Project PCA and tSNE - Learner's Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Ioq6KPw64VM"
   },
   "source": [
    "---------------------\n",
    "# Project: PCA and tSNE\n",
    "- **Marks: 25**\n",
    "--------------------\n",
    "\n",
    "Dear Learner,\n",
    "Welcome to the project on PCA and tSNE. In this project, we aim to generate and visualise the pca and tsne embeddings for iris flower(read more about the plant <a href = \"https://en.wikipedia.org/wiki/Iris_(plant)\">here</a>) dataset.\n",
    "\n",
    "<br>\n",
    "\n",
    "---------------------\n",
    "## Background and about data\n",
    "--------------------\n",
    "\n",
    "We have learnt that PCA and tSNE can be used to represent a high dimensional data in lower dimesions thus enabling us to visualise the same.\n",
    "The dataset used here is a classic dataset to study multiclass classification - Iris plant dataset. Every data science surely comes across this dataset in their journey and it has been subject for a lot of research as well. The data has 3 types of the iris flower -  \n",
    "- Iris-Setosa\n",
    "- Iris-Versicolour\n",
    "- Iris-Virginica\n",
    "\n",
    "Four different features are described for these 3 types which are-\n",
    "\n",
    "- sepal length (cm)\t\n",
    "- sepal width (cm)\t\n",
    "- petal length (cm)\t\n",
    "- petal width (cm)\n",
    "\n",
    "<br>\n",
    "\n",
    "-----------------------\n",
    "## Objective\n",
    "-------------------------\n",
    "\n",
    "- The idea in this project is to read the data from sklearn.datasets, load it into a dataframe appropriately, do some basic data exploration and then visualize using the 2D embeddings from PCA and tSNE to check if the types are reflected well in these visualisations.\n",
    "\n",
    "\n",
    "-------------------\n",
    "## Guidelines\n",
    "-------------------\n",
    "\n",
    "- The exercise consists of semi written code blocks. You need to fill the blocks as per the instructions to achieve the required results.\n",
    "- To be able to complete the assessment in the expected time, do not change the variable names. The codes might throw errors when the names are changed.\n",
    "- You can raise your issues on the discussion forum on the Olympus.\n",
    "You can use Jupyter Notebook/ Google Colab(recommended) for this assessment.\n",
    "- Uncomment the code snippets and work on them\n",
    "- All the requirements have been numbered. You can treat them as question numbers. Marks are also added in the requirement statement.\n",
    "\n",
    "\n",
    "--------------------\n",
    "## Resources\n",
    "---------------------\n",
    "\n",
    "- Documentation PCA - https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html\n",
    "- Documnetation tSNE - https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q1jHHmKT_isb"
   },
   "source": [
    "### Import the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "k0r2U2O060Ti"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I_REzQOF_o31"
   },
   "source": [
    "### 1. Load the iris data from sklearn.datasets (2 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Q5uJmADTx1Io"
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "X9fIIWI2yeiB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "#know more about the dataset\n",
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "ALF5jdlyvEEw"
   },
   "outputs": [],
   "source": [
    "# Now that we have loaded the data, let us extract the features, targets and feature names\n",
    "\n",
    "iris_x = iris.data\n",
    "iris_y = iris.target\n",
    "column_names = iris.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mAcSZqbvBlNl"
   },
   "source": [
    "### 2. Create the iris_df (3 marks)\n",
    "- Using the values above, create a dataframe using the hints below.\n",
    "- The final dataframe is printed as a hint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>Y</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>0</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>0</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>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     Y  \n",
       "0    0  \n",
       "1    0  \n",
       "2    0  \n",
       "3    0  \n",
       "4    0  \n",
       "..  ..  \n",
       "145  2  \n",
       "146  2  \n",
       "147  2  \n",
       "148  2  \n",
       "149  2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pass the iris data to create a dataframe without the target column\n",
    "\n",
    "iris_df = pd.DataFrame(iris_x)\n",
    "\n",
    "# name the column header using the column names retrieved above \n",
    "\n",
    "iris_df.columns = column_names\n",
    "\n",
    "# Add the target column to the dataframe using the iris target\n",
    "\n",
    "iris_df['Y'] = pd.DataFrame(iris_y)\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "executionInfo": {
     "elapsed": 1287,
     "status": "ok",
     "timestamp": 1604087083030,
     "user": {
      "displayName": "Shrish Chandra Pandey",
      "photoUrl": "",
      "userId": "03777729159992968707"
     },
     "user_tz": -330
    },
    "id": "OfEfwqcH5eeP",
    "outputId": "1c0fa40e-3005-459a-d432-681ea64bed0f"
   },
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>Y</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",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</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",
       "      <td>0.000000</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",
       "      <td>0.000000</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",
       "      <td>1.000000</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",
       "      <td>2.000000</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",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)           Y  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.199333    1.000000  \n",
       "std            0.762238    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zIsFUB_ZCsfN"
   },
   "source": [
    "### 3. We have learnt that there are 3 types of flowers in this dataset represented by 0,1 and 2 in the Y column. Create a boxplot by completing the function below that plots the distribution of the features over different types. (3 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the column that stores the type of flower in by argument\n",
    "\n",
    "iris_df.boxplot(by ='Y', layout = (1,5), figsize=(15,6) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "executionInfo": {
     "elapsed": 1745,
     "status": "ok",
     "timestamp": 1604087648846,
     "user": {
      "displayName": "Shrish Chandra Pandey",
      "photoUrl": "",
      "userId": "03777729159992968707"
     },
     "user_tz": -330
    },
    "id": "eYYK6q01zga2",
    "outputId": "2338eb6f-707e-427d-fe85-0230eade274f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffdeb11e990>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(iris_df[column_names].corr(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tGUzJjKCEbuE"
   },
   "source": [
    "### 4. Generate the covariance heatmap (2 marks)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361
    },
    "executionInfo": {
     "elapsed": 2089,
     "status": "ok",
     "timestamp": 1604087670233,
     "user": {
      "displayName": "Shrish Chandra Pandey",
      "photoUrl": "",
      "userId": "03777729159992968707"
     },
     "user_tz": -330
    },
    "id": "mty_45cVEzHv",
    "outputId": "4f51690a-5299-4858-e589-69f36bd972c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffdeb76d490>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAFGCAYAAADUy7AxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd3wU1drA8d+TBOlVSgKKAWlSpAUUASkCAsr1qiggoOhVLNhQvFZQsV/rq9iwIjYseAUEpQhSlBI6yEVBQCCF3iQgSZ73j5kkm0KyIZvMQJ6vn/mwO3P2zLNjdp89Z86cEVXFGGOM8bMwrwMwxhhj8mLJyhhjjO9ZsjLGGON7lqyMMcb4niUrY4wxvmfJyhhjjO9FeB2AydmI6AF2TQHw9cFfvQ7BN96LaOh1CL4xrnSK1yH4yrjNX0tB6zi264+gvnNKVK1b4H2dCEtWxhhjINXfPwAsWRljjAFN9TqCXFmyMsYYA6mWrIwxxvicpiSHpB4RKQXMBUri5JivVPXRLGVKAh8BrYHdQD9V3ZxbvTYa0BhjjNMNGMySt6NAV1VtDrQAeorI+VnK/AvYq6r1gJeB5/Kq1JKVMcYYZ4BFMEse1HHIfVrCXbKONLwMGOc+/gq4SERyHWVoycoYY0zQLSsRGSoisQHL0KxViUi4iKwAdgAzVHVRliK1gK0AqpoM7AdOzy08O2dljDEm6AEWqjoWGJtHmRSghYhUAr4RkaaquiagSE6tqFyv87KWlTHGGDQlOaglX3Wq7gPmAD2zbNoGnAkgIhFARWBPbnVZsjLGGBOyARYiUs1tUSEipYFuwP+yFJsEXOc+7gv8qHncCdi6AY0xxoRyBosoYJyIhOM0iL5Q1SkiMhqIVdVJwHvAeBHZgNOi6p9XpZasjDHGhGwGC1VdBbTMYf2ogMdHgKvyU68lK2OMMTaDhTHGmJOAzQ1ojDHG7zTlmNch5MqSlTHGGGtZGWOMOQnYOStjjDG+Zy0rY4wxvmd3CjbGGON7IbqfVWGxZGWMMca6AY0/NezUnMtGXUtYeBiLJsxm9puTspVpfsn59Lj7SlQhbt0WPr1rDACXPDCAc7o4F6jPeG0iK6csLNLYQ+XRZ+6nc7cOHEk6wojbR7J2Vdbpy6Bp83N4fswTlCpVkjkz5/P4g5nvEXfTsGt5aPS9tKrfib179nFZ397ccuf1APz112FGjniKdWt/K5L3E2qNXrmVqt1b8feu/SzuNCLb9qo9Y6h7fz80VdHkFH4f+SH7F6/3INLC0axTCwaOuoGw8DB+mjCL7978JtP2Dn270O/BwexNdOZfnTVuGj9NmEXtxtFc9+RQSpcrQ2pKKpNe/4rFU3724i3kjw2wOD4R6QyMUNVLg1kfgv39E/hNVX91n89x9xObx+uigHcKGo+IVAPGq2rWGYiLlIQJl4++nrGDnmZ/wm7umvQUv85YSuKG7ellqkZH0vW2yxhz5WMkHfiLcqdXAOCcLi2p1aQOL/V+gIjTSnDrhFH8b85Kjh5K8urtnJDO3ToQXbc2Xdr0oUVMM5584REu7zEoW7knX3iEh4aPZnnsKj6Y8DqdLmrPT7MWABBVswYdOrdj+9a49PJbt2ynX58bOLD/IJ0uas/TL4/Ksd6TQcLnc9j23vc0HjMsx+17565m8ffOR6ds49o0HTucRR2GF2WIhUbCwrh29E38Z9Bo9iTs5rFJz7F8xhLiNmzLVG7xlJ8Z/+i7mdYdTTrK2HteI3FzPJWqV+bxKc+zZu4KDh84XJRvIf98nqyK26zr/wQan8Dr7gHeKejOVXUnEC8i7QtaV0HUblGP3VsS2LN1BynHUlgx+Rea9IjJVOa8/l1Z8NF0kg78BcCh3QcAqFG/FhsXrSM1JZW/k44St24LjTo1L/L3UFDde3Vh4oTJAKyIXU2FiuWpVqNqpjLValSlXPmyLI9dBcDECZPp0btr+vaRT93Hs4+9TOBk0cuWrOTA/oMALI9dRWTNGoX9VgrNvoXrSN536LjbUw4fTX8cXqYk5D5p9kmlbot6JG5JYOfWRFKOJbNo8nxa9WgT1GsTN8WTuDkegH079nJg937KV6lYmOGGhGpKUItXck1WIlJWRL4TkZUiskZE+rnrW4vITyKyVER+cFseiMgcEXlFRH52y7d117d11y13/20YbIBuDO+LyBL39Ze564eIyEQR+V5EfheR/wS85l8i8psbzzsiMkZELgD+ATwvIitE5Gy3+FUistgt3/E4YVwJfO/WHS4iL4jIahFZJSJ3uOs3i8jTIvKLe/fMVu6x2SgitwTU9V9gYLDvvzBUrFGZfXG705/vi99NxRqVM5WpVjeSanWiGPbVY9zxzWgaugkpbt0WGnVuTolSp1GmcnnqtWtMpahcb/DpSzWiqhO/PTH9eXxcIpFR1TOViYyqTnxcRpmEuERquGW69exEQvyOXLv4+g26nJ9mzg9x5P5StVcbzpv/Ms0/fpB1w9/0OpyQqVyjCnvidqU/3xO/h8o1sv+dx/Q6nyenvcTtb4ygSg6fg7rN6xFRIoIdWxIKNd6QSE0NbvFIXt2APYE4Vb0EQEQqikgJ4DXgMlXd6Sawp4Ab3NeUVdULRORC4H2gKc69TC5U1WQR6QY8jZMAgvEwzr1ObnDvkbJYRGa621rgzO57FFgvIq8BKcBIoBVwEPgRWKmqP4vIJGCKqn7lvh+ACFVtKyK9gUdx7r2STkTqAHtVNe1n5FCgDtDSfT9VAopvVdV2IvIy8CHQHigFrAXecsvEAk8G+d4Lh2S/SWfWH8Vh4eFUrRPJm/2foFJkFW778lFeuPjf/DZvNWeeeza3T3ycv3YfZMuy30lJ8feQ15zkcAjIejsdybkQpUqXYtg9N3Htlbdk3+46v0Mbrh50OVf1HlLASP1t17Ql7Jq2hErnn0Pd+/ux4ipv/7RDJaf/91n/PpbPXMLCSfNI/juZLgN7cNOLd/DcNY+lb69YrRJDX7qTd0a8lu21vnSSjwZcDbwgIs/hfMnPE5GmOAlohvs/NByID3jNZwCqOldEKrgJpjzO/U3q49y6uEQ+YuwB/ENE0s7wlgJqu49nqep+ABH5FTgLqAr8pKp73PVfAg1yqX+i++9SIDqH7VHAzoDn3YC3VDXZfZ+Bd7dMG6WwGiinqgeBgyJyREQquXfN3AHUzCkQERmKkwzpXiWGc8vXyyXsE7c/YQ+Vamb8CqwUdToHduzNVmbL8t9JTU5hz7ad7PwjnmrRkWxd9QezXv8vs17/LwDX/N/t7Np0EvxqBAb/qx/9B18BwKrla4mqldFFF1WzBokJOzOVj49LJCqgGy/SLXNW9BmcUbsWU+d+kb5+8uzP+Wf3gezasZtGjevz7CuPcn2/Yezbu78I3pn39i1cR+noSEpUKc+xPQe9DqfA9iTspkrNjG7hKlFV2Lcj841s/wroIp3z2Uyuvj/j3GSpcqW554OH+frFz9i4/PfCDzgUfD4aMNduQFX9DWiN8+X7jIiMAgRYq6ot3KWZqvYIfFnWaoAngNmq2hTog5NwgiXAlQH7q62q69xtRwPKpeAk3xx+DucqrY6012eVlCVeIft7zFpXapbYUgPqLuXWmY2qjlXVGFWNKaxEBbB15UaqRkdS5YxqhJcIp0WfdqydsTRTmTXTY6nXrgkAZSqXp1qdKHb/uQMJE8pUKgdAVKPa1GxUm9/mrSq0WENp/HsTuKRzPy7p3I/pU2dzRb8+ALSIacbBA4fYmbgrU/mdibs4dOgvWsQ0A+CKfn2YMW0269dtoE2jLnRs2ZuOLXuTEJdIny792bVjNzVrRfLmuJe459aH2bRxS5G/x6JUOjojkZdrVoewEhGnRKIC2LRyAzWio6h6RnXCS0RwXp8OLJ+ReRxWxWqV0h+36h5D3EZngFJ4iQjufPvfLJg4hyVTfynSuAvkZO4GFJGawB5V/VhEDgFDgGeBaiLSTlV/cbsFG6jqWvdl/YDZItIB2K+q+0WkIpA21GxIPmP8AbhDRO5QVRWRlqq6PJfyi4GXRaQyTjfglTjJFvd5+Xzu/zcyt7imA7eIyJy0bsAsrau8NADW5DOGkEpNSeWbUR9y00cPIuFhLPliDom/b+Pi4X3ZunoTv85cyvqfVtKgYzPum/E8qSmpTHnmEw7vO0REyRIM+/JRAI4cSuLT4a+TmuLvX2Q5mT1jHl26d2BO7BSSko7w7zvS7wvHd3OcpAYwcsRT6UPXf5q1gDl5nIO6876bqVylEk88/xAAySkpXHbRNYX3RgpRk7fuotIFjSlRpTwXLH+TTc9/gUQ4XxlxH82g2qXnE3nVhWhyCqlH/mbN0Jc9jjh0UlNSGT/qXe77aCRh4WHM/eJHtv++lcuH92fz6g0snxlLj+svoWW3NqSkpPDXvkO8O8K5tOO8Sy6gYdvGlKtcng59uwDw7ogx/PnrZg/fURB83rKS3PpSReRi4HmclsEx4FZVjRWRFsCrQEWchPeKqr7jDgX/BegEVABuUNXFItIOGIfTnfYjMFhVo4MZui4ipYFXgAtwWjWb3fVDgBhVvd19zRTgBVWd43anjQDigHU4CfdhdxTeOzitnr44t1Ye4b6nqji3XI7O4TjMAm5W1Q0iEgH8B+d83jGcIe1jRGSzG8+uHGIL3DYCOKqqr+X2P2ZE9ICToJO78H198FevQ/CN9yKCHpd0yhtX+uQ7T1qYxm3+Or89StkkTXs1qO+c0r3uLPC+TkSuySrflQV53VJhE5FyqnrITSzfAO+r6jd5vS6X+i4HWqvqIyGIbS7O4JS9uZWzZOWwZJXBklUGS1aZhSRZffdKcMnqkrs9SVan6gwWj7mjDkvhdNv9tyCVqeo3IlLg8dnuRcEv5ZWojDGmyJ3kowHzRVU7h7K+E6Wq2eeGKXid7+ZdKs86dlLAxGmMMYXC5+esTtWWlTHGmPyw6ZaMMcb4nqYGt+RBRM4Ukdkisk5E1orIXTmU6Swi+93ZhFa4l0XlylpWxhhjQtmySgbuVdVlIlIeWCoiM9ImEA8wLz+Tg1uyMsYYAyGaNk1V43FnNVLVgyKyDqgFFGhor3UDGmOMCXoGCxEZ6k7WnbYMPV6VIhKNM3/rohw2txNnkvRpItIkr/CsZWWMMSbobkBVHQuMzauciJQDvgbuVtUDWTYvA85yr4ftjTNKun5u9VnLyhhjTMgGWAC40/B9DXyiqhOzblfVA6p6yH08FSjhziJ0XNayMsYYE7IBFuLcjuM9YJ2qvnScMpFAojvfa1uchtPunMqmsWRljDEmlHd6bg8MBlaLyAp33UO4t3ZS1bdw5ma9VUSSce5C0V/zmPvPkpUxxhhIDs10S6o6nzxu1aSqY4Ax+anXkpUxxhibbskYY4z/aaq/b/RgycoYY4zv5wa0ZGWMMca6AY0xxpwErBvQGGOM74VoNGBhsWRljDEmlNdZFQpLVsYYY2yAhTHGmJOAnbMyJ+LrgwW69cspJbJUFa9D8IUeOxd4HYJvVD1awesQTj02GtCYE2eJypiiocmhufliYbFkZYwxxroBjTHGnASsG9AYY4zvWcvKGGOM79nQdWOMMb5nLStjjDG+l2KjAY0xxvicWjegMcYY37NuQGOMMb5nycoYY4zv2XVWxhhjfM9aVsYYY/xOk/3dsgrzOgBjjDE+kJoa3JIHETlTRGaLyDoRWSsid+VQRkTkVRHZICKrRKRVXvVay8oYY0wouwGTgXtVdZmIlAeWisgMVQ2871EvoL67nAe86f57XNayMsYY4ySrYJY8qGq8qi5zHx8E1gG1shS7DPhIHQuBSiISlVu9lqyMMcagqkEtIjJURGIDlqHHq1NEooGWwKIsm2oBWwOebyN7QsvEugGNMcZAkAMsVHUsMDavciJSDvgauFtVD2TdnFPVudVnycoYYwwawqHrIlICJ1F9oqoTcyiyDTgz4PkZQFxudVo3oDHGmJCdsxIRAd4D1qnqS8cpNgm41h0VeD6wX1Xjc6vXWlbGGGMgdJdZtQcGA6tFZIW77iGgNoCqvgVMBXoDG4DDwPV5VWrJyhhjTMi6AVV1Pjmfkwoso8Cw/NRrycoYY4zvp1uyc1bFzKPP3M/sJZOZNvdLmpzbKMcyTZufw7R5XzF7yWQefeb+bNtvGnYtm3avpHKVSgBc1rc30+Z+ybS5X/LVtHGc06RBob6HUDu/cxs+nzuOL+d/zOBhA7Jtb3HeuXz4/dvM2zKTLpdcmGnbbQ8N5eNZ7/PxrPe56B9diirkQvXyS6P536/zWbZ0Bi1bNM2xzHeTP2Zp7AxWrviR18c8S1iY81Xy+GP3sWzpDGKXTGfad58SFVWjKEMPuSefe4hfln3Pjwv+S7PmjXMs88Ajd7F0zY9s3Babaf3Nw65j7sLJ/Ljgv3z57fuccWbNogj5hGmyBrV4xXfJSkQ6i8iUE3hdTRH56jjb5ohIjPv4oYD10SKyJsj67xaRa/MbVw713C4iefbPFobO3ToQXbc2Xdr04cF7RvPkC4/kWO7JFx7hoeGj6dKmD9F1a9Ppovbp26Jq1qBD53Zs35oxcGfrlu3063MDvS68itdeGMvTL48q9PcSKmFhYdz71F3cM+gBBnQZQvd/XkR0/bMylUnYnsgTw59jxn9nZVp/wUXn07BZfa7rcSM3XnobA2/pR5lyZYoy/JDr1bMr9evVoVHjDtx66/28PuaZHMv1v+YWWsd0p3mLrlSrVoW+fS8F4IUX36RV6+7EtOnBd1Nn8sjDw4sy/JC6qPuF1K17Fu1a9WTEXY/y3Is5/11P/34OvS7ql239mlXruLjLVXRt/0+mfDudkY+PKOyQCyY1yMUjvktWJ0pV41S1bxBFH8q7SGYiEgHcAHya78Cyex+4MwT15Fv3Xl2YOGEyACtiV1OhYnmq1aiaqUy1GlUpV74sy2NXATBxwmR69O6avn3kU/fx7GMv43Q5O5YtWcmB/QcBWB67isiaJ8+v6cYtG7Ftcxxxf8aTfCyZmd/+yIUXt89UJmFbIhvX/UFqlnnR6tQ/i+ULV5KSksqRpCNs+HUj7bq0LcrwQ65Pn4sZ/4nzm2/R4mVUrFSRyMjq2codPHgIgIiICE477TTS/hzS1gOULVsm09/Jyebi3l354vNvAVgWu5IKFStQvUa1bOWWxa5kR+LObOsXzFtMUtIRAJbGriTK558LTdWgFq/kO1mJSFkR+U5EVorIGhHp565vLSI/ichSEfkhbeoMt1Xzioj87JZv665v665b7v7bMI/9ThWRc93Hy0VklPv4CRG5MbCVJCKlReRzd4LECUBpd/2zQGkRWSEin7hVh4vIO+6Ei9NFpHQOu+8KLFPVZLeeeiIy0z0Gy0TkbLdF+JOIfCEiv4nIsyIyUEQWi8hqETkbQFUPA5vTjkNRqhFVnfjtienP4+MSiYzK/EUUGVWd+LiMMglxidRwy3Tr2YmE+B2sW/vbcffRb9Dl/DRzfogjLzzVIquyI25H+vMd8TupFlk1l1dk+P3XjbTrch4lS5WkYuUKtLqgBdVrZv8yO5nUqhnJtoBW8/Zt8dSqGZlj2alTPiF++0oOHjzE119ndIY8Mfp+Nm1cwoABl/PY488XesyFJSqqBnHbE9Kfx8clEBWVPXEH45pBV/LjzHmhCq1wnIItq55AnKo2V9WmwPfuBWCvAX1VtTVO6+GpgNeUVdULgNvcbQD/Ay5U1ZbAKODpPPY7F+goIhVwJkpM+/nbAcj6V3ArcFhVz3XjaA2gqg8ASaraQlUHumXrA6+rahNgH3BlDvtuDywNeP6J+5rmwAVA2vUBzYG7gGY4QzcbqGpb4F3gjoDXxwIds+4kcBqTg0d253owToTkMD4n6y9fybkQpUqXYtg9N/HyM28ct/7zO7Th6kGX8+zjrxQ01CKT0/sNtjWweG4sP/+4kLGTxjD6jZGsWforKT6/zUJe8nM8el86kDNqt6JkydPo2iWjNTpy1HPUObsNn332DcNu86THOyQK8rcR6Mqr+9C8ZVPeePW9UIRVaDQ1uMUrJ5KsVgPdROQ5EemoqvuBhkBTYIY7rv4RnCuS03wGoKpzgQoiUgmoCHzptoZeBprksd95wIU4yek7oJyIlAGiVXV9lrIXAh+7+1wFrMql3k2qmnYtwFIgOocyUcBOAHcW4Vqq+o1b/xG3tQSwxJ3E8SiwEZjurl+dpd4dQLazrao6VlVjVDWmfKnTcwk5eIP/1Y/v5kzguzkT2JGwk6haGV0RUTVrkJiQufsiPi4xU3dFpFvmrOgzOKN2LabO/YJ5y6cSWbMGk2d/TtXqTpyNGtfn2VceZeigu9m3d39IYi8KO+J3Ur1mxq/l6lHV2JUY/A+Fca9+wnU9buKuAfchImzdtK0wwixUt95yHbFLphO7ZDpx8QmZBgLUOiOKuPjE47726NGjTJ4ygz59Ls627bPPv+Hyy3sXSsyF5fobr2HmvInMnDeRhIQd1KyV0aqMqhlJQkL27r7cdOzUjrvuvZnrBtzG338fC3W4IaXJwS1eyXeyUtXfcFoqq4Fn3O44Ada6LZYWqtpMVXsEvixrNcATwGy3ddYHKJXHrpcAMTgtkrnAcuAmMrd4su4jGEcDHqeQ83D+pID4crt+ILCu1IDnqVnqLeXWWejGvzeBSzr345LO/Zg+dTZX9OsDQIuYZhw8cIidibsyld+ZuItDh/6iRUwzAK7o14cZ02azft0G2jTqQseWvenYsjcJcYn06dKfXTt2U7NWJG+Oe4l7bn2YTRu3FMXbCpl1K/7HmXVqEXVmJBElIuh2WVfmTf85qNeGhYVRoXIFAM4+py5nn1OXxT8tKcxwC8Wbb40jpk0PYtr0YNKkHxg80Dn1e17bVhzYf4CEhB2ZypctWyb9PFZ4eDi9enZl/foNANSrVye9XJ9Le7B+/cYieheh8cG7n9Kt4xV063gF3383i6v7XwZAq5jmHDxwMMdzU8fT9NxzeP6Vx7huwDB27dpTWCGHjs+7AfN9nZWI1AT2qOrHInIIGAI8C1QTkXaq+ovbLdhAVde6L+sHzBaRDjjTauwXkYrAdnf7kLz2q6p/i8hW4GqcRFcNeMFdspoLDHT32RQ4N2DbMREpoar5+ZmzDqjnxnFARLaJyD9V9b8iUhIIz0ddAA2ABfl8TYHNnjGPLt07MCd2CklJR/j3HRmjm76b4yQ1gJEjnuL5MU9QqlRJfpq1gDl5nIO6876bqVylEk8874xdSU5J4bKLrim8NxJCKSmpvPjIq7zy6X8ICwtjyoRpbPptMzeNuJ51K9czf8bPnNO8Ic++9wTlK5ajQ/d23Hjv9Qzsej0RJcJ5a+L/AfDXocM8fudTpKSc3N2AU6fNomfPrqxft4DDSUnceOM96dtil0wnpk0PypYtwzcTP6BkydMIDw9n9uwFvD12PABPP/UgDRqcTWpqKn/+uZ3bhj3g1VspsJnTf+Ki7heycPkPJB0+wt3DMsZmzZw3kW4drwBg5OMjuLzvJZQuU5pla2fz6fiveOHZ1xk1+j7Kli3DO+NeBpzzf9cNyNd1sEXKyy6+YEh++2BF5GLgeZwcewy4VVVjRaQF8CpO914E8IqqviMic4BfgE5ABeAGVV0sIu2AcTjdaz8Cg1U1WkQ6AyNU9dIc9v0EcJGqXuAmze1Aa/cmX9HAFFVt6g6S+ABoDKzASTR3unE+B/wDWAY8nPYat/4RQDlVfSzLfs8Cxqvqhe7z+sDbQFX3GFyFM5VIetzu+x7h7rNzlm3LgB6qmrlZE6DO6c1P3mFUIRRZqorXIfjGkp3HH9hS3FQtU8HrEHwlYd+6XGeMCMaOizoF9Z1TfdZPBd7Xich3ssr3DgK+tAt1R4VMRL4B/q2qvxewnpbAPao6OLdylqwclqwyWLLKYMkqs1Akq8QuwSWrGrO9SVanzHVWReABnIEWBVUVGBmCeowxJnRUgls8UuhzA6pq58LeR1FwRxxmHXV4IvXMCEE4xhgTUqnJ3iWiYNhEtsYYY3w/wMKSlTHGGNTDLr5gWLIyxhhjLStjjDH+p6nWsjLGGONzfp8g35KVMcYYUpP9fSWTJStjjDHWsjLGGON/ds7KGGOM7/l96Lq/OymNMcYUiVDefFFE3heRHWl3b89he2cR2e/etX1F2p3fc2MtK2OMMaSkhrTt8iEwBvgolzLzcrq7xvFYsjLGGBPSc1aqOte9bVPIWDegMcYYVINbQqidiKwUkWki0iSvwtayMsYYE3TLSkSGAkMDVo1V1bH53N0y4CxVPSQivYH/AvVze4ElK2OMMaQGORrQTUz5TU5Z6zgQ8HiqiLwhIlVzu3u6JStjjDFFOnRdRCKBRFVVEWmLc0pqd26vsWRljDGGlBAOsBCRz4DOQFUR2QY8CpQAUNW3gL7ArSKSDCQB/VVzPyNmycoYY0xIW1aqOiCP7WNwhrYHzZKVMcYYmxvQnJj3Ihp6HYIv9Ni5wOsQfCMpbp7XIfjGgib3ex3CKSfYARZesWRljDHG93MDWrIyxhhjLStjjDH+l2LJyhhjjN9ZN6AxxhjfC/LuH56xZGWMMQbFWlbGGGN8LtWuszLGGON3KT6/Y5QlK2OMMXbOyhhjjP/ZOStjjDG+Zy0rY4wxvmfJyhhjjO9ZN6AxxhjfSxZLVsYYY3zO55dZWbIyxhhj56yMMcacBFKtG9AYY4zfWTegMcYY37NuQGOMMb5nowGNMcb4nt+7Af09za4xxpgikSrBLcEQkfdFZIeIrDnOdhGRV0Vkg4isEpFWedVpLStDo1dupWr3Vvy9az+LO43Itr1qzxjq3t8PTVU0OYXfR37I/sXrPYi08Lz80mh69ezK4aQk/vWv4Sxfkf0z9t3kj4mMqkFERDjz5y/mjjsfIjU1lccfu48+fXqQmqrs3LGLG24cTnx8ogfvInSOHv2b64bdx9/HjpGSnEL3Lh24/cbBmcrErljNc//3Nr9t3MTzjz9Ajy4dPYq2cDV85VZO796aY7v2s6TTvdm2n94zhjr39wf387Fh5IfsX/w/DyItmBCfs/oQGAN8dJztvYD67nIe8Kb773EVWstKRIaISM0gyn0oIn2DXR+CuB4KeBx9vMyfw+vuFpFrQ7D/20Xk+oLWE0oJn89hRXkSuTcAACAASURBVP+nj7t979zVLO5yH0su+jfrhr9Jo5duKcLoCl+vnl2pX68OjRp34NZb7+f1Mc/kWK7/NbfQOqY7zVt0pVq1KvTteykAL7z4Jq1adyemTQ++mzqTRx4eXpThF4rTTivB+68+y8Rxb/DVuNdZsGgpK9esy1QmqkZ1nnz4Xnp37+JRlEUj4fM5rOr/1HG375u7htguI4i96D7+N/wNGp6knw8NcgmqLtW5wJ5cilwGfKSOhUAlEYnKrc7C7AYcAuSZrDzwUN5FMhORCOAG4NMQ7P994M4Q1BMy+xauI3nfoeNuTzl8NP1xeJmSoH7v3c6fPn0uZvwnXwGwaPEyKlaqSGRk9WzlDh50jlFERASnnXZa+mFIWw9QtmwZ9BQ4PiJCmTKlAUhOTiY5ORnJcgK+VlQNGtarQ5jPT8wX1P48Px9H0h+Hlyl10v7/T5bgFhEZKiKxAcvQE9hdLWBrwPNt7rrjCqobUESige+BRUBL4DfgWlU9LCKtgZeAcsAunCTVHogBPhGRJKAdcB/QBygN/AzcrEH+X81pH6oaLyJz3Ji6AJWAf6nqPBEpg9MMbQSsA6KBYUBfoLSIrADWAg8D4SLyDnABsB24TFWTsoTQFVimqsluPPWAt4BqQApwFXAm8DiQCLQAJgKrgbvc9/xPVd3oHrPNItJWVRcH8/79oGqvNpz98DWcVrUiKwfl3PI4WdWqGcm2rXHpz7dvi6dWzUgSEnZkKzt1yie0adOC73+YzddfT0lf/8To+xk0sC/7DxygW/eriiTuwpaSksLVN9zJn9vjGHDFpZzbpJHXIflW1V5tqfvwNZSoWpHVJ+nnI9huQFUdC4wt4O5y+oWTaz7IT8uqITBWVc8FDgC3iUgJ4DWgr6q2xmk1PKWqXwGxwEBVbeF++Y9R1Taq2hTny/vSYHZ6vH0EFIlQ1bbA3cCj7rrbgL1urE8ArQFU9QEgyY1poFu2PvC6qjYB9gFX5hBGe2BpwPNP3Nc0x0ly8e765jjJqRkwGGjgxvYucEfA62OBk6qDf9e0JSzqMJzVQ56n7v39vA4npLK2GIDj/jrufelAzqjdipIlT6Nrl/bp60eOeo46Z7fhs8++YdhtvurlPWHh4eF8Pe51Zn0zntW//sbvf2z2OiTf2jVtMYs73M2aIf+hzkn6+VAJbgmRbTg/8NOcAcQdpyyQv2S1VVUXuI8/BjrgJLCmwAy3tfKIu9OcdBGRRSKyGqel0iTI/ea1j4nuv0txWlC4sX0OoKprgFW51L9JVVfkUEegKGAngIiUB2qp6jdu/UdU9bBbbomqxqvqUWAjMN1dvzpLvTvIoYs0sHk9JemPXEL2zr6F6ygdHUmJKuW9DqVAbr3lOmKXTCd2yXTi4hM448yM/x21zogiLpcBEkePHmXylBn06XNxtm2fff4Nl1/eu1Bi9kqF8uVo0+pc5i+M9ToU39u/cB2lTtLPR2qQS4hMAq51RwWeD+xX1fjcXpCfZJX1p6biNOXWui2VFqraTFV7ZH2hiJQC3sBpHTUD3gFKBbnfvPaRdkIlhYxuzfzk/6MBjwPrCJQUEG9udQfWlRrwPDVLvaXcOjNR1bGqGqOqMZeWrptX3EWmdHSN9MflmtUhrEQEx/Yc9DCignvzrXHEtOlBTJseTJr0A4MHOmN5zmvbigP7D2TrAixbtkz6eazw8HB69ezK+vUbAKhXr056uT6X9mD9+o1F9C4Kz569+zjgnos7cvQoC5csp85ZZ+bxquKpdHRk+uOT+fMRymQlIp8BvwANRWSbiPxLRG4RkbTRJ1OBP4ANOPngtrzqzM/Q9doi0k5VfwEGAPOB9UC1tPVul10DVV0LHATSfl6kfdHvEpFyOOeOvgpyv7nt43jmA1cDs0WkMU63XJpjIlJCVY8FuX9wznvVA1DVA+7B/6eq/ldESgLh+agLoAGwIM9SRaTJW3dR6YLGlKhSnguWv8mm579AIpw/jbiPZlDt0vOJvOpCNDmF1CN/s2boyx5HHFpTp82iZ8+urF+3gMNJSdx44z3p22KXTCemTQ/Kli3DNxM/oGTJ0wgPD2f27AW8PXY8AE8/9SANGpxNamoqf/65nduGPeDVWwmZnbv38vCTL5CSmoqmKhd37Ujn9ucx5p2PaNKoAV06ns/qdeu5+8EnOHDwEHMWLOL1dz/m20/e9jr0kDvnrbuodEETSlQpT7vlb7Hp+S8Ii3A+8nEfzaDqpecReVUnNDmFlCN/8+tJ+vkI5bAQVR2Qx3bFGUcQNAlmjIM7wGIqMBfnHM3vwGB3sEAL4FWgIk7ye0VV3xGRK4GncVoQ7XAGM/QHNuOMAtmiqo+JyIfAFPc8V+A+09fnso85wAhVjRWRqkCsqkaLSFlgHE5SWI7TjdhfVX8XkeeAfwDL3JimuOfREJERQDlVfSxLLGcB41X1Qvd5feBtoCpwDGeARW03lkvdMoGxdc6ybRnQQ1V3He+Y/1jj6pNzSFGI9djrm5zuuaS4eV6H4BsLmtzvdQi+0jnxywKfTXq59qCgvnOG//mxJ8M/85Os0r/U/U5EwoESqnpERM4GZuG0xv4uQJ3fAP9W1d8LGFtL4B5VHZxbOUtWDktWGSxZZbBklVkoktWLQSarez1KVqfqDBZlcLoAS+CcY7q1IInK9QDOQIsCJSuc1tjIAtZhjDEh5fdfx0ElK1XdjNOVdlJQ1YM413mFss71OOfPClrPjBCEY4wxIRXsvH9eOVVbVsYYY/LB7mdljDHG906JbkBjjDGntmSfpytLVsYYY3yeqixZGWOMwc5ZGWOMOQnYaEBjjDG+l+rzjkBLVsYYY0jxOoA8WLIyxhhjLStjjDH+5+9UZcnKGGMMNhrQGGPMScC6AY0xxviev1OVJStjjDFAis/TlSUrY4wxds7KGGOM/9k5K2OMMb7n71RlycoYYwz+b1mFeR2AMcYY76WgQS3BEJGeIrJeRDaIyAM5bB8iIjtFZIW73JhXnday8qlxpf0+U1fRqHq0gtch+MaCJvd7HYJvfFba6wj8pXMI6gjVAAsRCQdeB7oD24AlIjJJVX/NUnSCqt4ebL3WsjLGGIMG+V8Q2gIbVPUPVf0b+By4rKDxWbIyxhhDapBLEGoBWwOeb3PXZXWliKwSka9E5My8KrVkZYwxhlTVoBYRGSoisQHL0CxV5XQbx6xNsslAtKqeC8wExuUVn52zMsYYE/RYQFUdC4zNpcg2ILCldAYQl6WO3QFP3wGey2u/1rIyxhhDCqlBLUFYAtQXkToichrQH5gUWEBEogKe/gNYl1el1rIyxhgTstGAqposIrcDPwDhwPuqulZERgOxqjoJuFNE/gEkA3uAIXnVa8nKGGNMSC8KVtWpwNQs60YFPH4QeDA/dVqyMsYYE+ywdM9YsjLGGGOzrhtjjPE/VWtZGWOM8blk6wY0xhjjd3bOyhhjjO/5/RYhlqyMMcbYOStjjDH+Z6MBjTHG+F6QUyl5xpKVMcYY6wY0xhjjfzbAwhhjjO/Z0HVjjDG+l2rdgMYYY/zO36nKkpUxxhgg2UYDGj9q1qkFA0fdQFh4GD9NmMV3b36TaXuHvl3o9+Bg9ibuAWDWuGn8NGEWtRtHc92TQyldrgypKalMev0rFk/52Yu3EFJPPvcQF3W/kKSkI9x120OsXvlrtjIPPHIXV/W/jEqVKnD2GTHp628edh0DB/clOSWF3bv2MPz2R9i2NS7b6082DV+5ldO7t+bYrv0s6XRvtu2n94yhzv39IVXR5BQ2jPyQ/Yv/50GkhaNJpxZcPep6wsLDmD9hFj+8+d9M29v17cyVDw5mn/sZmT1uGgsm/AjAFQ8MolnXVkiYsG7eKiY8/kGRx59fNhrQJSJDgOmqmuunWEQ+BKao6lf5rP8W4LCqfpRlfbRbX1MRaQHUdG8Mhog8BhxS1RfyqFuAWcA/VfVAfuLKoa6ZwFWqurcg9RQohrAwrh19E/8ZNJo9Cbt5bNJzLJ+xhLgN2zKVWzzlZ8Y/+m6mdUeTjjL2ntdI3BxPpeqVeXzK86yZu4LDBw4X5VsIqYu6X0jdumfRrlVPWsU057kXR9G7W/9s5aZ/P4f33/mUX5ZOy7R+zap1XNzlKpKSjnDdDf0Z+fgIbr7hnqIKv9AkfD6H7e99zzljbs9x+765a4j9fgQAZRvXpsnYe1jc4e6iDLHQSFgYA0b/i1cGPcHehD08OOkZVs2IJT7LZyR2ys98/uh7mdbVbdWAs2MaMrqnc2z+/dUTNDi/Mb8tzP4DyE/8PhowrAj3NQSoWViVq+pbWRNVDloAvU+g+t7AyoImKtd44LYQ1HPC6raoR+KWBHZuTSTlWDKLJs+nVY82Qb02cVM8iZvjAdi3Yy8Hdu+nfJWKhRluobu4d1e++PxbAJbFrqRCxQpUr1EtW7llsSvZkbgz2/oF8xaTlHQEgKWxK4mqWaNwAy4i+xeuI3nfoeNuTzl8JP1xeJlSvv9lnh91WtRjx5YEdm3dQcqxZGInL6B5j5i8X+gqUfI0IkpEEHFaBOER4RzYub8Qow0NDfI/r5xQy8ptrXwPLAJaAr8B16rqYRFpDbwElAN24SSp9kAM8ImIJAHtgPuAPkBp4GfgZj3OX7uIVAemqWprEWkOrADOUtU/RWQj0Az4N24ryY3hfeAwMN+t4zRgNFBaRDoAz7jVNxaROUBt4BVVfTWHEAYCYwPiuRYYgXNOcpWqDnZbhElAI+As4HrgOve9LlLVIe7LJwHzgKeOf4QLV+UaVdgTtyv9+Z74PZzdon62cjG9zqdh28YkbIrj0yc+YE/87kzb6zavR0SJCHZsSSj0mAtTVFQN4rZnvIf4uASioqrnmJjycs2gK/lx5rxQhudrVXu1pe7D11CiakVWD3om7xecJCrVqMLeuIy/973xe6iTw2ekVa/zqN/2HBI3xfPlEx+yN343fyz7jfW/rOE/S8YiCLM/+p6EjduLMvwT4vcfGwVpWTUExqrqucAB4DYRKQG8BvRV1bSE8ZTbpRcLDFTVFqqaBIxR1Taq2hQnYV16vB2p6g6glIhUADq6dXUUkbOAHaqatQ/qA+BOVW0XUMffwChgghvDBHdTI+BioC3wqPsesmoPLAUQkSbAw0BXVW0O3BVQrjLQFRgOTAZeBpoAzdwuSNzuv5Iicvrx3m9hc3o1M8v6h7p85hLu7XALj/S6h7ULVnHTi3dk2l6xWiWGvnQn7943xvd/5HkJ5ngE48qr+9C8ZVPeePW9vAufInZNW8ziDnezZsh/qHN/P6/DCZ3sfxKQ5W9i1cxYHupwG0/0GsH/FqxiyItOd2m1syKJqncGD5x/C/effzONLmhK/bbnFEHQBZOKBrV4pSDJaquqLnAffwx0wElgTYEZIrICeAQ44ziv7yIii0RkNc4XfJM89vczTtK4EHja/bcjTislnYhUBCqp6k/uqvF51Pudqh5V1V3ADiCnPpwqqnrQfdwV+Motj6ruCSg32W0drgYSVXW1qqYCa4HogHI7yKFLVESGikisiMT+dnBTHmGfuD0Ju6lSs2r68ypRVdi3Y0+mMn/tO0Ty38kAzPlsJtFN66ZvK1WuNPd88DBfv/gZG5f/XmhxFqbrb7yGmfMmMnPeRBISdlCzVmT6tqiakSQk5K9V1bFTO+6692auG3Abf/99LNTh+t7+hesoFR1JiSrlvQ4lJPYl7KFyzYzfk5Xz+IzM+2wWZ7mfkZYXt+WP5b9x9PARjh4+wpo5y6nTMnurzG9SNDWoxSsFSVZZU6zi/B5Z67ZcWqhqM1XtkfWFIlIKeAOnBdYMeAcolcf+5uEkp7OAb4HmOAlybtbqc4gtN0cDHqeQc9dosoikHavc6k+rKzVLvalZ6i2F02WYiaqOVdUYVY1pUL5OMLGfkE0rN1AjOoqqZ1QnvEQE5/XpwPIZsZnKVKxWKf1xq+4xxLndGOElIrjz7X+zYOIclkz9pdBiLGwfvPsp3TpeQbeOV/D9d7O4uv9lALSKac7BAwfz1QXY9NxzeP6Vx7huwDB27dqT9wtOEaWjMxJ8uWZ1CCsRwbE9B3N5xclj88oNVI+O4nT3MxLTpz0rs3xGKgR8Rpp3jyF+ozP4Yk/cLhqc15iw8DDCIsJpcF5jEjacBN2Ap+I5K1dtEWmnqr8AA3DODa0HqqWtd7vUGqjqWuAgkPazKy0x7RKRckBfIK/Rf3OBJ4G5qpoqIntwBj48GFhIVfeJyH4R6aCq83HON6UJjCE/1gN1gQ04owK/EZGXVXW3iFTJ0rrKlTuyMBLYfAJxhERqSirjR73LfR+NJCw8jLlf/Mj237dy+fD+bF69geUzY+lx/SW07NaGlJQU/tp3iHdHjAHgvEsuoGHbxpSrXJ4OfbsA8O6IMfz5q2dvp8BmTv+Ji7pfyMLlP5B0+Ah3D3soY9u8iXTreAUAIx8fweV9L6F0mdIsWzubT8d/xQvPvs6o0fdRtmwZ3hn3MgDbt8Vz3YBhnryXUDrnrbuodEETSlQpT7vlb7Hp+S8IiwgHIO6jGVS99Dwir+qEJqeQcuRvfh36sscRh05qSiqfj3qPuz56mLDwMBZ8MZv437fRZ3g/tqzeyKqZsXS9vjfNu8WQkpLC4X2H+HDE6wAsnbqQhhc0ZdQPL6IKv/60glWzlnr8jvLm9xks5ET65t0BFlNxEsgFwO/AYHeARQvgVaAiTjJ8RVXfEZErcbrv0gZYPAz0x/nS3gpsUdXHchu6LiJ/Ak+q6lgReQjo754zyzQMPcsAix9wWnBNRaSK+7wEzgCLcwgYui4ia4BLVXVzlv2OBOJV9V33+XU4A0RSgOWqOiQw7sDh8m75wG0xwIOqemVux/i66Cv9/ZdTRH7Y5+/hvkXp85JNvQ7BNz4r7XUE/vL25i9zOsuWL01qnBfUd87axEV57ktEegL/B4QD76rqs1m2lwQ+AloDu4F+Wb93s9VZgGSV/mV8qhORKOAjVe0egrr+D5ikqrNyK2fJymHJKoMlqwyWrDILRbI6p3rboL5z1u1YnOu+RCQcZ4R4d2AbsAQYoKq/BpS5DThXVW8Rkf7A5aqa6widorzO6qSlqvHAO+5oxIJak1eiMsaYohbCARZtgQ2q+oc7Cvtz4LIsZS4DxrmPvwIukpyG5QY4oWSlqpuLS6sqjap+EYqLglX1nVDEY4wxoRTsAIvAUcvuMjRLVbVwTu2k2eauy7GMqiYD+4FcL+exuQGNMcYEPcBCVccSMElCDnK8Su0EymRi3YDGGGNCOXR9G3BmwPMzgKxzwqaXEZEInAF5uY6qtmRljDEG1dSgliAsAeqLSB13mrv+ONPMBZqEMx0dOJcu/Xi86fbSWDegMcaYkE2lpKrJInI7zmVC4cD7qrpWREYDsao6CXgPGC8iG3BaVNlvc5CFJStjjDEhnUrJvQ3T1CzrRgU8PgJclZ86LVkZY4zx/YTUlqyMMcb4frolS1bGGGM8naQ2GJasjDHGWDegMcYY//PyxorBsGRljDGGlFTvbqwYDEtWxhhjrBvQGGOM/1k3oDHGGN+zlpUxxhjfs+usjDHG+F4op1sqDJasjDHGWDegMcYY/7MZLIwxxvietayMMcb4nt+Tlfg9QOMdERmqqmO9jsMP7FhksGORwY5F0bHb2pvcDPU6AB+xY5HBjkUGOxZFxJKVMcYY37NkZYwxxvcsWZncWF98BjsWGexYZLBjUURsgIUxxhjfs5aVMcYY37NkZYwxxvcsWRljjPE9m8HCACAi7YBBQEcgCkgC1gDfAR+r6n4PwytyIhKDcyxqknEsZqrqHk8D84Adi8xEpDIZx2Kzqs+nKz9F2AALg4hMA+KAb4FYYAdQCmgAdAH6AC+p6iTPgiwiIjIEuBPYBCwl87Foj/NFPVJV//QqxqJixyKDiFQEhgEDgNOAnTjHogawEHhDVWd7F+Gpz1pWBmCwqu7Ksu4QsMxdXhSRqkUflifKAu1VNSmnjSLSAqgPnPJf0NixCPQV8BHQUVX3BW4QkdbAYBGpq6rveRJdMWAtK5ONiFQg4IdMce3uMcb4h7WsTDoRuRkYjdMXn/YrRoG6ngXlERGpA9wBRJM5cf/Dq5i8YsciMxE5l+zHYqJnARUT1rIy6UTkd6BdDl2CxY6IrATeA1YD6SfQVfUnz4LyiB2LDCLyPnAusJaMY6GqeoN3URUP1rIygTYCh70OwieOqOqrXgfhE3YsMpyvqo29DqI4spaVSSciLYEPgEXA0bT1qnqnZ0F5RESuwRk8MJ3Mx2KZZ0F5xI5FBhF5D3hRVX/1OpbixlpWJtDbwI9k6e4pppoBg4GuBHT3uM+LGzsWGcYBv4hIAk7iFpxuwHO9DevUZy0rk05EflbVC7yOww9E5H/Auar6t9exeM2ORQYR2QDcQ/bzd1s8C6qYsJaVCTRbRIYCk8nc3VMch66vBCrhXAhb3NmxyPBncbg43o+sZWXSicimHFarqhbHoetzcEZ9LSFz4i52w7XtWGQQkTdwEnfWH3Q2dL2QWcvKpFPVOl7H4COPeh2Aj9ixyFAaJ0n1CFingCWrQmYtK5NORIYBn6RNJ+NO2DlAVd/wNrKi514IG6+qR9znpYEaqrrZ08A8YMfC+IHdIsQEuilw3jNV3Qvc5GE8XvqSzCMiU9x1xZEdC5eIjBORSgHPK7sXCptCZsnKBAoTEUl7IiLhODNMF0cRgaPf3Md2LCj2x+LcHH7QtfQwnmLDkpUJ9APwhYhcJCJdgc+A7z2OySs7RSR9AIGIXAYU12mo7FhkCHO7xwEQkSrYuf8iYeesTDoRCQOGAt1wLnacDryrqimeBuYBETkb+ATnJnsA23BupbLRu6i8Yccig4hcCzyIc8sQBa4GnlLV8Z4GVgxYsjImFyJSDudzctDrWLxmx8IhIo1xZu8QYJZNvVQ0LFkZRGQyMBb4XlWPZdlWFxiCc/vuU/5EsogMAj493q3K3VZGlKrOL9rIip4diwwiUk5VDxW0jDlx1tdqwBnxdw/wiojsIeOW3XWADcAYVf3Ww/iK0unAchFZinMr97RjUQ/ohHOu5gHvwitSdiwyfCsiK4BvgaWq+hek/5jrgtMd+A5O96ApBNayMpmISDQQhXMDxt9UtdjdMsQdBdkVaE/GsVgHTFPV4nAL93R2LDKISG9gIM6xqAIcA9YD3wHvqWqCh+Gd8ixZGWOM8T0bum6MMcb3LFkZY4zxPUtWxhhjfM9GA5p0ItIeeAw4C+dvI+0uqMXxFiElgSuBaAI+J6o62quYvGLHIjN30EkNMh+LYjXYxAuWrEyg94DhOMOUi92sFVl8C+zHORZH8yh7qrNj4RKRO3BumZJIxuS+inO/L1OIbDSgSScii1T1PK/j8AMRWaOqTb2Oww/sWGRwb2t/nqru9jqW4sZaVgYRaeU+nC0iz+PcSC7wLqjLPAnMWz+LSDNVXe11ID5gxyLDVpxWpili1rIyiMjsXDarqnYtsmA8JiKrcbp1IoD6wB84iTvt/F2x6e6xY5FBRO5xHzYBGuJcCBz4g+4lL+IqTqxlZVDVLuBMHaOqfwRuc6eTKU4u9ToAH7FjkaG8+++f7nIaGff0sl/8RcBaViadiCxT1VZZ1i1V1dZexeQVERmvqoPzWlcc2LHIICJXqeqXea0zoWctK4OINMLp3qgoIlcEbKqAM3FpcdQk8Ik7XLnYJW2XHYsMDwJZE1NO60yIWbIy4PTBXwpUAvoErD+IMyN7sSEiDwIPAaVF5EDaauBvnNuoFBt2LDKISC+gN1BLRF4N2FQBSPYmquLFugFNOhFpp6q/eB2HH4jIM6r6oNdx+IEdCxCR5kBL4HFgVMCmg8BsVd3rSWDFiCUrk05EXiP7yeL9QGxxuZ9VwDD+HBXHYfzHOSb7gS2qWqxaFSJSIusNSk3RsGRl0onIWKARGf3vVwJrgTOBP1T1bq9iKyoBw/hLATHASpyur3OBRarawavYvCIiC4FWwCqcY9EM57icDtyiqtM9DK9IBAzjz1FxGsbvFTtnZQLVA7qm/VoWkTeB6UB3oFhcEBowjP9zYGjahbAi0hQY4WVsHtoM/EtV1wKISGPgPuAJnAvIT/lkRcYw/mHuv+PdfwcCxe4GpV6wZGUC1QLKknGFflmgpqqmiEhxmxOuUeCMDaq6RkRaeBmQhxqlJSoAVf1VRFqq6h8i4mVcRUZVt4Az2bOqtg/Y9ICILACK5aS+RcmSlQn0H2CFiMzB6e65EHhaRMoCM70MzAPrRORd4GOc7p9BOLdzL47Wu63sz93n/YDf3NnYi9v5m7Ii0kFV5wOIyAU4P+pMIbNzViYTEYkC2uIkq8WqGudxSJ4QkVLArTgJG2Au8KaqHvEuKm+ISGngNqADzt/FfOAN4AhQRlUPeRhekRKR1sD7QEV31T7ghuI48KaoWbIymYhILTLuZwWAqs71LiJj/EdEKuB8f9qktkXEugFNOhF5DqeLZy2Z79VTbJKViHyhqlcfb/RXcRz1lcNNOQEoTjflFJFBqvpxwIS2aesBm8i2KFiyMoH+CTRU1eI2mCLQXe6/NolrBrspZ8Z5qfK5ljKFxroBTToRmQZcVZzOQRyPiNwAzFPV372OxWt2U84MIlKqOJ639ANrWZlAh3FGA84i87167vQuJM9EA4NE5CycFsU8nOS1wtOovGE35cywRkQScf4e5gIL7LxV0bCWlUknItfltF5VxxV1LH7hjoS7CeeC4FqqGu5xSEXuODfnLFY35QwkIrWBjkB7nMlt96lqcb0Gr8hYsjKZuF/OtVV1vdexeElEHsH5MioHLMcZrj1PVeM9Dcx4SkTOwElUnYDmwB5gvv5/e/cf6ndVx3H8+TLxxygp3LLWLyRq4K/aj9CJNSzxB7IIm0QSkQoFUhL9Vxaa1oYUCS3QwNqPonDgIDNoQphsamx5bT9C3BC0kP6IsDW0SctXf5zz/XG367V/MO98IQAABjVJREFU7vd82Xk94HLv93z+ef9x732fc97n8z72hqaBdSDJKoYkrQW+D5xm+9zaseFO259sHNrESZqhXP3wG+Ax4A+91ioknQOsp3Qzuaa2W1pt+yeNQ5s4Sa8Be4D1vTR3nhantA4gpsodlBeC/wlQ6zPntgyolXpj8ieA3dTeiJJ2tY2qmc3ADmBp/XwQOOmbGr+O5cBW4AZJT0raKunm1kH1IAcsYtwx24eP6/fW5dK7Nq4dbPesAv5KKar3aLHtbfUyRmwfk9TlEXbbeyU9BzxH+f34HKXLSXerzElLsopxByTdALxJ0geAW4EnGsfUyt2U7b8fAns6v8PoZUlnUycuki5h1Oy4K5L+CJxO+bvYBXxs0OQ2FlZqVjEkaRFwG3AlpQfcDuCuXms1UdTLFzcCFwAHgCXAOtv7mgbWgKQltv/eOo4eJVlFxBuSdCqwjDKJebbzlWY0kGQVSPo189+C2t1pwABJ18333Pb2ScUSkZpVQDmuHnG8tfM8M6WjRcREZGUVMSarzJhLVpntZWUVMVtWmTGXrDIby8oqIiKmXlZWEXOo75ltAM4DzhiM93ThYMxN0rXA+cz+vbizXUR9SLKK1Gnmtgm4HbgHuBy4kXJsuxup05xI0n3AIsrvxP3AOkpLrlhg2QYMJK2Z77ntxyYVy7SQ9JTtlZL2276wju20/dHWsU2KpE3zPLbtmyYWzJSQtM/2RWPf3wxst31l69hOdllZRZfJ6P9wVNIpwCFJXwZeBN7eOKaJsn1j6xim0L/r91ckLQX+QafNnictySqGUqeZ5auU7Z5bgbuAjwNzXk7Zg9Rphh6W9Fbge8AMZfv8/rYh9SHbgDFUr8AY1GnWUus0tm9vGlhDks6ibHkdaR1LK69Xp7Hd3dUYkk63/ergZ0ryPjoYi4WT+6xi3Jm2f0dJUC/YvoOyouiOpFWS9gP7KHdZ7ZW0snVcjVxq+/PAS7a/DawG3tM4plaeHPxg+1Xbh8fHYuFkGzDGdV+nGfNT4BbbOwEkXUY5IXhR06ja6L5OI+kdwLuAMyUtZ3Qy9CzKqjMWWJJVjEudZuTIIFEB2N4lqdetwNRp4CrgC8C7gR+Mjf8L+EaLgHqTmlWcIHUakHQPJXH/kvLP+TPAS8CDALZn2kU3WanTjEj6tO0HW8fRoySrGJK0irLV9ZY6dBi4yfZT7aJqQ9Kj8zy27W5qeZJmbK94o7Ee1O3A7wJLbV8j6Txgte1ca7/Asg0Y41KnqWxf3jqG1lKnmdOm+nVb/XwQeABIslpgSVYxLnWaStI5wHr6nkGnTnOixba3Sfo6gO1jkv7bOqgeJFnFuN2SfszsOs3vJa2Avuo0wGY6n0Hb3gJsSZ1mlpclnU3tpSnpEsp2eSyw1KxiKHWaEUl7bH9E0tO2l9exP9n+cOvYJi11mpE6cdsIXAAcAJYA62zvaxpYB7KyiqHUaWbJDHokdZrK9kxt/LyMUsN71vZ/GofVhSSrGEqdZpavAQ8B75f0OHUG3TakZlKnqSSdAdwCXEaZyOyUdJ/to20jO/ml3VKM2wzsAJbWzwcpLwp3p9bn1gCXAl8Czu94qyerzJGtlIa+G4EfUZo+/6xpRJ1Isopxi21vA16DMoMGep1BX0/plfhn4FPAA4ODJh06fpW5FfhK25CaWWb7ZtuP1q8vAh9sHVQPkqxiXGbQI9+yfaS+a3YVsAW4t3FMTWSVOcvT9e8CAEkXA483jKcbOQ0YQznpNDI4BShpA7Df9i/GTwb2ZK46DdBlnUbSM5TDFX+pQ+8FnqHsRth2dy/QT0qSVcwi6VRy0glJD1O6zl8BrKR0Ht9t+0NNA2tA0jbgCPDzOvRZ4G22r28XVRuS3jffc9svTCqW3iRZxVCt0/y2bn99E1gBfKezl4EBkLQIuJqyqjok6Z3AhbYfaRzaxEnae3ySnmssYiGlZhXjUqepbL9ie7vtQ/Xz33pMVFXqNNFcklWMG5z8uxa41/avgNMaxhPT4WLgCUnPS3qecjPuGkn7JXVXz4w28lJwjHux9ga8Ari73l2UCU1c3TqAiNSsYih1moiYVklWEREx9bLFExERUy/JKiIipl6SVURETL0kq4iImHpJVhERMfX+B4bNFSUEnJgJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(iris_df[column_names].cov(), annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nfER_sWeDoz6"
   },
   "source": [
    "### 5. Implement PCA with two components (5 marks)\n",
    "- Import and initialise the PCA object with 2 components (2 marks)\n",
    "- Transform the data with relevant column only.(2 marks)\n",
    "- Give names 'X1' and 'X2' to the columns (1 mark)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "CUc7bQDfzuhb"
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components = 2)\n",
    "\n",
    "iris_pca = pca.fit_transform(iris_x)\n",
    "\n",
    "c = pd.DataFrame(iris_pca)\n",
    "\n",
    "iris_pca.columns = ['X1', 'X2']\n",
    "iris_pca[\"target\"] = iris_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "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>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.684126</td>\n",
       "      <td>0.319397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.714142</td>\n",
       "      <td>-0.177001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.888991</td>\n",
       "      <td>-0.144949</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.745343</td>\n",
       "      <td>-0.318299</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.728717</td>\n",
       "      <td>0.326755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.944110</td>\n",
       "      <td>0.187532</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.527167</td>\n",
       "      <td>-0.375317</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.764346</td>\n",
       "      <td>0.078859</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1.900942</td>\n",
       "      <td>0.116628</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1.390189</td>\n",
       "      <td>-0.282661</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           X1        X2  target\n",
       "0   -2.684126  0.319397       0\n",
       "1   -2.714142 -0.177001       0\n",
       "2   -2.888991 -0.144949       0\n",
       "3   -2.745343 -0.318299       0\n",
       "4   -2.728717  0.326755       0\n",
       "..        ...       ...     ...\n",
       "145  1.944110  0.187532       2\n",
       "146  1.527167 -0.375317       2\n",
       "147  1.764346  0.078859       2\n",
       "148  1.900942  0.116628       2\n",
       "149  1.390189 -0.282661       2\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transformed data sample\n",
    "iris_pca.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "145    2\n",
       "146    2\n",
       "147    2\n",
       "148    2\n",
       "149    2\n",
       "Name: target, Length: 150, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_pca.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ap_kSQBpI_-V"
   },
   "source": [
    "### 6. Write a function that takes the the embedding dataframe like above and generate a scatterplot with target column as the hue.(5 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "EwirSNnb0SSb"
   },
   "outputs": [],
   "source": [
    "# A function that takes a dataframe of embeddings.\n",
    "import seaborn as sns\n",
    "\n",
    "def plot_scatter(iris_pca):\n",
    "  \n",
    "    plt.figure(figsize = (7,7))\n",
    "    \n",
    "    sns.scatterplot(x = iris_pca[\"X1\"], y = iris_pca[\"X2\"] , hue = iris_pca[\"target\"])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SeHjtKyvJszr"
   },
   "source": [
    "### 7. Call the above function with the pca embedding dataset to generate the visualisation(2 marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "YRF8sGRT0jqc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PCA embeddings visualised over target types\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"PCA embeddings visualised over target types\")\n",
    "print()\n",
    "\n",
    "\n",
    "plot_scatter(iris_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fJITkQ4MKWXO"
   },
   "source": [
    "### 8. Initialise tSNE with 2 components and generate the plot by calling the above function (3 marks)\n",
    "\n",
    " - Generate the embeddings (1)\n",
    " - Create the requried dataframe (1)\n",
    " - Call the above defined function (1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "V5seh5xB2dvL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tSNE embeddings visualized over target types\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialise the tSNE object\n",
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2)\n",
    "\n",
    "\n",
    "# fit and transform and generate the required dataframe\n",
    "iris_tsne = tsne.fit_transform(iris_x)\n",
    "iris_tsne =pd.DataFrame(iris_tsne) \n",
    "\n",
    "# assign column names and add target column\n",
    "iris_tsne.columns = ['X1', 'X2']\n",
    "iris_tsne[\"target\"] = iris_y\n",
    "\n",
    "#iris_tsne.head(3)\n",
    "\n",
    "# call the function above for tsne results\n",
    "\n",
    "print(\"tSNE embeddings visualized over target types\")\n",
    "\n",
    "plot_scatter(iris_tsne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPabZo/u9DwI58SWsZl7cla",
   "collapsed_sections": [
    "zIsFUB_ZCsfN",
    "tGUzJjKCEbuE",
    "nfER_sWeDoz6",
    "ap_kSQBpI_-V",
    "SeHjtKyvJszr",
    "fJITkQ4MKWXO"
   ],
   "name": "Project PCA and tSNE - Learner's Notebook.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}