Cluster_Iris.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction Using Supervised Machine Learning Part 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author: Moin Uddin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset for local directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Id</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>1</td>\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>2</td>\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>3</td>\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</td>\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</td>\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>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Datasets/Iris.csv')\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the Id column because it's of no use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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>"
      ],
      "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Id', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for any null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for any duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If found any then remove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(inplace=True)\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let us start finding the optimal number of cluster for this solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:, [0, 1, 2, 3]].values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Kmeans to achive our desired result as it one the algorithm to deduce the cluster problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++',\n",
    "                    max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The elbow method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS') \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elbow Method is used in finding the optimal number for cluster as in the above graph we see that when number of cluster becomes 3 we don't see a huge amount of change in the curve so that is going to be our optimal choice"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After which we fit our data with 3 cluster in our mind and start plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++',\n",
    "                max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_kmeans = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QBw4cEEIIMW/ePDF8+HCHZY4fPy4AiEOHDjXarr/+9a+ic+fOorq62ul6ExMTxZIlS4QQQmRnZwuLxVL3evXdd9994vbbb3eY9sQTT4ju3bs7fb0tW7aIoKAgcezYsbrn9+/fLwCIb7/9VgjhuF1rPfbYY2LQoEF1/25se9fX1PtLze9vHiEhcofZT7ITaXDmDJCfr/6MoxDycmfP6tMuAOjbt2+Tz0+ePBk5OTno0qULMjIysGXLlrrnHnnkETRv3rzuAQD33nsvCgsLsWvXLgDy0ZE+ffqga9euAIDc3Fx8/vnnDsvVPpefn99ou37729+iqqoKSUlJePjhh7F+/XpcvnzZaZtzcnIQHx+Pzp07O33+wIEDuOWWWxym3XLLLcjLy0NNTY3T+RMSEpCQkFA3rXv37oiOjsaBAwecb7hGuNrensSChEgrM55kJ/KAigr3li8v90w7nHF1Z0qfPn1w9OhRzJs3D1VVVRg3bhx+85vfAACee+455OTk1D0A+YLYoUOHYvXq1QCA1atXY+LEiXWvV1FRgZEjRzosl5OTg7y8PAwcOLDRdiUkJODQoUP4+9//DqvViv/5n//BwIEDcenSpQZttlqtmraFOywWC0S9itNZ24y8E4gFCZFWZj3JTuSmKwcPNIuM9Ew7tIqKisL48ePxxhtv4N1338XatWtx9uxZtG7dGp06dap71Jo4cSLeffdd7Ny5EwUFBbj33nvrnuvTpw/279+PDh06OCzbqVMnl7+srVYrRo4ciWXLlmHbtm3YuXMn9u3b12C+1NRUnDhxAocPH3b6Ot26dcP27dsdpm3fvh2dO3euu86k/vzHjx/H8ePH66b9+OOPsNls6N69OwD5Lp+TJ086LFdbpHkLCxIiLYQAli/XtuyyZbz7hkwtJgZITlY/Cqskycu1aqVPu5R4+eWX8c477+DgwYM4fPgw3n//fbRt2xbR0dGNLjNmzBiUl5fj0UcfxZAhQ9CuXbu656ZOnYqzZ89iwoQJ2L17N/Lz85GdnY0pU6Y4PV1SKysrC//v//0//PDDDygoKMDKlSthtVqRmJjYYN5BgwZh4MCBGDt2LLZu3YqjR4/i3//+NzZv3gwA+OMf/4hPP/0U8+bNw+HDh7FixQq88sorePzxx52ue9iwYejVqxcmTpyIvXv34ttvv8WkSZMwaNAg9OvXDwAwdOhQfPfdd3j77beRl5eHuXPn4ocfflCyiXXDgoRICzOfZCdykyQB06drWzYjw7vDyUdGRuKFF15Av3790L9/fxQWFmLTpk2wWBr/dRcZGYmRI0ciNzfX4XQNALRr1w7bt29HTU0Nhg8fjl69emHGjBmIjo5u8jWjo6Pxxhtv4JZbbkFqaio++eQTfPTRR4hpJCl57dq16N+/PyZMmIDu3bvjySefrCt4+vTpg/feew9r1qxBz5498b//+7947rnnMHnyZKevJUkSPvjgA7Rs2RIDBw7EsGHDkJSUhHfffbdunrS0NDz99NN48skn0b9/f5SXl2PSpEmN9scIkqh/EsmEzp07hxYtWqCsrAxRUVHebg6RPM5Ix47alz96FGjkVkQiT7tw4QKOHj2Kjh07Ijw8XNEyNhsQHy9fj63krKTFAlitwIkTQBMHI8gPNfX+UvP7m0dIiLTw9ZPsRC5ER8vDwUuSXGw0xWKR51u3jsUIaceChEgLXz7JTqRQWhqwcaN85EOSGr7da6dZrcCmTcDw4d5pJ/kHFiREWvjySXYiFdLS5NMwS5cCSUmOzyUlydN/+onFCLmP15AQacWT7OQjtFxD4owQ8vXY5eXyWcdWrVhbE68hIfI+nmSnACNJ8tnKDh3knyxGyJNYkJDvMkOyLk+yExF5BAsS8j1mS9blSXYiIrfxGhLyLdnZch5MZaX876vfvrVHJyIi5FMpaWnGt48n2cmEPHUNCZEzvIaEAo8vJOvyJDv5MSEESipLUGgrREllSYNwNiJ3sCAh38BkXSKvsV2wIXNXJlKWpyD2xVh0zOyI2BdjkbI8BZm7MmG7YPNq+yRJwoYNG3R57W3btkGSJNg88F2itp1ZWVlNZvD4GxYk5BuYrEvkFdlHshH/cjxmZs9EQWmBw3MFpQWYmT0T8S/HI/uIPkckJ0+ejFGjRjU5z8mTJ3HnnXfqsv6bb74ZJ0+eRIsWLdx+LbXtHD9+fKMJwP6IBQmZH5N1ibwi+0g2RqwegapLVRBX/rta7bSqS1UYsXqEbkVJY6qrqwEAbdu2RVhYmC7rCA0NRdu2bSE1cvq1pqYGdoV/KKltp9VqRevWrRXP7+tYkJD5MVmXyHC2CzaMfW8shBCwo+lfuHbYIYTA2PfG6nr6ZvDgwZg2bRpmzJiBa665BmlXLly/+lRIdXU1pk2bhri4OISHhyMxMRGLFi1y+nqHDx+GJEk4ePCgw/QlS5YgOTkZQMNTNrWnUT788EN0794dYWFhOHbsGE6ePIkRI0bAarWiY8eOWL16NTp06IClS5fWve7V7SwsLIQkSVi3bh2GDBmCiIgI9O7dGzt37qyb39kpm48++gj9+/dHeHg4rrnmGowePbruuf/7v/9Dv379EBkZibZt2+K+++7D6dOn1W5mr2FBQuZXUeHe8uXlnmkHUQBZkbMClZcqXRYjteywo/JSJd7O1fc06YoVKxAaGort27fjtddea/D8smXL8OGHH+K9997DoUOHsGrVKnRoJFm7c+fO6NevH1atWuUwfdWqVbjvvvsabUNlZSWef/55/POf/8T+/fvRunVrTJo0Cf/973+xbds2rF27Fv/4xz8UFQNz5szB448/jpycHHTu3BkTJkzA5cuXnc67ceNGjB49GnfddRf+85//4NNPP8UNN9xQ9/ylS5cwb9485ObmYsOGDSgsLMTkyZNdtsEsgr3dACKXmKxLZCghBJZ/q+006bJvlmH6DdMbPcXhrpSUFLzwwguNPn/s2DGkpKTgV7/6FSRJQmJiYpOvN3HiRLzyyiuYN28eAPmoyZ49e7By5cpGl7l06RL+/ve/o3fv3gCAgwcP4pNPPsHu3bvRr18/AMA///lPpKSkuOzP448/jhEjRgAAnn32WfTo0QNHjhxB165dG8y7YMEC3HvvvXj22WfrptW2AQB+97vf1f1/UlISli1bhv79+6OiogLN3f0eNQCPkJD5MVmXyFBnqs4gvzS/wTUjrggI5Jfm42yVfqdJ+/bt2+TzkydPRk5ODrp06YKMjAxs2bKl7rlHHnkEzZs3r3sAwL333ovCwkLs2rULgHx0pE+fPk4LglqhoaFITU2t+/ehQ4cQHByMPn361E3r1KkTWrZs6bI/V79OXFwcADR6ZCUnJwe33XZbo6+1Z88ejBw5Eu3bt0dkZCQGDRoEQC7SfAELEjI/JusSGaqi2r3TpOXV+p0mbdasWZPP9+nTB0ePHsW8efNQVVWFcePG4Te/+Q0A4LnnnkNOTk7dA5AvNB06dChWr14NAFi9ejUmTpzY5DqsVqvHjgCFhITU/X/tazZ2kazVam30dc6fP4+0tDRERUVh1apV2L17N9avXw/gl4t/zY4FCfmG9HR5BFZXIXa1LBZ5/kmT9G0XkR9qHure4f3IUO+eJo2KisL48ePxxhtv4N1338XatWtx9uxZtG7dGp06dap71Jo4cSLeffdd7Ny5EwUFBbj33ntVra9Lly64fPky/vOf/9RNO3LkCEpLSz3WJ0A+mvLpp586fe7gwYM4c+YMFi9ejFtvvRVdu3b1qQtaARYk5CuYrEtkmBhrDJJbJkOCuqMAEiQkt0xGK6v3TpO+/PLLeOedd3Dw4EEcPnwY77//Ptq2bdvkAGNjxoxBeXk5Hn30UQwZMgTt2rVTtc6uXbti2LBh+P3vf49vv/0W//nPf/D73//eo0dSAGDu3Ll45513MHfuXBw4cAD79u3D888/DwBo3749QkNDsXz5chQUFODDDz+suy7GV7AgId/BZF3tzJCMTD5DkiRMv0HbadKMGzN0u6BVicjISLzwwgvo168f+vfvj8LCQmzatAmWJv6QiYyMxMiRI5Gbm+vydE1j3n77bbRp0wYDBw7E6NGj8fDDDyMyMtKj2UGDBw/G+++/jw8//BDXXXcdhg4dim+//RYAEBsbi6ysLLz//vvo3r07Fi9ejJdeeslj6zYCw/XI99hs8gisy5bJ44zUSk6WrxlJTwc8MKqiX7DZ5FFuly9vuK2mT5e3FY8i+T0t4Xq2CzbEvxyPqktVim79tUgWWIOtODHrBKLDo91sse87ceIEEhIS8MknnzR5Iao/8FS4HgsS8l1M1m2a2ZORyTBa035rR2p1NTiaBRZIkoRNEzdheHJgHpn87LPPUFFRgV69euHkyZN48skn8dNPP+Hw4cMOF676I6b9EjFZt3G+kIxMppfWKQ0b79sIa4gV0pX/rlY7zRpiDehiBJDHJvnLX/6CHj16YPTo0YiNjcW2bdv8vhjxJLcKksWLF0OSJMyYMaPJ+d5//3107doV4eHh6NWrFzZt2uTOaomoKUxGJg9K65SGE7NOYOkdS5HUMsnhuaSWSVh6x1L8NOungC5GACAtLQ0//PADKisrUVxcjPXr17sclI0caR6pdffu3Xj99dcdBnVxZseOHZgwYQIWLVqEX//611i9ejVGjRqFvXv3omfPnlpXT0SNqU1GVno29upk5IwMfdtGPik6PBoZN2Zg+g3TcbbqLMqryxEZGolW1lZevYCV/IumIyQVFRWYOHEi3njjDZcj0WVmZuKOO+7AE088gW7dumHevHno06cPXnnlFU0NJqImMBmZdCRJEmIiYtAhugNiImJYjJBHaSpIpk6dihEjRmDYsGEu5925c2eD+dLS0hwSDeu7ePEizp075/AgIgWYjExEPkr1KZs1a9Zg79692L17t6L5T506hTZt2jhMa9OmDU6dOtXoMosWLXIIDyIihTyRjBwT45m2EBGpoOoIyfHjx/HYY49h1apVHh3spb7Zs2ejrKys7nH8+HHd1kXkV5iMTEQ+StURkj179uD06dMOiYY1NTX48ssv8corr+DixYsICgpyWKZt27YoLi52mFZcXIy2bds2up6wsDCEhYWpaRoRAb8kIxcUqDttI0lAUhKTkckFAeAMgAoAzQHEACqHlydqjKojJLfddhv27dvnkJbYr18/TJw4ETk5OQ2KEQAYMGBAgzCgrVu3YsCAAe61nIgaYjIy6cIGIBNACoBYAB2v/Ey5Mt3mrYaZ0rZt2yBJEmxN3EqflZXVZL5OIFJVkERGRqJnz54Oj2bNmiEmJqbuFt5JkyZh9uzZdcs89thj2Lx5M/7617/i4MGDeOaZZ/Ddd99h2rRpnu0JEcmYjEwelQ0gHsBMAAX1niu4Mj3+ynz6OHXqFKZPn46kpCSEhYUhISEBI0eObDT5VovBgwe7HFNLqZtvvhknT55EC0ZYqOLxkVqPHTuGkydP1v375ptvxurVq/GPf/wDvXv3xr/+9S9s2LCBY5AQ6YXJyOQx2QBGAKiCfLqm/mnA2mlVV+bzfFFSWFiIvn374rPPPsOLL76Iffv2YfPmzRgyZAimTp3q8fU1RQiBy5cvu5wvNDQUbdu25W3RagkfUFZWJgCIsrIybzeFAk1NjRAHDwrx1Vfyz5oab7dIuc2bhWjWTAhJkh+/DBj/y7RmzYTIzvZ2S0lnVVVV4scffxRVVVUqlioVQjQTQliEEFDwsFyZv9Rj7RZCiDvvvFNce+21oqKiomELS0vrfj744IPimmuuEZGRkWLIkCEiJyenbr65c+eK3r17i7ffflskJiaKqKgoMX78eHHu3DkhhBDp6em1lVXd4+jRo+Lzzz8XAMSmTZtEnz59REhIiPj888/FhQsXxPTp00VsbKwICwsTt9xyi/j222/r1le7XG37hBDirbfeEgkJCcJqtYpRo0aJl156SbRo0aLu+ZycHDF48GDRvHlzERkZKfr06SN2797t0W2pl6beX2p+fzPLhsiZoiJgzBggLAzo2hW49Vb5Z1iYPL2oyNstdC0tDThxAli6VL5g9WpJSfL0n34Chgf2kN/UmBUAKgEFSb8y+5X53/ZYC86ePYvNmzdj6tSpaNasWYPna6/B+O1vf4vTp0/j3//+N/bs2YM+ffrgtttuw9mrxtXJz8/Hhg0b8PHHH+Pjjz/GF198gcWLFwOQB/AcMGAAHn74YZw8eRInT55EQkJC3bJ//vOfsXjxYhw4cACpqal48sknsXbtWqxYsQJ79+5Fp06dkJaW5rC+q33zzTd48MEHMW3aNOTk5GDIkCGYP3++wzwTJ05EfHw8du/ejT179uDPf/5z4OXg6FEteRqPkJCh5s93PJrQ2GP+fG+3VDm7XYiSEiGOHpV/2u3ebhEZSP0RErsQIlkIIQllR0dqH9KV5Tzz/vrmm28EALFu3bpG5/nqq69EVFSUuHDhgsP05ORk8frrrwsh5CMkERERdUdEhBDiiSeeEDfeeGPdvwcNGiQee+wxh9eoPdKxYcOGumkVFRUiJCRErFq1qm5adXW1aNeunXjhhRcclqs9QjJhwgRx1113Obz2+PHjHY6QREZGiqysrCa2hnnxCAmRHhYsAJ56Stm8Tz0lz+8LmIxMqpwBkI+G14y4Iq4s55kRf4WCW9dzc3NRUVGBmJgYNG/evO5x9OhR5Ofn183XoUMHRF41zk5cXBxOnz6tqB39+vWr+//8/HxcunQJt9xyS920kJAQ3HDDDThw4IDT5Q8cOIAbb7zRYVr9O01nzZqFhx56CMOGDcPixYsd2h4oWJAQ1SoqUl6M1HrqKd84fUOkipsj/qLcI61ISUmBJEk4ePBgo/NUVFQgLi7OYTiKnJwcHDp0CE888UTdfPVPf0iSBLurNOwrnJ0u8rRnnnkG+/fvx4gRI/DZZ5+he/fuWL9+ve7rNRMWJES1Zs7UttysWZ5tB5HXuTniLzwz4m+rVq2QlpaGv/3tbzh//nyD5202G/r06YNTp04hODgYnTp1cnhcc801itcVGhqKmpoal/MlJycjNDQU27dvr5t26dIl7N69G927d3e6TLdu3fDNN984TNu1a1eD+Tp37oyZM2diy5YtGDNmDN566y3F7fcHLEiIAMBuBz76SNuyH34oL0/kN2IAJEP9KKzSleU8N+Lv3/72N9TU1OCGG27A2rVrkZeXhwMHDmDZsmUYMGAAhg0bhgEDBmDUqFHYsmULCgsLsWPHDsyZMwffffed4vV06NAB33zzDQoLC1FSUtLo0ZNmzZrh0UcfxRNPPIHNmzfjxx9/xMMPP4zKyko8+OCDTpfJyMjA5s2b8dJLLyEvLw+vvPIKNm/eXPd8VVUVpk2bhm3btqGoqAjbt2/H7t270a1bN3Uby8exICECgLw8QMH4Ak5dviwn5RL5DQmAxhF/kQFPDieflJSEvXv3YsiQIfjjH/+Inj174vbbb8enn36KV199FZIkYdOmTRg4cCCmTJmCzp07495770VRUVGDYNemPP744wgKCkL37t0RGxuLY8eONTrv4sWLMXbsWDzwwAPo06cPjhw5guzsbLRs2dLp/DfddBPeeOMNZGZmonfv3tiyZQueuur0cFBQEM6cOYNJkyahc+fOGDduHO68886AC5mVhJKrhrzs3LlzaNGiBcrKyhAVFeXt5pA/+vpr+dZerb76CvjVrzzXHiIPunDhAo4ePYqOHTuqCEa1QR6BtQrKbv21ALACOAEgWkszyUc19f5S8/ubR0iIACA21r3lVfwlRuQbogGshXy0w9WvCsuV+daBxQhpxYKECABSUoBgVeHXvwgOlhN2ifxOGoCNkI98SGh4KqZ2mhXAJgAcZI+0Y0FCBMiZLiNHalv27ruVB9kR+Zw0yKdhlgKoN+Ivkq5M/wksRshd/BYlqrVkibblXn7Zs+0gMp1oyBer5gEoAXD0ys+8K9OZakvuY0FCVCsxEaiXL+HSwoXyckQBQYJ8S3CHKz854i95jsaT5kQeVlMD7N4NHD8OJCQA/fsDQUHGt2POHPmnkhFbFy4EZs/Wtz3OCAGcOQNUVADNm3MoeFLMB26qpKsIIXDZfhl2YYdFsiDYEgzJw591T6zDU+8rFiTkXd9/D9x/P7BvX8PnevUCVq4EUlONbdOcOXKbZs2SBz27enyS4GD5mpElS4D27Y1tl80GrFgBLF/uOO5JcjIwfTqQng5cST8lulrtsOmVlZWwWq1ebg25ctl+GWcqz+D0+dO4WHOxbnpYUBhaN2uNmIgYBFvc+/XtyXVUVlYCaDg8v1och4S8Z/Jk+ResK+npQFaW3q1xzm6Xf/kXF8u39iYne+cC1uxsYOxY4MoHH1d/bGv/momIANauBdLSjG8fmd7Jkydhs9nQunVrREREePwvbfKM8ovlOFZ2DKKJYEMJEtq3aI/IMG1D9HtqHUIIVFZW4vTp04iOjkZcXFyDedT8/mZBQt6htBip5c2ixNuys4ERI+QipKkh6i0WuTjZuJFFCTUghMCpU6dgs9m83RRqRNWlKpw+ryyBGABaN2sNa4i6I156rCM6Ohpt27Z1WuSyICFz+/57oHdv9cvl5hp/+sbbbDYgPh6oqlKWl2OxAFYrcOIET9+QUzU1Nbh06ZK3m0H1nLt4DoOzBuPCpQuwKxgZ1wILwkPCsW3yNkSFKfu9qMc6QkJCENTE9X5qfn/zGhIy3v33a1vugQfkoiSQrFghn6ZR+neD3S7P//bbQEaGvm0jnxQUFNTkLxDyjtdzXsdB28EmT6PUJ0HCmoNrkHGjss+6EetwB2/7JWPV1Di/gFWJ77+Xlw8UQsgXsGqxbJnyIoaIvEoIgeXfavusL/tmmaK7XIxYh7tYkJCxdu92b/k9ezzTDl9w5ox8Qa3aLwIh5OXOntWnXUTkUWeqziC/NF/VkQsAEBDIL83H2SrXn3Uj1uEuFiRkrOPH3Vu+qMgz7fAFFRXuLV9e7pl2EJGuKqrd+6yXV7v+rBuxDnexICFjJSS4t3wgjYravLl7y0dquyWQiIzVPNS9z3pkqOvPuhHrcBcLEjJW//7uLd+3r2fa4QtiYuRxT9SOFyFJ8nKtWunTLiLyqBhrDJJbJkNSORS/BAnJLZPRyur6s27EOtzFgoSMFRQkj8CqRWqqd4aT9xZJkkdg1SIjg8PJE/kISZIw/QZtn/WMGzMUDXJnxDrcxYKEjLdypbbl/u//PNsOX5CeLo/AqnR0WItFnn/SJH3bRUQelX5dOiJCImBR+GvZIlkQERKBSb2Vf9aNWIc7WJCQ8VJT5V+0akyZEniDogHy4GZr18pHO1wVJbUjta5bx0HRiHxMdHg01o5bC0mSXBYMFlggQcK68esQHR5tqnW4gwUJeUdWlvKiZMoU4M033V+nEEBJCVBYKP9UcjutlmU8LS1NHg7eapULjvqHTmunWa3Apk3A8OHGt5GI3JbWKQ0b79sIa4gV0pX/rlY7zRpixaaJmzA8Wf1n3Yh1aMWChLwnKwv46iugXTvnz7drB2zf7n4xYrMBmZlASgoQGwt07Cj/TEmRpzvL9tCyjJ7S0uTh4JcuBZKSHJ9LSpKn//QTixEiH5fWKQ0nZp3A0juWIqml42c9qWUSlt6xFD/N+smtQsGIdWjBLBvynqsTbJ29DSXJ/QRbLSm5Zk/WFUIe9Ky8XL61t1UrXsBK5IeEEDhbdRbl1eWIDI1EK2srj19cqvc6GK5H5mdEgq2WdQBM1iUi8hAWJGRuRiTYal2HEMCFC0zWJSLyADW/v3kNCRmvNsFWyS99wDHBVs91nD+vf7uIiMgpFiRkLCMSbN1ZhxZM1iUichsLEjKWEQm2WtehBZN1iYg8ggUJGcuIBFt316EFk3WJiNzCgoSMZUSCrbvr0ILJukREbmFBQsYyIsFW6zq0YLIuEZFHsCAhYxmRYOvOOrRgsi4Rkds4DgkZj+OQEBEFBI5DQuZmRIKt1nWsW8dkXSIiL2BBYjZmSJf1BFf9MCLBVss6mKxLZCpCCJRUlqDQVoiSyhL4wEF90ogFiVmYLV1WKzX9MCLBVss6mKxL5HW2CzZk7spEyvIUxL4Yi46ZHRH7YixSlqcgc1cmbBds3m4ieRivITEDs6fLKuVOP4xIsNWyDibrEhku+0g2xr43FpWX5O8SgV++SyTIn7+IkAisHbcWaZ1M/J1IDNfzKUak3hrBX/pBRF6VfSQbI1aPgBACdjT+XWKBBZIkYeN9G1mUmBgLEl9hxN0mRvCXfhCRV9ku2BD/cjyqLlU1WYzUssACa4gVJ2adQHR4tP4NJNV4l42vMCL11gj+0g8i8qoVOStQealSUTECAHbYUXmpEm/n8rvEH7Ag8RYjUm+N4C/9ICKvEkJg+bfavkuWfbOMd9/4ARYk3mJE6q0R/KUfRORVZ6rOIL803+ECViUEBPJL83G2it8lvo4FibcYkXprBH/pBxF5VUW1e98l5dX8LvF1LEi8xYjUWyP4Sz+IyKuah7r3XRIZyu8SX8eCxFuMSL01gr/0g4i8KsYag+SWyXXjjCglQUJyy2S0svK7xNexIPEWI1JvjeAv/SAir5IkCdNv0PZdknFjBiR+l/g8jkPiTf4yfoe/9IOIvEr1OCSSBdZgjkNiZhyHxFcYkXprBH/pBxF5VXR4NNaOWwtJkmBx8evJAgskSFg3fh2LET/BgsTb/CVdtn4/nGmqH3Y7cOgQ8PXX8k8lR1rMmIxsxjYR+ZC0TmnYeN9GWEOskK78d7XaadYQKzZN3IThySb9TiTVWJCYgb+ky6alATt2AD17On++Z0/5+av7UVQEjBkDhIUBXbsCt94q/wwLk6cXFTV8HTMmI5uxTUQ+Kq1TGk7MOoGldyxFUkvH78SklklYesdS/DTrJxYjfobXkJiNL6fLLlgAPPWU6/nmzwfmzFE/P2DOZGQztonITwghcLbqLMqryxEZGolW1la8gNWHMFyPjKe0uKg1bBjwySfK558/H+jXz3yJwkw5JiJqlG4Xtb766qtITU1FVFQUoqKiMGDAAPz73/9udP6srCxIkuTwCA8PV7NK8gVFReqKEUBdMQLIrz9mjOtf/ID8vBDyUQs9T5XYbPI6zNQmIiIfpaogiY+Px+LFi7Fnzx589913GDp0KO655x7s37+/0WWioqJw8uTJukeRs2sCyLfNnGnMesyWKMyUYyIij3H7lE2rVq3w4osv4sEHH2zwXFZWFmbMmAGbm38R8pSNidnt8gWoly97uyUNSZJ8UXBenuevwxFCvmC1oEDdnTR6tomIyGQMGYekpqYGa9aswfnz5zFgwIBG56uoqEBiYiISEhJcHk2pdfHiRZw7d87hQSaVl2fOYgTQN1GYKcdERB6luiDZt28fmjdvjrCwMDzyyCNYv349unfv7nTeLl264M0338QHH3yAlStXwm634+abb8aJEyeaXMeiRYvQokWLukdCQoLaZpJRfv7Z2y1wTY9EYaYcExF5lOpTNtXV1Th27BjKysrwr3/9C//85z/xxRdfNFqUXO3SpUvo1q0bJkyYgHnz5jU638WLF3Hx4sW6f587dw4JCQk8ZWNGhw7J44aYWUmJHALo6deMjXVveU+3iYjIZNScsglW++KhoaHo1KkTAKBv377YvXs3MjMz8frrr7tcNiQkBNdffz2OHDnS5HxhYWEICwtT2zTyhpQUIDi48dM2MQCaA6gAcMbAdgG/XK+hR6Jwbcqx1mtImHJMROTA7ZFa7Xa7w9GMptTU1GDfvn2Ii4tzd7VkFhYLMHKk47QWADIA5AEoAVB45WfelektDGyfXonCTDkmIvIoVQXJ7Nmz8eWXX6KwsBD79u3D7NmzsW3bNkycOBEAMGnSJMyePbtu/ueeew5btmxBQUEB9u7di/vvvx9FRUV46KGHPNsL8q4lS375/+EATgBYAqDeKPhIujL9xJX51IqIcB3eV8tikeefNEnDihRKTzdfm4iIfJSqguT06dOYNGkSunTpgttuuw27d+9GdnY2br/9dgDAsWPHcPLkybr5S0tL8fDDD6Nbt2646667cO7cOezYsUPR9SbkQxIT5ZFUhwPYCMAK+Z1V/91VO80KYBPUFSULF8oJwWZKFGbKMRGRx3DoeLMRQr6ltKICaN5cvlbB04f37Xb5dt2ff/4lAE7pX/mNsgHVbYCgaiBIyfwWoDoYaF0NlLmYdeFCoPbI29W5Mc7eupIkH4VYt84zYYRKtpXSLBtPtcmkjHjrCiFwpuoMKqor0Dy0OWKsMS5zTbQsQ0SeYcg4JORhRqTFaknWVWwFEHpJYTECAHZ5/v1PAqmpzmdJTQW+//6XYgSQc2D27wdGjZIvpr1acLA8ff9+93/xq9lW/pLWrJERb13bBRsyd2UiZXkKYl+MRcfMjoh9MRYpy1OQuSsTtgsNV6JlGSLyHh4hMQMj0mK1JOsqJgCkACIfUPOHp5CAAgApAoCkrN9m31a+nNasgRG7I/tINsa+NxaVl+SVCPyyEunKGy4iJAJrx61FWqc0zcsQkecx7deXGJEWqzaJV3VRUgLAjTE5YgA0NnDp1f0G/GBb+Q8j3rrZR7IxYvUICCFgR+MrscACSZKw8T75faJ2GRYlRPpgQeIrbDYgPh6oqlIW0GaxAFarfHpA6YWRRUVAhw7q21ZYKF+sqsSJr4H4W9Wvo1YHAE2dLarttxDAhQu+va38hBFvXdsFG+JfjkfVpaomC4u6dcACa4gVQghcuHxB1TInZp1AdLjChhGRYryGxFcYkRarNYl31izl885ZpG0dtVyNom63A+fP+8e28hNGvHVX5KxA5aVKRYUFANhhx/lL51F5Wd0ylZcq8XYuE5iJvI1HSLzFiLRYd5J4g4OBixdd331jtwNhocCBGnmcETUlrh1XriFR3zxFzLat/IQRb10hBFKWp6CgtMDh+g89SJCQ1DIJedPzePcNkYfxCIkvMCIt1p0k3suX5fUoWkcNsFzbarBM43JKmG1b+Qkj3rpnqs4gvzRf92IEkC94zS/Nx9kqJjATeRMLEm8xIi3W3STe4mLl61gBoBJAjcLXrrkyvxFHys2yrfyEEW/dimo3V6JBeTUTmIm8iQWJtzRv7t7ykZGu53EnjRYA2rRRvo4yAGMh3wHsqiipuTLfGLgeFM0TzLKt/IQRb93moW6uRIPIUAUNIyLdsCDxltq0WLXnrCVJXk5JWmxtEq8WwcHyetSsYwuAEQCqIF8fUv+6wtppVQDuArBVW9MUM9u28hNGvHVjrDFIbplcN2aIniRISG6ZjFZWJjATeRMLEm8xIi3WWRKvUnffrewizfrr2AIgHsAMyBesXq3gyvRroX8xUstM28pPGPHWlSQJ02/QuBINMm7M4AWtRF7Gu2y8yV/GIWlqHa0AREK+tbf+NYOSpOzKSI5DYjqmHIdEssAarHIckivLcBwSIn3wLhtfYURabG0SrxoLF6r7BdvUOs5CHvSsfjEyebLcJzX91jvt14ht5SeMeOtGh0dj7bi1kCQJFhdfVRZYIEHCuvHrsG78OtXLsBgh8j4WJN6WliaPqW21yt/a9Q8b106zWoFNm7QFtM2Zo/wX7dXJunqu46231PfbX7aVnzBid6R1SsPG+zbCGmKFdOU/h3Vc+c8aYsWmiZswPHm4pmV8gd1ux6GSQ/j62Nc4VHIIdqWj0hH5CuEDysrKBABRVlbm7abop7RUiMxMIZKThZBPTsiP5GR5us3m/joKC4UYM0aI4GDHdQQHy9OLitxfR26uEKmpjq9f+0hNFeL77xu2afRo520aPVp+vj5/2VZ+wojdUVpVKjJ3ZYrkzGSBZ1D3SM5MFpm7MoWtquFKcosKReqC0QJPBzssg6eDReqC0eL7IifvLRMqLC0Uo9eMFsHPOfYj+LlgMXrNaFFY6hv9oMCk5vc3ryExGyPSYu12eYSq4mL5dtXkZM9clKk2+vXq+Z29DSWp6ahYX95WfsiI3SGEwNmqsyivLkdkaCRaWVs5vRh1wbvZeOr7sUDIlffW1bvMDrlhlyIwP3Ut5ow3b7Degi8X4KnPXYc9zh8yH3MGBmbII5kbw/XIeGqjX599Fpg7V9+oWApIC97NxlM/jgAgAEsT7y27BYCE+d03mrIoUVqM1GJRQmbEgoSMpeWWC7td/V02am7RoIBUVGxDh2XxQHBV08VILbsFuGxFYcYJJLaJ1r19ShXZitAhs4Pq5QofK0RidOBdZE3mxbtsyFhaol8B5WEoWqJiKSDNXLFCPk2jpBgB5PlCKjFrhbneWzOztSVPz8oOvORp8h8sSMg9QgDLtSbrqbRsmfpENwoYdrvAR8Ua3osC+LB4Gex2c7y37HY7Pjr8kaZlPzz8Ie++IZ/FgoTcozX6VS01UbEUkPJ+OoPLUfmApPK9aBG4HJWP/P+a472VdzYPl+3akqcv2y8jvzRwkqfJv7AgIfe4G/2qlpKoWApIP5e5914stpnjvfVzpXvJ08XnAyd5mvwLCxJyj7vRr2opiYqlgBTbwr33Yptoc7y3YiPcS55u0yxwkqfJv7AgIfdojX5VS01ULAWklGtjEHwuGbCrfC/aJQSfS0ZyO3O8t1JapSDYoi15OtgSjOSWgZM8Tf6FBQm5x53oV7WURsVSQLJYJIxsMx1Q+xaRgLvbZMBiMcd7y2KxYGRnbcnTd3e+GxYO3Ec+iuOQkPs4DgmZBMch4TgkZC4ch4SMpSX6dcEC9Wm/LEbIhcQ20ZifuhaAdGUk1iZcGal1Ye91pipGACAxOhHzh6hLnl44dCGLEfJpLEjUEAIoKQEKC+WfehxcUrsOux04dAj4+mv5p7fGIFAb/fqXv+gfFUtuMeLtroc549Mwv/tG4LIVEFLDa0rskjz9shULe2zC7HGO7y27XeDQ8RJ8/UMhDh0vUTQ+iRACJZUlKLQVoqSyBJ448Dxn4BzFRcnCoQsx+1bH5GktbVK7jDFfiZ7ftmROPGWjhM0mj0a6fLk8Fkat5GT5+on0dPf/ele7jqIiYOZM4KOPgMtXjVkQHAyMHAksWQIkeuGvJbXtstnkEViXLWvY74wMud8tWhjWfDLm7W6EomIbZq14Gx8WL5PHJ7ki+Fwy7m6TgSWT09G+dQuH+WeuWIGPipc3mH9km+lYkp7e4EiK7YINK3JWYPm3yx3G/0humYzpN0xH+nXpiA53XEZ1P2xFmJU9Cx8e/tBhfJJgSzDu7nw3ltyxBO1btHerTWqXMeQr0YBtS/pjlo0nqU2wNWIdCxYATykI3Zo/H5hjYNiWO+m9RkTFkktGvN2NZrcL5P/3LIpt5WgTHYnkdq0aXMDqmA4MwHJVx+2SfKFsvXTg7CPZGPveWFRekjeWwC/LSFeurI0IicDacWuR1sn9jWW325Ffmo/i88Vo06wNklsmN7iAVUub1C5jyFeiwduW9MOCxFPUJthqSaRVu47775f/NFHKqKLEiG1FugrUXaglHbhfX2DE6hEQQsCOxpexwAJJkrDxvo26/+LMPpKtuk2Aun4822Uj5k5M0/crUUM/WJSYFwsST9By54jaO0HUrkPpXSn1FRbqe/rGiG1FugrUXaj1rpyICIELly80+QuzlgUWWEOsODHrhG6nGGwXbIh/OR5Vl6pUtUkIdf2wV1shLTkBURXten4tX4ka+6HntiX38C4bT9CSYKs2kVbtOrTWjrN0TgA1YluRrgJ1F2pLBz6PysuVin5hAoAddlReqsTbufptrBU5K1B5SV2bzl9S3w+EVEKkKuuHpq9EDf3Qe9uScXiExBkhgJQUoKBAXREgSUBSEpCX5/r6B63r0CI4GLh40fUttloYsa1IV4G6C+12gbAnUnA5skBdIF/trCr6LEFCUssk5E3Pg+ThjSWEQMryFBSUFjhca6ELuwTYkoBleVCyAdR9JWrrh57bltzHIyTu0ppgqyaR1qiUXEC+2+XqS+E9yYhtRboK1F2oOR1YgurRYAUE8kvzcbbK8xvrTNUZ5Jfm61+MAPLFvq3yAauyfqj6StTYDz23LRmLBYkz7ibYKkmkNTolt1inBFAjthXpKlB3obvpwFqUV3t+Y1VUG98PhKnrh6KvRDf7oce2JWOxIHHG3QRbJYm0RqfkttEpAdSIbUW6CtRd6G46sBaRoZ7fWM1Dje8HLqrrh6KvRDf7oce2JWOxIHFGa4KtmkRao1JyAfkakmSdEkCN2Fakq0DdhZrTgQWg9uyIBAnJLZPRyur5jRVjjUFyy+S68Tl0ZZeAs8lAlbJ+qPpK1NgPPbctGYsFiTPuJNgqTaQ1MiX37rv1uaAVMGZbka4CdRdqTgcGNC2TcWOGLhddSpKE6TcY9F0iAfgmA2o2gPKvRO390GvbkrF4l01jzDgOSW1Krloch4RcCNRdaMg4JJIF1mCTjUNypU0+Pw6JAduW3MO7bDxBS4Kt2kRaLetIT1f++gCwcKH+mTZGbCvSVaDuQq3pwOvGr4MkSbC4+Aq1wAIJEtaNX6frL8zo8GisHbdWdZtU9UOSsCB1HSzV0fp9JWrsB4sR/8CCpClqE2y1JNKqXUdWljwcvBILFwKzZzecrjaiU8n8Rmwr0lWg7sKG6cD1ZrCjQTpwWqc0bLxvI6whVkhX/rta7TRriBWbJm7C8GT3N1ZNjcCu70vw/tZC7Pq+BDU1jp9DLW2qv4wzVy/zl/HD9f9K9MK2JZMQPqCsrEwAEGVlZd5pQGmpEJmZQiQnCyH/OpYfycnydJvN/XUUFgoxerQQwcGO6wgOlqcXFjacf8wY5/OPGSNEUZHzfixd6rwfS5fKz7szv1HbinQVqLswt6hQpC4YLfB0sMAz+OXxdLBIXTBafH+ssMEypVWlInNXpkjOTHZYJjkzWWTuyhS2Kvc3Vu6hUtHr4aUCGY7rQEay6PXwUpF7qNTtNn1V9JWIeynO8fWvPOJeihNfFX3luI5S/d8jRmxb0p+a39+8hkQNvRJp3UnJtdvlkYeKi+Vbe5OTnR9zVxvR6W6kJ9N7fV4g7UJ302WFEDhbdRbl1eWIDI1EK2srj1xkOXleNlZccJ1CnB6+FllPO7ZLaZsmr5+MFd+7DuxMT01H1uiseuvQ/z2i17YlYzBcz5eYMVH42WeBuXMDL/aVApJZ02Unz8vGisvKU4jTgzc2KEpcrkNhMVLLWVFC1BQWJL7CzHfyKE0W9pfbLSggmTVd9vvDNvReof7un9z0E0jtrKRdAvtPf4kRqwejoho4U6W8bbmP5CK1TaryBSig8S4bX2HGROHa+ZTWqf4S+0oByazpsve/pCWFuBIPvOSqXTYAmQBS0KP1YBTOAEqeBPKmAxk3Ai3CXK/qgXUPKGsTkUo8QuIt/pQo7OuxrxSQhEnTZWtqBIJnpQAtVaYQX0nivfxyHoKCnLUrG8BYAJUQApCuem27HYAEVF4Cxr4HbHGRxXn5qcsICgpS3jYKWDxC4gv8KVHY12NfKSCZNV129/4zcqKu2hTiK0m8ew44a1c2gBEAqgAIh2IEkM+8WiTAGgxsvA8Y7iJpYs/JPeraRqQACxJv8cdEYV+NfaWAZNZ02ePF7rWr6GT9dtkgHxkRaDjIiqMgi3zjztpxTZ++KSorcquNRM6wIPEWf0wU9tXYVwpIZk2XTWjjXrsS4+q3awWASrgqRmoFWYCIEGBS7ybW0ULn0Z8pILEg8RZ/ShT29dhXCkhmTZft3yNGTtRVm0J8JYm3b7er2yUALFffCCFf5NqYvnF91b8mkQssSLzF3xKFfTn2lQKSWdNlg4Ik9KrSkEIsAalVGfUuaD0DIB9QeZ2MxQJ0agW0sjZ8LrV1Ki9oJV2wIPGm9HR5tFNXSVW1LBZ5/kmT9F0HoLy40NImIpNIvy4dESERLoPcalkkCyJCIjCpt77v95WPpwOXIlwH/tWyW4BLEfi/x+u3y73rUSJDG077vzH/59ZrEjWGBYk3mTVReMGCK5fdB1DsKwUks6bLpnaORnq4uhTiKRHrnAyK5t71KOXVjv+e0nsKB0Uj3QRuQaI28VbrMq54I1HYmavX8Ze/uN8mPbaVFxjRDbsdOHQI+Ppr+aerMeyM2rRGtEvtOvTgiXTZmpoa7Dq+C+/vfx+7ju9CTU1Nk+tUsq2ynk5DerCyFOIpoZvw5hzHdgkhUFIpcKmmPYRQd/7HbgeOnAXOXjWC65TeU/DmqDdVvY4zcrtKUGgrREllCVwNhaV2frPS0g9/6btiHo3104lH0361JtiqXUZLu/SOz8zNFaJXL8fXr3306iU/fzW1CcS1/dB7WxnAiG6o3bxGbVoj2qXlraU3LemyuadyRa+/93Kaktvr771E7inHz5SWbbVpT66IerKXwNx665gLEfVkL7F5b711VJWKpTuX1vUj498QNXYIIZQ/auwQ0zfJ60n9e6rYV7zP3c3boF1Xb9+lO5eK0qpSt+Y3Ky398Je+C8G038ZpSbB1N/VWLb3iMxcsAJ56yvV88+cDc+ZoSyA2elvpxIhuuLM79GqTUe1Suw6jCZ1ScjVtqy8X4KnPXW+s+UPmY87AOU5Ti1uEASdmyYOeBSk4Ji6EBTX2EOSe2ojr2g72yAWsatOU3U1fNgst/fCXvtdiuJ4zWlJ1Af2TeI2g9DdArfR0YOXKgNxWRoQvG7E7tGxaI9r13Xfq1uGtosQVtSm5w65Jx+ePZanbVlZlxUit9NR0rNy3EsJJavHwZHkEVgmuihL5ehRgEwANp4edUJum/OzgZzF321zF8xuVvqyWlhRpAKZMnnaHbgXJq6++ildffRWFhYUAgB49euB///d/ceeddza6zPvvv4+nn34ahYWFSElJwfPPP4+77rpL6SoBeKAg0ZJ4Gx4ufzPomcRrhKIioEMH/V7fj7aVEeHLWneH3uHLRrQrPPyXowNqFBYCiSYah+v74u/R+7UmRg1zRgB4LRcodn1BqMUChLUuQtUjHVS3TYLU6FD4w5PlEVgjQuT2OF6zXnsEKALAOniqGFGbplzbfgsspkpfVktLinR4cDgkSTJd8rS7dMuyiY+Px+LFi7Fnzx589913GDp0KO655x7s37/f6fw7duzAhAkT8OCDD+I///kPRo0ahVGjRuGHH35Qs1r3aU3VPX9e3yReI8ycqe/r+9G2MiJ8Wevu0Dt82ah2aTFrlrbl9HL/uvu1LThaWUqu3Q5UDdK2Q5rK5dmSD8S/DMzYDBTY6j+bBGApgJ/gqWIEUJ+mXNt+s6Uvq6UpRfpyJc5fOu/zfXeH26dsWrVqhRdffBEPPvhgg+fGjx+P8+fP4+OPP66bdtNNN+G6667Da6+9pngdbh0hMSrxFjBf6q3dDoSFAZcve7slDZlsWxkRvmzU7lC7ac38NgGA4GDg4kXlQ+noqaamBsHzg9UvWPueevYyAFfXZNiBp8MAy2X1g6OpEGMFesS2x7bJeyBJMfD0yoTGNGW19E5fVsuofgPm67szhqT91tTUYM2aNTh//jwGDBjgdJ6dO3di2LBhDtPS0tKwc+fOJl/74sWLOHfunMNDM6MSbwF5HWZKvc3LM+9vGZNtKyPCl43aHWo3rZnfJoDctvx8b7dCtvu/u7UtKF15xClIyY3JA4L0LUYA4EwV8OWxYzhbVds4T7++tjRltfROX1bLqH4D5uu7u1QXJPv27UPz5s0RFhaGRx55BOvXr0f37t2dznvq1Cm0adPGYVqbNm1w6tSpJtexaNEitGjRou6RkJCgtpm/MDrxFjBP6u3PP3u7Ba6ZZFsZEb5s9O5Quml94W1SXOztFsiOnzvu3gu0VJCS28zYHaJXarG7acpq6dUPtYzuN2CevrtLdUHSpUsX5OTk4JtvvsGjjz6K9PR0/Pjjjx5t1OzZs1FWVlb3OH7cjS8BoxNvAfOk3sbGersFrplkWxkRvmz07lC6aX3hbVLv7xqvSYhy448jAChVcHXueWN3iF6pxe6mKaulVz/UMrrfgHn67i7VBUloaCg6deqEvn37YtGiRejduzcyMzOdztu2bVsU1/vTpri4GG3btm1yHWFhYYiKinJ4aGZU4i1gvtTblBT5BLwZmWxbGRG+bNTuULtpzfw2AeS2JSd7uxWy/u36a1tQXHmcVJCSeyYFqAmG3kf89U4t1pqmrJbe/VDLqH4D5uu7u9y+TMxut+PixYtOnxswYAA+/fRTh2lbt25t9JoTXRiZeAuYK/XWYgFGjvR2Kxpnom1lRPiykbtDzaY1+9vk7rvNcUErAAQFBaFX617aFi5OhesLWgHAAhweqfs1JIC+qcXupCmrpWc/1DKy34C5+u4uVXfZzJ49G3feeSfat2+P8vJyrF69Gs8//zyys7Nx++23Y9KkSbj22muxaNEiAPJtv4MGDcLixYsxYsQIrFmzBgsXLsTevXvRs2dPxY3kOCRu4DgkinEcEn3bxXFIvDsOicM6JAuswfqPYaH7OCQG9UMt1eOQSBaEB6kch8Skfa9Pt7tsTp8+jUmTJqFLly647bbbsHv37rpiBACOHTuGkydP1s1/8803Y/Xq1fjHP/6B3r1741//+hc2bNigqhjxCC2Jt+vX65/Ea4TERHm4SzUmTwaCggJuWxkRvqx1d+gdvmxEu9avV7+OhQvNVYwAQGqbVKSnpqta5vbWUxBUkqp4W21YkYj5Q9RtrMm9J8MiWUyVWqw2TdkiWbBg6ALTpS+rpSVFev29602ZPG2kwBk6HlAevLFu3S8JtlqWMSOl44IvXAjMnh3Q28qIbrizO5qKFnJ30xrxNlG7jqsJId+iXVEhX4gcE+P61JQQAmeqzqCiugLNQ5sjxhrT5CFupetQOnx8bUqupm2lMMtm4dCFmH3rbMU5KOvGr3OaWqwXte1ytx9q97letPTDrPtQK1W/vz0a66cTj6f9qk3VNWM0qRaFhUKMGeO8H2PGCFFU5Di/lm1lRGqxAYwKX05NdR6+nJoqxPffO85v1NtQ7dtES7vU9l1TSLfadFkN68g9lStS/57qNO3XWUqulm1VWFooxqwZI4KfC3Z4/eDngsWYNWNEkc1xh2hJLTaC2nZp6YcZU3K19sOM+1ALpv0qoTRVV0vqrdnZ7fJIU8XF8v2UyclNH3fXkkCsV2qxwfTqhtq3lTeClJW8TdwNhdar76rTZd3cvjU1Ndhzcg+KyoqQ2CIRfeP6NkjJdferxG63I780H8Xni9GmWRskt0yGpYnPrVCYWmw0te1SOr/ZU3K17A+z7kM1mPbrKUZEv1LAUfu2evZZYO5c870NjQjQ1tJ3JKtMl+2yEXMnpum6fflVoi8tybpmT8n1FyxIPMGIWy4o4Kh9W9XexWKxmOttqPXjIQRw4YJ+fQ+PtkH6o7qUVXu1FdKSExBV0Yr7oWb78qtEX1qSdX0hJddfGJJl4/eMiH6lgKP2bVX754LZ3oZaPh7nzxvQ984rcF5lyipCKiFSlW0wLduXXyX60pSs62cpuf6CR0icMSL6lQKOUcHTer8NjQzQVkcAGSlAywJAUtEwuwTYkoBleVAyGpma7cuvEn0Jjcm6vpCS6y94hMRdRkS/UsAxKnha77ehkQHaqkScAVrlqytGAMAi5OWsyjaYmu3LrxJ9aU3W9beUXH/BgsQZI6JfKeAYHTyt19vQGwHaioS62bAwdRtMyfblV4m+3E3W9ZeUXH/BgsQZI6JfKeAYHTyt19vQGwHailS72bCL6jaYku3LrxJ9uZus6y8puf6CBYkzRkS/UsAxKnha77ehkQHaqlTGAGeTAaGyYXZJXq5K2QZTs335VaIvrcm6/paS6y9YkDhjRPQrBRwjg6f1fBsaHaCtnAR8O119Sq4E4JsMqFlQ6fblV4m+3EnW9aeUXH/Bu2waw8EDSAcch4TjkOixjkCmJVnXF1Jy/QXvsvEEI6JfKeCofVtZLHIYndnehlo/HuvW6dv39e+oTFmVJCxIXQdLdbRu25dfJfrSkqzrbym5/oIFSVPS0uQxnK1W+Vui/uG92mlWK7Bpk6kTbMk86r+tnLn6bfWXv6ib39nbUAigpAQoLJR/euK4qJaPhxF9T+uUho33bYQ1xArpyn8O81/5zxpixaaJm/CX8cMd1yHZgZhDQPuv5Z+S3e3t642vEj32uVmp3ef1U3KFECipLEGhrRAllSUwy4kDs7ZLNx6N9dOJR9N+tfCTBFsyF7XJr7m5QvTq5Twht1cv+fn6tCTYqqXl46G2L5pSck+VitHPZ4rgWY6JqcGzksXo5zNF4SnHhuUWFYpeC0YLPO2Yqoung0WvBaNFblHDlajdvkZ8lRixz81KS6Kw2dKBzdwuLZj2qxc/SbAl71ObLrtgAfDUU65fd/58YM4cbetwl9KPh9q+aEr7vTpZFwIIPyuPM3IxErjQChIkx+375QI89bnrRs0fMh9zBs5psA6l7aplRIq02jb5E6EgJdes6cBmbZdWDNcjMjG1ya/33y/noSg1fz7Qr58502WVFiO10tOBlSv1TRS+//UFWHFCeaPmD5mPflVzTLd9mSisnFnTgc3aLnewICEyKa132agVEaH8bhaj7uooKgI6dFC/nNJtYLEA4eHy/Eq3L6KLgMc6qL5V2PpaIS6eTjTN9uWdPMqZNR3YrO1yF++yITIprWm/apkxXXbmTG3LKd0Gtf04f1553zFcW6OqBs4y1fZlorByZk0HNmu7jMQjJEQGMW9Krv7psnY7EBYGXL7s+dfWzg48HQZYLqs7QiIA2IOBeReh9G86PbcvE4WVEyZNBzZruzyBR0iITMi0KbnQP102L89sxQiAmDwgSGUxAsjzB10GWuYrXkTP7ctEYeXMmg5s1nYZjQUJkUFMm5J7Fcd0WQGgBEDhlZ/aK6mff3anVTpp5majIotVL6JHei8ThZUzazqwWdtlNBYkRAYxbUruVeR0WRuATAApAGIBdLzyM+XKdJvq142N9VADPem8m40qb6N6ET3Se5korJxZ04HN2i6jsSAhMohpU3JxdbpsNoB4ADMBFNSbq+DK9HgA2apePyUFCA72REs96EwKUBOs/sCPgLxcabLiRfRM72WisHJmTQc2a7uMxoKEyCDmTcmVvfRSNiRpBIAqyL916/+mrp1WBWAE1BQlFgswcqSHGuoxFuCQhkZJAA7dDbVfn3ql9zJRWDmzpgObtV1G4102RAbSMl6E4ltYr6J2HJK2bW04cSIeklQFKLrt0ALACuAEgGhFbeI4JByHxAzMmg5s1na5i3fZEJmUluTX9HR161i4UF2qriQBn366ApJUCWXFCK7MVwlA+RgIiYnyKLJqTJ78S/JvU+rSfter275B5YmY3F5doxYOXYj1WYmmSu9lorByZk0HNmu7jMSChHyWr6aZqk1+zcpS/ot84UJg9my16xDo2nW5xt4sQ+2pHSX7Y84cdX156y33E4VdLfPW7+Zg/hBljVo4dCFm3zrblEHgZmyTWbmbDhxo7TKMHul+nub1tF8yFX9JM1WbYFtYKMSYMc7nHzNGiKKihuv46ish4uKcp+rGxQnx9ddCCPGzEAKaHzZbier9obYvWlJyVacpn8oVqX9PdUz6vfJI/Xuq+L74+wbrMGMQuBnbZFZq04EDvV1aMO2X/Ja/pJk6pNE6+QRKUuP9sNvlAa2Ki4E2beQ7JZwdpp88WVko3x//WIiXXuqoqR8A0L37URw82AGA+v2htC+1lKbkqt2+VyesOhucSoLUZMKqGYPAzdgmsxIK0oHZLm0Yrkd+yV/STI3oh9JiBABiYkpQUqJ9TI7Y2BKUlMQ0+rzR+0Pt9n12VTbmHvKvhFUis2BBQn7HX+4iMKIf338P9O6tplUCeXkpSE4ugCQp/zqw2yUUFCQhJSUPrm5TMWp/qE5TttogZsbDEupfCatEZsG7bMjv+EuaqRH9uP9+ta2SsHz5dE0XBS9blgEl98watT9UpymnrgBCAjthlcgseISETM9f0kyN6EdNjbYRUVu0kMchadasCpLk+pdzTY0FVVVWxMefQFlZtKJ16L0/1G9fAWSkAC0LABVHhnwhYZXILHiEhPyKv6SZGtGP3bu1ta2sLBpjx66FfLSj6a8FISwQQsKYMesUFyPycvruD9XbN+IM0CpfVTEC+F/CKpFZsCAh0/OXNFMj+nH8uPbX37IlDV99tRHyCKwSGp6KkacJYcVdd23C1q3axkDQa3+o3r6hTFglMhMWJGR6/pJmakQ/EhLcW0d4eBrk4eCXAkiq92wSgKUoLf1JczEC6Lc/VG/faiasEpkJCxIyPX9JMzWiH/37a2tbrb59ATmbJgNAHoASAEev/MwDkIFWrVqYcn+o3r6VMcDZZMAe2AmrRGbBgoRMz1/STI3oR1AQ0KuXtnWkpsrL/0ICEAOgw5WfcgPMuj/Ut0sCvpmuOlgP8K+EVSKz4F025BM4Dome45DIcnPlokQJs+4P3cch8ZGEVSKz4F025Hf8Jc3UiH6kpqpPCJ4yRXkxAph3f6htl6U6Ggt6B3bCKpFZsCAhn+ELaaZKEm/d7YeSdWRlKS9KpkwB3nxT2bye7AcgZ3WUVJag0FaIksoSeOKArdp2/WVcgCeskmnp8fkwNc9n+3ke037pamZMM9WSQKy2H1rW8dVXQrRr5zztt107IbZv90zf1e6P0qpSsXTnUqdppkt3LhWlVU46o3O7/ClhlXybEZ8PozDtlwKCWdJM3U0gVtIPLetwJ1FYC8VJvFcl6wJwSNetPTrRVLKuXu36ZX7fT1gl32X050NvDNcjMogRyb1a1gGYMxk5+0g2Rqxmsi6RM/74+WBBQmQAI+400bKO8HC5yDDdHTAXbIh/OR5Vl5isS1Sfv34+eJcNkQGMSO7Vuo7z582XjLwiZwUqLzFZl8gZfj5YkBBpIgSwfLm2ZZctUxYA5846tFDaLi2EEFj+rbbOLPtmmf/fXUABjZ8PGQsSIg2MSO7Vug4tdE/irTqD/NJ8hwv0FLWLyboUAPj5kLEgIdLAiORed9ehhW5JvNVM1iVqDD8fMhYkRBoYkdzr7jq00C2JN5TJukSN4edDxoKESAMjknu1rkML3ZN4rTFIbpncYBRUl+1isi4FAH4+ZCxIiDQwIvHWnXVooW8Sr4TpN2jrDJN1yd/x8yHjOCREGnEcEnVUj7PAZF0KIP76+eA4JEQGMCLxVss61q83aRJveDTWjmOyLpEz/HywICEFlKTL+sI69OCZxNum+15/Hc7UX4dZk5HTOhmfrGu3A4cOAV9/Lf9UOmCc2YhAS34NQN74fJgJCxJqlM0GZGYCKSlAbCzQsaP8MyVFnm6z+cY69JaWBuzfD4waBQQFOT4XFCRP37+/4S99NX2/eh3BwY6vExzsfB1pafJpmKVLgaQkx2WSkuTpP/1kXDFS165OaTgx6wSW3rEUSS0dG5bUMglL71iKn2b95PaXbVERMGYMEBYGdO0K3Hqr/DMsTJ5eVOTWyxvGdsGGzF2ZSFmegtgXY9ExsyNiX4xFyvIUZO7KhO2CzdtNJA8y6vNhRryGhJxyN8HWLOswgpZUXbV9NyJR2Bv0StZdsAB46inX882fD8yZ4/bqdONvya+kjj8kTzNcj9xi1gRbMxYlRiTxPvssMHeu728roygtRmqZtSjxx+RXCjy6FSSLFi3CunXrcPDgQVitVtx88814/vnn0aVLl0aXycrKwpQpUxymhYWF4cKFC0pXy4LEQGa9c8SIu0DU0toPIYALF5QtI0ny/BaLb28roxQVAR06qF+usBBITPR0a7Tz1+RXCjy63WXzxRdfYOrUqdi1axe2bt2KS5cuYfjw4Th//nyTy0VFReHkyZN1jyJfOXkbgMycYKt3Gq1aWvpx/ry6ZWr/XPD1bWWUmTO1LTdrlmfb4S4mv1IgcuuUzc8//4zWrVvjiy++wMCBA53Ok5WVhRkzZsDmxtWJPEJiDCHkCyoLCtTd5SJJ8kWSeXmur0swYh1G0NoPI5htWxnFbpcvWL18Wf2ywcHAxYuub5M2ghACKctTUFBaoCpsTYKEpJZJyJue53PXGZD/MmwckrKyMgBAKxfjTVdUVCAxMREJCQm45557sH///ibnv3jxIs6dO+fwIP2ZOcFW7zRatYxM4lXLbNvKKHl52ooRQF4uP9+z7dGKya8UqDQXJHa7HTNmzMAtt9yCnj17Njpfly5d8Oabb+KDDz7AypUrYbfbcfPNN+PEiRONLrNo0SK0aNGi7pGQkKC1maSCLyTY6pVGq5Y3knjVMsu2MsrPP7u3fHGxZ9rhLia/UqDSXJBMnToVP/zwA9asWdPkfAMGDMCkSZNw3XXXYdCgQVi3bh1iY2Px+uuvN7rM7NmzUVZWVvc4fvy41maSCr6QYKtXGq1a3kjiVcss28oosbHuLd+mjWfa4S4mv1Kg0lSQTJs2DR9//DE+//xzxMfHq1o2JCQE119/PY4cOdLoPGFhYYiKinJ4kP7MnGCrdxqtWkYm8apltm1llJSUhoPGKRUcLG8zM2DyKwUqVQWJEALTpk3D+vXr8dlnn6Fjx46qV1hTU4N9+/YhLi5O9bKkL7Mn2OqZRquW0Um8aplpWxnFYgFGjtS27N13m+OCVoDJrxS4VN1l8z//8z9YvXo1PvjgA4exR1q0aAGr1QoAmDRpEq699losWrQIAPDcc8/hpptuQqdOnWCz2fDiiy9iw4YN2LNnD7p3765ovbzLxjgch0Q5jkNiPgE7DomPJL9S4NHtLptXX30VZWVlGDx4MOLi4uoe7777bt08x44dw8mTJ+v+XVpaiocffhjdunXDXXfdhXPnzmHHjh2KixEyllkTbI1Io1VLaz/WrVO+jMUijzzq69vKKImJ8siraixcaK5iBGDyKwUmDh1PTinNTlm3Tns4mxHrMIKWfqhdxl+2lVGUDh+/cCEwe7b+7dFKaZbNuvHr/DJsjXyfYeOQkP/SmmCrdh1mTKNVKy0N2LED6NWr4bgkQsjTd+50L4nXX7aVUebMkU/DjBnjPB25Nu3XzMUIENjJrxR4eISEnNKSYOsOs6bRKuFusqzavvvytvIGu10e9Ky4WL61NznZPBewquEPya8UeJj2S27xlyReI/hLsiwRkR5YkJBm/nIHjBH85Y4OIiK98BoS0sxfkniN4C/JskREZsAjJFTHX5J4jeAvybJERHriERLSxF+SeI3gL8myRERmwYKE6vhLEq8R/CVZlojILFiQUB1/SeI1gr8kyxIRmQULEqrjL0m8RvCXZFkiIrNgQUJ1/CWJ1wj+kixLRGQWvMuGHHAcEuU4DgkRUdN4lw1p5i9JvEbwl2RZIiIzYEGiJyGAkhL5T+KSEvX303pJWpo8HLzVKhcc9U/F1E6zWoFNm5wHuvlo11WbM0d5UWL2ZFl/FSjvRSJfx4JEDzYbkJkpX/kYGwt07Cj/TEmRp9ts3m6hS1rTZf2g66rNmQPk5gKpqc6fT00Fvv+exYjRAvG9SOTLeA2Jp10dkws4/jlWe6jBkzG5BlCaLuuHXVfE6GRkci1Q34tEZsNrSLylNia3qkr+Bqz/26l2WlWVPF92tnfaqZIkybcEd+gg/2ysGPHDrrtUv9/O+GO/zSxQ34tEvo5HSDwlgG9PCdSuB2q/zYz7hMhceITEGwI4JjdQux6o/TYz7hMi38UjJJ4QwDG5gdr1QO23mXGfEJkPj5AYLYBjcgO164HabzPjPiHybSxIPCGAY3IDteuB2m8z4z4h8m0sSDwhgGNyA7XrgdpvM+M+IfJtLEg8IYBjcgO164HabzPjPiHybSxIPCGAY3IDteuB2m8z4z4h8m28y8ZTAngAhEDteqD228y4T4jMhXfZeEMAx+QGatcDtd9mxn1C5LtYkHiSJ2JyfVSgdj1Q++2M3Q4cOgR8/bX8U+ngZGooSe7lPiHyTSxIPE1rTK4fCNSuB2q/axUVAWPGAGFhQNeuwK23yj/DwuTpRUXur0Ntcm+g7xMiX8RrSPSkNCbXDwVq1wOt3wsWAE895Xq++fOBOXO0rcPd5N5A2ydEZqLm9zcLEiLSRGkxUktLUVKb3CtE06eAaq8H2bjReVFCRN7BgoSIdFVUBHTooH65wkIgMVHZvLxjhsj38S4bItLVzJnalps1S/m8TO4lCiw8QkJEqtjt8gWrly+rXzY4GLh40fUtuUzuJfIPPEJCRLrJy9NWjADycvn5rudjci9R4GFBQkSq/Pyze8sXF7ueh8m9RIGHBQkRqRIb697ybdq4nofJvUSBhwUJEamSkiJfC6JFcLCcrOsKk3uJAg8LEiJSxWIBRo7Utuzdd7u+oBVgci9RIOJdNkSkGschISIleJcNEekqMVEeeVWNhQuVFyMAk3uJAg0LEiLSZM4c5UXJwoXA7Nnq18HkXqLAwYKEiDSbM0c+DTNmTMMLXYODf0n71VKM1GJyL1Fg4DUkROQRdrs8KFlxsXxrb3KysgtY1WByL5FvUfP7W+PNe0REjiwW+ZbglBT91iFJ8i3BMTH6rYOIvIOnbIiIiMjrWJAQERGR17EgISIiIq9jQUJERERex4KEiIiIvI4FCREREXkdCxIiIiLyOhYkRERE5HUsSIiIiMjrWJAQERGR13HoeNKFEMCZM0BFBdC8uTzUNzNHiIioMTxCQh5lswGZmXKeSWws0LGj/DMlRZ5us3m7hUREZEYsSMhjsrOB+Hhg5kygoMDxuYICeXp8vDwfERHR1ViQkEdkZwMjRgBVVfLpGiEcn6+dVlUlz8eihIiIrqaqIFm0aBH69++PyMhItG7dGqNGjcKhQ4dcLvf++++ja9euCA8PR69evbBp0ybNDSbzsdmAsWPlgsNub3peu12eb+xYnr4hIqJfqCpIvvjiC0ydOhW7du3C1q1bcenSJQwfPhznz59vdJkdO3ZgwoQJePDBB/Gf//wHo0aNwqhRo/DDDz+43XgyhxUrgMpK18VILbtdnv/tt/VtFxER+Q5JiPoH15X7+eef0bp1a3zxxRcYOHCg03nGjx+P8+fP4+OPP66bdtNNN+G6667Da6+9pmg9586dQ4sWLVBWVoaoqCitzSUdCCFfsFpQ0PA0TVMkCUhKAvLyePcNEZG/UvP7261rSMrKygAArVq1anSenTt3YtiwYQ7T0tLSsHPnzkaXuXjxIs6dO+fwIHM6cwbIz1dXjADy/Pn5wNmz+rSLiIh8i+aCxG63Y8aMGbjlllvQs2fPRuc7deoU2rRp4zCtTZs2OHXqVKPLLFq0CC1atKh7JCQkaG0m6ayiwr3ly8s90w4iIvJtmguSqVOn4ocffsCaNWs82R4AwOzZs1FWVlb3OH78uMfXQZ7RvLl7y0dGeqYdRETk2zSN1Dpt2jR8/PHH+PLLLxEfH9/kvG3btkVxcbHDtOLiYrRt27bRZcLCwhAWFqalaWSwmBggOVn7NSRNnO0jIqIAouoIiRAC06ZNw/r16/HZZ5+hY8eOLpcZMGAAPv30U4dpW7duxYABA9S1lExJkoDp07Utm5HBC1qJiEimqiCZOnUqVq5cidWrVyMyMhKnTp3CqVOnUFVVVTfPpEmTMHv27Lp/P/bYY9i8eTP++te/4uDBg3jmmWfw3XffYdq0aZ7rBXlVejoQEQFYFL6bLBZ5/kmT9G0XERH5DlUFyauvvoqysjIMHjwYcXFxdY933323bp5jx47h5MmTdf+++eabsXr1avzjH/9A79698a9//QsbNmxo8kJY8i3R0cDatfLRDldFicUiz7dunbwcERER4OY4JEbhOCS+ITtbHoG1slL+99XvrNpTMxERcjEyfLjx7SMiImMZNg4J0dXS0oATJ4ClS+ULVq+WlCRP/+knFiNERNQQj5CQLoSQBz0rL5dv7W3VihewEhEFGjW/vzXd9kvkiiTJtwTHxHi7JURE5At4yoaIiIi8jgUJEREReR0LEiIiIvI6FiRERETkdSxIiIiIyOtYkBAREZHX+cRtv7VDpZw7d87LLSEiIiKlan9vKxnyzCcKkvLycgBAQkKCl1tCREREapWXl6NFixZNzuMTI7Xa7Xb897//RWRkJCQfG+7z3LlzSEhIwPHjxwNulNlA7Xug9htg3wOx74Hab4B9V9J3IQTKy8vRrl07WFykr/rEERKLxYL4+HhvN8MtUVFRAfeGrRWofQ/UfgPseyD2PVD7DbDvrvru6shILV7USkRERF7HgoSIiIi8jgWJzsLCwjB37lyEhYV5uymGC9S+B2q/AfY9EPseqP0G2HdP990nLmolIiIi/8YjJEREROR1LEiIiIjI61iQEBERkdexICEiIiKvY0HiQYsXL4YkSZgxY0aj82RlZUGSJIdHeHi4cY30kGeeeaZBP7p27drkMu+//z66du2K8PBw9OrVC5s2bTKotZ6ltu/+ss8B4KeffsL999+PmJgYWK1W9OrVC999912Ty2zbtg19+vRBWFgYOnXqhKysLGMa62Fq+75t27YG+12SJJw6dcrAVruvQ4cOTvsxderURpfxh8+62n770+e8pqYGTz/9NDp27Air1Yrk5GTMmzfPZR6Nu591nxip1Rfs3r0br7/+OlJTU13OGxUVhUOHDtX929eGw6/Vo0cPfPLJJ3X/Dg5u/O20Y8cOTJgwAYsWLcKvf/1rrF69GqNGjcLevXvRs2dPI5rrUWr6DvjHPi8tLcUtt9yCIUOG4N///jdiY2ORl5eHli1bNrrM0aNHMWLECDzyyCNYtWoVPv30Uzz00EOIi4tDWlqaga13j5a+1zp06JDDSJatW7fWs6ket3v3btTU1NT9+4cffsDtt9+O3/72t07n95fPutp+A/7xOQeA559/Hq+++ipWrFiBHj164LvvvsOUKVPQokULZGRkOF3GI591QW4rLy8XKSkpYuvWrWLQoEHisccea3Tet956S7Ro0cKwtull7ty5onfv3ornHzdunBgxYoTDtBtvvFH84Q9/8HDL9Ke27/6yz//0pz+JX/3qV6qWefLJJ0WPHj0cpo0fP16kpaV5smm609L3zz//XAAQpaWl+jTKSx577DGRnJws7Ha70+f96bN+NVf99pfPuRBCjBgxQvzud79zmDZmzBgxceLERpfxxGedp2w8YOrUqRgxYgSGDRumaP6KigokJiYiISEB99xzD/bv369zC/WRl5eHdu3aISkpCRMnTsSxY8canXfnzp0Ntk9aWhp27typdzN1oabvgH/s8w8//BD9+vXDb3/7W7Ru3RrXX3893njjjSaX8Zf9rqXvta677jrExcXh9ttvx/bt23Vuqb6qq6uxcuVK/O53v2v0r39/2edXU9JvwD8+5wBw880349NPP8Xhw4cBALm5ufj6669x5513NrqMJ/Y7CxI3rVmzBnv37sWiRYsUzd+lSxe8+eab+OCDD7By5UrY7XbcfPPNOHHihM4t9awbb7wRWVlZ2Lx5M1599VUcPXoUt956K8rLy53Of+rUKbRp08ZhWps2bXzufDqgvu/+ss8LCgrw6quvIiUlBdnZ2Xj00UeRkZGBFStWNLpMY/v93LlzqKqq0rvJHqOl73FxcXjttdewdu1arF27FgkJCRg8eDD27t1rYMs9a8OGDbDZbJg8eXKj8/jTZ72Wkn77y+ccAP785z/j3nvvRdeuXRESEoLrr78eM2bMwMSJExtdxiOfdXUHcuhqx44dE61btxa5ubl101ydsqmvurpaJCcni6eeekqHFhqntLRUREVFiX/+859Onw8JCRGrV692mPa3v/1NtG7d2ojm6cpV3+vz1X0eEhIiBgwY4DBt+vTp4qabbmp0mZSUFLFw4UKHaRs3bhQARGVlpS7t1IOWvjszcOBAcf/993uyaYYaPny4+PWvf93kPP74WVfS7/p89XMuhBDvvPOOiI+PF++88474/vvvxdtvvy1atWolsrKyGl3GE591HiFxw549e3D69Gn06dMHwcHBCA4OxhdffIFly5YhODjY4YKoxtRWn0eOHDGgxfqJjo5G586dG+1H27ZtUVxc7DCtuLgYbdu2NaJ5unLV9/p8dZ/HxcWhe/fuDtO6devW5OmqxvZ7VFQUrFarLu3Ug5a+O3PDDTf43H6vVVRUhE8++QQPPfRQk/P522ddab/r89XPOQA88cQTdUdJevXqhQceeAAzZ85s8kyAJz7rLEjccNttt2Hfvn3Iycmpe/Tr1w8TJ05ETk4OgoKCXL5GTU0N9u3bh7i4OANarJ+Kigrk5+c32o8BAwbg008/dZi2detWDBgwwIjm6cpV3+vz1X1+yy23ONxBAACHDx9GYmJio8v4y37X0ndncnJyfG6/13rrrbfQunVrjBgxosn5/GWf11La7/p89XMOAJWVlbBYHMuDoKAg2O32RpfxyH5367gONVD/lM0DDzwg/vznP9f9+9lnnxXZ2dkiPz9f7NmzR9x7770iPDxc7N+/3wut1e6Pf/yj2LZtmzh69KjYvn27GDZsmLjmmmvE6dOnhRAN+719+3YRHBwsXnrpJXHgwAExd+5cERISIvbt2+etLmimtu/+ss+//fZbERwcLBYsWCDy8vLEqlWrREREhFi5cmXdPH/+85/FAw88UPfvgoICERERIZ544glx4MAB8be//U0EBQWJzZs3e6MLmmnp+5IlS8SGDRtEXl6e2Ldvn3jssceExWIRn3zyiTe64JaamhrRvn178ac//anBc/78WVfTb3/5nAshRHp6urj22mvFxx9/LI4ePSrWrVsnrrnmGvHkk0/WzaPHZ50FiYfVL0gGDRok0tPT6/49Y8YM0b59exEaGiratGkj7rrrLrF3717jG+qm8ePHi7i4OBEaGiquvfZaMX78eHHkyJG65+v3Wwgh3nvvPdG5c2cRGhoqevToITZu3Ghwqz1Dbd/9ZZ8LIcRHH30kevbsKcLCwkTXrl3FP/7xD4fn09PTxaBBgxymff755+K6664ToaGhIikpSbz11lvGNdiD1Pb9+eefF8nJySI8PFy0atVKDB48WHz22WcGt9ozsrOzBQBx6NChBs/582ddTb/96XN+7tw58dhjj4n27duL8PBwkZSUJObMmSMuXrxYN48en3VJCBdDrxERERHpjNeQEBERkdexICEiIiKvY0FCREREXseChIiIiLyOBQkRERF5HQsSIiIi8joWJEREROR1LEiIiIjI61iQEBERkdexICEiIiKvY0FCREREXseChIiIiLzu/wMgr8Rvt+iMdQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], s = 100, color = 'red', label = 'Iris-setosa')\n",
    "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], s = 100, color = 'blue', label = 'Iris-versicolour')\n",
    "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], s = 100, color = 'green', label = 'Iris-virginica')\n",
    "\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \n",
    "            s = 100, c = 'yellow', label = 'Centroids')\n",
    "\n",
    "plt.legend()\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}