{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "w8-afogrcOYz",
   "metadata": {
    "id": "w8-afogrcOYz"
   },
   "source": [
    "# DS620 - Iris Classification\n",
    "\n",
    "Iris dataset: 3 classes, 50 samples each, 4 features (sepal and petal length/width in cm). Setosa is linearly separable from the other two; versicolor and virginica are not."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yk0zweducOYz",
   "metadata": {
    "id": "yk0zweducOYz"
   },
   "source": [
    "## 1. Google Drive and Colab setup\n",
    "\n",
    "Created a DS620 folder in Google Drive, uploaded Iris.csv, and mount the Drive below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "qpCBFFfkcOY0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qpCBFFfkcOY0",
    "outputId": "b63d4454-506e-47e1-af9a-80e46b8706f1"
   },
   "outputs": [],
   "source": [
    "# Mount Google Drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2xVCybancOY0",
   "metadata": {
    "id": "2xVCybancOY0"
   },
   "source": [
    "## 2. Import the necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "RBB1m-IzcOY0",
   "metadata": {
    "id": "RBB1m-IzcOY0"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rdnQYh_ocOY0",
   "metadata": {
    "id": "rdnQYh_ocOY0"
   },
   "source": [
    "## 3. Load the dataset\n",
    "\n",
    "Read Iris.csv with pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YLb_bVpTcOY0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "YLb_bVpTcOY0",
    "outputId": "7cf17457-f4ff-465e-92f2-d3df579262dc"
   },
   "outputs": [],
   "source": [
    "path = '/content/drive/MyDrive/DS620/Iris.csv'\n",
    "df = pd.read_csv(path)\n",
    "print(\"Loaded:\", path, \"| shape:\", df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "PMMfRMfJcOY1",
   "metadata": {
    "id": "PMMfRMfJcOY1"
   },
   "source": [
    "## 4. Delete the ID column\n",
    "\n",
    "Id is just a row number, so we drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "-us61JEHcOY1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "-us61JEHcOY1",
    "outputId": "15f3b798-d4de-4a87-a362-7a6559ca5d27"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-bb23adf3-850b-4bb4-a406-9d20ad2b10d3\" 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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb23adf3-850b-4bb4-a406-9d20ad2b10d3')\"\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 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-bb23adf3-850b-4bb4-a406-9d20ad2b10d3 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-bb23adf3-850b-4bb4-a406-9d20ad2b10d3');\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",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['Id'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "uV3UV9nKcOY1",
   "metadata": {
    "id": "uV3UV9nKcOY1"
   },
   "source": [
    "## 5. Display the first 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hwTJNSZ3cOY1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "hwTJNSZ3cOY1",
    "outputId": "f5937d05-333b-4795-f9cc-edbd22c22337"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-263930b0-453d-44df-aa5a-506f2fc440b5\" 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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-263930b0-453d-44df-aa5a-506f2fc440b5')\"\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 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-263930b0-453d-44df-aa5a-506f2fc440b5 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-263930b0-453d-44df-aa5a-506f2fc440b5');\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",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa\n",
       "5            5.4           3.9            1.7           0.4  Iris-setosa\n",
       "6            4.6           3.4            1.4           0.3  Iris-setosa\n",
       "7            5.0           3.4            1.5           0.2  Iris-setosa\n",
       "8            4.4           2.9            1.4           0.2  Iris-setosa\n",
       "9            4.9           3.1            1.5           0.1  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Corv2QBecOY1",
   "metadata": {
    "id": "Corv2QBecOY1"
   },
   "source": [
    "## 6. Statistical description\n",
    "\n",
    "describe() shows count, mean, std, min, quartiles and max for each attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "C_ncgoINcOY2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "C_ncgoINcOY2",
    "outputId": "2f559477-9c14-4ef3-8860-1aa22c84b72a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-43648898-982d-4a11-9312-24e32e1891ca\" 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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43648898-982d-4a11-9312-24e32e1891ca')\"\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 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-43648898-982d-4a11-9312-24e32e1891ca 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-43648898-982d-4a11-9312-24e32e1891ca');\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",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count     150.000000    150.000000     150.000000    150.000000\n",
       "mean        5.843333      3.054000       3.758667      1.198667\n",
       "std         0.828066      0.433594       1.764420      0.763161\n",
       "min         4.300000      2.000000       1.000000      0.100000\n",
       "25%         5.100000      2.800000       1.600000      0.300000\n",
       "50%         5.800000      3.000000       4.350000      1.300000\n",
       "75%         6.400000      3.300000       5.100000      1.800000\n",
       "max         7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lXIdm_VkcOY2",
   "metadata": {
    "id": "lXIdm_VkcOY2"
   },
   "source": [
    "## 7. Attribute info\n",
    "\n",
    "info() shows the data type and number of values for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "LUOid8w9cOY2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LUOid8w9cOY2",
    "outputId": "cba2c8b3-17b2-4bbd-d82b-968bdd43ad53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   SepalLengthCm  150 non-null    float64\n",
      " 1   SepalWidthCm   150 non-null    float64\n",
      " 2   PetalLengthCm  150 non-null    float64\n",
      " 3   PetalWidthCm   150 non-null    float64\n",
      " 4   Species        150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sLhAk9W9cOY2",
   "metadata": {
    "id": "sLhAk9W9cOY2"
   },
   "source": [
    "## 8. Samples per class\n",
    "\n",
    "50 samples in each class (balanced)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "xscgpdZwcOY2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "id": "xscgpdZwcOY2",
    "outputId": "df2035d1-4a64-47d5-fca9-488083f7097c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "Species\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mQJhSc04cOY3",
   "metadata": {
    "id": "mQJhSc04cOY3"
   },
   "source": [
    "## 9. Null values?\n",
    "\n",
    "No missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8TCUH1btcOY3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8TCUH1btcOY3",
    "outputId": "823a4993-b979-41ef-f2da-a632e297328a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SepalLengthCm    0\n",
      "SepalWidthCm     0\n",
      "PetalLengthCm    0\n",
      "PetalWidthCm     0\n",
      "Species          0\n",
      "dtype: int64\n",
      "\n",
      "Total nulls: 0\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())\n",
    "print(\"\\nTotal nulls:\", df.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "H22dTm14cOY3",
   "metadata": {
    "id": "H22dTm14cOY3"
   },
   "source": [
    "## 10. Histograms\n",
    "\n",
    "Distribution of each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "MI_lcBZdcOY3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 802
    },
    "id": "MI_lcBZdcOY3",
    "outputId": "8ad2ca3a-3497-4486-b947-c25d23b3d61c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9cAAAMRCAYAAADmxkWAAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAArYBJREFUeJzs3XtcVOXa//EvKigoh0jDLagobBATBM1TpSliiVrsNMunR7Sy1ApNdvZktW1nu2fX09lT7MRTZZlZ2sFIMys761ZTt4YHMkJMMTQRYtBB1u8Pf4xOgAJrYGbg8369fOGsda97Xeuaw83FrHUvD8MwDAEAAAAAgFpr4uwAAAAAAABwdxTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcA4KYiIyOVnJzs7DBQB7766iuNGTNGV1xxhSIjI3XPPffU2b4ay+to06ZNioyM1Ny5c+2WJycnKzIy0klRAQAaEoprAHCiyMjIi/5iHx8fr8jISOXm5jpsv7m5uYqMjNSMGTMc1iccIzc3V/fcc49yc3M1atQopaSkaPjw4RfcprxwdOciedWqVYqMjNSqVasqXT937lxFRkZq06ZN9RyZeVUV9gCAhqWZswMAANRORkaGvL29nR0GHOzbb7/VqVOn9OCDD+r666+v8/01ltdRTEyMMjIydMkllzg7FABAA0VxDQBuKiwszNkhoA7k5eVJki677LJ62V9jeR15e3s3mmMFADgHp4UDgJuq7DTgoqIizZ8/XyNGjFCPHj0UFxenhIQETZs2Tbt27ZJ09vTawYMHS5JWr15tOzX9j6fklpWVafny5Ro1apTi4uIUGxurUaNG6Y033lBZWVmlMb3//vu68cYbFRMTo379+umBBx5QXl5epde1nn+q7M6dOzVx4kT17t3b7hT47777TjNnztSwYcPUo0cPxcTEaMSIEZo3b55OnTpVYf/nnzq8Zs0ajRw5Ut27d9fVV1+tJ598UqdPn5Z09tvh5ORk9ejRQ7169dIDDzyg3377rUJ/e/bs0V//+lfFx8erW7du6tu3r2688Ub97//+r6xWa3WfKmVkZOi///u/1bNnT8XExOj666/Xyy+/bIvnj/mQpHHjxtmel9qeCn3+qdZffPGFkpOT1bNnT7vnoravo4vZtWuXnnjiCd1www3q3bu3oqOjde211+qpp55SQUGBXdvk5GQ99NBDkqSHHnrI7jWZm5ur+Ph4zZs3r0Jezj+OGTNmKDIyUgcPHtRrr72m66+/XjExMbZju9ip2adPn9YLL7xge64TEhI0b948u+dIuvglFX98rc+YMUPjxo2TJM2bN88u9j8+r2vWrFFycrKuuOIKRUdHKzExUS+99FKFGCRpy5Ytmjx5sgYMGKBu3brpqquu0s0332zLEwCg/vHNNQA0EIZh6M4779T333+vuLg4jR49Wk2bNlVeXp42bdqkK664Qt26dVPv3r01btw4vfrqq+rSpYsSEhJsfURFRdn+/8ADD2jNmjX605/+pJtuukkeHh765JNPNGvWLG3dulXPPfec3f7T09P17LPPyt/fX3/5y1/k6+urb775Rv/1X/8lX1/fKuPevn27Xn75ZfXs2VOjRo3Sb7/9Jk9PT1ufP/30k+Li4nTNNdfo9OnT2rZtm+bOnatNmzZp6dKlatq0aYU+ly1bpi+++EIJCQnq3bu3vv76ay1dulQFBQUaPHiwUlNTNXDgQN1yyy36/vvv9f777+u3337TwoULbX3s2bNHN998szw8PBQfH6+QkBAVFRUpJydHy5cv17Rp02xxXsjzzz+vl19+WZdccolGjBghHx8fffnll3r++ef11VdfadGiRfLy8lJwcLBSUlK0efNmbd68WTfeeKOCg4MlyfazttatW6cvv/xSAwYM0JgxY/TLL79U2ba6r6OLeeutt/TJJ5+oV69euvLKK1VWVqbdu3dryZIl+uKLL/TWW2+pVatWkqQbb7xRvr6+2rBhgwYPHmz3OvTz89O4ceO0YcOGCnmpzP/+7/9qy5Ytuuaaa3TNNddU+vqozH333af//Oc/Gjp0qJo1a6YNGzZo7ty52rVrl9LS0uTh4VGtfv6o/P21evVq9e7dW71797atO/84HnroIa1atUpt27bVtddeKz8/P23fvl2zZ8/Wt99+qyVLlqhZs7O/tn3xxReaNGmSWrVqpfj4eAUFBenEiRM6cOCA3njjDaWkpNQqVgCAORTXAOACLjTR0cmTJ6vVx759+/T9998rISFB8+fPt1tXVlamwsJCSVKfPn0UHBysV199VVFRUZoyZUqFvtasWaM1a9aoa9euWrZsmVq2bClJmjZtmsaOHas1a9Zo4MCBtmuCDx48qBdffFGXXHKJVq9erT/96U+SzhZq999/vz788MMq4/7qq680a9YsjRkzpsK6xx57TCEhIRUKmxdffFFpaWlat26dhg0bVmG7b775RqtWrbKdBnz69GndeOONeu+99/TZZ59p8eLFtiKnrKxMEyZM0JdffqnMzExbYffuu+/q1KlTmj9/vt0fICSpoKCgWtcpf//993r55Zf1pz/9SStXrlSbNm0kSffff79SUlJssUyePFkhISGaMmWK5s6daysi+/Tpc9F9VMfGjRu1YMECDRgw4KJtq/s6uphJkybp73//e4XiduXKlfrb3/6mN954QxMnTpQkjRw5UpK0YcMGJSQk2B6Xu+2221RYWFitvOzevVurV69W+/btqxVnuQMHDujDDz+Uv7+/JCk1NVXjxo3TZ599pvfee09/+ctfatRfuYSEBPn6+tqK68reb6tWrdKqVas0ZMgQPfvss2rRooVt3dy5czVv3jy9/vrrGj9+vKSzOSwrK9Nrr72mLl262PV1/PjxWsUJADCP08IBwAXMmzevyn/VLWbKnf+LebkmTZrYiobqeOeddySdLQLLC2tJ8vHx0QMPPCDp7C/45T744AOVlpZq7NixtsJakjw8PHT//fdf8NvDqKioSgtrSWrfvn2l3xjedtttkqQvv/yy0u2Sk5Ptrq/18vJSYmKiysrKdM0119h9e9ikSRPdcMMNks5+W/1HleXT399fTZpcfAgtz+Pdd99tK6wlqVmzZnrwwQfVpEkTuzzWlcGDB1ersD6f2ddRcHBwpc/7TTfdpFatWumrr76qUTzVdeedd9a4sJbOPkfnH1vz5s3117/+VdK557GuvPrqq2rWrJn++c9/Vsj7Pffco4CAAH3wwQcVtmvevHmFZYGBgXUWJwDgwvjmGgBcwN69e6tcFx8fr0OHDl20j/DwcEVFRWnNmjU6dOiQBg8erJ49e6pbt27y8vKqUTw//PCDmjRpYleEluvVq5eaNm2qzMxM27Ly//fs2bNC++DgYLVt27bKY4iJiakyjuLiYr366qtav369srOz9fvvv8swDNv6o0ePVrpdZactBwUFSZIuv/zyKtcdOXLEtmzYsGF69dVXde+99+q6667TlVdeqR49eqhDhw5VxvtHP/zwgySpb9++FdZ16tRJbdu2VW5urgoLCy946rxZF8rxHznqdWS1WrVixQp9+OGH+vHHH1VYWGh3rX75xG2OVpNjPV9lr/WePXtWeK07msVi0Z49e3TJJZfolVdeqbSNl5eXfvzxR9vj66+/Xh9//LFuvvlmJSYmqm/fvurRo4fatm1bZ3ECAC6O4hoAGoimTZvqlVde0fz587Vu3To9++yzkqSWLVvqxhtv1F//+le7b6EvpLCwUP7+/pUWU82aNdMll1yiY8eO2bWXpNatW1faX+vWrassrqvaxmq1avz48dq5c6ciIiI0bNgwBQYG2q47rWyyqXKVFarl36JeaF1paaltWUxMjF5//XX961//0rp16/Tee+9JOlsUp6SkaMSIEZXu+3zleTn/W+vztWnTRr/88otOnjxZp8V1VTmujKNeR6mpqVq/fr3at2+vwYMHq3Xr1rbX0yuvvFKjCeFqoibHerHtKnutO9rJkydlGIaOHz9e7cnIrr32Wr388stavHixVq1apRUrVkg6+4ej+++/X1dddVWdxQsAqBrFNQA0IP7+/nr44Yf18MMP6+eff9bmzZu1YsUKLVu2TCdPntQzzzxTrX58fX1VUFAgq9VaYdKu0tJS/fbbb7bJqCTZ/p+fn68///nPFfrLz8+vcl9VTRS1YcMG7dy5UyNHjtSTTz5pt+7o0aP1MityXFycbVbvXbt26csvv9SyZct0//33KzAwUFdeeeUFty8vmPPz8yv9xvvXX3+1a1dXajoZl9nX0X/+8x+tX79eV155pdLT021/EJHOXrd9/sRxjlbbicfy8/PVrl07u2WVvdbLLwc4/w8x56vuHAnlyvvu2rWrVq9eXe3tBg4cqIEDB6q4uFg7duzQ559/ruXLl2vSpEl69913FR4eXqM4AADmcc01ADRQHTt21OjRo7Vs2TL5+Phow4YNtnXl39SeOXOm0m2joqJUVlamLVu2VFj373//W2fOnFHXrl3t2kvS1q1bK7Q/dOiQ3enW1ZWTkyNJGjJkSKUx1CcvLy/16NFD9913nx555BFJsstnVcrzUtmttH7++WcdOXJEISEh8vPzc2zADnSh11FVyp+7+Ph4u8Jaknbu3KmSkpIK25QXrVW9JsvXV3UbOLM2b95cYdnWrVt15syZCrOXS6r0NV1UVKTs7OwKyy/0fmvZsqX+/Oc/a//+/Tpx4kSN4/bx8VG/fv300EMPadKkSbJarfriiy9q3A8AwDyKawBoIA4ePKiDBw9WWF7+DfT5EyX5+fnJw8NDhw8frrSvUaNGSZKee+45WSwW23KLxWK7BddNN91kW3799derWbNmWrZsmV2fhmHo+eefr7JgupDy2xT9seg5ePCg7VTlurRt27ZKi8DyU4Qrm/Drj8rzmJaWZjeL85kzZ/R///d/Kisrs8ujK6jJ66gqVT13x44d0+OPP17pNpdccokkVfmaDAgIkKQL3kbMjLS0NLv7b586dUrPP/+8pHPPo3T2m+bOnTtr27ZtysrKsi0/c+aMnnzyyUpfM+WxV3Vst912m6xWqx5++OFKv/kuKCjQ7t27bY///e9/V/rNeU1emwAAx+O0cABoIPbu3auUlBRFR0crLCxMl112mY4fP64NGzbIarXqrrvusrVt2bKlunfvri1btuj+++9Xp06d1KRJE8XHx6tLly66/vrrtWHDBn300UcaPny4EhISbPe5zs3N1bBhw2wzbEtShw4dNHXqVD3//PNKSkpSYmKi7T7XJ06cUJcuXS44aVtlBg0apI4dO2rJkiXat2+foqKidPjwYX322WcaOHBgnRVZ5RYuXKjvvvtOV1xxhUJCQuTj46OsrCx98cUX8vf31y233HLRPnr06KE777xTCxcu1IgRI3TdddfJ29tbX375pfbt26eePXtqwoQJdXocNVWT11FVoqOj1aNHD3388ccaM2aMevTooWPHjumLL75Qp06ddNlll1XYJjY2Vt7e3nrllVd04sQJ2zXQycnJ8vX1Vd++fdWkSRM9//zz2r9/v+0b5Hvuucchx925c2cNHz7c7j7XOTk5GjhwoJKSkuzaTpgwQY888oj+67/+S0OHDlXz5s21adMmWa1WdenSpcKs8506dVJQUJA+/PBDNWvWTO3atZOHh4eSkpIUHBysm266Sbt379Ybb7yhIUOG6Oqrr9af/vQnFRQUKDc3V//+9781cuRI2x8mnnjiCeXl5alHjx4KDg6Wp6endu/ere+++07BwcEaPny4Q3ICAKgZimsAaCC6deumiRMnavPmzfryyy9VUFCgwMBAXX755UpOTtY111xj1/7pp5/Wk08+qa+++koffvihDMNQ27ZtbffNff7559WrVy+98847tgmTwsLCdMcdd+i//uu/Kux/0qRJCgoK0tKlS7Vq1Sq1bNlSV199tR544AFNmDDB7rrV6vDx8dErr7yiZ599Vps3b9aWLVvUvn173XPPPbr99tuVkZFRy0xVz6233ip/f3/t2LHDdnpwUFCQbr31Vt1+++22b2cv5oEHHrDdL/zdd99VaWmpOnTooGnTpumOO+6o8Uzuda2mr6PKNG3aVGlpaXrxxRf1xRdf6LXXXlNQUJBGjx6tu+++u9Liz9/fX3PmzNH8+fO1evVqFRcXS5JuuOEG+fr6KiwsTE899ZQWL16sN954Q6dOnZLkuOJ69uzZmj9/vj744AMdPXpUQUFBmjJliiZOnFjhOu6bbrpJhmFo6dKlWr16tfz9/TV48GClpqZq6tSpleZj3rx5eu6557R27VrbrPc9e/a0vY7+/ve/a8CAAXrzzTf1zTff2CYV/NOf/qQJEybY/TFr0qRJ+uSTT7Rr1y59++238vDwULt27TR58mSNHz++RrfdAwA4jodx/j1NAABwsKKiIl155ZWKioqyFekAAAANDddcAwAc4vjx4xVur1RaWqqnnnpKp06dUkJCgpMiAwAAqHucFg4AcIh169Zpzpw5uvLKK9W2bVsVFBTo3//+t7KzsxUVFaXk5GRnhwgAAFBnKK4BAA7RvXt39ezZU//+979ttxQKCQnR5MmTdddddzGDMQAAaNC45hoAAAAAAJO45hoAAAAAAJMorgEAAAAAMIniGgAAAAAAkyiuAQAAAAAwieIaAAAAAACTKK4BAAAAADCJ4hoAAAAAAJMorgEAAAAAMIniGgAAAAAAkyiuAQAAAAAwieIaAAAAAACTKK4BAAAAADCJ4hoAAAAAAJMorgEAAAAAMIniGgAAAAAAkyiuAQAAAAAwieIaAAAAAACTKK4BAAAAADCJ4hoAAAAAAJMorgE3ExkZqblz5zo7DIdITk7WiBEjnB0GAAAO5eixetOmTYqMjNSmTZsu2jY5OVnJycnV6pdxGHAsimvgD/bu3aupU6dq0KBBio6OVv/+/XX77bfrtddec3ZolVq1apUiIyP1n//8x9mhVCovL09z585VZmam6b6Kioo0b9483XDDDYqLi1NMTIxGjBihZ555Rnl5eQ6IFgDgDtxprM7IyFBkZKTWr19fYd0NN9ygyMhIfffddxXWDRw4UGPGjDG9f8ZhoP40c3YAgCvZtm2bxo0bp3bt2mn06NFq06aNDh8+rB07dujVV1+t9l+Ccc7Ro0c1b948BQcHKyoqqtb9HDx4ULfddpsOHz6soUOH6pZbbpGnp6f27t2rt99+W5988onWrVvnwMgBAK7I3cbqnj17SpK2bt2qIUOG2JYXFRVp//79atasmbZt26a+ffva1h0+fFiHDx/WsGHDJEm9evXSzp075enpWeP9Mw4D9YfiGjjPv/71L/n6+urtt9+Wn5+f3bpjx445KSqUlpYqJSVFx44d06uvvqorrrjCbn1qaqrS09OdFB0AoD6521gdFBSkkJAQbd261W75999/L8MwNHTo0Arryh+XF+ZNmjRR8+bN6yfgSjAOA9XDaeHAeXJychQeHl5hsJakSy+91O7xe++9p5EjRyomJka9e/dWamqqDh8+bNem/FqmXbt2acyYMYqJiVF8fLyWL19u1+706dOaPXu2Ro4cqZ49eyo2Nla33nprpaeJ1VZeXp4eeughXXnllerWrZuGDx+ut99+265N+TVdGRkZSktL04ABAxQdHa3x48fr559/rtDn66+/rsGDBysmJkY33XSTtmzZYnet16ZNm3TTTTdJkh566CFFRkYqMjJSq1atsusnKytLycnJ6t69u/r3719hgP7444+1Z88eTZ48ucKALkmtWrVSamqq7XF53vfs2aOxY8eqe/fuGjJkiNauXStJ2rx5s0aPHq2YmBhdd911+uabb2qRUQCAM7jjWN2zZ09lZmaqpKTEtmzbtm3685//rP79+2vHjh0qKyuzW+fh4aEePXpIqvqa6xUrVighIcFuHD4f4zBQvyiugfMEBwdr9+7d2rdv3wXbpaWl6cEHH1THjh01Y8YMjRs3Tt9++63++7//WydPnrRrW1BQoIkTJ+ryyy/XAw88oLZt2+qxxx6zK2yLioq0cuVK9e7dW9OnT1dKSoqOHz+uO++80yHXSOXn5+vmm2+2xfjII4+oQ4cOeuSRR7R06dIK7dPT07V+/XrdcccdmjRpknbs2KHp06fbtXnjjTf0+OOPq23btnrggQd0xRVX6N5779WRI0dsbcLCwjR16lRJ0i233KKnn35aTz/9tHr16mWXnzvvvFNdunTRgw8+qM6dO+vZZ5/Vxo0bbW02bNggSUpKSqr2MRcUFGjy5MmKiYnRAw88IC8vL/31r39VRkaG/vrXv+qaa67R/fffL4vFoqlTp6qoqKjafQMAnMcdx+qePXvKarVqx44dtmXbtm1TXFycevToocLCQrvj2bZtmzp37qxLLrmkyj5XrlypRx99VK1bt9YDDzygHj166O6777b74wHjMFDPDAA2X331lREVFWVERUUZt9xyi/H0008bX375pXH69Glbm9zcXCMqKspIS0uz23bv3r1G165d7ZaPHTvWiIiIMBYvXmxbdurUKSMpKcno16+frd/S0lLj1KlTdv0VFBQYV155pfHQQw/ZLY+IiDDmzJlje/zOO+8YERERxs6dO6s8rocffti46qqrjOPHj9stT01NNXr27GlYLBbDMAzju+++MyIiIozExES7eF555RUjIiLC2Lt3r+0YevfubYwaNcqwWq22dqtWrTIiIiKMsWPH2pbt3LnTiIiIMN55550KcZXnZ/Xq1Xb5ueqqq4wpU6bYlv3lL38xevbsWeXxVdXvBx98YFv2448/GhEREUaXLl2M7du325Z/+eWXVcYHAHA97jhW79+/34iIiDDmz59vGIZhWK1WIzY21jb+XXnllcayZcsMwzCMwsJCIyoqyvjb3/5m2758fP7uu+8MwzCM06dPG/369TOSkpLsYlqxYgXjMOBEfHMNnOeqq67Sm2++qfj4eO3Zs0cLFy7UhAkTNGDAANtfbdevX6+ysjIlJibq+PHjtn+tW7dWx44dK5yy1axZM91yyy22x15eXrrlllt07Ngx7d69W5LUtGlTeXl5SZLKysp04sQJlZaWqlu3bvrhhx9MHZNhGPr4448VHx8vwzDsYr766qtVWFhoi6PcyJEjbfFIsp0CdvDgQUnSrl27dOLECd18881q1uzc1A3XX3+9/P39axSfj4+P3V/Cvby8FB0dbduXdPbbgpYtW9a43+HDh9sed+7cWX5+fgoLC1P37t1ty8v/f/7+AACuyx3H6rCwMAUEBNiupd6zZ4+Ki4sVFxcnSYqLi9O2bdskSdu3b9eZM2ds11tXZteuXTp27JjGjBljN17feOON8vX1rVYeyzEOA47DhGbAH8TExGjevHk6ffq09uzZo08++URLly7Vfffdp3fffVfZ2dkyDEPXXnttpdufX2xK0mWXXSYfHx+7ZaGhoZKkQ4cOKTY2VpK0evVqLV68WD/99JOsVqutbUhIiKnjOX78uE6ePKkVK1ZoxYoVVbY5X7t27ewel1/XVn4a3S+//CJJ6tChg127Zs2aKTg4uEbxtW3bVh4eHnbL/P39tXfvXtvjVq1a1XjQraxfX19ftW3btsIySRVOEQQAuC53G6s9PDwUFxenLVu2qKysTNu2bdOll16qjh07SjpbXL/++uuSZCuyL1Rcl4/D5duX8/T0VPv27S8Yyx8xDgOOQ3ENVMHLy0sxMTGKiYlRaGioHnroIa1du1ZlZWXy8PBQenq6mjZtWmG7Pw7O1fHee+9pxowZSkhI0IQJE3TppZeqadOmevnll03/Jbd8gpQbbrhBN954Y6VtIiMj7R43aVL5SS2GYZiKpTKV5fCPOnfurB9++EGHDx/Wn/70J1P9VrW8Lo4NAFC33Gms7tmzpz777DPt27fPdr11ubi4OD399NPKy8vT1q1bddlll9W4SK4txmHAcSiugWro1q2bpLP3iuzQoYMMw1BISIg6dep00W2PHj2q4uJiu4E8Oztbkmzf8q5bt07t27fXvHnz7P7KO2fOHNOxBwYGqmXLliorK9OVV15puj/p3DfbOTk5dvflLC0t1aFDh+yK9T/+1bo2Bg0apDVr1uj999/XpEmTTPcHAGh4XH2sPv9+19u2bdP48ePtYvfy8tKmTZu0c+dODRgw4IJ9lY/DP//8s/r162dbbrValZubqy5dutiWMQ4D9YdrroHzfPfdd5X+5bR8xszOnTvr2muvVdOmTTVv3rwKbQ3D0G+//Wa3rLS01O507NOnT2vFihUKDAzU5ZdfLuncX3HP72/Hjh3avn276WNq2rSprrvuOq1bt67SmVX/eEp4dXTr1k0BAQF66623VFpaalv+wQcfqKCgwK6tt7e3JHOne1133XWKiIjQv/71L33//fcV1hcVFemFF16odf8AAPfhrmN1t27d1Lx5c33wwQfKy8uz++bay8tLl19+ud544w0VFxdf8JTw8r4CAwP15ptv6vTp07blq1evrjDeMg4D9YdvroHzPPHEE7JYLBoyZIg6d+4sq9Wqbdu26aOPPlJwcLBGjhwpPz8/TZs2Tc8995wOHTqkhIQEtWzZUrm5ufrkk0908803a8KECbY+L7vsMqWnp+vQoUMKDQ1VRkaGMjMz9Y9//EOenp6SpIEDB+rjjz/Wvffeq4EDByo3N1dvvvmmwsPDVVxcXK3Y33nnHX355ZcVlo8bN07333+/Nm3apJtvvlmjR49WeHi4CgoKtHv3bn377bfavHlzjfLk5eWlKVOm6B//+IfGjx+vxMREHTp0SKtWrapwHXaHDh3k5+enN998Uy1btpSPj49iYmJqdLqbp6en5s2bp9tvv11jx47V0KFD1aNHD3l6emr//v1as2aN/Pz87O6xCQBomNx1rC6fKGzLli3y8vKyfdNeLi4uTosXL5Z04eutpbPj4rRp0/Too49q/PjxGjZsmHJzc7Vq1aoK4yvjMFB/KK6B8/zP//yP1q5dq40bN2rFihWyWq1q166dbr31Vt199922ib0mTpyo0NBQLV26VPPnz5d0duKOq666SvHx8XZ9+vv766mnntITTzyht956S61bt9ajjz6qm2++2dZm5MiRys/P14oVK/TVV18pPDxczzzzjNauXVvtwnf58uWVLh85cqTatm2rlStXav78+Vq/fr2WL1+ugIAAhYeHV7h/dXWNHTtWhmFoyZIl+r//+z916dJFaWlpeuKJJ9S8eXNbO09PTz311FN6/vnn9dhjj6m0tFRPPvlkja8l69ixo959910tXbpU69ev14YNG1RWVqaOHTtq9OjRSk5OrtVxAADcizuP1T179tSWLVt0+eWX283yLUk9evTQ4sWL1bJlS7vTuqtyyy236MyZM1q0aJGefvppRUREKC0tTbNnz7ZrxzgM1B8Pg9kDgDqTnJys3377TWvWrHF2KPWirKxM/fr105AhQ/TEE084OxwAAC6qsY3VAOoO11wDqJVTp05VuI7t3Xff1YkTJ9S7d28nRQUAAAA4B6eFA6iV7du368knn9TQoUMVEBCgH374QW+//bYiIiI0dOhQZ4cHAAAA1CuKawC1EhwcrLZt2+q1115TQUGB/P39lZSUpOnTp1e4jgwAAABo6LjmGgAAAAAAk7jmGgAAAAAAk9zmtPDvv/9ehmHY7jUIAIC7sVqt8vDwUFxcnLNDMY1xGQDg7hw9LrvNN9eGYVSYmdjVGIah06dPu3yctcGxuSeOzT1xbO6pOsfmDmNZdTn6WBrya6M+kUfHIZeOQR4dh1w6xvl5dPRY5jbfXJf/ZTw6OtrJkVStuLhYmZmZCg8Pl4+Pj7PDcSiOzT1xbO6JY3NP1Tm2//znP/UcVd1x9LjckF8b9Yk8Og65dAzy6Djk0jHOz+OPP/7o0L7d5ptrAAAAAABcFcU1AAAAAAAmUVwDAAAAAGASxTUAAAAAACZRXAMAAAAAYBLFNQAAAAAAJlFcAwAAAABgEsU1AAAAAAAmUVwDAAAAAGASxTUAAAAAACZRXAMAAAAAYBLFNQAAAAAAJjVzdgCAo+Xk5Cg/P7/a7S0Wi7Kzs1VSUiJvb+86jOys1q1bq0OHDnW+HwAAcHFV/d5Q378fVIXfGwD3QXGNBiUnJ0eRXaJUYil2dihVauHto717MhkoAQBwMn5vAOBIFNdoUPLz81ViKVZsYqp8A0OcHU4Fhcdztf2jF5Sfn88gCQCAk/F7AwBHorhGg+QbGCL/oDBnhwEAANwAvzcAcAQmNAMAAAAAwCSKawAAAAAATKK4BgAAAADAJIprAAAAAABMorgGAAAAAMAkimsAAAAAAEyiuAYAAAAAwCTucw3AJicnR/n5+c4Oo0qtW7dWhw4dnB0GAAAAUAHFNQBJZwvryC5RKrEUOzuUKrXw9tHePZkU2AAAAHA5FNcAJEn5+fkqsRQrNjFVvoEhzg6ngsLjudr+0QvKz8+nuAYAAIDLobgGYMc3MET+QWHODgMAAABwK0xoBgAAAACASRTXAAAAAACYVKPTwj/66CO9//772r17t06ePKmOHTsqOTlZo0aNkoeHhyQpOTlZmzdvrrBtRkaGwsI41RQAAAAA0PDUqLheunSpgoODNWPGDF1yySX65ptvNHPmTB05ckQpKSm2dj169NCDDz5ot21IiOtNkAQAAAAAgCPUqLhOS0tTYGCg7XG/fv104sQJLVmyRPfcc4+aNDl7lrmfn59iY2MdGigAALC3ceNGpaenKysrS0VFRQoKClJCQoJSUlLk6+srSZoxY4ZWr15dYdv09HQNGDCgvkMGAKDBqlFxfX5hXS4qKkpvvfWWiouL1apVK4cFBgAALuzEiROKiYlRcnKyAgICtH//fs2dO1f79+/X4sWLbe3at2+vZ5991m5bLtUCAMCxTN+Ka+vWrQoKCrIrrDdv3qzY2FidOXNG3bt313333adevXqZ3ZUMw1BxcbHpfuqKxWKx+9mQuMuxuXp85SwWS728lmvyvLlb7tzlNVkbHJt7qs6xGYZhm6PEEZKSkuwe9+nTR15eXpo5c6by8vIUFBQkSWrRogVnlAEAUMdMFddbtmxRRkaG3fXVvXr1UlJSkkJDQ3X06FEtWrRIt99+u1577TXFxcWZCtZqtSozM9NUH/UhOzvb2SHUGVc/NlePr1x2drZatGhRr/tzRBtX8MfcuUvctcGxuaeLHZuXl1ed7j8gIEDS2TETAADUn1oX10eOHFFqaqr69OmjcePG2ZZPnTrVrt3AgQM1YsQIvfTSS0pPT699pJI8PT0VHh5uqo+6ZLFYlJ2drdDQUHl7ezs7HIdyl2MrKSlxdgjVEhoaqqioqDrfT02eN3fLnbu8JmuDY3NP1Tm2rKysOtn3mTNnVFpaqqysLM2fP1/x8fF2E4n+/PPP6tmzp06dOqWIiAjdc889SkhIML1fR55R1pDPaqhP5LFm3CVP9XXGW13gNek45NIxzs+jo88oq1VxffLkSd11110KCAjQ3LlzbROZVcbHx0fXXHON1q1bV+sgy3l4eMjHx8d0P3XN29vbLeKsDVc/Nnf5Zb2+81id/blr7lz9NWkGx+aeLnRsjhzAzzdo0CDl5eVJkvr376/nnnvOti4qKkrR0dEKDw9XYWGhli9frnvvvVezZ8/W0KFDTe23Ls4oa8hnNdQn8lg97pKn+j7jrS64S67dAbl0jPI8OvKMshoX1yUlJZo0aZIKCwu1YsUK22ykAADAORYsWCCLxaKsrCylpaVp8uTJWrJkiZo2barx48fbtY2Pj9eYMWM0Z84c08W1I88oa8hnNdQn8lgz7nbWljviNek45NIxzs/joUOHHNp3jYrr0tJSTZs2TQcOHNDrr79umyjlQoqLi/X5558rOjq61kECAICqdenSRZIUFxen6OhoJSUlaf369ZUWz02aNNG1116rZ555RiUlJaa+DauLM8oa8lkN9Yk8Vo+7FCgN4flsCMfgKsilY3h7ezv8jLIaFdezZs3SZ599phkzZqioqEjbt2+3revatat27typhQsXasiQIQoODtbRo0e1ZMkS/frrr5o9e7ZDAwcAABVFRkbK09NTOTk5zg4FAIBGpUbF9ddffy1Jeuqppyqs27Bhg9q0aSOr1aoXXnhBJ06ckLe3t+Li4jRr1izFxMQ4JmIAAFClHTt2yGq12k1odr6ysjKtXbtWf/7zn93+Gk4AAFxJjYrrTz/99KJtFi1aVOtgAABA9aWkpKhbt26KjIxUixYttGfPHi1atEiRkZFKSEjQoUOHNGPGDA0fPlwdO3ZUQUGBli9frl27dmnu3LnODh8AgAbF1H2uAQCA88TExCgjI0MLFiyQYRgKDg7W6NGjNWHCBHl5eally5Zq1aqV0tLSdOzYMXl6eqpbt25KT09X//79nR0+AAANCsU1AABuauLEiZo4cWKV6wMCApSWllaPEQEA0HhVfYNqAAAAAABQLRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEnNnB0AKpeTk6P8/Hxnh2FjsViUnZ2tkpISeXt7q3Xr1urQoYOzwwIAAAAAl0Bx7YJycnIU2SVKJZZiZ4dSpRbePtq7J5MCGwAAAABEce2S8vPzVWIpVmxiqnwDQ5wdTgWFx3O1/aMXlJ+fT3ENAAAAAKK4dmm+gSHyDwpzdhgAAAAAgItgQjMAAAAAAEyiuAYAAAAAwCSKawAAAAAATKK4BgAAAADAJIprAAAAAABMorgGAAAAAMAkbsUFAAAAuLDMzExnh1Cp1q1bq0OHDs4OA3AZFNcAAACACyr5/TfJw0Njx451diiVauHto717Mimwgf+P4hoAAABwQaWnfpcMQ7GJqfINDHF2OHYKj+dq+0cvKD8/n+Ia+P8orgEAAAAX5hsYIv+gMGeHAeAimNAMAAA3tXHjRo0dO1Z9+/ZVt27dNHjwYD355JMqLCy0a/fpp5/qhhtuUHR0tK677jq98847TooYAICGi2+uAQBwUydOnFBMTIySk5MVEBCg/fv3a+7cudq/f78WL14sSdqyZYtSUlJ000036eGHH9Z3332nRx55RC1bttTQoUOdfAQAADQcFNcAALippKQku8d9+vSRl5eXZs6cqby8PAUFBSktLU0xMTF6/PHHJUl9+/bVwYMHNWfOHIprAAAciNPCAQBoQAICAiRJVqtVp0+f1qZNmyoU0cOGDdOPP/6o3NxcJ0QIAEDDxDfXAAC4uTNnzqi0tFRZWVmaP3++4uPjFRISoqysLFmtVnXu3NmufVjY2YmRDhw4oJCQ2s9AbBiGiouLTcVezmKx2P1E7ZDHmiFP5lkslgt+DvCadBxy6Rjn59EwDHl4eDisb4prAADc3KBBg5SXlydJ6t+/v5577jlJUkFBgSTJz8/Prn354/L1tWW1WpWZmWmqjz/Kzs52aH+NFXmsHvJkXnZ2tlq0aFGtdnAMcukY5Xn08vJyWJ8U1wAAuLkFCxbIYrEoKytLaWlpmjx5spYsWVLn+/X09FR4eLhD+rJYLMrOzlZoaKi8vb0d0mdjRB5rpqSkxNkhuL3Q0FBFRUVVuZ7XpOOQS8c4P4+HDh1yaN8U1wAAuLkuXbpIkuLi4hQdHa2kpCStX7/eVvj+8dZcJ0+elCT5+/ub2q+Hh4d8fHxM9fFH3t7eDu+zMSKP1UOBYl51X2u8Jh2HXDqGt7e3Q08Jl5jQDACABiUyMlKenp7KyclRhw4d5OnpqQMHDti1KX/8x2uxAQBA7VFcAwDQgOzYsUNWq1UhISHy8vJSnz59tG7dOrs2GRkZCgsLMzWZGQAAsMdp4QAAuKmUlBR169ZNkZGRatGihfbs2aNFixYpMjJSCQkJkqS7775b48aN02OPPabExERt2rRJa9as0QsvvODk6AEAaFgorgEAcFMxMTHKyMjQggULZBiGgoODNXr0aE2YMME2++kVV1yhuXPn6sUXX9Tbb7+tdu3a6YknnlBiYqKTowcAoGGhuAYAwE1NnDhREydOvGi7wYMHa/DgwfUQEQAAjRfXXAMAAAAAYFKNiuuPPvpId999twYMGKDY2FglJSXp7bfflmEYdu1Wrlyp6667TtHR0brhhhv02WefOTRoAAAAAABcSY2K66VLl8rb21szZsxQWlqaBgwYoJkzZ2r+/Pm2Nh9++KFmzpypxMREpaenKzY2VikpKdq+fbujYwcAAAAAwCXU6JrrtLQ0BQYG2h7369dPJ06c0JIlS3TPPfeoSZMmmjNnjoYPH65p06ZJkvr27at9+/Zp/vz5Sk9Pd2jwAAAAAAC4ghp9c31+YV0uKipKRUVFKi4u1sGDB5WdnV1hBtJhw4bp22+/1enTp81FCwAAAACACzI9W/jWrVsVFBSkVq1aaevWrZKkTp062bUJCwuT1WrVwYMHFRYWVut9GYah4uJiU/HWJYvFYvfTbD+uzmKxuNzzQe4q7uf8n9Vp6+rKc+eo95sr4tjcU3WOzTAMeXh41FdIAACgHpkqrrds2aKMjAw9+OCDkqSCggJJkp+fn1278sfl62vLarUqMzPTVB/1ITs726nb15fs7Gy1aNHC2WHYIXdV788RbVzBH3PnLnHXBsfmni52bOX3nwYAAA1LrYvrI0eOKDU1VX369NG4ceMcGVOVPD09FR4eXi/7qg2LxaLs7GyFhobK29u71v2UlJQ4MKq6ExoaqqioKGeHYYfc2avJa9Ldcueo95sr4tjcU3WOLSsrq56jAgAA9aVWxfXJkyd11113KSAgQHPnzlWTJmcv3fb395ckFRYWqk2bNnbtz19fWx4eHvLx8THVR33w9vY2Fae7/MJp9jjrArmr/f7cNXeu+Dp0FI7NPV3o2DglHACAhqtGE5pJZ7/dmjRpkgoLC7Vw4UL5+vra1nXu3FmSdODAAbttDhw4IE9PT7Vv395kuAAAAAAAuJ4aFdelpaWaNm2aDhw4oIULFyooKMhuffv27RUaGqq1a9faLc/IyFC/fv24zgwAAAAA0CDV6LTwWbNm6bPPPtOMGTNUVFSk7du329Z17dpVXl5emjJliqZPn64OHTqoT58+ysjI0M6dO7Vs2TJHxw4AAAAAgEuoUXH99ddfS5KeeuqpCus2bNigkJAQjRgxQhaLRenp6VqwYIE6deqkefPmKS4uzjERAwAAAADgYmpUXH/66afVajd69GiNHj26VgEBAAAAAOBuajyhGQAAAAAAsEdxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJjUzNkBAACA2vnoo4/0/vvva/fu3Tp58qQ6duyo5ORkjRo1Sh4eHpKk5ORkbd68ucK2GRkZCgsLq++QAQBosCiuAbiVzMxMSZLFYlF2drZKSkrk7e3t5KjOat26tTp06ODsMNCILF26VMHBwZoxY4YuueQSffPNN5o5c6aOHDmilJQUW7sePXrowQcftNs2JCSkvsMFAKBBo7gG4BZKfv9N8vDQ2LFjnR1KlVp4+2jvnkwKbNSbtLQ0BQYG2h7369dPJ06c0JIlS3TPPfeoSZOzV3/5+fkpNjbWSVECANA4UFwDcAulp36XDEOxianyDXS9b9wKj+dq+0cvKD8/n+Ia9eb8wrpcVFSU3nrrLRUXF6tVq1ZOiAoAgMaJ4hqAW/ENDJF/ENeJAlXZunWrgoKC7ArrzZs3KzY2VmfOnFH37t113333qVevXqb3ZRiGiouLTfcjnb3U4/yfqB3yWDPkyTyLxXLBzwFek45DLh3j/DwahmGbo8QRKK4BAGggtmzZooyMDLvrq3v16qWkpCSFhobq6NGjWrRokW6//Xa99tpriouLM7U/q9VqmwfBUbKzsx3aX2NFHquHPJmXnZ2tFi1aVKsdHINcOkZ5Hr28vBzWJ8U1AAANwJEjR5Samqo+ffpo3LhxtuVTp061azdw4ECNGDFCL730ktLT003t09PTU+Hh4ab6KFc+SWFoaKjLTFLojshjzZSUlDg7BLcXGhqqqKioKtfzmnQccukY5+fx0KFDDu2b4hoAADd38uRJ3XXXXQoICNDcuXNtE5lVxsfHR9dcc43WrVtner8eHh7y8fEx3c/5vL29Hd5nY0Qeq4cCxbzqvtZ4TToOuXQMb29vh54SLlFcAwDg1kpKSjRp0iQVFhZqxYoV8vX1dXZIAAA0ShTXAAC4qdLSUk2bNk0HDhzQ66+/rqCgoItuU1xcrM8//1zR0dH1ECEAAI0HxTUAAG5q1qxZ+uyzzzRjxgwVFRVp+/bttnVdu3bVzp07tXDhQg0ZMkTBwcE6evSolixZol9//VWzZ892XuAAADRAFNcAALipr7/+WpL01FNPVVi3YcMGtWnTRlarVS+88IJOnDghb29vxcXFadasWYqJianvcNFI5eTkKD8/39lhVMrRs90DaNworgEAcFOffvrpRdssWrSoHiIBKpeTk6PILlEqsTjmfugA4MoorgEAAFAn8vPzVWIpVmxiqnwDQ5wdTgV5P23Vvm/ecHYYABoIimsAAADUKd/AEPkHhTk7jAqKjuc6OwQADUjVN8IEAAAAAADVQnENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJzZwdANAYZWZm1st+LBaLsrOzVVJSIm9vb5eICQAAAGiIKK6BelTy+2+Sh4fGjh3r7FAAAAAAOBDFNVCPSk/9LhmGYhNT5RsY4uxw7OT9tFX7vnnD2WEAAAAAboniGnAC38AQ+QeFOTsMO0XHc50dAgAAAOC2KK4BAADcXE5OjvLz82s010Z9YD4PAI0JxTUAAIAby8nJUWSXKJVYip0dCgA0ajUurn/++WctWrRIO3bs0P79+9W5c2etWbPGrk1ycrI2b95cYduMjAyFhbnWqbAAAADuLD8/XyWWYubzAAAnq3FxvX//fm3cuFHdu3dXWVmZDMOotF2PHj304IMP2i0LCXGtD3wAAICGgvk8AMC5alxcx8fHKyEhQZI0Y8YM7dq1q9J2fn5+io2NNRUcAAAAAADuoEmNN2hS400AAAAAAGjQ6mxCs82bNys2NlZnzpxR9+7ddd9996lXr16m+jQMQ8XFrjtZh8Visftpth9XZ7FYXO75cJfcoeFyxPvCUZ8lrqixH5thGPLw8KivkAAAQD2qk+K6V69eSkpKUmhoqI4ePapFixbp9ttv12uvvaa4uLha92u1Wt3ilg7Z2dlO3b6+ZGdnq0WLFs4Ow4675A4NlyPfFw359dyYj83Ly6t+AgEAAPWqTorrqVOn2j0eOHCgRowYoZdeeknp6em17tfT01Ph4eFmw6sz5feWDA0NNXVvyZKSEgdGVXdCQ0MVFRXl7DDsuEvu0HA54n3hqM8SV9TYjy0rK6ueowIAAPWlXu5z7ePjo2uuuUbr1q0z1Y+Hh4d8fHwcFFXd8fb2NhWnu/zCafY464K75A4NlyPfF674HnOUxnpsnBIOAEDDxexkAAAAAACYVC/FdXFxsT7//HNFR0fXx+4AAGgUPvroI919990aMGCAYmNjlZSUpLfffluGYdi1W7lypa677jpFR0frhhtu0GeffeakiAEAaLhqfFq4xWLRxo0bJUmHDh1SUVGR1q5dK0nq3bu3Dhw4oIULF2rIkCEKDg7W0aNHtWTJEv3666+aPXu2Y6MHAKARW7p0qYKDgzVjxgxdcskl+uabbzRz5kwdOXJEKSkpkqQPP/xQM2fO1OTJk9W3b19lZGQoJSVFr7/+umJjY517AAAANCA1Lq6PHTum++67z25Z+eNXX31Vbdu2ldVq1QsvvKATJ07I29tbcXFxmjVrlmJiYhwTNQAAUFpamgIDA22P+/XrpxMnTmjJkiW655571KRJE82ZM0fDhw/XtGnTJEl9+/bVvn37NH/+fFOTjAIAAHs1Lq5DQkK0d+/eC7ZZtGhRrQMCAADVc35hXS4qKkpvvfWWiouL9dtvvyk7O1sPPPCAXZthw4bp6aef1unTp7k1GAAADlIvs4UDAID6sXXrVgUFBalVq1baunWrJKlTp052bcLCwmS1WnXw4EGFhYXVel+GYai4uNhUvOUsFovdT1QfOYMzWSyWC34O8N52HHLpGOfn0TAMh97Jg+IaAIAGYsuWLcrIyNCDDz4oSSooKJAk+fn52bUrf1y+vrasVqsyMzNN9fFH2dnZDu2vMSBncKbs7Gy1aNGiWu3gGOTSMcrz6MgzuCiuUWuO/oXKEVwxJgCoD0eOHFFqaqr69OmjcePG1cs+PT09FR4e7pC+LBaLsrOzFRoaKm9vb4f02ViUlJQ4OwQ0YqGhoYqKiqpyPe9txyGXjnF+Hg8dOuTQvimuUWMlv/8meXho7Nixzg4FACDp5MmTuuuuuxQQEKC5c+eqSZOzd9r09/eXJBUWFqpNmzZ27c9fX1seHh7y8fEx1ccfeXt7O7zPho5fsuFM1X3P8t52HHLpGN7e3g49JVyiuEYtlJ76XTIMxSamyjcwxNnh2Mn7aav2ffOGs8MAgHpTUlKiSZMmqbCwUCtWrJCvr69tXefOnSVJBw4csP2//LGnp6fat29f7/ECANBQUVyj1nwDQ+QfVPuJcOpC0fFcZ4cAAPWmtLRU06ZN04EDB/T6668rKCjIbn379u0VGhqqtWvXKiEhwbY8IyND/fr1Y6ZwAAAciOIaAAA3NWvWLH322WeaMWOGioqKtH37dtu6rl27ysvLS1OmTNH06dPVoUMH9enTRxkZGdq5c6eWLVvmvMABAGiAKK4BAHBTX3/9tSTpqaeeqrBuw4YNCgkJ0YgRI2SxWJSenq4FCxaoU6dOmjdvnuLi4uo7XAAAGjSKawAA3NSnn35arXajR4/W6NGj6zgaAAAatybODgAAAAAAAHdHcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJjUzNkBAACA2vv555+1aNEi7dixQ/v371fnzp21Zs0auzbJycnavHlzhW0zMjIUFhZWX6ECANCgUVwDAODG9u/fr40bN6p79+4qKyuTYRiVtuvRo4cefPBBu2UhISH1ESIAAI0CxTUAAG4sPj5eCQkJkqQZM2Zo165dlbbz8/NTbGxsPUYGAEDjwjXXAAC4sSZNGMoBAHAFfHMNAEAjsHnzZsXGxurMmTPq3r277rvvPvXq1ctUn4ZhqLi42CHxWSwWu5+oPnIGZ7JYLBf8HOC97Tjk0jHOz6NhGPLw8HBY3xTXAAA0cL169VJSUpJCQ0N19OhRLVq0SLfffrtee+01xcXF1bpfq9WqzMxMB0YqZWdnO7S/xoCcwZmys7PVokWLarWDY5BLxyjPo5eXl8P6pLgGAKCBmzp1qt3jgQMHasSIEXrppZeUnp5e6349PT0VHh5uNjxJZ79ByM7OVmhoqLy9vR3SZ2NRUlLi7BDQiIWGhioqKqrK9by3HYdcOsb5eTx06JBD+65xcV2dW35I0sqVK7Vw4UL98ssv6tSpk1JTUzVo0CCHBA0AAGrPx8dH11xzjdatW2eqHw8PD/n4+DgoqrO8vb0d3mdDxy/ZcKbqvmd5bzsOuXQMb29vh54SLtViQrPyW3507Nixyntjfvjhh5o5c6YSExOVnp6u2NhYpaSkaPv27WbjBQAAAADA5dT4m+vq3PJjzpw5Gj58uKZNmyZJ6tu3r/bt26f58+ebOv0MAACYV1xcrM8//1zR0dHODgUAgAajxsX1xW75cfDgQWVnZ+uBBx6wWz5s2DA9/fTTOn36tEMvGgcAoDGzWCzauHGjJOnQoUMqKirS2rVrJUm9e/fWgQMHtHDhQg0ZMkTBwcE6evSolixZol9//VWzZ892ZugAADQoDp/Q7MCBA5KkTp062S0PCwuT1WrVwYMHqzyd/GIcecuPuuCo6fGZXh9wXxe7JUl1+zj/Z0PS2I/N0bf8kKRjx47pvvvus1tW/vjVV19V27ZtZbVa9cILL+jEiRPy9vZWXFycZs2apZiYGIfGAgBAY+bw4rqgoECS5OfnZ7e8/HH5+tqoi1t+1AWz0+MzvT7gvqp7S5Lq9tVQNeZjc/TZWyEhIdq7d+8F2yxatMih+wQAABW51a24HHnLj7rgqOnxuaUG4L4udkuS6mjIt9po7MeWlZVVz1EBAID64vDi2t/fX5JUWFioNm3a2JafPHnSbn1t1MUtP+qC2enxG9ovnEBj4sjbYzTkW2001mNz9CnhAADAddT4VlwX07lzZ0nnrr0ud+DAAXl6eqp9+/aO3iUAAAAAAE7l8OK6ffv2Cg0Ntc1UWi4jI0P9+vVjpnAAAAAAQINT49PCL3bLj8DAQE2ZMkXTp09Xhw4d1KdPH2VkZGjnzp1atmyZY6MHAAAAAMAF1Li4vtgtP/r06aMRI0bIYrEoPT1dCxYsUKdOnTRv3jzFxcU5JmoAAAAAAFxIjYvr6tzyQ5JGjx6t0aNH1yooAAAAAADcicOvuQYAAAAAoLGhuAYAAAAAwCSH3+faXeTk5Cg/P9+hfVosFmVnZ6ukpMTUvaozMzMdGBUAAAAAoK41yuI6JydHkV2iVGIpdnYoAAAAAIAGoFEW1/n5+SqxFCs2MVW+gSHODqeCvJ+2at83bzg7DAAAAABANTXK4rqcb2CI/IPCnB1GBUXHc50dAgAAAACgBpjQDAAAAAAAkyiuAQAAAAAwieIaAAAAAACTKK4BAAAAADCJ4hoAAAAAAJMorgEAAAAAMIniGgAAAAAAkyiuAQAAAAAwieIaAAAAAACTmjk7AAAAAABwtJycHOXn5zs7jEq1bt1aHTp0cHYYcDCKawAAAAANSk5OjiK7RKnEUuzsUCrVwttHe/dkUmA3MBTXAAAAABqU/Px8lViKFZuYKt/AEGeHY6fweK62f/SC8vPzKa4bGIprAADc2M8//6xFixZpx44d2r9/vzp37qw1a9ZUaLdy5UotXLhQv/zyizp16qTU1FQNGjTICREDQP3xDQyRf1CYs8NAI8GEZgAAuLH9+/dr48aN6tixo8LCKv8F8sMPP9TMmTOVmJio9PR0xcbGKiUlRdu3b6/fYAEAaMD45hoAADcWHx+vhIQESdKMGTO0a9euCm3mzJmj4cOHa9q0aZKkvn37at++fZo/f77S09PrM1wAABosvrkGAMCNNWly4aH84MGDys7OVmJiot3yYcOG6dtvv9Xp06frMjwAABoNvrkGAKABO3DggCSpU6dOdsvDwsJktVp18ODBKk8nvxjDMFRc7JiZeC0Wi91PVB85gzNZLJYLfg44673tDu+Li+Wusvbn/0TtnJ9HwzDk4eHhsL4prgEAaMAKCgokSX5+fnbLyx+Xr68Nq9WqzMzM2gdXiezsbIf21xiQMzhTdna2WrRoUa129ckd3hfVzV1l28G88jx6eXk5rE+KawAAUCuenp4KDw93SF8Wi0XZ2dkKDQ2Vt7e3Q/psLEpKSpwdAhqx0NBQRUVFVbneWe9td3hfXCx3f8TnpGOcn8dDhw45tG+KawAAGjB/f39JUmFhodq0aWNbfvLkSbv1teHh4SEfHx9zAf6Bt7e3w/ts6PglG85U3fdsfb+33eF9Uduc8DnpGN7e3g49JVxiQjMAABq0zp07Szp37XW5AwcOyNPTU+3bt3dGWAAANDgU1wAANGDt27dXaGio1q5da7c8IyND/fr1c+i1ZgAANGacFg4AgBuzWCzauHGjJOnQoUMqKiqyFdK9e/dWYGCgpkyZounTp6tDhw7q06ePMjIytHPnTi1btsyZoQMA0KBQXAMA4MaOHTum++67z25Z+eNXX31Vffr00YgRI2SxWJSenq4FCxaoU6dOmjdvnuLi4pwRMgAADRLFNQAAbiwkJER79+69aLvRo0dr9OjR9RARgMbkYrfjK5+ZuaSkpF4nGXP0bQKB6qC4BgAAAFAjJb//Jnl4aOzYsc4OBXAZFNcAAAAAaqT01O+SYSg2MVW+gSHODqeCvJ+2at83bzg7DDQyFNcAAAAAasU3MET+QWHODqOCouO5zg4BjRDFNQA4kCOu8aqL69Nat26tDh06OKSvupCTk6P8/Hxnh1EpV88dAABwDRTXAOAArn7tWQtvH+3dk+mSRWJOTo4iu0SpxFLs7FAq5cq5AwAAroPiGgAcwJWvPSs8nqvtH72g/Px8lywQ8/PzVWIpJncAAMCtUVwDgAO56rVn7oDcAQAAd9bE2QEAAAAAAODuKK4BAAAAADCJ4hoAAAAAAJPqpLhetWqVIiMjK/x79tln62J3AAAAAAA4VZ1OaLZw4UL5+vraHgcFBdXl7gAAAAAAcIo6La4vv/xyBQYG1uUuAAAAAABwOq65BgAAAADApDr95nrEiBH67bff1K5dO918882688471bRp01r3ZxiGiouLTcdlsVhM9wEA7mb79u1O//w7deqUDh8+rIKCAjVv3lyStHfvXqfGVB0Wi+Wi4095bi+UY8Mw5OHh4dDYUD9ycnKUn5/v7DAqlZmZ6ewQAACqo+K6TZs2mjJlirp37y4PDw99+umnevHFF5WXl6dHH3201v1arVaHDCDZ2dmm+wAAd1Hy+2+Sh4cmTJjg7FDcVnZ2tlq0aFHtthfi5eXlgIhQn3JychTZJUolFvN/4AcANFx1Ulz3799f/fv3tz2++uqr1bx5c73yyiuaPHmyLrvsslr16+npqfDwcNPxlZSUmO4DANxF6anfJcNQbGKqfANDnB1OBXk/bdW+b95wdhgXFBoaqqioqAu2sVgsys7OVmhoqLy9vSttk5WVVRfhoY7l5+erxFLMewgAcEF1elr4+RITE7V48WJlZmbWurj28PCQj4+P6Viq+qUHABoy38AQ+QeFOTuMCoqO5zo7hIvy9vau9vhzobacEu7eeA8BAC6ECc0AAAAAADCp3orrjIwMNW3aVF27dq2vXQIAAAAAUC/q5LTwCRMmqE+fPoqMjJQkbdiwQW+99ZbGjRunNm3a1MUuAQAAAABwmjoprjt16qR33nlHR44cUVlZmUJDQ/Xwww8rOTm5LnYHAAAAAIBT1Ulx/be//a0uugUAAAAAwCUxoRkAAAAAACZRXAMAAAAAYBLFNQAADdyqVasUGRlZ4d+zzz7r7NAAAGgw6uSaawAA4HoWLlwoX19f2+OgoCAnRgMAQMNCcQ0AQCNx+eWXKzAw0NlhAADQIHFaOAAAAAAAJvHNNQAAjcSIESP022+/qV27drr55pt15513qmnTprXuzzAMFRcXOyQ2i8Vi99OVuGJMANyfxWKp0WeoK39OupPz82gYhjw8PBzWN8U1AAANXJs2bTRlyhR1795dHh4e+vTTT/Xiiy8qLy9Pjz76aK37tVqtyszMdGCkUnZ2tkP7cwRXjAmA+8vOzlaLFi1qtR3MK8+jl5eXw/qkuAYAoIHr37+/+vfvb3t89dVXq3nz5nrllVc0efJkXXbZZbXq19PTU+Hh4Q6J0WKxKDs7W6GhofL29nZIn45SUlLi7BAANEChoaGKioqqdntX/px0J+fn8dChQw7tm+IaAIBGKDExUYsXL1ZmZmati2sPDw/5+Pg4NC5vb2+H92kWv8QCqAu1/bxzxc9Jd+Tt7e3QU8IlJjQDAAAAAMA0imsAABqhjIwMNW3aVF27dnV2KAAANAicFg4AQAM3YcIE9enTR5GRkZKkDRs26K233tK4cePUpk0bJ0cHAEDDQHENAEAD16lTJ73zzjs6cuSIysrKFBoaqocffljJycnODg0AgAaD4hoAgAbub3/7m7NDqJYjR46opKTE5SYQc/TtxgAADRPFNQAAcLqDBw9q1KibdOoUt70CALgnimsAAOB0+fn5OnWqRLGJqfINDHF2OHbyftqqfd+84ewwAAAujuIaAAC4DN/AEPkHhTk7DDtFx3OdHQIAwA1wKy4AAAAAAEyiuAYAAAAAwCSKawAAAAAATKK4BgAAAADAJIprAAAAAABMorgGAAAAAMAkimsAAAAAAEyiuAYAAAAAwCSKawAAAAAATKK4BgAAAADAJIprAAAAAABMaubsAAAAAACgscnMzKxRe4vFouzsbJWUlMjb27uOojrr1KlTat68eZ3uw4zWrVurQ4cOzg6jAoprAAAAAKgnJb//Jnl4aOzYsc4OpUoeHk1kGGXODqNKLbx9tHdPpssV2BTXAAAAAFBPSk/9LhmGYhNT5RsY4uxwKsj7aav2ffOGy8ZXeDxX2z96Qfn5+RTXAAAAANDY+QaGyD8ozNlhVFB0PFeS68bnypjQDAAAAAAAkyiuAQAAAAAwieIaAAAAAACTKK4BAAAAADCJ4hoAAAAAAJMorgEAAAAAMIniGgAAAAAAk+qsuP7xxx91++23KzY2VldddZWefvppnT59uq52BwAALoBxGQCAutWsLjotKCjQ+PHjFRoaqrlz5yovL09PPfWUSkpK9Oijj9bFLgEAQBUYlwEAqHt1Uly/+eab+v333zVv3jwFBARIks6cOaNZs2Zp0qRJCgoKqovdAgCASjAuAwBQ9+rktPAvvvhC/fr1sw3gkpSYmKiysjJ9/fXXdbFLAABQBcZlAADqnodhGIajO+3Xr59GjRql6dOn2y3v37+/kpKSKiyvjm3btskwDHl6epqO79SpU/rll1/U3MdfTZrWyZf3ppyxntLpkiLiqwVXjk1y7fhcOTaJ+Mxw5dgk146v7EypThUXqF27dmrevPkF2xqGodLSUjVr1kweHh6VtrFarfLw8FCPHj3qItwqufq4LLn22OzKr1HJteNz5dgk4jPDlWOTXDs+V45Ncv34ajI2V+b88bq0tNSh43KdZOvkyZPy8/OrsNzf318FBQW16rP8F5WqfmGpiRYtWqhz586m+6lblzk7gItw5fhcOTbJteNz5dgk4jPDlWOTXDu+S6vVysPDQ15eXhdt44hxrKZcfVyW3GFsduXXqOTa8blybBLxmeHKsUmuHZ8rxya5fnzVG5src/547ehx2fX+FFGFuLg4Z4cAAAD+P8ZlAADs1ck1135+fiosLKywvKCgQP7+/nWxSwAAUAXGZQAA6l6dFNedO3fWgQMH7JYVFhbq119/dfFTvgAAaHgYlwEAqHt1UlwPGDBA33zzjU6ePGlbtnbtWjVp0kRXXXVVXewSAABUgXEZAIC6VyezhRcUFGj48OHq1KmTJk2apLy8PD311FO6/vrr9eijjzp6dwAA4AIYlwEAqHt1UlxL0o8//qh//OMf+v7779WyZUslJSUpNTX1ojOpAgAAx2NcBgCgbtVZcQ0AAAAAQGNRJ9dcAwAAAADQmFBcAwAAAABgEsU1AAAAAAAmUVwDAAAAAGASxTUAAAAAACZRXAMAAAAAYFIzZwfgjn7//XclJiYqLy9Pb7/9tqKjo6tsGx8fr0OHDlVYvnPnTjVv3rwuw6yWVatW6aGHHqqw/K677tL06dOr3M4wDKWnp+uNN97Q8ePHFRUVpYceekixsbF1GG3N1PbYXP05O9/q1av1yiuv6Mcff5SPj4+io6M1b948tWjRosptVq5cqYULF+qXX35Rp06dlJqaqkGDBtVj1NVT02NLTk7W5s2bKyzPyMhQWFhYXYdbbVXFKUnPP/+8hg8fXuk6d3jP1fbY3OE9t2HDBv3rX/9SVlaWWrZsqZ49e2r69Olq3779Bbdzh+etrn300Ud6//33tXv3bp08eVIdO3ZUcnKyRo0aJQ8Pjyq3I3f2aptHd3h/1beNGzcqPT1dWVlZKioqUlBQkBISEpSSkiJfX98LbusuY2h9qG0e3WW8dpaa1Bl8Tl6YM2o2iutaeOmll3TmzJlqt7/uuut0xx132C3z8vJydFimLFy40O6DMCgo6ILt09PTNWfOHE2fPl2RkZF6/fXXdccdd+i999676C+b9a2mxya5x3OWlpam9PR0TZ48WbGxsfrtt9/07bffXvC1+eGHH2rmzJmaPHmy+vbtq4yMDKWkpOj11193qQ/i2hybJPXo0UMPPvig3bKQkJC6DLXG/v73v6uoqMhu2SuvvKKPP/5Y/fr1q3I7d3jP1fbYJNd+z23atEkpKSn6y1/+otTUVJ04cUKzZ8/WHXfcoQ8++OCCf8xyh+etri1dulTBwcGaMWOGLrnkEn3zzTeaOXOmjhw5opSUlCq3I3f2aptHybXfX85w4sQJxcTEKDk5WQEBAdq/f7/mzp2r/fv3a/HixVVu5y5jaH2pbR4l9xivnaUmdQafkxfmlJrNQI1kZWUZsbGxxvLly42IiAhj586dF2w/aNAgY9asWfUUXc298847RkREhHHs2LFqb1NSUmL06NHDeO6552zLTp06ZQwaNMj4+9//XgdR1k5tjs0wXP85MwzD+PHHH42uXbsan3/+eY22u/baa42//vWvdstuueUW484773RkeKbU9tjGjh1rTJw4sY6iqlvx8fHGXXfdVeV6d3nPVeZix2YYrv+emzlzphEfH2+UlZXZln377bdGRESE8e9//7vK7dz5eXOkyj6D//a3vxk9evQwzpw5U+k25K6i2uTRMFz//eUqVqxYYURERBhHjhypso07jKHOVp08uvN4XddqUmfwOXlhzqrZuOa6hp544gmNGTNGnTp1cnYoTrNt2zYVFRUpMTHRtszLy0tDhgzRF1984cTIGo9Vq1YpJCRE11xzTbW3OXjwoLKzs+2eN0kaNmyYvv32W50+fdrRYdZKbY7NnW3btk25ubm6/vrrL9jGHd9z1Tk2d1BaWqqWLVvanXpbfjaMYRhVbueuz5ujBQYGVlgWFRWloqIiFRcXV7oNuauoNnlE9QUEBEiSrFZrpevdZQx1tovlERdWkzqDz8kLc1bNRnFdA2vXrtW+fft077331mi7Dz74QN26dVNcXJzuuusu7d27t44irL0RI0YoKipKgwcP1ssvv3zBUygOHDggSercubPd8rCwMP3yyy8qKSmp01hrqibHVs7Vn7MdO3YoIiJCL730kvr166du3bppzJgx2rFjR5XblD9vf/yQCQsLk9Vq1cGDB+s05uqqzbGV27x5s2JjYxUdHa2xY8fq3//+dz1EbM6aNWvk4+OjwYMHV9nG3d5z5apzbOVc+T03cuRI/fjjj3r99ddVWFiogwcP6vnnn1fXrl3Vo0ePKrdz1+etPmzdulVBQUFq1apVpevJXfVcLI/lXPn95UxnzpzRqVOntHv3bs2fP1/x8fFVnprsLmOoM9Qkj+XccbyuazWtM/icrJozazauua4mi8Wip556SqmpqRcdxM4XHx+vmJgYtWvXTgcPHtS//vUv3XrrrXr33Xdd4lqINm3aaMqUKerevbs8PDz06aef6sUXX1ReXp4effTRSrc5efKkvLy8Klzc7+fnJ8MwVFBQcMFrEOtLbY5Ncv3nTJJ+/fVX7dq1S/v27dPf//53eXt761//+pfuuOMOffzxx7r00ksrbFNQUCDp7PN0vvLH5eudrTbHJkm9evVSUlKSQkNDdfToUS1atEi33367XnvtNcXFxdXzUVRPaWmpPvroI8XHx8vHx6fKdu7ynjtfdY9Ncv333BVXXKF58+bp/vvv1+OPPy7p7DeGCxcuVNOmTavczh2ft/qwZcsWZWRkVLje8nzk7uKqk0fJ9d9fzjRo0CDl5eVJkvr376/nnnuuyrbuMoY6Q03yKLnneF3XalNn8DlZOWfXbBTX1ZSWlqZLL71Uo0aNqtF2f/vb32z/v+KKK3TVVVcpMTFRixYt0mOPPebgKGuuf//+6t+/v+3x1VdfrebNm+uVV17R5MmTddlllzkxOnNqe2yu/pxJZ09FLS4u1uzZs9WlSxdJUvfu3RUfH69ly5bpvvvuc3KEtVfbY5s6dard44EDB2rEiBF66aWXlJ6eXudx18bXX3+t48ePa8SIEc4OxeFqcmyu/p7btm2b/ud//kc333yzBg4cqBMnTuill17SxIkT9cYbbzTKX15q68iRI0pNTVWfPn00btw4Z4fjtmqSR1d/fznTggULZLFYlJWVpbS0NE2ePFlLliy54B/NUFFN8+iO43Vdq22dgYqcXbNxWng1HDp0SIsXL9bUqVNVWFiokydP2q5vKi4u1u+//17tvi677DL17NlTu3fvrqtwTUtMTNSZM2eUmZlZ6Xo/Pz+dPn1ap06dslt+8uRJeXh4yN/fvz7CrJWLHVtlXPE58/PzU0BAgK34lM5e59S1a1dlZWVVuk3581JYWGi3/OTJk3brna02x1YZHx8fXXPNNS71vP3RmjVrFBAQoKuvvvqC7dzxPVfdY6uMq73nnnjiCfXt21czZsxQ3759NXToUC1YsEA//PCD3nvvvSq3c8fnrS6dPHlSd911lwICAjR37lw1aVL1ryDkrmo1yWNlXO395UxdunRRXFycRo8erZdeekmbNm3S+vXrK23rLmOoM9Qkj5Vxh/G6LtW2zuBzsiJXqNn45roacnNzZbVaNXHixArrxo0bp+7du+utt95yQmTOUX5tx08//WRXAB04cEDt2rXjW5x6EB4erpycnErX/fFDtlz583bgwAG763MOHDggT09Plzk9sDbH5o5KSkr0ySef6IYbbpCnp+cF27rbe64mx+YOfvzxxwrXjbdt21aXXHJJla9Vyf2et7pUUlKiSZMmqbCwUCtWrLjovYTJXeVqmkdUX2RkpDw9Pat8T7vLGOpsF8sjKqptncHnZEWuULNRXFdDVFSUXn31VbtlmZmZevLJJzVr1qwL3pD8j/Ly8rR161YlJSU5OkyHycjIUNOmTdW1a9dK1/fo0UOtWrXSRx99ZHszW61WffzxxxowYEB9hlpjFzu2yrjiczZo0CCtWrVKmZmZioqKkiT99ttv2r17t2677bZKt2nfvr1CQ0O1du1aJSQk2JZnZGSoX79+LnPP09ocW2WKi4v1+eef1+j9WZ8+/fRTFRcXV2smbXd7z9Xk2Crjau+5du3a6YcffrBbdujQIf32228KDg6ucjt3e97qSmlpqaZNm6YDBw7o9ddfV1BQ0EW3IXcV1SaPlXG195er2LFjh6xWa5UTcbnLGOpsF8tjZVx9vK5rta0z+JysyBVqNorravDz81OfPn0qXXf55Zfr8ssvlySNHz9ev/zyi+1UmDVr1uizzz7TNddco8suu0wHDx7UggUL1LRpU91+++31Fv+FTJgwQX369FFkZKQkacOGDXrrrbc0btw4tWnTRlLF42revLkmTZqkuXPnKjAwUBEREVq+fLlOnDihCRMmOO1Y/qg2x+YOz5kkJSQkKDo6WlOnTlVqaqqaN2+uBQsWyMvLS7feeqsk6eGHH9a7775rVxRMmTJF06dPV4cOHdSnTx9lZGRo586dWrZsmbMOpYLaHNuWLVu0cOFCDRkyRMHBwTp69KiWLFmiX3/9VbNnz3bm4VTpgw8+ULt27dSzZ88K69z1PVeuJsfmDu+5MWPG6J///KeeeOIJxcfH68SJE7Zrus6/BYq7P291ZdasWfrss880Y8YMFRUVafv27bZ1Xbt2lZeXF7mrhtrk0R3eX86QkpKibt26KTIyUi1atNCePXu0aNEiRUZG2gpndx1D61Nt8uiO43Vdq22dwedkRa5Qs1FcO1BZWZndbZ5CQkJ09OhR/fOf/1RhYaF8fX3Vt29fTZ061WVOH+rUqZPeeecdHTlyRGVlZQoNDdXDDz+s5ORkW5s/Hpck3XXXXTIMQ4sXL9bx48cVFRWlRYsWucxxSbU7Nnd4ziSpSZMmWrBggZ588kk9+uijslqtuuKKK/T666/b/nBQ2fM2YsQIWSwWpaena8GCBerUqZPmzZvnUrNz1ubY2rRpI6vVqhdeeEEnTpyQt7e34uLiNGvWLMXExDjrUKpUUFCgL7/8UuPHj7e7d3I5d33PSTU/Nnd4z40bN05eXl5avny53nnnHbVs2VKxsbF68cUXdckll9jaufPzVpe+/vprSdJTTz1VYd2GDRsUEhJC7qqhNnl0h/eXM8TExCgjI0MLFiyQYRgKDg7W6NGjNWHCBNs30O46htan2uTR3cZrV8LnpOPU5Welh2EYhqMDBgAAAACgMWG2cAAAAAAATKK4BgAAAADAJIprAAAAAABMorgGAAAAAMAkimsAAAAAAEyiuAYAAAAAwCSKawAAAAAATKK4BgAAAADAJIprAAAAAABMorgGAAAAAMAkimsAAAAAAEyiuAYAAAAAwCSKawAAAAAATKK4BgAAAADAJIprAAAAAABMorgGAAAAAMAkimsAAAAAAEyiuAYAAAAAwCSKawAAAAAATKK4BgAAAADAJIprwE1ERkZq7ty5zg7DIZKTkzVixAhnhwEAgEM5eqzetGmTIiMjtWnTpou2TU5OVnJycrX6ZRwG6gbFNVCFVatWKTIy0vYvOjpa1113nR5//HHl5+fXqK8PPvhAS5curZP4/vOf/zi0X0fJy8vT3LlzlZmZabqvoqIizZs3TzfccIPi4uIUExOjESNG6JlnnlFeXp4DogUAuCNXHqszMjIUGRmp9evXV1h3ww03KDIyUt99912FdQMHDtSYMWNM759xGKh/zZwdAODqpk6dqpCQEJ0+fVpbt27V8uXLtXHjRq1Zs0be3t7V6mPNmjXav3+/brvttroN1oUcPXpU8+bNU3BwsKKiomrdz8GDB3Xbbbfp8OHDGjp0qG655RZ5enpq7969evvtt/XJJ59o3bp1DowcAOBuXHGs7tmzpyRp69atGjJkiG15UVGR9u/fr2bNmmnbtm3q27evbd3hw4d1+PBhDRs2TJLUq1cv7dy5U56enjXeP+MwUP8oroGLGDBggKKjoyVJo0ePVkBAgJYsWaINGzZwSlUdKy0tVUpKio4dO6ZXX31VV1xxhd361NRUpaenOyk6AICrcMWxOigoSCEhIdq6davd8u+//16GYWjo0KEV1pU/Li/MmzRpoubNm9dPwJVgHAZqhtPCgRoq/wtzbm6uJOm9997TyJEjFRMTo969eys1NVWHDx+2tU9OTtbnn3+uQ4cO2U5bi4+PlySdPn1as2fP1siRI9WzZ0/Fxsbq1ltvrfQ0sdrKy8vTQw89pCuvvFLdunXT8OHD9fbbb9u1Kb+mKyMjQ2lpabZfUsaPH6+ff/65Qp+vv/66Bg8erJiYGN10003asmWL3bVemzZt0k033SRJeuihh2zHvWrVKrt+srKylJycrO7du6t///4VBuiPP/5Ye/bs0eTJkysM6JLUqlUrpaam2h6XX0O2Z88ejR07Vt27d9eQIUO0du1aSdLmzZs1evRoxcTE6LrrrtM333xTi4wCAFydq4zVPXv2VGZmpkpKSmzLtm3bpj//+c/q37+/duzYobKyMrt1Hh4e6tGjh6Sqr7lesWKFEhIS7Mbh8zEOA87BN9dADeXk5EiSAgIClJaWptmzZysxMVE33XSTjh8/rmXLlum///u/9e6778rPz0+TJ09WYWGhjhw5ooceekiS1LJlS0lnTw1buXKlRowYodGjR+v333/X22+/rTvvvFMrV640dRqXJOXn5+vmm2+Wh4eH/vu//1uBgYH64osv9Mgjj6ioqKjCqW/p6eny8PDQHXfcoaKiIi1cuFDTp0/XypUrbW3eeOMNPf7447riiit022236dChQ7r33nvl5+entm3bSpLCwsI0depUzZkzR7fccovtL/DlvyxIUkFBge68804NGTJEiYmJWrdunZ599llFRETommuukSRt2LBBkpSUlFTtYy4oKNDkyZM1bNgwDR06VMuXL9df//pXlZWV6Z///KfGjBmjESNGaNGiRZo6dao+//xztWrVqlb5BQC4JlcZq3v27Kn33ntPO3bsUJ8+fSSdLaDj4uLUo0cPFRYWat++ferSpYttXefOnXXJJZdU2efKlSv16KOPKi4uTuPHj9fBgwd19913y9/fX3/6058kMQ4DTmMAqNQ777xjREREGN98841x7Ngx4/Dhw8aHH35o9O7d24iJiTFyc3ONqKgoIy0tzW67vXv3Gl27drVbPnHiRGPQoEEV9lFaWmqcOnXKbllBQYFx5ZVXGg899JDd8oiICGPOnDkV4tu5c2eVx/Dwww8bV111lXH8+HG75ampqUbPnj0Ni8ViGIZhfPfdd0ZERISRmJhoF88rr7xiREREGHv37jUMwzBOnTpl9O7d2xg1apRhtVpt7VatWmVEREQYY8eOtS3buXOnERERYbzzzjsV4ho7dqwRERFhrF692rbs1KlTxlVXXWVMmTLFtuwvf/mL0bNnzyqPr6p+P/jgA9uyH3/80YiIiDC6dOlibN++3bb8yy+/rDI+AIB7cPWxev/+/UZERIQxf/58wzAMw2q1GrGxsbbx78orrzSWLVtmGIZhFBYWGlFRUcbf/vY32/bl4/N3331nGIZhnD592ujXr5+RlJRkF9OKFSsYhwEXwGnhwEXcdttt6tevn6655hqlpqaqZcuWmjdvntavX6+ysjIlJibq+PHjtn+tW7dWx44dq3XbjKZNm8rLy0uSVFZWphMnTqi0tFTdunXTDz/8YCpuwzD08ccfKz4+XoZh2MV49dVXq7CwULt377bbZuTIkbZ4JNlOATt48KAkadeuXTpx4oRuvvlmNWt27sSX66+/Xv7+/jWKz8fHx+4v4V5eXoqOjrbtSzr7bUH5Nwc16Xf48OG2x507d5afn5/CwsLUvXt32/Ly/5+/PwCAe3LVsTosLEwBAQG2a6n37Nmj4uJixcXFSZLi4uK0bds2SdL27dt15swZ27fMldm1a5eOHTumMWPG2I3XN954o3x9fS96LOdjHAYcj9PCgYt49NFH1alTJzVt2lStW7dWp06d1KRJE23YsEGGYejaa6+tdLvzi88LWb16tRYvXqyffvpJVqvVtjwkJMRU3MePH9fJkye1YsUKrVixoso252vXrp3dYz8/P0nSyZMnJUm//PKLJKlDhw527Zo1a6bg4OAaxde2bVt5eHjYLfP399fevXttj1u1alXjQbeyfn19fW2nrJ+/TDp3bAAA9+WqY7WHh4fi4uK0ZcsWlZWVadu2bbr00kvVsWNHSWeL69dff12SbEX2hYrr8nG4fPtynp6eat++fbWOpRzjMOB4FNfARcTExNhmID1fWVmZPDw8lJ6erqZNm1ZY7+Pjc9G+33vvPc2YMUMJCQmaMGGCLr30UjVt2lQvv/yy6b/klk+QcsMNN+jGG2+stE1kZKTd4yZNKj+ZxTAMU7FUprKc/VHnzp31ww8/6PDhw7bryGrbb1XL6+LYAAD1y5XH6p49e+qzzz7Tvn37bNdbl4uLi9PTTz+tvLw8bd26VZdddlmNi+TaYhwGHI/iGqilDh06yDAMhYSEqFOnThds+8e/4JZbt26d2rdvr3nz5tm1mTNnjun4AgMD1bJlS5WVlenKK6803Z907pvtnJwcu/tylpaW2mZYLVfVMdfEoEGDtGbNGr3//vuaNGmS6f4AAI2LK4zV59/vetu2bRo/frxtXbdu3eTl5aVNmzZp586dGjBgwAX7Kh+Hf/75Z/Xr18+23Gq1Kjc31zYx2oWOpyYYh4Ga4ZproJauvfZaNW3aVPPmzavwV1fDMPTbb7/ZHnt7e6uwsLBCH+V/xT1/+x07dmj79u2m42vatKmuu+46rVu3Tvv27auw/o+nhFdHt27dFBAQoLfeekulpaW25R988IEKCgrs2np7e0syd7rXddddp4iICP3rX//S999/X2F9UVGRXnjhhVr3DwBo2FxhrO7WrZuaN2+uDz74QHl5eXbfXHt5eenyyy/XG2+8oeLi4gueEl7eV2BgoN58802dPn3atnz16tUVxlvGYaD+8c01UEsdOnTQtGnT9Nxzz+nQoUNKSEhQy5YtlZubq08++UQ333yzJkyYIEm6/PLLlZGRoSeffFLR0dHy8fFRfHy8Bg4cqI8//lj33nuvBg4cqNzcXL355psKDw9XcXFxteJ455139OWXX1ZYPm7cON1///3atGmTbr75Zo0ePVrh4eEqKCjQ7t279e2332rz5s01OmYvLy9NmTJF//jHPzR+/HglJibq0KFDWrVqVYXrsDt06CA/Pz+9+eabatmypXx8fBQTE1Oj0908PT01b9483X777Ro7dqyGDh2qHj16yNPTU/v379eaNWvk5+dnd49NAADKucJYXT5R2JYtW+Tl5aVu3brZrY+Li9PixYslXfh6a+nsuDht2jQ9+uijGj9+vIYNG6bc3FytWrWqwvjKOAzUP4prwISJEycqNDRUS5cu1fz58yWdncjjqquuUnx8vK3drbfeqszMTK1atUpLly5VcHCw4uPjNXLkSOXn52vFihX66quvFB4ermeeeUZr166tduG7fPnySpePHDlSbdu21cqVKzV//nytX79ey5cvV0BAgMLDwzV9+vRaHfPYsWNlGIaWLFmi//u//1OXLl2UlpamJ554Qs2bN7e18/T01FNPPaXnn39ejz32mEpLS/Xkk0/W+Fqyjh076t1339XSpUu1fv16bdiwQWVlZerYsaNGjx6t5OTkWh0HAKBxcIWxumfPntqyZYsuv/xyu1m+pbP3nl68eLFatmxpd1p3VW655RadOXNGixYt0tNPP62IiAjbvbzPxzgM1D8Pg1kEAJhUVlamfv36aciQIXriiSecHQ4AAABQ77jmGkCNnDp1qsJ1a++++65OnDih3r17OykqAAAAwLk4LRxAjWzfvl1PPvmkhg4dqoCAAP3www96++23FRERoaFDhzo7PAAAAMApKK4B1EhwcLDatm2r1157TQUFBfL391dSUpKmT59e4ToyAAAAoLHgmmsAAAAAAEzimmsAAAAAAEyiuAYAAAAAwCS3ueb6+++/l2EY8vT0dHYoAADUitVqlYeHh+Li4pwdimmMywAAd+focdltvrk2DKPC7X/M9HX69GmH9efuyMc55OIccnEOuTiHXJxTm1w4cixztvOPhdeF45BLxyCPjkMuHYM8Oo4jc+nocdltvrku/8t4dHS06b6Ki4uVmZmp8PBw+fj4mO7P3ZGPc8jFOeTiHHJxDrk4pza5+M9//lPHUdWf88dlXheOQy4dgzw6Drl0DPLoOI7MpaPHZbf55hoAAAAAAFdFcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACYRHENAAAAAIBJFNcAAAAAAJhEcQ0AAAAAgEkU1wAAAAAAmERxDQAAAACASRTXAAAAAACY1MzZATjLkSNHVFJSIm9vb2eHUqnWrVurQ4cOzg4DAIB6k5OTo/z8fGeHUSnGZQDAxTTK4vrgwYMaNeomnTpV4uxQqtTC20d792QykAMAGoWcnBxFdolSiaXY2aFUinEZAHAxjbK4zs/P16lTJYpNTJVvYIizw6mg8Hiutn/0gvLz8xnEAQCNQn5+vkosxS45NjMuAwCqo1EW1+V8A0PkHxTm7DAAAHCI33//XYmJicrLy9Pbb7+t6Oho27qVK1dq4cKF+uWXX9SpUyelpqZq0KBBToy2cozNAAB3xYRmAAA0EC+99JLOnDlTYfmHH36omTNnKjExUenp6YqNjVVKSoq2b99e/0ECANBAUVwDANAA/Pjjj3rjjTc0ZcqUCuvmzJmj4cOHa9q0aerbt68ef/xxRUdHa/78+U6IFACAhoniGgCABuCJJ57QmDFj1KlTJ7vlBw8eVHZ2thITE+2WDxs2TN9++61Onz5dn2ECANBgNeprrgEAaAjWrl2rffv2ae7cudq9e7fdugMHDkhShaI7LCxMVqtVBw8eVFhY7a5xNgxDxcXFslgskmT7WRtmtq0vFotFxcV1O5u5I3IJ8uhI5NIxyKPjODKXhmHIw8PDdD/lKK4BAHBjFotFTz31lFJTU9WqVasK6wsKCiRJfn5+dsvLH5evrw2r1arMzEzb4+zs7Fr3ZWbb+pKdna0WLVrU275gHnl0HHLpGOTRcRyVSy8vL4f0I1FcAwDg1tLS0nTppZdq1KhR9b5vT09PhYeHy2KxKDs7W6GhofL29q5VXyUlJQ6OzvFCQ0MVFRVVp/twRC5BHh2JXDoGeXQcR+YyKyvLQVGdRXENAICbOnTokBYvXqz58+ersLBQkmynLRcXF+v333+Xv7+/JKmwsFBt2rSxbXvy5ElJsq2vDQ8PD/n4+Ngee3t72z2uCXf4ZdPM8bnyvhoy8ug45NIxyKPjOCKXjjwlXKK4BgDAbeXm5spqtWrixIkV1o0bN07du3fXc889J+nstdedO3e2rT9w4IA8PT3Vvn37eosXAICGrEbF9caNG5Wenq6srCwVFRUpKChICQkJSklJka+vryRpxowZWr16dYVt09PTNWDAAMdEDQAAFBUVpVdffdVuWWZmpp588knNmjVL0dHRat++vUJDQ7V27VolJCTY2mVkZKhfv34OvdYMAIDGrEbF9YkTJxQTE6Pk5GQFBARo//79mjt3rvbv36/Fixfb2rVv317PPvus3ba1nYkUAABUzs/PT3369Kl03eWXX67LL79ckjRlyhRNnz5dHTp0UJ8+fZSRkaGdO3dq2bJl9RkuAAANWo2K66SkJLvHffr0kZeXl2bOnKm8vDwFBQVJklq0aKHY2FiHBQkAAGpvxIgRslgsSk9P14IFC9SpUyfNmzdPcXFxzg4NAIAGw/Q11wEBAZLO3o4DAAA4V58+fbR3794Ky0ePHq3Ro0c7ISIAABqHWhXXZ86cUWlpqbKysjR//nzFx8crJCTEtv7nn39Wz549derUKUVEROiee+6xu86rtgzDsM2CasapU6dM91EfLBaLQ463Ovs5/2djRi7OIRfnkItzyMU5tcmFYRgOn5kUAAC4hloV14MGDVJeXp4kqX///raZSKWzk6tER0crPDxchYWFWr58ue69917Nnj1bQ4cONRWs1WpVZmamqT4k6fDhw6b7qA/Z2dlq0aJFve4PZ5GLc8jFOeTiHHJxTk1zwQRiAAA0TLUqrhcsWCCLxaKsrCylpaVp8uTJWrJkiZo2barx48fbtY2Pj9eYMWM0Z84c08W1p6enwsPDTfUhSQUFBab7qA+hoaGKioqq8/1wU/tzyMU55OIccnEOuTinNrnIysqq46gAAICz1Kq47tKliyQpLi5O0dHRSkpK0vr16ystnps0aaJrr71WzzzzjEpKSkx9E+vh4eGQm643b97cdB/1ob5vMs9N7c8hF+eQi3PIxTnk4pya5IJTwgEAaLiamO0gMjJSnp6eysnJcUQ8AAAAAAC4HdPF9Y4dO2S1Wu0mNDtfWVmZ1q5dqz//+c/1ev0wAAAAAAD1pUanhaekpKhbt26KjIxUixYttGfPHi1atEiRkZFKSEjQoUOHNGPGDA0fPlwdO3ZUQUGBli9frl27dmnu3Ll1dQwAAAAAADhVjYrrmJgYZWRkaMGCBTIMQ8HBwRo9erQmTJggLy8vtWzZUq1atVJaWpqOHTsmT09PdevWTenp6erfv39dHQMAAAAAAE5Vo+J64sSJmjhxYpXrAwIClJaWZjooAAAAAADcielrrgEAAAAAaOworgEAAAAAMIniGgAAAAAAkyiuAQAAAAAwieIaAAAAAACTKK4BAAAAADCJ4hoAAAAAAJMorgEAAAAAMIniGgAAAAAAkyiuAQAAAAAwieIaAAAAAACTKK4BAAAAADCJ4hoAAAAAAJMorgEAAAAAMKmZswMAAAC1s3HjRqWnpysrK0tFRUUKCgpSQkKCUlJS5OvrK0maMWOGVq9eXWHb9PR0DRgwoL5DBgCgwaK4BgDATZ04cUIxMTFKTk5WQECA9u/fr7lz52r//v1avHixrV379u317LPP2m0bFhZW3+ECANCgUVwDAOCmkpKS7B736dNHXl5emjlzpvLy8hQUFCRJatGihWJjY50QIQAAjQfXXAMA0IAEBARIkqxWq3MDAQCgkeGbawAA3NyZM2dUWlqqrKwszZ8/X/Hx8QoJCbGt//nnn9WzZ0+dOnVKERERuueee5SQkGB6v4ZhqLi4WBaLRZJsP2vDzLb1xWKxqLi4uM73cf5P1A55dBxy6Rjk0XEcmUvDMOTh4WG6n3IU1wAAuLlBgwYpLy9PktS/f38999xztnVRUVGKjo5WeHi4CgsLtXz5ct17772aPXu2hg4damq/VqtVmZmZtsfZ2dm17svMtvUlOztbLVq0qLd9wTzy6Djk0jHIo+M4KpdeXl4O6UeiuAYAwO0tWLBAFotFWVlZSktL0+TJk7VkyRI1bdpU48ePt2sbHx+vMWPGaM6cOaaLa09PT4WHh8tisSg7O1uhoaHy9vauVV8lJSWmYqkPoaGhioqKqtN9OCKXII+ORC4dgzw6jiNzmZWV5aCozqK4BgDAzXXp0kWSFBcXp+joaCUlJWn9+vWVFs9NmjTRtddeq2eeeUYlJSWmvon18PCQj4+P7bG3t7fd45pwh182zRyfK++rISOPjkMuHYM8Oo4jcunIU8IlJjQDAKBBiYyMlKenp3JycpwdCgAAjQrFNQAADciOHTtktVrtJjQ7X1lZmdauXas///nP9Xb9MAAAjQGnhQMA4KZSUlLUrVs3RUZGqkWLFtqzZ48WLVqkyMhIJSQk6NChQ5oxY4aGDx+ujh07qqCgQMuXL9euXbs0d+5cZ4cPAECDQnENAICbiomJUUZGhhYsWCDDMBQcHKzRo0drwoQJ8vLyUsuWLdWqVSulpaXp2LFj8vT0VLdu3ZSenq7+/fs7O3wAABqUGhXXGzduVHp6urKyslRUVKSgoCAlJCQoJSVFvr6+tnaffvqpXnzxRf30009q166dJk6cqFGjRjk8eAAAGrOJEydq4sSJVa4PCAhQWlpaPUYEAEDjVaPi+sSJE4qJiVFycrICAgK0f/9+zZ07V/v379fixYslSVu2bFFKSopuuukmPfzww/ruu+/0yCOPqGXLlqZv+QEAAAAAgCuqUXGdlJRk97hPnz7y8vLSzJkzlZeXp6CgIKWlpSkmJkaPP/64JKlv3746ePCgQ+6nCQAAAACAKzI9W3hAQIAkyWq16vTp09q0aVOFInrYsGH68ccflZuba3Z3AAAAAAC4nFpNaHbmzBmVlpYqKytL8+fPV3x8vEJCQpSVlSWr1arOnTvbtQ8LC5MkHThwoMpbg1SHYRgqLi6u9fblTp06ZbqP+mCxWBxyvNXZz/k/GzNycQ65OIdcnEMuzqlNLgzDkIeHR12FBAAAnKhWxfWgQYOUl5cnSerfv7+ee+45SVJBQYEkyc/Pz659+ePy9bVltVqVmZlpqg9JOnz4sOk+6kN2dna93oM0Ozu73vbl6sjFOeTiHHJxDrk4p6a58PLyqptAAACAU9WquF6wYIEsFouysrKUlpamyZMna8mSJY6OrQJPT0+Fh4eb7sdskV9fQkNDFRUVVef7sVgsys7OVmhoqLy9vet8f66MXJxDLs4hF+eQi3Nqk4usrKw6jgpAQ5OTk6P8/Hyn7b/8s66kpKTCZ13r1q3VoUMHJ0UGuJ5aFdddunSRJMXFxSk6OlpJSUlav369rfAtLCy0a3/y5ElJkr+/v5lY5eHhIR8fH1N9SFLz5s1N91EfvL29HXK8rro/V0YuziEX55CLc8jFOTXJBaeEA6iJnJwcRXaJUoml7i8TrI0W3j7auyeTAhv4/2pVXJ8vMjJSnp6eysnJUXx8vDw9PXXgwAH179/f1ubAgQOSVOFabAAAAACVy8/PV4mlWLGJqfINrP28RXWh8Hiutn/0gvLz8ymugf/PdHG9Y8cOWa1WhYSEyMvLS3369NG6des0fvx4W5uMjAyFhYWZmswMAAAAaIx8A0PkHxTm7DAAXESNiuuUlBR169ZNkZGRatGihfbs2aNFixYpMjJSCQkJkqS7775b48aN02OPPabExERt2rRJa9as0QsvvFAnBwAAAAAAgLPVqLiOiYlRRkaGFixYIMMwFBwcrNGjR2vChAm22U+vuOIKzZ07Vy+++KLefvtttWvXTk888YQSExPr5AAAAAAAAHC2GhXXEydO1MSJEy/abvDgwRo8eHCtgwKAyjhrxtQLzZR6PmZNBQAAaLxMX3MNAPXB1WdMlZg1FQAAoDGjuAbgFlx5xlSJWVMBAAAaO4prAG6FGVMBAADgipo4OwAAAAAAANwdxTUAAAAAACZRXAMAAAAAYBLFNQAAAAAAJlFcAwAAAABgEsU1AAAAAAAmUVwDAAAAAGASxTUAAG5q48aNGjt2rPr27atu3bpp8ODBevLJJ1VYWGjX7tNPP9UNN9yg6OhoXXfddXrnnXecFDEAAA1XM2cHAAAAaufEiROKiYlRcnKyAgICtH//fs2dO1f79+/X4sWLJUlbtmxRSkqKbrrpJj388MP67rvv9Mgjj6hly5YaOnSok48AAICGg+IaAAA3lZSUZPe4T58+8vLy0syZM5WXl6egoCClpaUpJiZGjz/+uCSpb9++OnjwoObMmUNxDQCAA3FaOAAADUhAQIAkyWq16vTp09q0aVOFInrYsGH68ccflZub64QIAQBomPjmGgAAN3fmzBmVlpYqKytL8+fPV3x8vEJCQpSVlSWr1arOnTvbtQ8LC5MkHThwQCEhIbXer2EYKi4ulsVikSTbz9ows219sVgsKi4urvN9nP8TtdNQ8ugO8dfH+6IhaCivSVfgyFwahiEPDw/T/ZSjuAYAwM0NGjRIeXl5kqT+/fvrueeekyQVFBRIkvz8/Ozalz8uX19bVqtVmZmZtsfZ2dm17svMtvUlOztbLVq0qLd9wTx3z6M7xF+f74uGwB2eU3fhqFx6eXk5pB+J4hoAALe3YMECWSwWZWVlKS0tTZMnT9aSJUvqfL+enp4KDw+XxWJRdna2QkND5e3tXau+SkpKHByd44WGhioqKqpO9+GIXKLh5JH3RcPRUF6TrsCRuczKynJQVGdRXAMA4Oa6dOkiSYqLi1N0dLSSkpK0fv16hYeHS1KFW3OdPHlSkuTv729qvx4eHvLx8bE99vb2tntcE+7wy6aZ43PlfTVk7p5H3hcND/lyHEfk0pGnhEtMaAYAQIMSGRkpT09P5eTkqEOHDvL09NSBAwfs2pQ//uO12AAAoPYorgEAaEB27Nghq9WqkJAQeXl5qU+fPlq3bp1dm4yMDIWFhZmazAwAANjjtHAAANxUSkqKunXrpsjISLVo0UJ79uzRokWLFBkZqYSEBEnS3XffrXHjxumxxx5TYmKiNm3apDVr1uiFF15wcvQAADQsFNcAALipmJgYZWRkaMGCBTIMQ8HBwRo9erQmTJhgm/30iiuu0Ny5c/Xiiy/q7bffVrt27fTEE08oMTHRydEDANCwUFwDsMnJyVF+fr6kczMxlpSUuMSEKuff7gfAWRMnTtTEiRMv2m7w4MEaPHhwPUQEAEDjRXENQNLZwjqyS5RKLMXODgUAAABwOxTXACRJ+fn5KrEUKzYxVb6BrjfJUd5PW7XvmzecHQYAAABQKYprAHZ8A0PkHxTm7DAqKDqe6+wQAAAAgCrVqLj+6KOP9P7772v37t06efKkOnbsqOTkZI0aNcp2A+7k5GRt3ry5wrblt/0AAAAAAKChqVFxvXTpUgUHB2vGjBm65JJL9M0332jmzJk6cuSIUlJSbO169OihBx980G5b7qUJAAAAAGioalRcp6WlKTAw0Pa4X79+OnHihJYsWaJ77rlHTZo0kST5+fkpNjbWoYECAAAAAOCqmtSk8fmFdbmoqCgVFRWpuJgZhgEAAAAAjZPpCc22bt2qoKAgtWrVyrZs8+bNio2N1ZkzZ9S9e3fdd9996tWrl9ldyTAMhxTxp06dMt1HfbBYLPXyRwuLxWL3szFrzLlojMdcF+rrfessjfk98ke1yYVhGLY5SgAAQMNiqrjesmWL/l979x8cRZ3nf/w1YfNjovlhlhhXAk4SSgwaSNjFkFJEWCgrkpU/PK6sLcBSjoNTBFLFFXFL8HJr3VFbtcfKT48k4s/d9efduRjQO0C8AusocYGSikDMjqASIEJ+MTNhgPn+wZcsYwAz093TPTPPR5WF09P96dd8pntm3pnpz6e5uTns+urx48drxowZ8ng8OnnypJqamvTYY4/p1VdfVUVFhaGwwWBQLS0thtqQpOPHjxtuIxa8Xq8yMjJiuj9ckox9kYyP2QqxPm/twvHyV5H2RVpamjVBAACAraIurtvb21VbW6vKykrNmTOnf/miRYvC1rv//vtVU1Oj9evXq6GhIfqkklJTUzVy5EhDbUhSV1eX4TZiwePxqLS01PL9+P1+eb1eeTweud1uy/fnZMncF4FAwO4ICSFW561dkvkc+b5o+qK1tdXiVAAAwC5RFdfd3d2aN2+ecnNztWbNmv6BzK4mMzNTkyZN0gcffBB1yMtcLpcyMzMNt5Oenm64jVhwu92mPF6n7s/JkrEvkr1QMkuyHDvJ8jgHI5K+4CfhAAAkroiL60AgoPnz56unp0dvvPGGsrKyrMgFAAAAAEDciKi4Pn/+vJYsWaK2tja9/vrrKigo+MFtfD6fPvroI5WVlUUdEgAAAAAAJ4uouK6vr9eOHTtUV1en3t5e7du3r/++0aNH68CBA2psbNS0adM0bNgwnTx5Ups2bdKpU6f0/PPPm50dAAAAAABHiKi43rVrlyRp5cqVA+7btm2b8vPzFQwGtWrVKnV2dsrtdquiokL19fUaM2aMOYkBAAAAAHCYiIrr7du3/+A6TU1NUYcBAAAAACAeXXuYbwAAAAAAMCgU1wAAAAAAGERxDQAAAACAQRTXAAAAAAAYRHENAAAAAIBBFNcAAAAAABgU0VRcAADAObZs2aL33ntPBw8eVHd3t2677TbNnj1bDz/8sFwulyRp9uzZ2rNnz4Btm5ubVVJSEuvIAAAkLIprAADi1EsvvaRhw4aprq5ON910k3bv3q3ly5ervb1dCxcu7F9v3LhxWrZsWdi2hYWFsY4LAEBCo7gGACBObdiwQXl5ef23q6qq1NnZqU2bNumJJ55QSsqlq7+ys7NVXl5uU0oAAJID11wDABCnriysLystLVVvb698Pp8NiQAASF58cw0AQALZu3evCgoKdOONN/Yv27Nnj8rLy3XhwgWNHTtWixcv1vjx4w3vKxQKyefzye/3S1L/v9Ewsm2s+P1+y/9oYUZfInH6MR7yx+K8SASJckw6gZl9GQqF+scoMQPFNQAACeLTTz9Vc3Nz2PXV48eP14wZM+TxeHTy5Ek1NTXpscce06uvvqqKigpD+wsGg2ppaem/7fV6o27LyLax4vV6lZGREbN9wbh478d4yB/L8yIRxMNzGi/M6su0tDRT2pEorgEASAjt7e2qra1VZWWl5syZ07980aJFYevdf//9qqmp0fr169XQ0GBon6mpqRo5cqT8fr+8Xq88Ho/cbndUbQUCAUNZYsHj8ai0tNTSfZjRl0icfuS8SByJckw6gZl92draalKqSyiuAQCIc93d3Zo3b55yc3O1Zs2a/oHMriYzM1OTJk3SBx98YHi/LpdLmZmZ/bfdbnfY7UjEw4dNI4/PyftKZPHej5wXiYf+Mo8ZfWnmT8IlimsAAOJaIBDQ/Pnz1dPTozfeeENZWVl2RwIAIClRXAMAEKfOnz+vJUuWqK2tTa+//roKCgp+cBufz6ePPvpIZWVlMUgIAEDyoLgGACBO1dfXa8eOHaqrq1Nvb6/27dvXf9/o0aN14MABNTY2atq0aRo2bJhOnjypTZs26dSpU3r++eftCw4AQAKiuAYAIE7t2rVLkrRy5coB923btk35+fkKBoNatWqVOjs75Xa7VVFRofr6eo0ZMybWcQEASGgU1wAAxKnt27f/4DpNTU0xSAIAAK49nCgAAAAAABgUimsAAAAAAAyiuAYAAAAAwCCKawAAAAAADKK4BgAAAADAIEYLBwAAAJBwjh49qo6ODrtjSJL8fr+8Xq8CgYDcbreGDh2qESNG2B0LJqO4BgAAAJBQjh49qlF3lCrg99kd5aoy3Jk69EULBXaCiai43rJli9577z0dPHhQ3d3duu222zR79mw9/PDDcrlc/eu99dZbamxs1LfffquioiLV1tZq8uTJpocHAAAAgO/r6OhQwO9TeXWtsvIK7Y4Tpuf019q3ZZU6OjoorhNMRMX1Sy+9pGHDhqmurk433XSTdu/ereXLl6u9vV0LFy6UJL3//vtavny5FixYoAkTJqi5uVkLFy7U66+/rvLyciseAwAAAAAMkJVXqJyCErtjIElEVFxv2LBBeXl5/berqqrU2dmpTZs26YknnlBKSopWr16t6dOna8mSJZKkCRMm6PDhw1q3bp0aGhpMDQ8AAAAAgBNENFr4lYX1ZaWlpert7ZXP59OxY8fk9XpVXV0dts6DDz6oTz75ROfOnTOWFgAAAAAABzI8oNnevXtVUFCgG2+8UXv37pUkFRUVha1TUlKiYDCoY8eOqaQk+p9lhEIh+XzGByXo6+sz3EYs+P1+Ux7vYPZz5b/JLJn7IhkfsxVidd7aJZnPke+Lpi9CoVDYGCUAACBxGCquP/30UzU3N2vZsmWSpK6uLklSdnZ22HqXb1++P1rBYFAtLS2G2pCk48ePG24jFrxerzIyMmK6P1ySjH2RjI/ZCrE+b+3C8fJXkfZFWlqaNUEAAICtoi6u29vbVVtbq8rKSs2ZM8fMTNeUmpqqkSNHGm7HaJEfKx6PR6WlpZbv5/K8ex6PR2632/L9OVky90UgELA7QkKI1Xlrl2Q+R74vmr5obW21OBUAALBLVMV1d3e35s2bp9zcXK1Zs0YpKZcu3c7JyZEk9fT0KD8/P2z9K++PlsvlUmZmpqE2JCk9Pd1wG7HgdrtNebxO3Z+TJWNfJHuhZJZkOXaS5XEORiR9wU/CAQBIXBENaCZd+nZr/vz56unpUWNjo7KysvrvKy4uliS1tbWFbdPW1qbU1FQNHz7cYFwAAAAAAJwnouL6/PnzWrJkidra2tTY2KiCgoKw+4cPHy6Px6OtW7eGLW9ublZVVRXXmQEAAAAAElJEPwuvr6/Xjh07VFdXp97eXu3bt6//vtGjRystLU1PPfWUli5dqhEjRqiyslLNzc06cOCAXnvtNbOzAwAAAADgCBEV17t27ZIkrVy5csB927ZtU2FhoWpqauT3+9XQ0KCNGzeqqKhIa9euVUVFhTmJAQAAAABwmIiK6+3btw9qvZkzZ2rmzJlRBQIAAAAAIN5EPKAZAAAAAAAIR3ENAAAAAIBBFNcAAAAAABhEcQ0AQJzasmWL/uEf/kH33XefysvLNWPGDL399tsKhUJh67311lt64IEHVFZWpoceekg7duywKTEAAImL4hoAgDj10ksvye12q66uThs2bNB9992n5cuXa926df3rvP/++1q+fLmqq6vV0NCg8vJyLVy4MGw6TQAAYFxEo4UDAADn2LBhg/Ly8vpvV1VVqbOzU5s2bdITTzyhlJQUrV69WtOnT9eSJUskSRMmTNDhw4e1bt06NTQ02JQcAIDEwzfXAADEqSsL68tKS0vV29srn8+nY8eOyev1qrq6OmydBx98UJ988onOnTsXq6gAACQ8vrkGABO1tLTYHeGqhg4dqhEjRtgdAzGwd+9eFRQU6MYbb9TevXslSUVFRWHrlJSUKBgM6tixYyopKYl6X6FQSD6fT36/X5L6/42GkW1jxe/3y+fzWb6PK/9FdBKlH+MhfyzOi2jQd4nLzPM7FArJ5XIZbucyimsAMEHg7BnJ5dKsWbPsjnJVGe5MHfqihQI7wX366adqbm7WsmXLJEldXV2SpOzs7LD1Lt++fH+0gsFg2B+UvF5v1G0Z2TZWvF6vMjIyYrYvGBfv/RgP+WN5XkSCvkt8Zj3HaWlpprQjUVwDgCnO952VQiGVV9cqK6/Q7jhhek5/rX1bVqmjo4PiOoG1t7ertrZWlZWVmjNnTkz2mZqaqpEjR8rv98vr9crj8cjtdkfVViAQMDmd+Twej0pLSy3dhxl9icTpR86L6NF3icvM87u1tdWkVJdQXAOAibLyCpVTEP3PbIFodHd3a968ecrNzdWaNWuUknJpSJWcnBxJUk9Pj/Lz88PWv/L+aLlcLmVmZvbfdrvdYbcjEQ8FkJHH5+R9JbJ470fOi+jRd4nPjP4z8yfhEgOaAQAQ1wKBgObPn6+enh41NjYqKyur/77i4mJJUltbW9g2bW1tSk1N1fDhw2OaFQCAREZxDQBAnDp//ryWLFmitrY2NTY2qqCgIOz+4cOHy+PxaOvWrWHLm5ubVVVVZep1ZgAAJDt+Fg4AQJyqr6/Xjh07VFdXp97eXu3bt6//vtGjRystLU1PPfWUli5dqhEjRqiyslLNzc06cOCAXnvtNfuCAwCQgCiuAQCIU7t27ZIkrVy5csB927ZtU2FhoWpqauT3+9XQ0KCNGzeqqKhIa9euVUVFRazjAgCQ0CiuAQCIU9u3bx/UejNnztTMmTMtTgMAQHLjmmsAAAAAAAyiuAYAAAAAwCCKawAAAAAADKK4BgAAAADAIIprAAAAAAAMorgGAAAAAMAgimsAAAAAAAyiuAYAAAAAwCCKawAAAAAADKK4BgAAAADAoB9FusFXX32lpqYm7d+/X0eOHFFxcbE2b94cts7s2bO1Z8+eAds2NzerpKQk+rQAAAAAADhQxMX1kSNHtHPnTo0dO1YXL15UKBS66nrjxo3TsmXLwpYVFhZGlxIAAAAAAAeLuLieMmWKpk6dKkmqq6vT559/ftX1srOzVV5ebigcAAAAAADxIOJrrlNSuEwbAAAAAIArRfzN9WDt2bNH5eXlunDhgsaOHavFixdr/PjxhtoMhULy+XyGs/X19RluIxb8fr8pj3cw+7ny32SWzH2RjI852ZjxmpLM58j3RdMXoVBILpfLqkgAAMBGlhTX48eP14wZM+TxeHTy5Ek1NTXpscce06uvvqqKioqo2w0Gg2ppaTGc7/jx44bbiAWv16uMjIyY7g+XJGNfJONjTjZmvqZwvPxVpH2RlpZmTRAAAGArS4rrRYsWhd2+//77VVNTo/Xr16uhoSHqdlNTUzVy5Eij8dTV1WW4jVjweDwqLS21fD9+v19er1cej0dut9vy/TlZMvdFIBCwOwIsZsZrSjKfI98XTV+0trZanAoAANjFsp+FXykzM1OTJk3SBx98YKgdl8ulzMxMw3nS09MNtxELbrfblMfr1P05WTL2RbIXSsnAzOM6Gc+Ra4mkL/hJOAAAiYvRyQAAAAAAMCgmxbXP59NHH32ksrKyWOwOAAAAAICYivhn4X6/Xzt37pQkffPNN+rt7dXWrVslSXfffbfa2trU2NioadOmadiwYTp58qQ2bdqkU6dO6fnnnzc3PQAAAAAADhBxcf3dd99p8eLFYcsu337llVd0yy23KBgMatWqVers7JTb7VZFRYXq6+s1ZswYc1IDAAAAAOAgERfXhYWFOnTo0HXXaWpqijoQAAAYvK+++kpNTU3av3+/jhw5ouLiYm3evDlsndmzZ2vPnj0Dtm1ublZJSUmsogIAkNBiMlo4AACwxpEjR7Rz506NHTtWFy9eVCgUuup648aN07Jly8KWFRYWxiIiAABJgeIaAIA4NmXKFE2dOlWSVFdXp88///yq62VnZ6u8vDyGyQAASC5MxQUAQBxLSeGtHAAAJ+CbawAAksCePXtUXl6uCxcuaOzYsVq8eLHGjx9vqM1QKCSfzye/3y9J/f9Gw8i2seL3++Xz+Szfx5X/IjqJ0o/xkD8W50U06LvEZeb5HQqF5HK5DLdzGcU1AAAJbvz48ZoxY4Y8Ho9OnjyppqYmPfbYY3r11VdVUVERdbvBYFAtLS39t71eb9RtGdk2VrxerzIyMmK2LxgX7/0YD/ljeV5Egr5LfGY9x2lpaaa0I1FcAwCQ8BYtWhR2+/7771dNTY3Wr1+vhoaGqNtNTU3VyJEj5ff75fV65fF45Ha7o2orEAhEnSNWPB6PSktLLd2HGX2JxOlHzovo0XeJy8zzu7W11aRUl1BcAwCQZDIzMzVp0iR98MEHhtpxuVzKzMzsv+12u8NuRyIeCiAjj8/J+0pk8d6PnBfRo+8Snxn9Z+ZPwiWKa0e78qd2Vrr8159AIDDoF6KhQ4dqxIgRFicDAAAAgPhAce1AgbNnJJdLs2bNsjvKNWW4M3XoixYKbACIQz6fTx999JHKysrsjgIAQMKguHag831npVBI5dW1ysortDvOAD2nv9a+LavU0dFBcQ0ANvP7/dq5c6ck6ZtvvlFvb6+2bt0qSbr77rvV1tamxsZGTZs2TcOGDdPJkye1adMmnTp1Ss8//7yd0QEASCgU1w6WlVeonIISu2MAABzsu+++0+LFi8OWXb79yiuv6JZbblEwGNSqVavU2dkpt9utiooK1dfXa8yYMXZEBgA43NGjR9XR0WF3jKvy+/3q7Ox05GBwFNcAAMSxwsJCHTp06LrrNDU1xSgNACDeHT16VKPuKFXA79w5uNPTM7R//z6NGjXK7ihhKK4BAAAAAJKkjo4OBfy+uLhEleIaAAAAAOBoXKIauRS7AwAAAAAAEO8orgEAAAAAMIjiGgAAAAAAgyiuAQAAAAAwiOIaAAAAAACDKK4BAAAAADCI4hoAAAAAAIMorgEAAAAAMIjiGgAAAAAAg35kdwAAQGy0tLQYbsPv98vr9SoQCMjtdpuQ6pKhQ4dqxIgRprUHAAAQaxTXAJDgAmfPSC6XZs2aZXeUa8pwZ+rQFy0U2AAAIG5RXANAgjvfd1YKhVReXausvEK74wzQc/pr7duySh0dHRTXAAAgblFcA0CSyMorVE5Bid0xAAAAElLEA5p99dVXWrFihWbMmKHRo0erpqbmquu99dZbeuCBB1RWVqaHHnpIO3bsMBwWAAAAAAAniri4PnLkiHbu3KnbbrtNJSVX/wbk/fff1/Lly1VdXa2GhgaVl5dr4cKF2rdvn9G8AAAAAAA4TsQ/C58yZYqmTp0qSaqrq9Pnn38+YJ3Vq1dr+vTpWrJkiSRpwoQJOnz4sNatW6eGhgZjiQEAAAAAcJiIv7lOSbn+JseOHZPX61V1dXXY8gcffFCffPKJzp07F+kuAQAAAABwNNMHNGtra5MkFRUVhS0vKSlRMBjUsWPHrvlz8h8SCoXk8/kMZ+zr6zPcBi7Nd2vG8+Ekfr8/7N9kkoyPGc4Sb68p0bxehEIhuVwuqyIBAAAbmV5cd3V1SZKys7PDll++ffn+aASDQbW0tEQf7v87fvy44TYgeb1eZWRk2B3DEl6v1+4IMZeMjxnOEq+vKZGeO2lpadYEAQAAtoqrqbhSU1M1cuRIw+0YKfDxVx6PR6WlpXbHMJXf75fX65XH45Hb7bY7TkwFAgG7IyDJxdtrSjSvF62trRanAgAAdjG9uM7JyZEk9fT0KD8/v395d3d32P3RcLlcyszMNBZQUnp6uuE2ILndblOeDydK5Md2Lcn2xwQ4T7yed5HktuIn4V999ZWampq0f/9+HTlyRMXFxdq8efOA9d566y01Njbq22+/VVFRkWprazV58mTT8wAAkKwiHtDshxQXF0v667XXl7W1tSk1NVXDhw83e5cAACQtpsgEAMAZTC+uhw8fLo/Ho61bt4Ytb25uVlVVFdeaAQBgoilTpmjnzp1avXq17rzzzquuc+UUmRMmTNA///M/q6ysTOvWrYtxWgAAElfEPwv3+/3auXOnJOmbb75Rb29vfyF99913Ky8vT0899ZSWLl2qESNGqLKyUs3NzTpw4IBee+01c9MDAJDkBjtF5j/+4z+GLX/wwQf1m9/8RufOneMP3wAAmCDi4vq7777T4sWLw5Zdvv3KK6+osrJSNTU18vv9amho0MaNG1VUVKS1a9eqoqLCnNQAAGBQYjFFphnTGMbDdICxmC4umaeENFOi9GM85HfqNIr0XfTioe+kS9MrG+0/s6fIjLi4Liws1KFDh35wvZkzZ2rmzJlRhQIAAOaI5RSZRqb0i4fpAGM5XVw89Ec8iPd+jIf8Tp1Gkb6LXjz0nXRpemUzpmk289dbcTUVFwAAcI7LU2SaMY1hPEwHGIvp4pJ5SkgzJUo/cl5Ej76LXjz0nST95Cc/Mdx/Zk+RSXENAEACi+UUmUamU4uHAiiW08XF69R0ThPv/ch5ET36Lnrx0HfSpemVjfaf2VNkmj5aOAAAcA6myAQAIDYorgEASGBMkQkAQGzws3AAAOIYU2QCAOAMFNcAAMQxpsgEgPhkxkjXVnBqrnhAcQ3E2NGjR9XR0WF3jAF4IQXiE1NkAkB8CZw9I7lcmjVrlt1RYDKKayCGjh49qlF3lCrgNzbhPQAAAOLT+b6zUiik8upaZeUV2h1ngBN/2avDu39vd4y4RHENxFBHR4cCfp8jX0x5IQUAAIidrLxC5RSU2B1jgN7TX9sdIW5RXAM2cOKLKS+kAAAAQPSYigsAAAAAAIMorgEAAAAAMIifhQMAAAxCLGZV8Pv98nq9CgQCcrvdg95u6NChGjFihIXJEpdTZ/GQmMkDiDcU1wAAANcRD9PmZLgzdeiLFgrsCDGLBwAzUVwDAABch9Onzek5/bX2bVmljo4OiusIOXkWD4mZPIB4Q3ENAAAwCE6c6QHmcOpzy0weQHxhQDMAAAAAAAyiuAYAAAAAwCCKawAAAAAADOKaawAAAFjGjqmuBjulGVNdATATxTUAAAAswVRXAJIJxTUAAAAswVRXAJIJxTUAAAAsxVRXAJIBA5oBAAAAAGAQxTUAAAAAAAZRXAMAAAAAYJAl11y/++67evrppwcsnzdvnpYuXWrFLgEAAADEmFOnM3NqLiQ2Swc0a2xsVFZWVv/tgoICK3cHAAAAIAYCZ89ILpdmzZpldxTAMSwtru+8807l5eVZuQsAAAAAMXa+76wUCjHNGnAFpuICAAAAEBWmWQP+ytLiuqamRmfOnNGtt96qv/3bv9Xf/d3faciQIVG3FwqF5PP5DOfq6+sz3AYkv99vyvPhJH6/P+xfq9oHMNC+ffsceY4MHTpUw4cPH7A8mteLUCgkl8tlWrbBYiwUAACsZ0lxnZ+fr6eeekpjx46Vy+XS9u3b9bvf/U4nTpzQihUrom43GAyaMjjB8ePHDbcByev1KiMjw+4YlvB6vXHVLhDPLl+3N3fuXLujXFV6eobeeedt3XLLLVe9P9LzOi0tzYRU0WEsFAAArGNJcT1x4kRNnDix//a9996r9PR0vfzyy1qwYIFuvvnmqNpNTU3VyJEjDefr6uoy3AYkj8ej0tJSu2OYyu/3y+v1yuPxyO12m95+IBAwvU0g3jn5ur2e019r35ZVys3NHfB6F83rRWtrqxUxB42xUAAAsE7Mrrmurq7Wiy++qJaWlqiLa5fLpczMTMNZ0tPTDbcBye12m/J8OJFVj82Kgh1IFE69bk+6/mtCJK8XdvwkHAAAxAYDmgEAkCSsGgvFjPEqnHi9fbxx4lgoPK8ArNLX12f4Nc/ssVBiVlw3NzdryJAhGj16dKx2CQAAFLuxUIyMK8GYFMY5cSwUnlcAVjl+/Lgp43GZORaKJcX13LlzVVlZqVGjRkmStm3bpjfffFNz5sxRfn6+FbsEAADXYPVYKGaMV8GYFMY5cSwUnlcAVvnJT35i+DXP7LFQLCmui4qK9M4776i9vV0XL16Ux+PRr371K82ePduK3QEAgAhZMRaKkfEqGJPCOCeOhcLzCsAq6enphl/zzB4LxZLi+plnnrGiWQAAAAAAHCnF7gAAACD2GAsFAABzMVo4AAAJjrFQAACwHsU1AAAJjrFQAACwHsU1AAAJjrFQAACwHtdcAwAAAABgEMU1AAAAAAAG8bNwJJyjR4+qo6Mjqm39fr+8Xq8CgYAlc3O2tLSY3iYAAAAA+1FcI6EcPXpUo+4oVcDvszsKAAAAgCRCcY2E0tHRoYDfp/LqWmXlFdodZ4ATf9mrw7t/b3cMAAAAACajuEZCysorVE5Bid0xBug9/bXdEQAAAABYgAHNAAAAAAAwiOIaAAAAAACDKK4BAAAAADCI4hoAAAAAAIMorgEAAAAAMIjiGgAAAAAAgyiuAQAAAAAwiOIaAAAAAACDKK4BAAAAADCI4hoAAAAAAIMorgEAAAAAMIjiGgAAAAAAgyiuAQAAAAAw6Ed2BwAAAIBxLS0tdkcYwImZAMAqFNcAAABxLHD2jORyadasWXZHAYCkRnENAAAQx873nZVCIZVX1yorr9DuOGFO/GWvDu/+vd0xACAmLCuuv/zySz333HP685//rBtuuEEzZszQkiVLlJaWZtUuAQDANfC+nPiy8gqVU1Bid4wwvae/tjsCAMSMJcV1V1eXHn30UXk8Hq1Zs0YnTpzQypUrFQgEtGLFCit2CQAAroH3ZQAArGdJcf3HP/5RZ8+e1dq1a5WbmytJunDhgurr6zV//nwVFBRYsVsAAHAVvC8DAGA9S6bi+vjjj1VVVdX/Bi5J1dXVunjxonbt2mXFLgEAwDXwvgwAgPVcoVAoZHajVVVVevjhh7V06dKw5RMnTtSMGTMGLB+Mzz77TKFQSKmpqYbz9fX16dtvv1V6Zo5ShjhvTLcLwT6dC/Q6Nt/FC+fV5+tSfn6+Kc+HmYLBoE6dOuXYvnPyc+vkbBL5jHByNsnZ+S6/3t16661KT08Puy8UCun8+fP60Y9+JJfLNaj2gsGgXC6Xxo0bZ0Xca7L6fTmavvg+J783O/kYlZydz8nZJPIZ4eRskrPzOTmb5Px813tvjpTZ78uW9FZ3d7eys7MHLM/JyVFXV1dUbV5+s472TftKGRkZKi4uNtyOtW62O8AP+LHdAa4qIyNDWVlZdsf4AU5+bp2cTSKfEU7OJjk739Vf71wuV8SDgblcLlPexyJl9ftyNH3xfc5/b3byMSo5O5+Ts0nkM8LJ2SRn53NyNsn5+cypRcx+X3benyKuoaKiwu4IAADg/+N9GQCAcJZcc52dna2enp4By7u6upSTk2PFLgEAwDXwvgwAgPUsKa6Li4vV1tYWtqynp0enTp1y+E++AABIPLwvAwBgPUuK6/vuu0+7d+9Wd3d3/7KtW7cqJSVF99xzjxW7BAAA18D7MgAA1rNktPCuri5Nnz5dRUVFmj9/vk6cOKGVK1fqF7/4hVasWGH27gAAwHXwvgwAgPUsKa4l6csvv9Svf/1r/fnPf9YNN9ygGTNmqLa21vBoogAAIHK8LwMAYC3LimsAAAAAAJKFJddcAwAAAACQTCiuAQAAAAAwiOIaAAAAAACDKK4BAAAAADCI4hoAAAAAAIMorgEAAAAAMOhHdgeIpa+++kpNTU3av3+/jhw5ouLiYm3evNnuWLbYsmWL3nvvPR08eFDd3d267bbbNHv2bD388MNyuVx2x4upnTt3qqGhQa2trert7VVBQYGmTp2qhQsXKisry+54tjp79qyqq6t14sQJvf322yorK7M7Usy8++67evrppwcsnzdvnpYuXWpDIvv9x3/8h15++WV9+eWXyszMVFlZmdauXauMjAy7o8XU7NmztWfPnqve92//9m+aPn16jBNZ68svv9Rzzz0XNj/2kiVLfnB+7FAopIaGBv3+97/X6dOnVVpaqqefflrl5eWxCe5A0fbllClT9M033wxYfuDAAaWnp1sV17Gi/TzHMRku2n7keAwX7WdqjseBou1LJx2TSVVcHzlyRDt37tTYsWN18eJFJfMU3y+99JKGDRumuro63XTTTdq9e7eWL1+u9vZ2LVy40O54MdXZ2akxY8Zo9uzZys3N1ZEjR7RmzRodOXJEL774ot3xbLV+/XpduHDB7hi2amxsDPsjS0FBgY1p7LNhwwY1NDRowYIFKi8v15kzZ/TJJ58k5fHx7LPPqre3N2zZyy+/rA8//FBVVVU2pbJGV1eXHn30UXk8Hq1Zs0YnTpzQypUrFQgEtGLFiutu29DQoNWrV2vp0qUaNWqUXn/9dT3++OP6r//6Lw0fPjxGj8A5jPSlJD3wwAN6/PHHw5b9UFGeqKL9PMcxGc7I52KOx7+K9jM1x+NARuoTxxyToSRy4cKF/v9ftmxZaPr06Tamsdd33303YNkzzzwTGjduXFg/Jas33ngjdPvtt4fa29vtjmKb1tbWUHl5eegPf/hD6Pbbbw8dOHDA7kgx9c4774Ruv/32q54ryebLL78MjR49OvTRRx/ZHcWxpkyZEpo3b57dMUz3wgsvhMrLy0NnzpzpX/bHP/4xVFpaet3Xx0AgEBo3blzot7/9bf+yvr6+0OTJk0PPPvushYmdK9q+DIVCocmTJ4fq6+stThg/ovk8xzE5ULSfizkew0XzmZrj8eqirU+cdEwm1TXXKSlJ9XCvKy8vb8Cy0tJS9fb2yufz2ZDIWXJzcyVJwWDQ3iA2eu655/TII4+oqKjI7iiw2bvvvqvCwkJNmjTJ7iiO9Nlnn+nrr7/WL37xC7ujmO7jjz9WVVVV/2uiJFVXV+vixYvatWvXNbf77LPP1Nvbq+rq6v5laWlpmjZtmj7++GMrIztWtH2JgaL5PMcxORCfi80RzWdqjserS4T6hLMK/fbu3auCggLdeOONdkexxYULF9TX16eDBw9q3bp1mjJligoLC+2OZYutW7fq8OHDevLJJ+2OYruamhqVlpbq5z//uf793/89KX8GvX//ft1+++1av369qqqqdNddd+mRRx7R/v377Y7mCJs3b1ZmZqZ+/vOf2x3FdG1tbSouLg5blp2drfz8fLW1tV13O0kDti0pKdG3336rQCBgfliHi7YvL/vTn/6ku+66SxUVFZo3b54OHTpkVdSExDFpLo7H6/uhz9Qcj4M32PrEKcdkUl1zjWv79NNP1dzcrGXLltkdxTaTJ0/WiRMnJEkTJ07Ub3/7W5sT2cPv92vlypWqra1N2j+0SFJ+fr6eeuopjR07Vi6XS9u3b9fvfvc7nThxYlDXRyaSU6dO6fPPP9fhw4f17LPPyu1264UXXtDjjz+uDz/8UD/+8Y/tjmib8+fPa8uWLZoyZYoyMzPtjmO67u5uZWdnD1iek5Ojrq6u626XlpY2YCCZ7OxshUIhdXV1Jd1AeNH2pXRpsJ4xY8bo1ltv1bFjx/TCCy/ol7/8pf7zP/8zaa/NjBTHpHk4Hq9vMJ+pOR4HZ7D1iZOOSYprqL29XbW1taqsrNScOXPsjmObjRs3yu/3q7W1VRs2bNCCBQu0adMmDRkyxO5oMbVhwwb9+Mc/1sMPP2x3FFtNnDhREydO7L997733Kj09XS+//LIWLFigm2++2cZ0sRUKheTz+fT888/rjjvukCSNHTtWU6ZM0WuvvabFixfbnNA+u3bt0unTp1VTU2N3FCSwZ555pv//f/azn+mee+5RdXW1mpqa9E//9E/2BUNS4ni8Nj5TmyeSvnTSMcnPwpNcd3e35s2bp9zcXK1Zsyapr7+54447VFFRoZkzZ2r9+vX6v//7P/33f/+33bFi6ptvvtGLL76oRYsWqaenR93d3f3XuPh8Pp09e9bmhPaqrq7WhQsX1NLSYneUmMrOzlZubm5/YS1dGpdg9OjRam1ttTGZ/TZv3qzc3Fzde++9dkexRHZ2tnp6egYs7+rqUk5OznW3O3funPr6+sKWd3d3y+VyXXfbRBVtX17NzTffrJ/+9Kc6ePCgWfESHsekdTgeL4nkMzXH4/UZrU/sPCb55jqJBQIBzZ8/Xz09PXrjjTeSfk7nK40aNUqpqak6evSo3VFi6uuvv1YwGNTf//3fD7hvzpw5Gjt2rN58800bksFOI0eOvOa58P0PBskkEAjof/7nf/TQQw8pNTXV7jiWKC4uHnA9cE9Pj06dOjXgWsHvbydJf/nLX8L+KNPW1qZbb701KX/uGG1fwhwck7BSpJ+pOR6vLd7rk+T9mjLJnT9/XkuWLFFbW5saGxuTdu7ea9m/f7+CwWDSDWhWWlqqV155Jey/p59+WpJUX1+vZ5991uaE9mpubtaQIUM0evRou6PE1OTJk9XZ2Rn2jf2ZM2d08OBB3XnnnTYms9f27dvl8/kScpTwy+677z7t3r1b3d3d/cu2bt2qlJQU3XPPPdfcbty4cbrxxhu1ZcuW/mXBYFAffvih7rvvPkszO1W0fXk1J06c0N69e1VWVmZ2zITFMWmdZD8eo/lMzfF4dWbVJ3Yek0n1zbXf79fOnTslXfr5a29vr7Zu3SpJuvvuu686/Huiqq+v144dO1RXV6fe3l7t27ev/77Ro0fbM+m6TRYuXKi77rpLo0aNUkZGhr744gs1NTVp1KhRmjp1qt3xYio7O1uVlZVXve/OO+9MqkJq7ty5qqys1KhRoyRJ27Zt05tvvqk5c+YoPz/f5nSxNXXqVJWVlWnRokWqra1Venq6Nm7cqLS0NP3yl7+0O55t/vSnP+nWW2/VT3/6U7ujWOaRRx7Rq6++qieffFLz58/XiRMn9Jvf/EaPPPJI2IeeRx99VN9++23/pTTp6emaP3++1qxZo7y8PN1+++36wx/+oM7OTs2dO9euh2OraPty8+bN2rFjhyZNmqSbb75Zx44d08aNGzVkyBA99thjdj0cWw3m8xzH5A+Lph85HgcazGdqjsfBiaYvnXZMJlVx/d133w0YeOfy7VdeeeWaRUUiujyn5sqVKwfct23btqT6xnbMmDFqbm7Wxo0bFQqFNGzYMM2cOVNz585Nqj8yIFxRUZHeeecdtbe36+LFi/J4PPrVr36l2bNn2x0t5lJSUrRx40b967/+q1asWKFgMKif/exnev3115PuDw2XdXV16X//93/16KOPyuVy2R3HMjk5OXr55Zf161//Wk8++aRuuOEG/c3f/I1qa2vD1rt48eKAaermzZunUCikF198UadPn1ZpaamampqSdjThaPuysLBQJ0+e1L/8y7+op6dHWVlZmjBhghYtWpS0fTmYz3Mckz8smn7keBxoMJ+pOR4HJ5q+dNox6QqFQqGY7xUAAAAAgATCNdcAAAAAABhEcQ0AAAAAgEEU1wAAAAAAGERxDQAAAACAQRTXAAAAAAAYRHENAAAAAIBBFNcAAAAAABhEcQ0AAAAAgEEU1wAAAAAAGERxDQAAAACAQRTXAAAAAAAY9P8A3UeTvJXf/4YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(10, 8), edgecolor='black')\n",
    "plt.suptitle(\"Histograms of Iris attributes\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OWiyofmGcOY3",
   "metadata": {
    "id": "OWiyofmGcOY3"
   },
   "source": [
    "## 11. Scatterplots per class\n",
    "\n",
    "Colours: virginica red, versicolor orange, setosa blue.\n",
    "Pairs: sepal length vs width, petal length vs width, sepal length vs petal length, sepal width vs petal width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "LSYNjGNpcOY3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "LSYNjGNpcOY3",
    "outputId": "fa8a8232-82a0-4a6c-8f42-0416d336d5af"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = {'Iris-virginica': 'red', 'Iris-versicolor': 'orange', 'Iris-setosa': 'blue'}\n",
    "\n",
    "pairs = [\n",
    "    ('SepalLengthCm', 'SepalWidthCm'),\n",
    "    ('PetalLengthCm', 'PetalWidthCm'),\n",
    "    ('SepalLengthCm', 'PetalLengthCm'),\n",
    "    ('SepalWidthCm',  'PetalWidthCm'),\n",
    "]\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(13, 10))\n",
    "for ax, (x, y) in zip(axes.ravel(), pairs):\n",
    "    for species, color in colors.items():\n",
    "        sub = df[df['Species'] == species]\n",
    "        ax.scatter(sub[x], sub[y], c=color, label=species, edgecolor='k', alpha=0.7)\n",
    "    ax.set_xlabel(x); ax.set_ylabel(y)\n",
    "    ax.set_title(f\"{x} vs {y}\")\n",
    "    ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "RcUVIxgTcOY3",
   "metadata": {
    "id": "RcUVIxgTcOY3"
   },
   "source": [
    "### Interpretation\n",
    "\n",
    "Setosa forms a separate cluster with small petals, so it is easy to separate. Versicolor and virginica overlap, especially on the sepal measurements. The petal features separate the classes better than the sepal features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "BFmo_AKNcOY3",
   "metadata": {
    "id": "BFmo_AKNcOY3"
   },
   "source": [
    "## 12. Correlation matrix\n",
    "\n",
    "Correlation between the 4 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "iB8AHLOKcOY3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "iB8AHLOKcOY3",
    "outputId": "3317c7dd-7d6b-4a8a-97b5-e7917d484a56"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"corr\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5087331794989353,\n        \"min\": -0.10936924995064931,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.10936924995064931,\n          0.8179536333691642,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6614868453597539,\n        \"min\": -0.4205160964011539,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          -0.35654408961380507,\n          -0.10936924995064931\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6847985519785192,\n        \"min\": -0.4205160964011539,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.4205160964011539,\n          0.9627570970509662,\n          0.8717541573048716\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6465103986023225,\n        \"min\": -0.35654408961380507,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -0.35654408961380507,\n          1.0,\n          0.8179536333691642\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "corr"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-97f77ca3-1b47-4890-809b-5fdb50a7d250\" 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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97f77ca3-1b47-4890-809b-5fdb50a7d250')\"\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 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-97f77ca3-1b47-4890-809b-5fdb50a7d250 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-97f77ca3-1b47-4890-809b-5fdb50a7d250');\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",
       "\n",
       "\n",
       "  <div id=\"id_3f99b3e9-9890-44c4-9856-2561fb4512ba\">\n",
       "    <style>\n",
       "      .colab-df-generate {\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-generate: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-generate {\n",
       "        background-color: #3B4455;\n",
       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate: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",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('corr')\"\n",
       "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_3f99b3e9-9890-44c4-9856-2561fb4512ba button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('corr');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "SepalLengthCm       1.000000     -0.109369       0.871754      0.817954\n",
       "SepalWidthCm       -0.109369      1.000000      -0.420516     -0.356544\n",
       "PetalLengthCm       0.871754     -0.420516       1.000000      0.962757\n",
       "PetalWidthCm        0.817954     -0.356544       0.962757      1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.drop(columns=['Species']).corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Ymgljk6DcOY4",
   "metadata": {
    "id": "Ymgljk6DcOY4"
   },
   "source": [
    "## 13. Heatmap of the correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "xUr6Cnn9cOY4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 578
    },
    "id": "xUr6Cnn9cOY4",
    "outputId": "da4645b1-debd-48b4-f7b0-3fa814c0585d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', square=True)\n",
    "plt.title(\"Correlation matrix of Iris attributes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "AR2YAE1xcOY4",
   "metadata": {
    "id": "AR2YAE1xcOY4"
   },
   "source": [
    "### Interpretation\n",
    "\n",
    "Petal length and petal width are very strongly correlated (about 0.96). Petal length is also strongly correlated with sepal length (about 0.87). Sepal width has weak or slightly negative correlation with the others, so it is the least useful feature."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "o4H8vsY0cOY4",
   "metadata": {
    "id": "o4H8vsY0cOY4"
   },
   "source": [
    "## 14. Encode the class (LabelEncoder)\n",
    "\n",
    "LabelEncoder gives each class a number in alphabetical order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "M8Yf9iSTcOY4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 196
    },
    "id": "M8Yf9iSTcOY4",
    "outputId": "3ff0975a-8bcf-4df9-cba1-7abba32be336"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris-setosa -> 0\n",
      "Iris-versicolor -> 1\n",
      "Iris-virginica -> 2\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df[['Species', 'Species_encoded']]\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species_encoded\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-2811b130-38e1-45a2-9159-e544981bc29e\" 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>Species</th>\n",
       "      <th>Species_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2811b130-38e1-45a2-9159-e544981bc29e')\"\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 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\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",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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-2811b130-38e1-45a2-9159-e544981bc29e 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-2811b130-38e1-45a2-9159-e544981bc29e');\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",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "             Species  Species_encoded\n",
       "0        Iris-setosa                0\n",
       "50   Iris-versicolor                1\n",
       "100   Iris-virginica                2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "df['Species_encoded'] = le.fit_transform(df['Species'])\n",
    "\n",
    "# Show the mapping: class name -> integer code\n",
    "for i, cls in enumerate(le.classes_):\n",
    "    print(f\"{cls} -> {i}\")\n",
    "\n",
    "df[['Species', 'Species_encoded']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "SwQafh6GcOY4",
   "metadata": {
    "id": "SwQafh6GcOY4"
   },
   "source": [
    "## 15. Code of the third class?\n",
    "\n",
    "0 = setosa, 1 = versicolor, 2 = virginica. The third class is virginica, code 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "jiIJBFBTcOY4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jiIJBFBTcOY4",
    "outputId": "43c6bb10-2561-44c4-b4c7-c70dce786d1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Third class: Iris-virginica -> code 2\n"
     ]
    }
   ],
   "source": [
    "third_class = le.classes_[2]\n",
    "print(f\"Third class: {third_class} -> code {le.transform([third_class])[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Jxv8-SkdcOY4",
   "metadata": {
    "id": "Jxv8-SkdcOY4"
   },
   "source": [
    "## 16. Split 70% train / 30% test\n",
    "\n",
    "stratify keeps the class balance, random_state makes the split reproducible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pNva6XaRcOY4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pNva6XaRcOY4",
    "outputId": "5d850b3d-d6fb-4411-e1a2-3bf3aaed5d17"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (105, 4) | Test: (45, 4)\n"
     ]
    }
   ],
   "source": [
    "X = df[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]\n",
    "y = df['Species_encoded']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.30, random_state=42, stratify=y\n",
    ")\n",
    "print(\"Train:\", X_train.shape, \"| Test:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "--j0kfaZcOY4",
   "metadata": {
    "id": "--j0kfaZcOY4"
   },
   "source": [
    "## 17. Logistic Regression classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "XyBJEbescOY4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XyBJEbescOY4",
    "outputId": "cc5f8fef-d7d3-45d6-a8f8-0444fa504d77"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression accuracy: 0.9333\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(max_iter=200)\n",
    "logreg.fit(X_train, y_train)\n",
    "acc_logreg = accuracy_score(y_test, logreg.predict(X_test))\n",
    "print(f\"Logistic Regression accuracy: {acc_logreg:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qPRWGpuncOY4",
   "metadata": {
    "id": "qPRWGpuncOY4"
   },
   "source": [
    "## 18. K-Nearest Neighbors classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ganfJnipcOY4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ganfJnipcOY4",
    "outputId": "b24b2ea2-4843-4be0-d4e2-ccd378ef1662"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN (k=5) accuracy: 0.9778\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "acc_knn = accuracy_score(y_test, knn.predict(X_test))\n",
    "print(f\"KNN (k=5) accuracy: {acc_knn:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "FakDqjpJcOY5",
   "metadata": {
    "id": "FakDqjpJcOY5"
   },
   "source": [
    "## 19. Decision Tree classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "l_1NHXrLcOY5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l_1NHXrLcOY5",
    "outputId": "558704b6-019a-47c7-c187-233126f608e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy: 0.9333\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(random_state=42)\n",
    "tree.fit(X_train, y_train)\n",
    "acc_tree = accuracy_score(y_test, tree.predict(X_test))\n",
    "print(f\"Decision Tree accuracy: {acc_tree:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5Sleb25PcOY5",
   "metadata": {
    "id": "5Sleb25PcOY5"
   },
   "source": [
    "## 20. Compare the accuracies of each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bEUITSWOcOY5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "bEUITSWOcOY5",
    "outputId": "a3bc9e72-83b0-4fad-ffed-10438f7e167d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Model  Accuracy\n",
      "0            KNN (k=5)  0.977778\n",
      "1  Logistic Regression  0.933333\n",
      "2        Decision Tree  0.933333\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Model': ['Logistic Regression', 'KNN (k=5)', 'Decision Tree'],\n",
    "    'Accuracy': [acc_logreg, acc_knn, acc_tree]\n",
    "}).sort_values('Accuracy', ascending=False).reset_index(drop=True)\n",
    "print(results)\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "sns.barplot(data=results, x='Model', y='Accuracy')\n",
    "plt.ylim(0.8, 1.0)\n",
    "plt.title(\"Model accuracy comparison (default hyperparameters)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9pjqg7cwcOY5",
   "metadata": {
    "id": "9pjqg7cwcOY5"
   },
   "source": [
    "All three models score high (about 0.93 to 1.00) because the classes are easy to separate on the petal features. Small differences come from the random split."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GJGL0Wr_cOY5",
   "metadata": {
    "id": "GJGL0Wr_cOY5"
   },
   "source": [
    "## 21. Change the hyperparameters\n",
    "\n",
    "Logistic Regression: smaller C. KNN: different k. Decision Tree: limit max_depth and change the criterion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "keCBWB8XcOY5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "keCBWB8XcOY5",
    "outputId": "fc9f7604-e715-456b-cfa7-25e1d4347791"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression (C=0.01): 0.8222  (was 0.9333)\n",
      "\n",
      "KNN across k:\n",
      "  k= 1: 0.9333\n",
      "  k= 3: 0.9556\n",
      "  k= 5: 0.9778\n",
      "  k= 7: 0.9556\n",
      "  k= 9: 0.9556\n",
      "  k=11: 0.9333\n",
      "  k=15: 0.9556\n",
      "\n",
      "Decision Tree (max_depth=3, entropy): 0.9333  (was 0.9333)\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression: stronger regularisation\n",
    "logreg2 = LogisticRegression(max_iter=500, C=0.01)\n",
    "logreg2.fit(X_train, y_train)\n",
    "acc_logreg2 = accuracy_score(y_test, logreg2.predict(X_test))\n",
    "print(f\"Logistic Regression (C=0.01): {acc_logreg2:.4f}  (was {acc_logreg:.4f})\")\n",
    "\n",
    "# KNN: sweep k to see the effect\n",
    "print(\"\\nKNN across k:\")\n",
    "for k in [1, 3, 5, 7, 9, 11, 15]:\n",
    "    m = KNeighborsClassifier(n_neighbors=k).fit(X_train, y_train)\n",
    "    print(f\"  k={k:>2}: {accuracy_score(y_test, m.predict(X_test)):.4f}\")\n",
    "\n",
    "# Decision Tree: limit depth + change criterion\n",
    "tree2 = DecisionTreeClassifier(random_state=42, max_depth=3, criterion='entropy')\n",
    "tree2.fit(X_train, y_train)\n",
    "acc_tree2 = accuracy_score(y_test, tree2.predict(X_test))\n",
    "print(f\"\\nDecision Tree (max_depth=3, entropy): {acc_tree2:.4f}  (was {acc_tree:.4f})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CpoZfX8PcOY5",
   "metadata": {
    "id": "CpoZfX8PcOY5"
   },
   "source": [
    "## 22. Any changes in accuracy?\n",
    "\n",
    "Yes, but small, because Iris is easy. KNN: k=1 is noisy, k=3 to 9 is stable. Logistic Regression: a very small C under-fits and lowers accuracy. Decision Tree: limiting depth avoids overfitting, and Gini vs entropy makes little difference. On easy data tuning changes little; it matters more on harder data."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}