AI1703_LinearRegression_HW_exercise3_passenger1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1EX4UeQlEkd7wrdRlBhl_RMLL2R8kTkeX",
      "authorship_tag": "ABX9TyNkuZ9x3XcHg8MHQ5WSfhf/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thanhtruongphan/HomeworkAI/blob/main/AI1703_LinearRegression_HW_passenger1_ex3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Passenger 1**"
      ],
      "metadata": {
        "id": "rRAULoZNehTq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Linear bậc 1**"
      ],
      "metadata": {
        "id": "Od186RVwOoqz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Rb2lSyh7Gdqm"
      },
      "outputs": [],
      "source": [
        "import pandas as pd "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url = '/content/drive/MyDrive/A_HOC TAP/Nam 4, Hoc ky 2/Artificial Intelligence/Code_AI/files/passenger1.csv'\n",
        "data= pd.read_csv(url)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rt7G8RtZGx8U",
        "outputId": "be938168-c9b5-4166-bbda-32069d0b744f"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Passenger  Miles(X)  Dollars (Y)\n",
              "0         19      4267         5738\n",
              "1         23      5090         6321\n",
              "2         16      3643         5298\n",
              "3         10       246         3694\n",
              "4         15      3466         4244"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8a98a2f9-5eda-4b67-9404-d00cc16fe1f2\">\n",
              "    <div 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>Passenger</th>\n",
              "      <th>Miles(X)</th>\n",
              "      <th>Dollars (Y)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>4267</td>\n",
              "      <td>5738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</td>\n",
              "      <td>5090</td>\n",
              "      <td>6321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16</td>\n",
              "      <td>3643</td>\n",
              "      <td>5298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>246</td>\n",
              "      <td>3694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15</td>\n",
              "      <td>3466</td>\n",
              "      <td>4244</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a98a2f9-5eda-4b67-9404-d00cc16fe1f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8a98a2f9-5eda-4b67-9404-d00cc16fe1f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8a98a2f9-5eda-4b67-9404-d00cc16fe1f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "data.hist()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "gxLadJTlHFVS",
        "outputId": "251e25fa-dacd-4916-d61e-5f874389b19d"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/Folder')"
      ],
      "metadata": {
        "id": "woAoW15BHMxP"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = data.corr()\n",
        "print(correlation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9J5W_TAHayx",
        "outputId": "333a04cf-0ddf-4a14-f6da-4db8cd359f17"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             Passenger  Miles(X)  Dollars (Y)\n",
            "Passenger     1.000000  0.939166     0.980384\n",
            "Miles(X)      0.939166  1.000000     0.921729\n",
            "Dollars (Y)   0.980384  0.921729     1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "sns.heatmap(correlation, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "MXWzEmJ3H4xV",
        "outputId": "e8d8c581-6e91-46d5-e72e-7c049c07bf85"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qNubdsxrKKl0"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "6fsWtxiSJZ_0",
        "outputId": "55c7b547-68f3-417d-e313-60fce2251727"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f2f4c0630a0>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhUAAAIVCAYAAABm5A1+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAABPC0lEQVR4nO3de7xcVX3//9f7hEAUwi0cQhoSQxSlYBVoCnjBL4piQL9iWw3gpdGvfYRWKCr6q2D91nsf2AsWvCBRaINFAW8lVQqmqBUVkYDckRJi+JKYy0lAQoIBkvP5/bHXicPhzDkz5+w9e/ae9/PxmMeZ2bNnz9pnPmefNWt91lqKCMzMzMwmqq/sApiZmVk9uFJhZmZmuXClwszMzHLhSoWZmZnlwpUKMzMzy4UrFWZmZpaLWlYq5s+fH4BvvhV1G5Nj0LeCb6Ny/PlW8K2pWlYqNm7cWHYRrMc5Bq1Mjj8rSy0rFWZmZtZ5XVOpkDRL0g8k3SPpbknvSds/KmmNpNvS7aSyy2pmZmbPtEvZBWiwHXh/RNwqaSpwi6Rl6bnPRMQ/llg2M7OOGBwMVm3ayvrN25i+5xTmTNudvj6VXSzrAXnEXtdUKiJiLbA23X9M0r3AzHJLZWbWOYODwbV3r+Psq25j21ODTJncx/kLDmf+YQe4YmGFyiv2uqb7o5GkOcARwE1p05mS7pB0qaR9yiuZmVlxVm3auvOiDrDtqUHOvuo2Vm3aWnLJrO7yir2uq1RI2gP4JvDeiNgMXAQ8FzicrCXjn5q8bpGk5ZKWDwwMdKq4Zjs5Bm2i1m/etvOiPmTbU4NseGzbmK91/NlETCT2GnVVpULSZLIKxeUR8S2AiFgfETsiYhD4EnDUSK+NiMURMS8i5vX393eu0GaJY9AmavqeU5gy+emX5SmT+9h/6pQxX+v4s4mYSOw16ppKhSQBlwD3RsT5DdtnNOz2x8BdnS6bmVknzJm2O+cvOHznxX2oX3vOtN1LLpnVXV6x1zWJmsDLgLcDd0q6LW37EHCapMPJZvFaBZxeRuHMzPLULNN+/mEHcMhZx7LhsW3sP9WjP6wzhmLv0Pccy/rNT7D1ye08Z9/2K7NdU6mIiB8DI/3lXNPpspiZFWmsTPu5/Xswt3+PsotpPeietY9NaARI13R/mJn1Co/ysG6UR1y6UmFm1mF5Zdqb5SmPuHSlwsysw/LKtDfLUx5x6UqFmVmHDA4GKwe2sGnrE3z6T1/kUR6Wm6HYuvGBjawc2MLg4KgrlI8ojxEgXZOoaWZWZ8OTM58z7Vksfvs8Jk+S1/iwCcltiu0cRh+5pcLMrAOGJ8E9uOm3LPrKcqbvOYW5/Xu4QmHjlmfi79Doo2Pm7jeuuHSlwsysA5ycaUXppthypcLMrAOcnGlF6abYcqXCzKwDPAW3FaWbYsuJmmZmOWk29TbkkwRnNpLhsXXAnlPYMQg3/WpTx5OAXakwM8tBKxn4noLbijIUW3Om7Z7LSJBxl6PwdzAz6wGeetu6Qdlx6EqFmVkOuikD33pX2XHoSoWZWQ66KQPfelfZcehKhZlZDropA996V9lx6ERNM7M2NBvh4dEdVrTRRhcNKTsOXakwM2vRWCM8PLrDitLO+h5lxqG7P8zMWlR2Zr31rqrEnisVZmYtKjuz3npXVWLPlQozsxaVnVlvvasqsedKhVXWzFmzkVTIbeas2WWfnnWhsjPrrXdVJfa6JlFT0izgMmA6EMDiiLhA0r7AlcAcYBWwICIeKauc1j1+vfohTrn4p4Uc+8rTX1rIca36XjB9Kl9465HsvtsuTJ+6G7P39QgPK16rozpaGSFSpK6pVADbgfdHxK2SpgK3SFoGvAO4PiLOk3QOcA7wwRLLaWY9qFn2/ex9u+ubotXXWKM62hkhUlgZO/IuLYiItRFxa7r/GHAvMBM4GViSdlsCvLGUAppZT6tK9r31rm6I0a6pVDSSNAc4ArgJmB4Ra9NT68i6R0Z6zSJJyyUtHxgY6ExBzRo4Buut27PvHX/WDTHadZUKSXsA3wTeGxGbG5+LiCDLt3iGiFgcEfMiYl5/f38HSmr2dI7Beuv27HvHn3VDjHZVpULSZLIKxeUR8a20eb2kGen5GcCGsspnZr2rKtn31ru6IUa7JlFTkoBLgHsj4vyGp5YCC4Hz0s+rSyiemfWQZhn0XtvDipLHqI1uiNGuqVQALwPeDtwp6ba07UNklYmrJL0LeBBYUE7xzKwXjJVB77U9LG95jtooO0a7pvsjIn4cEYqIF0XE4el2TURsiojjI+LgiHh1RDxcdlnNrL66IYPeekudYq5rKhVmZt2gGzLorbfUKeZcqTAza9ANGfTWW+oUc65UmJk16IYMeustdYq5bkrUNDPrKI/ysDyNdwRHnWLOlQoz60ke5WF5mugIjrrEnLs/zKwn1Snj3srneMq4UmFmPalOGfdWPsdTxpUKM+tJdcq4t/I5njKuVJhZT6pTxr2Vz/GUcaKmmfWEkTLz65Jxb8Uba2RHnUZwTIQrFWZWe6Nl5tch496K1erIjrqM4JgId3+YWe05M98mwvHTukIqFZL6JL20iGObmbXLmfk2EY6f1hVSqYiIQeDzRRzbzKxdzsy3iXD8tK7I7o/rJf2ppK7KUpk5azaSCrntsusUH7uDxzZrlTPzbSIcP60rMlHzdOBsYIek3wICIiL2LPA9x/Tr1Q9xysU/LeTYV57+Uh+7w8c2G87reVjexhs/410LpMoKq1RExNSijm1mNhKv52FFaTd+JroWSFUV1v2hzNsk/d/0eJako4p6PzMzZ+lbt+jVWCwyp+ILwEuAt6THW3DyppkVyFn61i16NRaLrFQcHRFnANsAIuIRYNcC38/Mepyz9K1b9GosFlmpeErSJCAAJPUDg6O9QNKlkjZIuqth20clrZF0W7qdVGCZzazCnKVv3aJXY7HI0R8XAt8G9pf0KeBNwIfHeM2/Ap8DLhu2/TMR8Y+5l9DMKs3reVg7Ojkao1dHHBU5+uNySbcAx5MNJ31jRNw7xmt+JGlOUWUys/rweh7WjjJGY/TiiKMiR3/sC2wAvgZ8FVgvafI4D3empDtS98g+uRXSzCqrV7PrbXwcL51RZE7FrcAA8D/A/en+Kkm3SvrDNo5zEfBc4HBgLfBPI+0kaZGk5ZKWDwwMTKjgZuPhGOysXs2ub8bxNzrHS2cUWalYBpwUEftFxDTgROA7wLvJhpu2JCLWR8SOtJ7Il4AR57qIiMURMS8i5vX39+dQfLP2OAY7q1ez65tx/I3O8dIZRVYqjomI64YeRMT3gJdExM+A3Vo9iKQZDQ//GLir2b5m1jt6Nbvexsfx0hlFjv5YK+mDwBXp8SlkeRWTaDK0VNLXgOOA/SStBj4CHCfpcLKhqavI1hQxsx7i9TxsosYTL724dsdEFVmpeAtZpeDf0+OfpG2TgAUjvSAiThth8yVFFM7MqsHreVhe2omXXl27Y6IK6/6IiI0R8VcRcUS6nRkRAxHxZESsKOp9zaxenLVvZXDcjU9hLRWSng98AJjT+D4R8aqi3tPM6me0rH23UFhRHHfjU2T3x9eBLwJfBnYU+D5mVmNDWfuNF3hn7VvRHHfjU+Toj+0RcVFE/Dwibhm6Ffh+ZlYTg4PByoEt3PjARiLgc285wln7PawxHlYObGFwMAp/T48WGZ8iWyr+Q9K7ydb/eGJoY0Q8XOB7mlnFNUuQu/Y9x7Jus0d59JqyEiY9umh8imypWAj8f8BPgVvSbXmB72dmNdAsQW4w4Ji5+zG3fw9f2HtImQmTQ6NFHHetK3JBsYOKOraZ1ZcT5KyR46FailxQ7NmSPixpcXp8sKTXF/V+ZlYPnk7ZGjkeqqXI7o9/AZ4EXpoerwE+WeD7mVkNOEHOGjkeqqXIRM3nRsQpkk4DiIjHJblDysyA5lMgO0HOGrUbD55au1xFViqelPQssjU7kPRcGkaBmFnvGiuj39NvW6NW48FTa5evyO6PjwDXArMkXQ5cD/x1ge9nZhXhKZCtCI6r8hU5+mOZpFuBYwAB74mIjUW9n5lVhzP6rQiOq/IVOfrjZcC2iPgusDfwIUnPKer9zKw6nNFvRXBcla/I7o+LgMclvRg4G3gAuKzA9zOzinBGvxXBcVW+IhM1t0dESDoZ+HxEXCLpXQW+n5l1ucbM/BdMn+qpt+0ZJjJ6wyOHyldkpeIxSecCbwNeIakPmFzg+5lZF3Nmvo0ljxjxyKFyFdn9cQrZENJ3RcQ64EDgHwp8PzPrYs7Mt7E4RqqvyErFY8AFEXGDpOcDhwNfK/D9zKyLjZaZbwaOkTooslLxI2A3STOB7wFvB/61wPczsy7mzHwbi2Ok+oqsVCgiHgf+BPhCRLwZeGGB72dmXcyZ+TYWx0j1FZmoKUkvAd4KDI36GLUSI+lS4PXAhoh4Ydq2L3AlMAdYBSyIiEcKKrNZYXp9TQJn5ttYWomRXv876nZFVireA5wLfDsi7pY0F/jBGK/5V+BzPH0+i3OA6yPiPEnnpMcfLKC8ZoXxyIeMM/NtLKPFiP+Oul9h3R8R8aOIeENEfDo9XhkRZ431GuDhYZtPBpak+0uAN+ZdVrOiOavdbOL8d9T9ipymu1/SP0i6RtL3h27jONT0iFib7q8Dpjd5v0WSlktaPjAwMO5ym43XaDHorHYrWi9cA/131P2KTNS8HPglcBDwMbJ8iJsncsCICNJS6iM8tzgi5kXEvP7+/om8jdm4jBaDzmq3ovXCNdB/R92vyErFtIi4BHgqIv47Iv4P8KpxHGe9pBkA6eeGPAtp1gnOajebOP8ddb8iEzWfSj/XSnod8Gtg33EcZymwEDgv/bw6n+KZdU6vjHxwZr6NxWt71FuRlYpPStoLeD/wWWBP4H2jvUDS14DjgP0krQY+QlaZuCotRvYgsKDAMpsVpu4jH5yZb2Px2h71l3ulQtIU4C+A5wEzgUsi4pWtvDYiTmvy1PE5Fc/MCtIsM/+Qs471PwADHCO9oIiciiXAPOBO4ETgnwp4DzPrMs7Mt7E4RuqviO6PQyPiDwAkXQL8vID3MLMuM5SZ3/hPw5n51sgxUn9FtFQMJWgSEdsLOL6ZdSFn5ttYHCP1V0RLxYslbU73BTwrPRbZVBN7FvCeZlaC4Zn8J/z+dK5xZr414bU96i/3SkVETMr7mGbWfUbL5HfSnTXjtT3qrcjJr8ysxrwOg+XNMVV9rlSY2bg4k9/y5piqPlcqzGxcvA6D5c0xVX2uVJjZuDiT3/LmmKq+IqfpNrMaaszOP3TGVL77V8cysMWjPWx0rYzq8Noe1edKhZm1zNn5Nh7txI3X9qg2d3+YWcucnW/j4bjpHa5UmFnLnJ1v4+G46R2uVJhZy5ydb+PhuOkdrlSYWcucnW/j4bjpHU7UNLMxNWbuv2D6VK59z7Gs2+zs/F6Qx1ocHtXRO1ypMLNRecRH78rzs/eojt7g7g8zG5Uz93uXP3trlysVZjYqZ+73Ln/21i5XKsxsVM7c713+7K1dlalUSFol6U5Jt0laXnZ5zHqFM/d7lz97a1fVEjVfGREbyy6EWZ2NlO3vzP3e1NcnTvj96Vy56BjWPrqNGXtN4bAZe/mzt6aqVqkwswKNlu3vzP3eMzgYfO/e9R75Yy2rTPcHEMD3JN0iaVHZhTGrI2f7WyPHg7WrSpWKl0fEkcCJwBmSXtH4pKRFkpZLWj4wMFBOCa2n1SEGne1fXUXEn+PB2lWZSkVErEk/NwDfBo4a9vziiJgXEfP6+/vLKKL1uDrEoLP9q6uI+HM8WLsqUamQtLukqUP3gROAu8otlVk9DA4GKwe2cOMDG4mAz73lCGf7G9B89MfsfZ69M2ZWDmxhcDBKLql1i6okak4Hvi0JsjJ/NSKuLbdIZtXXLDHTa3sYjLxmx+x9nu3kTWuqEi0VEbEyIl6cbodFxKfKLpNZHTRLxBsMOGbufszt38P/KHrc0JodQ/Hw/x553Mmb1lQlKhVmVgwn4lm7HDM2GlcqzHqYE/GsXY4ZG40rFWY9zNMwW7scMzaaqiRqmtkEjDT1dl+fRkzEc2Jmb2kWG804Zmw0rlSY1dxoU28PVSw8BXdvGis2mnHMWDPu/jCrOU+1bM04NixvrlSY1Zyz9a0Zx4blzZUKs5pztr4149iwvLlSYVZzzta3ZhwbljcnaprV2FBmf//UXbly0TE8/uSOljL8rTe0M5Kj3VEi1ptcqTCrqWaZ/UcfNM3/DGynVkZyjHeUiPUed3+Y1ZQz+y0vjiVrlSsVZjXlzH7Li2PJWuVKhVlNObPf8uJYsla5UmFWU87st7w4lqxVTtQ0q5HhGfon/P50rvEaDcbERm94vQ9rlSsVZjUxWoa+12jobXmM3vB6H9YKd3+Y1YQz9K0Zx4Z1iisVZjXhDH1rxrFhneJKhVlNOEPfmnFsWKe4UmFWE87Qt2YcG9YplUnUlDQfuACYBHw5Is4ruUhmXcUZ+taMY8M6pRKVCkmTgM8DrwFWAzdLWhoR95RbMrPu4gx9a8axYZ1Qle6Po4AVEbEyIp4ErgBOLrlMZmZm1qAqlYqZwEMNj1enbTtJWiRpuaTlAwMDHS2cGTgGrVyOP+sGioiyyzAmSW8C5kfEn6fHbweOjogzm+w/AGwFNnaulKXZD59np22MiPmj7SDpMeC+DpWnU7rpM8hTFc9r1BhM18AH2zxmFX8Po6nb+UD3nFPT+KtETgWwBpjV8PjAtG1EEdEvaXlEzCu8ZCXzeXat+ypW3jFV8DNoSR3PKyL6231N3X4PdTsfqMY5VaX742bgYEkHSdoVOBVYWnKZzMzMrEElWioiYrukM4HryIaUXhoRd5dcLDMzM2tQiUoFQERcA1zTxksWF1WWLuPz7E5VK28r6nhOUN/zalfdfg91Ox+owDlVIlHTzMzMul9VcirMzMysy7lSYWZmZrlwpcLMzMxy4UqFmZmZ5cKVCjMzM8tFLSsV8+fPD8A334q6jckx6FvBt1E5/nwr+NZULSsVGzd2w9To1sscg1Ymx5+VpZaVCjMzM+s8VyrMzMwsF5WZptusEwYHg1WbtrJ+8zam7zmFOdN2p69PZRfLzKxweVz/XKkwSwYHg2vvXsfZV93GtqcGmTK5j/MXHM78ww5wxcLMai2v65+7P8ySVZu27vyDAtj21CBnX3UbqzZtLblkZmbFyuv650qFWbJ+87adf1BDtj01yIbHtpVUou4yc9ZsJBVymzlrdtmnZ9bT8rr+ufvDLJm+5xSmTO572h/WlMl97D91Soml6h6/Xv0Qp1z800KOfeXpLy3kuGbWmryuf26pMEvmTNud8xcczpTJ2Z/FUJ/inGm7l1wyM7Ni5XX9c0uF9ayRMp3nH3YAh5x1LBse28b+Uz36w8x6Q1+fmH/YARz6nmNZv/kJtj65nefs2/4XKlcqrCeNluk8t38P5vbvUXYRzcw67p61j01oBIi7P6wneaSHmdnT5XFddKXCam9wMFg5sIUbH9jIyoEtDA6GR3qYWa2MdJ1rVx7XRXd/WK016+Z4wfSpHulhZrWQ18RVeYwAcUuF1Vqz5rxJfXikh5nVQl7duXmMACm0pULSKuAxYAewPSLmSdoXuBKYA6wCFkTEI5IEXACcBDwOvCMibk3HWQh8OB32kxGxpMhyW300a85bt3mbR3qYWS2M1m3RTtL50AiQiVwXO9H98cqI2Njw+Bzg+og4T9I56fEHgROBg9PtaOAi4OhUCfkIMA8I4BZJSyPikQ6U3SputOa8vj55pIeZVV6eE/dN9LpYRvfHycBQS8MS4I0N2y+LzM+AvSXNAF4LLIuIh1NFYhkwv8NltoryhFZmVnfddJ0ruqUigO9JCuDiiFgMTI+Iten5dcD0dH8m8FDDa1enbc22P42kRcAigNmzvY6AZfJozmuVY9DK5PjrXZ28zo2l6ErFyyNijaT9gWWSftn4ZEREqnBMWKqwLAaYN29eLse0euhUN4dj0Mrk+Ott3dKdW2j3R0SsST83AN8GjgLWp24N0s8Nafc1wKyGlx+YtjXbbvY0eYzTNjOrujKvhYW1VEjaHeiLiMfS/ROAjwNLgYXAeenn1eklS4EzJV1Blqj5aESslXQd8HeS9kn7nQCcW1S5rZryGqdtZlZlZV8Li2ypmA78WNLtwM+B70bEtWSViddIuh94dXoMcA2wElgBfAl4N0BEPAx8Arg53T6etpnt5Gm3zczKvxYW1lIRESuBF4+wfRNw/AjbAzijybEuBS7Nu4xWH3mN0zYzq7Kyr4WeUdNqYWicdiNPu21mvabsa6ErFVYL3TRO28ysLGVfC72gmNVCN43TNjMrS9nXQlcqrDa6ZZy2mVmZyrwWulJhlTM4GKzatJX1m7cxfU+3SJhZb6jCtc+VCutqw/+IZu/zbL5373rPR2FmPaXV+SfKrni4UmFda6Q/ok//6Ys4f9l9zxiDfchZx7rbw8xqq9n8E43XvrInvgKP/rAuNtIf0Qe/eQevf9HT15MbGoNtMHPWbCQVcquqIn8nM2d54S7rjNHmnxhS9sRX4JYK62LN/ogmDasKez6K3/n16oc45eKfFnLsK09/aSHHLZp/J1YHQ/NPNF4Th1/7yp74CtxSYV2s2SQu856zr+ejMLOe0sr8E2VPfAVuqbAuNvRHNLx/8KVzp3GN56Mwsx7SyvwTza6ZnfzS5UqFdYVmGcvN/og8H4WZ1UkrozbGuvaVPfEVuFJhXWCsjGVXIMyszvIctVH2NdM5FVa6bshYNjMrS52uga5UWOlaGSplZlZXdboGulJhpeuGjGUzs7LU6RroSoWVruyles3MylSna6ATNa2j2h3lYWZWFeNdd6NO18DCKxWSJgHLgTUR8XpJBwFXANOAW4C3R8STknYDLgP+ENgEnBIRq9IxzgXeBewAzoqI64out+XPozzMrK4mOoKjLtfATnR/vAe4t+Hxp4HPRMTzgEfIKgukn4+k7Z9J+yHpUOBU4DBgPvCFVFGxiqlThrOZWSNf3zKFViokHQi8DvhyeizgVcA30i5LgDem+yenx6Tnj0/7nwxcERFPRMSvgBXAUUWW24pRpwxnM7NGvr5lim6p+Gfgr4Gh3/Q04DcRsT09Xg0MLTk5E3gIID3/aNp/5/YRXrOTpEWSlktaPjAwkPNpWB7qlOE8EseglcnxV666X99aVVilQtLrgQ0RcUtR79EoIhZHxLyImNff39+Jt7RRDA4GKwe2cOMDG1k5sIXBwahVhvNIHINWJsdfsUa6pjWq+/WtVUUmar4MeIOkk4ApwJ7ABcDeknZJrREHAmvS/muAWcBqSbsAe5ElbA5tH9L4GutCoyUs1SXD2cx6RytJmHUawTERhbVURMS5EXFgRMwhS7T8fkS8FfgB8Ka020Lg6nR/aXpMev77ERFp+6mSdksjRw4Gfl5UuW3iRktYGspwPmbufszt36Pn/uDMrHpaTcL09a2cya8+CJwtaQVZzsQlafslwLS0/WzgHICIuBu4CrgHuBY4IyJ2dLzU1jInLJlZnfia1rqOTH4VET8Efpjur2SE0RsRsQ14c5PXfwr4VHEltDwNJSw1/hH2YsKSmdWDr2mt8zTdljsnLJlZnfia1jpP020TNtLUtE5YMrO6GG8S5nin7a4yVypsQkbLiq7DlLNmZtD+NNoTnba7qtz9YRPiqWnNzJ6pV6+NrlTYhDgr2szsmXr12ujuD5sQZ0VbLvp2IVvqp2IKLPfvHTiLNQ/9v0KObcXr1WujKxXWspGSjoayoof3Gzor2toyuJ1TLv5pIYe+8vSXFnJcoLrl7lGdTJzs1WujKxXWEk+9bWZV1unEyV6dtrvlSoWkecCxwO8BvwXuApZFxCMFlc26SLOko0POOnZnRrRHephZtxrrGlaEdkeM1MGYiZqS3inpVuBc4FnAfcAG4OXAf0laIml2scW0svVq0pGZ1YOvYZ3RSkvFs4GXRcRvR3pS0uFki3w5o6jGejXpyMzqwdewzhizpSIiPj9KhWKXiLgtIq7Pv2jWTTxNrZlVma9hnTFmS4Wk/wDOjIgHh21/NfDPwAuLKZp1k15NOjKzevA1rDNa6f64AviBpEuAvwf6ySoTzwEWFlc0K0uzYVe9mHRkZvUxnqm2e23tjokas1IREZdL+g5ZheJeYDLZMuRfiogouHzWYb06X72ZWSNfC8en1Wm6DwWOAn4OPAFMx3Nc1FKvzldvZtbI18LxaWVI6SXA54F3R8RbgCOAvYDbJZ1QcPmsQIODwcqBLdz4wEZWDmxhcDA87MrMus5I16qi+Vo4Pq20NtwFLIqIHQARsRX4gKQlwBeA7xVYPitIs6a9Q2dM9bArM+saZXVDeAjq+LQypPQzQxWKYdvvjIhjm71O0hRJP5d0u6S7JX0sbT9I0k2SVki6UtKuaftu6fGK9PychmOdm7bfJ+m14zpTe5pmTXs7BvGwKzPrGmV1Q3gI6vi0MqT0S8CFEXHnCM/tDpwCPBERlw97+gngVRGxRdJk4MeS/hM4G/hMRFwh6YvAu4CL0s9HIuJ5kk4FPg2cIulQ4FTgMLIpwv9L0vNHquhY65o17Q1s2eZhV2bWNUbrhihyJJqHoI5PK90fnwf+r6Q/IOsKGQCmkM2iuSdwKTC8QkEaGbIlPZycbgG8CnhL2r4E+ChZpeLkdB/gG8DnlK0pfDJwRUQ8AfxK0gqypNEb2zhPG2a0pj0PHTWzblFmN4Svhe1rpfvjtohYAPwRWQXjBmAp8OcR8eKIuCD9w38GSZMk3Ua2Vsgy4AHgNxGxPe2yGpiZ7s8EHkrvuR14FJjWuH2E1zS+1yJJyyUtHxgYGOu0ep6b9vLnGLQy1TX+fK2qlpaHhUbEFuCHAJL2AWa18JodwOGS9ga+DRwyrlK2Vr7FwGKAefPmef6MMbhpL3+OQStTXePP16pqaWfp8x8Cb0ivuQXYIOmnEfG+sV4bEb+R9APgJcDeac2Q7cCBwJq02xqyispqSbuQDVvd1LB9SONrbALctGdmVeBrVXW0OvkVwF4RsRn4E+CyiDgaOL7ZzpL6UwsFkp4FvIZsRs4fAG9Kuy0Erk73l/K7ab/fBHw/5WUsBU5No0MOIsvl+Hkb5TbKGedtZtZpvtaVq51ZMXeRNANYAPxNC/vPAJZImkRWebkqIr4j6R7gCkmfBH4BXJL2vwT4SkrEfJhsxAcRcbekq4B7gO3AGR750R5PN2tmvcDXuvK1U6n4OHAd8JOIuFnSXOD+ZjtHxB1ks28O376SbPTG8O3bgDc3OdanyNYbsXFoNs77kLOOdXOimdWGr3XlaydR8+vA1xserwT+tIhC2fiNtKpeWeO8zczaNZGVQX2tK187iZrPJ5tPYnpEvFDSi4A3RMQnCyudtcVTb5tZlU20+8JTa5evnUTNLwHnAk/Bzu6NU4solI2Pp942syqb6JTcntOifO3kVDw7In6eTXK50/ZmO1vneeptM6uyiXZfeE6L8rVTqdgo6blkU20j6U3A2kJKZePiqbfNrMry6L7wta5c7XR/nAFcDBwiaQ3wXuAviyiUjY+b/sysynwNq752Rn+sBF6dVibti4jHiiuWjYeb/sysynwNq75Wlj4/u8l2ACLi/JzLZBPgpj8zqzJfw6qtlZaKqYWXwpqayJhtM7Nu42tavY1ZqYiIj3WiIPZMnnLWzOrE17T6GzNRU9Jfp5+flXTh8FvxRexdEx2zbWbWTXxNq79Wuj/uTT+XF1kQeyZPOWtmdeJrWv210v3xH+nnkuKL09uG9zXO2MtTzppZfbQyD4VzLqqtldEfS0d7PiLekF9xelezvsbPveUIzvzqL562zWO2zayKhuahGH6dG7qmOeei+lrp/ngJ8BDwNeAmwJ9sAZr1NX73r47lGo/ZNrMaGGseCi9dXn2tVCoOAF4DnAa8Bfgu8LWIuLvIgtVZO8uTD2zZxjFz9/MflJlV2vDr3lFzpj3jC5JzLqqvlZyKHcC1wLWSdiOrXPxQ0sci4nNFF7BumjXvvWC6lyc3s3pqtVvDS5dXX0trf0jaTdKfAP9GtgbIhcC3iyxYXTVr3pvU5+XJzayeWh1K6rU/qq+VRM3LgBcC1wAfi4i7WjmwpFnAZcB0spVNF0fEBZL2Ba4E5gCrgAUR8Yiyeb8vAE4CHgfeERG3pmMtBD6cDv3JqoxEaaebY91mL09uZt0nj9EYrXZreO2P6mslp+JtwFbgPcBZQ2t+kCVsRkTs2eR124H3R8StkqYCt0haBrwDuD4izpN0DnAO8EHgRODgdDsauAg4OlVCPgLMI6uc3CJpaUQ80vbZdlCz5r5DZzTv5vCc92bWTfIajdFOt4avg9U2ZvdHRPRFxNR027PhNnWUCgURsXaopSGtaHovMBM4GRhqaVgCvDHdPxm4LDI/A/aWNAN4LbAsIh5OFYllwPzxnW7nNGvu2zHobg4zq4a8ZsB0t0bvaHnp84mQNAc4gmxI6vSIWJueWkfWPQJZheOhhpetTtuabe9qo43mcPOemVVBXqMx3K3ROwqvVEjaA/gm8N6I2NzQfUJEhKTI6X0WAYsAZs+encchJ2S05j4379VTt8Wg9ZYi4i/P0Ri+7vWGlkZ/jJekyWQVissj4ltp8/rUrUH6uSFtXwPManj5gWlbs+1PExGLI2JeRMzr7+/P90TGMDgYrBzYwo0PbGTlwBYGB8PNfT2ozBg0KyL+fB2zdhXWUpFGc1wC3BsR5zc8tRRYCJyXfl7dsP1MSVeQJWo+GhFrJV0H/J2kfdJ+JwDnFlXudo2WyOTmPjOrul13EYteMZfBgD5lj82aKbL742XA24E7Jd2Wtn2IrDJxlaR3AQ8CC9Jz15ANJ11BNqT0nQAR8bCkTwA3p/0+HhEPF1jutow1rayb+8ysqlZt2rpz7aEhUyb3cY2nzbYmCqtURMSPab5OyPEj7B9kE2uNdKxLgUvzK11+PK2smdWVr2/Wro6M/qgTL09uZr2iWaLmAXtOYeXAFi9Pbs/gSkUbvDy5mfWSkZYq/9xbjuCetY95eXIbkSsVbfDy5GbWS0aaXyICXvfZG7w8uY3IlYo2eHlyM+s1w+eXuPGBjc6zsKYKnaeibob6Fxs5f8LMeomvgzYaVyra4IlgzKzX+Tpoo3H3Rxs8f72Z9TpfB200rlQ0MXzo6NAfjeevN7M6aXatG42vg9aMKxUjGG3qbdfGzawufK2zvDmnYgTNho6u2rS15JKZmeXH1zrLW89XKkZaYXS0qWnNzOqinWvdSNdKs+F6uvujWdPfC6ZP9dTbZlZ7zabhHn6tczeJtaqnWyqaNf1N6sNDpsys9lodHupuEmtVz7RUjJTh3Kzpb93mbR4yZWa10myURyvXOq9Waq3qiUpFs6a7Q2c07+bwkCkzq4uxui/Guta12k1i1hPdH82a7nYMupvDzOpvot0XnkXTWtUTLRWjLQTmbg4zq7uJdl94Fk1rVU9UKkZrunM3h5nVXR7dF75WWit6ovvDTXdm1st8DbROKaylQtKlwOuBDRHxwrRtX+BKYA6wClgQEY9IEnABcBLwOPCOiLg1vWYh8OF02E9GxJJ2y+KmOzPrZb4GWqcU2VLxr8D8YdvOAa6PiIOB69NjgBOBg9NtEXAR7KyEfAQ4GjgK+IikfcZTmKGmu2Pm7sfc/j38x2RmPcXXQOuEwioVEfEj4OFhm08GhloalgBvbNh+WWR+BuwtaQbwWmBZRDwcEY8Ay3hmRcXMzMy6QKdzKqZHxNp0fx0wPd2fCTzUsN/qtK3Z9meQtEjScknLBwYG8i21WQscg1Ymx591g9ISNSMigNxWpImIxRExLyLm9ff353VYs5Y5Bq1Mjj/rBp2uVKxP3RqknxvS9jXArIb9Dkzbmm03MzOzLtPpSsVSYGG6vxC4umH7nylzDPBo6ia5DjhB0j4pQfOEtM2ssmbOmo2kQm5mZmUqckjp14DjgP0krSYbxXEecJWkdwEPAgvS7teQDSddQTak9J0AEfGwpE8AN6f9Ph4Rw5M/zSrl16sf4pSLf1rIsa88/aWFHNfMrBWFVSoi4rQmTx0/wr4BnNHkOJcCl+ZYNDMzMytAT8yoaWZmZsVzpcLMzMxy4UqFmZmZ5cKVCjMzM8uFKxVmZmaWC1cqzMzMLBeuVJiZmVkuXKkwMzOzXLhSYWZmZrlwpcLMzMxy4UqFmZmZ5aKwtT+sWIODwapNW1m/eRvT95zCnGm709fnVSrNrN587eturlRU0OBgcO3d6zj7qtvY9tQgUyb3cf6Cw5l/2AH+4zKz2vK1r/u5+6OCVm3auvOPCmDbU4OcfdVtrNq0teSSmZkVx9e+7udKRQWt37xt5x/VkG1PDbLhsW0llcjMrHi+9nU/VyoqaPqeU5gy+ekf3ZTJfew/dUpJJTIzK56vfd3PlYoKmjNtd85fcPjOP66hfsU503YvuWRmZsXxta/7OVGzgvr6xPzDDuCQs45lw2Pb2H+qM6DNrP587et+lalUSJoPXABMAr4cEeeVXKRS9fWJuf17MLd/j7KLYmbWMb72dbdKdH9ImgR8HjgROBQ4TdKh5ZbKzMzMGlWiUgEcBayIiJUR8SRwBXByyWUyMzOzBlWpVMwEHmp4vDpt20nSIknLJS0fGBjoaOHMwDFo5XL8WTdQRJRdhjFJehMwPyL+PD1+O3B0RJzZZP8BYCuwsXOlLM1++Dw7bWNEzB9tB0mPAfd1qDyd0k2fQZ6qeF6jxmC6Bj7Y5jGr+HsYTd3OB7rnnJrGX1USNdcAsxoeH5i2jSgi+iUtj4h5hZesZD7PrnVfxco7pgp+Bi2p43lFRH+7r6nb76Fu5wPVOKeqdH/cDBws6SBJuwKnAktLLpOZmZk1qERLRURsl3QmcB3ZkNJLI+LukotlZmZmDSpRqQCIiGuAa9p4yeKiytJlfJ7dqWrlbUUdzwnqe17tqtvvoW7nAxU4p0okapqZmVn3q0pOhZmZmXU5VyrMzMwsF65UmJmZWS5cqTAzM7Nc1LJSMX/+/AB8862o25gcg74VfBuV48+3gm9N1bJSsXFjN8xiar3MMWhlcvxZWWpZqTAzM7POc6XCzMzMclGZGTXN8jY4GKzatJX1m7cxfc8pzJm2O319KrtY1iMcf1ZHhVUqJL0AuLJh01zgb4HL0vY5wCpgQUQ8IknABcBJwOPAOyLi1nSshcCH03E+GRFLiiq39YbBweDau9dx9lW3se2pQaZM7uP8BYcz/7ADfGG3wjn+rK4K6/6IiPsi4vCIOBz4Q7KKwreBc4DrI+Jg4Pr0GOBE4OB0WwRcBCBpX+AjwNHAUcBHJO1TVLmtN6zatHXnBR1g21ODnH3Vbfxq41ZWDmzhxgc2snJgC4ODoyY6m41Ls/hbtWkrg4PhGLTK6lT3x/HAAxHxoKSTgePS9iXAD4EPAicDl0W2GMnPJO0taUbad1lEPAwgaRkwH/hah8puNbR+87adF/Qh254a5N51m/nA12/3t0crVLP4W795G79c95hbMKyyOpWoeSq/qwRMj4i16f46YHq6PxN4qOE1q9O2ZtufRtIiScslLR8YGMiz7FZD0/ecwpTJTw//KZP7+J/1j4347bEVjkFrVbP4e/auk5q2YIyl1fibOWs2kgq5zZw1e2K/GKu8wlsqJO0KvAE4d/hzERGScmnbi4jFpGVh582b5/ZCG9Wcabtz/oLDn/aN8O/++A/4h+vue9p+254aZMNj25jbv8eYx3QMWqtGir/zFxzOkzsGR2zBaCUGW42/X69+iFMu/mkOZ/FMV57+0kKOa9XRie6PE4FbI2J9erxe0oyIWJu6Nzak7WuAWQ2vOzBtW8PvukuGtv+w0BJb7fX1ifmHHcAhZx3Lhse2sf/UKfQJHnn8yaftN2VyH/tPnVJSKa2uRoq/OdN2Z9WmrUyZ3Pe0ioVj0KqkE90fp/H0/IelwMJ0fyFwdcP2P1PmGODR1E1yHXCCpH1SguYJaZvZhPT1ibn9e3DM3P2Y278Hs/fNvj0ONUsPfXucM233kktqdTQ8/vr6tLMFwzFoVVVoS4Wk3YHXAKc3bD4PuErSu4AHgQVp+zVkw0lXkI0UeSdARDws6RPAzWm/jw8lbZrlqdm3RyfIWac4Bq3qCq1URMRWYNqwbZvIRoMM3zeAM5oc51Lg0iLKaNZo6NtjKzkUZkVwDFqVeZpuMzMzy4UrFWZmZpYLVyrMzMwsF65UmJmZWS5cqTAzM7NcuFJhZmZmuejUgmJmhRscDFZt2sr6zduYvqfH91vnOQat17lSYV2t1Yv04GBw7d3rvLqjFWq0eHQMmrn7w7rY0EX6pAtv4LQv3cRJF97AtXevY3DwmWslrdq0ddyrO5q1Yqx4dAyauVJhXaydi/T6zduaru44OBisHNjCjQ9sZOXAlhErJWZjGSseR4vBIY5Fqzt3f1jXGu0iPXwK4+l7Thlxdcf+Paa4SdpyMVY8NovBoRVG3T1ivcAtFda1hi7SjZotA91sdcdJfbhJ2nIxVjyOtcKou0esF7ilwrrW0EV6+De7kZaBbra6402/2tRya4fZaMaKx7FWGG2n5c2sqlypsK7V7jLQI63uOFaTtFmrWonH0VYYdSxaL3D3h3W1oYv0MXP3Y27/Hm33PY/VJG3WjonEo2PReoFbKqzW2m3tMCuKY9F6gSsVVjntzlo4WpO0WbsmMmumY9HqzpUKqxQPy7MyOf7MRldoToWkvSV9Q9IvJd0r6SWS9pW0TNL96ec+aV9JulDSCkl3SDqy4TgL0/73S1pYZJmtu3lYnpXJ8WfdZOas2Ugq5DZz1uxxlanolooLgGsj4k2SdgWeDXwIuD4izpN0DnAO8EHgRODgdDsauAg4WtK+wEeAeUAAt0haGhGPFFx260IelmdlcvxZN/n16oc45eKfFnLsK09/6bheV1hLhaS9gFcAlwBExJMR8RvgZGBJ2m0J8MZ0/2Tgssj8DNhb0gzgtcCyiHg4VSSWAfOLKrd1t3YmxDLLm+PPbHRFdn8cBAwA/yLpF5K+LGl3YHpErE37rAOmp/szgYcaXr86bWu2/WkkLZK0XNLygYGBnE/FukU3D8tzDNaf489sdEV2f+wCHAn8VUTcJOkCsq6OnSIiJOWyok5ELAYWA8ybN8+r9NRUNw/LcwzWn+PPbHRFVipWA6sj4qb0+BtklYr1kmZExNrUvbEhPb8GmNXw+gPTtjXAccO2/7DAcluX87A8K5Pjz6y5wro/ImId8JCkF6RNxwP3AEuBoREcC4Gr0/2lwJ+lUSDHAI+mbpLrgBMk7ZNGipyQtpmZmVkXKXr0x18Bl6eRHyuBd5JVZK6S9C7gQWBB2vca4CRgBfB42peIeFjSJ4Cb034fj4iHCy63ddhEJhQyy5vj0Wx8Cq1URMRtZENBhzt+hH0DOKPJcS4FLs21cNY1PKGQdRPHo9n4eUExK50nFLJu4ng0Gz9XKqx0o00oZNZpjkez8XOlwkrnCYWsmzgezcbPlQorXTdPKGS9x/FoNn5epdRK180TClnvcTyajZ8rFdYVPKGQdRPHo9n4uPvDzMzMcuFKhZmZmeXClQozMzPLhSsVZmZmlgsnatqEeZ0E6yaOR7PyuFJhE+J1EqybOB7NyuXuD5sQr5Ng3cTxaFautioVknaXNKmowlj1eJ0E6yaOR7Nyjdr9IakPOBV4K/BHwBPAbpI2At8FLo6IFYWX0rrW0DoJjRfy0dZJcH+3FcnxaFausVoqfgA8FzgXOCAiZkXE/sDLgZ8Bn5b0toLLaF2snXUShvq7T7rwBk770k2cdOENXHv3OgYHo9PFtppyPJqVa6xEzVdHxFPDN0bEw8A3gW9KmlxIyawS2lknoVl/9yFnHevpkC0XjseS9e2CVExLz6TJu7HjqScKOfbvHTiLNQ/9v0KO3WvGqlRcLendEbGq2Q4jVTqst7S6TsJo/d2+iFteHI8lGtzOKRf/tJBDX3n6Sws9tuVjrO6PfwG+J+lvxtMiIWmVpDsl3SZpedq2r6Rlku5PP/dJ2yXpQkkrJN0h6ciG4yxM+98vaWG75bDuMNTf3Wi0/m6zIjkezfI3aqUiIr4OHAnsCSyX9AFJZw/dWnyPV0bE4RExLz0+B7g+Ig4Grk+PAU4EDk63RcBFkFVCgI8ARwNHAR8ZqohYtbTT321WNMejWf5amfzqSWArsBswFRgcffcxnQwcl+4vAX4IfDBtvywiAviZpL0lzUj7Lkt5HEhaBswHvjbBcliHtdPfbVY0x6NZ/sYaUjofOB9YChwZEY+3efwg6z4JsuGni4HpEbE2Pb8OmJ7uzwQeanjt6rSt2fbhZV1E1sLB7Nmz2yymdUqr/d1V5BisnjrFo+PPusFYORV/A7w5Is4ZR4UC4OURcSRZ18YZkl7R+GRqlchl/FZELI6IeRExr7+/P49DmrXFMWhlcvxZNxirUvG6iLh7tB0kNa3iR8Sa9HMD8G2ynIj1qVuD9HND2n0NMKvh5Qembc22WxcbHAxWDmzhxgc2snJgi8f+W+kck2bFG6tS8W1J/yTpFZJ2Zi9JmivpXZKuI8tveIY0pffUofvACcBdZF0pQyM4FgJXp/tLgT9Lo0COAR5N3STXASdI2iclaJ6QtlmX8qRC1m0ck2adMdboj+PJRmicDtwt6VFJm4B/Aw4AFkbEN5q8fDrwY0m3Az8HvhsR1wLnAa+RdD/w6vQY4BpgJbAC+BLw7lSGh4FPADen28eHkjatO3lRJ+s2jkmzzhhz9EdEXEP2D78tEbESePEI2zcBx4+wPYAzmhzrUuDSdstg4zPR9RA8qZDlzTFpVg2tDCm1HjLUTDz0rW5o7P78ww5o+SLe7qJOZqNxTJpVR1tLn1v95dFM7EmFLE+OSbPqcEuFPU0ezcSeVMjy5Jg0q46WKhWSngusjognJB0HvIhs9svfFFc0K0NezcR1mlTIyuWYNKuOVrs/vgnskPQ8YDHZvBFfLaxUVho3E1u3cUyaVUer3R+DEbFd0h8Dn42Iz0r6RZEFs3K4mdi6jWPSrDparVQ8Jek0ssmq/nfa1vZS6FYNbia2buOYNKuGVrs/3gm8BPhURPxK0kHAV4orlpmZmVXNmC0VkiYBfxMRbx3aFhG/Aj5dZMHMzMysWsZsqYiIHcBzJO3agfKYmZlZRbWaU7ES+ImkpcDOGWci4vxCSmVmZmaV02ql4oF06wOmFlccMzMzq6qWKhUR8bGiC2Ldb6KLOpnlzTFp1l1anVGzH/hr4DBg5zR2EfGqgsplXSaPRZ3M8uSYNOs+rQ4pvRz4JXAQ8DFgFXBzQWWyLpTHok5meXJMWm76dkFSIbdddp1S2LG7Uas5FdMi4hJJ74mI/wb+W5IrFT0kj0WdzPLkmLTcDG7nlIt/Wsihrzz9pYUeu9u02lLxVPq5VtLrJB0B7FtQmawLDS3q1Gg8izqZ5cUxadZ9Wq1UfFLSXsD7gQ8AXwbeV1iprOt4USfrNo5Js+7T6uiP76S7jwKvbOcN0oycy4E1EfH6NMX3FcA04Bbg7RHxpKTdgMuAPwQ2AadExKp0jHOBdwE7gLMi4rp2ymATz5L3ok6WN8ekWf2MWqmQ9Fkgmj0fEWe18B7vAe4F9kyPPw18JiKukPRFssrCRennIxHxPEmnpv1OkXQocCrZyJPfA/5L0vPTTJ/Wgryy5L2ok+XFMWlWT2N1fywna01odhuVpAOB15F1l6AsXfVVwDfSLkuAN6b7J6fHpOePT/ufDFwREU+kNUdWAEe1cG6WOEveuo1j0qyeRm2piIgloz3fgn8mm99iaBbOacBvImJ7erwamJnuzwQeSu+7XdKjaf+ZwM8ajtn4mp0kLQIWAcyePXuCxa4XZ8l3hmOwdY7J/Dn+rBuM1f3xH4ze/fGGUV77emBDRNwi6bjxFrBVEbEYWAwwb968pmXuRUNZ8o0XcWfJ588x2DrHZP4cf9YNxkrU/McJHPtlwBsknUQ2C+eewAXA3pJ2Sa0VBwJr0v5rgFnAakm7AHuRJWwObR/S+BprwVCW/PD+a2fJW1kck2b1NFb3x38P3U9Lnz8/PbwvIp4a+VU7X3sucG567XHAByLirZK+DryJbATIQuDq9JKl6fGN6fnvR0SklVG/Kul8skTNg4Gft3GOPc9Z8tZtHJNm9dTq2h/HkSVRrgIEzJK0MCJ+NI73/CBwhaRPAr8ALknbLwG+ImkF8DDZiA8i4m5JVwH3ANuBMzzyo33Okrdu45g0q59Wp+n+J+CEiLgPQNLzga+RzSkxpoj4IfDDdH8lI4zeiIhtwJubvP5TwKdaLGtP8OqM1ePPzMzqrtVKxeShCgVARPyPpMkFlcnG4NUZq8efmZn1glan6V4u6cuSjku3L5HNYWEl8Bj/6vFnZma9oNVKxV+S5TSclW73pG1WgtHG+Ft38mdmZr2g1bU/npD0FeArETFQcJlsDHmN8Xcff+d4Xob2ODbNqmnUlgplPippI3AfcJ+kAUl/25ni2UjyWJ1xqI//pAtv4LQv3cRJF97AtXevY3DQc+YUwStqts6xaVZdY7VUvI9sEqs/SutuIGkucJGk90XEZ4ouoD1THmP8m/XxH3LWsR7iVwDPy9A6x6ZZdY1VqXg78JqI2Di0ISJWSnob8D3AlYqSTHSMv9de6DzPy9Aax6ZZdY2VqDm5sUIxJOVVeEhphQ318TdyH791A8emWXWNVal4cpzP2TgMDgYrB7Zw4wMbWTmwZVx9yK0ew338lod2Y7aV/R2bZtU1VvfHiyVtHmG7yBYJs5zkMTlSO8dwH79NVLsx2+r+jk2z6hq1pSIiJkXEniPcpkaEuz9ylMfkSO0eY6iP/5i5+zG3fw9ftK0t7cZbO/s7Ns2qqdXJr6xgeUyO5AmWrJPajTfHp1n9uVLRJfJITnOCm3VSu/Hm+DSrP1cqukQeyWlOcLNOajfeHJ9m9dfqKqVWsDyS05zgZp3Ubrw5Ps3qz5WKLpLH5EieYMk6qd14c3ya1ZsrFV3OCytZlThezXqbKxVdLI+5K8w6xfFqZoUlakqaIunnkm6XdLekj6XtB0m6SdIKSVdK2jVt3y09XpGen9NwrHPT9vskvbaoMndKq7MQ5jF3hVnemsWv49XMimypeAJ4VURskTQZ+LGk/wTOBj4TEVdI+iLwLuCi9PORiHiepFOBTwOnSDoUOBU4DPg94L8kPT8idhRY9sK0823OCytZtxktfh2vZlZYS0VktqSHk9MtgFcB30jblwBvTPdPTo9Jzx8vSWn7FRHxRFp+fQVwVFHlLlo73+Y8rt+6zWjx63g1s0LnqZA0SdJtwAZgGfAA8JuI2J52WQ3MTPdnAg8BpOcfBaY1bh/hNY3vtUjScknLBwYGCjibfLQzq6DH9VdLVWJwIkaLX8druXoh/qz7FZqombooDpe0N/Bt4JAC32sxsBhg3rx57S/v2SFD3+YaL8zNvs15XH+1VCUGJ2K0+HW8lqsX4s+6X0dm1IyI3wA/AF4C7C1pqDJzILAm3V8DzAJIz+8FbGrcPsJrKqfdb3NeWMm6yVjx63g1622FtVRI6geeiojfSHoW8Bqy5MsfAG8CrgAWAlenlyxNj29Mz38/IkLSUuCrks4nS9Q8GPh5UeUumr/NWZU5fs1sNEV2f8wAlkiaRNYiclVEfEfSPcAVkj4J/AK4JO1/CfAVSSuAh8lGfBARd0u6CrgH2A6cUdWRH0M8q6BVmePXzJoprFIREXcAR4ywfSUjjN6IiG3Am5sc61PAp/Iuo5mZmeXHq5SamZlZLlypMDMzs1y4UmFmZma5cKXCzMzMcuFKhZmZmeXCS5+3aXAwWLVpK+s3b2P6nh6jb9XnmDazvLhS0YZ2Vhg1qwLHtJnlqee7PwYHg5UDW7jxgY2sHNjC4GDzKfPbWWHUrBuMFd+OaTPLU0+3VLT7LW20FRo9u6B1m1bi2zFtZnnq6ZaKdr+lDa3Q2GjK5D7695jScmuHWae0Et/NYvpZkyc5ns2sbT1dqRjtW9pImq3Q+KtNWzjpwhs47Us3cdKFN3Dt3esYHIy2ulbM8tZKfI8U05984ws564pfPC2et28fdCyb2Zh6uvtj6Fta44V3yuQ+9p86ZcT9R1qhsU8w/4IbnvFt8ND3HMs9ax9zApyVppX4Hh7Tz5o8ibOu+AUPbvotkMXzp6+9l6d2DPLBb97hWDazUfV0S0Wzloc503Zv+pqhFRqPmbsfc/v3YO2jI38bXL/5CSfAWalaje/GmH78yR07KxRDXv+imTsrFOBYNrPmerqlYqSWh3bH6Df7Nrj1ye1OgLNSjSe+R4rnSX04ls2sJT3dUgHPbHlotzm32bfB5+y7+4gJcM26VsyK0G58jxTPf/ScfR3LZtaSnm6pyEOzb4MA5y84/Bk5FaN1rZiVbaR4nr3Psx3LZtYSVypyMPRtcHhT8ES7VszKMFI8O5bNrBWuVBSoWWXDrGocy2bWisJyKiTNkvQDSfdIulvSe9L2fSUtk3R/+rlP2i5JF0paIekOSUc2HGth2v9+SQuLKnMjzzFhdeJ4NrNOKLKlYjvw/oi4VdJU4BZJy4B3ANdHxHmSzgHOAT4InAgcnG5HAxcBR0vaF/gIMA+IdJylEfFIUQX3IktWJ45nM+uUwloqImJtRNya7j8G3AvMBE4GlqTdlgBvTPdPBi6LzM+AvSXNAF4LLIuIh1NFYhkwv6hygxdZsnpxPJtZp3RkSKmkOcARwE3A9IhYm55aB0xP92cCDzW8bHXa1mz78PdYJGm5pOUDAwMTKm+703ebQb4xmCfHc2/o1viz3lJ4pULSHsA3gfdGxObG5yIiyLo0JiwiFkfEvIiY19/fP6FjNVtkyePybTR5xmCeHM+9oVvjz3pLoZUKSZPJKhSXR8S30ub1qVuD9HND2r4GmNXw8gPTtmbbCzOe6bvNupXj2cw6pbBETUkCLgHujYjzG55aCiwEzks/r27YfqakK8gSNR+NiLWSrgP+bmiUCHACcG675RkcDFZt2sr6zduYvufo4+zzmL7brAzN4tzxbGadUOToj5cBbwfulHRb2vYhssrEVZLeBTwILEjPXQOcBKwAHgfeCRARD0v6BHBz2u/jEfFwOwUZT/a7x+Vb1YwV545nMytaYZWKiPgx0Oyr0PEj7B/AGU2OdSlw6XjL0iz7/ZCzjvVF1mrDcW5mZeuJBcWc/W69wHFuZmXriUqFs9+tFzjOzaxsPVGpcPa79QLHuZmVrScWFHP2u/UCx7mZla0nKhXg0RzWGxznZlamnuj+MDMzs+K5UmFmZma5UDY9RL1IGgC2AhvLLksH7IfPs9M2RsSoK+VKegy4r0Pl6ZRu+gzyVMXzGjUG0zXwwTaPWcXfw2jqdj7QPefUNP5qWakAkLQ8IuaVXY6i+Ty7U9XK24o6nhPU97zaVbffQ93OB6pxTu7+MDMzs1y4UmFmZma5qHOlYnHZBegQn2d3qlp5W1HHc4L6nle76vZ7qNv5QAXOqbY5FWZmZtZZdW6pMDMzsw6qZaVC0nxJ90laIemcssuTF0mXStog6a6GbftKWibp/vRznzLLOFGSZkn6gaR7JN0t6T1pe2XOs0rx1+7vW5kL07ndIenIhmMtTPvfL2lhWefUUJ5Jkn4h6Tvp8UGSbkplv1LSrmn7bunxivT8nIZjnJu23yfptSWdyrjU9bOt4+cqaW9J35D0S0n3SnpJZT+niKjVDZgEPADMBXYFbgcOLbtcOZ3bK4Ajgbsatv09cE66fw7w6bLLOcFznAEcme5PBf4HOLQq51m1+Gv39w2cBPwnIOAY4Ka0fV9gZfq5T7q/T8nndjbwVeA76fFVwKnp/heBv0z33w18Md0/Fbgy3T80fX67AQelz3VS2Z9Zr3+2dfxcgSXAn6f7uwJ7V/VzKj3wC/hwXgJc1/D4XODcssuV4/nN4emVivuAGen+DOC+ssuY8/leDbymKudZ9fgb6/cNXAyc1rD/fen504CLG7Y/bb8SzuNA4HrgVcB30gV4I7DL8M8JuA54Sbq/S9pPwz+7xv2qeKvDZ1vHzxXYC/gVKcdx+O+/ap9THbs/ZgIPNTxenbbV1fSIWJvurwOml1mYPKXmyiOAm6jOeVY2/lr8fTc7v247738G/hoYTI+nAb+JiO3pcWP5dpY9Pf9o2r/bzmncavTZ/jP1+1wPAgaAf0ndOl+WtDsV/ZzqWKnoWZFVT2sxnEfSHsA3gfdGxObG5+p0nt2iTr9vSa8HNkTELWWXpRvU5bOt8ee6C1m39kURcQTZEhNPy8Wq0udUx0rFGmBWw+MD07a6Wi9pBkD6uaHk8kyYpMlkF8HLI+JbaXNVzrNy8dfm77vZ+XXTeb8MeIOkVcAVZE3lFwB7S9ol7dNYvp1lT8/vBWyiu85pXGr22db1c10NrI6Im9Ljb5BVMir5OdWxUnEzcHDKCN6VLEFnacllKtJSYCjLdyFZv2llSRJwCXBvRJzf8FRVzrNS8TeO3/dS4M9SBvoxwKOpifY64ARJ+6Qs9RPSto6LiHMj4sCImEP2+/9+RLwV+AHwprTb8HMaOtc3pf0jbT81jSI4CDgY+HmHTmPC6vbZ1vVzjYh1wEOSXpA2HQ/cQ0U/p1ISU4q+kWXH/g9ZVu/flF2eHM/ra8Ba4Cmy2u27yPoIrwfuB/4L2Lfsck7wHF9O1sx3B3Bbup1UpfOsUvy1+/smS3T7fDq3O4F5Dcf6P8CKdHtn2eeWynQcvxslMJfsn8cK4OvAbmn7lPR4RXp+bsPr/yad633AiWWfjz/ben6uwOHA8vRZ/TvZ6I1Kfk6eUdPMzMxyUcfuDzMzMyuBKxVmZmaWC1cqzMzMLBeuVJiZmVkuXKkwMzOzXOwy9i5WNkk7yIYO7QLcCyyMiMfLLZWZmdnTuaWiGn4bEYdHxAuBJ4G/KLtA4yFpUtllsPxICkn/1vB4F0kDDUtSv0Fp6XdJH5X0gXG+zxGSLkn3z5Z0acNzb5X03XT/TEn/ZyLnZN1H0g5Jtylbvv12Se+XNOr/LknHNcThOyR9roBySdL3Je0l6ceSTmx47s2SrpW0q6QfNcz4WXuuVFTPDcDzJP1vSTelBWj+S9J0AEn/K/0B3paemyppRgrs2yTdJenYtO8Jkm6UdKukr6c1ApC0StLH0vY7JR2StvdLWpb+uL8s6UFJ+6Xn3ibp5+k9Lh6qQEjaIumfJN1OtoKg1cdW4IWSnpUev4aGaYEjYmlEnJfD+3wIuDDdvxA4UtLLJO0NfBL4q/TcpQ33rT6GvlQdRhZjJwIfKerN2qgAnATcHhGPkn3RO1/SlHQd/TvgjIh4kmwCq1OKKW33caWiQlKwn0jWFfJj4JjIFqC5gmzlPoAPkAXz4cCxwG+Bt5AtB3w48GLgtlQZ+DDw6og4kmw2t7Mb3m5j2n5ROiZkf8jfT3/c3wBmp3L9PtkfzcvSe+wA3ppesztwU0S8OCJ+nN9vw7rENcDr0v3TyGZ9BZp/Q5T03PQt7hZJNzRUWt+cKr23S/pR2jYVeFFE3A47V5t8N9mMgn8PXBoRK9NzjwOrJB1V2NlaqSJiA7AIODO1FEyR9C/py88vJL1ytNeP8mXso5K+IuknwFckHdbwJekOSQePcLi3kqbOjoi7gP8APgj8LXBZRDyQ9vt3fnc9rL2eaZKpuGdJui3dv4FsPv8XAFcqW2hmV+BX6fmfkNWYLwe+FRGrJd0MXKpscaF/j4jbJP0v4FDgJ5JIx7ix4T2HFh+6BfiTdP/lwB8DRMS1kh5J248H/hC4OR3rWfxu8ZsdZAsaWT1dAfxtamp+EVlrwbFjvGYx8BcRcb+ko4EvkC0O9bfAayNiTWqFAJgH3NX44oj4qaR7gVcDvz/s2MvT+1dmjQ5rT0SsTC2h+wNvyzbFH6TK6fckPX+Ulw99GQtJf072Zez96blDgZdHxG8lfRa4ICIuV7aGz0hdty8DTm94/DHgVrIu6nkN2+8C/qj9M60mVyqq4bepBWCnFPTnR8RSSccBHwWIiPOU9TGfRFZheG1E/EjSK8i+Uf6rpPOBR4BlEXFak/d8Iv3cwdhxImBJRJw7wnPbImLHWCdo1RQRd0iaQ9ZKcc1Y+6em4ZcCX08VUIDd0s+fkMXnVfyuUjsDGBjhGPOAyUA/2To4QzYAh4znXKySXg58FiAifinpQWC0SsWBjPxlDGBpRPw23b8R+BtJB5J9Obt/hGPtGxGPDT2IiK2SrgS2RMQTDdt3SHpS0tTG/evK3R/VtRe/678eWskOSc+NiDsj4tNkK2YeIuk5wPqI+BLwZbJldX8GvEzS89Lrdh+jhg/ZRX9B2v8EskVvIOszfJOk/dNz+6b3tN6wFPhHGro+RtEH/Cb1kQ/dfh8gIv6CrEtuFnCLpGlk3XdThh3jY8C/AZ8CPjPsuSnpNVZTkuaSfdnZMNa+I/gs8LmI+AOyVobG2No6dCcivgq8gSyWrpH0qhGOtV3PTBgdTLfhdgO2jaO8leNKRXV9lOzb3i3Axobt70390neQrWb6n2Qr+t0u6RdkuQ8XRMQA8A7ga2nfGxn7G97HyJbWvQt4M7AOeCwi7iH7Z/C9dKxlZN8wrTdcCnwsIu4ca8eI2Az8StKbYWcG/YvT/edGxE0R8bdkrROzyIZQP2/o9ZL+gKzF7dNk3ShzJL2m4S2ez7DuEqsPSf3AF8kqBkHWHfzW9NzzyfK87hvlECN+GRvhfeYCKyPiQrK8iReNsNt9ZCukjlXmaWQ5ak+NtW8duPujAiJijxG2XU1KEhq2faTs9yXpNnzf7zNCX19EzGm4v5ysUgLwKFmf93ZJLwH+aKiZLyKuBK5spexWLxGxmt+NzmjFW4GLJH2YrAvjCuB24B9SQpzIWr9uT33fe6WEzS1kicPvi4htAJL+ErhM0uEp0/5lpK5Aq42hnLLJwHbgK8D56bkvkMXSnem5d0TEEw1da8N9lOzL2CPA94GDmuy3AHi7pKfIvjz93Qj7fJfs2rhijPK/Mu3bE7z0ubUsXfCvImvhehJ4d0TcXG6prO4kvY+sRezLY+x3BHB2RLy9MyWzXpbyMi6LiNeMsd+3gHMi4n86U7JyuVJhZl1N0hTgzRHxlTH2ew1wf0Ss6kjBrOdJWgBcm7r1Rnp+V+DUiLissyUrjysVZmZmlgsnapqZmVkuXKkwMzOzXLhSYWZmZrlwpcLMzMxy4UqFmZmZ5eL/B2zoiwZDmyvrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "no7hWj1AJg_M"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#1 import library \n",
        "from sklearn import linear_model"
      ],
      "metadata": {
        "id": "oPJWwlTzKLlL"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2 fit-model, find intercept & coef\n",
        "model = linear_model.LinearRegression()\n",
        "model.fit(X=pd.DataFrame(data['Miles(X)']),y= data['Dollars (Y)'])\n",
        "print(\"He so intercept la : \", model.intercept_)\n",
        "print(\"He so coef la : \", model.coef_)"
      ],
      "metadata": {
        "id": "If92E7dgKSsQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61492c74-c16c-4550-bbd7-899c37c87890"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "He so intercept la :  934.5366384552531\n",
            "He so coef la :  [1.07802533]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X=pd.DataFrame(data['Miles(X)']),y= data['Dollars (Y)'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4-9cjAbLPjp",
        "outputId": "3e17afdb-b94f-4c66-e57b-d6ab2eb73350"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8495847755547029"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j-sufbKqMNRw"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X=pd.DataFrame(data['Miles(X)']))\n",
        "data.plot(kind='scatter',x='Miles(X)',y='Dollars (Y)',figsize=(7,7),color='orange', xlim=(-1000,7000))\n",
        "plt.plot(data['Miles(X)'],y_pred,color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "J-wioYrTNJCw",
        "outputId": "c40d7946-99a6-4eab-c203-75a042409e1e"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2f4c6a7730>]"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5xBVaveYNkZh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### bậc 27 (cao nhất) "
      ],
      "metadata": {
        "id": "zAsrRFFTcQ76"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n",
        "X=pd.DataFrame([data['Miles(X)'],data['Miles(X)']**2,\n",
        "                data['Miles(X)']**3,data['Miles(X)']**4,\n",
        "                data['Miles(X)']**5,data['Miles(X)']**6,\n",
        "                data['Miles(X)']**7,data['Miles(X)']**8,\n",
        "                data['Miles(X)']**9,data['Miles(X)']**10,\n",
        "                data['Miles(X)']**11,data['Miles(X)']**12,\n",
        "                data['Miles(X)']**13,data['Miles(X)']**14,\n",
        "                data['Miles(X)']**15,data['Miles(X)']**16,\n",
        "                data['Miles(X)']**17,data['Miles(X)']**18,\n",
        "                data['Miles(X)']**19,data['Miles(X)']**20,\n",
        "                data['Miles(X)']**21,data['Miles(X)']**22,\n",
        "                data['Miles(X)']**23,data['Miles(X)']**24]).T \n",
        "y=data['Dollars (Y)']\n",
        "model.fit(X,y)\n",
        "print('Intercept cua mo hinh : ', model.intercept_)\n",
        "print('Coef cua mo hinh la : ',model.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjGNWO_UcRPv",
        "outputId": "069470a9-f00c-4db7-9ecf-44155d0ae63f"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept cua mo hinh :  4531.155058991336\n",
            "Coef cua mo hinh la :  [-2.04787653e-06 -1.99178495e-03  1.27622373e-06 -2.51678380e-10\n",
            "  1.57837680e-14 -4.75801052e-17  2.41701173e-17 -1.22827406e-17\n",
            " -1.10931582e-16  4.61143835e-17 -1.23852407e-17 -8.70223216e-17\n",
            "  3.00240237e-17  3.43348011e-17  9.24348878e-17  2.11609161e-17\n",
            " -1.46765201e-18  4.78471971e-17  3.96275894e-17 -1.21322223e-16\n",
            " -3.67544536e-17  7.32378568e-17 -1.51788304e-17 -1.26960074e-16]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPHlurMscRSb",
        "outputId": "8cc46435-0007-469a-b061-9ea99ac8d454"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9977188388016233"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "range = np.arange(0,6000,0.1)\n",
        "X_range=pd.DataFrame([range**1,range**2,range**3,range**4,range**5,range**6,range**7,range**8,range**9,range**10,range**11,range**12,range**13,range**14,range**15,range**16,range**17,range**18,range**19,range**20,range**21,range**22,range**23,range**24]).T\n",
        "y_pred = model.predict(X_range)\n",
        "data.plot(kind='scatter',x='Miles(X)',y='Dollars (Y)',figsize=(9,9),color='blue',xlim=(-1000,7000))\n",
        "plt.plot(range,y_pred,color='red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "CvVXUw6vC24u",
        "outputId": "1cb13e74-bc84-4a09-89cc-19df2eb7cf00"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2f4beb1100>]"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### dùng 2 biến "
      ],
      "metadata": {
        "id": "HsiPKSpoNNCi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = linear_model.LinearRegression()\n",
        "model.fit(X=data.loc[:,['Passenger','Miles(X)']],y=data['Dollars (Y)'])\n",
        "print('intercept la : ',model.intercept_)\n",
        "print('coef la : ',model.coef_)\n",
        "print('do chinh xac la : ',model.score(X=data.loc[:,['Passenger','Miles(X)']],y=data['Dollars (Y)']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6AFRCi_M2KK",
        "outputId": "9b9047fe-5888-4983-cf9a-dd131c3d6d0f"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "intercept la :  1368.2489732097583\n",
            "coef la :  [2.20474008e+02 9.77291220e-03]\n",
            "do chinh xac la :  0.96116047036539\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YK325l15NGHw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}