      train_loss = 0.0\n",
        "\n",
        "      for seq, labels in train_loader:\n",
        "          optimizer.zero_grad()\n",
        "          model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
        "                              torch.zeros(1, 1, model.hidden_layer_size))\n",
        "\n",
        "          y_pred = model(seq)\n",
        "          loss = loss_function(y_pred, labels)\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "\n",
        "          train_loss += loss.item()\n",
        "\n",
        "      train_loss /= len(train_loader)\n",
        "\n",
        "      # Validation loop\n",
        "      model.eval()  # Set the model to evaluation mode\n",
        "      val_loss = 0.0\n",
        "\n",
        "      with torch.no_grad():\n",
        "          for seq_val, labels_val in val_loader:\n",
        "              model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
        "                                    torch.zeros(1, 1, model.hidden_layer_size))\n",
        "\n",
        "              y_pred_val = model(seq_val)\n",
        "              loss_val = loss_function(y_pred_val, labels_val)\n",
        "              val_loss += loss_val.item()\n",
        "\n",
        "      val_loss /= len(val_loader)\n",
        "\n",
        "    if(val_loss < best_loss):\n",
        "      best_hyperparameters[\"hidden_layer_sizes\"] = hidden_layer_size\n",
        "      best_hyperparameters[\"dropout_values\"] = dropout\n",
        "      best_hyperparameters[\"num_layers_values\"] = num_layers\n",
        "      best_hyperparameters[\"learning_rate\"]=lr\n",
        "\n",
        "      best_loss = val_loss\n",
        "\n",
        "# Print the best hyperparameters and their corresponding validation loss\n",
        "print(\"Best Hyperparameters:\")\n",
        "print(best_hyperparameters)\n",
        "print(\"Best Validation Loss:\", best_loss)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LObhbum7fR0e",
        "outputId": "0bdeb372-dae8-4faa-af37-dd3556b10f76"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py:82: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py:535: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py:82: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py:82: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.3 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters:\n",
            "{'hidden_layer_sizes': 32, 'dropout_values': 0.2, 'num_layers_values': 2, 'learning_rate': 0.1}\n",
            "Best Validation Loss: 0.026398887537652627\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Train the model using the best hyperparameters**"
      ],
      "metadata": {
        "id": "Ncrvbmgk24Zn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 250\n",
        "\n",
        "model = LSTM(hidden_layer_size=best_hyperparameters[\"hidden_layer_sizes\"], p=best_hyperparameters[\"dropout_values\"], num_layers=best_hyperparameters[\"num_layers_values\"])\n",
        "loss_function = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=best_hyperparameters[\"learning_rate\"])\n",
        "\n",
        "train_losses, val_losses = train_val(epochs, train_loader, val_loader, loss_function, optimizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMeMPoPqpaWM",
        "outputId": "a7375ea6-db02-4776-cfc3-186c1d778516"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py:82: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py:535: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/250], Train Loss: 0.0582, Val Loss: 0.2125\n",
            "Epoch [26/250], Train Loss: 0.0310, Val Loss: 0.1631\n",
            "Epoch [51/250], Train Loss: 0.0180, Val Loss: 0.1176\n",
            "Epoch [76/250], Train Loss: 0.0179, Val Loss: 0.1287\n",
            "Epoch [101/250], Train Loss: 0.0142, Val Loss: 0.1420\n",
            "Epoch [126/250], Train Loss: 0.0137, Val Loss: 0.1589\n",
            "Epoch [151/250], Train Loss: 0.0164, Val Loss: 0.1727\n",
            "Epoch [176/250], Train Loss: 0.0130, Val Loss: 0.2057\n",
            "Epoch [201/250], Train Loss: 0.0156, Val Loss: 0.1921\n",
            "Epoch [226/250], Train Loss: 0.0124, Val Loss: 0.1666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Plot the model train and validation loss vs epochs**"
      ],
      "metadata": {
        "id": "E4AURakN6ASt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot(epochs, train_losses, val_losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "PUlMGFv25_5_",
        "outputId": "803e6947-39cd-47c4-88fc-64551c15ae82"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Evaluate the model**"
      ],
      "metadata": {
        "id": "mkoVv67s6IDe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "aWsvffnn6IVJ",
        "outputId": "770232fe-d6d4-48b2-8005-5abe14381651"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test MSE: 0.0508\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Finetuning**"
      ],
      "metadata": {
        "id": "xWAP4u7Po9j3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Choosing Best Optimizer**"
      ],
      "metadata": {
        "id": "GRinNASG3Gd9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer1 = optim.Adam(model.parameters(), lr=0.001)\n",
        "optimizer2 = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
        "optimizer3 = optim.Adagrad(model.parameters(), lr=0.001)\n",
        "optimizer4 = optim.RMSprop(model.parameters(), lr=0.001, alpha=0.9)\n",
        "\n",
        "# Store the optimizers in a list\n",
        "optimizers_list = [optimizer1, optimizer2, optimizer3, optimizer4]\n",
        "list_opt = {0: \"Adam\", 1: \"SGD\", 2: \"Adagrad\", 3: \"RMSProp\"}\n",
        "\n",
        "opt = \"\"\n",
        "\n",
        "t = 0\n",
        "best_loss = float('inf')\n",
        "epochs = 20\n",
        "\n",
        "loss_list = []\n",
        "\n",
        "for i in range(len(optimizers_list)):\n",
        "  optimizer = optimizers_list[i]\n",
        "\n",
        "  # Training loop\n",
        "  for epoch in range(1, epochs + 1):\n",
        "    model.train()  # Set the model to training mode\n",
        "    train_loss = 0.0\n",
        "\n",
        "    for seq, labels in train_loader:\n",
        "        optimizer.zero_grad()\n",
        "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
        "                            torch.zeros(1, 1, model.hidden_layer_size))\n",
        "\n",
        "        y_pred = model(seq)\n",
        "        loss = loss_function(y_pred, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        train_loss += loss.item()\n",
        "\n",
        "    train_loss /= len(train_loader)\n",
        "\n",
        "    # Validation loop\n",
        "    model.eval()  # Set the model to evaluation mode\n",
        "    val_loss = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for seq_val, labels_val in val_loader:\n",
        "            model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
        "                                  torch.zeros(1, 1, model.hidden_layer_size))\n",
        "\n",
        "            y_pred_val = model(seq_val)\n",
        "            loss_val = loss_function(y_pred_val, labels_val)\n",
        "            val_loss += loss_val.item()\n",
        "\n",
        "    val_loss /= len(val_loader)\n",
        "\n",
        "  loss_list.append(val_loss)\n",
        "  if(val_loss < best_loss):\n",
        "    opt = list_opt[i]\n",
        "    t = i\n",
        "    best_loss = val_loss\n",
        "\n",
        "print(\"Best Optimizer :\", opt, \"val_loss:\", loss_list[t])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4crP93KpES0",
        "outputId": "3c0da8e1-c59f-44bb-9e49-9901788826c9"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py:535: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Optimizer : RMSProp val_loss: 0.2553142920136452\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Train using the best optimizer**"
      ],
      "metadata": {
        "id": "_X5DM7Pa3Mhz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 250\n",
        "optimizer = optimizer3\n",
        "\n",
        "train_losses, val_losses = train_val(epochs, train_loader, val_loader, loss_function, optimizer)"
      ],
      "metadata": {
        "id": "NENVCHSkfm6z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9c66d88-ab98-4fa3-e7b5-0991d25489fa"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/250], Train Loss: 0.0190, Val Loss: 0.2553\n",
            "Epoch [26/250], Train Loss: 0.0190, Val Loss: 0.2545\n",
            "Epoch [51/250], Train Loss: 0.0190, Val Loss: 0.2540\n",
            "Epoch [76/250], Train Loss: 0.0190, Val Loss: 0.2535\n",
            "Epoch [101/250], Train Loss: 0.0190, Val Loss: 0.2531\n",
            "Epoch [126/250], Train Loss: 0.0190, Val Loss: 0.2527\n",
            "Epoch [151/250], Train Loss: 0.0189, Val Loss: 0.2523\n",
            "Epoch [176/250], Train Loss: 0.0189, Val Loss: 0.2520\n",
            "Epoch [201/250], Train Loss: 0.0189, Val Loss: 0.2517\n",
            "Epoch [226/250], Train Loss: 0.0189, Val Loss: 0.2513\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Plotted the best optimizer curve of trin and validation loss vs epochs**"
      ],
      "metadata": {
        "id": "ayfDzTXC3VLU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot(epochs, train_losses, val_losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "JYLnaObT3T-q",
        "outputId": "9d65d638-b6cd-4d5d-9405-eae112a65ed3"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Evaluate the model**"
      ],
      "metadata": {
        "id": "sjy-YbU53UpT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "15elcOna3Vhl",
        "outputId": "4293251c-9f6e-4713-f826-a805e61f120a"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test MSE: 0.0742\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Gradient Clipping**"
      ],
      "metadata": {
        "id": "hGnmPGO8zSTu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Train and validate the model**"
      ],
      "metadata": {
        "id": "uu0Mb7z5325y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LSTM()\n",
        "loss_function = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "train_losses = []\n",
        "val_losses = []\n",
        "\n",
        "epochs = 250\n",
        "# Training loop\n",
        "for epoch in range(1, epochs + 1):\n",
        "    model.train()  # Set the model to training mode\n",
        "    train_loss = 0.0\n",
        "\n",
        "    for seq, labels in train_loader:\n",
        "        optimizer.zero_grad()\n",
        "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
        "                            torch.zeros(1, 1, model.hidden_layer_size))\n",
        "\n",
        "        y_pred = model(seq)\n",
        "        loss = loss_function(y_pred, labels)\n",
        "        loss.backward()\n",
        "\n",
        "        nn.utils.clip_grad_norm_(model.parameters() , 0.1 ) # using gradient clipping\n",
        "\n",
        "        optimizer.step()\n",
        "\n",
        "        train_loss += loss.item()\n",
        "\n",
        "    train_loss /= len(train_loader)\n",
        "    train_losses.append(train_loss)\n",
        "\n",
        "    # Validation loop\n",
        "    model.eval()  # Set the model to evaluation mode\n",
        "    val_loss = 0.0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for seq_val, labels_val in val_loader:\n",
        "            model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
        "                                  torch.zeros(1, 1, model.hidden_layer_size))\n",
        "\n",
        "            y_pred_val = model(seq_val)\n",
        "            loss_val = loss_function(y_pred_val, labels_val)\n",
        "            val_loss += loss_val.item()\n",
        "\n",
        "    val_loss /= len(val_loader)\n",
        "    val_losses.append(val_loss)\n",
        "\n",
        "    if epoch % 25 == 1:\n",
        "        print(f'Epoch [{epoch}/{epochs}], Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzbNwB0ZzXRf",
        "outputId": "6d584422-3441-49ff-9d06-4ad66f00bf54"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py:82: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.1 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py:535: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  return F.mse_loss(input, target, reduction=self.reduction)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/250], Train Loss: 0.0257, Val Loss: 0.2717\n",
            "Epoch [26/250], Train Loss: 0.0128, Val Loss: 0.0621\n",
            "Epoch [51/250], Train Loss: 0.0108, Val Loss: 0.3004\n",
            "Epoch [76/250], Train Loss: 0.0108, Val Loss: 0.2020\n",
            "Epoch [101/250], Train Loss: 0.0113, Val Loss: 0.3899\n",
            "Epoch [126/250], Train Loss: 0.0137, Val Loss: 0.1445\n",
            "Epoch [151/250], Train Loss: 0.0101, Val Loss: 0.2182\n",
            "Epoch [176/250], Train Loss: 0.0107, Val Loss: 0.0700\n",
            "Epoch [201/250], Train Loss: 0.0088, Val Loss: 0.1333\n",
            "Epoch [226/250], Train Loss: 0.0073, Val Loss: 0.1417\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Plot the model train and validation loss vs epochs**"
      ],
      "metadata": {
        "id": "dvmU9Pj937G6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot(epochs, train_losses, val_losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Ll5c39wwz0uE",
        "outputId": "a63a56d7-21c8-41bc-e992-9ac6f1c8d92c"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Evaluate the model**"
      ],
      "metadata": {
        "id": "wnm3pZhO4CDX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "VWrWvsxs1b7I",
        "outputId": "bf72d453-4d0b-4f4e-fea1-b30abb4bd5d0"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test MSE: 0.2731\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}