STINTSY MP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group: I-Need-More-Boolets\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Members:\n",
    "Go, Ryan Jefferson <br>\n",
    "Ejercito, Joshua Carl <br>\n",
    "Moraña, Anton Louis <br>\n",
    "Nieva, Samuel\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Section: S11\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to the problem/task and dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This major course output tasked our group to select a real-word dataset from the selection given. This group has selected the stars dataset, which is a dataset containing spectral characteristics derived from pictures taken by the SDSS (Sloan Digital Sky Survey). This dataset's main use is spectral classification, i.e., classifying whether a celestial body is a star, a quasar or an entire galaxy.\n",
    "\n",
    "Using the dataset, the group is to perform the following:\n",
    "\n",
    "- Describe the dataset\n",
    "- Perform Data Pre-processing and Cleaning\n",
    "- Perform Exploratory Data Analysis\n",
    "- Select 3 Machine Learning Models\n",
    "- Perform model training\n",
    "- Perform hyperparameter tuning\n",
    "- Extract insights from the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated earlier, the Stars datasets contains spectral characteristics from celestial bodies that can be used to classify it as either a quasar, star, or galaxy. This dataset is made up of 100,000 obersavations of space taken by the SDSS (Sloan Digital Sky Survey) with each observations having 18 features. The SDSS takes these observations through the use of a 2.5m wide located at Apache Point Observatory in New Mexico. The telescope is described in detail in a paper by Gunn et al. (2006), and it is through this powerful telescope that a robust astronomical survey is created, from which datasets such as this are derived.\n",
    "\n",
    "The following are the description of their features:\n",
    "\n",
    "1. obj_ID =  the unique value that identifies the object in the image catalog used by th CAS (Chinese Academy of Sciences).\n",
    "2. alpha = it contains the right ascension angle, it is measured from the start of a point which is called vernal equinox and go eastward. It is expressed in hours, minutes, and seconds. Vernal equinox is when the sun is exactly above the equator and day and night are of equal day. It is similar to the longitude in space.\n",
    "3. delta = it contains the declination angle, which shows the angle between the celestial equator and a point on the celestial sphere. It is like the latitude in space which results can either be positive or negative. Positive means that the object is located north while negative means it is located south.\n",
    "4. u – Ultraviolet filter in the photometric system.\n",
    "5. g – Green filter in the photometric system.\n",
    "6. r – Red filter in the photometric system.\n",
    "7. i – Near Infrared filter in the photometric system.\n",
    "8. z – Infrared filter in the photometric system.\n",
    "9. run_ID – Run Number used to identify the specific scan.\n",
    "10. rerun_ID – Rerun Number to specify how the image was processed.\n",
    "11. cam_col – Camera column to identify the scanline within the run.\n",
    "12. field_ID – Field number to identify each field.\n",
    "13. spec_obj_ID – Unique ID used for optical spectroscopic objects (this means that 2 different observations with the same spec_obj_ID must share the output class).\n",
    "14. class – object class (galaxy, star or quasar object). This is the label the group will be tasking the models to predict.\n",
    "15. redshift – redshift value based on the increase in wavelength.\n",
    "16. plate – plate ID, identifies each plate in SDSS (Sloan Digital Sky Survey).\n",
    "17. MJD – Modified Julian Date, used to indicate when a given piece of SDSS (Sloan Digital Sky Survey) data was taken.\n",
    "18. fiber_ID – identifies the fiber that pointed the light at the focal plane in each observation.\n",
    "\n",
    "The first few rows of the dataset look like this:\n",
    "\n"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stars dataset shape:  (100000, 18)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obj_ID</th>\n",
       "      <th>alpha</th>\n",
       "      <th>delta</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>run_ID</th>\n",
       "      <th>rerun_ID</th>\n",
       "      <th>cam_col</th>\n",
       "      <th>field_ID</th>\n",
       "      <th>spec_obj_ID</th>\n",
       "      <th>class</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
       "      <th>MJD</th>\n",
       "      <th>fiber_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.237661e+18</td>\n",
       "      <td>135.689107</td>\n",
       "      <td>32.494632</td>\n",
       "      <td>23.87882</td>\n",
       "      <td>22.27530</td>\n",
       "      <td>20.39501</td>\n",
       "      <td>19.16573</td>\n",
       "      <td>18.79371</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>6.543777e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.634794</td>\n",
       "      <td>5812</td>\n",
       "      <td>56354</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.237665e+18</td>\n",
       "      <td>144.826101</td>\n",
       "      <td>31.274185</td>\n",
       "      <td>24.77759</td>\n",
       "      <td>22.83188</td>\n",
       "      <td>22.58444</td>\n",
       "      <td>21.16812</td>\n",
       "      <td>21.61427</td>\n",
       "      <td>4518</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>1.176014e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>10445</td>\n",
       "      <td>58158</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.237661e+18</td>\n",
       "      <td>142.188790</td>\n",
       "      <td>35.582444</td>\n",
       "      <td>25.26307</td>\n",
       "      <td>22.66389</td>\n",
       "      <td>20.60976</td>\n",
       "      <td>19.34857</td>\n",
       "      <td>18.94827</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>5.152200e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.644195</td>\n",
       "      <td>4576</td>\n",
       "      <td>55592</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.237663e+18</td>\n",
       "      <td>338.741038</td>\n",
       "      <td>-0.402828</td>\n",
       "      <td>22.13682</td>\n",
       "      <td>23.77656</td>\n",
       "      <td>21.61162</td>\n",
       "      <td>20.50454</td>\n",
       "      <td>19.25010</td>\n",
       "      <td>4192</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>214</td>\n",
       "      <td>1.030107e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.932346</td>\n",
       "      <td>9149</td>\n",
       "      <td>58039</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.237680e+18</td>\n",
       "      <td>345.282593</td>\n",
       "      <td>21.183866</td>\n",
       "      <td>19.43718</td>\n",
       "      <td>17.58028</td>\n",
       "      <td>16.49747</td>\n",
       "      <td>15.97711</td>\n",
       "      <td>15.54461</td>\n",
       "      <td>8102</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>6.891865e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.116123</td>\n",
       "      <td>6121</td>\n",
       "      <td>56187</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "         obj_ID       alpha      delta         u         g         r  \\\n",
       "0  1.237661e+18  135.689107  32.494632  23.87882  22.27530  20.39501   \n",
       "1  1.237665e+18  144.826101  31.274185  24.77759  22.83188  22.58444   \n",
       "2  1.237661e+18  142.188790  35.582444  25.26307  22.66389  20.60976   \n",
       "3  1.237663e+18  338.741038  -0.402828  22.13682  23.77656  21.61162   \n",
       "4  1.237680e+18  345.282593  21.183866  19.43718  17.58028  16.49747   \n",
       "\n",
       "          i         z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID  \\\n",
       "0  19.16573  18.79371    3606       301        2        79  6.543777e+18   \n",
       "1  21.16812  21.61427    4518       301        5       119  1.176014e+19   \n",
       "2  19.34857  18.94827    3606       301        2       120  5.152200e+18   \n",
       "3  20.50454  19.25010    4192       301        3       214  1.030107e+19   \n",
       "4  15.97711  15.54461    8102       301        3       137  6.891865e+18   \n",
       "\n",
       "    class  redshift  plate    MJD  fiber_ID  \n",
       "0  GALAXY  0.634794   5812  56354       171  \n",
       "1  GALAXY  0.779136  10445  58158       427  \n",
       "2  GALAXY  0.644195   4576  55592       299  \n",
       "3  GALAXY  0.932346   9149  58039       775  \n",
       "4  GALAXY  0.116123   6121  56187       842  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "seed_num = 12\n",
    "\n",
    "\n",
    "np.random.seed(seed_num)\n",
    "\n",
    "stars = pd.read_csv('stars.csv')\n",
    "print(\"stars dataset shape: \", stars.shape)\n",
    "stars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following shows the number of data points for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "GALAXY    59445\n",
       "STAR      21594\n",
       "QSO       18961\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following shows the datatypes of each feature within the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   obj_ID       100000 non-null  float64\n",
      " 1   alpha        100000 non-null  float64\n",
      " 2   delta        100000 non-null  float64\n",
      " 3   u            100000 non-null  float64\n",
      " 4   g            100000 non-null  float64\n",
      " 5   r            100000 non-null  float64\n",
      " 6   i            100000 non-null  float64\n",
      " 7   z            100000 non-null  float64\n",
      " 8   run_ID       100000 non-null  int64  \n",
      " 9   rerun_ID     100000 non-null  int64  \n",
      " 10  cam_col      100000 non-null  int64  \n",
      " 11  field_ID     100000 non-null  int64  \n",
      " 12  spec_obj_ID  100000 non-null  float64\n",
      " 13  class        100000 non-null  object \n",
      " 14  redshift     100000 non-null  float64\n",
      " 15  plate        100000 non-null  int64  \n",
      " 16  MJD          100000 non-null  int64  \n",
      " 17  fiber_ID     100000 non-null  int64  \n",
      "dtypes: float64(10), int64(7), object(1)\n",
      "memory usage: 13.7+ MB\n"
     ]
    }
   ],
   "source": [
    "stars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List of requirements\n",
    "\n",
    "The group will be using Python's scikit learn library for their classification models (I think), scipy, scikit, numpy and pandas array for processing data, and matplotlib for visualizing data (add stuff if need pa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import metrics as metrics\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Cleaning and Preprocessing\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, we assign a set of columns that will be the focus for data cleaning and preprocessing, namely the `alpha`, `delta` `u`, `g`, `r`, `i`, `z`, and `redshift` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "columns_to_clean = ['alpha', 'delta', 'u', 'g', 'r', 'i', 'z', 'redshift']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replacement of `obj_ID` column\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `obj_ID` column assigns a unique ID for each observation in the dataset, although it is represented in a floating point format. For the purposes of the study, this will be replaced with a similarly-named column that utilizes integer IDs instead in order to simplify the access to each element.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>obj_ID</th>\n",
       "      <th>alpha</th>\n",
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       "      <th>field_ID</th>\n",
       "      <th>spec_obj_ID</th>\n",
       "      <th>class</th>\n",
       "      <th>redshift</th>\n",
       "      <th>plate</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>135.689107</td>\n",
       "      <td>32.494632</td>\n",
       "      <td>23.87882</td>\n",
       "      <td>22.27530</td>\n",
       "      <td>20.39501</td>\n",
       "      <td>19.16573</td>\n",
       "      <td>18.79371</td>\n",
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       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>6.543777e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.634794</td>\n",
       "      <td>5812</td>\n",
       "      <td>56354</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>144.826101</td>\n",
       "      <td>31.274185</td>\n",
       "      <td>24.77759</td>\n",
       "      <td>22.83188</td>\n",
       "      <td>22.58444</td>\n",
       "      <td>21.16812</td>\n",
       "      <td>21.61427</td>\n",
       "      <td>4518</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>1.176014e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>10445</td>\n",
       "      <td>58158</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>142.188790</td>\n",
       "      <td>35.582444</td>\n",
       "      <td>25.26307</td>\n",
       "      <td>22.66389</td>\n",
       "      <td>20.60976</td>\n",
       "      <td>19.34857</td>\n",
       "      <td>18.94827</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>5.152200e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.644195</td>\n",
       "      <td>4576</td>\n",
       "      <td>55592</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>338.741038</td>\n",
       "      <td>-0.402828</td>\n",
       "      <td>22.13682</td>\n",
       "      <td>23.77656</td>\n",
       "      <td>21.61162</td>\n",
       "      <td>20.50454</td>\n",
       "      <td>19.25010</td>\n",
       "      <td>4192</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>214</td>\n",
       "      <td>1.030107e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.932346</td>\n",
       "      <td>9149</td>\n",
       "      <td>58039</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>345.282593</td>\n",
       "      <td>21.183866</td>\n",
       "      <td>19.43718</td>\n",
       "      <td>17.58028</td>\n",
       "      <td>16.49747</td>\n",
       "      <td>15.97711</td>\n",
       "      <td>15.54461</td>\n",
       "      <td>8102</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>6.891865e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.116123</td>\n",
       "      <td>6121</td>\n",
       "      <td>56187</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>99995</td>\n",
       "      <td>39.620709</td>\n",
       "      <td>-2.594074</td>\n",
       "      <td>22.16759</td>\n",
       "      <td>22.97586</td>\n",
       "      <td>21.90404</td>\n",
       "      <td>21.30548</td>\n",
       "      <td>20.73569</td>\n",
       "      <td>7778</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>581</td>\n",
       "      <td>1.055431e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9374</td>\n",
       "      <td>57749</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>99996</td>\n",
       "      <td>29.493819</td>\n",
       "      <td>19.798874</td>\n",
       "      <td>22.69118</td>\n",
       "      <td>22.38628</td>\n",
       "      <td>20.45003</td>\n",
       "      <td>19.75759</td>\n",
       "      <td>19.41526</td>\n",
       "      <td>7917</td>\n",
       "      <td>301</td>\n",
       "      <td>1</td>\n",
       "      <td>289</td>\n",
       "      <td>8.586351e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.404895</td>\n",
       "      <td>7626</td>\n",
       "      <td>56934</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>99997</td>\n",
       "      <td>224.587407</td>\n",
       "      <td>15.700707</td>\n",
       "      <td>21.16916</td>\n",
       "      <td>19.26997</td>\n",
       "      <td>18.20428</td>\n",
       "      <td>17.69034</td>\n",
       "      <td>17.35221</td>\n",
       "      <td>5314</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>308</td>\n",
       "      <td>3.112008e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.143366</td>\n",
       "      <td>2764</td>\n",
       "      <td>54535</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>99998</td>\n",
       "      <td>212.268621</td>\n",
       "      <td>46.660365</td>\n",
       "      <td>25.35039</td>\n",
       "      <td>21.63757</td>\n",
       "      <td>19.91386</td>\n",
       "      <td>19.07254</td>\n",
       "      <td>18.62482</td>\n",
       "      <td>3650</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>131</td>\n",
       "      <td>7.601080e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.455040</td>\n",
       "      <td>6751</td>\n",
       "      <td>56368</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>99999</td>\n",
       "      <td>196.896053</td>\n",
       "      <td>49.464643</td>\n",
       "      <td>22.62171</td>\n",
       "      <td>21.79745</td>\n",
       "      <td>20.60115</td>\n",
       "      <td>20.00959</td>\n",
       "      <td>19.28075</td>\n",
       "      <td>3650</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>8.343152e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.542944</td>\n",
       "      <td>7410</td>\n",
       "      <td>57104</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       obj_ID       alpha      delta         u         g         r         i  \\\n",
       "0           0  135.689107  32.494632  23.87882  22.27530  20.39501  19.16573   \n",
       "1           1  144.826101  31.274185  24.77759  22.83188  22.58444  21.16812   \n",
       "2           2  142.188790  35.582444  25.26307  22.66389  20.60976  19.34857   \n",
       "3           3  338.741038  -0.402828  22.13682  23.77656  21.61162  20.50454   \n",
       "4           4  345.282593  21.183866  19.43718  17.58028  16.49747  15.97711   \n",
       "...       ...         ...        ...       ...       ...       ...       ...   \n",
       "99995   99995   39.620709  -2.594074  22.16759  22.97586  21.90404  21.30548   \n",
       "99996   99996   29.493819  19.798874  22.69118  22.38628  20.45003  19.75759   \n",
       "99997   99997  224.587407  15.700707  21.16916  19.26997  18.20428  17.69034   \n",
       "99998   99998  212.268621  46.660365  25.35039  21.63757  19.91386  19.07254   \n",
       "99999   99999  196.896053  49.464643  22.62171  21.79745  20.60115  20.00959   \n",
       "\n",
       "              z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID   class  \\\n",
       "0      18.79371    3606       301        2        79  6.543777e+18  GALAXY   \n",
       "1      21.61427    4518       301        5       119  1.176014e+19  GALAXY   \n",
       "2      18.94827    3606       301        2       120  5.152200e+18  GALAXY   \n",
       "3      19.25010    4192       301        3       214  1.030107e+19  GALAXY   \n",
       "4      15.54461    8102       301        3       137  6.891865e+18  GALAXY   \n",
       "...         ...     ...       ...      ...       ...           ...     ...   \n",
       "99995  20.73569    7778       301        2       581  1.055431e+19  GALAXY   \n",
       "99996  19.41526    7917       301        1       289  8.586351e+18  GALAXY   \n",
       "99997  17.35221    5314       301        4       308  3.112008e+18  GALAXY   \n",
       "99998  18.62482    3650       301        4       131  7.601080e+18  GALAXY   \n",
       "99999  19.28075    3650       301        4        60  8.343152e+18  GALAXY   \n",
       "\n",
       "       redshift  plate    MJD  fiber_ID  \n",
       "0      0.634794   5812  56354       171  \n",
       "1      0.779136  10445  58158       427  \n",
       "2      0.644195   4576  55592       299  \n",
       "3      0.932346   9149  58039       775  \n",
       "4      0.116123   6121  56187       842  \n",
       "...         ...    ...    ...       ...  \n",
       "99995  0.000000   9374  57749       438  \n",
       "99996  0.404895   7626  56934       866  \n",
       "99997  0.143366   2764  54535        74  \n",
       "99998  0.455040   6751  56368       470  \n",
       "99999  0.542944   7410  57104       851  \n",
       "\n",
       "[100000 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars = stars.drop(['obj_ID'], axis=1)\n",
    "stars['obj_ID'] = np.arange(len(stars))\n",
    "stars.insert(0, 'obj_ID', stars.pop('obj_ID'))\n",
    "stars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for empty observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking was done for any entries with missing/null features by instantiating another stars dataset whose entries are filtered off of null features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stars dataset shape (no null):  (100000, 18)\n"
     ]
    }
   ],
   "source": [
    "nan_variables = stars[stars.isnull().any(axis=1)].index\n",
    "stars_nonull = stars.drop(labels=nan_variables).reset_index(drop=True)\n",
    "print(\"stars dataset shape (no null): \", stars_nonull.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown by the resulting filtered dataset and comparing its shape to the original dataset, it was shown that all entries have all their features accounted for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for duplicate entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any duplicated entries was checked first by comparing all entries' columns to each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "As shown, there are no duplicated entries within the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The removal of outliers aims to craft a dataset whose subset can be used for training data that is generally representative of each classes. We will first check for the existence of outliers in this instance, and any suspicious outliers that implies default/placeholder values will be removed from analysis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97884, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars[(np.abs(stats.zscore(stars[columns_to_clean])) < 3).all(axis=1)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2116 data points that are considered as outliers based from the z-score metric (>= 3 standard deviations from the mean). In order to know more about these outliers, the features are drawn in a boxplot, displaying their minimum and maximum values respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" alpha \" minimum value:  0.0055278279239701\n",
      "\" alpha \" maximum value:  359.999809770956\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" delta \" minimum value:  -18.7853280771825\n",
      "\" delta \" maximum value:  83.00051858898\n"
     ]
    },
    {
     "data": {
      "image/png": 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ee0yPPPKImpubvZn8/Hx1dHTonXfeUVlZmdavX69NmzY55waKi4vV1tamI0eOaNGiRfrud7+rn/zkJ/wOAwCMIM3stl8Rxqii0ajy8/MViUQ4+YwJl0gk1NbWptraWg5xYsIls/+6q18xAAA+eoQBAOAgDAAAB2EAADgIAwDAQRgAAA7CAABwEAYAgIMwAAAchAEA4CAMAAAHYQAAOAgDAMBBGAAADsIAAHAQBgCAgzAAAByEAQDgIAwAAAdhAAA4CAMAwEEYAAAOwgAAcBAGAICDMAAAHIQBAOAgDAAAB2EAADgIAwDAQRgAAA7CAABwEAYAgIMwAAAchAEA4CAMAAAHYQAAOAgDAMBBGAAADsIAAHAQBgCAgzAAAByEAQDgIAwAAAdhAAA4CAMAwEEYAAAOwgAAcBAGAICDMAAAHIQBAOAgDAAAB2EAADhSHoZf/epXWrJkibKzsxUIBPSlL33J+Xpvb69Wrlyp3NxcBQIBbdiwQfF43Jnp7u5WRUWFsrOzNXfuXG3fvl1m5swcPXpUpaWlysrK0vz587V3795Jf2wA8HGUkco7b2lp0dq1a/X3f//3evDBB2Vm6u7u9r4+ODiouro6zZ49WydOnNB7772nNWvWyMy0a9cuSVI0GlVVVZUqKyvV1dWlc+fO6YknnlBubq42b94sSbpw4YJqa2u1du1aPf/883r55Ze1fv16zZ49W48++mhKHjsA3LUsRRKJhM2dO9eeffbZUWfa2tosPT3d+vr6vG379+83v99vkUjEzMx2795t+fn5duvWLW+mqanJQqGQDQ0NmZnZli1bbOHChc5tr1u3zpYuXZrUmiORiEny7huYSPF43FpbWy0ej6d6KbgHJbP/StkrhtOnT6uvr0/p6elavHixwuGwFi1apObmZn32s5+VJHV2dqqkpEShUMi7Xk1NjWKxmE6dOqXKykp1dnaqoqJCfr/fmWloaFBPT4+Ki4vV2dmp6upq5/5ramq0b98+JRIJ+Xy+EdcYi8UUi8W8y9FoVJKUSCSUSCQm7O8CkOQ9p3huYTIk87xKWRjOnz8vSWpsbNTOnTtVVFSkH/3oR6qoqNC5c+c0c+ZMhcNhBYNB53ozZsxQZmamwuGwJCkcDquoqMiZGb5OOBxWcXHxiLcTDAY1MDCgK1euaM6cOSOusampSdu2bbtje3t7u3Jycj7U4wY+SEdHR6qXgHvQzZs3xz074WFobGwccWf6fl1dXRoaGpIkffvb3/aO8z/33HOaN2+eXnzxRa1bt06SlJaWdsf1zczZfvuM/eXEc7Izt2toaNCmTZu8y9FoVIWFhaqurlZeXt6YjxFIViKRUEdHh6qqqkZ9FQt8WMNHPMZjwsNQX1+vVatWjTlTVFSk69evS5IeeOABb7vf79f8+fPV29srSSooKNDJkyed6169elWJRMJ7BVBQUOC9ehh2+fJlSfrAmYyMDM2aNWvUdfr9fucQ1TCfz8c3LiYNzy9MhmSeUxMehkAgoEAg8IFzpaWl8vv9evvtt/WFL3xB0p9/Yurp6dH9998vSSovL9eOHTt06dIl73BPe3u7/H6/SktLvZmtW7cqHo8rMzPTmwmFQt4hpvLych06dMi5//b2dpWVlfENCAC3m+wz4WP55je/aXPnzrV///d/t7feesuefPJJu+++++w//uM/zMxsYGDASkpKbPny5Xb69Gk7fPiwzZs3z+rr673buHbtmgWDQVu9erV1d3fbwYMHLS8vz5qbm72Z8+fPW05Ojm3cuNHOnj1r+/btM5/PZwcOHEhqvbwrCZOJdyVhMiWz/0ppGOLxuG3evNnuu+8+mz59uv3t3/6tvfnmm87MxYsXra6uzrKzs23mzJlWX1/vvDXVzOyNN96wZcuWmd/vt4KCAmtsbPTeqjrsyJEjtnjxYsvMzLSioiLbs2dP0uslDJhMhAGTKZn9V5rZbb8ijFFFo1Hl5+crEolw8hkTLpFIqK2tTbW1tRzixIRLZv+V8o/EAADcXQgDAMBBGAAADsIAAHAQBgCAgzAAAByEAQDgIAwAAAdhAAA4CAMAwEEYAAAOwgAAcBAGAICDMAAAHIQBAOAgDAAAB2EAADgIAwDAQRgAAA7CAABwEAYAgIMwAAAchAEA4CAMAAAHYQAAOAgDAMBBGAAADsIAAHAQBgCAgzAAAByEAQDgIAwAAAdhAAA4CAMAwEEYAAAOwgAAcBAGAICDMAAAHIQBAOAgDAAAB2EAADgIAwDAQRgAAA7CAABwEAYAgIMwAAAchAEA4CAMAAAHYQAAOAgDAMBBGAAAjpSG4dy5c3r44YcVCASUl5enz3/+83rppZecmd7eXq1cuVK5ubkKBALasGGD4vG4M9Pd3a2KigplZ2dr7ty52r59u8zMmTl69KhKS0uVlZWl+fPna+/evZP++ADg4yilYairq9PAwIB+85vf6NSpU1q0aJFWrFihcDgsSRocHFRdXZ1u3LihEydO6Je//KVaWlq0efNm7zai0aiqqqoUCoXU1dWlXbt2qbm5WTt37vRmLly4oNraWi1btkxnzpzR1q1btWHDBrW0tHzkjxkA7nqWIu+++65JsmPHjnnbotGoSbLDhw+bmVlbW5ulp6dbX1+fN7N//37z+/0WiUTMzGz37t2Wn59vt27d8maamposFArZ0NCQmZlt2bLFFi5c6Nz/unXrbOnSpUmtORKJmCTvvoGJFI/HrbW11eLxeKqXgntQMvuvlL1imDVrlj7zmc/on//5n3Xjxg0NDAzopz/9qYLBoEpLSyVJnZ2dKikpUSgU8q5XU1OjWCymU6dOeTMVFRXy+/3OTH9/v3p6eryZ6upq5/5ramr02muvKZFITPIjBYCPl4xU3XFaWpo6Ojr08MMPa/r06UpPT1cwGNS//du/6ZOf/KQkKRwOKxgMOtebMWOGMjMzvcNN4XBYRUVFzszwdcLhsIqLi0e8nWAwqIGBAV25ckVz5swZcY2xWEyxWMy7HI1GJUmJRIKgYMINP6d4bmEyJPO8mvAwNDY2atu2bWPOdHV1qbS0VOvXr9d9992n48ePKzs7W88++6xWrFihrq4ub2edlpZ2x/XNzNl++4z95cRzsjO3a2pqGvGxtLe3KycnZ8zHCHxYHR0dqV4C7kE3b94c9+yEh6G+vl6rVq0ac6aoqEi/+c1v9K//+q+6evWq8vLyJEm7d+9WR0eHfv7zn+tb3/qWCgoKdPLkSee6V69eVSKR8F4BFBQUeK8ehl2+fFmSPnAmIyNDs2bNGnWdDQ0N2rRpk3c5Go2qsLBQ1dXV3pqBiZJIJNTR0aGqqir5fL5ULwf3mOEjHuMx4WEIBAIKBAIfODdcr/R09zRHenq6hoaGJEnl5eXasWOHLl265L2CaG9vl9/v985DlJeXa+vWrYrH48rMzPRmQqGQd4ipvLxchw4dcu6nvb1dZWVlY34D+v1+59zFMJ/PxzcuJg3PL0yGpJ5Tk30mfDTvvvuuzZo1y770pS/Z66+/bm+//bY99dRT5vP57PXXXzczs4GBASspKbHly5fb6dOn7fDhwzZv3jyrr6/3bufatWsWDAZt9erV1t3dbQcPHrS8vDxrbm72Zs6fP285OTm2ceNGO3v2rO3bt898Pp8dOHAgqTXzriRMJt6VhMmUzP4rZWEwM+vq6rLq6mqbOXOmTZ8+3ZYuXWptbW3OzMWLF62urs6ys7Nt5syZVl9f77w11czsjTfesGXLlpnf77eCggJrbGz03qo67MiRI7Z48WLLzMy0oqIi27NnT9LrJQyYTIQBkymZ/Vea2W2/IoxRRaNR5efnKxKJcI4BEy6RSKitrU21tbUcSsKES2b/xWclAQAchAEA4CAMAAAHYQAAOAgDAMBBGAAADsIAAHAQBgCAgzAAAByEAQDgIAwAAAdhAAA4CAMAwEEYAAAOwgAAcBAGAICDMAAAHIQBAOAgDAAAB2EAADgIAwDAQRgAAA7CAABwEAYAgIMwAAAchAEA4CAMAAAHYQAAOAgDAMBBGAAADsIAAHAQBgCAgzAAAByEAQDgIAwAAAdhAAA4CAMAwEEYAAAOwgAAcBAGAICDMAAAHIQBAOAgDAAAB2EAADgIAwDAQRgAAA7CAABwEAYAgIMwAAAchAEA4CAMAAAHYQAAOCY1DDt27NDnPvc55eTk6JOf/OSIM729vVq5cqVyc3MVCAS0YcMGxeNxZ6a7u1sVFRXKzs7W3LlztX37dpmZM3P06FGVlpYqKytL8+fP1969e++4r5aWFj3wwAPy+/164IEH9C//8i8T9liBv8bg4KCOHj2qY8eO6ejRoxocHEz1kjCFTWoY4vG4vvzlL+sb3/jGiF8fHBxUXV2dbty4oRMnTuiXv/ylWlpatHnzZm8mGo2qqqpKoVBIXV1d2rVrl5qbm7Vz505v5sKFC6qtrdWyZct05swZbd26VRs2bFBLS4s309nZqa985St6/PHH9dvf/laPP/64HnvsMZ08eXLy/gKAcTh48KAWLFigqqoq7dy5U1VVVVqwYIEOHjyY6qVhqrKPwHPPPWf5+fl3bG9ra7P09HTr6+vztu3fv9/8fr9FIhEzM9u9e7fl5+fbrVu3vJmmpiYLhUI2NDRkZmZbtmyxhQsXOre9bt06W7p0qXf5sccesy9+8YvOTE1Nja1atWrcjyMSiZgkb23AX6ulpcXS0tJs5cqVdvz4cdu/f78dP37cVq5caWlpadbS0pLqJeIekcz+K6XnGDo7O1VSUqJQKORtq6mpUSwW06lTp7yZiooK+f1+Z6a/v189PT3eTHV1tXPbNTU1eu2115RIJMaceeWVVybjoQEfaHBwUJs3b9aKFSvU2tqqJUuWKDs7W0uWLFFra6tWrFihp556isNK+MhlpPLOw+GwgsGgs23GjBnKzMxUOBz2ZoqKipyZ4euEw2EVFxePeDvBYFADAwO6cuWK5syZM+rM8P2MJBaLKRaLeZej0agkKZFIeMEBPqyjR4+qp6dHv/jFLzQ4OOg9p4b///TTT+tv/uZv9NJLL6mioiKVS8U9IJl9VtJhaGxs1LZt28ac6erqUllZ2bhuLy0t7Y5tZuZsv33G/nLieSJmRrr/YU1NTSM+1vb2duXk5Ix6PWA8jh07Jkl655139N5773nbOzo6JEn/+Z//KUn69a9/rRs3bnz0C8Q95ebNm+OeTToM9fX1WrVq1Zgzt/+EP5qCgoI7Tv5evXpViUTC++m+oKDgjp/qL1++LEkfOJORkaFZs2aNOXP7q4j3a2ho0KZNm7zL0WhUhYWFqq6uVl5e3rgeIzCa3Nxc7dy5U/PmzdOSJUuUSCTU0dGhqqoq+Xw+vfrqq5Kkhx56iFcM+KsNH/EYj6TDEAgEFAgEkr3aiMrLy7Vjxw5dunRJc+bMkfTnn8b9fr9KS0u9ma1btyoejyszM9ObCYVCXoDKy8t16NAh57bb29tVVlYmn8/nzXR0dGjjxo3OzOc+97lR1+f3+51zG8N8Pp93u8CHVVlZqaKiIv3gBz9Qa2urt93n82natGn64Q9/qOLiYlVWVmratGmpWyjuCUntsybzLPjFixftzJkztm3bNvvEJz5hZ86csTNnztj169fNzGxgYMBKSkps+fLldvr0aTt8+LDNmzfP6uvrvdu4du2aBYNBW716tXV3d9vBgwctLy/PmpubvZnz589bTk6Obdy40c6ePWv79u0zn89nBw4c8GZefvllmzZtmn3/+9+33//+9/b973/fMjIy7NVXXx334+FdSZho739X0rFjx2z//v127Ngx3pWECZfM/mtSw7BmzRqTdMd/L730kjdz8eJFq6urs+zsbJs5c6bV19c7b001M3vjjTds2bJl5vf7raCgwBobG723qg47cuSILV682DIzM62oqMj27Nlzx3pefPFF+/SnP20+n88WLlyY9DcdYcBkaGlpsaKiIud7pLi4mChgQiWz/0ozu+1XiDGqaDSq/Px8RSIRzjFgQg0ODuqll17Sr3/9az300EMcPsKES2b/ldK3qwL4s2nTpqmiokI3btxQRUUFUUBK8SF6AAAHYQAAOAgDAMBBGAAADsIAAHAQBgCAgzAAABz8HkMShn8XMJkPowLGK5FI6ObNm4pGo3wWFybc8H5rPL/TTBiScP36dUlSYWFhilcCAB/O9evXlZ+fP+YMH4mRhKGhIfX392v69Olj/jsOwIcx/LHuf/zjH/nIFUw4M9P169cVCoWUnj72WQTCANwl+Cwu3C04+QwAcBAGAICDMAB3Cb/fr+985zsj/quBwEeJcwwAAAevGAAADsIAAHAQBgCAgzAAAByEAQDgIAwAAAdhAFKsqKhIzzzzjLNt0aJFamxsTMl6AMIAAHAQBgCAgzAAAByEAUix9PT0O/5VrUQikaLVAIQBSLnZs2fr0qVL3uVoNKoLFy6kcEWY6ggDkGIPPvigfvGLX+j48eN68803tWbNGk2bNi3Vy8IUxr/5DKRYQ0ODzp8/rxUrVig/P1/f/e53ecWAlOJjtwEADg4lAQAchAEA4CAMAAAHYQAAOAgDAMBBGAAADsIAAHAQBgCAgzAAAByEAQDgIAwAAAdhAAA4/j+36yFTBYLUwgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" u \" minimum value:  -9999.0\n",
      "\" u \" maximum value:  32.78139\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" g \" minimum value:  -9999.0\n",
      "\" g \" maximum value:  31.60224\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" r \" minimum value:  9.82207\n",
      "\" r \" maximum value:  29.57186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" i \" minimum value:  9.469903\n",
      "\" i \" maximum value:  32.14147\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" z \" minimum value:  -9999.0\n",
      "\" z \" maximum value:  29.38374\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" redshift \" minimum value:  -0.009970667\n",
      "\" redshift \" maximum value:  7.011245\n"
     ]
    }
   ],
   "source": [
    "for col in columns_to_clean:\n",
    "    stars.boxplot(col, figsize=(4, 4))\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    print(\"\\\"\",col,\"\\\" minimum value: \", stars[col].min())\n",
    "    print(\"\\\"\",col,\"\\\" maximum value: \", stars[col].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some features have values equated to a whole value of \\-9999. This may indicate that these features were not recorded/not available for that observation, thus putting a default value for those features. To resolve this, we will remove the associated entries that has default values for these certain features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99999, 18)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_value = stars['u'].min()\n",
    "stars = stars[stars['u'] != default_value]\n",
    "stars = stars[stars['g'] != default_value]\n",
    "stars = stars[stars['z'] != default_value]\n",
    "stars.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based from the filtered dataset, there is only one entry that contains such default values. Any other outliers' effects will be resolved by applying the robust scaling method for normalization.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" alpha \" minimum value:  0.0055278279239701\n",
      "\" alpha \" maximum value:  359.999809770956\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" delta \" minimum value:  -18.7853280771825\n",
      "\" delta \" maximum value:  83.00051858898\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" u \" minimum value:  10.99623\n",
      "\" u \" maximum value:  32.78139\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" g \" minimum value:  10.4982\n",
      "\" g \" maximum value:  31.60224\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" r \" minimum value:  9.82207\n",
      "\" r \" maximum value:  29.57186\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" i \" minimum value:  9.469903\n",
      "\" i \" maximum value:  32.14147\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" z \" minimum value:  9.612333\n",
      "\" z \" maximum value:  29.38374\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" redshift \" minimum value:  -0.009970667\n",
      "\" redshift \" maximum value:  7.011245\n"
     ]
    }
   ],
   "source": [
    "for col in columns_to_clean:\n",
    "    stars.boxplot(col, figsize=(4, 4))\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    print(\"\\\"\",col,\"\\\" minimum value: \", stars[col].min())\n",
    "    print(\"\\\"\",col,\"\\\" maximum value: \", stars[col].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplots indicates that any extreme outliers that comes from the default values were successfully removed. However, the `redshift` feature may require additional feature engineering in the form of logarithmic transformation, which will be done in the normalization phase.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logarithmic transformation of the `redshift` feature\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `redshift` feature will be transformed by implementing a helper function to be used in the normalization process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def log_transform_feature(df, feature):\n",
    "    feature_min = df[feature].min()\n",
    "    feature_max = df[feature].max()\n",
    "    # shifter to convert negaative values to positive, and to keep any values above zero for log computation\n",
    "    shifter = abs(feature_min) + 0.001\n",
    "        \n",
    "    df[feature] = np.log(df[feature] + shifter)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixing `obj_ID` after the removal of outliers\n",
    "\n",
    "Since the outliers were removed, the row obj_ID may not reflect it's actual position in the dataset. To fix this, we simply reassign the obj_IDs incrementally.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
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     "outputs_hidden": false
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   },
   "outputs": [
    {
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       "      <th>obj_ID</th>\n",
       "      <th>alpha</th>\n",
       "      <th>delta</th>\n",
       "      <th>u</th>\n",
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       "      <th>redshift</th>\n",
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       "      <th>MJD</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>135.689107</td>\n",
       "      <td>32.494632</td>\n",
       "      <td>23.87882</td>\n",
       "      <td>22.27530</td>\n",
       "      <td>20.39501</td>\n",
       "      <td>19.16573</td>\n",
       "      <td>18.79371</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>6.543777e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.634794</td>\n",
       "      <td>5812</td>\n",
       "      <td>56354</td>\n",
       "      <td>171</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>144.826101</td>\n",
       "      <td>31.274185</td>\n",
       "      <td>24.77759</td>\n",
       "      <td>22.83188</td>\n",
       "      <td>22.58444</td>\n",
       "      <td>21.16812</td>\n",
       "      <td>21.61427</td>\n",
       "      <td>4518</td>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>119</td>\n",
       "      <td>1.176014e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.779136</td>\n",
       "      <td>10445</td>\n",
       "      <td>58158</td>\n",
       "      <td>427</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>142.188790</td>\n",
       "      <td>35.582444</td>\n",
       "      <td>25.26307</td>\n",
       "      <td>22.66389</td>\n",
       "      <td>20.60976</td>\n",
       "      <td>19.34857</td>\n",
       "      <td>18.94827</td>\n",
       "      <td>3606</td>\n",
       "      <td>301</td>\n",
       "      <td>2</td>\n",
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       "      <td>GALAXY</td>\n",
       "      <td>0.644195</td>\n",
       "      <td>4576</td>\n",
       "      <td>55592</td>\n",
       "      <td>299</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>338.741038</td>\n",
       "      <td>-0.402828</td>\n",
       "      <td>22.13682</td>\n",
       "      <td>23.77656</td>\n",
       "      <td>21.61162</td>\n",
       "      <td>20.50454</td>\n",
       "      <td>19.25010</td>\n",
       "      <td>4192</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>214</td>\n",
       "      <td>1.030107e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.932346</td>\n",
       "      <td>9149</td>\n",
       "      <td>58039</td>\n",
       "      <td>775</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>345.282593</td>\n",
       "      <td>21.183866</td>\n",
       "      <td>19.43718</td>\n",
       "      <td>17.58028</td>\n",
       "      <td>16.49747</td>\n",
       "      <td>15.97711</td>\n",
       "      <td>15.54461</td>\n",
       "      <td>8102</td>\n",
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       "      <td>6.891865e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.116123</td>\n",
       "      <td>6121</td>\n",
       "      <td>56187</td>\n",
       "      <td>842</td>\n",
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       "      <td>99994</td>\n",
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       "      <td>-2.594074</td>\n",
       "      <td>22.16759</td>\n",
       "      <td>22.97586</td>\n",
       "      <td>21.90404</td>\n",
       "      <td>21.30548</td>\n",
       "      <td>20.73569</td>\n",
       "      <td>7778</td>\n",
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       "      <td>581</td>\n",
       "      <td>1.055431e+19</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>9374</td>\n",
       "      <td>57749</td>\n",
       "      <td>438</td>\n",
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       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>99995</td>\n",
       "      <td>29.493819</td>\n",
       "      <td>19.798874</td>\n",
       "      <td>22.69118</td>\n",
       "      <td>22.38628</td>\n",
       "      <td>20.45003</td>\n",
       "      <td>19.75759</td>\n",
       "      <td>19.41526</td>\n",
       "      <td>7917</td>\n",
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       "      <td>289</td>\n",
       "      <td>8.586351e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.404895</td>\n",
       "      <td>7626</td>\n",
       "      <td>56934</td>\n",
       "      <td>866</td>\n",
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       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>99996</td>\n",
       "      <td>224.587407</td>\n",
       "      <td>15.700707</td>\n",
       "      <td>21.16916</td>\n",
       "      <td>19.26997</td>\n",
       "      <td>18.20428</td>\n",
       "      <td>17.69034</td>\n",
       "      <td>17.35221</td>\n",
       "      <td>5314</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>308</td>\n",
       "      <td>3.112008e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.143366</td>\n",
       "      <td>2764</td>\n",
       "      <td>54535</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>99997</td>\n",
       "      <td>212.268621</td>\n",
       "      <td>46.660365</td>\n",
       "      <td>25.35039</td>\n",
       "      <td>21.63757</td>\n",
       "      <td>19.91386</td>\n",
       "      <td>19.07254</td>\n",
       "      <td>18.62482</td>\n",
       "      <td>3650</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>131</td>\n",
       "      <td>7.601080e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.455040</td>\n",
       "      <td>6751</td>\n",
       "      <td>56368</td>\n",
       "      <td>470</td>\n",
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       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>99998</td>\n",
       "      <td>196.896053</td>\n",
       "      <td>49.464643</td>\n",
       "      <td>22.62171</td>\n",
       "      <td>21.79745</td>\n",
       "      <td>20.60115</td>\n",
       "      <td>20.00959</td>\n",
       "      <td>19.28075</td>\n",
       "      <td>3650</td>\n",
       "      <td>301</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>8.343152e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.542944</td>\n",
       "      <td>7410</td>\n",
       "      <td>57104</td>\n",
       "      <td>851</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>99999 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "       obj_ID       alpha      delta         u         g         r         i  \\\n",
       "0           0  135.689107  32.494632  23.87882  22.27530  20.39501  19.16573   \n",
       "1           1  144.826101  31.274185  24.77759  22.83188  22.58444  21.16812   \n",
       "2           2  142.188790  35.582444  25.26307  22.66389  20.60976  19.34857   \n",
       "3           3  338.741038  -0.402828  22.13682  23.77656  21.61162  20.50454   \n",
       "4           4  345.282593  21.183866  19.43718  17.58028  16.49747  15.97711   \n",
       "...       ...         ...        ...       ...       ...       ...       ...   \n",
       "99995   99994   39.620709  -2.594074  22.16759  22.97586  21.90404  21.30548   \n",
       "99996   99995   29.493819  19.798874  22.69118  22.38628  20.45003  19.75759   \n",
       "99997   99996  224.587407  15.700707  21.16916  19.26997  18.20428  17.69034   \n",
       "99998   99997  212.268621  46.660365  25.35039  21.63757  19.91386  19.07254   \n",
       "99999   99998  196.896053  49.464643  22.62171  21.79745  20.60115  20.00959   \n",
       "\n",
       "              z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID   class  \\\n",
       "0      18.79371    3606       301        2        79  6.543777e+18  GALAXY   \n",
       "1      21.61427    4518       301        5       119  1.176014e+19  GALAXY   \n",
       "2      18.94827    3606       301        2       120  5.152200e+18  GALAXY   \n",
       "3      19.25010    4192       301        3       214  1.030107e+19  GALAXY   \n",
       "4      15.54461    8102       301        3       137  6.891865e+18  GALAXY   \n",
       "...         ...     ...       ...      ...       ...           ...     ...   \n",
       "99995  20.73569    7778       301        2       581  1.055431e+19  GALAXY   \n",
       "99996  19.41526    7917       301        1       289  8.586351e+18  GALAXY   \n",
       "99997  17.35221    5314       301        4       308  3.112008e+18  GALAXY   \n",
       "99998  18.62482    3650       301        4       131  7.601080e+18  GALAXY   \n",
       "99999  19.28075    3650       301        4        60  8.343152e+18  GALAXY   \n",
       "\n",
       "       redshift  plate    MJD  fiber_ID  \n",
       "0      0.634794   5812  56354       171  \n",
       "1      0.779136  10445  58158       427  \n",
       "2      0.644195   4576  55592       299  \n",
       "3      0.932346   9149  58039       775  \n",
       "4      0.116123   6121  56187       842  \n",
       "...         ...    ...    ...       ...  \n",
       "99995  0.000000   9374  57749       438  \n",
       "99996  0.404895   7626  56934       866  \n",
       "99997  0.143366   2764  54535        74  \n",
       "99998  0.455040   6751  56368       470  \n",
       "99999  0.542944   7410  57104       851  \n",
       "\n",
       "[99999 rows x 18 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars = stars.drop(['obj_ID'], axis=1)\n",
    "stars['obj_ID'] = np.arange(len(stars))\n",
    "stars.insert(0, 'obj_ID', stars.pop('obj_ID'))\n",
    "stars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before proceeding to the normalization process, we will only assign the feature columns that we have deemed as necessary, such as the photometric data that was recorded by the instrumentation, rather than the parameters of the instrumentations since we have deemed them to have negligible effects or may overfit the machine learning models with unneeded features that we will utilize.\n",
    "\n",
    "Normalization is performed for the included feature columns in order to equally weight\\-in each of the features for the model training and evaluation. The robust scaler will be applied as normalization technique to account for the massive outliers for some features such as `u`, `g,` and `z`.\n",
    "\n",
    "In order to begin the normalization process, the original dataset is split into training and test dataset alongside splitting their features and labels, and are then normalized. This was done to simulate the prediction on unseen data by only doing the normalization computations within the split datasets themselves to avoid imposing any bias for the evaluation. Additionally, the redshift feature is log\\-transformed first in order to balance\\-out its skewed distribution.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# For use in EDA\n",
    "og_stars = stars.copy()\n",
    "\n",
    "cols = ['u','g','r','i','z','redshift']\n",
    "X = stars[cols]\n",
    "y = stars[\"class\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=seed_num, test_size=0.3, stratify=y)\n",
    "\n",
    "# log-transform redshift features first\n",
    "X_train = log_transform_feature(X_train, \"redshift\")\n",
    "scaler = RobustScaler().fit(X_train[cols])\n",
    "X_train[cols] = scaler.transform(X_train[cols])\n",
    "\n",
    "X_test = log_transform_feature(X_test, \"redshift\")\n",
    "scaler = RobustScaler().fit(X_test[cols])\n",
    "X_test[cols] = scaler.transform(X_test[cols])\n",
    "\n",
    "X_train = X_train.to_numpy()\n",
    "X_test = X_test.to_numpy()\n",
    "\n",
    "y_train = y_train.to_numpy()\n",
    "y_test = y_test.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original stars dataset is also normalized for comparison in the EDA process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>obj_ID</th>\n",
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       "      <td>1</td>\n",
       "      <td>-0.339118</td>\n",
       "      <td>0.219477</td>\n",
       "      <td>0.779129</td>\n",
       "      <td>0.548340</td>\n",
       "      <td>0.845354</td>\n",
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       "      <td>1.176014e+19</td>\n",
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       "      <td>0.249518</td>\n",
       "      <td>10445</td>\n",
       "      <td>58158</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.924697</td>\n",
       "      <td>0.495154</td>\n",
       "      <td>0.166535</td>\n",
       "      <td>-0.021237</td>\n",
       "      <td>-0.022896</td>\n",
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       "      <td>5.152200e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.171176</td>\n",
       "      <td>4576</td>\n",
       "      <td>55592</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.483780</td>\n",
       "      <td>-0.691984</td>\n",
       "      <td>-0.012689</td>\n",
       "      <td>0.847428</td>\n",
       "      <td>0.510936</td>\n",
       "      <td>0.412648</td>\n",
       "      <td>0.099785</td>\n",
       "      <td>4192</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>214</td>\n",
       "      <td>1.030107e+19</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>0.323658</td>\n",
       "      <td>9149</td>\n",
       "      <td>58039</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.545274</td>\n",
       "      <td>-0.070858</td>\n",
       "      <td>-0.822160</td>\n",
       "      <td>-1.114330</td>\n",
       "      <td>-1.247111</td>\n",
       "      <td>-1.286691</td>\n",
       "      <td>-1.406334</td>\n",
       "      <td>8102</td>\n",
       "      <td>301</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>6.891865e+18</td>\n",
       "      <td>GALAXY</td>\n",
       "      <td>-0.514844</td>\n",
       "      <td>6121</td>\n",
       "      <td>56187</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obj_ID     alpha     delta         u         g         r         i  \\\n",
       "0       0 -0.425010  0.254593  0.509638  0.372125  0.092712 -0.089865   \n",
       "1       1 -0.339118  0.219477  0.779129  0.548340  0.845354  0.661718   \n",
       "2       2 -0.363910  0.343441  0.924697  0.495154  0.166535 -0.021237   \n",
       "3       3  1.483780 -0.691984 -0.012689  0.847428  0.510936  0.412648   \n",
       "4       4  1.545274 -0.070858 -0.822160 -1.114330 -1.247111 -1.286691   \n",
       "\n",
       "          z  run_ID  rerun_ID  cam_col  field_ID   spec_obj_ID   class  \\\n",
       "0 -0.085718    3606       301        2        79  6.543777e+18  GALAXY   \n",
       "1  1.060716    4518       301        5       119  1.176014e+19  GALAXY   \n",
       "2 -0.022896    3606       301        2       120  5.152200e+18  GALAXY   \n",
       "3  0.099785    4192       301        3       214  1.030107e+19  GALAXY   \n",
       "4 -1.406334    8102       301        3       137  6.891865e+18  GALAXY   \n",
       "\n",
       "   redshift  plate    MJD  fiber_ID  \n",
       "0  0.165130   5812  56354       171  \n",
       "1  0.249518  10445  58158       427  \n",
       "2  0.171176   4576  55592       299  \n",
       "3  0.323658   9149  58039       775  \n",
       "4 -0.514844   6121  56187       842  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars = log_transform_feature(stars, \"redshift\")\n",
    "scaler = RobustScaler().fit(stars[columns_to_clean])\n",
    "stars[columns_to_clean] = scaler.transform(stars[columns_to_clean])\n",
    "stars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proceeding with the exploratory data analysis process, we will first assign the photometric feature columns to a new dataset, in addition to two instrumentation features which are the \"alpha\" and \"delta\" to demonstrate the negligible effects of instrumentation data on the modelling process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tomat\\AppData\\Local\\Temp\\ipykernel_29500\\3563447924.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  stars_df['class'] = stars_df['class'].map(labels)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>delta</th>\n",
       "      <th>u</th>\n",
       "      <th>g</th>\n",
       "      <th>r</th>\n",
       "      <th>i</th>\n",
       "      <th>z</th>\n",
       "      <th>redshift</th>\n",
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       "      <td>0.509638</td>\n",
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       "      <td>0.092712</td>\n",
       "      <td>-0.089865</td>\n",
       "      <td>-0.085718</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.339118</td>\n",
       "      <td>0.219477</td>\n",
       "      <td>0.779129</td>\n",
       "      <td>0.548340</td>\n",
       "      <td>0.845354</td>\n",
       "      <td>0.661718</td>\n",
       "      <td>1.060716</td>\n",
       "      <td>0.249518</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.363910</td>\n",
       "      <td>0.343441</td>\n",
       "      <td>0.924697</td>\n",
       "      <td>0.495154</td>\n",
       "      <td>0.166535</td>\n",
       "      <td>-0.021237</td>\n",
       "      <td>-0.022896</td>\n",
       "      <td>0.171176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.483780</td>\n",
       "      <td>-0.691984</td>\n",
       "      <td>-0.012689</td>\n",
       "      <td>0.847428</td>\n",
       "      <td>0.510936</td>\n",
       "      <td>0.412648</td>\n",
       "      <td>0.099785</td>\n",
       "      <td>0.323658</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.545274</td>\n",
       "      <td>-0.070858</td>\n",
       "      <td>-0.822160</td>\n",
       "      <td>-1.114330</td>\n",
       "      <td>-1.247111</td>\n",
       "      <td>-1.286691</td>\n",
       "      <td>-1.406334</td>\n",
       "      <td>-0.514844</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99994</th>\n",
       "      <td>-1.328101</td>\n",
       "      <td>-0.755034</td>\n",
       "      <td>-0.003463</td>\n",
       "      <td>0.593924</td>\n",
       "      <td>0.611459</td>\n",
       "      <td>0.713276</td>\n",
       "      <td>0.703612</td>\n",
       "      <td>-1.539587</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>-1.423299</td>\n",
       "      <td>-0.110709</td>\n",
       "      <td>0.153532</td>\n",
       "      <td>0.407262</td>\n",
       "      <td>0.111626</td>\n",
       "      <td>0.132286</td>\n",
       "      <td>0.166915</td>\n",
       "      <td>-0.018958</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>0.410679</td>\n",
       "      <td>-0.228628</td>\n",
       "      <td>-0.302836</td>\n",
       "      <td>-0.579370</td>\n",
       "      <td>-0.660376</td>\n",
       "      <td>-0.643642</td>\n",
       "      <td>-0.671624</td>\n",
       "      <td>-0.433603</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>0.294876</td>\n",
       "      <td>0.662192</td>\n",
       "      <td>0.950880</td>\n",
       "      <td>0.170218</td>\n",
       "      <td>-0.072688</td>\n",
       "      <td>-0.124843</td>\n",
       "      <td>-0.154364</td>\n",
       "      <td>0.028665</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>0.150366</td>\n",
       "      <td>0.742881</td>\n",
       "      <td>0.132702</td>\n",
       "      <td>0.220837</td>\n",
       "      <td>0.163575</td>\n",
       "      <td>0.226872</td>\n",
       "      <td>0.112243</td>\n",
       "      <td>0.100951</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99999 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           alpha     delta         u         g         r         i         z  \\\n",
       "obj_ID                                                                         \n",
       "0      -0.425010  0.254593  0.509638  0.372125  0.092712 -0.089865 -0.085718   \n",
       "1      -0.339118  0.219477  0.779129  0.548340  0.845354  0.661718  1.060716   \n",
       "2      -0.363910  0.343441  0.924697  0.495154  0.166535 -0.021237 -0.022896   \n",
       "3       1.483780 -0.691984 -0.012689  0.847428  0.510936  0.412648  0.099785   \n",
       "4       1.545274 -0.070858 -0.822160 -1.114330 -1.247111 -1.286691 -1.406334   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "99994  -1.328101 -0.755034 -0.003463  0.593924  0.611459  0.713276  0.703612   \n",
       "99995  -1.423299 -0.110709  0.153532  0.407262  0.111626  0.132286  0.166915   \n",
       "99996   0.410679 -0.228628 -0.302836 -0.579370 -0.660376 -0.643642 -0.671624   \n",
       "99997   0.294876  0.662192  0.950880  0.170218 -0.072688 -0.124843 -0.154364   \n",
       "99998   0.150366  0.742881  0.132702  0.220837  0.163575  0.226872  0.112243   \n",
       "\n",
       "        redshift  class  \n",
       "obj_ID                   \n",
       "0       0.165130      0  \n",
       "1       0.249518      0  \n",
       "2       0.171176      0  \n",
       "3       0.323658      0  \n",
       "4      -0.514844      0  \n",
       "...          ...    ...  \n",
       "99994  -1.539587      0  \n",
       "99995  -0.018958      0  \n",
       "99996  -0.433603      0  \n",
       "99997   0.028665      0  \n",
       "99998   0.100951      0  \n",
       "\n",
       "[99999 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_use = ['obj_ID','alpha','delta', 'u', 'g', 'r', 'i', 'z', 'redshift', 'class']\n",
    "columns_data = ['alpha','delta','u','g','r','i','z','redshift']\n",
    "\n",
    "labels = {'GALAXY':0,'STAR':1,'QSO':2}\n",
    "\n",
    "stars_df = stars[columns_to_use]\n",
    "stars_df['class'] = stars_df['class'].map(labels)\n",
    "\n",
    "stars_df.set_index('obj_ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Central Tendencies\n",
    "\n",
    "To gain a better understanding of the data, we can begin with examining the central tendencies that are applicable for the data, which are the mean and the median.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Means\n",
    "\n",
    "We can take the means of each feature, pertaining to each class and compare them to one another. Putting the means together in a table, it would look like this:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw data mean:\n",
      " class         GALAXY         QSO        STAR\n",
      "alpha     177.954727  180.474314  174.232180\n",
      "delta      23.513911   26.808621   23.499671\n",
      "u          22.587379   21.547619   21.153830\n",
      "g          20.906101   20.926193   19.617142\n",
      "r          19.587552   20.624089   18.947005\n",
      "i          18.851990   20.431173   18.543760\n",
      "z          18.449156   20.266732   18.334295\n",
      "redshift    0.421596    1.719676   -0.000115\n",
      "\n",
      "\n",
      "Normalized data mean:\n",
      " class       GALAXY       QSO      STAR\n",
      "alpha    -0.027692 -0.004007 -0.062686\n",
      "delta    -0.003814  0.090987 -0.004224\n",
      "u         0.122408 -0.189358 -0.307433\n",
      "g        -0.061367 -0.055006 -0.469454\n",
      "r        -0.184860  0.171461 -0.405056\n",
      "i        -0.207625  0.385110 -0.323317\n",
      "z        -0.225763  0.513001 -0.272450\n",
      "redshift -0.123088  0.513845 -1.544378\n"
     ]
    }
   ],
   "source": [
    "means_df = og_stars.groupby('class').mean()\n",
    "means_df = means_df[['alpha','delta','u','g','r','i','z','redshift']].T\n",
    "\n",
    "normalized_means_df = stars.groupby('class').mean()\n",
    "normalized_means_df = normalized_means_df[['alpha','delta','u','g','r','i','z','redshift']].T\n",
    "\n",
    "print(\"Raw data mean:\\n\", means_df)\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Normalized data mean:\\n\", normalized_means_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then graph it to a bar plot to make it easier to compare to one another. The bar chart for the raw data means is shown by the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x3200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(nrows=len(means_df), figsize=(8, 4 * len(means_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(means_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Raw Data Row \\\"{index}\\\"')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from the raw data graph, the magnitudes of the raw features' values are massive, which may hinder certain models from deriving the differences between the classes; models that are sensitive to unnormalized data such as regression-based models and k-nearest neighbors. As such, normalization was performed in order to rescale the features relative to each other.\n",
    "\n",
    "The following shows the bar chart for the means of the normalized features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mJkYNGzaUp6enqlWrphkzZmTrs3TpUtWuXVseHh6qXbu2li9fnl/lAwAAAEVSoQoWn3/+ufr27auhQ4dq586datasmdq0aaNDhw7l2D8xMVFt27ZVs2bNtHPnTg0ZMkR9+vTR0qVL7X1iY2PVpUsXRUZGKj4+XpGRkercubO2bNlSUIcFAAAAFHqF6lSo4OBgNWjQQNOnT7e3BQUFqVOnTho3bly2/gMHDtSKFSuUkJBgb+vdu7fi4+MVGxsrSerSpYtSUlL03//+194nPDxcZcqU0ZIlS/LxaAAAAICio9DcIO/SpUvasWOHBg0a5NAeFhamTZs25TgmNjZWYWFhDm1PPPGE5s6dq8uXL8vNzU2xsbHq169ftj6TJk26YS3p6elKT0+3P8/KytLp06dVrlw5hxstAQAAAIWZYRhKTU1VpUqVVKJE7ic7FZpgcfLkSWVmZsrPz8+h3c/PTzabLccxNpstx/4ZGRk6efKkKlaseMM+N9qnJI0bN06jRo26zSMBAAAACpfDhw+rcuXKufYpNMHiqutnBAzDyHWWIKf+17fndZ+DBw9WVFSU/XlycrLuu+8+HT58WD4+Pjc/iEKszogfnF0C7qDdo55wdgm4w/iMFi18RosWPp9FT3H4jKakpKhKlSry9va+ad9CEyx8fX3l4uKSbSbh+PHj2WYcrvL398+xv6urq8qVK5drnxvtU5I8PDzk4eGRrd3Hx6fIB4sSHiWdXQLuoKL+97U44jNatPAZLVr4fBY9xekzeiun+xeaVaHc3d3VsGFDRUdHO7RHR0eradOmOY4JCQnJ1n/16tVq1KiR3Nzccu1zo30CAAAAyK7QzFhIUlRUlCIjI9WoUSOFhIRo1qxZOnTokHr37i3pyilKf/31lxYuXCjpygpQU6dOVVRUlHr27KnY2FjNnTvXYbWnN954Q4899pjGjx+vJ598Ut98843WrFmjjRs3OuUYAQAAgMKoUAWLLl266NSpUxo9erSSkpJUp04drVy5UlWrVpUkJSUlOdzTIjAwUCtXrlS/fv300UcfqVKlSpo8ebKeeeYZe5+mTZvqs88+07BhwzR8+HBVr15dn3/+uYKDgwv8+AAAAIDCqlDdx+JulZKSIqvVquTk5CJ/rl3AoO+dXQLuoAPvtnN2CbjD+IwWLXxGixY+n0VPcfiM5uV7bqG5xgIAAADA3YtgAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANFdnFwAAuHMOvNvO2SUAAIopZiwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphWaYHHmzBlFRkbKarXKarUqMjJSZ8+ezXWMYRgaOXKkKlWqJC8vL7Vo0UJ79uxx6DNr1iy1aNFCPj4+slgsN90nAAAAgOwKTbCIiIhQXFycVq1apVWrVikuLk6RkZG5jpkwYYImTpyoqVOnatu2bfL391doaKhSU1Ptfc6fP6/w8HANGTIkvw8BAAAAKLJcnV3ArUhISNCqVau0efNmBQcHS5Jmz56tkJAQ7du3TzVr1sw2xjAMTZo0SUOHDtXTTz8tSfr444/l5+enxYsXq1evXpKkvn37SpI2bNhQIMcCAAAAFEWFYsYiNjZWVqvVHiokqUmTJrJardq0aVOOYxITE2Wz2RQWFmZv8/DwUPPmzW84BgAAAMDtKRQzFjabTRUqVMjWXqFCBdlsthuOkSQ/Pz+Hdj8/Px08eNBUPenp6UpPT7c/T0lJMbU/AAAAoLBz6ozFyJEjZbFYcn1s375dkmSxWLKNNwwjx/ZrXb/9VsbczLhx4+wXkVutVlWpUsXU/gAAAIDCzqkzFq+//rq6du2aa5+AgADt2rVLx44dy7btxIkT2WYkrvL395d0ZeaiYsWK9vbjx4/fcMytGjx4sKKiouzPU1JSCBcAAAAo1pwaLHx9feXr63vTfiEhIUpOTtbWrVvVuHFjSdKWLVuUnJyspk2b5jgmMDBQ/v7+io6OVv369SVJly5dUkxMjMaPH2+qbg8PD3l4eJjaBwAAAFCUFIqLt4OCghQeHq6ePXtq8+bN2rx5s3r27Kn27ds7rAhVq1YtLV++XNKVU6D69u2rsWPHavny5dq9e7d69OihkiVLKiIiwj7GZrMpLi5Of/zxhyTpl19+UVxcnE6fPl2wBwkAAAAUYoXi4m1J+vTTT9WnTx/7Kk8dO3bU1KlTHfrs27dPycnJ9ucDBgzQhQsX9Oqrr+rMmTMKDg7W6tWr5e3tbe8zY8YMjRo1yv78sccekyTNnz9fPXr0yMcjAgAAAIoOi2EYhrOLKOxSUlJktVqVnJwsHx8fZ5eTrwIGfe/sEnAHHXi3nbNLAIBig39Di57i8O9oXr7nFopToQAAAADc3QgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCt0ASLM2fOKDIyUlarVVarVZGRkTp79myuYwzD0MiRI1WpUiV5eXmpRYsW2rNnj3376dOn9a9//Us1a9ZUyZIldd9996lPnz5KTk7O56MBAAAAipZCEywiIiIUFxenVatWadWqVYqLi1NkZGSuYyZMmKCJEydq6tSp2rZtm/z9/RUaGqrU1FRJ0tGjR3X06FG9//77+uWXX7RgwQKtWrVKL730UkEcEgAAAFBkuDq7gFuRkJCgVatWafPmzQoODpYkzZ49WyEhIdq3b59q1qyZbYxhGJo0aZKGDh2qp59+WpL08ccfy8/PT4sXL1avXr1Up04dLV261D6mevXqGjNmjJ5//nllZGTI1bVQvD0AAACA0xWKGYvY2FhZrVZ7qJCkJk2ayGq1atOmTTmOSUxMlM1mU1hYmL3Nw8NDzZs3v+EYSUpOTpaPjw+hAgAAAMiDQvHt2WazqUKFCtnaK1SoIJvNdsMxkuTn5+fQ7ufnp4MHD+Y45tSpU3r77bfVq1evXOtJT09Xenq6/XlKSkqu/QEAAICizqkzFiNHjpTFYsn1sX37dkmSxWLJNt4wjBzbr3X99huNSUlJUbt27VS7dm2NGDEi132OGzfOfhG51WpVlSpVbnaoAAAAQJHm1BmL119/XV27ds21T0BAgHbt2qVjx45l23bixIlsMxJX+fv7S7oyc1GxYkV7+/Hjx7ONSU1NVXh4uEqXLq3ly5fLzc0t15oGDx6sqKgo+/OUlBTCBQAAAIo1pwYLX19f+fr63rRfSEiIkpOTtXXrVjVu3FiStGXLFiUnJ6tp06Y5jgkMDJS/v7+io6NVv359SdKlS5cUExOj8ePH2/ulpKToiSeekIeHh1asWCFPT8+b1uPh4SEPD49bOUQAAACgWCgUF28HBQUpPDxcPXv21ObNm7V582b17NlT7du3d1gRqlatWlq+fLmkK6dA9e3bV2PHjtXy5cu1e/du9ejRQyVLllRERISkKzMVYWFhSktL09y5c5WSkiKbzSabzabMzEynHCsAAABQGBWKi7cl6dNPP1WfPn3sqzx17NhRU6dOdeizb98+h5vbDRgwQBcuXNCrr76qM2fOKDg4WKtXr5a3t7ckaceOHdqyZYskqUaNGg77SkxMVEBAQD4eEQAAAFB0WAzDMJxdRGGXkpIiq9VqX6q2KAsY9L2zS8AddODdds4uAQCKDf4NLXqKw7+jefmeWyhOhQIAAABwdyNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADDN1dkFoHA58G47Z5cAAACAuxAzFgAAAABMy3OwOHz4sI4cOWJ/vnXrVvXt21ezZs26o4UBAAAAKDzyHCwiIiK0fv16SZLNZlNoaKi2bt2qIUOGaPTo0Xe8QAAAAAB3vzwHi927d6tx48aSpC+++EJ16tTRpk2btHjxYi1YsOBO1wcAAACgEMhzsLh8+bI8PDwkSWvWrFHHjh0lSbVq1VJSUtKdre4aZ86cUWRkpKxWq6xWqyIjI3X27NlcxxiGoZEjR6pSpUry8vJSixYttGfPHoc+vXr1UvXq1eXl5aXy5cvrySef1K+//ppvxwEAAAAURXkOFg8++KBmzJihH3/8UdHR0QoPD5ckHT16VOXKlbvjBV4VERGhuLg4rVq1SqtWrVJcXJwiIyNzHTNhwgRNnDhRU6dO1bZt2+Tv76/Q0FClpqba+zRs2FDz589XQkKCfvjhBxmGobCwMGVmZubbsQAAAABFjcUwDCMvAzZs2KCnnnpKKSkpeuGFFzRv3jxJ0pAhQ/Trr79q2bJld7zIhIQE1a5dW5s3b1ZwcLAkafPmzQoJCdGvv/6qmjVrZhtjGIYqVaqkvn37auDAgZKk9PR0+fn5afz48erVq1eOr7Vr1y499NBD+uOPP1S9evVbqi8lJUVWq1XJycny8fG5zaMEAABFWcCg751dAu6w4rAMf16+5+b5PhYtWrTQyZMnlZKSojJlytjbX3nlFZUsWTLv1d6C2NhYWa1We6iQpCZNmshqtWrTpk05BovExETZbDaFhYXZ2zw8PNS8eXNt2rQpx2CRlpam+fPnKzAwUFWqVMmXYwEAAACKotu6j4VhGNqxY4dmzpxpP63I3d0934KFzWZThQoVsrVXqFBBNpvthmMkyc/Pz6Hdz88v25hp06apdOnSKl26tFatWqXo6Gi5u7vfsJ709HSlpKQ4PAAAAIDiLM/B4uDBg6pbt66efPJJvfbaazpx4oSkK9cz9O/fP0/7GjlypCwWS66P7du3S5IsFku28YZh5Nh+reu35zTmueee086dOxUTE6P7779fnTt31sWLF2+4z3HjxtkvIrdarcxuAAAAoNjL86lQb7zxhho1aqT4+HiHi7Wfeuopvfzyy3na1+uvv66uXbvm2icgIEC7du3SsWPHsm07ceJEthmJq/z9/SVdmbmoWLGivf348ePZxlwNCPfff7+aNGmiMmXKaPny5erWrVuO+x48eLCioqLsz1NSUggXAAAAKNbyHCw2btyon376KdupQlWrVtVff/2Vp335+vrK19f3pv1CQkKUnJysrVu32u+hsWXLFiUnJ6tp06Y5jgkMDJS/v7+io6NVv359SdKlS5cUExOj8ePH5/p6hmEoPT39hts9PDzsS+4CAAAAuI1TobKysnJcivXIkSPy9va+I0VdLygoSOHh4erZs6c2b96szZs3q2fPnmrfvr3Dhdu1atXS8uXLJV05Bapv374aO3asli9frt27d6tHjx4qWbKkIiIiJEl//vmnxo0bpx07dujQoUOKjY1V586d5eXlpbZt2+bLsQAAAABFUZ6DRWhoqCZNmmR/brFYdO7cOY0YMSJfv4x/+umnqlu3rsLCwhQWFqZ69epp0aJFDn327dun5ORk+/MBAwaob9++evXVV9WoUSP99ddfWr16tT0AeXp66scff1Tbtm1Vo0YNde7cWaVKldKmTZtyvFgcAAAAQM7yfB+Lo0ePqmXLlnJxcdHvv/+uRo0a6ffff5evr6/+97//Fcsv5NzHAgAA3Az3sSh6uI+FozxfY1GpUiXFxcVpyZIl+vnnn5WVlaWXXnpJzz33nLy8vG67aAAAAACFV56DhSR5eXnpxRdf1Isvvnin6wEAAABQCOU5WCxcuDDX7d27d7/tYgAAAAAUTrd1H4trXb58WefPn7ffeZtgAQAAABQ/eV4V6syZMw6Pc+fOad++fXr00Ue1ZMmS/KgRAAAAwF0uz8EiJ/fff7/efffdbLMZAAAAAIqHOxIsJMnFxUVHjx69U7sDAAAAUIjk+RqLFStWODw3DENJSUmaOnWqHnnkkTtWGAAAAIDCI8/BolOnTg7PLRaLypcvr1atWuk///nPnaoLAAAAQCGS52CRlZWVH3UAAAAAKMTu2DUWAAAAAIqvW5qxiIqKuuUdTpw48baLAQAAAFA43VKw2Llz5y3tzGKxmCoGAAAAQOF0S8Fi/fr1+V0HAAAAgEKMaywAAAAAmJbnVaEkadu2bfryyy916NAhXbp0yWHbsmXL7khhAAAAAAqPPM9YfPbZZ3rkkUe0d+9eLV++XJcvX9bevXu1bt06Wa3W/KgRAAAAwF0uz8Fi7Nix+uCDD/Tdd9/J3d1dH374oRISEtS5c2fdd999+VEjAAAAgLtcnoPF/v371a5dO0mSh4eH0tLSZLFY1K9fP82aNeuOFwgAAADg7pfnYFG2bFmlpqZKku69917t3r1bknT27FmdP3/+zlYHAAAAoFC45WARFxcnSWrWrJmio6MlSZ07d9Ybb7yhnj17qlu3bnr88cfzpUgAAAAAd7dbXhWqQYMGql+/vjp16qRu3bpJkgYPHiw3Nzdt3LhRTz/9tIYPH55vhQIAAAC4e93yjMVPP/2kBg0a6P3331f16tX1/PPPKyYmRgMGDNCKFSs0ceJElSlTJj9rBQAAAHCXuuVgERISotmzZ8tms2n69Ok6cuSIWrdurerVq2vMmDE6cuRIftYJAAAA4C6W54u3vby89MILL2jDhg367bff1K1bN82cOVOBgYFq27ZtftQIAAAA4C6X52BxrerVq2vQoEEaOnSofHx89MMPP9ypugAAAAAUIrd88fb1YmJiNG/ePC1dulQuLi7q3LmzXnrppTtZGwAAAIBCIk/B4vDhw1qwYIEWLFigxMRENW3aVFOmTFHnzp1VqlSp/KoRAAAAwF3uloNFaGio1q9fr/Lly6t79+568cUXVbNmzfysDQAAAEAhccvBwsvLS0uXLlX79u3l4uKSnzUBAAAAKGRuOVisWLEiP+sAAAAAUIiZWhUKAAAAAKRCFCzOnDmjyMhIWa1WWa1WRUZG6uzZs7mOMQxDI0eOVKVKleTl5aUWLVpoz549N+zbpk0bWSwWff3113f+AAAAAIAirNAEi4iICMXFxWnVqlVatWqV4uLiFBkZmeuYCRMmaOLEiZo6daq2bdsmf39/hYaGKjU1NVvfSZMmyWKx5Ff5AAAAQJF22/exKEgJCQlatWqVNm/erODgYEnS7NmzFRISon379uW4OpVhGJo0aZKGDh2qp59+WpL08ccfy8/PT4sXL1avXr3sfePj4zVx4kRt27ZNFStWLJiDAgAAAIqQQjFjERsbK6vVag8VktSkSRNZrVZt2rQpxzGJiYmy2WwKCwuzt3l4eKh58+YOY86fP69u3bpp6tSp8vf3v6V60tPTlZKS4vAAAAAAirNCESxsNpsqVKiQrb1ChQqy2Ww3HCNJfn5+Du1+fn4OY/r166emTZvqySefvOV6xo0bZ7/Ww2q1qkqVKrc8FgAAACiKnBosRo4cKYvFkutj+/btkpTj9Q+GYdz0uojrt187ZsWKFVq3bp0mTZqUp7oHDx6s5ORk++Pw4cN5Gg8AAAAUNU69xuL1119X165dc+0TEBCgXbt26dixY9m2nThxItuMxFVXT2uy2WwO100cP37cPmbdunXav3+/7rnnHoexzzzzjJo1a6YNGzbkuG8PDw95eHjkWjcAAABQnDg1WPj6+srX1/em/UJCQpScnKytW7eqcePGkqQtW7YoOTlZTZs2zXFMYGCg/P39FR0drfr160uSLl26pJiYGI0fP16SNGjQIL388ssO4+rWrasPPvhAHTp0MHNoAAAAQLFSKFaFCgoKUnh4uHr27KmZM2dKkl555RW1b9/eYUWoWrVqady4cXrqqadksVjUt29fjR07Vvfff7/uv/9+jR07ViVLllRERISkK7MaOV2wfd999ykwMLBgDg4AAAAoAgpFsJCkTz/9VH369LGv8tSxY0dNnTrVoc++ffuUnJxsfz5gwABduHBBr776qs6cOaPg4GCtXr1a3t7eBVo7AAAAUNRZDMMwnF1EYZeSkiKr1ark5GT5+Pg4uxwAAHAXChj0vbNLwB124N12zi4h3+Xle26hWG4WAAAAwN2NYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wpNsDhz5owiIyNltVpltVoVGRmps2fP5jrGMAyNHDlSlSpVkpeXl1q0aKE9e/Y49GnRooUsFovDo2vXrvl4JAAAAEDRU2iCRUREhOLi4rRq1SqtWrVKcXFxioyMzHXMhAkTNHHiRE2dOlXbtm2Tv7+/QkNDlZqa6tCvZ8+eSkpKsj9mzpyZn4cCAAAAFDmuzi7gViQkJGjVqlXavHmzgoODJUmzZ89WSEiI9u3bp5o1a2YbYxiGJk2apKFDh+rpp5+WJH388cfy8/PT4sWL1atXL3vfkiVLyt/fv2AOBgAAACiCCsWMRWxsrKxWqz1USFKTJk1ktVq1adOmHMckJibKZrMpLCzM3ubh4aHmzZtnG/Ppp5/K19dXDz74oPr3759tRuN66enpSklJcXgAAAAAxVmhmLGw2WyqUKFCtvYKFSrIZrPdcIwk+fn5ObT7+fnp4MGD9ufPPfecAgMD5e/vr927d2vw4MGKj49XdHT0DesZN26cRo0adTuHAgAAABRJTp2xGDlyZLYLp69/bN++XZJksViyjTcMI8f2a12//foxPXv2VOvWrVWnTh117dpVX331ldasWaOff/75hvscPHiwkpOT7Y/Dhw/n5bABAACAIsepMxavv/76TVdgCggI0K5du3Ts2LFs206cOJFtRuKqq9dM2Gw2VaxY0d5+/PjxG46RpAYNGsjNzU2///67GjRokGMfDw8PeXh45Fo3AAAAUJw4NVj4+vrK19f3pv1CQkKUnJysrVu3qnHjxpKkLVu2KDk5WU2bNs1xzNXTm6Kjo1W/fn1J0qVLlxQTE6Px48ff8LX27Nmjy5cvO4QRAAAAALkrFBdvBwUFKTw8XD179tTmzZu1efNm9ezZU+3bt3dYEapWrVpavny5pCunQPXt21djx47V8uXLtXv3bvXo0UMlS5ZURESEJGn//v0aPXq0tm/frgMHDmjlypV69tlnVb9+fT3yyCNOOVYAAACgMCoUF29LV1Zu6tOnj32Vp44dO2rq1KkOffbt26fk5GT78wEDBujChQt69dVXdebMGQUHB2v16tXy9vaWJLm7u2vt2rX68MMPde7cOVWpUkXt2rXTiBEj5OLiUnAHBwAAABRyFsMwDGcXUdilpKTIarUqOTlZPj4+zi4HAADchQIGfe/sEnCHHXi3nbNLyHd5+Z5bKE6FAgAAAHB3I1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwLRCEyzOnDmjyMhIWa1WWa1WRUZG6uzZs7mOMQxDI0eOVKVKleTl5aUWLVpoz5492frFxsaqVatWKlWqlO655x61aNFCFy5cyKcjAQAAAIqeQhMsIiIiFBcXp1WrVmnVqlWKi4tTZGRkrmMmTJigiRMnaurUqdq2bZv8/f0VGhqq1NRUe5/Y2FiFh4crLCxMW7du1bZt2/T666+rRIlC89YAAAAATmcxDMNwdhE3k5CQoNq1a2vz5s0KDg6WJG3evFkhISH69ddfVbNmzWxjDMNQpUqV1LdvXw0cOFCSlJ6eLj8/P40fP169evWSJDVp0kShoaF6++23b7u+lJQUWa1WJScny8fH57b3AwAAiq6AQd87uwTcYQfebefsEvJdXr7nFoqf5WNjY2W1Wu2hQroSCKxWqzZt2pTjmMTERNlsNoWFhdnbPDw81Lx5c/uY48ePa8uWLapQoYKaNm0qPz8/NW/eXBs3bszfAwIAAACKmEIRLGw2mypUqJCtvUKFCrLZbDccI0l+fn4O7X5+fvZtf/75pyRp5MiR6tmzp1atWqUGDRro8ccf1++//37DetLT05WSkuLwAAAAAIozpwaLkSNHymKx5PrYvn27JMlisWQbbxhGju3Xun77tWOysrIkSb169dI//vEP1a9fXx988IFq1qypefPm3XCf48aNs19EbrVaVaVKlTwdNwAAAFDUuDrzxV9//XV17do11z4BAQHatWuXjh07lm3biRMnss1IXOXv7y/pysxFxYoV7e3Hjx+3j7naXrt2bYexQUFBOnTo0A1rGjx4sKKiouzPU1JSCBcAAAAo1pwaLHx9feXr63vTfiEhIUpOTtbWrVvVuHFjSdKWLVuUnJyspk2b5jgmMDBQ/v7+io6OVv369SVJly5dUkxMjMaPHy/pSmipVKmS9u3b5zD2t99+U5s2bW5Yj4eHhzw8PG7pGAEAAIDioFBcYxEUFKTw8HD17NlTmzdv1ubNm9WzZ0+1b9/eYUWoWrVqafny5ZKunALVt29fjR07VsuXL9fu3bvVo0cPlSxZUhEREfY+b731liZPnqyvvvpKf/zxh4YPH65ff/1VL730klOOFQAAACiMnDpjkReffvqp+vTpY1/lqWPHjpo6dapDn3379ik5Odn+fMCAAbpw4YJeffVVnTlzRsHBwVq9erW8vb3tffr27auLFy+qX79+On36tB566CFFR0erevXqBXNgAAAAQBFQKO5jcbfjPhYAAOBmuI9F0cN9LBwVilOhAAAAANzdCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMKzXKzAAAAhVlxWEEIxRszFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANFdnF1AUGIYhSUpJSXFyJQAAAMCdc/X77dXvu7khWNwBqampkqQqVao4uRIAAADgzktNTZXVas21j8W4lfiBXGVlZeno0aPy9vaWxWJxdjkwKSUlRVWqVNHhw4fl4+Pj7HIAXIfPKHB34zNatBiGodTUVFWqVEklSuR+FQUzFndAiRIlVLlyZWeXgTvMx8eH/yACdzE+o8Ddjc9o0XGzmYqruHgbAAAAgGkECwAAAACmESyA63h4eGjEiBHy8PBwdikAcsBnFLi78Rktvrh4GwAAAIBpzFgAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWCBYm3KlCnOLgHALdi2bZuioqLUvn17dejQQVFRUdq+fbuzywJwE8uWLVO9evWcXQYKCMECxdqIESMUGhqqI0eOOLsUADcwYMAABQcHa86cOTpy5IgOHTqk2bNnKzg4WAMHDnR2eUCxN3v2bD377LOKiIjQli1bJEnr1q1T/fr19fzzzyskJMTJFaKgECxQrO3evVseHh6qW7euFi1a5OxyAFzn448/1pQpUzR58mSdOnVKcXFxio+P1+nTp/XBBx9o8uTJWrhwobPLBIqt999/X6+99poSExP1zTffqFWrVho7dqw6d+6sTp066dChQ5o5c6azy0QB4QZ5gKQFCxYoKipKLVq00LBhw+Tq6uqwnWlcwDkaN26sbt26qV+/fjlunzhxoj777DNt3bq1gCsDIElBQUF666239OKLL2rDhg1q1aqVWrVqpa+++kr33HOPs8tDASNYAP/fmjVrFB4eLsMwZBiGLBaL/X8zMzOdXR5QLJUqVUq//PKLqlWrluP2P//8U3Xr1lVaWloBVwZAkkqWLKlff/1V9913nyTJw8ND//vf/xQcHOzkyuAMnAoF6Mqvnk8++aSef/55/fbbb0pMTNSff/5p/18AzuHi4qJLly7dcPvly5fl4uJSgBUBuNbFixfl6elpf+7u7q7y5cs7sSI4k+vNuwBF159//qnu3btr//79Wrx4sZ588klnlwTgGg0bNtSnn36qt99+O8ftixYtUoMGDQq4KgDXmjNnjkqXLi1JysjI0IIFC+Tr6+vQp0+fPs4oDQWMU6FQrJUuXVrh4eGaMWNGtv8IAnC+7777Tp06dVJUVJTefPNN+fn5SZJsNpv+85//aNKkSVq+fLnat2/v5EqB4ikgIEAWiyXXPhaLhdn/YoJggWLtk08+0fPPP+/sMgDkYsqUKerfv78yMjJktVolScnJyXJxcdGECRPUt29f5xYIAJDEqVAo5nI7d1uSUlNT1a9fP82ZM6eAKgJwvX/961966qmn9OWXX+r333+XJD3wwAN65plnVKVKFSdXByA3p06d0qJFi/gBoJhgxgLFmtVqVbNmzTRnzhz5+/s7bPvhhx/Us2dPlSlTRvHx8U6qEACAwsUwDK1evVpz587VN998Ix8fH504ccLZZaEAsCoUirX4+HilpaXpwQcf1JIlSyRdmaV46aWX1LFjR3Xv3l3bt293cpVA8fXHH39ox44dDm1r165Vy5Yt1bhxY40dO9ZJlQG43oEDB/Tvf/9bVatWVdu2beXp6anvv/9eNpvN2aWhgBAsUKwFBARo/fr1Gj58uHr27Kn27durTp062rp1qzZt2qR33nlHbm5uzi4TKLbeeustff311/bniYmJ6tChg9zd3RUSEqJx48Zp0qRJTqsPKO7S09O1ZMkSPf744woKCtLu3bs1ceJElShRQoMGDVLr1q1ZEroY4VQoQNKFCxf03HPP6euvv1apUqW0ceNGPfTQQ84uCyj2qlSpoi+++EIhISGSpHfeeUdfffWV4uLiJElz587VlClT7M8BFCxfX1/Vrl1bzz//vJ599lmVKVNGkuTm5qb4+HjVrl3byRWiIDFjgWLvp59+0kMPPaR9+/Zp1apVatOmjUJCQvTBBx84uzSg2Dt58qQqV65sf75+/Xp16NDB/rxFixY6cOCAEyoDIEmZmZmyWCyyWCzMTIBggeLtzTffVKtWrdShQwf9/PPPCgsL0xdffKH58+dr7Nixeuyxx1h7G3CismXLKikpSZKUlZWl7du3Kzg42L790qVLYuIdcJ6kpCS98sorWrJkifz9/fXMM89o+fLlN723BYomggWKtW+++UZr1qzRf/7zH3l4eNjbu3Tpot27d6ts2bKcEgU4UfPmzfX222/r8OHDmjRpkjIzM9WyZUv79r179yogIMB5BQLFnKenp5577jmtW7dOv/zyi4KCgtSnTx9lZGRozJgxio6OVmZmprPLRAHhGgsUa2lpaSpVqlSufRYtWqTIyMgCqgjAtRITExUaGqo///xTJUqU0OTJk/Xqq6/at3fq1EmBgYGcugg4ycKFC9WlSxeHH+eysrL0ww8/aO7cufr222/l7e2tkydPOrFKFBSCBZCLX375RXPnzmXVGcCJLl++rL1796p8+fJyd3eXu7u7fHx8JF1ZMrpy5coqV66ck6sEiicXFxclJSWpQoUKOW4/ceKEFi1apKioqAKuDM7AqVDAdVJSUjRz5kw1btxYDz30kDZs2ODskoBiLS0tTbNmzVK9evXk5+enMmXKyN/fX4MHD9b9999PqACc6Ga/T5cvX55QUYy4OrsA4G4RExOjuXPnaunSpbp48aLeeustLV68WDVq1HB2aUCxdfr0aYWEhOivv/7Sc889p6CgIBmGoYSEBE2ZMkXR0dHauHGj4uPjtWXLFvXp08fZJQPFDhdq4ypOhUKxlpSUpPnz52vevHlKS0tTt27dFBERoZCQENbfBu4Cffv21dq1a7VmzRr5+fk5bLPZbAoLC1PNmjW1evVqTZ48WS+88IKTKgWKpxIlSqhNmzYO11jkZNmyZQVUEZyJGQsUa4GBgXr22Wf10UcfKTQ0VCVKcHYgcDf5+uuvNXPmzGyhQpL8/f01YcIEtW3bViNGjCBUAE7i7e0tLy8vZ5eBuwAzFijWatasqUuXLikiIkKRkZGqVauWJO4YCtwtPDw8tH//foeb5F3ryJEjCggIUEZGRgFXBkC6MmNhs9luePE2ihd+nkWxtm/fPn3yySdKSkrSww8/rIYNG9qXreScUcD5fH19c72zdmJiIl9oACfi30pcixkL4P87d+6clixZonnz5mnLli1q3ry5IiIi1KlTJ5UvX97Z5QHF0ksvvaQ//vhD0dHRcnd3d9iWnp6uJ554QtWqVdO8efOcVCFQvDFjgWsRLIAcJCQkaO7cuVq0aJFOnz6ty5cvO7skoFg6cuSIGjVqJA8PD7322mv20xX37t2radOmKT09Xdu2bdN9993n5EqB4undd9/Vgw8+qA4dOtjbFi5cqBEjRigtLU2dOnXSlClTbnpxN4oGggWQi4yMDK1YsUJPP/20s0sBiq3ExES9+uqrWr16tX3NfIvFotDQUE2dOpUloQEnCg8PV8uWLTVw4EBJV24s26BBA/Xo0UNBQUF677331KtXL40cOdK5haJAECyAXCQkJKhdu3b6888/nV0KUOydOXNGv//+uySpRo0aKlu2rJMrAlCxYkV9++23atSokSRp6NChiomJ0caNGyVJX375pUaMGKG9e/c6s0wUEJabBXJx6dIlHTx40NllAJBUpkwZNW7c2NllALjGmTNnHJaDjomJUXh4uP35ww8/rMOHDzujNDgBq0IBAADgtvj5+SkxMVHSlR/jfv75Z4WEhNi3p6amys3NzVnloYARLAAAAHBbwsPDNWjQIP34448aPHiwSpYsqWbNmtm379q1S9WrV3dihShInAoFAACA2/LOO+/o6aefVvPmzVW6dGl9/PHHDktDz5s3T2FhYU6sEAWJi7dRrJUpUybXm/tkZGQoLS1NmZmZBVgVAACFS3JyskqXLi0XFxeH9tOnT6t06dLZ7kODookZCxRrkyZNcnYJAAAUelarNcd2Vm8rXpixAG4iIyNDrq5kcAAAgNxw8TZwA3v37tWbb76pe++919mlAAAA3PUIFsA1zp07pzlz5igkJET16tXTli1bNGjQIGeXBQAAcNfj/A5A0saNGzVnzhwtXbpUgYGB2rt3r2JiYvTII484uzQAAIBCgRkLFGsTJkxQrVq11LVrV5UvX14bN27Url27ZLFYVKZMGWeXBwAAUGhw8TaKNVdXVw0cOFCjR492WCLPzc1N8fHxql27thOrAwAAKDyYsUCxNnr0aH355ZcKDAzUwIEDtXv3bmeXBAAAUCgRLFCsDRkyRL/99psWLVokm82mJk2a6KGHHpJhGDpz5oyzywMAACg0OBUKuEZKSooWL16sefPmaceOHQoODtbf//53RUVFObs0AACAuxrBAriB3bt3a+7cufr00091/PhxZ5cDAABwV2O5WRRrFy5c0Nq1a9W+fXtJ0uDBg5Wenm7f7urqqv379zurPAAAgEKDYIFibeHChfruu+/swWLq1Kl68MEH5eXlJUnat2+fKlWqpH79+jmzTAAAgLsep0KhWHvsscfUr18/PfXUU5Ikb29vxcfHq1q1apKkTz75RB999JFiY2OdWSYAAMBdj1WhUKz99ttveuCBB+zPPT09VaLE/30sGjdurL179zqjNAAAgEKFU6FQrCUnJ8vV9f8+BidOnHDYnpWV5XDNBQAAAHLGjAWKtcqVK+d6U7xdu3apcuXKBVgRAABA4USwQLHWtm1b/fvf/9bFixezbbtw4YJGjRqldu3aOaEyAACAwoWLt1GsHTt2TH/729/k7u6u119/XQ888IAsFot+/fVXTZ06VRkZGdq5c6f8/PycXSoAAMBdjWCBYi8xMVH//Oc/FR0drasfB4vFotDQUE2bNs2+QhQAAABujGAB/H+nT5/WH3/8IUmqUaOGypYt6+SKAAAACg+CBQAAAADTuHgbAAAAgGkECwAAAACmESwAAAAAmEawAADcVQ4cOCCLxaK4uDhnlwIAyAOCBQAAAADTCBYAAAAATCNYAACcIisrS+PHj1eNGjXk4eGh++67T2PGjMnWLzMzUy+99JICAwPl5eWlmjVr6sMPP3Tos2HDBjVu3FilSpXSPffco0ceeUQHDx6UJMXHx6tly5by9vaWj4+PGjZsqO3btxfIMQJAceLq7AIAAMXT4MGDNXv2bH3wwQd69NFHlZSUpF9//TVbv6ysLFWuXFlffPGFfH19tWnTJr3yyiuqWLGiOnfurIyMDHXq1Ek9e/bUkiVLdOnSJW3dulUWi0WS9Nxzz6l+/fqaPn26XFxcFBcXJzc3t4I+XAAo8rhBHgCgwKWmpqp8+fKaOnWqXn75ZYdtBw4cUGBgoHbu3Km//e1vOY5/7bXXdOzYMX311Vc6ffq0ypUrpw0bNqh58+bZ+vr4+GjKlCl64YUX8uNQAAD/H6dCAQAKXEJCgtLT0/X444/fUv8ZM2aoUaNGKl++vEqXLq3Zs2fr0KFDkqSyZcuqR48eeuKJJ9ShQwd9+OGHSkpKso+NiorSyy+/rNatW+vdd9/V/v378+WYAKC4I1gAAAqcl5fXLff94osv1K9fP7344otavXq14uLi9I9//EOXLl2y95k/f75iY2PVtGlTff7553rggQe0efNmSdLIkSO1Z88etWvXTuvWrVPt2rW1fPnyO35MAFDccSoUAKDAXbx4UWXLltXkyZNveirUv/71L+3du1dr166192ndurVOnjx5w3tdhISE6OGHH9bkyZOzbevWrZvS0tK0YsWKO3pMAFDcMWMBAChwnp6eGjhwoAYMGKCFCxdq//792rx5s+bOnZutb40aNbR9+3b98MMP+u233zR8+HBt27bNvj0xMVGDBw9WbGysDh48qNWrV+u3335TUFCQLly4oNdff10bNmzQwYMH9dNPP2nbtm0KCgoqyMMFgGKBVaEAAE4xfPhwubq66t///reOHj2qihUrqnfv3tn69e7dW3FxcerSpYssFou6deumV199Vf/9738lSSVLltSvv/6qjz/+WKdOnVLFihX1+uuvq1evXsrIyNCpU6fUvXt3HTt2TL6+vnr66ac1atSogj5cACjyOBUKAAAAgGmcCgUAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUA5MGCBQtksVjk6empgwcPZtveokUL1alTxwmV3Rk9evRQQECAQ1tAQIB69OhRoHUcOHBAFotFCxYsyLXfhg0bZLFY7A8XFxeVL19eHTp00Pbt2wum2Fxc/fsi/V+tBw4cuOm4Fi1aqEWLFrf1mtf/eR09elQjR45UXFzcbe1Pcqz96p/Nhg0bbnt/AIomggUA3Ib09HQNGzbM2WUUiOXLl2v48OHOLiNXY8eOVWxsrDZs2KDhw4dr06ZNat68uX7//Xdnl+Z0R48e1ahRo0wFCwC4FQQLALgN4eHhWrx4seLj4/P1dS5cuJCv+78V9evXV/Xq1Z1dRq7uv/9+NWnSRM2aNVOfPn30wQcf6Pz58/rkk0+cXRoAFBsECwC4DQMGDFC5cuU0cODAm/a9ePGiBg8erMDAQLm7u+vee+/Va6+9prNnzzr0CwgIUPv27bVs2TLVr19fnp6eGjVqlP00lMWLF2vgwIGqWLGiSpcurQ4dOujYsWNKTU3VK6+8Il9fX/n6+uof//iHzp0757Dvjz76SI899pgqVKigUqVKqW7dupowYYIuX7580/qvP7WmRYsWDqcfXfu49tQlm82mXr16qXLlynJ3d1dgYKBGjRqljIwMh/0fPXpUnTt3lre3t6xWq7p06SKbzXbTunLTqFEjSdKxY8cc2jdu3KjHH39c3t7eKlmypJo2barvv//evj0lJUWurq5677337G0nT55UiRIlZLVaHWrv06ePypcvL8MwbrtOwzA0YcIEVa1aVZ6enmrQoIH++9//5tg3JSVF/fv3d/h71LdvX6Wlpd1w/xs2bNDDDz8sSfrHP/5h/3MaOXKkJGn79u3q2rWrAgIC5OXlpYCAAHXr1i3H0/wA4GZcnV0AABRG3t7eGjZsmN544w2tW7dOrVq1yrGfYRjq1KmT1q5dq8GDB6tZs2batWuXRowYodjYWMXGxsrDw8Pe/+eff1ZCQoKGDRumwMBAlSpVyv7FcciQIWrZsqUWLFigAwcOqH///urWrZtcXV310EMPacmSJdq5c6eGDBkib29vTZ482b7f/fv3KyIiwv6lND4+XmPGjNGvv/6qefPm5enYp02bppSUFIe24cOHa/369apZs6akK6GicePGKlGihP7973+revXqio2N1TvvvKMDBw5o/vz5kq7MyLRu3VpHjx7VuHHj9MADD+j7779Xly5d8lTT9RITEyVJDzzwgL0tJiZGoaGhqlevnubOnSsPDw9NmzZNHTp00JIlS9SlSxf5+Pjo4Ycf1po1a/TWW29JktauXSsPDw+lpqZq69atatq0qSRpzZo1atWqlf0aipz06NHDHspatGiRLYSMGjVKo0aN0ksvvaS///3vOnz4sHr27KnMzEz7eylJ58+fV/PmzXXkyBENGTJE9erV0549e/Tvf/9bv/zyi9asWZNjHQ0aNND8+fP1j3/8Q8OGDVO7du0kSZUrV5Z05VqWmjVrqmvXripbtqySkpI0ffp0Pfzww9q7d698fX1zrN1MmAJQhBkAgFs2f/58Q5Kxbds2Iz093ahWrZrRqFEjIysryzAMw2jevLnx4IMP2vuvWrXKkGRMmDDBYT+ff/65IcmYNWuWva1q1aqGi4uLsW/fPoe+69evNyQZHTp0cGjv27evIcno06ePQ3unTp2MsmXL3vAYMjMzjcuXLxsLFy40XFxcjNOnT9u3vfDCC0bVqlUd+letWtV44YUXbri/9957L9ux9OrVyyhdurRx8OBBh77vv/++IcnYs2ePYRiGMX36dEOS8c033zj069mzpyHJmD9//g1f1zD+7735/PPPjcuXLxvnz583fvrpJ6NmzZpG7dq1jTNnztj7NmnSxKhQoYKRmppqb8vIyDDq1KljVK5c2f5nOGzYMMPLy8u4ePGiYRiG8fLLLxvh4eFGvXr1jFGjRhmGYRh//fVXtmPOqzNnzhienp7GU0895dD+008/GZKM5s2b29vGjRtnlChRwti2bZtD36+++sqQZKxcudLedv2f17Zt227pvTSMK+/HuXPnjFKlShkffvjhbR0XgOKLU6EA4Da5u7vrnXfe0fbt2/XFF1/k2GfdunWSlG1VpWeffValSpXS2rVrHdrr1avn8Cv7tdq3b+/wPCgoSJLsv0Jf23769GmH06F27typjh07qly5cnJxcZGbm5u6d++uzMxM/fbbbzc/2BtYsmSJBgwYoGHDhqlnz5729u+++04tW7ZUpUqVlJGRYX+0adNG0pXZA0lav369vL291bFjR4f9RkRE5KmOLl26yM3NTSVLltQjjzyilJQUff/997rnnnskSWlpadqyZYv+/ve/q3Tp0vZxLi4uioyM1JEjR7Rv3z5J0uOPP64LFy5o06ZNkq7MTISGhqp169aKjo62t0lS69at81TntWJjY3Xx4kU999xzDu1NmzZV1apVHdq+++471alTR3/7298c3s8nnnjC1ApN586d08CBA1WjRg25urrK1dVVpUuXVlpamhISEm730AAUUwQLADCha9euatCggYYOHZrj9QqnTp2Sq6urypcv79BusVjk7++vU6dOObRXrFjxhq9VtmxZh+fu7u65tl+8eFGSdOjQITVr1kx//fWXPvzwQ/3444/atm2bPvroI0m3f4H4+vXr1aNHD3Xv3l1vv/22w7Zjx47p22+/lZubm8PjwQcflHTlugXpyvvj5+eXbd/+/v55qmX8+PHatm2bYmJiNHToUB07dkydOnVSenq6JOnMmTMyDCPH97dSpUr2WqQrX+xLliypNWvW6I8//tCBAwfswWLLli06d+6c1qxZo2rVqikwMDBPdV7r6uvldKzXtx07dky7du3K9n56e3vLMAz7+5lXERERmjp1ql5++WX98MMP2rp1q7Zt26by5cvfFQsHAChcuMYCAEywWCwaP368QkNDNWvWrGzby5Urp4yMDJ04ccIhXBiGIZvNZr+w9tr93Wlff/210tLStGzZModfws0sP7pr1y516tRJzZs31+zZs7Nt9/X1Vb169TRmzJgcx1/9Ml+uXDlt3bo12/a8XrxdrVo1+wXbjz32mLy8vDRs2DBNmTJF/fv3V5kyZVSiRAklJSVlG3v06FF7zdKVYPboo49qzZo1qly5svz9/VW3bl1Vq1ZN0pULoteuXZttBimvypUrJynnY7XZbA73E/H19ZWXl9cNr4e5WnteJCcn67vvvtOIESM0aNAge3t6erpOnz6d5/0BADMWAGBS69atFRoaqtGjR2dbjenxxx+XpGzLni5dulRpaWn27fnpali59iJxwzByDAS34tChQ2rTpo2qVaumpUuXys3NLVuf9u3ba/fu3apevboaNWqU7XE1WLRs2VKpqalasWKFw/jFixffVm1XDRgwQDVq1NC7776r1NRUlSpVSsHBwVq2bJnDL/FZWVn65JNPVLlyZYdT0Fq3bq0dO3Zo6dKl9tOdSpUqpSZNmmjKlCk6evSoqdOgJKlJkyby9PTUp59+6tC+adOmbKsytW/fXvv371e5cuVyfD+vv6nhta7+uV8/A2GxWGQYhsPfC0maM2eOMjMzTRwZgOKKGQsAuAPGjx+vhg0b6vjx4/bTfSQpNDRUTzzxhAYOHKiUlBQ98sgj9lWh6tevr8jIyHyvLTQ0VO7u7urWrZsGDBigixcvavr06Tpz5sxt7a9NmzY6e/aspk6dqj179jhsq169usqXL6/Ro0crOjpaTZs2VZ8+fVSzZk1dvHhRBw4c0MqVKzVjxgxVrlxZ3bt31wcffKDu3btrzJgxuv/++7Vy5Ur98MMPpo7Zzc1NY8eOVefOnfXhhx9q2LBhGjdunEJDQ9WyZUv1799f7u7umjZtmnbv3q0lS5Y4zBY9/vjjyszM1Nq1a/Xxxx/b21u3bq0RI0bIYrHccCWwW1WmTBn1799f77zzjl5++WU9++yzOnz4sEaOHJntVKi+fftq6dKleuyxx9SvXz/Vq1dPWVlZOnTokFavXq0333xTwcHBOb5O9erV5eXlpU8//VRBQUEqXbq0KlWqpEqVKumxxx7Te++9J19fXwUEBCgmJkZz5861X5sCAHni1EvHAaCQuXZVqOtFREQYkhxWhTIMw7hw4YIxcOBAo2rVqoabm5tRsWJF45///KfDikWGcWU1n3bt2mXb79WVj7788stbqmXEiBGGJOPEiRP2tm+//dZ46KGHDE9PT+Pee+813nrrLeO///2vIclYv369vd+trAol6YaPa1ceOnHihNGnTx8jMDDQcHNzM8qWLWs0bNjQGDp0qHHu3Dl7vyNHjhjPPPOMUbp0acPb29t45plnjE2bNuVpVajr35urgoODjTJlyhhnz541DMMwfvzxR6NVq1ZGqVKlDC8vL6NJkybGt99+m21cVlaW4evra0gy/vrrL3v71RWbGjRokGtdtyorK8sYN26cUaVKFcPd3d2oV6+e8e233xrNmzd3WBXKMAzj3LlzxrBhw4yaNWsa7u7uhtVqNerWrWv069fPsNls9n45reK1ZMkSo1atWoabm5shyRgxYoRhGP/33pcpU8bw9vY2wsPDjd27d990JTAAyInFMFiMGgAAAIA5XGMBAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANO4Qd4dkJWVpaNHj8rb29vhBksAAABAYWYYhlJTU1WpUiWVKJH7nATB4g44evSoqlSp4uwyAAAAgHxx+PBhVa5cOdc+BIs7wNvbW9KVN9zHx8fJ1QAAAAB3RkpKiqpUqWL/vpsbgsUdcPX0Jx8fH4IFAAAAipxbOd2fi7cBAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgmquzCwAA3DkBg753dgm4gw68287ZJQDALWPGAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYFqhCxbTpk1TYGCgPD091bBhQ/3444+59o+JiVHDhg3l6empatWqacaMGdn6TJo0STVr1pSXl5eqVKmifv366eLFi/l1CAAAAECRU6iCxeeff66+fftq6NCh2rlzp5o1a6Y2bdro0KFDOfZPTExU27Zt1axZM+3cuVNDhgxRnz59tHTpUnufTz/9VIMGDdKIESOUkJCguXPn6vPPP9fgwYML6rAAAACAQs9iGIbh7CJuVXBwsBo0aKDp06fb24KCgtSpUyeNGzcuW/+BAwdqxYoVSkhIsLf17t1b8fHxio2NlSS9/vrrSkhI0Nq1a+193nzzTW3duvWmsyFXpaSkyGq1Kjk5WT4+Prd7eABgWsCg751dAu6gA++2c3YJAIq5vHzPLTQzFpcuXdKOHTsUFhbm0B4WFqZNmzblOCY2NjZb/yeeeELbt2/X5cuXJUmPPvqoduzYoa1bt0qS/vzzT61cuVLt2t34P+bp6elKSUlxeAAAAADFmauzC7hVJ0+eVGZmpvz8/Bza/fz8ZLPZchxjs9ly7J+RkaGTJ0+qYsWK6tq1q06cOKFHH31UhmEoIyND//znPzVo0KAb1jJu3DiNGjXK/EEBAAAARUShmbG4ymKxODw3DCNb2836X9u+YcMGjRkzRtOmTdPPP/+sZcuW6bvvvtPbb799w30OHjxYycnJ9sfhw4dv93AAAACAIqHQzFj4+vrKxcUl2+zE8ePHs81KXOXv759jf1dXV5UrV06SNHz4cEVGRurll1+WJNWtW1dpaWl65ZVXNHToUJUokT17eXh4yMPD404cFgAAAFAkFJoZC3d3dzVs2FDR0dEO7dHR0WratGmOY0JCQrL1X716tRo1aiQ3NzdJ0vnz57OFBxcXFxmGoUJ0XTsAAADgVIUmWEhSVFSU5syZo3nz5ikhIUH9+vXToUOH1Lt3b0lXTlHq3r27vX/v3r118OBBRUVFKSEhQfPmzdPcuXPVv39/e58OHTpo+vTp+uyzz5SYmKjo6GgNHz5cHTt2lIuLS4EfIwAAAFAYFZpToSSpS5cuOnXqlEaPHq2kpCTVqVNHK1euVNWqVSVJSUlJDve0CAwM1MqVK9WvXz999NFHqlSpkiZPnqxnnnnG3mfYsGGyWCwaNmyY/vrrL5UvX14dOnTQmDFjCvz4AAAAgMKqUN3H4m7FfSwA3C24j0XRwn0sADhbkbyPBQAAAIC7F8ECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaYUuWEybNk2BgYHy9PRUw4YN9eOPP+baPyYmRg0bNpSnp6eqVaumGTNmZOtz9uxZvfbaa6pYsaI8PT0VFBSklStX5tchAAAAAEVOoQoWn3/+ufr27auhQ4dq586datasmdq0aaNDhw7l2D8xMVFt27ZVs2bNtHPnTg0ZMkR9+vTR0qVL7X0uXbqk0NBQHThwQF999ZX27dun2bNn69577y2owwIAAAAKPYthGIazi7hVwcHBatCggaZPn25vCwoKUqdOnTRu3Lhs/QcOHKgVK1YoISHB3ta7d2/Fx8crNjZWkjRjxgy99957+vXXX+Xm5nZbdaWkpMhqtSo5OVk+Pj63tQ8AuBMCBn3v7BJwBx14t52zSwBQzOXle26hmbG4dOmSduzYobCwMIf2sLAwbdq0KccxsbGx2fo/8cQT2r59uy5fvixJWrFihUJCQvTaa6/Jz89PderU0dixY5WZmXnDWtLT05WSkuLwAAAAAIqzQhMsTp48qczMTPn5+Tm0+/n5yWaz5TjGZrPl2D8jI0MnT56UJP3555/66quvlJmZqZUrV2rYsGH6z3/+ozFjxtywlnHjxslqtdofVapUMXl0AAAAQOFWaILFVRaLxeG5YRjZ2m7W/9r2rKwsVahQQbNmzVLDhg3VtWtXDR061OF0q+sNHjxYycnJ9sfhw4dv93AAAACAIsHV2QXcKl9fX7m4uGSbnTh+/Hi2WYmr/P39c+zv6uqqcuXKSZIqVqwoNzc3ubi42PsEBQXJZrPp0qVLcnd3z7ZfDw8PeXh4mD0kAAAAoMgoNDMW7u7uatiwoaKjox3ao6Oj1bRp0xzHhISEZOu/evVqNWrUyH6h9iOPPKI//vhDWVlZ9j6//fabKlasmGOoAAAAAJBdoQkWkhQVFaU5c+Zo3rx5SkhIUL9+/XTo0CH17t1b0pVTlLp3727v37t3bx08eFBRUVFKSEjQvHnzNHfuXPXv39/e55///KdOnTqlN954Q7/99pu+//57jR07Vq+99lqBHx8AAABQWBWaU6EkqUuXLjp16pRGjx6tpKQk1alTRytXrlTVqlUlSUlJSQ73tAgMDNTKlSvVr18/ffTRR6pUqZImT56sZ555xt6nSpUqWr16tfr166d69erp3nvv1RtvvKGBAwcW+PEBAAAAhVWhuo/F3Yr7WAC4W3Afi6KF+1gAcLYieR8LAAAAAHcvggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwLR8DxaHDx/WkSNH7M+3bt2qvn37atasWfn90gAAAAAKSL4Hi4iICK1fv16SZLPZFBoaqq1bt2rIkCEaPXp0fr88AAAAgAKQ78Fi9+7daty4sSTpiy++UJ06dbRp0yYtXrxYCxYsyO+XBwAAAFAA8j1YXL58WR4eHpKkNWvWqGPHjpKkWrVqKSkpKb9fHgAAAEAByPdg8eCDD2rGjBn68ccfFR0drfDwcEnS0aNHVa5cufx+eQAAAAAFIN+Dxfjx4zVz5ky1aNFC3bp100MPPSRJWrFihf0UKQAAAACFm2t+v0CLFi108uRJpaSkqEyZMvb2V155RSVLlszvlwcAAABQAArkPhaGYWjHjh2aOXOmUlNTJUnu7u4ECwAAAKCIyPcZi4MHDyo8PFyHDh1Senq6QkND5e3trQkTJujixYuaMWNGfpcAAAAAIJ/l+4zFG2+8oUaNGunMmTPy8vKytz/11FNau3Ztfr88AAAAgAKQ7zMWGzdu1E8//SR3d3eH9qpVq+qvv/7K75cHAAAAUADyfcYiKytLmZmZ2dqPHDkib2/v/H55AAAAAAUg34NFaGioJk2aZH9usVh07tw5jRgxQm3bts3vlwcAAABQAPL9VKgPPvhALVu2VO3atXXx4kVFRETo999/l6+vr5YsWZLfLw8AAACgAOR7sKhUqZLi4uK0ZMkS/fzzz8rKytJLL72k5557zuFibgAAAACFV74HC0ny8vLSiy++qBdffLEgXg4AAABAAcv3YLFw4cJct3fv3j2/SwAAAACQz/I9WLzxxhsOzy9fvqzz58/b77xNsAAAAAAKv3xfFerMmTMOj3Pnzmnfvn169NFHuXgbAAAAKCLyPVjk5P7779e7776bbTYDAAAAQOHklGAhSS4uLjp69KizXh4AAADAHZTv11isWLHC4blhGEpKStLUqVP1yCOP5PfLAwAAACgA+R4sOnXq5PDcYrGofPnyatWqlf7zn//k98sDAAAAKAD5HiyysrLy+yUAAAAAOJnTrrEAAAAAUHTky4xFVFTULfedOHFinvY9bdo0vffee0pKStKDDz6oSZMmqVmzZjfsHxMTo6ioKO3Zs0eVKlXSgAED1Lt37xz7fvbZZ+rWrZuefPJJff3113mqCwAAACjO8iVY7Ny585b6WSyWPO33888/V9++fTVt2jQ98sgjmjlzptq0aaO9e/fqvvvuy9Y/MTFRbdu2Vc+ePfXJJ5/op59+0quvvqry5cvrmWeeceh78OBB9e/fP9eQAgAAACBnFsMwDGcXcauCg4PVoEEDTZ8+3d4WFBSkTp06ady4cdn6Dxw4UCtWrFBCQoK9rXfv3oqPj1dsbKy9LTMzU82bN9c//vEP/fjjjzp79myeZixSUlJktVqVnJwsHx+f2zs4ALgDAgZ97+wScAcdeLeds0sAUMzl5XtuobnG4tKlS9qxY4fCwsIc2sPCwrRp06Ycx8TGxmbr/8QTT2j79u26fPmyvW306NEqX768XnrppVuqJT09XSkpKQ4PAAAAoDjL91WhJGnbtm368ssvdejQIV26dMlh27Jly25pHydPnlRmZqb8/Pwc2v38/GSz2XIcY7PZcuyfkZGhkydPqmLFivrpp580d+5cxcXF3fLxjBs3TqNGjbrl/gAAAEBRl+8zFp999pkeeeQR7d27V8uXL9fly5e1d+9erVu3TlarNc/7u/66DMMwcr1WI6f+V9tTU1P1/PPPa/bs2fL19b3lGgYPHqzk5GT74/Dhw3k4AgAAAKDoyfcZi7Fjx+qDDz7Qa6+9Jm9vb3344YcKDAxUr169VLFixVvej6+vr1xcXLLNThw/fjzbrMRV/v7+OfZ3dXVVuXLltGfPHh04cEAdOnSwb7963w1XV1ft27dP1atXz7ZfDw8PeXh43HLtAAAAQFGX7zMW+/fvV7t2Vy4+8/DwUFpamiwWi/r166dZs2bd8n7c3d3VsGFDRUdHO7RHR0eradOmOY4JCQnJ1n/16tVq1KiR3NzcVKtWLf3yyy+Ki4uzPzp27KiWLVsqLi5OVapUyePRAgAAAMVTvs9YlC1bVqmpqZKke++9V7t371bdunV19uxZnT9/Pk/7ioqKUmRkpBo1aqSQkBDNmjVLhw4dst+XYvDgwfrrr7+0cOFCSVdWgJo6daqioqLUs2dPxcbGau7cuVqyZIkkydPTU3Xq1HF4jXvuuUeSsrUDAAAAuLF8CxZxcXH629/+pmbNmik6Olp169ZV586d9cYbb2jdunWKjo7W448/nqd9dunSRadOndLo0aOVlJSkOnXqaOXKlapataokKSkpSYcOHbL3DwwM1MqVK9WvXz999NFHqlSpkiZPnpztHhYAAAAAzMm3+1iUKFFC9evXV6dOnfTyyy+rYsWKysrK0vvvv6+NGzeqRo0aGj58uMqUKZMfL1+guI8FgLsF97EoWriPBQBnuyvuY/HTTz+pQYMGev/991W9enU9//zziomJ0YABA7RixQpNnDixSIQKAAAAAPkYLEJCQjR79mzZbDZNnz5dR44cUevWrVW9enWNGTNGR44cya+XBgAAAFDA8n1VKC8vL73wwgvasGGDfvvtN3Xr1k0zZ85UYGCg2rZtm98vDwAAAKAA5HuwuFb16tU1aNAgDR06VD4+Pvrhhx8K8uUBAAAA5JN8X272qpiYGM2bN09Lly6Vi4uLOnfurJdeeqmgXh4AAABAPsrXYHH48GEtWLBACxYsUGJiopo2baopU6aoc+fOKlWqVH6+NAAAAIAClG/BIjQ0VOvXr1f58uXVvXt3vfjii6pZs2Z+vRwAAAAAJ8q3YOHl5aWlS5eqffv2cnFxya+XAQAAAHAXyLdgsWLFivzaNQAAAIC7TIGuCgUAAACgaCJYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwrdMFi2rRpCgwMlKenpxo2bKgff/wx1/4xMTFq2LChPD09Va1aNc2YMcNh++zZs9WsWTOVKVNGZcqUUevWrbV169b8PAQAAACgyClUweLzzz9X3759NXToUO3cuVPNmjVTmzZtdOjQoRz7JyYmqm3btmrWrJl27typIUOGqE+fPlq6dKm9z4YNG9StWzetX79esbGxuu+++xQWFqa//vqroA4LAAAAKPQshmEYzi7iVgUHB6tBgwaaPn26vS0oKEidOnXSuHHjsvUfOHCgVqxYoYSEBHtb7969FR8fr9jY2BxfIzMzU2XKlNHUqVPVvXv3W6orJSVFVqtVycnJ8vHxyeNRAcCdEzDoe2eXgDvowLvtnF0CgGIuL99zC82MxaVLl7Rjxw6FhYU5tIeFhWnTpk05jomNjc3W/4knntD27dt1+fLlHMecP39ely9fVtmyZW9YS3p6ulJSUhweAAAAQHFWaILFyZMnlZmZKT8/P4d2Pz8/2Wy2HMfYbLYc+2dkZOjkyZM5jhk0aJDuvfdetW7d+oa1jBs3Tlar1f6oUqVKHo8GAAAAKFoKTbC4ymKxODw3DCNb283659QuSRMmTNCSJUu0bNkyeXp63nCfgwcPVnJysv1x+PDhvBwCAAAAUOS4OruAW+Xr6ysXF5dssxPHjx/PNitxlb+/f479XV1dVa5cOYf2999/X2PHjtWaNWtUr169XGvx8PCQh4fHbRwFAAAAUDQVmhkLd3d3NWzYUNHR0Q7t0dHRatq0aY5jQkJCsvVfvXq1GjVqJDc3N3vbe++9p7ffflurVq1So0aN7nzxAAAAQBFXaIKFJEVFRWnOnDmaN2+eEhIS1K9fPx06dEi9e/eWdOUUpWtXcurdu7cOHjyoqKgoJSQkaN68eZo7d6769+9v7zNhwgQNGzZM8+bNU0BAgGw2m2w2m86dO1fgxwcAAAAUVoXmVChJ6tKli06dOqXRo0crKSlJderU0cqVK1W1alVJUlJSksM9LQIDA7Vy5Ur169dPH330kSpVqqTJkyfrmWeesfeZNm2aLl26pL///e8OrzVixAiNHDmyQI4LAAAAKOwK1X0s7lbcxwLA3YL7WBQt3McCgLMVyftYAAAAALh7ESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYVuiCxbRp0xQYGChPT081bNhQP/74Y679Y2Ji1LBhQ3l6eqpatWqaMWNGtj5Lly5V7dq15eHhodq1a2v58uX5VT4AAABQJBWqYPH555+rb9++Gjp0qHbu3KlmzZqpTZs2OnToUI79ExMT1bZtWzVr1kw7d+7UkCFD1KdPHy1dutTeJzY2Vl26dFFkZKTi4+MVGRmpzp07a8uWLQV1WAAAAEChZzEMw3B2EbcqODhYDRo00PTp0+1tQUFB6tSpk8aNG5et/8CBA7VixQolJCTY23r37q34+HjFxsZKkrp06aKUlBT997//tfcJDw9XmTJltGTJkluqKyUlRVarVcnJyfLx8bndwwMA0wIGfe/sEnAHHXi3nbNLAFDM5eV7bqGZsbh06ZJ27NihsLAwh/awsDBt2rQpxzGxsbHZ+j/xxBPavn27Ll++nGufG+1TktLT05WSkuLwAAAAAIozV2cXcKtOnjypzMxM+fn5ObT7+fnJZrPlOMZms+XYPyMjQydPnlTFihVv2OdG+5SkcePGadSoUbd5JIUbv4YWLfwaWvTwZwrcvfg3tOjhv7mOCs2MxVUWi8XhuWEY2dpu1v/69rzuc/DgwUpOTrY/Dh8+fMv1AwAAAEVRoZmx8PX1lYuLS7aZhOPHj2ebcbjK398/x/6urq4qV65crn1utE9J8vDwkIeHx+0cBgAAAFAkFZoZC3d3dzVs2FDR0dEO7dHR0WratGmOY0JCQrL1X716tRo1aiQ3N7dc+9xonwAAAACyKzQzFpIUFRWlyMhINWrUSCEhIZo1a5YOHTqk3r17S7pyitJff/2lhQsXSrqyAtTUqVMVFRWlnj17KjY2VnPnznVY7emNN97QY489pvHjx+vJJ5/UN998ozVr1mjjxo1OOUYAAACgMCpUwaJLly46deqURo8eraSkJNWpU0crV65U1apVJUlJSUkO97QIDAzUypUr1a9fP3300UeqVKmSJk+erGeeecbep2nTpvrss880bNgwDR8+XNWrV9fnn3+u4ODgAj8+AAAAoLAqVPexuFsVp/tYsKJF0cJqFgBQcPg3tOgpDv+OFsn7WAAAAAC4exEsAAAAAJhGsAAAAABgGsECAAAAgGmFalUoOF9xuEgJAAAAeUewAAAAKAD8OIeijlOhAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYJqrswsoCgzDkCSlpKQ4uRIAAADgzrn6/fbq993cECzugNTUVElSlSpVnFwJAAAAcOelpqbKarXm2sdi3Er8QK6ysrJ09OhReXt7y2KxOLscmJSSkqIqVaro8OHD8vHxcXY5AK7DZxS4u/EZLVoMw1BqaqoqVaqkEiVyv4qCGYs7oESJEqpcubKzy8Ad5uPjw38QgbsYn1Hg7sZntOi42UzFVVy8DQAAAMA0ggUAAAAA0wgWwHU8PDw0YsQIeXh4OLsUADngMwrc3fiMFl9cvA0AAADANGYsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawQLE2ZcoUZ5cA4BZs27ZNUVFRat++vTp06KCoqCht377d2WUBuIlly5apXr16zi4DBYRggWJtxIgRCg0N1ZEjR5xdCoAbGDBggIKDgzVnzhwdOXJEhw4d0uzZsxUcHKyBAwc6uzyg2Js9e7aeffZZRUREaMuWLZKkdevWqX79+nr++ecVEhLi5ApRUAgWKNZ2794tDw8P1a1bV4sWLXJ2OQCu8/HHH2vKlCmaPHmyTp06pbi4OMXHx+v06dP64IMPNHnyZC1cuNDZZQLF1vvvv6/XXntNiYmJ+uabb9SqVSuNHTtWnTt3VqdOnXTo0CHNnDnT2WWigHCDPEDSggULFBUVpRYtWmjYsGFydXV12M40LuAcjRs3Vrdu3dSvX78ct0+cOFGfffaZtm7dWsCVAZCkoKAgvfXWW3rxxRe1YcMGtWrVSq1atdJXX32le+65x9nloYARLID/b82aNQoPD5dhGDIMQxaLxf6/mZmZzi4PKJZKlSqlX375RdWqVctx+59//qm6desqLS2tgCsDIEklS5bUr7/+qvvuu0+S5OHhof/9738KDg52cmVwBk6FAnTlV88nn3xSzz//vH777TclJibqzz//tP8vAOdwcXHRpUuXbrj98uXLcnFxKcCKAFzr4sWL8vT0tD93d3dX+fLlnVgRnMn15l2AouvPP/9U9+7dtX//fi1evFhPPvmks0sCcI2GDRvq008/1dtvv53j9kWLFqlBgwYFXBWAa82ZM0elS5eWJGVkZGjBggXy9fV16NOnTx9nlIYCxqlQKNZKly6t8PBwzZgxI9t/BAE433fffadOnTopKipKb775pvz8/CRJNptN//nPfzRp0iQtX75c7du3d3KlQPEUEBAgi8WSax+LxcLsfzFBsECx9sknn+j55593dhkAcjFlyhT1799fGRkZslqtkqTk5GS5uLhowoQJ6tu3r3MLBABI4lQoFHO5nbstSampqerXr5/mzJlTQBUBuN6//vUvPfXUU/ryyy/1+++/S5IeeOABPfPMM6pSpYqTqwOQm1OnTmnRokX8AFBMMGOBYs1qtapZs2aaM2eO/P39Hbb98MMP6tmzp8qUKaP4+HgnVQgAQOFiGIZWr16tuXPn6ptvvpGPj49OnDjh7LJQAFgVCsVafHy80tLS9OCDD2rJkiWSrsxSvPTSS+rYsaO6d++u7du3O7lKoPj6448/tGPHDoe2tWvXqmXLlmrcuLHGjh3rpMoAXO/AgQP697//rapVq6pt27by9PTU999/L5vN5uzSUEAIFijWAgICtH79eg0fPlw9e/ZU+/btVadOHW3dulWbNm3SO++8Izc3N2eXCRRbb731lr7++mv788TERHXo0EHu7u4KCQnRuHHjNGnSJKfVBxR36enpWrJkiR5//HEFBQVp9+7dmjhxokqUKKFBgwapdevWLAldjHAqFCDpwoULeu655/T111+rVKlS2rhxox566CFnlwUUe1WqVNEXX3yhkJAQSdI777yjr776SnFxcZKkuXPnasqUKfbnAAqWr6+vateureeff17PPvusypQpI0lyc3NTfHy8ateu7eQKUZCYsUCx99NPP+mhhx7Svn37tGrVKrVp00YhISH64IMPnF0aUOydPHlSlStXtj9fv369OnToYH/eokULHThwwAmVAZCkzMxMWSwWWSwWZiZAsEDx9uabb6pVq1bq0KGDfv75Z4WFhemLL77Q/PnzNXbsWD322GOsvQ04UdmyZZWUlCRJysrK0vbt2xUcHGzffunSJTHxDjhPUlKSXnnlFS1ZskT+/v565plntHz58pve2wJFE8ECxdo333yjNWvW6D//+Y88PDzs7V26dNHu3btVtmxZTokCnKh58+Z6++23dfjwYU2aNEmZmZlq2bKlffvevXsVEBDgvAKBYs7T01PPPfec1q1bp19++UVBQUHq06ePMjIyNGbMGEVHRyszM9PZZaKAcI0FirW0tDSVKlUq1z6LFi1SZGRkAVUE4FqJiYkKDQ3Vn3/+qRIlSmjy5Ml69dVX7ds7deqkwMBATl0EnGThwoXq0qWLw49zWVlZ+uGHHzR37lx9++238vb21smTJ51YJQoKwQLIxS+//KK5c+ey6gzgRJcvX9bevXtVvnx5ubu7y93dXT4+PpKuLBlduXJllStXzslVAsWTi4uLkpKSVKFChRy3nzhxQosWLVJUVFQBVwZn4FQo4DopKSmaOXOmGjdurIceekgbNmxwdklAsZaWlqZZs2apXr168vPzU5kyZeTv76/Bgwfr/vvvJ1QATnSz36fLly9PqChGXJ1dAHC3iImJ0dy5c7V06VJdvHhRb731lhYvXqwaNWo4uzSg2Dp9+rRCQkL0119/6bnnnlNQUJAMw1BCQoKmTJmi6Ohobdy4UfHx8dqyZYv69Onj7JKBYocLtXEVp0KhWEtKStL8+fM1b948paWlqVu3boqIiFBISAjrbwN3gb59+2rt2rVas2aN/Pz8HLbZbDaFhYWpZs2aWr16tSZPnqwXXnjBSZUCxVOJEiXUpk0bh2sscrJs2bICqgjOxIwFirXAwEA9++yz+uijjxQaGqoSJTg7ELibfP3115o5c2a2UCFJ/v7+mjBhgtq2basRI0YQKgAn8fb2lpeXl7PLwF2AGQsUazVr1tSlS5cUERGhyMhI1apVSxJ3DAXuFh4eHtq/f7/DTfKudeTIEQUEBCgjI6OAKwMgXZmxsNlsN7x4G8ULP8+iWNu3b58++eQTJSUl6eGHH1bDhg3ty1ZyzijgfL6+vrneWTsxMZEvNIAT8W8lrsWMBfD/nTt3TkuWLNG8efO0ZcsWNW/eXBEREerUqZPKly/v7PKAYumll17SH3/8oejoaLm7uztsS09P1xNPPKFq1app3rx5TqoQKN6YscC1CBZADhISEjR37lwtWrRIp0+f1uXLl51dElAsHTlyRI0aNZKHh4dee+01++mKe/fu1bRp05Senq5t27bpvvvuc3KlQPH07rvv6sEHH1SHDh3sbQsXLtSIESOUlpamTp06acqUKTe9uBtFA8ECyEVGRoZWrFihp59+2tmlAMVWYmKiXn31Va1evdq+Zr7FYlFoaKimTp3KktCAE4WHh6tly5YaOHCgpCs3lm3QoIF69OihoKAgvffee+rVq5dGjhzp3EJRIAgWQC4SEhLUrl07/fnnn84uBSj2zpw5o99//12SVKNGDZUtW9bJFQGoWLGivv32WzVq1EiSNHToUMXExGjjxo2SpC+//FIjRozQ3r17nVkmCgjLzQK5uHTpkg4ePOjsMgBIKlOmjBo3buzsMgBc48yZMw7LQcfExCg8PNz+/OGHH9bhw4edURqcgFWhAAAAcFv8/PyUmJgo6cqPcT///LNCQkLs21NTU+Xm5uas8lDACBYAAAC4LeHh4Ro0aJB+/PFHDR48WCVLllSzZs3s23ft2qXq1as7sUIUJE6FAgAAwG1555139PTTT6t58+YqXbq0Pv74Y4eloefNm6ewsDAnVoiCxMXbKNbKlCmT6819MjIylJaWpszMzAKsCgCAwiU5OVmlS5eWi4uLQ/vp06dVunTpbPehQdHEjAWKtUmTJjm7BAAACj2r1ZpjO6u3FS/MWAA3kZGRIVdXMjgAAEBuuHgbuIG9e/fqzTff1L333uvsUgAAAO56BAvgGufOndOcOXMUEhKievXqacuWLRo0aJCzywIAALjrcX4HIGnjxo2aM2eOli5dqsDAQO3du1cxMTF65JFHnF0aAABAocCMBYq1CRMmqFatWuratavKly+vjRs3ateuXbJYLCpTpoyzywMAACg0uHgbxZqrq6sGDhyo0aNHOyyR5+bmpvj/x96dx1VV7f8ffx9BUBFOIjiFCs6zmaaimSNEin3NyqlQG0x/ZZZWGg453MzSyhzSyhxyvNlVy5tdc/abCSoqmolYJg4J5gikiQz790dfz+0EkraBDZzX8/E4j+tee+29P5s6Xd6utfY+cEANGjSwsDoAAICigxELuLRJkybps88+U1BQkEaNGqVDhw5ZXRIAAECRRLCASxs9erSOHj2qJUuWKCkpSa1bt1bTpk1lGIYuXbpkdXkAAABFBlOhgD9ISUnR8uXLtWDBAu3du1etWrXSI488ohEjRlhdGgAAQKFGsABu4tChQ5o/f76WLVumX375xepyAAAACjUeNwuX9ttvv2nz5s0KDw+XJEVGRiotLc2x393dXceOHbOqPAAAgCKDYAGXtnjxYn355ZeOYDF79mw1bNhQpUuXliTFx8erSpUqGj58uJVlAgAAFHpMhYJLu++++zR8+HA99NBDkiRvb28dOHBANWrUkCQtXbpU77//vqKioqwsEwAAoNDjqVBwaUePHlWdOnUc26VKlVKJEv/9WrRs2VKHDx+2ojQAAIAihalQcGnJyclyd//v1+DcuXNO+7OyspzWXAAAACBnjFjApQUEBOT6UryDBw8qICCgACsCAAAomggWcGldu3bVa6+9pmvXrmXb99tvv2nixInq1q2bBZUBAAAULSzehks7e/as7rrrLnl4eGjo0KGqU6eObDabjhw5otmzZysjI0P79+9XxYoVrS4VAACgUCNYwOUdP35c/+///T9t3LhRN74ONptNISEhmjNnjuMJUQAAALg5ggXwfy5evKgff/xRklSrVi35+vpaXBEAAEDRQbAAAAAAYBqLtwEAAACYRrAAAAAAYBrBAgAAAIBpBAsAQKGSkJAgm82m2NhYq0sBANwGggUAAAAA0wgWAAAAAEwjWAAALJGVlaW33npLtWrVkqenp6pVq6bJkydn65eZmamnnnpKQUFBKl26tOrWrasZM2Y49dm2bZtatmwpLy8v3XHHHWrbtq1OnDghSTpw4IA6duwob29v+fj4qHnz5oqJiSmQewQAV+JudQEAANcUGRmpefPmafr06br33nuVmJioI0eOZOuXlZWlgIAArVy5Un5+ftq5c6eeeeYZVa5cWb169VJGRoZ69OihQYMGacWKFbp+/bp2794tm80mSXrsscfUrFkzzZ07V25uboqNjVXJkiUL+nYBoNjjBXkAgAKXmpoqf39/zZ49W08//bTTvoSEBAUFBWn//v266667cjz+ueee09mzZ/Wvf/1LFy9eVPny5bVt2za1b98+W18fHx/NmjVLAwYMyI9bAQD8H6ZCAQAKXFxcnNLS0tS5c+db6v/BBx+oRYsW8vf3V9myZTVv3jydPHlSkuTr66uBAwfq/vvvV/fu3TVjxgwlJiY6jh0xYoSefvppdenSRW+++aaOHTuWL/cEAK6OYAEAKHClS5e+5b4rV67U8OHD9eSTT2rDhg2KjY3VE088oevXrzv6LFy4UFFRUWrTpo0+/fRT1alTR9HR0ZKkCRMm6Pvvv1e3bt20ZcsWNWjQQGvWrMnzewIAV8dUKABAgbt27Zp8fX01c+bMv5wK9fzzz+vw4cPavHmzo0+XLl10/vz5m77rIjg4WPfcc49mzpyZbV/fvn115coVrV27Nk/vCQBcHSMWAIACV6pUKY0aNUojR47U4sWLdezYMUVHR2v+/PnZ+taqVUsxMTH6+uuvdfToUY0bN0579uxx7D9+/LgiIyMVFRWlEydOaMOGDTp69Kjq16+v3377TUOHDtW2bdt04sQJffvtt9qzZ4/q169fkLcLAC6Bp0IBACwxbtw4ubu767XXXtOZM2dUuXJlDRkyJFu/IUOGKDY2Vr1795bNZlPfvn317LPP6j//+Y8kqUyZMjpy5Ig++eQTXbhwQZUrV9bQoUM1ePBgZWRk6MKFC+rfv7/Onj0rPz8/9ezZUxMnTizo2wWAYo+pUAAAAABMYyoUAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAFCAFi1aJJvNplKlSunEiRPZ9nfo0EGNGjWyoLK8MXDgQAUGBjq1BQYGauDAgQVaR0JCgmw2mxYtWpRrv23btslmszk+bm5u8vf3V/fu3RUTE1Mwxebixr8v0n9rTUhIuO3z3Ph5bNu2TZJu6WcDALfL3eoCAMAVpaWlaezYsVqyZInVpeS7NWvWyMfHx+oycvXGG2+oY8eOSk9P1/79+zVx4kS1b99esbGxql27ttXlAUCRwIgFAFggLCxMy5cv14EDB/L1Or/99lu+nv9WNGvWTDVr1rS6jFzVrl1brVu3Vrt27TRs2DBNnz5dV69e1dKlS60uDQCKDIIFAFhg5MiRKl++vEaNGvWXfa9du6bIyEgFBQXJw8NDd955p5577jldvnzZqV9gYKDCw8O1evVqNWvWTKVKldLEiRMdU2iWL1+uUaNGqXLlyipbtqy6d++us2fPKjU1Vc8884z8/Pzk5+enJ554Qr/++qvTud9//33dd999qlChgry8vNS4cWNNnTpV6enpf1n/n6dCdejQwWn60R8/f5yek5SUpMGDBysgIEAeHh4KCgrSxIkTlZGR4XT+M2fOqFevXvL29pbdblfv3r2VlJT0l3XlpkWLFpKks2fPOrXv2LFDnTt3lre3t8qUKaM2bdpo3bp1jv0pKSlyd3fXtGnTHG3nz59XiRIlZLfbnWofNmyY/P39ZRjG367zZtPMOnTooA4dOvzt8wLA38FUKACwgLe3t8aOHasXXnhBW7ZsUadOnXLsZxiGevTooc2bNysyMlLt2rXTwYMHNX78eEVFRSkqKkqenp6O/vv27VNcXJzGjh2roKAgeXl56cqVK5Kk0aNHq2PHjlq0aJESEhL08ssvq2/fvnJ3d1fTpk21YsUK7d+/X6NHj5a3t7dmzpzpOO+xY8fUr18/R7g5cOCAJk+erCNHjmjBggW3de9z5sxRSkqKU9u4ceO0detW1a1bV9LvoaJly5YqUaKEXnvtNdWsWVNRUVF6/fXXlZCQoIULF0r6fUSmS5cuOnPmjKZMmaI6depo3bp16t27923V9GfHjx+XJNWpU8fRtn37doWEhKhJkyaaP3++PD09NWfOHHXv3l0rVqxQ79695ePjo3vuuUebNm3SK6+8IknavHmzPD09lZqaqt27d6tNmzaSpE2bNqlTp06ONRQ5GThwoCM4dOjQ4W+HkMDAQKdjzYQZALgpAwBQYBYuXGhIMvbs2WOkpaUZNWrUMFq0aGFkZWUZhmEY7du3Nxo2bOjov379ekOSMXXqVKfzfPrpp4Yk46OPPnK0Va9e3XBzczPi4+Od+m7dutWQZHTv3t2p/cUXXzQkGcOGDXNq79Gjh+Hr63vTe8jMzDTS09ONxYsXG25ubsbFixcd+wYMGGBUr17dqX/16tWNAQMG3PR806ZNy3YvgwcPNsqWLWucOHHCqe/bb79tSDK+//57wzAMY+7cuYYk44svvnDqN2jQIEOSsXDhwpte1zD++7P59NNPjfT0dOPq1avGt99+a9StW9do0KCBcenSJUff1q1bGxUqVDBSU1MdbRkZGUajRo2MgIAAxz/DsWPHGqVLlzauXbtmGIZhPP3000ZYWJjRpEkTY+LEiYZhGMbPP/+c7Z7/jpv9bNu3b2+0b9/e1LkB4HYxFQoALOLh4aHXX39dMTExWrlyZY59tmzZIknZprs8+uij8vLy0ubNm53amzRp4vS37H8UHh7utF2/fn1JUrdu3bK1X7x40Wk61P79+/Xggw+qfPnycnNzU8mSJdW/f39lZmbq6NGjf32zN7FixQqNHDlSY8eO1aBBgxztX375pTp27KgqVaooIyPD8XnggQck/T56IElbt26Vt7e3HnzwQafz9uvX77bq6N27t0qWLKkyZcqobdu2SklJ0bp163THHXdIkq5cuaJdu3bpkUceUdmyZR3Hubm5KSIiQqdPn1Z8fLwkqXPnzvrtt9+0c+dOSb+PTISEhKhLly7auHGjo02SunTpclt1AkBhRrAAAAv16dNHd999t8aMGZPjeoULFy7I3d1d/v7+Tu02m02VKlXShQsXnNorV65802v5+vo6bXt4eOTafu3aNUnSyZMn1a5dO/3888+aMWOGvvnmG+3Zs0fvv/++pL+/QHzr1q0aOHCg+vfvr3/84x9O+86ePat///vfKlmypNOnYcOGkn5ftyD9/vOpWLFitnNXqlTptmp56623tGfPHm3fvl1jxozR2bNn1aNHD6WlpUmSLl26JMMwcvz5VqlSxVGLJLVp00ZlypTRpk2b9OOPPyohIcERLHbt2qVff/1VmzZtUo0aNRQUFHRbdQJAYcYaCwCwkM1m01tvvaWQkBB99NFH2faXL19eGRkZOnfunFO4MAxDSUlJuueee7KdL699/vnnunLlilavXq3q1as72mNjY//2OQ8ePKgePXqoffv2mjdvXrb9fn5+atKkiSZPnpzj8Td+mS9fvrx2796dbf/tLt6uUaOGY8H2fffdp9KlS2vs2LGaNWuWXn75ZZUrV04lSpRQYmJitmPPnDnjqFn6PZjde++92rRpkwICAlSpUiU1btxYNWrUkPT7+yg2b96cbQTp7yhVqpQj/PzR+fPnHfUAQEFhxAIALNalSxeFhIRo0qRJ2Z7G1LlzZ0nK9tjTVatW6cqVK479+elGWPnjInHDMHIMBLfi5MmTeuCBB1SjRg2tWrVKJUuWzNYnPDxchw4dUs2aNdWiRYtsnxvBomPHjkpNTdXatWudjl++fPnfqu2GkSNHqlatWnrzzTeVmpoqLy8vtWrVSqtXr3YaocnKytLSpUsVEBDgNAWtS5cu2rt3r1atWuWY7uTl5aXWrVtr1qxZOnPmTJ5MgwoMDNTBgwed2o4ePeqYlgUABYlgAQCFwFtvvaVz585p7969Tu0hISG6//77NWrUKE2cOFGbNm3Su+++qyeeeELNmjVTREREvtcWEhIiDw8P9e3bV//5z3+0Zs0a3X///bp06dLfOt8DDzygy5cv67XXXtP333+v6Ohox+fcuXOSpEmTJqlkyZJq06aN5s6dqy1btuirr77SnDlzFB4ertOnT0uS+vfvrzp16qh///56//33tWHDBr344ov6+uuvTd1zyZIl9cYbb+jChQuaMWOGJGnKlCm6cOGCOnbsqH/9619au3atunbtqkOHDuntt992Gi3q3LmzMjMztXnzZoWEhDjau3Tpog0bNshms930SWC3IyIiQocPH9azzz6rzZs3a8GCBXrwwQezTZ0DgIJAsACAQqBZs2bq27dvtnabzabPP/9cI0aM0MKFC9W1a1e9/fbbioiI0JYtW5xGEfJLvXr1tGrVKl26dEk9e/bU888/r7vuusvpcbS34/Dhw7p69ap69uyp4OBgp8+Nd0JUrlxZMTExCg0N1bRp0xQWFqaIiAgtWLBAd911l8qVKydJKlOmjLZs2aIuXbro1Vdf1SOPPKLTp0/rn//8p+n7fvTRR9WqVSu9++67Sk5OVvv27bVlyxZ5eXlp4MCB6tOnj5KTk7V27dpsj7dt1qyZYyrSH0cmbvy5WbNmKl++vOka+/Xrp6lTp+rrr79WeHi45s6dq7lz5950AT8A5CebYfAwawAAAADmMGIBAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMc7e6gOIgKytLZ86ckbe3t9ObVwEAAICizDAMpaamqkqVKipRIvcxCYJFHjhz5oyqVq1qdRkAAABAvjh16pQCAgJy7UOwyAPe3t6Sfv+B+/j4WFwNAAAAkDdSUlJUtWpVx++7uSFY5IEb0598fHwIFgAAACh2bmW6P4u3AQAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYxpu3cVsCX11ndQnIQwlvdrO6BAAAUEwwYgEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwrVgGizlz5igoKEilSpVS8+bN9c0339y0b2Jiovr166e6deuqRIkSevHFFwuuUAAAAKCYKHbB4tNPP9WLL76oMWPGaP/+/WrXrp0eeOABnTx5Msf+aWlp8vf315gxY9S0adMCrhYAAAAoHopdsHj33Xf11FNP6emnn1b9+vX13nvvqWrVqpo7d26O/QMDAzVjxgz1799fdru9gKsFAAAAiodiFSyuX7+uvXv3KjQ01Kk9NDRUO3fuzLPrpKWlKSUlxekDAAAAuLJiFSzOnz+vzMxMVaxY0am9YsWKSkpKyrPrTJkyRXa73fGpWrVqnp0bAAAAKIqKVbC4wWazOW0bhpGtzYzIyEglJyc7PqdOncqzcwMAAABFkbvVBeQlPz8/ubm5ZRud+OWXX7KNYpjh6ekpT0/PPDsfAAAAUNQVqxELDw8PNW/eXBs3bnRq37hxo9q0aWNRVQAAAEDxV6xGLCRpxIgRioiIUIsWLRQcHKyPPvpIJ0+e1JAhQyT9Po3p559/1uLFix3HxMbGSpJ+/fVXnTt3TrGxsfLw8FCDBg2suAUAAACgyCl2waJ37966cOGCJk2apMTERDVq1EhfffWVqlevLun3F+L9+Z0WzZo1c/x57969Wr58uapXr66EhISCLB0AAAAosmyGYRhWF1HUpaSkyG63Kzk5WT4+PlaXk68CX11ndQnIQwlvdrO6BAAAUIjdzu+5xWqNBQAAAABrECwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGnFMljMmTNHQUFBKlWqlJo3b65vvvkm1/7bt29X8+bNVapUKdWoUUMffPBBAVUKAAAAFA/FLlh8+umnevHFFzVmzBjt379f7dq10wMPPKCTJ0/m2P/48ePq2rWr2rVrp/3792v06NEaNmyYVq1aVcCVAwAAAEWXzTAMw+oi8lKrVq109913a+7cuY62+vXrq0ePHpoyZUq2/qNGjdLatWsVFxfnaBsyZIgOHDigqKioW7pmSkqK7Ha7kpOT5ePjY/4mCrHAV9dZXQLyUMKb3awuAQAAFGK383tusRqxuH79uvbu3avQ0FCn9tDQUO3cuTPHY6KiorL1v//++xUTE6P09PQcj0lLS1NKSorTBwAAAHBl7lYXkJfOnz+vzMxMVaxY0am9YsWKSkpKyvGYpKSkHPtnZGTo/Pnzqly5crZjpkyZookTJ+Zd4UUIf8MNFG6MKhYv/De3eOH7WfzwHXVWrEYsbrDZbE7bhmFka/ur/jm13xAZGank5GTH59SpUyYrBgAAAIq2YjVi4efnJzc3t2yjE7/88ku2UYkbKlWqlGN/d3d3lS9fPsdjPD095enpmTdFAwAAAMVAsRqx8PDwUPPmzbVx40an9o0bN6pNmzY5HhMcHJyt/4YNG9SiRQuVLFky32oFAAAAipNiFSwkacSIEfr444+1YMECxcXFafjw4Tp58qSGDBki6fdpTP3793f0HzJkiE6cOKERI0YoLi5OCxYs0Pz58/Xyyy9bdQsAAABAkVOspkJJUu/evXXhwgVNmjRJiYmJatSokb766itVr15dkpSYmOj0TougoCB99dVXGj58uN5//31VqVJFM2fO1MMPP2zVLQAAAABFTrF7j4UVXOk9FgAKN546U7zwxJnihe9n8eMK31GXfY8FAAAAAGsQLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYFq+BItTp07p9OnTju3du3frxRdf1EcffZQflwMAAABgsXwJFv369dPWrVslSUlJSQoJCdHu3bs1evRoTZo0KT8uCQAAAMBC+RIsDh06pJYtW0qSVq5cqUaNGmnnzp1avny5Fi1alB+XBAAAAGChfAkW6enp8vT0lCRt2rRJDz74oCSpXr16SkxMzI9LAgAAALBQvgSLhg0b6oMPPtA333yjjRs3KiwsTJJ05swZlS9fPj8uCQAAAMBC+RIs3nrrLX344Yfq0KGD+vbtq6ZNm0qS1q5d65giBQAAAKD4cM+Pk3bo0EHnz59XSkqKypUr52h/5plnVKZMmfy4JAAAAAAL5dt7LAzD0N69e/Xhhx8qNTVVkuTh4UGwAAAAAIqhfBmxOHHihMLCwnTy5EmlpaUpJCRE3t7emjp1qq5du6YPPvggPy4LAAAAwCL5MmLxwgsvqEWLFrp06ZJKly7taH/ooYe0efPm/LgkAAAAAAvly4jFjh079O2338rDw8OpvXr16vr555/z45IAAAAALJQvIxZZWVnKzMzM1n769Gl5e3vnxyUBAAAAWChfgkVISIjee+89x7bNZtOvv/6q8ePHq2vXrvlxSQAAAAAWypepUNOnT1fHjh3VoEEDXbt2Tf369dMPP/wgPz8/rVixIj8uCQAAAMBC+RIsqlSpotjYWK1YsUL79u1TVlaWnnrqKT322GNOi7kBAAAAFA/5EiwkqXTp0nryySf15JNP5tclAAAAABQS+RIsFi9enOv+/v3758dlAQAAAFgkX4LFCy+84LSdnp6uq1evOt68TbAAAAAAipd8eSrUpUuXnD6//vqr4uPjde+997J4GwAAACiG8iVY5KR27dp68803s41mAAAAACj6CixYSJKbm5vOnDmTb+e/dOmSIiIiZLfbZbfbFRERocuXL+d6zOrVq3X//ffLz89PNptNsbGx+VYfAAAAUFzlyxqLtWvXOm0bhqHExETNnj1bbdu2zY9LSpL69eun06dPa/369ZKkZ555RhEREfr3v/9902OuXLmitm3b6tFHH9WgQYPyrTYAAACgOMuXYNGjRw+nbZvNJn9/f3Xq1EnvvPNOflxScXFxWr9+vaKjo9WqVStJ0rx58xQcHKz4+HjVrVs3x+MiIiIkSQkJCflSFwAAAOAK8iVYZGVl5cdpcxUVFSW73e4IFZLUunVr2e127dy586bBAgAAAIB5+faCvIKWlJSkChUqZGuvUKGCkpKS8vRaaWlpSktLc2ynpKTk6fkBAACAoibPgsWIESNuue+77757y30nTJigiRMn5tpnz549kn6fcvVnhmHk2G7GlClT/rImAAAAwJXkWbDYv3//LfW73V/yhw4dqj59+uTaJzAwUAcPHtTZs2ez7Tt37pwqVqx4W9f8K5GRkU5BKiUlRVWrVs3TawAAAABFSZ4Fi61bt+bVqZz4+fnJz8/vL/sFBwcrOTlZu3fvVsuWLSVJu3btUnJystq0aZOnNXl6esrT0zNPzwkAAAAUZQX6Hov8VL9+fYWFhWnQoEGKjo5WdHS0Bg0apPDwcKeF2/Xq1dOaNWsc2xcvXlRsbKwOHz4sSYqPj1dsbGyer8sAAAAAirN8W7y9Z88effbZZzp58qSuX7/utG/16tX5cs1ly5Zp2LBhCg0NlSQ9+OCDmj17tlOf+Ph4JScnO7bXrl2rJ554wrF9Y9rV+PHjNWHChHypEwAAAChu8iVY/POf/1T//v0VGhqqjRs3KjQ0VD/88IOSkpL00EMP5cclJUm+vr5aunRprn0Mw3DaHjhwoAYOHJhvNQEAAACuIF+mQr3xxhuaPn26vvzyS3l4eGjGjBmKi4tTr169VK1atfy4JAAAAAAL5UuwOHbsmLp16ybp94XOV65ckc1m0/Dhw/XRRx/lxyUBAAAAWChfgoWvr69SU1MlSXfeeacOHTokSbp8+bKuXr2aH5cEAAAAYKE8DRaxsbGSpHbt2mnjxo2SpF69eumFF17QoEGD1LdvX3Xu3DkvLwkAAACgEMjTxdt33323mjVrph49eqhv376Sfn+ZXMmSJbVjxw717NlT48aNy8tLAgAAACgE8nTE4ttvv9Xdd9+tt99+WzVr1tTjjz+u7du3a+TIkVq7dq3effddlStXLi8vCQAAAKAQyNNgERwcrHnz5ikpKUlz587V6dOn1aVLF9WsWVOTJ0/W6dOn8/JyAAAAAAqJfFm8Xbp0aQ0YMEDbtm3T0aNH1bdvX3344YcKCgpS165d8+OSAAAAACyUL8Hij2rWrKlXX31VY8aMkY+Pj77++uv8viQAAACAApYvb96+Yfv27VqwYIFWrVolNzc39erVS0899VR+XhIAAACABfI8WJw6dUqLFi3SokWLdPz4cbVp00azZs1Sr1695OXlldeXAwAAAFAI5GmwCAkJ0datW+Xv76/+/fvrySefVN26dfPyEgAAAAAKoTwNFqVLl9aqVasUHh4uNze3vDw1AAAAgEIsT4PF2rVr8/J0AAAAAIqIfH8qFAAAAIDij2ABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMc7e6AABA3kl4s5vVJQAAXBQjFgAAAABMK1bB4tKlS4qIiJDdbpfdbldERIQuX7580/7p6ekaNWqUGjduLC8vL1WpUkX9+/fXmTNnCq5oAAAAoBgoVsGiX79+io2N1fr167V+/XrFxsYqIiLipv2vXr2qffv2ady4cdq3b59Wr16to0eP6sEHHyzAqgEAAICir9issYiLi9P69esVHR2tVq1aSZLmzZun4OBgxcfHq27dutmOsdvt2rhxo1PbrFmz1LJlS508eVLVqlUrkNoBAACAoq7YjFhERUXJbrc7QoUktW7dWna7XTt37rzl8yQnJ8tms+mOO+7IhyoBAACA4qnYjFgkJSWpQoUK2dorVKigpKSkWzrHtWvX9Oqrr6pfv37y8fG5ab+0tDSlpaU5tlNSUm6/YAAAAKAYKfQjFhMmTJDNZsv1ExMTI0my2WzZjjcMI8f2P0tPT1efPn2UlZWlOXPm5Np3ypQpjgXidrtdVatW/Xs3BwAAABQThX7EYujQoerTp0+ufQIDA3Xw4EGdPXs2275z586pYsWKuR6fnp6uXr166fjx49qyZUuuoxWSFBkZqREjRji2U1JSCBcAAABwaYU+WPj5+cnPz+8v+wUHBys5OVm7d+9Wy5YtJUm7du1ScnKy2rRpc9PjboSKH374QVu3blX58uX/8lqenp7y9PS89ZsAAAAAirlCPxXqVtWvX19hYWEaNGiQoqOjFR0drUGDBik8PNzpiVD16tXTmjVrJEkZGRl65JFHFBMTo2XLlikzM1NJSUlKSkrS9evXrboVAAAAoMgpNsFCkpYtW6bGjRsrNDRUoaGhatKkiZYsWeLUJz4+XsnJyZKk06dPa+3atTp9+rTuuusuVa5c2fG5nSdJAQAAAK6u0E+Fuh2+vr5aunRprn0Mw3D8OTAw0GkbAAAAwN9TrEYsAAAAAFiDYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCtWweLSpUuKiIiQ3W6X3W5XRESELl++nOsxEyZMUL169eTl5aVy5cqpS5cu2rVrV8EUDAAAABQTxSpY9OvXT7GxsVq/fr3Wr1+v2NhYRURE5HpMnTp1NHv2bH333XfasWOHAgMDFRoaqnPnzhVQ1QAAAEDRZzMMw7C6iLwQFxenBg0aKDo6Wq1atZIkRUdHKzg4WEeOHFHdunVv6TwpKSmy2+3atGmTOnfufFvHJCcny8fH52/fAwAAKL4CX11ndQnIYwlvdrO6hHx3O7/nFpsRi6ioKNntdkeokKTWrVvLbrdr586dt3SO69ev66OPPpLdblfTpk1v2i8tLU0pKSlOHwAAAMCVFZtgkZSUpAoVKmRrr1ChgpKSknI99ssvv1TZsmVVqlQpTZ8+XRs3bpSfn99N+0+ZMsWxjsNut6tq1aqm6wcAAACKskIfLCZMmCCbzZbrJyYmRpJks9myHW8YRo7tf9SxY0fFxsZq586dCgsLU69evfTLL7/ctH9kZKSSk5Mdn1OnTpm7SQAAAKCIc7e6gL8ydOhQ9enTJ9c+gYGBOnjwoM6ePZtt37lz51SxYsVcj/fy8lKtWrVUq1YttW7dWrVr19b8+fMVGRmZY39PT095enre+k0AAAAAxVyhDxZ+fn65Tku6ITg4WMnJydq9e7datmwpSdq1a5eSk5PVpk2b27qmYRhKS0v7W/UCAAAArqjQT4W6VfXr11dYWJgGDRqk6OhoRUdHa9CgQQoPD3d6IlS9evW0Zs0aSdKVK1c0evRoRUdH68SJE9q3b5+efvppnT59Wo8++qhVtwIAAAAUOYV+xOJ2LFu2TMOGDVNoaKgk6cEHH9Ts2bOd+sTHxys5OVmS5ObmpiNHjuiTTz7R+fPnVb58ed1zzz365ptv1LBhwwKvHwAAFF+u8GhSuLZi8x4LK/EeCwAAABRHLvkeCwAAAADWIVgAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATHO3uoDi4MbLy1NSUiyuBAAAAMg7N36/vfH7bm4IFnkgNTVVklS1alWLKwEAAADyXmpqqux2e659bMatxA/kKisrS2fOnJG3t7dsNpvV5cCklJQUVa1aVadOnZKPj4/V5QD4E76jQOHGd7R4MQxDqampqlKlikqUyH0VBSMWeaBEiRIKCAiwugzkMR8fH/6DCBRifEeBwo3vaPHxVyMVN7B4GwAAAIBpBAsAAAAAphEsgD/x9PTU+PHj5enpaXUpAHLAdxQo3PiOui4WbwMAAAAwjRELAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEs4NJmzZpldQkAAADFAo+bhUvz9fVV8+bNtXDhQgUEBFhdDoCb2LNnj1asWKGjR4/KZrOpdu3a6tevn1q0aGF1aQBysXr1ak2YMEEHDx60uhQUAEYs4NIOHTokT09PNW7cWEuWLLG6HAA5GDlypFq1aqWPP/5Yp0+f1smTJzVv3jy1atVKo0aNsro8wOXNmzdPjz76qPr166ddu3ZJkrZs2aJmzZrp8ccfV3BwsMUVoqAwYgFIWrRokUaMGKEOHTpo7Nixcnd3d9rfpEkTiyoDXNsnn3yiIUOGaNq0aRo8eLBKliwpSUpPT9fcuXM1atQoffjhh+rfv7/FlQKu6e2339bo0aPVpEkTxcXFSZLGjBmjd999V88//7yee+45+fn5WVwlCgrBAvg/mzZtUlhYmAzDkGEYstlsjv/NzMy0ujzAJbVs2VJ9+/bV8OHDc9z/7rvv6p///Kd2795dwJUBkKT69evrlVde0ZNPPqlt27apU6dO6tSpk/71r3/pjjvusLo8FDCCBaDffzkZN26cHn30UY0bNy7biEX16tUtqgxwbV5eXvruu+9Uo0aNHPf/9NNPaty4sa5cuVLAlQGQpDJlyujIkSOqVq2aJMnT01P/+7//q1atWllcGazg/tddgOLrp59+Uv/+/XXs2DEtX75c//M//2N1SQD+wM3NTdevX7/p/vT0dLm5uRVgRQD+6Nq1aypVqpRj28PDQ/7+/hZWBCsRLODSmjRporCwMH3++efMAQUKoebNm2vZsmX6xz/+keP+JUuW6O677y7gqgD80ccff6yyZctKkjIyMrRo0aJs/586bNgwK0pDAWMqFFza0qVL9fjjj1tdBoCb+PLLL9WjRw+NGDFCL730kipWrChJSkpK0jvvvKP33ntPa9asUXh4uMWVAq4pMDBQNpst1z42m00//fRTAVUEKzFiAZeW2xQLSUpNTdXw4cP18ccfF1BFAP4oPDxc06dP18svv6x33nlHdrtdkpScnCw3NzdNmzaNUAFYKCEhweoSUIgwYgGXZrfb1a5dO3388ceqVKmS076vv/5agwYNUrly5XTgwAGLKgQgSadPn9Znn32mH374QZJUp04dPfzww6patarFlQHIzYULF7RkyRK9+OKLVpeCAkCwgEtLSEjQE088oYMHD2r27Nnq27evUlNT9eKLL2rp0qV65ZVXNH78eMez8wEAQO4Mw9CGDRs0f/58ffHFF/Lx8dG5c+esLgsFgDdvw6UFBgZq69atGjdunAYNGqTw8HA1atRIu3fv1s6dO/X6668TKgAL/fjjj9q7d69T2+bNm9WxY0e1bNlSb7zxhkWVAfizhIQEvfbaa6pevbq6du2qUqVKad26dUpKSrK6NBQQggUgafDgwQoNDdVXX32lixcvaunSpWrevLnVZQEu75VXXtHnn3/u2D5+/Li6d+8uDw8PBQcHa8qUKXrvvfcsqw9wdWlpaVqxYoU6d+6s+vXr69ChQ3r33XdVokQJvfrqq+rSpQuPhHYhBAu4vG+//VZNmzZVfHy81q9frwceeEDBwcGaPn261aUBLi8mJkZdu3Z1bC9btkx16tTR119/rRkzZui9997TokWLrCsQcHF33nmn5s6dq969e+vMmTNavXq1HnnkEavLgkUIFnBpL730kjp16qTu3btr3759Cg0N1cqVK7Vw4UK98cYbuu+++3hEHmCh8+fPKyAgwLG9detWde/e3bHdoUMHnkoDWCgzM1M2m002m42RCRAs4Nq++OILbdq0Se+88448PT0d7b1799ahQ4fk6+urpk2bWlgh4Np8fX2VmJgoScrKylJMTIxatWrl2H/9+nXxDBLAOomJiXrmmWe0YsUKVapUSQ8//LDWrFnzl++2QPFEsIBLO3DggNq1a5fjvooVK+rzzz/XnDlzCrgqADe0b99e//jHP3Tq1Cm99957yszMVMeOHR37Dx8+rMDAQOsKBFxcqVKl9Nhjj2nLli367rvvVL9+fQ0bNkwZGRmaPHmyNm7cqMzMTKvLRAEhWMCleXl55br/u+++y/ZEGgAFZ/LkyYqLi1P16tU1cuRITZ061el7u2TJEnXq1MnCCgHXtnjxYqWlpUmSatasqddff10nTpzQunXrlJaWpvDwcFWsWNHiKlFQeI8F8CcpKSlasWKF5s+fr5iYGDVp0kSxsbFWlwW4rPT0dB0+fFj+/v7y8PCQh4eHfHx8JP0+6hgQEKDy5ctbXCXgmtzc3JSYmKgKFSrkuP/cuXNasmSJRowYUcCVwQqMWAD/Z/v27erfv78qV66sZ599Vp06ddLRo0cJFYDFrly5oo8++khNmjRRxYoVVa5cOVWqVEmRkZGqXbs2oQKw0F/9/bS/vz+hwoUwYgGXlpiYqIULF2rBggW6cuWK+vbtq379+ik4OFgHDhxQgwYNrC4RcGkXL15UcHCwfv75Zz322GOqX7++DMNQXFycli9frnr16mnHjh06cOCAdu3apWHDhlldMuBSSpQoobNnz8rf39/qUlAIuFtdAGCloKAgPfroo3r//fcVEhKiEiUYxAMKk0mTJsnDw0PHjh3LNk970qRJCg0NVUREhDZs2KCZM2daVCXg2gYOHOj0ZMWcrF69uoCqgZUIFnBp1atX144dO1StWjVVr15d9erVs7okAH/w+eef68MPP8xx8WelSpU0depUde3aVePHj9eAAQMsqBCAt7e3SpcubXUZKAQIFnBp8fHx+vbbbzV//nzdc889qlOnjh5//HFJ4hncQCGQmJiohg0b3nR/o0aNVKJECY0fP74AqwLwRzNnzrzp4m24FuZ9wOW1bdtWCxYsUGJiooYMGaKVK1cqMzNTzz77rObNm6dz585ZXSLgsvz8/HJ9s/bx48f5hQawEH8Jhz9i8TaQg7i4OM2fP19LlizRxYsXlZ6ebnVJgEt66qmn9OOPP2rjxo3y8PBw2peWlqb7779fNWrU0IIFCyyqEHBtJUqUUFJSEgEfkggWQK4yMjK0du1a9ezZ0+pSAJd0+vRptWjRQp6ennruuecc66AOHz6sOXPmKC0tTXv27FG1atUsrhRwTW+++aYaNmyo7t27O9oWL16s8ePH68qVK+rRo4dmzZr1l4u7UTwQLIBcxMXFqVu3bvrpp5+sLgVwWcePH9ezzz6rDRs2OJ6Zb7PZFBISotmzZ6tWrVoWVwi4rrCwMHXs2FGjRo2SJH333Xe6++67NXDgQNWvX1/Tpk3T4MGDNWHCBGsLRYEgWAC5OHDggO6++25lZmZaXQrg8i5duqQffvhBklSrVi35+vpaXBGAypUr69///rdatGghSRozZoy2b9+uHTt2SJI+++wzjR8/XocPH7ayTBQQngoFACgSypUrp5YtW1pdBoA/uHTpktPjoLdv366wsDDH9j333KNTp05ZURoswFOhAAAA8LdUrFhRx48flyRdv35d+/btU3BwsGN/amqqSpYsaVV5KGAECwAAAPwtYWFhevXVV/XNN98oMjJSZcqUUbt27Rz7Dx48qJo1a1pYIQoSU6Hg0sqVK5frM7gzMjIKsBoAAIqW119/XT179lT79u1VtmxZffLJJ06Phl6wYIFCQ0MtrBAFicXbcGmffPLJLfUbMGBAPlcCAEDRlZycrLJly8rNzc2p/eLFiypbtmy299CgeCJYAH8hIyND7u4M7gEAAOSGNRbATRw+fFgvvfSS7rzzTqtLAQAAKPQIFsAf/Prrr/r4448VHBysJk2aaNeuXXr11VetLgsAAKDQY34HIGnHjh36+OOPtWrVKgUFBenw4cPavn272rZta3VpAAAARQIjFnBpU6dOVb169dSnTx/5+/trx44dOnjwoGw2m8qVK2d1eQAAAEUGi7fh0tzd3TVq1ChNmjTJ6UkWJUuW1IEDB9SgQQMLqwMAACg6GLGAS5s0aZI+++wzBQUFadSoUTp06JDVJQEAABRJBAu4tNGjR+vo0aNasmSJkpKS1Lp1azVt2lSGYejSpUtWlwcAAFBkMBUK+IOUlBQtX75cCxYs0N69e9WqVSs98sgjGjFihNWlAQAAFGoEC+AmDh06pPnz52vZsmX65ZdfrC4HAACgUONxs3Bpv/32mzZv3qzw8HBJUmRkpNLS0hz73d3ddezYMavKAwAAKDIIFnBpixcv1pdffukIFrNnz1bDhg1VunRpSVJ8fLyqVKmi4cOHW1kmAABAocdUKLi0++67T8OHD9dDDz0kSfL29taBAwdUo0YNSdLSpUv1/vvvKyoqysoyAQAACj2eCgWXdvToUdWpU8exXapUKZUo8d+vRcuWLXX48GErSgMAAChSmAoFl5acnCx39/9+Dc6dO+e0Pysry2nNBQAAAHLGiAVcWkBAQK4vxTt48KACAgIKsCIAAICiiWABl9a1a1e99tprunbtWrZ9v/32myZOnKhu3bpZUBkAAEDRwuJtuLSzZ8/qrrvukoeHh4YOHao6derIZrPpyJEjmj17tjIyMrR//35VrFjR6lIBAAAKNYIFXN7x48f1//7f/9PGjRt14+tgs9kUEhKiOXPmOJ4QBQAAgJsjWAD/5+LFi/rxxx8lSbVq1ZKvr6/FFQEAABQdBAsAAAAAprF4GwAAAIBpBAsAAAAAphEsAAAAAJhGsAAAFCoJCQmy2WyKjY21uhQAwG0gWAAAAAAwjWABAAAAwDSCBQDAEllZWXrrrbdUq1YteXp6qlq1apo8eXK2fpmZmXrqqacUFBSk0qVLq27dupoxY4ZTn23btqlly5by8vLSHXfcobZt2+rEiROSpAMHDqhjx47y9vaWj4+PmjdvrpiYmAK5RwBwJe5WFwAAcE2RkZGaN2+epk+frnvvvVeJiYk6cuRItn5ZWVkKCAjQypUr5efnp507d+qZZ55R5cqV1atXL2VkZKhHjx4aNGiQVqxYoevXr2v37t2y2WySpMcee0zNmjXT3Llz5ebmptjYWJUsWbKgbxcAij1ekAcAKHCpqany9/fX7Nmz9fTTTzvtS0hIUFBQkPbv36+77rorx+Ofe+45nT17Vv/617908eJFlS9fXtu2bVP79u2z9fXx8dGsWbM0YMCA/LgVAMD/YSoUAKDAxcXFKS0tTZ07d76l/h988IFatGghf39/lS1bVvPmzdPJkyclSb6+vho4cKDuv/9+de/eXTNmzFBiYqLj2BEjRujpp59Wly5d9Oabb+rYsWP5ck8A4OoIFgCAAle6dOlb7rty5UoNHz5cTz75pDZs2KDY2Fg98cQTun79uqPPwoULFRUVpTZt2ujTTz9VnTp1FB0dLUmaMGGCvv/+e3Xr1k1btmxRgwYNtGbNmjy/JwBwdUyFAgAUuGvXrsnX11czZ878y6lQzz//vA4fPqzNmzc7+nTp0kXnz5+/6bsugoODdc8992jmzJnZ9vXt21dXrlzR2rVr8/SeAMDVMWIBAChwpUqV0qhRozRy5EgtXrxYx44dU3R0tObPn5+tb61atRQTE6Ovv/5aR48e1bhx47Rnzx7H/uPHjysyMlJRUVE6ceKENmzYoKNHj6p+/fr67bffNHToUG3btk0nTpzQt99+qz179qh+/foFebsA4BJ4KhQAwBLjxo2Tu7u7XnvtNZ05c0aVK1fWkCFDsvUbMmSIYmNj1bt3b9lsNvXt21fPPvus/vOf/0iSypQpoyNHjuiTTz7RhQsXVLlyZQ0dOlSDBw9WRkaGLly4oP79++vs2bPy8/NTz549NXHixIK+XQAo9pgKBQAAAMA0pkIBAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAVo0aJFstlsKlWqlE6cOJFtf4cOHdSoUSMLKssbAwcOVGBgoFNbYGCgBg4cWKB1JCQkyGazadGiRbn227Ztm2w2m+Pj5uYmf39/de/eXTExMQVTbC5u/Psi/bfWhISE2z7PjZ/Htm3bJOmWfjYAcLvcrS4AAFxRWlqaxo4dqyVLllhdSr5bs2aNfHx8rC4jV2+88YY6duyo9PR07d+/XxMnTlT79u0VGxur2rVrW10eABQJjFgAgAXCwsK0fPlyHThwIF+v89tvv+Xr+W9Fs2bNVLNmTavLyFXt2rXVunVrtWvXTsOGDdP06dN19epVLV261OrSAKDIIFgAgAVGjhyp8uXLa9SoUX/Z99q1a4qMjFRQUJA8PDx055136rnnntPly5ed+gUGBio8PFyrV69Ws2bNVKpUKU2cONExhWb58uUaNWqUKleurLJly6p79+46e/asUlNT9cwzz8jPz09+fn564okn9Ouvvzqd+/3339d9992nChUqyMvLS40bN9bUqVOVnp7+l/X/eSpUhw4dnKYf/fHzx+k5SUlJGjx4sAICAuTh4aGgoCBNnDhRGRkZTuc/c+aMevXqJW9vb9ntdvXu3VtJSUl/WVduWrRoIUk6e/asU/uOHTvUuXNneXt7q0yZMmrTpo3WrVvn2J+SkiJ3d3dNmzbN0Xb+/HmVKFFCdrvdqfZhw4bJ399fhmH87ToNw9Abb7yh6tWrq1SpUmrRooU2btyoDh06qEOHDn/7vADwdzAVCgAs4O3trbFjx+qFF17Qli1b1KlTpxz7GYahHj16aPPmzYqMjFS7du108OBBjR8/XlFRUYqKipKnp6ej/759+xQXF6exY8cqKChIXl5eunLliiRp9OjR6tixoxYtWqSEhAS9/PLL6tu3r9zd3dW0aVOtWLFC+/fv1+jRo+Xt7a2ZM2c6znvs2DH169fPEW4OHDigyZMn68iRI1qwYMFt3fucOXOUkpLi1DZu3Dht3bpVdevWlfR7qGjZsqVKlCih1157TTVr1lRUVJRef/11JSQkaOHChZJ+H5Hp0qWLzpw5oylTpqhOnTpat26devfufVs1/dnx48clSXXq1HG0bd++XSEhIWrSpInmz58vT09PzZkzR927d9eKFSvUu3dv+fj46J577tGmTZv0yiuvSJI2b94sT09Ppaamavfu3WrTpo0kadOmTerUqZNjDUVOBg4c6AhlHTp0yBZCxowZoylTpuiZZ55Rz549derUKT399NNKT093qj0wMNDpWDNhBgBuygAAFJiFCxcakow9e/YYaWlpRo0aNYwWLVoYWVlZhmEYRvv27Y2GDRs6+q9fv96QZEydOtXpPJ9++qkhyfjoo48cbdWrVzfc3NyM+Ph4p75bt241JBndu3d3an/xxRcNScawYcOc2nv06GH4+vre9B4yMzON9PR0Y/HixYabm5tx8eJFx74BAwYY1atXd+pfvXp1Y8CAATc937Rp07Ldy+DBg42yZcsaJ06ccOr79ttvG5KM77//3jAMw5g7d64hyfjiiy+c+g0aNMiQZCxcuPCm1zWM//5sPv30UyM9Pd24evWq8e233xp169Y1GjRoYFy6dMnRt3Xr1kaFChWM1NRUR1tGRobRqFEjIyAgwPHPcOzYsUbp0qWNa9euGYZhGE8//bQRFhZmNGnSxJg4caJhGIbx888/Z7vn23Xx4kXD09PT6N27t1N7VFSUIclo37793z43APwdTIUCAIt4eHjo9ddfV0xMjFauXJljny1btkhStqcqPfroo/Ly8tLmzZud2ps0aeL0N9V/FB4e7rRdv359SVK3bt2ytV+8eNFpOtT+/fv14IMPqnz58nJzc1PJkiXVv39/ZWZm6ujRo399szexYsUKjRw5UmPHjtWgQYMc7V9++aU6duyoKlWqKCMjw/F54IEHJP0+eiBJW7dulbe3tx588EGn8/br1++26ujdu7dKliypMmXKqG3btkpJSdG6det0xx13SJKuXLmiXbt26ZFHHlHZsmUdx7m5uSkiIkKnT59WfHy8JKlz58767bfftHPnTkm/j0yEhISoS5cu2rhxo6NNkrp06XJbdf5RdHS00tLS1KtXL6f21q1bZ3syFwAUBIIFAFioT58+uvvuuzVmzJgc1ytcuHBB7u7u8vf3d2q32WyqVKmSLly44NReuXLlm17L19fXadvDwyPX9mvXrkmSTp48qXbt2unnn3/WjBkz9M0332jPnj16//33Jf39BeJbt27VwIED1b9/f/3jH/9w2nf27Fn9+9//VsmSJZ0+DRs2lPT7ugXp959PxYoVs527UqVKt1XLW2+9pT179mj79u0aM2aMzp49qx49eigtLU2SdOnSJRmGkePPt0qVKo5aJKlNmzYqU6aMNm3apB9//FEJCQmOYLFr1y79+uuv2rRpk2rUqKGgoKDbqvOPblwvp/vPqQ0A8htrLADAQjabTW+99ZZCQkL00UcfZdtfvnx5ZWRk6Ny5c07hwjAMJSUl6Z577sl2vrz2+eef68qVK1q9erWqV6/uaI+Njf3b5zx48KB69Oih9u3ba968edn2+/n5qUmTJpo8eXKOx9/4Zb58+fLavXt3tv23u3i7Ro0ajgXb9913n0qXLq2xY8dq1qxZevnll1WuXDmVKFFCiYmJ2Y49c+aMo2bp92B27733atOmTQoICFClSpXUuHFj1ahRQ9Lv76PYvHlzthGk21W+fHlJ2ReYS7/fP6MWAAoaIxYAYLEuXbooJCREkyZNyvY0ps6dO0tStseerlq1SleuXHHsz083wsofF4kbhpFjILgVJ0+e1AMPPKAaNWpo1apVKlmyZLY+4eHhOnTokGrWrKkWLVpk+9wIFh07dlRqaqrWrl3rdPzy5cv/Vm03jBw5UrVq1dKbb76p1NRUeXl5qVWrVlq9erXTCE1WVpaWLl2qgIAApyloXbp00d69e7Vq1SrHdCcvLy+1bt1as2bN0pkzZ0xNg5KkVq1aydPTU59++qlTe3R0dI4vXwSA/MaIBQAUAm+99ZaaN2+uX375xTHdR5JCQkJ0//33a9SoUUpJSVHbtm0dT4Vq1qyZIiIi8r22kJAQeXh4qG/fvho5cqSuXbumuXPn6tKlS3/rfA888IAuX76s2bNn6/vvv3faV7NmTfn7+2vSpEnauHGj2rRpo2HDhqlu3bq6du2aEhIS9NVXX+mDDz5QQECA+vfvr+nTp6t///6aPHmyateura+++kpff/21qXsuWbKk3njjDfXq1UszZszQ2LFjNWXKFIWEhKhjx456+eWX5eHhoTlz5ujQoUNasWKF02hR586dlZmZqc2bN+uTTz5xtHfp0kXjx4+XzWa76ZPAbpWvr69GjBihKVOmqFy5cnrooYd0+vRpTZw4UZUrV1aJEvzdIYCCxX91AKAQaNasmfr27Zut3Waz6fPPP9eIESO0cOFCde3aVW+//bYiIiK0ZcsWp1GE/FKvXj2tWrVKly5dUs+ePfX888/rrrvucnoc7e04fPiwrl69qp49eyo4ONjpc+OdEJUrV1ZMTIxCQ0M1bdo0hYWFKSIiQgsWLNBdd92lcuXKSZLKlCmjLVu2qEuXLnr11Vf1yCOP6PTp0/rnP/9p+r4fffRRtWrVSu+++66Sk5PVvn17bdmyRV5eXho4cKD69Omj5ORkrV27NtvjbZs1a+aYGvXHkYkbf27WrJljKpMZkydP1uuvv65169bpwQcf1MyZMzV37lxVqFDBsfAcAAqKzTB4mDUAAMXF8ePHVa9ePY0fP16jR4+2uhwALoRgAQBAEXXgwAGtWLFCbdq0kY+Pj+Lj4zV16lSlpKTo0KFDPB0KQIFijQUAAEWUl5eXYmJiNH/+fF2+fFl2u10dOnTQ5MmTCRUAChwjFgAAAABMK5aLt+fMmaOgoCCVKlVKzZs31zfffJNr/+3bt6t58+YqVaqUatSooQ8++KCAKgUAAACKh2IXLD799FO9+OKLGjNmjPbv36927drpgQce0MmTJ3Psf/z4cXXt2lXt2rXT/v37NXr0aA0bNkyrVq0q4MoBAACAoqvYTYVq1aqV7r77bs2dO9fRVr9+ffXo0UNTpkzJ1n/UqFFau3at4uLiHG1DhgzRgQMHFBUVVSA1AwAAAEVdsRqxuH79uvbu3avQ0FCn9tDQUO3cuTPHY6KiorL1v//++xUTE6P09PR8qxUAAAAoTorVU6HOnz+vzMzMbE/CqFixopKSknI8JikpKcf+GRkZOn/+vCpXrpztmLS0NKWlpTm2s7KydPHiRZUvX97pzasAAABAUWYYhlJTU1WlShWVKJH7mESxChY3/PmXe8Mwcv2FP6f+ObXfMGXKFE2cONFklQAAAEDRcOrUKQUEBOTap1gFCz8/P7m5uWUbnfjll19u+jzvSpUq5djf3d1d5cuXz/GYyMhIjRgxwrGdnJysatWq6dSpU/Lx8TF5F4Vbo/FfW10C8tChifdbXQLyGN/R4oXvaPHC97P4cYXvaEpKiqpWrSpvb++/7FusgoWHh4eaN2+ujRs36qGHHnK0b9y4Uf/zP/+T4zHBwcH697//7dS2YcMGtWjRQiVLlszxGE9PT3l6emZr9/HxKfbBooRnGatLQB4q7v++uiK+o8UL39Hihe9n8eNK39Fbme5frBZvS9KIESP08ccfa8GCBYqLi9Pw4cN18uRJDRkyRNLvow39+/d39B8yZIhOnDihESNGKC4uTgsWLND8+fP18ssvW3ULAAAAQJFTrEYsJKl37966cOGCJk2apMTERDVq1EhfffWVqlevLklKTEx0eqdFUFCQvvrqKw0fPlzvv/++qlSpopkzZ+rhhx+26hYAAACAIqfYBQtJevbZZ/Xss8/muG/RokXZ2tq3b699+/blc1UAAABA8VXspkIBAAAAKHgECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGnuVhcAAMg7CW92s7oEAICLYsQCAAAAgGkECwAAAACmMRUKt4VpFgAAAMgJIxYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMK1bB4tKlS4qIiJDdbpfdbldERIQuX76c6zGrV6/W/fffLz8/P9lsNsXGxhZIrQAAAEBxUqyCRb9+/RQbG6v169dr/fr1io2NVURERK7HXLlyRW3bttWbb75ZQFUCAAAAxY+71QXklbi4OK1fv17R0dFq1aqVJGnevHkKDg5WfHy86tatm+NxN4JHQkJCQZUKAAAAFDvFZsQiKipKdrvdESokqXXr1rLb7dq5c2eeXistLU0pKSlOHwAAAMCVFZtgkZSUpAoVKmRrr1ChgpKSkvL0WlOmTHGs47Db7apatWqenh8AAAAoagp9sJgwYYJsNluun5iYGEmSzWbLdrxhGDm2mxEZGank5GTH59SpU3l6fgAAAKCoKfRrLIYOHao+ffrk2icwMFAHDx7U2bNns+07d+6cKlasmKc1eXp6ytPTM0/PCQAAABRlhT5Y+Pn5yc/P7y/7BQcHKzk5Wbt371bLli0lSbt27VJycrLatGmT32UCAAAALq3QT4W6VfXr11dYWJgGDRqk6OhoRUdHa9CgQQoPD3d6IlS9evW0Zs0ax/bFixcVGxurw4cPS5Li4+MVGxub5+syAAAAgOKs2AQLSVq2bJkaN26s0NBQhYaGqkmTJlqyZIlTn/j4eCUnJzu2165dq2bNmqlbt26SpD59+qhZs2b64IMPCrR2AAAAoCizGYZhWF1EUZeSkiK73a7k5GT5+PhYXQ4AACiEAl9dZ3UJyGMJb3azuoR8dzu/5xarEQsAAAAA1iBYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwrVsHi0qVLioiIkN1ul91uV0REhC5fvnzT/unp6Ro1apQaN24sLy8vValSRf3799eZM2cKrmgAAACgGChWwaJfv36KjY3V+vXrtX79esXGxioiIuKm/a9evap9+/Zp3Lhx2rdvn1avXq2jR4/qwQcfLMCqAQAAgKLP3eoC8kpcXJzWr1+v6OhotWrVSpI0b948BQcHKz4+XnXr1s12jN1u18aNG53aZs2apZYtW+rkyZOqVq1agdQOAAAAFHXFZsQiKipKdrvdESokqXXr1rLb7dq5c+ctnyc5OVk2m0133HHHTfukpaUpJSXF6QMAAAC4smITLJKSklShQoVs7RUqVFBSUtItnePatWt69dVX1a9fP/n4+Ny035QpUxzrOOx2u6pWrfq36wYAAACKg0IfLCZMmCCbzZbrJyYmRpJks9myHW8YRo7tf5aenq4+ffooKytLc+bMybVvZGSkkpOTHZ9Tp079vZsDAAAAiolCv8Zi6NCh6tOnT659AgMDdfDgQZ09ezbbvnPnzqlixYq5Hp+enq5evXrp+PHj2rJlS66jFZLk6ekpT0/Pvy4eAAAAcBGFPlj4+fnJz8/vL/sFBwcrOTlZu3fvVsuWLSVJu3btUnJystq0aXPT426Eih9++EFbt25V+fLl86x2AAAAwFUU+qlQt6p+/foKCwvToEGDFB0drejoaA0aNEjh4eFOT4SqV6+e1qxZI0nKyMjQI488opiYGC1btkyZmZlKSkpSUlKSrl+/btWtAAAAAEVOsQkWkrRs2TI1btxYoaGhCg0NVZMmTbRkyRKnPvHx8UpOTpYknT59WmvXrtXp06d11113qXLlyo7P7TxJCgAAAHB1+TIV6tSpU7LZbAoICJAk7d69W8uXL1eDBg30zDPP5MclJUm+vr5aunRprn0Mw3D8OTAw0GkbAAAAwN+TLyMW/fr109atWyX9/hjYkJAQ7d69W6NHj9akSZPy45IAAAAALJQvweLQoUOOBdQrV65Uo0aNtHPnTi1fvlyLFi3Kj0sCAAAAsFC+BIv09HTH41g3bdqkBx98UNLvC6cTExPz45IAAAAALJQvwaJhw4b64IMP9M0332jjxo0KCwuTJJ05c4bHuQIAAADFUL4Ei7feeksffvihOnTooL59+6pp06aSpLVr1zqmSAEAAAAoPvLlqVAdOnTQ+fPnlZKSonLlyjnan3nmGZUpUyY/LgkAAADAQvn2HgvDMLR37159+OGHSk1NlSR5eHgQLAAAAIBiKF9GLE6cOKGwsDCdPHlSaWlpCgkJkbe3t6ZOnapr167pgw8+yI/LAgAAALBIvoxYvPDCC2rRooUuXbqk0qVLO9ofeughbd68OT8uCQAAAMBC+TJisWPHDn377bfy8PBwaq9evbp+/vnn/LgkAAAAAAvly4hFVlaWMjMzs7WfPn1a3t7e+XFJAAAAABbKl2AREhKi9957z7Fts9n066+/avz48eratWt+XBIAAACAhfJlKtT06dPVsWNHNWjQQNeuXVO/fv30ww8/yM/PTytWrMiPSwIAAACwUL4EiypVqig2NlYrVqzQvn37lJWVpaeeekqPPfaY02JuAAAAAMVDvgQLSSpdurSefPJJPfnkk/l1CQAAAACFRL4Ei8WLF+e6v3///vlxWQAAAAAWyZdg8cILLzhtp6en6+rVq443bxMsAAAAgOIlX54KdenSJafPr7/+qvj4eN17770s3gYAAACKoXwJFjmpXbu23nzzzWyjGQAAAACKvgILFpLk5uamM2fOFOQlAQAAABSAfFljsXbtWqdtwzCUmJio2bNnq23btvlxSQAAAAAWypdg0aNHD6dtm80mf39/derUSe+8805+XBIAAACAhfIlWGRlZeXHaQEAAAAUUgW6xgIAAABA8ZRnIxYjRoy45b7vvvtuXl0WAAAAQCGQZ8Fi//79t9TPZrPl1SUBAAAAFBJ5Fiy2bt2aV6cCAAAAUMSwxgIAAACAafnyVChJ2rNnjz777DOdPHlS169fd9q3evXq/LosAAAAAAvky4jFP//5T7Vt21aHDx/WmjVrlJ6ersOHD2vLli2y2+35cUkAAAAAFsqXYPHGG29o+vTp+vLLL+Xh4aEZM2YoLi5OvXr1UrVq1fLjkgAAAAAslC/B4tixY+rWrZskydPTU1euXJHNZtPw4cP10Ucf5cclAQAAAFgoX4KFr6+vUlNTJUl33nmnDh06JEm6fPmyrl69mh+XBAAAAGChPA0WsbGxkqR27dpp48aNkqRevXrphRde0KBBg9S3b1917tw5Ly8JAAAAoBDI06dC3X333WrWrJl69Oihvn37SpIiIyNVsmRJ7dixQz179tS4cePy8pIAAAAACoE8HbH49ttvdffdd+vtt99WzZo19fjjj2v79u0aOXKk1q5dq3fffVflypXLy0sCAAAAKATyNFgEBwdr3rx5SkpK0ty5c3X69Gl16dJFNWvW1OTJk3X69Om8vBwAAACAQiJfFm+XLl1aAwYM0LZt23T06FH17dtXH374oYKCgtS1a9f8uCQAAAAAC+VLsPijmjVr6tVXX9WYMWPk4+Ojr7/+Or8vCQAAAKCA5eni7T/bvn27FixYoFWrVsnNzU29evXSU089lZ+XBAAAAGCBPA8Wp06d0qJFi7Ro0SIdP35cbdq00axZs9SrVy95eXnl9eUAAAAAFAJ5GixCQkK0detW+fv7q3///nryySdVt27dvLwEAAAAgEIoT4NF6dKltWrVKoWHh8vNzS0vTw0AAACgEMvTYLF27dq8PB0AAACAIiLfnwoFAAAAoPgrVsHi0qVLioiIkN1ul91uV0REhC5fvpzrMRMmTFC9evXk5eWlcuXKqUuXLtq1a1fBFAwAAAAUE8UqWPTr10+xsbFav3691q9fr9jYWEVEROR6TJ06dTR79mx999132rFjhwIDAxUaGqpz584VUNUAAABA0WczDMOwuoi8EBcXpwYNGig6OlqtWrWSJEVHRys4OFhHjhy55adTpaSkyG63a9OmTercufNtHZOcnCwfH5+/fQ8AAKD4Cnx1ndUlII8lvNnN6hLy3e38nltsRiyioqJkt9sdoUKSWrduLbvdrp07d97SOa5fv66PPvpIdrtdTZs2za9SAQAAgGInX9+8XZCSkpJUoUKFbO0VKlRQUlJSrsd++eWX6tOnj65evarKlStr48aN8vPzu2n/tLQ0paWlObZTUlL+fuEAAABAMVDoRywmTJggm82W6ycmJkaSZLPZsh1vGEaO7X/UsWNHxcbGaufOnQoLC1OvXr30yy+/3LT/lClTHAvE7Xa7qlatau4mAQAAgCKu0I9YDB06VH369Mm1T2BgoA4ePKizZ89m23fu3DlVrFgx1+O9vLxUq1Yt1apVS61bt1bt2rU1f/58RUZG5tg/MjJSI0aMcGynpKQQLgAAAODSCn2w8PPzy3Va0g3BwcFKTk7W7t271bJlS0nSrl27lJycrDZt2tzWNQ3DcJrq9Geenp7y9PS8rXMCAAAAxVmhnwp1q+rXr6+wsDANGjRI0dHRio6O1qBBgxQeHu70RKh69eppzZo1kqQrV65o9OjRio6O1okTJ7Rv3z49/fTTOn36tB599FGrbgUAAAAocopNsJCkZcuWqXHjxgoNDVVoaKiaNGmiJUuWOPWJj49XcnKyJMnNzU1HjhzRww8/rDp16ig8PFznzp3TN998o4YNG1pxCwAAAECRVOinQt0OX19fLV26NNc+f3xtR6lSpbR69er8LgsAAAAo9orViAUAAAAAaxAsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYVqyCxaVLlxQRESG73S673a6IiAhdvnz5lo8fPHiwbDab3nvvvXyrEQAAACiOilWw6Nevn2JjY7V+/XqtX79esbGxioiIuKVjP//8c+3atUtVqlTJ5yoBAACA4sfd6gLySlxcnNavX6/o6Gi1atVKkjRv3jwFBwcrPj5edevWvemxP//8s4YOHaqvv/5a3bp1K6iSAQAAgGKj2IxYREVFyW63O0KFJLVu3Vp2u107d+686XFZWVmKiIjQK6+8ooYNG97StdLS0pSSkuL0AQAAAFxZsQkWSUlJqlChQrb2ChUqKCkp6abHvfXWW3J3d9ewYcNu+VpTpkxxrOOw2+2qWrXq36oZAAAAKC4KfbCYMGGCbDZbrp+YmBhJks1my3a8YRg5tkvS3r17NWPGDC1atOimfXISGRmp5ORkx+fUqVN/7+YAAACAYqLQr7EYOnSo+vTpk2ufwMBAHTx4UGfPns2279y5c6pYsWKOx33zzTf65ZdfVK1aNUdbZmamXnrpJb333ntKSEjI8ThPT095enre+k0AAAAAxVyhDxZ+fn7y8/P7y37BwcFKTk7W7t271bJlS0nSrl27lJycrDZt2uR4TEREhLp06eLUdv/99ysiIkJPPPGE+eIBAAAAF1Hog8Wtql+/vsLCwjRo0CB9+OGHkqRnnnlG4eHhTk+EqlevnqZMmaKHHnpI5cuXV/ny5Z3OU7JkSVWqVCnXp0gBAAAAcFbo11jcjmXLlqlx48YKDQ1VaGiomjRpoiVLljj1iY+PV3JyskUVAgAAAMVTsRmxkCRfX18tXbo01z6GYeS6/2brKgAAAADcXLEasQAAAABgDYIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwzd3qAgAAAFxBwpvdrC4ByFeMWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA09ytLqA4MAxDkpSSkmJxJQAAAEDeufH77Y3fd3NDsMgDqampkqSqVataXAkAAACQ91JTU2W323PtYzNuJX4gV1lZWTpz5oy8vb1ls9msLgcmpaSkqGrVqjp16pR8fHysLgfAn/AdBQo3vqPFi2EYSk1NVZUqVVSiRO6rKBixyAMlSpRQQECA1WUgj/n4+PAfRKAQ4zsKFG58R4uPvxqpuIHF2wAAAABMI1gAAAAAMI1gAfyJp6enxo8fL09PT6tLAZADvqNA4cZ31HWxeBsAAACAaYxYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAZc2a9Ysq0sAAAAoFnjcLFyar6+vmjdvroULFyogIMDqcgDcxJ49e7RixQodPXpUNptNtWvXVr9+/dSiRQurSwOQi9WrV2vChAk6ePCg1aWgADBiAZd26NAheXp6qnHjxlqyZInV5QDIwciRI9WqVSt9/PHHOn36tE6ePKl58+apVatWGjVqlNXlAS5v3rx5evTRR9WvXz/t2rVLkrRlyxY1a9ZMjz/+uIKDgy2uEAWFEQtA0qJFizRixAh16NBBY8eOlbu7u9P+Jk2aWFQZ4No++eQTDRkyRNOmTdPgwYNVsmRJSVJ6errmzp2rUaNG6cMPP1T//v0trhRwTW+//bZGjx6tJk2aKC4uTpI0ZswYvfvuu3r++ef13HPPyc/Pz+IqUVAIFsD/2bRpk8LCwmQYhgzDkM1mc/xvZmam1eUBLqlly5bq27evhg8fnuP+d999V//85z+1e/fuAq4MgCTVr19fr7zyip588klt27ZNnTp1UqdOnfSvf/1Ld9xxh9XloYARLAD9/svJuHHj9Oijj2rcuHHZRiyqV69uUWWAa/Py8tJ3332nGjVq5Lj/p59+UuPGjXXlypUCrgyAJJUpU0ZHjhxRtWrVJEmenp763//9X7Vq1criymAF97/uAhRfP/30k/r3769jx45p+fLl+p//+R+rSwLwB25ubrp+/fpN96enp8vNza0AKwLwR9euXVOpUqUc2x4eHvL397ewIliJYAGX1qRJE4WFhenzzz9nDihQCDVv3lzLli3TP/7xjxz3L1myRHfffXcBVwXgjz7++GOVLVtWkpSRkaFFixZl+//UYcOGWVEaChhToeDSli5dqscff9zqMgDcxJdffqkePXpoxIgReumll1SxYkVJUlJSkt555x299957WrNmjcLDwy2uFHBNgYGBstlsufax2Wz66aefCqgiWIkRC7i03KZYSFJqaqqGDx+ujz/+uIAqAvBH4eHhmj59ul5++WW98847stvtkqTk5GS5ublp2rRphArAQgkJCVaXgEKEEQu4NLvdrnbt2unjjz9WpUqVnPZ9/fXXGjRokMqVK6cDBw5YVCEASTp9+rQ+++wz/fDDD5KkOnXq6OGHH1bVqlUtrgxAbi5cuKAlS5boxRdftLoUFACCBVxaQkKCnnjiCR08eFCzZ89W3759lZqaqhdffFFLly7VK6+8ovHjxzuenQ8AAHJnGIY2bNig+fPn64svvpCPj4/OnTtndVkoALx5Gy4tMDBQW7du1bhx4zRo0CCFh4erUaNG2r17t3bu3KnXX3+dUAFY6Mcff9TevXud2jZv3qyOHTuqZcuWeuONNyyqDMCfJSQk6LXXXlP16tXVtWtXlSpVSuvWrVNSUpLVpaGAECwASYMHD1ZoaKi++uorXbx4UUuXLlXz5s2tLgtwea+88oo+//xzx/bx48fVvXt3eXh4KDg4WFOmTNF7771nWX2Aq0tLS9OKFSvUuXNn1a9fX4cOHdK7776rEiVK6NVXX1WXLl14JLQLIVjA5X377bdq2rSp4uPjtX79ej3wwAMKDg7W9OnTrS4NcHkxMTHq2rWrY3vZsmWqU6eOvv76a82YMUPvvfeeFi1aZF2BgIu78847NXfuXPXu3VtnzpzR6tWr9cgjj1hdFixCsIBLe+mll9SpUyd1795d+/btU2hoqFauXKmFCxfqjTfe0H333ccj8gALnT9/XgEBAY7trVu3qnv37o7tDh068FQawEKZmZmy2Wyy2WyMTIBgAdf2xRdfaNOmTXrnnXfk6enpaO/du7cOHTokX19fNW3a1MIKAdfm6+urxMRESVJWVpZiYmLUqlUrx/7r16+LZ5AA1klMTNQzzzyjFStWqFKlSnr44Ye1Zs2av3y3BYonggVc2oEDB9SuXbsc91WsWFGff/655syZU8BVAbihffv2+sc//qFTp07pvffeU2Zmpjp27OjYf/jwYQUGBlpXIODiSpUqpccee0xbtmzRd999p/r162vYsGHKyMjQ5MmTtXHjRmVmZlpdJgoIwQIuzcvLK9f93333XbYn0gAoOJMnT1ZcXJyqV6+ukSNHaurUqU7f2yVLlqhTp04WVgi4tsWLFystLU2SVLNmTb3++us6ceKE1q1bp7S0NIWHh6tixYoWV4mCwnssgD9JSUnRihUrNH/+fMXExKhJkyaKjY21uizAZaWnp+vw4cPy9/eXh4eHPDw85OPjI+n3UceAgACVL1/e4ioB1+Tm5qbExERVqFAhx/3nzp3TkiVLNGLEiAKuDFZgxAL4P9u3b1f//v1VuXJlPfvss+rUqZOOHj1KqAAsduXKFX300Udq0qSJKlasqHLlyqlSpUqKjIxU7dq1CRWAhf7q76f9/f0JFS6EEQu4tMTERC1cuFALFizQlStX1LdvX/Xr10/BwcE6cOCAGjRoYHWJgEu7ePGigoOD9fPPP+uxxx5T/fr1ZRiG4uLitHz5ctWrV087duzQgQMHtGvXLg0bNszqkgGXUqJECZ09e1b+/v5Wl4JCwN3qAgArBQUF6dFHH9X777+vkJAQlSjBIB5QmEyaNEkeHh46duxYtnnakyZNUmhoqCIiIrRhwwbNnDnToioB1zZw4ECnJyvmZPXq1QVUDaxEsIBLq169unbs2KFq1aqpevXqqlevntUlAfiDzz//XB9++GGOiz8rVaqkqVOnqmvXrho/frwGDBhgQYUAvL29Vbp0aavLQCFAsIBLi4+P17fffqv58+frnnvuUZ06dfT4449LEs/gBgqBxMRENWzY8Kb7GzVqpBIlSmj8+PEFWBWAP5o5c+ZNF2/DtTDvAy6vbdu2WrBggRITEzVkyBCtXLlSmZmZevbZZzVv3jydO3fO6hIBl+Xn55frm7WPHz/OLzSAhfhLOPwRi7eBHMTFxWn+/PlasmSJLl68qPT0dKtLAlzSU089pR9//FEbN26Uh4eH0760tDTdf//9qlGjhhYsWGBRhYBrK1GihJKSkgj4kESwAHKVkZGhtWvXqmfPnlaXArik06dPq0WLFvL09NRzzz3nWAd1+PBhzZkzR2lpadqzZ4+qVatmcaWAa3rzzTfVsGFDde/e3dG2ePFijR8/XleuXFGPHj00a9asv1zcjeKBYAHkIi4uTt26ddNPP/1kdSmAyzp+/LieffZZbdiwwfHMfJvNppCQEM2ePVu1atWyuELAdYWFhaljx44aNWqUJOm7777T3XffrYEDB6p+/fqaNm2aBg8erAkTJlhbKAoEwQLIxYEDB3T33XcrMzPT6lIAl3fp0iX98MMPkqRatWrJ19fX4ooAVK5cWf/+97/VokULSdKYMWO0fft27dixQ5L02Wefafz48Tp8+LCVZaKA8FQoAECRUK5cObVs2dLqMgD8waVLl5weB719+3aFhYU5tu+55x6dOnXKitJgAZ4KBQAAgL+lYsWKOn78uCTp+vXr2rdvn4KDgx37U1NTVbJkSavKQwEjWAAAAOBvCQsL06uvvqpvvvlGkZGRKlOmjNq1a+fYf/DgQdWsWdPCClGQmAoFl1auXLlcn8GdkZFRgNUAAFC0vP766+rZs6fat2+vsmXL6pNPPnF6NPSCBQsUGhpqYYUoSCzehkv75JNPbqnfgAED8rkSAACKruTkZJUtW1Zubm5O7RcvXlTZsmWzvYcGxRPBAvgLGRkZcndncA8AACA3rLEAbuLw4cN66aWXdOedd1pdCgAAQKFHsAD+4Ndff9XHH3+s4OBgNWnSRLt27dKrr75qdVkAAACFHvM7AEk7duzQxx9/rFWrVikoKEiHDx/W9u3b1bZtW6tLAwAAKBIYsYBLmzp1qurVq6c+ffrI399fO3bs0MGDB2Wz2VSuXDmrywMAACgyWLwNl+bu7q5Ro0Zp0qRJTk+yKFmypA4cOKAGDRpYWB0AAEDRwYgFXNqkSZP02WefKSgoSKNGjdKhQ4esLgkAAKBIIljApY0ePVpHjx7VkiVLlJSUpNatW6tp06YyDEOXLl2yujwAAIAig6lQwB+kpKRo+fLlWrBggfbu3atWrVrpkUce0YgRI6wuDQAAoFAjWAA3cejQIc2fP1/Lli3TL7/8YnU5AAAAhRqPm4VL++2337R582aFh4dLkiIjI5WWlubY7+7urmPHjllVHgAAQJFBsIBLW7x4sb788ktHsJg9e7YaNmyo0qVLS5Li4+NVpUoVDR8+3MoyAQAACj2mQsGl3XfffRo+fLgeeughSZK3t7cOHDigGjVqSJKWLl2q999/X1FRUVaWCQAAUOjxVCi4tKNHj6pOnTqO7VKlSqlEif9+LVq2bKnDhw9bURoAAECRwlQouLTk5GS5u//3a3Du3Dmn/VlZWU5rLgAAAJAzRizg0gICAnJ9Kd7BgwcVEBBQgBUBAAAUTQQLuLSuXbvqtdde07Vr17Lt++233zRx4kR169bNgsoAAACKFhZvw6WdPXtWd911lzw8PDR06FDVqVNHNptNR44c0ezZs5WRkaH9+/erYsWKVpcKAABQqBEs4PKOHz+u//f//p82btyoG18Hm82mkJAQzZkzx/GEKAAAANwcwQL4PxcvXtSPP/4oSapVq5Z8fX0trggAAKDoIFgAAAAAMI3F2wAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAIBCJSEhQTabTbGxsVaXAgC4DQQLAAAAAKYRLAAAAACYRrAAAFgiKytLb731lmrVqiVPT09Vq1ZNkydPztYvMzNTTz31lIKCglS6dGnVrVtXM2bMcOqzbds2tWzZUl5eXrrjjjvUtm1bnThxQpJ04MABdezYUd7e3vLx8VHz5s0VExNTIPcIAK7E3eoCAACuKTIyUvPmzdP06dN17733KjExUUeOHMnWLysrSwEBAVq5cqX8/Py0c+dOPfPMM6pcubJ69eqljIwM9ejRQ4MGDdKKFSt0/fp17d69WzabTZL02GOPqVmzZpo7d67c3NwUGxurkiVLFvTtAkCxx5u3AQAFLjU1Vf7+/po9e7aefvppp30JCQkKCgrS/v37ddddd+V4/HPPPaezZ8/qX//6ly5evKjy5ctr27Ztat++fba+Pj4+mjVrlgYMGJAftwIA+D9MhQIAFLi4uDilpaWpc+fOt9T/gw8+UIsWLeTv76+yZctq3rx5OnnypCTJ19dXAwcO1P3336/u3btrxowZSkxMdBw7YsQIPf300+rSpYvefPNNHTt2LF/uCQBcHcECAFDgSpcufct9V65cqeHDh+vJJ5/Uhg0bFBsbqyeeeELXr1939Fm4cKGioqLUpk0bffrpp6pTp46io6MlSRMmTND333+vbt26acuWLWrQoIHWrFmT5/cEAK6OqVAAgAJ37do1+fr6aubMmX85Fer555/X4cOHtXnzZkefLl266Pz58zd910VwcLDuuecezZw5M9u+vn376sqVK1q7dm2e3hMAuDpGLAAABa5UqVIaNWqURo4cqcWLF+vYsWOKjo7W/Pnzs/WtVauWYmJi9PXXX+vo0aMaN26c9uzZ49h//PhxRUZGKioqSidOnNCGDRt09OhR1a9fX7/99puGDh2qbdu26cSJE/r222+1Z88e1a9fvyBvFwBcAk+FAgBYYty4cXJ3d9drr72mM2fOqHLlyhoyZEi2fkOGDFFsbKx69+4tm82mvn376tlnn9V//vMfSVKZMmV05MgRffLJJ7pw4YIqV66soUOHavDgwcrIyNCFCxfUv39/nT17Vn5+furZs6cmTpxY0LcLAMUeU6EAAAAAmMZUKAAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLADAAosWLZLNZlOpUqV04sSJbPs7dOigRo0aWVBZ3hg4cKACAwOd2gIDAzVw4MACrSMhIUE2m02LFi3Ktd+2bdtks9kcHzc3N/n7+6t79+6KiYkpmGJzcePfF+m/tSYkJNzSsR06dHD83AcOHKgOHTrkT5EAXJ671QUAgCtLS0vT2LFjtWTJEqtLyXdr1qyRj4+P1WXk6o033lDHjh2Vnp6u/fv3a+LEiWrfvr1iY2NVu3Ztq8sDgEKNEQsAsFBYWJiWL1+uAwcO5Ot1fvvtt3w9/61o1qyZatasaXUZuapdu7Zat26tdu3aadiwYZo+fbquXr2qpUuXWl3abbt69arVJQBwMQQLALDQyJEjVb58eY0aNeov+167dk2RkZEKCgqSh4eH7rzzTj333HO6fPmyU7/AwECFh4dr9erVatasmUqVKqWJEyc6ptAsX75co0aNUuXKlVW2bFl1795dZ8+eVWpqqp555hn5+fnJz89PTzzxhH799Venc7///vu67777VKFCBXl5ealx48aaOnWq0tPT/7L+P0+F6tChg9P0oz9+/jh1KSkpSYMHD1ZAQIA8PDwUFBSkiRMnKiMjw+n8Z86cUa9eveTt7S273a7evXsrKSnpL+vKTYsWLSRJZ8+edWrfsWOHOnfuLG9vb5UpU0Zt2rTRunXrHPtTUlLk7u6uadOmOdrOnz+vEiVKyG63O9U+bNgw+fv7yzCMv13njalz//u//6s2bdqoTJkyevLJJ//2+QDg72AqFABYyNvbW2PHjtULL7ygLVu2qFOnTjn2MwxDPXr00ObNmxUZGal27drp4MGDGj9+vKKiohQVFSVPT09H/3379ikuLk5jx45VUFCQvLy8dOXKFUnS6NGj1bFjRy1atEgJCQl6+eWX1bdvX7m7u6tp06ZasWKF9u/fr9GjR8vb21szZ850nPfYsWPq16+fI9wcOHBAkydP1pEjR7RgwYLbuvc5c+YoJSXFqW3cuHHaunWr6tatK+n3UNGyZUuVKFFCr732mmrWrKmoqCi9/vrrSkhI0MKFCyX9PiLTpUsXnTlzRlOmTFGdOnW0bt069e7d+7Zq+rPjx49LkurUqeNo2759u0JCQtSkSRPNnz9fnp6emjNnjrp3764VK1aod+/e8vHx0T333KNNmzbplVdekSRt3rxZnp6eSk1N1e7du9WmTRtJ0qZNm9SpUyfHGoqcDBw40BHKOnTokGMISUxM1OOPP66RI0fqjTfeUIkSv//d4bZt2xx9/mqtCQCYYgAACtzChQsNScaePXuMtLQ0o0aNGkaLFi2MrKwswzAMo3379kbDhg0d/devX29IMqZOnep0nk8//dSQZHz00UeOturVqxtubm5GfHy8U9+tW7cakozu3bs7tb/44ouGJGPYsGFO7T169DB8fX1veg+ZmZlGenq6sXjxYsPNzc24ePGiY9+AAQOM6tWrO/WvXr26MWDAgJueb9q0adnuZfDgwUbZsmWNEydOOPV9++23DUnG999/bxiGYcydO9eQZHzxxRdO/QYNGmRIMhYuXHjT6xrGf382n376qZGenm5cvXrV+Pbbb426desaDRo0MC5duuTo27p1a6NChQpGamqqoy0jI8No1KiRERAQ4PhnOHbsWKN06dLGtWvXDMMwjKefftoICwszmjRpYkycONEwDMP4+eefs93z39G+fXtDkrF58+b/z96dx+d05v8ff98SCULuIgRFEhSxRC1FGLUmtUS/pottmtBF+bVGybRIUUuX0EWpltrVOtqxjGmNinWoNSrUCFoVsSQaS5M0rUji/v3h6/72biKlJ8lJ7vv1fDzOY9zXuc45n8Ok7rfrus4xdB4AMIKpUABgMg8PD73xxhuKjY3Vp59+mmefbdu2SVKupyo9+eST8vLy0tatWx3ag4KCHP6V/dfCwsIcPgcGBkqSevXqlav96tWrDtOhDh8+rEcffVSVK1eWm5ubSpcurYiICOXk5OjUqVO/f7N3sGrVKo0ePVrjx4/XkCFD7O2ff/65OnfurBo1aig7O9u+9ejRQ9Kt0QNJ2r59uypUqKBHH33U4bwDBw68pzr69eun0qVLq1y5cmrfvr3S0tL0xRdf6L777pMkZWRkaP/+/XriiSdUvnx5+3Fubm4KDw/X+fPndfLkSUlS165d9csvv2jPnj2Sbo1MhISEqFu3boqJibG3SVK3bt3uqc68VKxY8Y4jXgBQFAgWAFAM9O/fXy1atNC4cePyXK9w5coVubu7q0qVKg7tFotF1apV05UrVxzaq1evfsdrVapUyeGzh4dHvu3Xr1+XJCUmJqpDhw66cOGCZs6cqV27dungwYP66KOPJP3xBeLbt2/X4MGDFRERoddff91h36VLl/Svf/1LpUuXdtgaN24s6da6BenW74+vr2+uc1erVu2eapk2bZoOHjyonTt3aty4cbp06ZL69OmjzMxMSdK1a9dks9ny/P2tUaOGvRZJ9rUOW7Zs0XfffaeEhAR7sNi/f79++uknbdmyRXXq1FFAQMA91ZmX/P7MAaAosMYCAIoBi8WiadOmKSQkRPPmzcu1v3LlysrOzlZKSopDuLDZbEpOTtZDDz2U63wFbf369crIyNDatWvl5+dnb4+Li/vD5zx69Kj69Omjjh07av78+bn2+/j4KCgoSG+++Waex9/+Ml+5cmUdOHAg1/57Xbxdp04d+4Lthx9+WGXLltX48eM1a9Ysvfzyy6pYsaJKlSqlpKSkXMdevHjRXrN0K5j96U9/0pYtW1SzZk1Vq1ZNTZs2VZ06dSTdWvuwdevWXCNIf1Rh/JkDwL1gxAIAiolu3bopJCREU6ZMyfU0pq5du0pSrseerlmzRhkZGfb9hen2F9dfLxK32Wx5BoK7kZiYqB49eqhOnTpas2aNSpcunatPWFiYjh07prp166pVq1a5ttvBonPnzkpPT9eGDRscjl+5cuUfqu220aNHq169epo6darS09Pl5eWlNm3aaO3atQ4jNDdv3tTy5ctVs2ZNhylo3bp106FDh7RmzRr7dCcvLy+1bdtWs2bN0sWLFwtkGhQAFAcECwAoRqZNm6aUlBQdOnTIoT0kJESPPPKIxowZo8mTJ2vLli2aPn26nn76aTVv3lzh4eGFXltISIg8PDw0YMAA/fvf/9a6dev0yCOP6Nq1a3/ofD169NCPP/6o1157Tf/973+1b98++5aSkiJJmjJlikqXLq127dppzpw52rZtmzZu3KjZs2crLCxM58+flyRFRESofv36ioiI0EcffaTNmzdr5MiR+vLLLw3dc+nSpfXWW2/pypUrmjlzpiQpOjpaV65cUefOnfWPf/xDGzZsUM+ePXXs2DG9++67DiMHXbt2VU5OjrZu3aqQkBB7e7du3bR582ZZLBbWRQBwGgQLAChGmjdvrgEDBuRqt1gsWr9+vSIjI7V48WL17NlT7777rsLDw7Vt2zaHUYTC0rBhQ61Zs0bXrl3TY489pr/+9a968MEHHR5Hey+OHz+un3/+WY899piCg4MdttvvhKhevbpiY2MVGhqqd955R927d1d4eLgWLVqkBx98UBUrVpQklStXTtu2bVO3bt00duxYPfHEEzp//rz+/ve/G77vJ598Um3atNH06dOVmpqqjh07atu2bfLy8tLgwYPVv39/paamasOGDbkeb9u8eXP71Khfj0zc/nXz5s1VuXJlwzUCQHFgsdkMvJEHAAAAAMSIBQAAAIACQLAAAAAAYJhTBovZs2crICBAZcqUUcuWLbVr16479l27dq1CQkJUpUoVeXt7Kzg42PBiPwAAAMDVOF2wWL16tUaOHKlx48bp8OHD6tChg3r06KHExMQ8+//nP/9RSEiINm7cqEOHDqlz587q3bu3Dh8+XMSVAwAAACWX0y3ebtOmjVq0aKE5c+bY2wIDA9WnTx9FR0ff1TkaN26sfv366bXXXiusMgEAAACn4lQjFjdu3NChQ4cUGhrq0B4aGqo9e/bc1Tlu3ryp9PR0VapUqTBKBAAAAJySu9kFFKTLly8rJydHvr6+Du2+vr5KTk6+q3O89957ysjIUN++fe/YJzMzU5mZmfbPN2/e1NWrV1W5cmWHFyMBAAAAJZnNZlN6erpq1KihUqXyH5NwqmBx22+/3Ntstrv6wr9q1SpNmjRJ//znP1W1atU79ouOjtbkyZMN1wkAAACUBOfOnVPNmjXz7eNUwcLHx0dubm65Rid++OGHXKMYv7V69Wo9++yz+uyzzxzejpqXqKgoRUZG2j+npqaqdu3aOnfunLy9vf/4DQAAAADFSFpammrVqqUKFSr8bl+nChYeHh5q2bKlYmJi9Oc//9neHhMTo//5n/+543GrVq3SM888o1WrVqlXr16/ex1PT095enrmavf29iZYAAAAwOnczewfpwoWkhQZGanw8HC1atVKwcHBmjdvnhITEzVs2DBJt0YbLly4oKVLl0q6FSoiIiI0c+ZMtW3b1j7aUbZsWVmtVtPuAwAAAChJnC5Y9OvXT1euXNGUKVOUlJSkJk2aaOPGjfLz85MkJSUlObzTYu7cucrOztaLL76oF1980d4+aNAgLVmypKjLBwAAAEokp3uPhRnS0tJktVqVmprKVCgAAAA4jXv5nutU77EAAAAAYA6CBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMOc7j0WAODK/Md+YXYJKEAJU3uZXQIA3DVGLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYJhTBovZs2crICBAZcqUUcuWLbVr16479k1KStLAgQPVoEEDlSpVSiNHjiy6QgEAAAAn4XTBYvXq1Ro5cqTGjRunw4cPq0OHDurRo4cSExPz7J+ZmakqVapo3LhxatasWRFXCwAAADgHpwsW06dP17PPPqvnnntOgYGBmjFjhmrVqqU5c+bk2d/f318zZ85URESErFZrEVcLAAAAOAenChY3btzQoUOHFBoa6tAeGhqqPXv2mFQVAAAA4PzczS6gIF2+fFk5OTny9fV1aPf19VVycnKBXSczM1OZmZn2z2lpaQV2bgAAAKAkcqoRi9ssFovDZ5vNlqvNiOjoaFmtVvtWq1atAjs3AAAAUBI5VbDw8fGRm5tbrtGJH374IdcohhFRUVFKTU21b+fOnSuwcwMAAAAlkVMFCw8PD7Vs2VIxMTEO7TExMWrXrl2BXcfT01Pe3t4OGwAAAODKnGqNhSRFRkYqPDxcrVq1UnBwsObNm6fExEQNGzZM0q3RhgsXLmjp0qX2Y+Li4iRJP/30k1JSUhQXFycPDw81atTIjFsAAAAAShynCxb9+vXTlStXNGXKFCUlJalJkybauHGj/Pz8JN16Id5v32nRvHlz+68PHTqklStXys/PTwkJCUVZOgAAAFBiWWw2m83sIkq6tLQ0Wa1WpaamMi0KgKn8x35hdgkoQAlTe5ldAgAXdy/fc51qjQUAAAAAcxAsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIY5ZbCYPXu2AgICVKZMGbVs2VK7du3Kt//OnTvVsmVLlSlTRnXq1NHHH39cRJUCAAAAzsHpgsXq1as1cuRIjRs3TocPH1aHDh3Uo0cPJSYm5tn/zJkz6tmzpzp06KDDhw/r1Vdf1YgRI7RmzZoirhwAAAAouSw2m81mdhEFqU2bNmrRooXmzJljbwsMDFSfPn0UHR2dq/+YMWO0YcMGxcfH29uGDRumI0eOaO/evXd1zbS0NFmtVqWmpsrb29v4TQDAH+Q/9guzS0ABSpjay+wSALi4e/me61QjFjdu3NChQ4cUGhrq0B4aGqo9e/bkeczevXtz9X/kkUcUGxurrKysQqsVAAAAcCbuZhdQkC5fvqycnBz5+vo6tPv6+io5OTnPY5KTk/Psn52drcuXL6t69eq5jsnMzFRmZqb9c1paWgFUDwAAAJRcThUsbrNYLA6fbTZbrrbf659X+23R0dGaPHmywSpLJqZZOBemWTgf/kyB4ou/Q50P/8115FRToXx8fOTm5pZrdOKHH37INSpxW7Vq1fLs7+7ursqVK+d5TFRUlFJTU+3buXPnCuYGAAAAgBLKqYKFh4eHWrZsqZiYGIf2mJgYtWvXLs9jgoODc/XfvHmzWrVqpdKlS+d5jKenp7y9vR02AAAAwJU5VbCQpMjISC1YsECLFi1SfHy8Ro0apcTERA0bNkzSrdGGiIgIe/9hw4bp7NmzioyMVHx8vBYtWqSFCxfq5ZdfNusWAAAAgBLH6dZY9OvXT1euXNGUKVOUlJSkJk2aaOPGjfLz85MkJSUlObzTIiAgQBs3btSoUaP00UcfqUaNGvrggw/0+OOPm3ULAAAAQInjdO+xMIMrvceChWfOhUVnAFB0+DvU+bjC36Mu+x4LAAAAAOYgWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwolWJw7d07nz5+3fz5w4IBGjhypefPmFcblAAAAAJisUILFwIEDtX37dklScnKyQkJCdODAAb366quaMmVKYVwSAAAAgIkKJVgcO3ZMrVu3liR9+umnatKkifbs2aOVK1dqyZIlhXFJAAAAACYqlGCRlZUlT09PSdKWLVv06KOPSpIaNmyopKSkwrgkAAAAABMVSrBo3LixPv74Y+3atUsxMTHq3r27JOnixYuqXLlyYVwSAAAAgIkKJVhMmzZNc+fOVadOnTRgwAA1a9ZMkrRhwwb7FCkAAAAAzsO9ME7aqVMnXb58WWlpaapYsaK9/fnnn1e5cuUK45IAAAAATFRo77Gw2Ww6dOiQ5s6dq/T0dEmSh4cHwQIAAABwQoUyYnH27Fl1795diYmJyszMVEhIiCpUqKC3335b169f18cff1wYlwUAAABgkkIZsXjppZfUqlUrXbt2TWXLlrW3//nPf9bWrVsL45KSpGvXrik8PFxWq1VWq1Xh4eH68ccf8z1m7dq1euSRR+Tj4yOLxaK4uLhCqw8AAABwVoUSLHbv3q3x48fLw8PDod3Pz08XLlwojEtKuvVivri4OG3atEmbNm1SXFycwsPD8z0mIyND7du319SpUwutLgAAAMDZFcpUqJs3byonJydX+/nz51WhQoXCuKTi4+O1adMm7du3T23atJEkzZ8/X8HBwTp58qQaNGiQ53G3g0dCQkKh1AUAAAC4gkIZsQgJCdGMGTPsny0Wi3766SdNnDhRPXv2LIxLau/evbJarfZQIUlt27aV1WrVnj17CvRamZmZNTQyzwAA0ZNJREFUSktLc9gAAAAAV1YoweL999/Xzp071ahRI12/fl0DBw6Uv7+/Lly4oGnTphXGJZWcnKyqVavmaq9ataqSk5ML9FrR0dH2dRxWq1W1atUq0PMDAAAAJU2hBIsaNWooLi5OL7/8soYOHarmzZtr6tSpOnz4cJ5f/vMzadIkWSyWfLfY2FhJt0ZGfstms+XZbkRUVJRSU1Pt27lz5wr0/AAAAEBJUyhrLCSpbNmyeuaZZ/TMM88YOs/w4cPVv3//fPv4+/vr6NGjunTpUq59KSkp8vX1NVTDb3l6esrT07NAzwkAAACUZIUSLJYuXZrv/oiIiLs+l4+Pj3x8fH63X3BwsFJTU3XgwAG1bt1akrR//36lpqaqXbt2d309AAAAAPeuUILFSy+95PA5KytLP//8s/3N2/cSLO5WYGCgunfvriFDhmju3LmSpOeff15hYWEOT4Rq2LChoqOj9ec//1mSdPXqVSUmJurixYuSpJMnT0qSqlWrpmrVqhV4nQAAAIAzKpQ1FteuXXPYfvrpJ508eVJ/+tOftGrVqsK4pCRpxYoVatq0qUJDQxUaGqqgoCAtW7bMoc/JkyeVmppq/7xhwwY1b95cvXr1kiT1799fzZs35+3gAAAAwD0otDUWv/XAAw9o6tSpeuqpp3TixIlCuUalSpW0fPnyfPvYbDaHz4MHD9bgwYMLpR4AAADAVRTKiMWduLm52accAQAAAHAehTJisWHDBofPNptNSUlJ+vDDD9W+ffvCuCQAAAAAExVKsOjTp4/DZ4vFoipVqqhLly567733CuOSAAAAAExUKMHi5s2bhXFaAAAAAMVUka6xAAAAAOCcCmzEIjIy8q77Tp8+vaAuCwAAAKAYKLBgcfjw4bvqZ7FYCuqSAAAAAIqJAgsW27dvL6hTAQAAAChhWGMBAAAAwLBCe/P2wYMH9dlnnykxMVE3btxw2Ld27drCuiwAAAAAExTKiMXf//53tW/fXsePH9e6deuUlZWl48ePa9u2bbJarYVxSQAAAAAmKpRg8dZbb+n999/X559/Lg8PD82cOVPx8fHq27evateuXRiXBAAAAGCiQgkWp0+fVq9evSRJnp6eysjIkMVi0ahRozRv3rzCuCQAAAAAExVKsKhUqZLS09MlSffff7+OHTsmSfrxxx/1888/F8YlAQAAAJioQINFXFycJKlDhw6KiYmRJPXt21cvvfSShgwZogEDBqhr164FeUkAAAAAxUCBPhWqRYsWat68ufr06aMBAwZIkqKiolS6dGnt3r1bjz32mCZMmFCQlwQAAABQDBToiMVXX32lFi1a6N1331XdunX11FNPaefOnRo9erQ2bNig6dOnq2LFigV5SQAAAADFQIEGi+DgYM2fP1/JycmaM2eOzp8/r27duqlu3bp68803df78+YK8HAAAAIBiolAWb5ctW1aDBg3Sjh07dOrUKQ0YMEBz585VQECAevbsWRiXBAAAAGCiQgkWv1a3bl2NHTtW48aNk7e3t7788svCviQAAACAIlagi7d/a+fOnVq0aJHWrFkjNzc39e3bV88++2xhXhIAAACACQo8WJw7d05LlizRkiVLdObMGbVr106zZs1S37595eXlVdCXAwAAAFAMFGiwCAkJ0fbt21WlShVFRETomWeeUYMGDQryEgAAAACKoQINFmXLltWaNWsUFhYmNze3gjw1AAAAgGKsQIPFhg0bCvJ0AAAAAEqIQn8qFAAAAADnR7AAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgWIG/eRvOLWFqL7NLAAAAQDHEiAUAAAAAw5wqWFy7dk3h4eGyWq2yWq0KDw/Xjz/+eMf+WVlZGjNmjJo2bSovLy/VqFFDERERunjxYtEVDQAAADgBpwoWAwcOVFxcnDZt2qRNmzYpLi5O4eHhd+z/888/6+uvv9aECRP09ddfa+3atTp16pQeffTRIqwaAAAAKPmcZo1FfHy8Nm3apH379qlNmzaSpPnz5ys4OFgnT55UgwYNch1jtVoVExPj0DZr1iy1bt1aiYmJql27dpHUDgAAAJR0TjNisXfvXlmtVnuokKS2bdvKarVqz549d32e1NRUWSwW3XfffXfsk5mZqbS0NIcNAAAAcGVOEyySk5NVtWrVXO1Vq1ZVcnLyXZ3j+vXrGjt2rAYOHChvb+879ouOjrav47BarapVq9YfrhsAAABwBsU+WEyaNEkWiyXfLTY2VpJksVhyHW+z2fJs/62srCz1799fN2/e1OzZs/PtGxUVpdTUVPt27ty5P3ZzAAAAgJMo9msshg8frv79++fbx9/fX0ePHtWlS5dy7UtJSZGvr2++x2dlZalv3746c+aMtm3blu9ohSR5enrK09Pz94sHAAAAXESxDxY+Pj7y8fH53X7BwcFKTU3VgQMH1Lp1a0nS/v37lZqaqnbt2t3xuNuh4ttvv9X27dtVuXLlAqsdAAAAcBXFfirU3QoMDFT37t01ZMgQ7du3T/v27dOQIUMUFhbm8ESohg0bat26dZKk7OxsPfHEE4qNjdWKFSuUk5Oj5ORkJScn68aNG2bdCgAAAFDiOE2wkKQVK1aoadOmCg0NVWhoqIKCgrRs2TKHPidPnlRqaqok6fz589qwYYPOnz+vBx98UNWrV7dv9/IkKQAAAMDVFfupUPeiUqVKWr58eb59bDab/df+/v4OnwEAAAD8MU41YgEAAADAHAQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgmFMFi2vXrik8PFxWq1VWq1Xh4eH68ccf8z1m0qRJatiwoby8vFSxYkV169ZN+/fvL5qCAQAAACfhVMFi4MCBiouL06ZNm7Rp0ybFxcUpPDw832Pq16+vDz/8UN988412794tf39/hYaGKiUlpYiqBgAAAEo+i81ms5ldREGIj49Xo0aNtG/fPrVp00aStG/fPgUHB+vEiRNq0KDBXZ0nLS1NVqtVW7ZsUdeuXe/pmNTUVHl7e//hewAAAM7Lf+wXZpeAApYwtZfZJRS6e/me6zQjFnv37pXVarWHCklq27atrFar9uzZc1fnuHHjhubNmyer1apmzZoVVqkAAACA03E3u4CCkpycrKpVq+Zqr1q1qpKTk/M99vPPP1f//v31888/q3r16oqJiZGPj88d+2dmZiozM9P+OS0t7Y8XDgAAADiBYj9iMWnSJFkslny32NhYSZLFYsl1vM1my7P91zp37qy4uDjt2bNH3bt3V9++ffXDDz/csX90dLR9gbjValWtWrWM3SQAAABQwhX7EYvhw4erf//++fbx9/fX0aNHdenSpVz7UlJS5Ovrm+/xXl5eqlevnurVq6e2bdvqgQce0MKFCxUVFZVn/6ioKEVGRto/p6WlES4AAADg0op9sPDx8cl3WtJtwcHBSk1N1YEDB9S6dWtJ0v79+5Wamqp27drd0zVtNpvDVKff8vT0lKen5z2dEwAAAHBmxX4q1N0KDAxU9+7dNWTIEO3bt0/79u3TkCFDFBYW5vBEqIYNG2rdunWSpIyMDL366qvat2+fzp49q6+//lrPPfeczp8/ryeffNKsWwEAAABKHKcJFpK0YsUKNW3aVKGhoQoNDVVQUJCWLVvm0OfkyZNKTU2VJLm5uenEiRN6/PHHVb9+fYWFhSklJUW7du1S48aNzbgFAAAAoEQq9lOh7kWlSpW0fPnyfPv8+rUdZcqU0dq1awu7LAAAAMDpOdWIBQAAAABzECwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhjlVsLh27ZrCw8NltVpltVoVHh6uH3/88a6PHzp0qCwWi2bMmFFoNQIAAADOyKmCxcCBAxUXF6dNmzZp06ZNiouLU3h4+F0du379eu3fv181atQo5CoBAAAA5+NudgEFJT4+Xps2bdK+ffvUpk0bSdL8+fMVHByskydPqkGDBnc89sKFCxo+fLi+/PJL9erVq6hKBgAAAJyG04xY7N27V1ar1R4qJKlt27ayWq3as2fPHY+7efOmwsPD9corr6hx48ZFUSoAAADgdJxmxCI5OVlVq1bN1V61alUlJyff8bhp06bJ3d1dI0aMuOtrZWZmKjMz0/45LS3t3ooFAAAAnEyxH7GYNGmSLBZLvltsbKwkyWKx5DreZrPl2S5Jhw4d0syZM7VkyZI79slLdHS0fYG41WpVrVq1/tjNAQAAAE6i2I9YDB8+XP3798+3j7+/v44ePapLly7l2peSkiJfX988j9u1a5d++OEH1a5d296Wk5Ojv/3tb5oxY4YSEhLyPC4qKkqRkZH2z2lpaYQLAAAAuDSLzWazmV1EQYiPj1ejRo20f/9+tW7dWpK0f/9+tW3bVidOnMhz8faVK1eUlJTk0PbII48oPDxcTz/9dL4Lvn8tLS1NVqtVqamp8vb2Nn4zAAAAQDFwL99zi/2Ixd0KDAxU9+7dNWTIEM2dO1eS9PzzzyssLMwhIDRs2FDR0dH685//rMqVK6ty5coO5yldurSqVat216ECAAAAQAlYY3EvVqxYoaZNmyo0NFShoaEKCgrSsmXLHPqcPHlSqampJlUIAAAAOCenmQplJqZCAQAAwBndy/dcpxqxAAAAAGAOggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADDM3ewCnMHtdwympaWZXAkAAABQcG5/v72bd2oTLApAenq6JKlWrVomVwIAAAAUvPT0dFmt1nz7WGx3Ez+Qr5s3b+rixYuqUKGCLBaL2eXAoLS0NNWqVUvnzp373VfXAyh6/IwCxRs/o87FZrMpPT1dNWrUUKlS+a+iYMSiAJQqVUo1a9Y0uwwUMG9vb/6DCBRj/IwCxRs/o87j90YqbmPxNgAAAADDCBYAAAAADCNYAL/h6empiRMnytPT0+xSAOSBn1GgeONn1HWxeBsAAACAYYxYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1jApc2aNcvsEgAAAJwCT4WCS6tUqZJatmypxYsX8/Z0oBg7ePCgVq1apVOnTsliseiBBx7QwIED1apVK7NLA5CPtWvXatKkSTp69KjZpaAIMGIBl3bs2DF5enqqadOmWrZsmdnlAMjD6NGj1aZNGy1YsEDnz59XYmKi5s+frzZt2mjMmDFmlwe4vPnz5+vJJ5/UwIEDtX//fknStm3b1Lx5cz311FMKDg42uUIUFUYsAElLlixRZGSkOnXqpPHjx8vd3d1hf1BQkEmVAa7tk08+0bBhw/TOO+9o6NChKl26tCQpKytLc+bM0ZgxYzR37lxFRESYXCngmt599129+uqrCgoKUnx8vCRp3Lhxmj59uv7617/qxRdflI+Pj8lVoqgQLID/tWXLFnXv3l02m002m00Wi8X+vzk5OWaXB7ik1q1ba8CAARo1alSe+6dPn66///3vOnDgQBFXBkCSAgMD9corr+iZZ57Rjh071KVLF3Xp0kX/+Mc/dN9995ldHooYwQLQrS8nEyZM0JNPPqkJEybkGrHw8/MzqTLAtXl5eembb75RnTp18tz//fffq2nTpsrIyCjiygBIUrly5XTixAnVrl1bkuTp6an//Oc/atOmjcmVwQzuv98FcF7ff/+9IiIidPr0aa1cuVL/8z//Y3ZJAH7Fzc1NN27cuOP+rKwsubm5FWFFAH7t+vXrKlOmjP2zh4eHqlSpYmJFMBPBAi4tKChI3bt31/r165kDChRDLVu21IoVK/T666/nuX/ZsmVq0aJFEVcF4NcWLFig8uXLS5Kys7O1ZMmSXH+njhgxwozSUMSYCgWXtnz5cj311FNmlwHgDj7//HP16dNHkZGR+tvf/iZfX19JUnJyst577z3NmDFD69atU1hYmMmVAq7J399fFosl3z4Wi0Xff/99EVUEMzFiAZeW3xQLSUpPT9eoUaO0YMGCIqoIwK+FhYXp/fff18svv6z33ntPVqtVkpSamio3Nze98847hArARAkJCWaXgGKEEQu4NKvVqg4dOmjBggWqVq2aw74vv/xSQ4YMUcWKFXXkyBGTKgQgSefPn9dnn32mb7/9VpJUv359Pf7446pVq5bJlQHIz5UrV7Rs2TKNHDnS7FJQBAgWcGkJCQl6+umndfToUX344YcaMGCA0tPTNXLkSC1fvlyvvPKKJk6caH92PgAAyJ/NZtPmzZu1cOFC/fOf/5S3t7dSUlLMLgtFgDdvw6X5+/tr+/btmjBhgoYMGaKwsDA1adJEBw4c0J49e/TGG28QKgATfffddzp06JBD29atW9W5c2e1bt1ab731lkmVAfithIQEvfbaa/Lz81PPnj1VpkwZffHFF0pOTja7NBQRggUgaejQoQoNDdXGjRt19epVLV++XC1btjS7LMDlvfLKK1q/fr3985kzZ9S7d295eHgoODhY0dHRmjFjhmn1Aa4uMzNTq1atUteuXRUYGKhjx45p+vTpKlWqlMaOHatu3brxSGgXQrCAy/vqq6/UrFkznTx5Ups2bVKPHj0UHBys999/3+zSAJcXGxurnj172j+vWLFC9evX15dffqmZM2dqxowZWrJkiXkFAi7u/vvv15w5c9SvXz9dvHhRa9eu1RNPPGF2WTAJwQIu7W9/+5u6dOmi3r176+uvv1ZoaKg+/fRTLV68WG+99ZYefvhhHpEHmOjy5cuqWbOm/fP27dvVu3dv++dOnTrxVBrARDk5ObJYLLJYLIxMgGAB1/bPf/5TW7Zs0XvvvSdPT097e79+/XTs2DFVqlRJzZo1M7FCwLVVqlRJSUlJkqSbN28qNjZWbdq0se+/ceOGeAYJYJ6kpCQ9//zzWrVqlapVq6bHH39c69at+913W8A5ESzg0o4cOaIOHTrkuc/X11fr16/X7Nmzi7gqALd17NhRr7/+us6dO6cZM2YoJydHnTt3tu8/fvy4/P39zSsQcHFlypTRX/7yF23btk3ffPONAgMDNWLECGVnZ+vNN99UTEyMcnJyzC4TRYRgAZfm5eWV7/5vvvkm1xNpABSdN998U/Hx8fLz89Po0aP19ttvO/zcLlu2TF26dDGxQsC1LV26VJmZmZKkunXr6o033tDZs2f1xRdfKDMzU2FhYfL19TW5ShQV3mMB/EZaWppWrVqlhQsXKjY2VkFBQYqLizO7LMBlZWVl6fjx46pSpYo8PDzk4eEhb29vSbdGHWvWrKnKlSubXCXgmtzc3JSUlKSqVavmuT8lJUXLli1TZGRkEVcGMzBiAfyvnTt3KiIiQtWrV9cLL7ygLl266NSpU4QKwGQZGRmaN2+egoKC5Ovrq4oVK6patWqKiorSAw88QKgATPR7/z5dpUoVQoULYcQCLi0pKUmLFy/WokWLlJGRoQEDBmjgwIEKDg7WkSNH1KhRI7NLBFza1atXFRwcrAsXLugvf/mLAgMDZbPZFB8fr5UrV6phw4bavXu3jhw5ov3792vEiBFmlwy4lFKlSunSpUuqUqWK2aWgGHA3uwDATAEBAXryySf10UcfKSQkRKVKMYgHFCdTpkyRh4eHTp8+nWue9pQpUxQaGqrw8HBt3rxZH3zwgUlVAq5t8ODBDk9WzMvatWuLqBqYiWABl+bn56fdu3erdu3a8vPzU8OGDc0uCcCvrF+/XnPnzs1z8We1atX09ttvq2fPnpo4caIGDRpkQoUAKlSooLJly5pdBooBggVc2smTJ/XVV19p4cKFeuihh1S/fn099dRTksQzuIFiICkpSY0bN77j/iZNmqhUqVKaOHFiEVYF4Nc++OCDOy7ehmth3gdcXvv27bVo0SIlJSVp2LBh+vTTT5WTk6MXXnhB8+fPV0pKitklAi7Lx8cn3zdrnzlzhi80gIn4Rzj8Gou3gTzEx8dr4cKFWrZsma5evaqsrCyzSwJc0rPPPqvvvvtOMTEx8vDwcNiXmZmpRx55RHXq1NGiRYtMqhBwbaVKlVJycjIBH5IIFkC+srOztWHDBj322GNmlwK4pPPnz6tVq1by9PTUiy++aF8Hdfz4cc2ePVuZmZk6ePCgateubXKlgGuaOnWqGjdurN69e9vbli5dqokTJyojI0N9+vTRrFmzfndxN5wDwQLIR3x8vHr16qXvv//e7FIAl3XmzBm98MIL2rx5s/2Z+RaLRSEhIfrwww9Vr149kysEXFf37t3VuXNnjRkzRpL0zTffqEWLFho8eLACAwP1zjvvaOjQoZo0aZK5haJIECyAfBw5ckQtWrRQTk6O2aUALu/atWv69ttvJUn16tVTpUqVTK4IQPXq1fWvf/1LrVq1kiSNGzdOO3fu1O7duyVJn332mSZOnKjjx4+bWSaKCE+FAgCUCBUrVlTr1q3NLgPAr1y7ds3hcdA7d+5U9+7d7Z8feughnTt3zozSYAKeCgUAAIA/xNfXV2fOnJEk3bhxQ19//bWCg4Pt+9PT01W6dGmzykMRI1gAAADgD+nevbvGjh2rXbt2KSoqSuXKlVOHDh3s+48ePaq6deuaWCGKElOh4NIqVqyY7zO4s7Ozi7AaAABKljfeeEOPPfaYOnbsqPLly+uTTz5xeDT0okWLFBoaamKFKEos3oZL++STT+6q36BBgwq5EgAASq7U1FSVL19ebm5uDu1Xr15V+fLlc72HBs6JYAH8juzsbLm7M7gHAACQH9ZYAHdw/Phx/e1vf9P9999vdikAAADFHsEC+JWffvpJCxYsUHBwsIKCgrR//36NHTvW7LIAAACKPeZ3AJJ2796tBQsWaM2aNQoICNDx48e1c+dOtW/f3uzSAAAASgRGLODS3n77bTVs2FD9+/dXlSpVtHv3bh09elQWi0UVK1Y0uzwAAIASg8XbcGnu7u4aM2aMpkyZ4vAki9KlS+vIkSNq1KiRidUBAACUHIxYwKVNmTJFn332mQICAjRmzBgdO3bM7JIAAABKJIIFXNqrr76qU6dOadmyZUpOTlbbtm3VrFkz2Ww2Xbt2zezyAAAASgymQgG/kpaWppUrV2rRokU6dOiQ2rRpoyeeeEKRkZFmlwYAAFCsESyAOzh27JgWLlyoFStW6IcffjC7HAAAgGKNx83Cpf3yyy/aunWrwsLCJElRUVHKzMy073d3d9fp06fNKg8AAKDEIFjApS1dulSff/65PVh8+OGHaty4scqWLStJOnnypGrUqKFRo0aZWSYAAECxx1QouLSHH35Yo0aN0p///GdJUoUKFXTkyBHVqVNHkrR8+XJ99NFH2rt3r5llAgAAFHs8FQou7dSpU6pfv779c5kyZVSq1P/9WLRu3VrHjx83ozQAAIAShalQcGmpqalyd/+/H4OUlBSH/Tdv3nRYcwEAAIC8MWIBl1azZs18X4p39OhR1axZswgrAgAAKJkIFnBpPXv21Guvvabr16/n2vfLL79o8uTJ6tWrlwmVAQAAlCws3oZLu3Tpkh588EF5eHho+PDhql+/viwWi06cOKEPP/xQ2dnZOnz4sHx9fc0uFQAAoFgjWMDlnTlzRv/v//0/xcTE6PaPg8ViUUhIiGbPnm1/QhQAAADujGAB/K+rV6/qu+++kyTVq1dPlSpVMrkiAACAkoNgAQAAAMAwFm8DAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAipWEhARZLBbFxcWZXQoA4B4QLAAAAAAYRrAAAAAAYBjBAgBgips3b2ratGmqV6+ePD09Vbt2bb355pu5+uXk5OjZZ59VQECAypYtqwYNGmjmzJkOfXbs2KHWrVvLy8tL9913n9q3b6+zZ89Kko4cOaLOnTurQoUK8vb2VsuWLRUbG1sk9wgArsTd7AIAAK4pKipK8+fP1/vvv68//elPSkpK0okTJ3L1u3nzpmrWrKlPP/1UPj4+2rNnj55//nlVr15dffv2VXZ2tvr06aMhQ4Zo1apVunHjhg4cOCCLxSJJ+stf/qLmzZtrzpw5cnNzU1xcnEqXLl3UtwsATo83bwMAilx6erqqVKmiDz/8UM8995zDvoSEBAUEBOjw4cN68MEH8zz+xRdf1KVLl/SPf/xDV69eVeXKlbVjxw517NgxV19vb2/NmjVLgwYNKoxbAQD8L6ZCAQCKXHx8vDIzM9W1a9e76v/xxx+rVatWqlKlisqXL6/58+crMTFRklSpUiUNHjxYjzzyiHr37q2ZM2cqKSnJfmxkZKSee+45devWTVOnTtXp06cL5Z4AwNURLAAARa5s2bJ33ffTTz/VqFGj9Mwzz2jz5s2Ki4vT008/rRs3btj7LF68WHv37lW7du20evVq1a9fX/v27ZMkTZo0Sf/973/Vq1cvbdu2TY0aNdK6desK/J4AwNUxFQoAUOSuX7+uSpUq6YMPPvjdqVB//etfdfz4cW3dutXep1u3brp8+fId33URHByshx56SB988EGufQMGDFBGRoY2bNhQoPcEAK6OEQsAQJErU6aMxowZo9GjR2vp0qU6ffq09u3bp4ULF+bqW69ePcXGxurLL7/UqVOnNGHCBB08eNC+/8yZM4qKitLevXt19uxZbd68WadOnVJgYKB++eUXDR8+XDt27NDZs2f11Vdf6eDBgwoMDCzK2wUAl8BToQAAppgwYYLc3d312muv6eLFi6pevbqGDRuWq9+wYcMUFxenfv36yWKxaMCAAXrhhRf073//W5JUrlw5nThxQp988omuXLmi6tWra/jw4Ro6dKiys7N15coVRURE6NKlS/Lx8dFjjz2myZMnF/XtAoDTYyoUAAAAAMOYCgUAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAmGTJkiWyWCwqU6aMzp49m2t/p06d1KRJExMqKxiDBw+Wv7+/Q5u/v78GDx5cpHUkJCTIYrFoyZIl+fbbsWOHLBaLfXNzc1OVKlXUu3dvxcbGFk2x+bj9/xfp/2pNSEiw77/T7/ekSZMk3fr/U1H/3gNwLQQLADBZZmamxo8fb3YZRWLdunWaMGGC2WXk66233tLevXu1Y8cOTZgwQXv27FHHjh317bffml1aviZMmKB169aZXQYAF0awAACTde/eXStXrtSRI0cK9Tq//PJLoZ7/bjRv3lx169Y1u4x8PfDAA2rbtq06dOigESNG6P3339fPP/+s5cuXm11avurWravmzZubXQYAF0awAACTjR49WpUrV9aYMWN+t+/169cVFRWlgIAAeXh46P7779eLL76oH3/80aGfv7+/wsLCtHbtWjVv3lxlypTR5MmT7VNoVq5cqTFjxqh69eoqX768evfurUuXLik9PV3PP/+8fHx85OPjo6efflo//fSTw7k/+ugjPfzww6pataq8vLzUtGlTvf3228rKyvrd+n87FapTp04O049+vf166lJycrKGDh2qmjVrysPDQwEBAZo8ebKys7Mdzn/x4kX17dtXFSpUkNVqVb9+/ZScnPy7deWnVatWkqRLly45tO/evVtdu3ZVhQoVVK5cObVr105ffPGFfX9aWprc3d31zjvv2NsuX76sUqVKyWq1OtQ+YsQIValSRTab7Q/XmddUKAAoSu5mFwAArq5ChQoaP368XnrpJW3btk1dunTJs5/NZlOfPn20detWRUVFqUOHDjp69KgmTpyovXv3au/evfL09LT3//rrrxUfH6/x48crICBAXl5eysjIkCS9+uqr6ty5s5YsWaKEhAS9/PLLGjBggNzd3dWsWTOtWrVKhw8f1quvvqoKFSrogw8+sJ/39OnTGjhwoD3cHDlyRG+++aZOnDihRYsW3dO9z549W2lpaQ5tEyZM0Pbt29WgQQNJt0JF69atVapUKb322muqW7eu9u7dqzfeeEMJCQlavHixpFsjMt26ddPFixcVHR2t+vXr64svvlC/fv3uqabfOnPmjCSpfv369radO3cqJCREQUFBWrhwoTw9PTV79mz17t1bq1atUr9+/eTt7a2HHnpIW7Zs0SuvvCJJ2rp1qzw9PZWenq4DBw6oXbt2kqQtW7aoS5cu9jUUeRk8eLA9lHXq1OmuQsiv12Ds2LHjHu8cAO6RDQBgisWLF9sk2Q4ePGjLzMy01alTx9aqVSvbzZs3bTabzdaxY0db48aN7f03bdpkk2R7++23Hc6zevVqmyTbvHnz7G1+fn42Nzc328mTJx36bt++3SbJ1rt3b4f2kSNH2iTZRowY4dDep08fW6VKle54Dzk5ObasrCzb0qVLbW5ubrarV6/a9w0aNMjm5+fn0N/Pz882aNCgO57vnXfeyXUvQ4cOtZUvX9529uxZh77vvvuuTZLtv//9r81ms9nmzJljk2T75z//6dBvyJAhNkm2xYsX3/G6Ntv//d6sXr3alpWVZfv5559tX331la1Bgwa2Ro0a2a5du2bv27ZtW1vVqlVt6enp9rbs7GxbkyZNbDVr1rT/GY4fP95WtmxZ2/Xr1202m8323HPP2bp3724LCgqyTZ482Waz2WwXLlzIdc9/RF6/3wBQlJgKBQDFgIeHh9544w3Fxsbq008/zbPPtm3bJCnXk32efPJJeXl5aevWrQ7tQUFBDv/K/mthYWEOnwMDAyVJvXr1ytV+9epVh+lQhw8f1qOPPqrKlSvLzc1NpUuXVkREhHJycnTq1Knfv9k7WLVqlUaPHq3x48dryJAh9vbPP/9cnTt3Vo0aNZSdnW3fevToIenW6IEkbd++XRUqVNCjjz7qcN6BAwfeUx39+vVT6dKlVa5cObVv315paWn64osvdN9990mSMjIytH//fj3xxBMqX768/Tg3NzeFh4fr/PnzOnnypCSpa9eu+uWXX7Rnzx5Jt0YmQkJC1K1bN8XExNjbJKlbt273VCcAFDcECwAoJvr3768WLVpo3Lhxea5XuHLlitzd3VWlShWHdovFomrVqunKlSsO7dWrV7/jtSpVquTw2cPDI9/269evS5ISExPVoUMHXbhwQTNnztSuXbt08OBBffTRR5L++ALx7du3a/DgwYqIiNDrr7/usO/SpUv617/+pdKlSztsjRs3lnRr3YJ06/fH19c317mrVat2T7VMmzZNBw8e1M6dOzVu3DhdunRJffr0UWZmpiTp2rVrstlsef7+1qhRw16LJLVr107lypXTli1b9N133ykhIcEeLPbv36+ffvpJW7ZsUZ06dRQQEHBPdQJAccMaCwAoJiwWi6ZNm6aQkBDNmzcv1/7KlSsrOztbKSkpDuHCZrMpOTlZDz30UK7zFbT169crIyNDa9eulZ+fn709Li7uD5/z6NGj6tOnjzp27Kj58+fn2u/j46OgoCC9+eabeR5/+8t85cqVdeDAgVz773Xxdp06dewLth9++GGVLVtW48eP16xZs/Tyyy+rYsWKKlWqlJKSknIde/HiRXvN0q1g9qc//UlbtmxRzZo1Va1aNTVt2lR16tSRdGvdw9atW3ONIAFAScSIBQAUI926dVNISIimTJmS62lMXbt2laRcjz1ds2aNMjIy7PsL0+2w8utF4jabLc9AcDcSExPVo0cP1alTR2vWrFHp0qVz9QkLC9OxY8dUt25dtWrVKtd2O1h07txZ6enp2rBhg8PxK1eu/EO13TZ69GjVq1dPU6dOVXp6ury8vNSmTRutXbvWYYTm5s2bWr58uWrWrOkwBa1bt246dOiQ1qxZY5/u5OXlpbZt22rWrFm6ePEi06AAOAWCBQAUM9OmTVNKSooOHTrk0B4SEqJHHnlEY8aM0eTJk7VlyxZNnz5dTz/9tJo3b67w8PBCry0kJEQeHh4aMGCA/v3vf2vdunV65JFHdO3atT90vh49eujHH3/Ua6+9pv/+97/at2+ffUtJSZEkTZkyRaVLl1a7du00Z84cbdu2TRs3btTs2bMVFham8+fPS5IiIiJUv359RURE6KOPPtLmzZs1cuRIffnll4buuXTp0nrrrbd05coVzZw5U5IUHR2tK1euqHPnzvrHP/6hDRs2qGfPnjp27Jjeffddh9Girl27KicnR1u3blVISIi9vVu3btq8ebMsFssdnwQGACUJwQIAipnmzZtrwIABudotFovWr1+vyMhILV68WD179tS7776r8PBwbdu2zWEUobA0bNhQa9as0bVr1/TYY4/pr3/9qx588EGHx9Hei+PHj+vnn3/WY489puDgYIft9jshqlevrtjYWIWGhuqdd95R9+7dFR4erkWLFunBBx9UxYoVJUnlypXTtm3b1K1bN40dO1ZPPPGEzp8/r7///e+G7/vJJ59UmzZtNH36dKWmpqpjx47atm2bvLy8NHjwYPXv31+pqanasGFDrsfbNm/e3D416tcjE7d/3bx5c1WuXNlwjQBgNovNZuBtPAAAAAAgRiwAAAAAFACCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMPczS7AGdy8eVMXL15UhQoVHF6KBAAAAJRkNptN6enpqlGjhkqVyn9MgmBRAC5evKhatWqZXQYAAABQKM6dO6eaNWvm24dgUQAqVKgg6dZvuLe3t8nVAAAAAAUjLS1NtWrVsn/fzQ/BogDcnv7k7e1NsAAAAIDTuZvp/k65eHv27NkKCAhQmTJl1LJlS+3ateuujvvqq6/k7u6uBx98sHALBAAAAJyM0wWL1atXa+TIkRo3bpwOHz6sDh06qEePHkpMTMz3uNTUVEVERKhr165FVCkAAADgPCw2m81mdhEFqU2bNmrRooXmzJljbwsMDFSfPn0UHR19x+P69++vBx54QG5ublq/fr3i4uLu+pppaWmyWq1KTU1lKhQAAACcxr18z3WqEYsbN27o0KFDCg0NdWgPDQ3Vnj177njc4sWLdfr0aU2cOPGurpOZmam0tDSHDQAAAHBlThUsLl++rJycHPn6+jq0+/r6Kjk5Oc9jvv32W40dO1YrVqyQu/vdrWWPjo6W1Wq1bzxqFgAAAK7OqYLFbb9dtW6z2fJcyZ6Tk6OBAwdq8uTJql+//l2fPyoqSqmpqfbt3LlzhmsGAAAASjKnetysj4+P3Nzcco1O/PDDD7lGMSQpPT1dsbGxOnz4sIYPHy7p1lu0bTab3N3dtXnzZnXp0iXXcZ6envL09CycmwAAAABKIKcasfDw8FDLli0VExPj0B4TE6N27drl6u/t7a1vvvlGcXFx9m3YsGFq0KCB4uLi1KZNm6IqHQAAACjRnGrEQpIiIyMVHh6uVq1aKTg4WPPmzVNiYqKGDRsm6dY0pgsXLmjp0qUqVaqUmjRp4nB81apVVaZMmVztAAAAAO7M6YJFv379dOXKFU2ZMkVJSUlq0qSJNm7cKD8/P0lSUlLS777TAgAAAMC9cbr3WJiB91gAKC78x35hdgkoQAlTe5ldAgAX57LvsQAAAABgDoIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADDMKYPF7NmzFRAQoDJlyqhly5batWvXHfvu3r1b7du3V+XKlVW2bFk1bNhQ77//fhFWCwAAAJR87mYXUNBWr16tkSNHavbs2Wrfvr3mzp2rHj166Pjx46pdu3au/l5eXho+fLiCgoLk5eWl3bt3a+jQofLy8tLzzz9vwh0AAAAAJY/FZrPZzC6iILVp00YtWrTQnDlz7G2BgYHq06ePoqOj7+ocjz32mLy8vLRs2bK76p+Wliar1arU1FR5e3v/oboBoCD4j/3C7BJQgBKm9jK7BAAu7l6+5zrVVKgbN27o0KFDCg0NdWgPDQ3Vnj177uochw8f1p49e9SxY8fCKBEAAABwSk41Fery5cvKycmRr6+vQ7uvr6+Sk5PzPbZmzZpKSUlRdna2Jk2apOeee+6OfTMzM5WZmWn/nJaWZqxwAAAAoIRzqhGL2ywWi8Nnm82Wq+23du3apdjYWH388ceaMWOGVq1adce+0dHRslqt9q1WrVoFUjcAAABQUjnViIWPj4/c3NxyjU788MMPuUYxfisgIECS1LRpU126dEmTJk3SgAED8uwbFRWlyMhI++e0tDTCBQAAAFyaU41YeHh4qGXLloqJiXFoj4mJUbt27e76PDabzWGq0295enrK29vbYQMAAABcmVONWEhSZGSkwsPD1apVKwUHB2vevHlKTEzUsGHDJN0abbhw4YKWLl0qSfroo49Uu3ZtNWzYUNKt91q8++67+utf/2raPQAAAAAljdMFi379+unKlSuaMmWKkpKS1KRJE23cuFF+fn6SpKSkJCUmJtr737x5U1FRUTpz5ozc3d1Vt25dTZ06VUOHDjXrFgAAAIASx+neY2EG3mMBoLjgPRbOhfdYADCby77HAgAAAIA5CBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDnDJYzJ49WwEBASpTpoxatmypXbt23bHv2rVrFRISoipVqsjb21vBwcH68ssvi7BaAAAAoORzumCxevVqjRw5UuPGjdPhw4fVoUMH9ejRQ4mJiXn2/89//qOQkBBt3LhRhw4dUufOndW7d28dPny4iCsHAAAASi6LzWazmV1EQWrTpo1atGihOXPm2NsCAwPVp08fRUdH39U5GjdurH79+um11167q/5paWmyWq1KTU2Vt7f3H6obAAqC/9gvzC4BBShhai+zSwDg4u7le65TjVjcuHFDhw4dUmhoqEN7aGio9uzZc1fnuHnzptLT01WpUqXCKBEAAABwSu5mF1CQLl++rJycHPn6+jq0+/r6Kjk5+a7O8d577ykjI0N9+/a9Y5/MzExlZmbaP6elpf2xggEAAAAn4VQjFrdZLBaHzzabLVdbXlatWqVJkyZp9erVqlq16h37RUdHy2q12rdatWoZrhkAAAAoyZwqWPj4+MjNzS3X6MQPP/yQaxTjt1avXq1nn31Wn376qbp165Zv36ioKKWmptq3c+fOGa4dAAAAKMmcKlh4eHioZcuWiomJcWiPiYlRu3bt7njcqlWrNHjwYK1cuVK9ev3+QjlPT095e3s7bAAAAIArc6o1FpIUGRmp8PBwtWrVSsHBwZo3b54SExM1bNgwSbdGGy5cuKClS5dKuhUqIiIiNHPmTLVt29Y+2lG2bFlZrVbT7gMAAAAoSZwuWPTr109XrlzRlClTlJSUpCZNmmjjxo3y8/OTJCUlJTm802Lu3LnKzs7Wiy++qBdffNHePmjQIC1ZsqSoywcAAABKJKd7j4UZeI8FgOKC91g4F95jAcBsLvseCwAAAADmIFgAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMc8pgMXv2bAUEBKhMmTJq2bKldu3adce+SUlJGjhwoBo0aKBSpUpp5MiRRVcoAAAA4CScLlisXr1aI0eO1Lhx43T48GF16NBBPXr0UGJiYp79MzMzVaVKFY0bN07NmjUr4moBAAAA5+B0wWL69Ol69tln9dxzzykwMFAzZsxQrVq1NGfOnDz7+/v7a+bMmYqIiJDVai3iagEAAADn4FTB4saNGzp06JBCQ0Md2kNDQ7Vnz54Cu05mZqbS0tIcNgAAAMCVOVWwuHz5snJycuTr6+vQ7uvrq+Tk5AK7TnR0tKxWq32rVatWgZ0bAAAAKImcKljcZrFYHD7bbLZcbUZERUUpNTXVvp07d67Azg0AAACURO5mF1CQfHx85Obmlmt04ocffsg1imGEp6enPD09C+x8AAAAQEnnVCMWHh4eatmypWJiYhzaY2Ji1K5dO5OqAgAAAJxfoYxYnDt3ThaLRTVr1pQkHThwQCtXrlSjRo30/PPPF8Yl7SIjIxUeHq5WrVopODhY8+bNU2JiooYNGybp1jSmCxcuaOnSpfZj4uLiJEk//fSTUlJSFBcXJw8PDzVq1KhQawUAAACcRaEEi4EDB+r5559XeHi4kpOTFRISosaNG2v58uVKTk7Wa6+9VhiXlST169dPV65c0ZQpU5SUlKQmTZpo48aN8vPzk3TrhXi/fadF8+bN7b8+dOiQVq5cKT8/PyUkJBRanQAAAIAzsdhsNltBn7RixYrat2+fGjRooA8++ECrV6/WV199pc2bN2vYsGH6/vvvC/qSpkpLS5PValVqaqq8vb3NLgeAC/Mf+4XZJaAAJUztZXYJAFzcvXzPLZQ1FllZWfbFzVu2bNGjjz4qSWrYsKGSkpIK45IAAAAATFQowaJx48b6+OOPtWvXLsXExKh79+6SpIsXL6py5cqFcUkAAAAAJiqUYDFt2jTNnTtXnTp10oABA9SsWTNJ0oYNG9S6devCuCQAAAAAExXK4u1OnTrp8uXLSktLU8WKFe3tzz//vMqVK1cYlwQAAABgokJ7j4XNZtOhQ4c0d+5cpaenS7r1ngmCBQAAAOB8CmXE4uzZs+revbsSExOVmZmpkJAQVahQQW+//bauX7+ujz/+uDAuCwAAAMAkhTJi8dJLL6lVq1a6du2aypYta2//85//rK1btxbGJQEAAACYqFBGLHbv3q2vvvpKHh4eDu1+fn66cOFCYVwSAAAAgIkKZcTi5s2bysnJydV+/vx5VahQoTAuCQAAAMBEhRIsQkJCNGPGDPtni8Win376SRMnTlTPnj0L45IAAAAATFQoU6Hef/99de7cWY0aNdL169c1cOBAffvtt/Lx8dGqVasK45IAAAAATFQowaJGjRqKi4vTqlWr9PXXX+vmzZt69tln9Ze//MVhMTcAAAAA51AowUKSypYtq2eeeUbPPPNMYV0CAAAAQDFRKMFi6dKl+e6PiIgojMsCAAAAMEmhBIuXXnrJ4XNWVpZ+/vln+5u3CRYAAACAcymUp0Jdu3bNYfvpp5908uRJ/elPf2LxNgAAAOCECiVY5OWBBx7Q1KlTc41mAAAAACj5iixYSJKbm5suXrxYlJcEAAAAUAQKZY3Fhg0bHD7bbDYlJSXpww8/VPv27QvjkgAAAABMVCjBok+fPg6fLRaLqlSpoi5duui9994rjEsCAAAAMFGhBIubN28WxmkBAAAAFFNFusYCAAAAgHMqsBGLyMjIu+47ffr0grosAAAAgGKgwILF4cOH76qfxWIpqEsCAAAAKCYKLFhs3769oE5l2OzZs/XOO+8oKSlJjRs31owZM9ShQ4c79t+5c6ciIyP13//+VzVq1NDo0aM1bNiwIqwYAAAAKNmcbo3F6tWrNXLkSI0bN06HDx9Whw4d1KNHDyUmJubZ/8yZM+rZs6c6dOigw4cP69VXX9WIESO0Zs2aIq4cAAAAKLksNpvNVhgnPnjwoD777DMlJibqxo0bDvvWrl1bGJeUJLVp00YtWrTQnDlz7G2BgYHq06ePoqOjc/UfM2aMNmzYoPj4eHvbsGHDdOTIEe3du/eurpmWliar1arU1FR5e3sbvwkA+IP8x35hdgkoQAlTe5ldAgAXdy/fcwtlxOLvf/+72rdvr+PHj2vdunXKysrS8ePHtW3bNlmt1sK4pCTpxo0bOnTokEJDQx3aQ0NDtWfPnjyP2bt3b67+jzzyiGJjY5WVlVVotQIAAADOpFCCxVtvvaX3339fn3/+uTw8PDRz5kzFx8erb9++ql27dmFcUpJ0+fJl5eTkyNfX16Hd19dXycnJeR6TnJycZ//s7Gxdvnw5z2MyMzOVlpbmsAEAAACurFBekHf69Gn16nVr+NbT01MZGRmyWCwaNWqUunTposmTJxfGZe1+++Qpm82W79Oo8uqfV/tt0dHRhX4PxRXTLJwL0yycD3+mQPHF36HOh//mOiqUEYtKlSopPT1dknT//ffr2LFjkqQff/xRP//8c2FcUpLk4+MjNze3XKMTP/zwQ65RiduqVauWZ393d3dVrlw5z2OioqKUmppq386dO1cwNwAAAACUUAUaLOLi4iRJHTp0UExMjCSpb9++eumllzRkyBANGDBAXbt2LchLOvDw8FDLli3t174tJiZG7dq1y/OY4ODgXP03b96sVq1aqXTp0nke4+npKW9vb4cNAAAAcGUFGixatGihli1bKjAwUAMGDJB061/3X375ZV26dEmPPfaYFi5cWJCXzCUyMlILFizQokWLFB8fr1GjRikxMdH+XoqoqChFRETY+w8bNkxnz55VZGSk4uPjtWjRIi1cuFAvv/xyodYJAAAAOJMCXWPx1VdfadGiRXr33XcVHR2txx57TM8++6xGjx6t0aNHF+Sl7qhfv366cuWKpkyZoqSkJDVp0kQbN26Un5+fJCkpKcnhnRYBAQHauHGjRo0apY8++kg1atTQBx98oMcff7xI6gUAAACcQaG8x+KXX37Rp59+qsWLF2vXrl3y9/fXM888o0GDBqlmzZoFfTnTudJ7LFh45lxYdAYARYe/Q52PK/w9avp7LMqWLatBgwZpx44dOnXqlAYMGKC5c+cqICBAPXv2LIxLAgAAADBRoQSLX6tbt67Gjh2rcePGydvbW19++WVhXxIAAABAESuU91jctnPnTi1atEhr1qyRm5ub+vbtq2effbYwLwkAAADABAUeLM6dO6clS5ZoyZIlOnPmjNq1a6dZs2apb9++8vLyKujLAQAAACgGCjRYhISEaPv27apSpYoiIiL0zDPPqEGDBgV5CQAAAADFUIEGi7Jly2rNmjUKCwuTm5tbQZ4aAAAAQDFWoMFiw4YNBXk6AAAAACVEoT8VCgAAAIDzI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwzKmCxbVr1xQeHi6r1Sqr1arw8HD9+OOP+R6zdu1aPfLII/Lx8ZHFYlFcXFyR1AoAAAA4E6cKFgMHDlRcXJw2bdqkTZs2KS4uTuHh4fkek5GRofbt22vq1KlFVCUAAADgfNzNLqCgxMfHa9OmTdq3b5/atGkjSZo/f76Cg4N18uRJNWjQIM/jbgePhISEoioVAAAAcDpOM2Kxd+9eWa1We6iQpLZt28pqtWrPnj0Feq3MzEylpaU5bAAAAIArc5pgkZycrKpVq+Zqr1q1qpKTkwv0WtHR0fZ1HFarVbVq1SrQ8wMAAAAlTbEPFpMmTZLFYsl3i42NlSRZLJZcx9tstjzbjYiKilJqaqp9O3fuXIGeHwAAAChpiv0ai+HDh6t///759vH399fRo0d16dKlXPtSUlLk6+tboDV5enrK09OzQM8JAAAAlGTFPlj4+PjIx8fnd/sFBwcrNTVVBw4cUOvWrSVJ+/fvV2pqqtq1a1fYZQIAAAAurdhPhbpbgYGB6t69u4YMGaJ9+/Zp3759GjJkiMLCwhyeCNWwYUOtW7fO/vnq1auKi4vT8ePHJUknT55UXFxcga/LAAAAAJyZ0wQLSVqxYoWaNm2q0NBQhYaGKigoSMuWLXPoc/LkSaWmpto/b9iwQc2bN1evXr0kSf3791fz5s318ccfF2ntAAAAQElW7KdC3YtKlSpp+fLl+fax2WwOnwcPHqzBgwcXYlUAAACA83OqEQsAAAAA5iBYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADHOqYHHt2jWFh4fLarXKarUqPDxcP/744x37Z2VlacyYMWratKm8vLxUo0YNRURE6OLFi0VXNAAAAOAEnCpYDBw4UHFxcdq0aZM2bdqkuLg4hYeH37H/zz//rK+//loTJkzQ119/rbVr1+rUqVN69NFHi7BqAAAAoORzN7uAghIfH69NmzZp3759atOmjSRp/vz5Cg4O1smTJ9WgQYNcx1itVsXExDi0zZo1S61bt1ZiYqJq165dJLUDAAAAJZ3TjFjs3btXVqvVHiokqW3btrJardqzZ89dnyc1NVUWi0X33XffHftkZmYqLS3NYQMAAABcmdMEi+TkZFWtWjVXe9WqVZWcnHxX57h+/brGjh2rgQMHytvb+479oqOj7es4rFaratWq9YfrBgAAAJxBsQ8WkyZNksViyXeLjY2VJFksllzH22y2PNt/KysrS/3799fNmzc1e/bsfPtGRUUpNTXVvp07d+6P3RwAAADgJIr9Govhw4erf//++fbx9/fX0aNHdenSpVz7UlJS5Ovrm+/xWVlZ6tu3r86cOaNt27blO1ohSZ6envL09Pz94gEAAAAXUeyDhY+Pj3x8fH63X3BwsFJTU3XgwAG1bt1akrR//36lpqaqXbt2dzzudqj49ttvtX37dlWuXLnAandGCVN7mV0CAAAAiqFiPxXqbgUGBqp79+4aMmSI9u3bp3379mnIkCEKCwtzeCJUw4YNtW7dOklSdna2nnjiCcXGxmrFihXKyclRcnKykpOTdePGDbNuBQAAAChxnCZYSNKKFSvUtGlThYaGKjQ0VEFBQVq2bJlDn5MnTyo1NVWSdP78eW3YsEHnz5/Xgw8+qOrVq9u3e3mSFAAAAODqiv1UqHtRqVIlLV++PN8+NpvN/mt/f3+HzwAAAAD+GKcasQAAAABgDoIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADDMqYLFtWvXFB4eLqvVKqvVqvDwcP3444/5HjNp0iQ1bNhQXl5eqlixorp166b9+/cXTcEAAACAk3CqYDFw4EDFxcVp06ZN2rRpk+Li4hQeHp7vMfXr19eHH36ob775Rrt375a/v79CQ0OVkpJSRFUDAAAAJZ/FZrPZzC6iIMTHx6tRo0bat2+f2rRpI0nat2+fgoODdeLECTVo0OCuzpOWliar1aotW7aoa9eu93RMamqqvL29//A9AAAA5+U/9guzS0ABS5jay+wSCt29fM91mhGLvXv3ymq12kOFJLVt21ZWq1V79uy5q3PcuHFD8+bNk9VqVbNmzQqrVAAAAMDpuJtdQEFJTk5W1apVc7VXrVpVycnJ+R77+eefq3///vr5559VvXp1xcTEyMfH5479MzMzlZmZaf+clpb2xwsHAAAAnECxH7GYNGmSLBZLvltsbKwkyWKx5DreZrPl2f5rnTt3VlxcnPbs2aPu3burb9+++uGHH+7YPzo62r5A3Gq1qlatWsZuEgAAACjhiv2IxfDhw9W/f/98+/j7++vo0aO6dOlSrn0pKSny9fXN93gvLy/Vq1dP9erVU9u2bfXAAw9o4cKFioqKyrN/VFSUIiMj7Z/T0tIIFwAAAHBpxT5Y+Pj45Dst6bbg4GClpqbqwIEDat26tSRp//79Sk1NVbt27e7pmjabzWGq0295enrK09Pzns4JAAAAOLNiPxXqbgUGBqp79+4aMmSI9u3bp3379mnIkCEKCwtzeCJUw4YNtW7dOklSRkaGXn31Ve3bt09nz57V119/reeee07nz5/Xk08+adatAAAAACWO0wQLSVqxYoWaNm2q0NBQhYaGKigoSMuWLXPoc/LkSaWmpkqS3NzcdOLECT3++OOqX7++wsLClJKSol27dqlx48Zm3AIAAABQIhX7qVD3olKlSlq+fHm+fX792o4yZcpo7dq1hV0WAAAA4PScasQCAAAAgDkIFgAAAAAMc6qpUAAAAMVVwtReZpcAFCpGLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYe5mF+AMbDabJCktLc3kSgAAAICCc/v77e3vu/khWBSA9PR0SVKtWrVMrgQAAAAoeOnp6bJarfn2sdjuJn4gXzdv3tTFixdVoUIFWSwWs8uBQWlpaapVq5bOnTsnb29vs8sB8Bv8jALFGz+jzsVmsyk9PV01atRQqVL5r6JgxKIAlCpVSjVr1jS7DBQwb29v/oMIFGP8jALFGz+jzuP3RipuY/E2AAAAAMMIFgAAAAAMI1gAv+Hp6amJEyfK09PT7FIA5IGfUaB442fUdbF4GwAAAIBhjFgAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWMClzZo1y+wSAAAAnAJPhYJLq1Spklq2bKnFixfz9nSgGDt48KBWrVqlU6dOyWKx6IEHHtDAgQPVqlUrs0sDkI+1a9dq0qRJOnr0qNmloAgwYgGXduzYMXl6eqpp06ZatmyZ2eUAyMPo0aPVpk0bLViwQOfPn1diYqLmz5+vNm3aaMyYMWaXB7i8+fPn68knn9TAgQO1f/9+SdK2bdvUvHlzPfXUUwoODja5QhQVRiwASUuWLFFkZKQ6deqk8ePHy93d3WF/UFCQSZUBru2TTz7RsGHD9M4772jo0KEqXbq0JCkrK0tz5szRmDFjNHfuXEVERJhcKeCa3n33Xb366qsKCgpSfHy8JGncuHGaPn26/vrXv+rFF1+Uj4+PyVWiqBAsgP+1ZcsWde/eXTabTTabTRaLxf6/OTk5ZpcHuKTWrVtrwIABGjVqVJ77p0+frr///e86cOBAEVcGQJICAwP1yiuv6JlnntGOHTvUpUsXdenSRf/4xz903333mV0eihjBAtCtLycTJkzQk08+qQkTJuQasfDz8zOpMsC1eXl56ZtvvlGdOnXy3P/999+radOmysjIKOLKAEhSuXLldOLECdWuXVuS5Onpqf/85z9q06aNyZXBDO6/3wVwXt9//70iIiJ0+vRprVy5Uv/zP/9jdkkAfsXNzU03bty44/6srCy5ubkVYUUAfu369esqU6aM/bOHh4eqVKliYkUwE8ECLi0oKEjdu3fX+vXrmQMKFEMtW7bUihUr9Prrr+e5f9myZWrRokURVwXg1xYsWKDy5ctLkrKzs7VkyZJcf6eOGDHCjNJQxJgKBZe2fPlyPfXUU2aXAeAOPv/8c/Xp00eRkZH629/+Jl9fX0lScnKy3nvvPc2YMUPr1q1TWFiYyZUCrsnf318WiyXfPhaLRd9//30RVQQzMWIBl5bfFAtJSk9P16hRo7RgwYIiqgjAr4WFhen999/Xyy+/rPfee09Wq1WSlJqaKjc3N73zzjuECsBECQkJZpeAYoQRC7g0q9WqDh06aMGCBapWrZrDvi+//FJDhgxRxYoVdeTIEZMqBCBJ58+f12effaZvv/1WklS/fn09/vjjqlWrlsmVAcjPlStXtGzZMo0cOdLsUlAECBZwaQkJCXr66ad19OhRffjhhxowYIDS09M1cuRILV++XK+88oomTpxof3Y+AADIn81m0+bNm7Vw4UL985//lLe3t1JSUswuC0WAN2/Dpfn7+2v79u2aMGGChgwZorCwMDVp0kQHDhzQnj179MYbbxAqABN99913OnTokEPb1q1b1blzZ7Vu3VpvvfWWSZUB+K2EhAS99tpr8vPzU8+ePVWmTBl98cUXSk5ONrs0FBGCBSBp6NChCg0N1caNG3X16lUtX75cLVu2NLsswOW98sorWr9+vf3zmTNn1Lt3b3l4eCg4OFjR0dGaMWOGafUBri4zM1OrVq1S165dFRgYqGPHjmn69OkqVaqUxo4dq27duvFIaBdCsIDL++qrr9SsWTOdPHlSmzZtUo8ePRQcHKz333/f7NIAlxcbG6uePXvaP69YsUL169fXl19+qZkzZ2rGjBlasmSJeQUCLu7+++/XnDlz1K9fP128eFFr167VE088YXZZMAnBAi7tb3/7m7p06aLevXvr66+/VmhoqD799FMtXrxYb731lh5++GEekQeY6PLly6pZs6b98/bt29W7d2/7506dOvFUGsBEOTk5slgsslgsjEyAYAHX9s9//lNbtmzRe++9J09PT3t7v379dOzYMVWqVEnNmjUzsULAtVWqVElJSUmSpJs3byo2NlZt2rSx779x44Z4BglgnqSkJD3//PNatWqVqlWrpscff1zr1q373XdbwDkRLODSjhw5og4dOuS5z9fXV+vXr9fs2bOLuCoAt3Xs2FGvv/66zp07pxkzZignJ0edO3e27z9+/Lj8/f3NKxBwcWXKlNFf/vIXbdu2Td98840CAwM1YsQIZWdn680331RMTIxycnLMLhNFhGABl+bl5ZXv/m+++SbXE2kAFJ0333xT8fHx8vPz0+jRo/X22287/NwuW7ZMXbp0MbFCwLUtXbpUmZmZkqS6devqjTfe0NmzZ/XFF18oMzNTYWFh8vX1NblKFBXeYwH8RlpamlatWqWFCxcqNjZWQUFBiouLM7sswGVlZWXp+PHjqlKlijw8POTh4SFvb29Jt0Yda9asqcqVK5tcJeCa3NzclJSUpKpVq+a5PyUlRcuWLVNkZGQRVwYzMGIB/K+dO3cqIiJC1atX1wsvvKAuXbro1KlThArAZBkZGZo3b56CgoLk6+urihUrqlq1aoqKitIDDzxAqABM9Hv/Pl2lShVChQthxAIuLSkpSYsXL9aiRYuUkZGhAQMGaODAgQoODtaRI0fUqFEjs0sEXNrVq1cVHBysCxcu6C9/+YsCAwNls9kUHx+vlStXqmHDhtq9e7eOHDmi/fv3a8SIEWaXDLiUUqVK6dKlS6pSpYrZpaAYcDe7AMBMAQEBevLJJ/XRRx8pJCREpUoxiAcUJ1OmTJGHh4dOnz6da572lClTFBoaqvDwcG3evFkffPCBSVUCrm3w4MEOT1bMy9q1a4uoGpiJYAGX5ufnp927d6t27dry8/NTw4YNzS4JwK+sX79ec+fOzXPxZ7Vq1fT222+rZ8+emjhxogYNGmRChQAqVKigsmXLml0GigGCBVzayZMn9dVXX2nhwoV66KGHVL9+fT311FOSxDO4gWIgKSlJjRs3vuP+Jk2aqFSpUpo4cWIRVgXg1z744IM7Lt6Ga2HeB1xe+/bttWjRIiUlJWnYsGH69NNPlZOToxdeeEHz589XSkqK2SUCLsvHxyffN2ufOXOGLzSAifhHOPwai7eBPMTHx2vhwoVatmyZrl69qqysLLNLAlzSs88+q++++04xMTHy8PBw2JeZmalHHnlEderU0aJFi0yqEHBtpUqVUnJyMgEfkggWQL6ys7O1YcMGPfbYY2aXArik8+fPq1WrVvL09NSLL75oXwd1/PhxzZ49W5mZmTp48KBq165tcqWAa5o6daoaN26s3r1729uWLl2qiRMnKiMjQ3369NGsWbN+d3E3nAPBAshHfHy8evXqpe+//97sUgCXdebMGb3wwgvavHmz/Zn5FotFISEh+vDDD1WvXj2TKwRcV/fu3dW5c2eNGTNGkvTNN9+oRYsWGjx4sAIDA/XOO+9o6NChmjRpkrmFokgQLIB8HDlyRC1atFBOTo7ZpQAu79q1a/r2228lSfXq1VOlSpVMrghA9erV9a9//UutWrWSJI0bN047d+7U7t27JUmfffaZJk6cqOPHj5tZJooIT4UCAJQIFStWVOvWrc0uA8CvXLt2zeFx0Dt37lT37t3tnx966CGdO3fOjNJgAp4KBQAAgD/E19dXZ86ckSTduHFDX3/9tYKDg+3709PTVbp0abPKQxEjWAAAAOAP6d69u8aOHatdu3YpKipK5cqVU4cOHez7jx49qrp165pYIYoSU6Hg0ipWrJjvM7izs7OLsBoAAEqWN954Q4899pg6duyo8uXL65NPPnF4NPSiRYsUGhpqYoUoSizehkv75JNP7qrfoEGDCrkSAABKrtTUVJUvX15ubm4O7VevXlX58uVzvYcGzolgAfyO7OxsubszuAcAAJAf1lgAd3D8+HH97W9/0/333292KQAAAMUewQL4lZ9++kkLFixQcHCwgoKCtH//fo0dO9bssgAAAIo95ncAknbv3q0FCxZozZo1CggI0PHjx7Vz5061b9/e7NIAAABKBEYs4NLefvttNWzYUP3791eVKlW0e/duHT16VBaLRRUrVjS7PAAAgBKDxdtwae7u7hozZoymTJni8CSL0qVL68iRI2rUqJGJ1QEAAJQcjFjApU2ZMkWfffaZAgICNGbMGB07dszskgAAAEokggVc2quvvqpTp05p2bJlSk5OVtu2bdWsWTPZbDZdu3bN7PIAAABKDKZCAb+SlpamlStXatGiRTp06JDatGmjJ554QpGRkWaXBgAAUKwRLIA7OHbsmBYuXKgVK1bohx9+MLscAACAYo3HzcKl/fLLL9q6davCwsIkSVFRUcrMzLTvd3d31+nTp80qDwAAoMQgWMClLV26VJ9//rk9WHz44Ydq3LixypYtK0k6efKkatSooVGjRplZJgAAQLHHVCi4tIcfflijRo3Sn//8Z0lShQoVdOTIEdWpU0eStHz5cn300Ufau3evmWUCAAAUezwVCi7t1KlTql+/vv1zmTJlVKrU//1YtG7dWsePHzejNAAAgBKFqVBwaampqXJ3/78fg5SUFIf9N2/edFhzAQAAgLwxYgGXVrNmzXxfinf06FHVrFmzCCsCAAAomQgWcGk9e/bUa6+9puvXr+fa98svv2jy5Mnq1auXCZUBAACULCzehku7dOmSHnzwQXl4eGj48OGqX7++LBaLTpw4oQ8//FDZ2dk6fPiwfH19zS4VAACgWCNYwOWdOXNG/+///T/FxMTo9o+DxWJRSEiIZs+ebX9CFAAAAO6MYAH8r6tXr+q7776TJNWrV0+VKlUyuSIAAICSg2ABAAAAwDAWbwMAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAACKlYSEBFksFsXFxZldCgDgHhAsAAAAABhGsAAAAABgGMECAGCKmzdvatq0aapXr548PT1Vu3Ztvfnmm7n65eTk6Nlnn1VAQIDKli2rBg0aaObMmQ59duzYodatW8vLy0v33Xef2rdvr7Nnz0qSjhw5os6dO6tChQry9vZWy5YtFRsbWyT3CACuxN3sAgAArikqKkrz58/X+++/rz/96U9KSkrSiRMncvW7efOmatasqU8//VQ+Pj7as2ePnn/+eVWvXl19+/ZVdna2+vTpoyFDhmjVqlW6ceOGDhw4IIvFIkn6y1/+oubNm2vOnDlyc3NTXFycSpcuXdS3CwBOjzdvAwCKXHp6uqpUqaIPP/xQzz33nMO+hIQEBQQE6PDhw3rwwQfzPP7FF1/UpUuX9I9//ENXr15V5cqVtWPHDnXs2DFXX29vb82aNUuDBg0qjFsBAPwvpkIBAIpcfHy8MjMz1bVr17vq//HHH6tVq1aqUqWKypcvr/nz5ysxMVGSVKlSJQ0ePFiPPPKIevfurZkzZyopKcl+bGRkpJ577jl169ZNU6dO1enTpwvlngDA1REsAABFrmzZsnfd99NPP9WoUaP0zDPPaPPmzYqLi9PTTz+tGzdu2PssXrxYe/fuVbt27bR69WrVr19f+/btkyRNmjRJ//3vf9WrVy9t27ZNjRo10rp16wr8ngDA1TEVCgBQ5K5fv65KlSrpgw8++N2pUH/96191/Phxbd261d6nW7duunz58h3fdREcHKyHHnpIH3zwQa59AwYMUEZGhjZs2FCg9wQAro4RCwBAkStTpozGjBmj0aNHa+nSpTp9+rT27dunhQsX5upbr149xcbG6ssvv9SpU6c0YcIEHTx40L7/zJkzioqK0t69e3X27Flt3rxZp06dUmBgoH755RcNHz5cO3bs0NmzZ/XVV1/p4MGDCgwMLMrbBQCXwFOhAACmmDBhgtzd3fXaa6/p4sWLql69uoYNG5ar37BhwxQXF6d+/frJYrFowIABeuGFF/Tvf/9bklSuXDmdOHFCn3zyia5cuaLq1atr+PDhGjp0qLKzs3XlyhVFRETo0qVL8vHx0WOPPabJkycX9e0CgNNjKhQAAAAAw5gKBQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAUsSVLlshisahMmTI6e/Zsrv2dOnVSkyZNTKisYAwePFj+/v4Obf7+/ho8eHCR1pGQkCCLxaIlS5bk22/Hjh2yWCz2zc3NTVWqVFHv3r0VGxtbNMXm4/b/X6T/qzUhIeGeztGpUyf77//gwYPVqVOngi0SACS5m10AALiqzMxMjR8/XsuWLTO7lEK3bt06eXt7m11Gvt566y117txZWVlZOnz4sCZPnqyOHTsqLi5ODzzwgNnlAUCxx4gFAJike/fuWrlypY4cOVKo1/nll18K9fx3o3nz5qpbt67ZZeTrgQceUNu2bdWhQweNGDFC77//vn7++WctX77c7NIAoEQgWACASUaPHq3KlStrzJgxv9v3+vXrioqKUkBAgDw8PHT//ffrxRdf1I8//ujQz9/fX2FhYVq7dq2aN2+uMmXKaPLkyfYpNCtXrtSYMWNUvXp1lS9fXr1799alS5eUnp6u559/Xj4+PvLx8dHTTz+tn376yeHcH330kR5++GFVrVpVXl5eatq0qd5++21lZWX9bv2/nQrVqVMnh+lHv95+PXUpOTlZQ4cOVc2aNeXh4aGAgABNnjxZ2dnZDue/ePGi+vbtqwoVKshqtapfv35KTk7+3bry06pVK0nSpUuXHNp3796trl27qkKFCipXrpzatWunL774wr4/LS1N7u7ueuedd+xtly9fVqlSpWS1Wh1qHzFihKpUqSKbzfaHavztNK5fb7+djgYAhY2pUABgkgoVKmj8+PF66aWXtG3bNnXp0iXPfjabTX369NHWrVsVFRWlDh066OjRo5o4caL27t2rvXv3ytPT097/66+/Vnx8vMaPH6+AgAB5eXkpIyNDkvTqq6+qc+fOWrJkiRISEvTyyy9rwIABcnd3V7NmzbRq1SodPnxYr776qipUqKAPPvjAft7Tp09r4MCB9nBz5MgRvfnmmzpx4oQWLVp0T/c+e/ZspaWlObRNmDBB27dvV4MGDSTdChWtW7dWqVKl9Nprr6lu3brau3ev3njjDSUkJGjx4sWSbo3IdOvWTRcvXlR0dLTq16+vL774Qv369bunmn7rzJkzkqT69evb23bu3KmQkBAFBQVp4cKF8vT01OzZs9W7d2+tWrVK/fr1k7e3tx566CFt2bJFr7zyiiRp69at8vT0VHp6ug4cOKB27dpJkrZs2aIuXbrY11DkZfDgwfZQ1qlTJ4cQ0qJFC+3du9eh/7fffqtnn31WjRs3trft2LHD/uvfW3MCAH+YDQBQpBYvXmyTZDt48KAtMzPTVqdOHVurVq1sN2/etNlsNlvHjh1tjRs3tvfftGmTTZLt7bffdjjP6tWrbZJs8+bNs7f5+fnZ3NzcbCdPnnTou337dpskW+/evR3aR44caZNkGzFihEN7nz59bJUqVbrjPeTk5NiysrJsS5cutbm5udmuXr1q3zdo0CCbn5+fQ38/Pz/boEGD7ni+d955J9e9DB061Fa+fHnb2bNnHfq+++67Nkm2//73vzabzWabM2eOTZLtn//8p0O/IUOG2CTZFi9efMfr2mz/93uzevVqW1ZWlu3nn3+2ffXVV7YGDRrYGjVqZLt27Zq9b9u2bW1Vq1a1paen29uys7NtTZo0sdWsWdP+Zzh+/Hhb2bJlbdevX7fZbDbbc889Z+vevbstKCjINnnyZJvNZrNduHAh1z0bdenSJVudOnVsjRs3dqgbAIoCU6EAwEQeHh564403FBsbq08//TTPPtu2bZOkXE9VevLJJ+Xl5aWtW7c6tAcFBTn8K/uvhYWFOXwODAyUJPXq1StX+9WrVx2mQx0+fFiPPvqoKleuLDc3N5UuXVoRERHKycnRqVOnfv9m72DVqlUaPXq0xo8fryFDhtjbP//8c3Xu3Fk1atRQdna2fevRo4ekW6MHkrR9+3ZVqFBBjz76qMN5Bw4ceE919OvXT6VLl1a5cuXUvn17paWl6YsvvtB9990nScrIyND+/fv1xBNPqHz58vbj3NzcFB4ervPnz+vkyZOSpK5du+qXX37Rnj17JN0amQgJCVG3bt0UExNjb5Okbt263VOdd5KRkaFevXrp+vXr+ve//22vGwCKCsECAEzWv39/tWjRQuPGjctzvcKVK1fk7u6uKlWqOLRbLBZVq1ZNV65ccWivXr36Ha9VqVIlh88eHh75tl+/fl2SlJiYqA4dOujChQuaOXOmdu3apYMHD+qjjz6S9McXiG/fvl2DBw9WRESEXn/9dYd9ly5d0r/+9S+VLl3aYbs9xefy5cuSbv3++Pr65jp3tWrV7qmWadOm6eDBg9q5c6fGjRunS5cuqU+fPsrMzJQkXbt2TTabLc/f3xo1athrkaR27dqpXLly2rJli7777jslJCTYg8X+/fv1008/acuWLapTp44CAgLuqc68ZGdn64knntCpU6e0ceNG1apVy/A5AeBescYCAExmsVg0bdo0hYSEaN68ebn2V65cWdnZ2UpJSXEIFzabTcnJyXrooYdyna+grV+/XhkZGVq7dq38/Pzs7XFxcX/4nEePHlWfPn3UsWNHzZ8/P9d+Hx8fBQUF6c0338zz+Ntf5itXrqwDBw7k2n+vi7fr1KljX7D98MMPq2zZsho/frxmzZqll19+WRUrVlSpUqWUlJSU69iLFy/aa5ZuBbM//elP2rJli2rWrKlq1aqpadOmqlOnjqRbax62bt2aawTpj3r++ee1detWbdy4Uc2aNSuQcwLAvWLEAgCKgW7duikkJERTpkzJ9TSmrl27SlKux56uWbNGGRkZ9v2F6XZY+fUicZvNlmcguBuJiYnq0aOH6tSpozVr1qh06dK5+oSFhenYsWOqW7euWrVqlWu7HSw6d+6s9PR0bdiwweH4lStX/qHabhs9erTq1aunqVOnKj09XV5eXmrTpo3Wrl3rMEJz8+ZNLV++XDVr1nSYgtatWzcdOnRIa9assU938vLyUtu2bTVr1ixdvHixQKZBjR8/XosXL9aCBQsKbFoVAPwRjFgAQDExbdo0tWzZUj/88IPDE31CQkL0yCOPaMyYMUpLS1P79u3tT4X6/+3deVwW5f7/8fctCKICqSBiKqDmrqWYih5zhXDra1aaFuSpTE+ZKZlKZi5l2uZSprnvmnXUjic9Jq7HfUs0w7RMBBXcA7QExfv3R8f71x1I2nAzcN+v5+Mxj5xrrpn5DHrn/fa6ZqZhw4aKiopyeG3h4eHy8PBQz549NWTIEF27dk3Tpk3T5cuX/9LxOnTooJ9//llTpkzRd999Z7etWrVq8vf315gxYxQXF6fmzZtrwIABqlmzpq5du6bExEStWbNGn376qSpVqqTo6GhNnDhR0dHRGjt2rO677z6tWbNGX3/9taFrLl68uN555x11795dkydP1htvvKFx48YpPDxcbdq00eDBg+Xh4aGpU6fq8OHDWrp0qd1oUbt27ZSdna0NGzZo/vz5tvb27dtr5MiRslgst30S2J364osvNHbsWD3++OOqUaOGdu3aZdvm6emphg0bGjo+ANwNRiwAoJBo2LChevbsmaPdYrHoyy+/VExMjObOnauOHTvqgw8+UFRUlDZu3Gg3iuAotWrV0vLly3X58mV169ZNL7/8sh544AG7x9HejYSEBP3yyy/q1q2bwsLC7JZb74QIDAzUvn37FBERoffff1+RkZGKiorSnDlz9MADD6hMmTKSpJIlS2rjxo1q3769hg0bpscff1ynTp3SZ599Zvi6n3jiCTVt2lQTJkxQWlqaWrVqpY0bN6pUqVLq3bu3nnzySaWlpWnVqlU5Hm/bsGFD29So348k3Pp1w4YNVa5cOUP13Qpl//znP3P8HB999FFDxwaAu2WxWv/iW3kAAAAA4H8YsQAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYbwgLx/cvHlTZ86ckbe3t93LkQAAAICizGq1KiMjQxUrVlSxYnmPSRAs8sGZM2dUuXJls8sAAAAAHCI5OVmVKlXKsw/BIh94e3tL+u0H7uPjY3I1AAAAQP5IT09X5cqVbd9380KwyAe3pj/5+PgQLAAAAOB07mS6PzdvAwAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwzCmDxdSpUxUSEqISJUooNDRUW7duvW3fzZs3y2Kx5Fi+//77AqwYAAAAKNrczS4gvy1btkwDBw7U1KlT1aJFC02fPl0dOnRQQkKCqlSpctv9jh49Kh8fH9u6v79/QZQLAPkqeNhqs0tAPkoc38nsEgDgjjndiMWECRP03HPP6fnnn1ft2rU1adIkVa5cWdOmTctzv/Lly6tChQq2xc3NrYAqBgAAAIo+pwoWWVlZ2r9/vyIiIuzaIyIitGPHjjz3bdiwoQIDA9WuXTtt2rTJkWUCAAAATseppkJduHBB2dnZCggIsGsPCAhQampqrvsEBgZqxowZCg0NVWZmphYuXKh27dpp8+bNeuihh3LdJzMzU5mZmbb19PT0/LsIAAAAoAhyqmBxi8VisVu3Wq052m6pWbOmatasaVsPCwtTcnKyPvjgg9sGi3Hjxmn06NH5VzAAAABQxDnVVCg/Pz+5ubnlGJ04d+5cjlGMvDRr1kw//PDDbbfHxsYqLS3NtiQnJ//lmgEAAABn4FTBwsPDQ6GhoYqLi7Nrj4uLU/Pmze/4OAcOHFBgYOBtt3t6esrHx8duAQAAAFyZ002FiomJUVRUlBo3bqywsDDNmDFDSUlJ6tevn6TfRhtOnz6tBQsWSJImTZqk4OBg1a1bV1lZWVq0aJGWL1+u5cuXm3kZAAAAQJHidMGiR48eunjxosaMGaOUlBTVq1dPa9asUVBQkCQpJSVFSUlJtv5ZWVkaPHiwTp8+LS8vL9WtW1erV69Wx44dzboEAAAAoMixWK1Wq9lFFHXp6eny9fVVWloa06IAmIoX5DkXXpAHwGx38z3Xqe6xAAAAAGAOggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMOcMlhMnTpVISEhKlGihEJDQ7V169Y72m/79u1yd3fXAw884NgCAQAAACfjdMFi2bJlGjhwoIYPH64DBw6oZcuW6tChg5KSkvLcLy0tTdHR0WrXrl0BVQoAAAA4D6cLFhMmTNBzzz2n559/XrVr19akSZNUuXJlTZs2Lc/9+vbtq169eiksLKyAKgUAAACch1MFi6ysLO3fv18RERF27REREdqxY8dt95s7d66OHz+ukSNH3tF5MjMzlZ6ebrcAAAAArsypgsWFCxeUnZ2tgIAAu/aAgAClpqbmus8PP/ygYcOGafHixXJ3d7+j84wbN06+vr62pXLlyoZrBwAAAIoypwoWt1gsFrt1q9Wao02SsrOz1atXL40ePVo1atS44+PHxsYqLS3NtiQnJxuuGQAAACjK7uyf6IsIPz8/ubm55RidOHfuXI5RDEnKyMjQvn37dODAAfXv31+SdPPmTVmtVrm7u2vdunVq27Ztjv08PT3l6enpmIsAAAAAiiCnGrHw8PBQaGio4uLi7Nrj4uLUvHnzHP19fHz07bffKj4+3rb069dPNWvWVHx8vJo2bVpQpQMAAABFmlONWEhSTEyMoqKi1LhxY4WFhWnGjBlKSkpSv379JP02jen06dNasGCBihUrpnr16tntX758eZUoUSJHOwAAAIDbc7pg0aNHD128eFFjxoxRSkqK6tWrpzVr1igoKEiSlJKS8qfvtAAAAABwdyxWq9VqdhFFXXp6unx9fZWWliYfHx+zywHgwoKHrTa7BOSjxPGdzC4BgIu7m++5TnWPBQAAAABzECwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhjllsJg6dapCQkJUokQJhYaGauvWrbftu23bNrVo0ULlypWTl5eXatWqpYkTJxZgtQAAAEDR5252Aflt2bJlGjhwoKZOnaoWLVpo+vTp6tChgxISElSlSpUc/UuVKqX+/furQYMGKlWqlLZt26a+ffuqVKlSeuGFF0y4AgAAAKDosVitVqvZReSnpk2bqlGjRpo2bZqtrXbt2uratavGjRt3R8fo1q2bSpUqpYULF95R//T0dPn6+iotLU0+Pj5/qW4AyA/Bw1abXQLyUeL4TmaXAMDF3c33XKeaCpWVlaX9+/crIiLCrj0iIkI7duy4o2McOHBAO3bsUKtWrW7bJzMzU+np6XYLAAAA4MqcKlhcuHBB2dnZCggIsGsPCAhQampqnvtWqlRJnp6eaty4sV566SU9//zzt+07btw4+fr62pbKlSvnS/0AAABAUeVUweIWi8Vit261WnO0/dHWrVu1b98+ffrpp5o0aZKWLl16276xsbFKS0uzLcnJyflSNwAAAFBUOdXN235+fnJzc8sxOnHu3Lkcoxh/FBISIkmqX7++zp49q1GjRqlnz5659vX09JSnp2f+FA0AAAA4AacasfDw8FBoaKji4uLs2uPi4tS8efM7Po7ValVmZmZ+lwcAAAA4LacasZCkmJgYRUVFqXHjxgoLC9OMGTOUlJSkfv36SfptGtPp06e1YMECSdInn3yiKlWqqFatWpJ+e6/FBx98oJdfftm0awAAAACKGqcLFj169NDFixc1ZswYpaSkqF69elqzZo2CgoIkSSkpKUpKSrL1v3nzpmJjY3XixAm5u7urWrVqGj9+vPr27WvWJQAAAABFjtO9x8IMvMcCQGHBeyycC++xAGA2l32PBQAAAABzECwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhjllsJg6dapCQkJUokQJhYaGauvWrbftu2LFCoWHh8vf318+Pj4KCwvT119/XYDVAgAAAEWf0wWLZcuWaeDAgRo+fLgOHDigli1bqkOHDkpKSsq1/3//+1+Fh4drzZo12r9/v9q0aaMuXbrowIEDBVw5AAAAUHRZrFar1ewi8lPTpk3VqFEjTZs2zdZWu3Ztde3aVePGjbujY9StW1c9evTQm2++eUf909PT5evrq7S0NPn4+PylugEgPwQPW212CchHieM7mV0CABd3N99znWrEIisrS/v371dERIRde0REhHbs2HFHx7h586YyMjJUtmzZ2/bJzMxUenq63QIAAAC4MqcKFhcuXFB2drYCAgLs2gMCApSamnpHx/jwww919epVde/e/bZ9xo0bJ19fX9tSuXJlQ3UDAAAARZ1TBYtbLBaL3brVas3RlpulS5dq1KhRWrZsmcqXL3/bfrGxsUpLS7MtycnJhmsGAAAAijJ3swvIT35+fnJzc8sxOnHu3Lkcoxh/tGzZMj333HP64osv1L59+zz7enp6ytPT03C9AAAAgLNwqhELDw8PhYaGKi4uzq49Li5OzZs3v+1+S5cuVe/evbVkyRJ16sSNcgAAAMDdcqoRC0mKiYlRVFSUGjdurLCwMM2YMUNJSUnq16+fpN+mMZ0+fVoLFiyQ9FuoiI6O1uTJk9WsWTPbaIeXl5d8fX1Nuw4AAACgKHG6YNGjRw9dvHhRY8aMUUpKiurVq6c1a9YoKChIkpSSkmL3Tovp06frxo0beumll/TSSy/Z2p955hnNmzevoMsHAAAAiiSHvMciOTlZFotFlSpVkiTt2bNHS5YsUZ06dfTCCy/k9+lMx3ssABQWvMfCufAeCwBmM/09Fr169dKmTZskSampqQoPD9eePXv0+uuva8yYMY44JQAAAAATOSRYHD58WE2aNJEkff7556pXr5527NihJUuWML0IAAAAcEIOCRbXr1+3PY51/fr1euSRRyRJtWrVUkpKiiNOCQAAAMBEDgkWdevW1aeffqqtW7cqLi5OkZGRkqQzZ86oXLlyjjglAAAAABM5JFi8++67mj59ulq3bq2ePXvq/vvvlyStWrXKNkUKAAAAgPNwyONmW7durQsXLig9PV1lypSxtb/wwgsqWbKkI04JAAAAwEQOe/O21WrV/v37NX36dGVkZEj67c3YBAsAAADA+ThkxOLkyZOKjIxUUlKSMjMzFR4eLm9vb7333nu6du2aPv30U0ecFgAAAIBJHDJi8corr6hx48a6fPmyvLy8bO2PPvqoNmzY4IhTAgAAADCRQ0Ystm3bpu3bt8vDw8OuPSgoSKdPn3bEKQEAAACYyCEjFjdv3lR2dnaO9lOnTsnb29sRpwQAAABgIocEi/DwcE2aNMm2brFYdOXKFY0cOVIdO3Z0xCkBAAAAmMghU6EmTpyoNm3aqE6dOrp27Zp69eqlH374QX5+flq6dKkjTgkAAADARA4JFhUrVlR8fLyWLl2qb775Rjdv3tRzzz2np556yu5mbgAAAADOwSHBQpK8vLz07LPP6tlnn3XUKQAAAAAUEg4JFgsWLMhze3R0tCNOCwAAAMAkDgkWr7zyit369evX9csvv9jevE2wAAAAAJyLQ54KdfnyZbvlypUrOnr0qP72t79x8zYAAADghBwSLHJz3333afz48TlGMwAAAAAUfQUWLCTJzc1NZ86cKchTAgAAACgADrnHYtWqVXbrVqtVKSkpmjJlilq0aOGIUwIAAAAwkUOCRdeuXe3WLRaL/P391bZtW3344YeOOKWdqVOn6v3331dKSorq1q2rSZMmqWXLlrn2TUlJ0auvvqr9+/frhx9+0IABA+zeGg4AAADgzzkkWNy8edMRh70jy5Yt08CBAzV16lS1aNFC06dPV4cOHZSQkKAqVark6J+ZmSl/f38NHz5cEydONKFiAAAAoOgr0HssCsKECRP03HPP6fnnn1ft2rU1adIkVa5cWdOmTcu1f3BwsCZPnqzo6Gj5+voWcLUAAACAc8i3EYuYmJg77jthwoT8Oq2drKws7d+/X8OGDbNrj4iI0I4dO/LtPJmZmcrMzLStp6en59uxAQAAgKIo34LFgQMH7qifxWLJr1PmcOHCBWVnZysgIMCuPSAgQKmpqfl2nnHjxmn06NH5djwAAACgqMu3YLFp06b8OpRhfwwvVqs1XwNNbGys3QhNenq6KleunG/HBwAAAIoah9y8bRY/Pz+5ubnlGJ04d+5cjlEMIzw9PeXp6ZlvxwMAAACKOocFi7179+qLL75QUlKSsrKy7LatWLHCIef08PBQaGio4uLi9Oijj9ra4+Li9H//938OOScAAAAABz0V6rPPPlOLFi2UkJCglStX6vr160pISNDGjRsd/uSlmJgYzZo1S3PmzNGRI0c0aNAgJSUlqV+/fpJ+m8YUHR1tt098fLzi4+N15coVnT9/XvHx8UpISHBonQAAAIAzcciIxTvvvKOJEyfqpZdekre3tyZPnqyQkBD17dtXgYGBjjilTY8ePXTx4kWNGTNGKSkpqlevntasWaOgoCBJv70QLykpyW6fhg0b2n69f/9+LVmyREFBQUpMTHRorQAAAICzsFitVmt+H7RUqVL67rvvFBwcLD8/P23atEn169fXkSNH1LZtW6WkpOT3KU2Vnp4uX19fpaWlycfHx+xyALiw4GGrzS4B+ShxfCezSwDg4u7me65DpkKVLVtWGRkZkqR7771Xhw8fliT9/PPP+uWXXxxxSgAAAAAmytdgER8fL0lq2bKl4uLiJEndu3fXK6+8oj59+qhnz55q165dfp4SAAAAQCGQr/dYNGrUSA0bNlTXrl3Vs2dPSb/dLF28eHFt27ZN3bp104gRI/LzlAAAAAAKgXwdsdi+fbsaNWqkDz74QNWqVdPTTz+tLVu2aMiQIVq1apUmTJigMmXK5OcpAQAAABQC+RoswsLCNHPmTKWmpmratGk6deqU2rdvr2rVqmns2LE6depUfp4OAAAAQCHhkJu3vby89Mwzz2jz5s06duyYevbsqenTpyskJEQdO3Z0xCkBAAAAmMghweL3qlWrpmHDhmn48OHy8fHR119/7ehTAgAAAChgDnlB3i1btmzRnDlztHz5crm5ual79+567rnnHHlKAAAAACbI92CRnJysefPmad68eTpx4oSaN2+ujz/+WN27d1epUqXy+3QAAAAACoF8DRbh4eHatGmT/P39FR0drWeffVY1a9bMz1MAAAAAKITyNVh4eXlp+fLl6ty5s9zc3PLz0AAAAAAKsXwNFqtWrcrPwwEAAAAoIhz+VCgAAAAAzo9gAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADDMKYPF1KlTFRISohIlSig0NFRbt27Ns/+WLVsUGhqqEiVKqGrVqvr0008LqFIAAADAOThdsFi2bJkGDhyo4cOH68CBA2rZsqU6dOigpKSkXPufOHFCHTt2VMuWLXXgwAG9/vrrGjBggJYvX17AlQMAAABFl8VqtVrNLiI/NW3aVI0aNdK0adNsbbVr11bXrl01bty4HP2HDh2qVatW6ciRI7a2fv366eDBg9q5c+cdnTM9PV2+vr5KS0uTj4+P8YsAgL8oeNhqs0tAPkoc38nsEgC4uLv5nutUIxZZWVnav3+/IiIi7NojIiK0Y8eOXPfZuXNnjv4PP/yw9u3bp+vXrzusVgAAAMCZuJtdQH66cOGCsrOzFRAQYNceEBCg1NTUXPdJTU3Ntf+NGzd04cIFBQYG5tgnMzNTmZmZtvX09PR8qB4AAAAoupwqWNxisVjs1q1Wa462P+ufW/st48aN0+jRow1WWTQxzcK5MM3C+fB7ChRe/B3qfPh/rj2nmgrl5+cnNze3HKMT586dyzEqcUuFChVy7e/u7q5y5crluk9sbKzS0tJsS3Jycv5cAAAAAFBEOVWw8PDwUGhoqOLi4uza4+Li1Lx581z3CQsLy9F/3bp1aty4sYoXL57rPp6envLx8bFbAAAAAFfmVMFCkmJiYjRr1izNmTNHR44c0aBBg5SUlKR+/fpJ+m20ITo62ta/X79+OnnypGJiYnTkyBHNmTNHs2fP1uDBg826BAAAAKDIcbp7LHr06KGLFy9qzJgxSklJUb169bRmzRoFBQVJklJSUuzeaRESEqI1a9Zo0KBB+uSTT1SxYkV99NFHeuyxx8y6BAAAAKDIcbr3WJjBld5jwY1nzoWbzgCg4PB3qPNxhb9HXfY9FgAAAADMQbAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGOZUweLy5cuKioqSr6+vfH19FRUVpZ9//jnPfVasWKGHH35Yfn5+slgsio+PL5BaAQAAAGfiVMGiV69eio+P19q1a7V27VrFx8crKioqz32uXr2qFi1aaPz48QVUJQAAAOB83M0uIL8cOXJEa9eu1a5du9S0aVNJ0syZMxUWFqajR4+qZs2aue53K3gkJiYWVKkAAACA03GaEYudO3fK19fXFiokqVmzZvL19dWOHTtMrAwAAABwfk4zYpGamqry5cvnaC9fvrxSU1Pz9VyZmZnKzMy0raenp+fr8QEAAICiptCPWIwaNUoWiyXPZd++fZIki8WSY3+r1ZpruxHjxo2z3SDu6+urypUr5+vxAQAAgKKm0I9Y9O/fX08++WSefYKDg3Xo0CGdPXs2x7bz588rICAgX2uKjY1VTEyMbT09PZ1wAQAAAJdW6IOFn5+f/Pz8/rRfWFiY0tLStGfPHjVp0kSStHv3bqWlpal58+b5WpOnp6c8PT3z9ZgAAABAUVbop0Ldqdq1aysyMlJ9+vTRrl27tGvXLvXp00edO3e2eyJUrVq1tHLlStv6pUuXFB8fr4SEBEnS0aNHFR8fn+/3ZQAAAADOzGmChSQtXrxY9evXV0REhCIiItSgQQMtXLjQrs/Ro0eVlpZmW1+1apUaNmyoTp06SZKefPJJNWzYUJ9++mmB1g4AAAAUZYV+KtTdKFu2rBYtWpRnH6vVarfeu3dv9e7d24FVAQAAAM7PqUYsAAAAAJiDYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwzKmCxeXLlxUVFSVfX1/5+voqKipKP//88237X79+XUOHDlX9+vVVqlQpVaxYUdHR0Tpz5kzBFQ0AAAA4AacKFr169VJ8fLzWrl2rtWvXKj4+XlFRUbft/8svv+ibb77RiBEj9M0332jFihU6duyYHnnkkQKsGgAAACj63M0uIL8cOXJEa9eu1a5du9S0aVNJ0syZMxUWFqajR4+qZs2aOfbx9fVVXFycXdvHH3+sJk2aKCkpSVWqVCmQ2gEAAICizmlGLHbu3ClfX19bqJCkZs2aydfXVzt27Ljj46Slpcliseiee+5xQJUAAACAc3KaEYvU1FSVL18+R3v58uWVmpp6R8e4du2ahg0bpl69esnHx+e2/TIzM5WZmWlbT09Pv/uCAQAAACdS6EcsRo0aJYvFkueyb98+SZLFYsmxv9VqzbX9j65fv64nn3xSN2/e1NSpU/PsO27cONsN4r6+vqpcufJfuzgAAADASRT6EYv+/fvrySefzLNPcHCwDh06pLNnz+bYdv78eQUEBOS5//Xr19W9e3edOHFCGzduzHO0QpJiY2MVExNjW09PTydcAAAAwKUV+mDh5+cnPz+/P+0XFhamtLQ07dmzR02aNJEk7d69W2lpaWrevPlt97sVKn744Qdt2rRJ5cqV+9NzeXp6ytPT884vAgAAAHByhX4q1J2qXbu2IiMj1adPH+3atUu7du1Snz591LlzZ7snQtWqVUsrV66UJN24cUOPP/649u3bp8WLFys7O1upqalKTU1VVlaWWZcCAAAAFDlOEywkafHixapfv74iIiIUERGhBg0aaOHChXZ9jh49qrS0NEnSqVOntGrVKp06dUoPPPCAAgMDbcvdPEkKAAAAcHWFfirU3ShbtqwWLVqUZx+r1Wr7dXBwsN06AAAAgL/GqUYsAAAAAJiDYAEAAADAMKeaCgXHSxzfyewSAAAAUAgxYgEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMPczS4AAADAFSSO72R2CYBDMWIBAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADDMqYLF5cuXFRUVJV9fX/n6+ioqKko///xznvuMGjVKtWrVUqlSpVSmTBm1b99eu3fvLpiCAQAAACfhVMGiV69eio+P19q1a7V27VrFx8crKioqz31q1KihKVOm6Ntvv9W2bdsUHBysiIgInT9/voCqBgAAAIo+i9VqtZpdRH44cuSI6tSpo127dqlp06aSpF27diksLEzff/+9ataseUfHSU9Pl6+vr9avX6927drd1T5paWny8fH5y9cAAAAAFCZ38z3XaUYsdu7cKV9fX1uokKRmzZrJ19dXO3bsuKNjZGVlacaMGfL19dX9999/236ZmZlKT0+3WwAAAABX5jTBIjU1VeXLl8/RXr58eaWmpua571dffaXSpUurRIkSmjhxouLi4uTn53fb/uPGjbPdx+Hr66vKlSsbrh8AAAAoygp9sBg1apQsFkuey759+yRJFoslx/5WqzXX9t9r06aN4uPjtWPHDkVGRqp79+46d+7cbfvHxsYqLS3NtiQnJxu7SAAAAKCIcze7gD/Tv39/Pfnkk3n2CQ4O1qFDh3T27Nkc286fP6+AgIA89y9VqpSqV6+u6tWrq1mzZrrvvvs0e/ZsxcbG5trf09NTnp6ed34RAAAAgJMr9MHCz88vz2lJt4SFhSktLU179uxRkyZNJEm7d+9WWlqamjdvflfntFqtyszMvKv+krjXAgAAAE7l1vfbO3rek9WJREZGWhs0aGDduXOndefOndb69etbO3fubNenZs2a1hUrVlitVqv1ypUr1tjYWOvOnTutiYmJ1v3791ufe+45q6enp/Xw4cN3fN7k5GSrJBYWFhYWFhYWFhanXJKTk//0O3GhH7G4G4sXL9aAAQMUEREhSXrkkUc0ZcoUuz5Hjx5VWlqaJMnNzU3ff/+95s+frwsXLqhcuXJ68MEHtXXrVtWtW/eOz1uxYkUlJyfL29v7T+/nQOGXnp6uypUrKzk5mccHA4UQn1GgcOMz6lysVqsyMjJUsWLFP+3rNO+xAPIL7yUBCjc+o0DhxmfUdRX6p0IBAAAAKPwIFgAAAAAMI1gAf+Dp6amRI0fySGGgkOIzChRufEZdF/dYAAAAADCMEQsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEEC7i0jz/+2OwSAAAAnAJPhYJLK1u2rEJDQzV37lxVqlTJ7HIA3MbevXu1dOlSHTt2TBaLRffdd5969eqlxo0bm10agDysWLFCo0aN0qFDh8wuBQWAEQu4tMOHD8vT01P169fXwoULzS4HQC6GDBmipk2batasWTp16pSSkpI0c+ZMNW3aVEOHDjW7PMDlzZw5U0888YR69eql3bt3S5I2btyohg0b6umnn1ZYWJjJFaKgMGIBSJo3b55iYmLUunVrvfHGG3J3d7fb3qBBA5MqA1zb/Pnz1a9fP73//vvq27evihcvLkm6fv26pk2bpqFDh2r69OmKjo42uVLANX3wwQd6/fXX1aBBAx05ckSSNHz4cE2YMEEvv/yyXnrpJfn5+ZlcJQoKwQL4n/Xr1ysyMlJWq1VWq1UWi8X23+zsbLPLA1xSkyZN1LNnTw0aNCjX7RMmTNBnn32mPXv2FHBlACSpdu3aeu211/Tss89q8+bNatu2rdq2bat//vOfuueee8wuDwWMYAHoty8nI0aM0BNPPKERI0bkGLEICgoyqTLAtZUqVUrffvutqlatmuv2n376SfXr19fVq1cLuDIAklSyZEl9//33qlKliiTJ09NT//3vf9W0aVOTK4MZ3P+8C+C8fvrpJ0VHR+v48eNasmSJ/u///s/skgD8jpubm7Kysm67/fr163JzcyvAigD83rVr11SiRAnbuoeHh/z9/U2sCGYiWMClNWjQQJGRkfryyy+ZAwoUQqGhoVq8eLHeeuutXLcvXLhQjRo1KuCqAPzerFmzVLp0aUnSjRs3NG/evBx/pw4YMMCM0lDAmAoFl7Zo0SI9/fTTZpcB4Da++uorde3aVTExMXr11VcVEBAgSUpNTdWHH36oSZMmaeXKlercubPJlQKuKTg4WBaLJc8+FotFP/30UwFVBDMxYgGXltcUC0nKyMjQoEGDNGvWrAKqCMDvde7cWRMnTtTgwYP14YcfytfXV5KUlpYmNzc3vf/++4QKwESJiYlml4BChBELuDRfX1+1bNlSs2bNUoUKFey2ff311+rTp4/KlCmjgwcPmlQhAEk6deqUvvjiC/3www+SpBo1auixxx5T5cqVTa4MQF4uXryohQsXauDAgWaXggJAsIBLS0xM1N///ncdOnRIU6ZMUc+ePZWRkaGBAwdq0aJFeu211zRy5Ejbs/MBAEDerFar1q1bp9mzZ+tf//qXfHx8dP78ebPLQgHgzdtwacHBwdq0aZNGjBihPn36qHPnzqpXr5727NmjHTt26O233yZUACb68ccftX//fru2DRs2qE2bNmrSpIneeecdkyoD8EeJiYl68803FRQUpI4dO6pEiRJavXq1UlNTzS4NBYRgAUjq27evIiIitGbNGl26dEmLFi1SaGio2WUBLu+1117Tl19+aVs/ceKEunTpIg8PD4WFhWncuHGaNGmSafUBri4zM1NLly5Vu3btVLt2bR0+fFgTJkxQsWLFNGzYMLVv355HQrsQggVc3vbt23X//ffr6NGjWrt2rTp06KCwsDBNnDjR7NIAl7dv3z517NjRtr548WLVqFFDX3/9tSZPnqxJkyZp3rx55hUIuLh7771X06ZNU48ePXTmzBmtWLFCjz/+uNllwSQEC7i0V199VW3btlWXLl30zTffKCIiQp9//rnmzp2rd955Rw899BCPyANMdOHCBVWqVMm2vmnTJnXp0sW23rp1a55KA5goOztbFotFFouFkQkQLODa/vWvf2n9+vX68MMP5enpaWvv0aOHDh8+rLJly+r+++83sULAtZUtW1YpKSmSpJs3b2rfvn1q2rSpbXtWVpZ4BglgnpSUFL3wwgtaunSpKlSooMcee0wrV67803dbwDkRLODSDh48qJYtW+a6LSAgQF9++aWmTp1awFUBuKVVq1Z66623lJycrEmTJik7O1tt2rSxbU9ISFBwcLB5BQIurkSJEnrqqae0ceNGffvtt6pdu7YGDBigGzduaOzYsYqLi1N2drbZZaKAECzg0kqVKpXn9m+//TbHE2kAFJyxY8fqyJEjCgoK0pAhQ/Tee+/ZfW4XLlyotm3bmlgh4NoWLFigzMxMSVK1atX09ttv6+TJk1q9erUyMzPVuXNnBQQEmFwlCgrvsQD+ID09XUuXLtXs2bO1b98+NWjQQPHx8WaXBbis69evKyEhQf7+/vLw8JCHh4d8fHwk/TbqWKlSJZUrV87kKgHX5ObmppSUFJUvXz7X7efPn9fChQsVExNTwJXBDIxYAP+zZcsWRUdHKzAwUC+++KLatm2rY8eOESoAk129elUzZsxQgwYNFBAQoDJlyqhChQqKjY3VfffdR6gATPRn/z7t7+9PqHAhjFjApaWkpGju3LmaM2eOrl69qp49e6pXr14KCwvTwYMHVadOHbNLBFzapUuXFBYWptOnT+upp55S7dq1ZbVadeTIES1ZskS1atXStm3bdPDgQe3evVsDBgwwu2TApRQrVkxnz56Vv7+/2aWgEHA3uwDATCEhIXriiSf0ySefKDw8XMWKMYgHFCZjxoyRh4eHjh8/nmOe9pgxYxQREaGoqCitW7dOH330kUlVAq6td+/edk9WzM2KFSsKqBqYiWABlxYUFKRt27apSpUqCgoKUq1atcwuCcDvfPnll5o+fXquN39WqFBB7733njp27KiRI0fqmWeeMaFCAN7e3vLy8jK7DBQCBAu4tKNHj2r79u2aPXu2HnzwQdWoUUNPP/20JPEMbqAQSElJUd26dW+7vV69eipWrJhGjhxZgFUB+L2PPvrotjdvw7Uw7wMur0WLFpozZ45SUlLUr18/ff7558rOztaLL76omTNn6vz582aXCLgsPz+/PN+sfeLECb7QACbiH+Hwe9y8DeTiyJEjmj17thYuXKhLly7p+vXrZpcEuKTnnntOP/74o+Li4uTh4WG3LTMzUw8//LCqVq2qOXPmmFQh4NqKFSum1NRUAj4kESyAPN24cUOrVq1St27dzC4FcEmnTp1S48aN5enpqZdeesl2H1RCQoKmTp2qzMxM7d27V1WqVDG5UsA1jR8/XnXr1lWXLl1sbQsWLNDIkSN19epVde3aVR9//PGf3twN50CwAPJw5MgRderUST/99JPZpQAu68SJE3rxxRe1bt062zPzLRaLwsPDNWXKFFWvXt3kCgHXFRkZqTZt2mjo0KGSpG+//VaNGjVS7969Vbt2bb3//vvq27evRo0aZW6hKBAECyAPBw8eVKNGjZSdnW12KYDLu3z5sn744QdJUvXq1VW2bFmTKwIQGBiof//732rcuLEkafjw4dqyZYu2bdsmSfriiy80cuRIJSQkmFkmCghPhQIAFAllypRRkyZNzC4DwO9cvnzZ7nHQW7ZsUWRkpG39wQcfVHJyshmlwQQ8FQoAAAB/SUBAgE6cOCFJysrK0jfffKOwsDDb9oyMDBUvXtys8lDACBYAAAD4SyIjIzVs2DBt3bpVsbGxKlmypFq2bGnbfujQIVWrVs3EClGQmAoFl1amTJk8n8F948aNAqwGAICi5e2331a3bt3UqlUrlS5dWvPnz7d7NPScOXMUERFhYoUoSNy8DZc2f/78O+r3zDPPOLgSAACKrrS0NJUuXVpubm527ZcuXVLp0qVzvIcGzolgAfyJGzduyN2dwT0AAIC8cI8FcBsJCQl69dVXde+995pdCgAAQKFHsAB+58qVK5o1a5bCwsLUoEED7d69W8OGDTO7LAAAgEKP+R2ApG3btmnWrFlavny5QkJClJCQoC1btqhFixZmlwYAAFAkMGIBl/bee++pVq1aevLJJ+Xv769t27bp0KFDslgsKlOmjNnlAQAAFBncvA2X5u7urqFDh2rMmDF2T7IoXry4Dh48qDp16phYHQAAQNHBiAVc2pgxY/TFF18oJCREQ4cO1eHDh80uCQAAoEgiWMClvf766zp27JgWLlyo1NRUNWvWTPfff7+sVqsuX75sdnkAAABFBlOhgN9JT0/XkiVLNGfOHO3fv19NmzbV448/rpiYGLNLAwAAKNQIFsBtHD58WLNnz9bixYt17tw5s8sBAAAo1HjcLFzar7/+qg0bNqhz586SpNjYWGVmZtq2u7u76/jx42aVBwAAUGQQLODSFixYoK+++soWLKZMmaK6devKy8tLknT06FFVrFhRgwYNMrNMAACAQo+pUHBpDz30kAYNGqRHH31UkuTt7a2DBw+qatWqkqRFixbpk08+0c6dO80sEwAAoNDjqVBwaceOHVONGjVs6yVKlFCxYv//Y9GkSRMlJCSYURoAAECRwlQouLS0tDS5u///j8H58+fttt+8edPungsAAADkjhELuLRKlSrl+VK8Q4cOqVKlSgVYEQAAQNFEsIBL69ixo958801du3Ytx7Zff/1Vo0ePVqdOnUyoDAAAoGjh5m24tLNnz+qBBx6Qh4eH+vfvrxo1ashisej777/XlClTdOPGDR04cEABAQFmlwoAAFCoESzg8k6cOKF//OMfiouL062Pg8ViUXh4uKZOnWp7QhQAAABuj2AB/M+lS5f0448/SpKqV6+usmXLmlwRAABA0UGwAAAAAGAYN28DAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAACpXExERZLBbFx8ebXQoA4C4QLAAAAAAYRrAAAAAAYBjBAgBgips3b+rdd99V9erV5enpqSpVqmjs2LE5+mVnZ+u5555TSEiIvLy8VLNmTU2ePNmuz+bNm9WkSROVKlVK99xzj1q0aKGTJ09Kkg4ePKg2bdrI29tbPj4+Cg0N1b59+wrkGgHAlbibXQAAwDXFxsZq5syZmjhxov72t78pJSVF33//fY5+N2/eVKVKlfT555/Lz89PO3bs0AsvvKDAwEB1795dN27cUNeuXdWnTx8tXbpUWVlZ2rNnjywWiyTpqaeeUsOGDTVt2jS5ubkpPj5exYsXL+jLBQCnx5u3AQAFLiMjQ/7+/poyZYqef/55u22JiYkKCQnRgQMH9MADD+S6/0svvaSzZ8/qn//8py5duqRy5cpp8+bNatWqVY6+Pj4++vjjj/XMM8844lIAAP/DVCgAQIE7cuSIMjMz1a5duzvq/+mnn6px48by9/dX6dKlNXPmTCUlJUmSypYtq969e+vhhx9Wly5dNHnyZKWkpNj2jYmJ0fPPP6/27dtr/PjxOn78uEOuCQBcHcECAFDgvLy87rjv559/rkGDBunZZ5/VunXrFB8fr7///e/Kysqy9Zk7d6527typ5s2ba9myZapRo4Z27dolSRo1apS+++47derUSRs3blSdOnW0cuXKfL8mAHB1TIUCABS4a9euqWzZsvroo4/+dCrUyy+/rISEBG3YsMHWp3379rpw4cJt33URFhamBx98UB999FGObT179tTVq1e1atWqfL0mAHB1jFgAAApciRIlNHToUA0ZMkQLFizQ8ePHtWvXLs2ePTtH3+rVq2vfvn36+uuvdezYMY0YMUJ79+61bT9x4oRiY2O1c+dOnTx5UuvWrdOxY8dUu3Zt/frrr+rfv782b96skydPavv27dq7d69q165dkJcLAC6Bp0IBAEwxYsQIubu7680339SZM2cUGBiofv365ejXr18/xcfHq0ePHrJYLOrZs6defPFF/ec//5EklSxZUt9//73mz5+vixcvKjAwUP3791ffvn1148YNXbx4UdHR0Tp79qz8/PzUrVs3jR49uqAvFwCcHlOhAAAAABjGVCgAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAuLx58+bJYrGoRIkSOnnyZI7trVu3Vr169UyoLH/07t1bwcHBdm3BwcHq3bt3gdaRmJgoi8WiefPm5dlv8+bNslgstsXNzU3+/v7q0qWL9u3bVzDF5uHWnxfp/9eamJhYIOe2WCwaNWrUXe1zp39+b/f7s2zZMtWtW1deXl6yWCyKj4/X1KlTc/19vHWMzZs32+r9s99vAM7D3ewCAKCwyMzM1BtvvKGFCxeaXYrDrVy5Uj4+PmaXkad33nlHbdq00fXr13XgwAGNHj1arVq1Unx8vO677z6zy3M6gYGB2rlzp6pVq2ZrO3/+vKKiohQZGampU6fK09NTNWrU0NNPPy0/P78CD6cACjdGLADgfyIjI7VkyRIdPHjQoef59ddfHXr8O9GwYUO7L5CF0X333admzZqpZcuWGjBggCZOnKhffvlFixYtMru0u/bLL7+YXcKf8vT0VLNmzeTv729rO3bsmK5fv66nn35arVq1UrNmzVSyZEkTqwRQmBEsAOB/hgwZonLlymno0KF/2vfatWuKjY1VSEiIPDw8dO+99+qll17Szz//bNcvODhYnTt31ooVK9SwYUOVKFFCo0ePtk2hWbJkiYYOHarAwECVLl1aXbp00dmzZ5WRkaEXXnhBfn5+8vPz09///ndduXLF7tiffPKJHnroIZUvX16lSpVS/fr19d577+n69et/Wv8fp0K1bt3abvrR75ffT2VJTU1V3759ValSJXl4eCgkJESjR4/WjRs37I5/5swZde/eXd7e3vL19VWPHj2Umpr6p3XlpXHjxpKks2fP2rVv27ZN7dq1k7e3t0qWLKnmzZtr9erVtu3p6elyd3fX+++/b2u7cOGCihUrJl9fX7vaBwwYIH9/f1mt1r9c562pR//973/VvHlzlSxZUs8++6ytlsGDB9v9uRk4cKCuXr1qd4z09HT16dNH5cqVU+nSpRUZGaljx47lONf58+f1wgsvqHLlyvL09JS/v79atGih9evX5+i7d+9etWzZUiVLllTVqlU1fvx43bx507b9j1Ohevfurb/97W+SpB49eshisah169YKDg7Wd999py1bttj+jPxxqh0A18RUKAD4H29vb73xxht65ZVXtHHjRrVt2zbXflarVV27dtWGDRsUGxurli1b6tChQxo5cqR27typnTt3ytPT09b/m2++0ZEjR/TGG28oJCREpUqVsn2RfP3119WmTRvNmzdPiYmJGjx4sHr27Cl3d3fdf//9Wrp0qQ4cOKDXX39d3t7e+uijj2zHPX78uHr16mX7knrw4EGNHTtW33//vebMmXNX1z516lSlp6fbtY0YMUKbNm1SzZo1Jf0WKpo0aaJixYrpzTffVLVq1bRz5069/fbbSkxM1Ny5cyX9NiLTvn17nTlzRuPGjVONGjW0evVq9ejR465q+qMTJ05IkmrUqGFr27Jli8LDw9WgQQPNnj1bnp6emjp1qrp06aKlS5eqR48e8vHx0YMPPqj169frtddekyRt2LBBnp6eysjI0J49e9S8eXNJ0vr169W2bVvbPRS56d27ty2UtW7dOtcQkpKSoqefflpDhgzRO++8o2LFiumXX35Rq1atdOrUKb3++utq0KCBvvvuO7355pv69ttvtX79elksFtufrx07dujNN9/Ugw8+qO3bt6tDhw45zhMVFaVvvvlGY8eOVY0aNfTzzz/rm2++0cWLF+36paam6qmnntKrr76qkSNHauXKlYqNjVXFihUVHR2d63WOGDFCTZo00UsvvWSblubj46PMzEw9/vjj8vX11dSpUyXJ9uc9ODjY7udhJKABKIKsAODi5s6da5Vk3bt3rzUzM9NatWpVa+PGja03b960Wq1Wa6tWrax169a19V+7dq1VkvW9996zO86yZcuskqwzZsywtQUFBVnd3NysR48eteu7adMmqyRrly5d7NoHDhxolWQdMGCAXXvXrl2tZcuWve01ZGdnW69fv25dsGCB1c3NzXrp0iXbtmeeecYaFBRk1z8oKMj6zDPP3PZ477//fo5r6du3r7V06dLWkydP2vX94IMPrJKs3333ndVqtVqnTZtmlWT917/+ZdevT58+VknWuXPn3va8Vuv//9ksW7bMev36desvv/xi3b59u7VmzZrWOnXqWC9fvmzr26xZM2v58uWtGRkZtrYbN25Y69WrZ61UqZLt9/CNN96wenl5Wa9du2a1Wq3W559/3hoZGWlt0KCBdfTo0Var1Wo9ffp0jmv+K1q1amWVZN2wYYNd+7hx46zFihWz7t271679n//8p1WSdc2aNVar1Wr9z3/+Y5VknTx5sl2/sWPHWiVZR44caWsrXbq0deDAgXdUz+7du+3a69SpY3344Ydt6ydOnMjx+3Pr9+KLL76w27du3brWVq1a5XleAK6HqVAA8DseHh56++23tW/fPn3++ee59tm4caMk5bhx9YknnlCpUqW0YcMGu/YGDRrY/Sv773Xu3NluvXbt2pKkTp065Wi/dOmS3XSoAwcO6JFHHlG5cuXk5uam4sWLKzo6WtnZ2blOm7lTS5cu1ZAhQ/TGG2+oT58+tvavvvpKbdq0UcWKFXXjxg3bcutf0rds2SJJ2rRpk7y9vfXII4/YHbdXr153VUePHj1UvHhxlSxZUi1atFB6erpWr16te+65R5J09epV7d69W48//rhKly5t28/NzU1RUVE6deqUjh49Kklq166dfv31V+3YsUPSbyMT4eHhat++veLi4mxtktS+ffu7qjM3ZcqUyTHi9dVXX6levXp64IEH7H5+Dz/8sN2TlDZt2iRJeuqpp+z2z+3n16RJE82bN09vv/22du3addtpcBUqVFCTJk3s2ho0aJDrU9AA4K8iWADAHzz55JNq1KiRhg8fnusXtYsXL8rd3d3uJlfpt0drVqhQIcc0lMDAwNueq2zZsnbrHh4eebZfu3ZNkpSUlKSWLVvq9OnTmjx5srZu3aq9e/fqk08+kfTXbxDftGmTevfurejoaL311lt2286ePat///vfKl68uN1St25dSb/dtyD99vMJCAjIcewKFSrcVS3vvvuu9u7dqy1btmj48OE6e/asunbtqszMTEnS5cuXZbVac/35VqxY0VaLJNu9DuvXr9ePP/6oxMREW7DYvXu3rly5ovXr16tq1aoKCQm5qzpzk1tNZ8+e1aFDh3L8/Ly9vWW1Wu1+fu7u7ipXrpzd/rn9/JYtW6ZnnnlGs2bNUlhYmMqWLavo6Ogc97P88VjSb9OXCsODBAA4D+6xAIA/sFgsevfddxUeHq4ZM2bk2F6uXDnduHFD58+ftwsXVqtVqampevDBB3McL799+eWXunr1qlasWKGgoCBbe3x8/F8+5qFDh9S1a1e1atVKM2fOzLHdz89PDRo00NixY3Pd/9aX+XLlymnPnj05tt/tzdtVq1a13bD90EMPycvLS2+88YY+/vhjDR48WGXKlFGxYsWUkpKSY98zZ87YapZ+C2Z/+9vftH79elWqVEkVKlRQ/fr1VbVqVUm/vY9iw4YNOUaQ/qrcfs/9/Pzk5eV12/tfbtV668/XxYsX7QJBbj8/Pz8/TZo0SZMmTVJSUpJWrVqlYcOG6dy5c1q7dm2+XAsA3ClGLAAgF+3bt1d4eLjGjBmT42lM7dq1k6Qcjz1dvny5rl69atvuSLe+uP7+JnGr1ZprILgTSUlJ6tChg6pWrarly5erePHiOfp07txZhw8fVrVq1dS4ceMcy61g0aZNG2VkZGjVqlV2+y9ZsuQv1XbLkCFDVL16dY0fP14ZGRkqVaqUmjZtqhUrVtj9y/vNmze1aNEiVapUyW4KWvv27bV//34tX77cNt2pVKlSatasmT7++GOdOXMmX6ZB3U7nzp11/PhxlStXLtef360nK7Vp00aStHjxYrv9/+znV6VKFfXv31/h4eH65ptvHHINtzDaASA3jFgAwG28++67Cg0N1blz52zTfSQpPDxcDz/8sIYOHar09HS1aNHC9lSohg0bKioqyuG1hYeHy8PDQz179tSQIUN07do1TZs2TZcvX/5Lx+vQoYN+/vlnTZkyRd99953dtmrVqsnf319jxoxRXFycmjdvrgEDBqhmzZq6du2aEhMTtWbNGn366aeqVKmSoqOjNXHiREVHR2vs2LG67777tGbNGn399deGrrl48eJ655131L17d02ePFlvvPGGxo0bp/DwcLVp00aDBw+Wh4eHpk6dqsOHD2vp0qV2Iwft2rVTdna2NmzYoPnz59va27dvr5EjR8pisdz2SWD5YeDAgVq+fLkeeughDRo0SA0aNNDNmzeVlJSkdevW6dVXX1XTpk0VERGhhx56SEOGDNHVq1fVuHFjbd++PceLG9PS0tSmTRv16tVLtWrVkre3t/bu3au1a9eqW7duDrsOSapfv74+++wzLVu2TFWrVlWJEiVUv359h54TQOFHsACA22jYsKF69uyZ41+KLRaLvvzyS40aNUpz587V2LFj5efnp6ioKL3zzjt2owiOUqtWLS1fvlxvvPGGunXrpnLlyqlXr16KiYnJ9bGkfyYhIUGScv1COnfuXPXu3VuBgYHat2+f3nrrLb3//vs6deqUvL29FRISosjISJUpU0aSVLJkSW3cuFGvvPKKhg0bJovFooiICH322We2x7r+VU888YSaNm2qCRMm6OWXX1arVq20ceNGjRw5Ur1799bNmzd1//33a9WqVTmmNTVs2FB+fn66cOGC3cjErWDRsGHDXO9FyC+lSpXS1q1bNX78eM2YMUMnTpyQl5eXqlSpovbt29tGLIoVK6ZVq1YpJiZG7733nrKystSiRQutWbNGtWrVsh2vRIkSatq0qRYuXKjExERdv35dVapU0dChQzVkyBCHXYckjR49WikpKerTp48yMjIUFBSkxMREh54TQOFnsVp5yDQAAAAAY7jHAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACG8R6LfHDz5k2dOXNG3t7edi9jAgAAAIoyq9WqjIwMVaxYUcWK5T0mQbDIB2fOnFHlypXNLgMAAABwiOTkZFWqVCnPPgSLfODt7S3ptx+4j4+PydUAAAAA+SM9PV2VK1e2fd/NC8EiH9ya/uTj40OwAAAAgNO5k+n+3LwNAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMc8pgMXXqVIWEhKhEiRIKDQ3V1q1bb9t38+bNslgsOZbvv/++ACsGAAAAijanCxbLli3TwIEDNXz4cB04cEAtW7ZUhw4dlJSUlOd+R48eVUpKim257777CqhiAAAAoOhzumAxYcIEPffcc3r++edVu3ZtTZo0SZUrV9a0adPy3K98+fKqUKGCbXFzcyugigEAAICiz93sAvJTVlaW9u/fr2HDhtm1R0REaMeOHXnu27BhQ127dk116tTRG2+8oTZt2jiyVABwiOBhq80uAfkocXwns0sAgDvmVMHiwoULys7OVkBAgF17QECAUlNTc90nMDBQM2bMUGhoqDIzM7Vw4UK1a9dOmzdv1kMPPZTrPpmZmcrMzLStp6en599FAAAAAEWQUwWLWywWi9261WrN0XZLzZo1VbNmTdt6WFiYkpOT9cEHH9w2WIwbN06jR4/Ov4IBAACAIs6p7rHw8/OTm5tbjtGJc+fO5RjFyEuzZs30ww8/3HZ7bGys0tLSbEtycvJfrhkAAABwBk4VLDw8PBQaGqq4uDi79ri4ODVv3vyOj3PgwAEFBgbedrunp6d8fHzsFgAAAMCVOd1UqJiYGEVFRalx48YKCwvTjBkzlJSUpH79+kn6bbTh9OnTWrBggSRp0qRJCg4OVt26dZWVlaVFixZp+fLlWr58uZmXAQAAABQpThcsevTooYsXL2rMmDFKSUlRvXr1tGbNGgUFBUmSUlJS7N5pkZWVpcGDB+v06dPy8vJS3bp1tXr1anXs2NGsSwAAAACKHIvVarWaXURRl56eLl9fX6WlpTEtCoCpeNysc+FxswDMdjffc53qHgsAAAAA5iBYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADDMKYPF1KlTFRISohIlSig0NFRbt27Ns/+WLVsUGhqqEiVKqGrVqvr0008LqFIAAADAOThdsFi2bJkGDhyo4cOH68CBA2rZsqU6dOigpKSkXPufOHFCHTt2VMuWLXXgwAG9/vrrGjBggJYvX17AlQMAAABFl8VqtVrNLiI/NW3aVI0aNdK0adNsbbVr11bXrl01bty4HP2HDh2qVatW6ciRI7a2fv366eDBg9q5c+cdnTM9PV2+vr5KS0uTj4+P8YsAgL8oeNhqs0tAPkoc38nsEgC4uLv5nutUIxZZWVnav3+/IiIi7NojIiK0Y8eOXPfZuXNnjv4PP/yw9u3bp+vXrzusVgAAAMCZuJtdQH66cOGCsrOzFRAQYNceEBCg1NTUXPdJTU3Ntf+NGzd04cIFBQYG5tgnMzNTmZmZtvX09PR8qB4AAAAoupwqWNxisVjs1q1Wa462P+ufW/st48aN0+jRow1WWTQxzcK5MM3C+fB7ChRe/B3qfPh/rj2nmgrl5+cnNze3HKMT586dyzEqcUuFChVy7e/u7q5y5crluk9sbKzS0tJsS3Jycv5cAAAAAFBEOVWw8PDwUGhoqOLi4uza4+Li1Lx581z3CQsLy9F/3bp1aty4sYoXL57rPp6envLx8bFbAAAAAFfmVMFCkmJiYjRr1izNmTNHR44c0aBBg5SUlKR+/fpJ+m20ITo62ta/X79+OnnypGJiYnTkyBHNmTNHs2fP1uDBg826BAAAAKDIcbp7LHr06KGLFy9qzJgxSklJUb169bRmzRoFBQVJklJSUuzeaRESEqI1a9Zo0KBB+uSTT1SxYkV99NFHeuyxx8y6BAAAAKDIcbr3WJjBld5jwY1nzoWbzgCg4PB3qPNxhb9HXfY9FgAAAADMQbAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGOZudgEoWlzhDZMAAAC4e4xYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMcEiySk5N16tQp2/qePXs0cOBAzZgxwxGnAwAAAGAyhwSLXr16adOmTZKk1NRUhYeHa8+ePXr99dc1ZswYR5wSAAAAgIkcEiwOHz6sJk2aSJI+//xz1atXTzt27NCSJUs0b948R5wSAAAAgIkcEiyuX78uT09PSdL69ev1yCOPSJJq1aqllJQUR5wSAAAAgIkcEizq1q2rTz/9VFu3blVcXJwiIyMlSWfOnFG5cuUccUoAAAAAJnJIsHj33Xc1ffp0tW7dWj179tT9998vSVq1apVtihQAAAAA5+HuiIO2bt1aFy5cUHp6usqUKWNrf+GFF1SyZElHnBIAAACAiRz2Hgur1ar9+/dr+vTpysjIkCR5eHgQLAAAAAAn5JARi5MnTyoyMlJJSUnKzMxUeHi4vL299d577+natWv69NNPHXFaAAAAACZxyIjFK6+8osaNG+vy5cvy8vKytT/66KPasGGDI04JAAAAwEQOGbHYtm2btm/fLg8PD7v2oKAgnT592hGnBAAAAGAih4xY3Lx5U9nZ2TnaT506JW9vb0ecEgAAAICJHBIswsPDNWnSJNu6xWLRlStXNHLkSHXs2NERpwQAAABgIodMhZo4caLatGmjOnXq6Nq1a+rVq5d++OEH+fn5aenSpY44JQAAAAATOWTEomLFioqPj9fgwYPVt29fNWzYUOPHj9eBAwdUvnx5R5xSknT58mVFRUXJ19dXvr6+ioqK0s8//5znPr1795bFYrFbmjVr5rAaAQAAAGfkkBELSfLy8tKzzz6rZ5991lGnyKFXr146deqU1q5dK+m3F/JFRUXp3//+d577RUZGau7cubb1P950DgAAACBvDgkWCxYsyHN7dHR0vp/zyJEjWrt2rXbt2qWmTZtKkmbOnKmwsDAdPXpUNWvWvO2+np6eqlChQr7XBAAAALgKhwSLV155xW79+vXr+uWXX2xv3nZEsNi5c6d8fX1toUKSmjVrJl9fX+3YsSPPYLF582aVL19e99xzj1q1aqWxY8fmOWUrMzNTmZmZtvX09PT8uQgAAACgiHLIPRaXL1+2W65cuaKjR4/qb3/7m8Nu3k5NTc01DJQvX16pqam33a9Dhw5avHixNm7cqA8//FB79+5V27Zt7YLDH40bN852H4evr68qV66cL9cAAAAAFFUOCRa5ue+++zR+/Pgcoxl/ZtSoUTlurv7jsm/fPkm/Pdb2j6xWa67tt/To0UOdOnVSvXr11KVLF/3nP//RsWPHtHr16tvuExsbq7S0NNuSnJx8V9cEAAAAOBuH3bydGzc3N505c+au9unfv7+efPLJPPsEBwfr0KFDOnv2bI5t58+fV0BAwB2fLzAwUEFBQfrhhx9u28fT01Oenp53fEwAAADA2TkkWKxatcpu3Wq1KiUlRVOmTFGLFi3u6lh+fn7y8/P7035hYWFKS0vTnj171KRJE0nS7t27lZaWpubNm9/x+S5evKjk5GQFBgbeVZ0AAACAK3NIsOjatavdusVikb+/v9q2basPP/zQEadU7dq1FRkZqT59+mj69OmSfnvcbOfOne1u3K5Vq5bGjRunRx99VFeuXNGoUaP02GOPKTAwUImJiXr99dfl5+enRx991CF1AgAAAM7IIcHi5s2bjjjsn1q8eLEGDBigiIgISdIjjzyiKVOm2PU5evSo0tLSJP02Nevbb7/VggUL9PPPPyswMFBt2rTRsmXL5O3tXeD1AwAAAEVVgd5j4Whly5bVokWL8uxjtVptv/by8tLXX3/t6LIAAAAAp5dvwSImJuaO+06YMCG/TgsAAACgEMi3YHHgwIE76pfXo18BAAAAFE35Fiw2bdqUX4cCAAAAUMQU2AvyAAAAADgvh928vXfvXn3xxRdKSkpSVlaW3bYVK1Y46rQAAAAATOCQEYvPPvtMLVq0UEJCglauXKnr168rISFBGzdulK+vryNOCQAAAMBEDgkW77zzjiZOnKivvvpKHh4emjx5so4cOaLu3burSpUqjjglAAAAABM5JFgcP35cnTp1kiR5enrq6tWrslgsGjRokGbMmOGIUwIAAAAwkUOCRdmyZZWRkSFJuvfee3X48GFJ0s8//6xffvnFEacEAAAAYKJ8DRbx8fGSpJYtWyouLk6S1L17d73yyivq06ePevbsqXbt2uXnKQEAAAAUAvn6VKhGjRqpYcOG6tq1q3r27ClJio2NVfHixbVt2zZ169ZNI0aMyM9TAgAAACgE8nXEYvv27WrUqJE++OADVatWTU8//bS2bNmiIUOGaNWqVZowYYLKlCmTn6cEAAAAUAjka7AICwvTzJkzlZqaqmnTpunUqVNq3769qlWrprFjx+rUqVP5eToAAAAAhYRDbt728vLSM888o82bN+vYsWPq2bOnpk+frpCQEHXs2NERpwQAAABgIocEi9+rVq2ahg0bpuHDh8vHx0dff/21o08JAAAAoIDl683bf7RlyxbNmTNHy5cvl5ubm7p3767nnnvOkacEAAAAYIJ8DxbJycmaN2+e5s2bpxMnTqh58+b6+OOP1b17d5UqVSq/TwcAAACgEMjXYBEeHq5NmzbJ399f0dHRevbZZ1WzZs38PAUAAACAQihfg4WXl5eWL1+uzp07y83NLT8PDQAAAKAQy9dgsWrVqvw8HAAAAIAiwuFPhQIAAADg/AgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADHOqYDF27Fg1b95cJUuW1D333HNH+1itVo0aNUoVK1aUl5eXWrdure+++86xhQIAAABOxqmCRVZWlp544gn94x//uON93nvvPU2YMEFTpkzR3r17VaFCBYWHhysjI8OBlQIAAADOxamCxejRozVo0CDVr1//jvpbrVZNmjRJw4cPV7du3VSvXj3Nnz9fv/zyi5YsWeLgagEAAADn4VTB4m6dOHFCqampioiIsLV5enqqVatW2rFjh4mVAQAAAEWLu9kFmCk1NVWSFBAQYNceEBCgkydP3na/zMxMZWZm2tbT09MdUyAAAABQRBT6EYtRo0bJYrHkuezbt8/QOSwWi9261WrN0fZ748aNk6+vr22pXLmyofMDAAAARV2hH7Ho37+/nnzyyTz7BAcH/6VjV6hQQdJvIxeBgYG29nPnzuUYxfi92NhYxcTE2NbT09MJFwAAAHBphT5Y+Pn5yc/PzyHHDgkJUYUKFRQXF6eGDRtK+u3JUlu2bNG777572/08PT3l6enpkJoAAACAoqjQT4W6G0lJSYqPj1dSUpKys7MVHx+v+Ph4XblyxdanVq1aWrlypaTfpkANHDhQ77zzjlauXKnDhw+rd+/eKlmypHr16mXWZQAAAABFTqEfsbgbb775pubPn29bvzUKsWnTJrVu3VqSdPToUaWlpdn6DBkyRL/++qtefPFFXb58WU2bNtW6devk7e1doLUDAAAARZnFarVazS6iqEtPT5evr6/S0tLk4+NjdjkAAKAQCh622uwSkM8Sx3cyuwSHu5vvuU41FQoAAACAOQgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAxzqmAxduxYNW/eXCVLltQ999xzR/v07t1bFovFbmnWrJljCwUAAACcjFMFi6ysLD3xxBP6xz/+cVf7RUZGKiUlxbasWbPGQRUCAAAAzsnd7ALy0+jRoyVJ8+bNu6v9PD09VaFCBQdUBAAAALgGpxqx+Ks2b96s8uXLq0aNGurTp4/OnTtndkkAAABAkeJUIxZ/RYcOHfTEE08oKChIJ06c0IgRI9S2bVvt379fnp6eue6TmZmpzMxM23p6enpBlQsAAAAUSoV+xGLUqFE5bq7+47Jv376/fPwePXqoU6dOqlevnrp06aL//Oc/OnbsmFavXn3bfcaNGydfX1/bUrly5b98fgAAAMAZFPoRi/79++vJJ5/Ms09wcHC+nS8wMFBBQUH64YcfbtsnNjZWMTExtvX09HTCBQAAAFxaoQ8Wfn5+8vPzK7DzXbx4UcnJyQoMDLxtH09Pz9tOkwIAAABcUaGfCnU3kpKSFB8fr6SkJGVnZys+Pl7x8fG6cuWKrU+tWrW0cuVKSdKVK1c0ePBg7dy5U4mJidq8ebO6dOkiPz8/Pfroo2ZdBgAAAFDkFPoRi7vx5ptvav78+bb1hg0bSpI2bdqk1q1bS5KOHj2qtLQ0SZKbm5u+/fZbLViwQD///LMCAwPVpk0bLVu2TN7e3gVePwAAAFBUWaxWq9XsIoq69PR0+fr6Ki0tTT4+PmaXAwAAAOSLu/me61RToQAAAACYg2ABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAw9zNLsAZWK1WSVJ6errJlQAAAAD559b321vfd/NCsMgHGRkZkqTKlSubXAkAAACQ/zIyMuTr65tnH4v1TuIH8nTz5k2dOXNG3t7eslgsZpcDg9LT01W5cmUlJyfLx8fH7HIA/AGfUaBw4zPqXKxWqzIyMlSxYkUVK5b3XRSMWOSDYsWKqVKlSmaXgXzm4+PD/xCBQozPKFC48Rl1Hn82UnELN28DAAAAMIxgAQAAAMAwggXwB56enho5cqQ8PT3NLgVALviMAoUbn1HXxc3bAAAAAAxjxAIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAi7t448/NrsEAAAAp8BToeDSypYtq9DQUM2dO5e3pwOF2N69e7V06VIdO3ZMFotF9913n3r16qXGjRubXRqAPKxYsUKjRo3SoUOHzC4FBYARC7i0w4cPy9PTU/Xr19fChQvNLgdALoYMGaKmTZtq1qxZOnXqlJKSkjRz5kw1bdpUQ4cONbs8wOXNnDlTTzzxhHr16qXdu3dLkjZu3KiGDRvq6aefVlhYmMkVoqAwYgFImjdvnmJiYtS6dWu98cYbcnd3t9veoEEDkyoDXNv8+fPVr18/vf/+++rbt6+KFy8uSbp+/bqmTZumoUOHavr06YqOjja5UsA1ffDBB3r99dfVoEEDHTlyRJI0fPhwTZgwQS+//LJeeukl+fn5mVwlCgrBAvif9evXKzIyUlarVVarVRaLxfbf7Oxss8sDXFKTJk3Us2dPDRo0KNftEyZM0GeffaY9e/YUcGUAJKl27dp67bXX9Oyzz2rz5s1q27at2rZtq3/+85+65557zC4PBYxgAei3LycjRozQE088oREjRuQYsQgKCjKpMsC1lSpVSt9++62qVq2a6/affvpJ9evX19WrVwu4MgCSVLJkSX3//feqUqWKJMnT01P//e9/1bRpU5Mrgxnc/7wL4Lx++uknRUdH6/jx41qyZIn+7//+z+ySAPyOm5ubsrKybrv9+vXrcnNzK8CKAPzetWvXVKJECdu6h4eH/P39TawIZiJYwKU1aNBAkZGR+vLLL5kDChRCoaGhWrx4sd56661cty9cuFCNGjUq4KoA/N6sWbNUunRpSdKNGzc0b968HH+nDhgwwIzSUMCYCgWXtmjRIj399NNmlwHgNr766it17dpVMTExevXVVxUQECBJSk1N1YcffqhJkyZp5cqV6ty5s8mVAq4pODhYFoslzz4Wi0U//fRTAVUEMzFiAZeW1xQLScrIyNCgQYM0a9asAqoIwO917txZEydO1ODBg/Xhhx/K19dXkpSWliY3Nze9//77hArARImJiWaXgEKEEQu4NF9fX7Vs2VKzZs1ShQoV7LZ9/fXX6tOnj8qUKaODBw+aVCEASTp16pS++OIL/fDDD5KkGjVq6LHHHlPlypVNrgxAXi5evKiFCxdq4MCBZpeCAkCwgEtLTEzU3//+dx06dEhTpkxRz549lZGRoYEDB2rRokV67bXXNHLkSNuz8wEAQN6sVqvWrVun2bNn61//+pd8fHx0/vx5s8tCAeDN23BpwcHB2rRpk0aMGKE+ffqoc+fOqlevnvbs2aMdO3bo7bffJlQAJvrxxx+1f/9+u7YNGzaoTZs2atKkid555x2TKgPwR4mJiXrzzTcVFBSkjh07qkSJElq9erVSU1PNLg0FhGABSOrbt68iIiK0Zs0aXbp0SYsWLVJoaKjZZQEu77XXXtOXX35pWz9x4oS6dOkiDw8PhYWFady4cZo0aZJp9QGuLjMzU0uXLlW7du1Uu3ZtHT58WBMmTFCxYsU0bNgwtW/fnkdCuxCCBVze9u3bdf/99+vo0aNau3atOnTooLCwME2cONHs0gCXt2/fPnXs2NG2vnjxYtWoUUNff/21Jk+erEmTJmnevHnmFQi4uHvvvVfTpk1Tjx49dObMGa1YsUKPP/642WXBJAQLuLRXX31Vbdu2VZcuXfTNN98oIiJCn3/+uebOnat33nlHDz30EI/IA0x04cIFVapUyba+adMmdenSxbbeunVrnkoDmCg7O1sWi0UWi4WRCRAs4Nr+9a9/af369frwww/l6elpa+/Ro4cOHz6ssmXL6v777zexQsC1lS1bVikpKZKkmzdvat++fWratKlte1ZWlngGCWCelJQUvfDCC1q6dKkqVKigxx57TCtXrvzTd1vAOREs4NIOHjyoli1b5rotICBAX375paZOnVrAVQG4pVWrVnrrrbeUnJysSZMmKTs7W23atLFtT0hIUHBwsHkFAi6uRIkSeuqpp7Rx40Z9++23ql27tgYMGKAbN25o7NixiouLU3Z2ttllooAQLODSSpUqlef2b7/9NscTaQAUnLFjx+rIkSMKCgrSkCFD9N5779l9bhcuXKi2bduaWCHg2hYsWKDMzExJUrVq1fT222/r5MmTWr16tTIzM9W5c2cFBASYXCUKCu+xAP4gPT1dS5cu1ezZs7Vv3z41aNBA8fHxZpcFuKzr168rISFB/v7+8vDwkIeHh3x8fCT9NupYqVIllStXzuQqAdfk5uamlJQUlS9fPtft58+f18KFCxUTE1PAlcEMjFgA/7NlyxZFR0crMDBQL774otq2batjx44RKgCTXb16VTNmzFCDBg0UEBCgMmXKqEKFCoqNjdV9991HqABM9Gf/Pu3v70+ocCGMWMClpaSkaO7cuZozZ46uXr2qnj17qlevXgoLC9PBgwdVp04ds0sEXNqlS5cUFham06dP66mnnlLt2rVltVp15MgRLVmyRLVq1dK2bdt08OBB7d69WwMGDDC7ZMClFCtWTGfPnpW/v7/ZpaAQcDe7AMBMISEheuKJJ/TJJ58oPDxcxYoxiAcUJmPGjJGHh4eOHz+eY572mDFjFBERoaioKK1bt04fffSRSVUCrq137952T1bMzYoVKwqoGpiJYAGXFhQUpG3btqlKlSoKCgpSrVq1zC4JwO98+eWXmj59eq43f1aoUEHvvfeeOnbsqJEjR+qZZ54xoUIA3t7e8vLyMrsMFAIEC7i0o0ePavv27Zo9e7YefPBB1ahRQ08//bQk8QxuoBBISUlR3bp1b7u9Xr16KlasmEaOHFmAVQH4vY8++ui2N2/DtTDvAy6vRYsWmjNnjlJSUtSvXz99/vnnys7O1osvvqiZM2fq/PnzZpcIuCw/P78836x94sQJvtAAJuIf4fB73LwN5OLIkSOaPXu2Fi5cqEuXLun69etmlwS4pOeee04//vij4uLi5OHhYbctMzNTDz/8sKpWrao5c+aYVCHg2ooVK6bU1FQCPiQRLIA83bhxQ6tWrVK3bt3MLgVwSadOnVLjxo3l6empl156yXYfVEJCgqZOnarMzEzt3btXVapUMblSwDWNHz9edevWVZcuXWxtCxYs0MiRI3X16lV17dpVH3/88Z/e3A3nQLAA8nDkyBF16tRJP/30k9mlAC7rxIkTevHFF7Vu3TrbM/MtFovCw8M1ZcoUVa9e3eQKAdcVGRmpNm3aaOjQoZKkb7/9Vo0aNVLv3r1Vu3Ztvf/+++rbt69GjRplbqEoEAQLIA8HDx5Uo0aNlJ2dbXYpgMu7fPmyfvjhB0lS9erVVbZsWZMrAhAYGKh///vfaty4sSRp+PDh2rJli7Zt2yZJ+uKLLzRy5EglJCSYWSYKCE+FAgAUCWXKlFGTJk3MLgPA71y+fNnucdBbtmxRZGSkbf3BBx9UcnKyGaXBBDwVCgAAAH9JQECATpw4IUnKysrSN998o7CwMNv2jIwMFS9e3KzyUMAIFgAAAPhLIiMjNWzYMG3dulWxsbEqWbKkWrZsadt+6NAhVatWzcQKUZCYCgWXVqZMmTyfwX3jxo0CrAYAgKLl7bffVrdu3dSqVSuVLl1a8+fPt3s09Jw5cxQREWFihShI3LwNlzZ//vw76vfMM884uBIAAIqutLQ0lS5dWm5ubnbtly5dUunSpXO8hwbOiWAB/IkbN27I3Z3BPQAAgLxwjwVwGwkJCXr11Vd17733ml0KAABAoUewAH7nypUrmjVrlsLCwtSgQQPt3r1bw4YNM7ssAACAQo/5HYCkbdu2adasWVq+fLlCQkKUkJCgLVu2qEWLFmaXBgAAUCQwYgGX9t5776lWrVp68skn5e/vr23btunQoUOyWCwqU6aM2eUBAAAUGdy8DZfm7u6uoUOHasyYMXZPsihevLgOHjyoOnXqmFgdAABA0cGIBVzamDFj9MUXXygkJERDhw7V4cOHzS4JAACgSCJYwKW9/vrrOnbsmBYuXKjU1FQ1a9ZM999/v6xWqy5fvmx2eQAAAEUGU6GA30lPT9eSJUs0Z84c7d+/X02bNtXjjz+umJgYs0sDAAAo1AgWwG0cPnxYs2fP1uLFi3Xu3DmzywEAACjUeNwsXNqvv/6qDRs2qHPnzpKk2NhYZWZm2ra7u7vr+PHjZpUHAABQZBAs4NIWLFigr776yhYspkyZorp168rLy0uSdPToUVWsWFGDBg0ys0wAAIBCj6lQcGkPPfSQBg0apEcffVSS5O3trYMHD6pq1aqSpEWLFumTTz7Rzp07zSwTAACg0OOpUHBpx44dU40aNWzrJUqUULFi//9j0aRJEyUkJJhRGgAAQJHCVCi4tLS0NLm7//+Pwfnz5+2237x50+6eCwAAAOSOEQu4tEqVKuX5UrxDhw6pUqVKBVgRAABA0USwgEvr2LGj3nzzTV27di3Htl9//VWjR49Wp06dTKgMAACgaOHmbbi0s2fP6oEHHpCHh4f69++vGjVqyGKx6Pvvv9eUKVN048YNHThwQAEBAWaXCgAAUKgRLODyTpw4oX/84x+Ki4vTrY+DxWJReHi4pk6dantCFAAAAG6PYAH8z6VLl/Tjjz9KkqpXr66yZcuaXBEAAEDRQbAAAAAAYBg3bwMAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAKlcTERFksFsXHx5tdCgDgLhAsAAAAABhGsAAAAABgGMECAGCKmzdv6t1331X16tXl6empKlWqaOzYsTn6ZWdn67nnnlNISIi8vLxUs2ZNTZ482a7P5s2b1aRJE5UqVUr33HOPWrRooZMnT0qSDh48qDZt2sjb21s+Pj4KDQ3Vvn37CuQaAcCVuJtdAADANcXGxmrmzJmaOHGi/va3vyklJUXff/99jn43b95UpUqV9Pnnn8vPz087duzQCy+8oMDAQHXv3l03btxQ165d1adPHy1dulRZWVnas2ePLBaLJOmpp55Sw4YNNW3aNLm5uSk+Pl7Fixcv6MsFAKfHm7cBAAUuIyND/v7+mjJlip5//nm7bYmJiQoJCdGBAwf0wAMP5Lr/Sy+9pLNnz+qf//ynLl26pHLlymnz5s1q1apVjr4+Pj76+OOP9cwzzzjiUgAA/8NUKABAgTty5IgyMzPVrl27O+r/6aefqnHjxvL391fp0qU1c+ZMJSUlSZLKli2r3r176+GHH1aXLl00efJkpaSk2PaNiYnR888/r/bt22v8+PE6fvy4Q64JAFwdwQIAUOC8vLzuuO/nn3+uQYMG6dlnn9W6desUHx+vv//978rKyrL1mTt3rnbu3KnmzZtr2bJlqlGjhnbt2iVJGjVqlL777jt16tRJGzduVJ06dbRy5cp8vyYAcHVMhQIAFLhr166pbNmy+uijj/50KtTLL7+shIQEbdiwwdanffv2unDhwm3fdREWFqYHH3xQH330UY5tPXv21NWrV7Vq1ap8vSYAcHWMWAAAClyJEiU0dOhQDRkyRAsWLNDx48e1a9cuzZ49O0ff6tWra9++ffr666917NgxjRgxQnv37rVtP3HihGJjY7Vz506dPHlS69at07Fjx1S7dm39+uuv6t+/vzZv3qyTJ09q+/bt2rt3r2rXrl2QlwsALoGnQgEATDFixAi5u7vrzTff1JkzZxQYGKh+/frl6NevXz/Fx8erR48eslgs6tmzp1588UX95z//kSSVLFlS33//vebPn6+LFy8qMDBQ/fv3V9++fXXjxg1dvHhR0dHROnv2rPz8/NStWzeNHj26oC8XAJweU6EAAAAAGMZUKAAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGH/D88j6pUyPLOOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x3200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(normalized_means_df), figsize=(8, 4 * len(normalized_means_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(normalized_means_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Normalized Row \\\"{index}\\\"')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By rescaling the features through the usage of robust scaling, the differences between the classes are now more apparent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median\n",
    "The median for the raw features are also compared to their normalized counterparts in order to further verify the effects of normalization.\n",
    "\n",
    "The following shows the median values for the raw data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class         GALAXY         QSO        STAR\n",
      "alpha     181.315787  183.682504  174.149512\n",
      "delta      22.301731   29.299167   23.400590\n",
      "u          22.841770   21.503240   21.010650\n",
      "g          21.582200   21.056290   19.542830\n",
      "r          20.104590   20.772720   18.955130\n",
      "i          19.223660   20.582110   18.592390\n",
      "z          18.766630   20.373970   18.319230\n",
      "redshift    0.456274    1.617232   -0.000076\n",
      "\n",
      "\n",
      "class       GALAXY       QSO      STAR\n",
      "alpha     0.003904  0.026152 -0.063463\n",
      "delta    -0.038693  0.162649 -0.007075\n",
      "u         0.198685 -0.202664 -0.350364\n",
      "g         0.152688 -0.013817 -0.492982\n",
      "r        -0.007123  0.222555 -0.402263\n",
      "i        -0.068121  0.441764 -0.305064\n",
      "z        -0.096724  0.556589 -0.278573\n",
      "redshift  0.029772  0.551986 -1.542501\n"
     ]
    }
   ],
   "source": [
    "median_df = og_stars.groupby('class').median()\n",
    "median_df = median_df[['alpha','delta','u','g','r','i','z','redshift']].T\n",
    "\n",
    "normalized_median_df = stars.groupby('class').median()\n",
    "normalized_median_df = normalized_median_df[['alpha','delta','u','g','r','i','z','redshift']].T\n",
    "\n",
    "\n",
    "print(median_df)\n",
    "print(\"\\n\")\n",
    "print(normalized_median_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart for the median of the raw data is shown by the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x3200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(median_df), figsize=(8, 4 * len(median_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(median_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Row \\\"{index}\\\"')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, based from the bar chart for the raw data means, the feature scales for the raw data median are also massive.\n",
    "\n",
    "The following shows the bar chart for the median of the normalized features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x3200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=len(normalized_median_df), figsize=(8, 4 * len(normalized_median_df)))\n",
    "\n",
    "# Iterate through each row and plot a bar graph\n",
    "for i, (index, row) in enumerate(normalized_median_df.iterrows()):\n",
    "    # Create a bar plot for each row\n",
    "    ax = axes[i]\n",
    "    row.plot(kind='bar', ax=ax, title=f'Normalized Row \\\"{index}\\\"')\n",
    "    ax.set_ylabel('Values')\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the bar chart of the means of the normalized values, the differences between the features are more apparent through normalization.\n",
    "\n",
    "The following shows the box plot for the normalized data, which can be helpful to further visualize the differences between the classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'alpha'}, xlabel='class'>,\n",
       "        <Axes: title={'center': 'delta'}, xlabel='class'>,\n",
       "        <Axes: title={'center': 'u'}, xlabel='class'>],\n",
       "       [<Axes: title={'center': 'g'}, xlabel='class'>,\n",
       "        <Axes: title={'center': 'r'}, xlabel='class'>,\n",
       "        <Axes: title={'center': 'i'}, xlabel='class'>],\n",
       "       [<Axes: title={'center': 'z'}, xlabel='class'>,\n",
       "        <Axes: title={'center': 'redshift'}, xlabel='class'>, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_df.boxplot(columns_data, by=\"class\", figsize=(20,18))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplots for the normalized data above implies that the photometric features have certain distinctions between their classes when visually analyzed side-by-side, in contrast to the instrumentation data for alpha and delta. As such the instrumentation features will not be included for the model training part since they imply little distinction between the three classes and may not be useful for model training.\n",
    "\n",
    "One thing to also note about the classes is the overlapping regions between the interquartile ranges of the different classes, which may imply that certain values that may exist for a certain class can also be categorized for another class. As such, the complete utilization of the feature set for the photometric data may be needed in order to derive an effective model from the given data. Although interestingly, the redshift feature might become pivotal for the classification tasks as can be seen from the differences between the classes of the labels.\n",
    "\n",
    "Another thing to note of above is the relatively balanced distributions that the features for each classes have in terms of the positioning of the median values relative to their interquartile ranges and outliers, which may help with models that are sensitive against skewed data such as k-nearest neighbors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers\n",
    "The outlier samples of each feature will also be shown in order to determine their proportion relative to the size of the sample. All outliers will be defined as feature samples at and beyond 3 standard deviations from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\" u \" outliers:  8\n",
      "\" g \" outliers:  25\n",
      "\" r \" outliers:  19\n",
      "\" i \" outliers:  73\n",
      "\" z \" outliers:  26\n",
      "\" redshift \" outliers:  0\n",
      "\n",
      "\n",
      "\" u \"outlier percentage:  0.008000080000800007  %\n",
      "\" g \"outlier percentage:  0.025000250002500026  %\n",
      "\" r \"outlier percentage:  0.01900019000190002  %\n",
      "\" i \"outlier percentage:  0.07300073000730008  %\n",
      "\" z \"outlier percentage:  0.026000260002600025  %\n",
      "\" redshift \"outlier percentage:  0.0  %\n"
     ]
    }
   ],
   "source": [
    "outliers_df = pd.DataFrame()\n",
    "\n",
    "for feature in cols:\n",
    "    tmp = (stats.zscore(stars[feature]) >= 3)\n",
    "    outliers_df[feature] = tmp\n",
    "    print(\"\\\"\", feature, \"\\\" outliers: \", len(tmp[tmp==True]))\n",
    "\n",
    "print(\"\\n\")\n",
    "    \n",
    "for feature in cols:\n",
    "    print(\"\\\"\", feature, \"\\\"outlier percentage: \", 100 * sum(outliers_df[feature])/len(stars), \" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based from the values above, the outliers may have negligible impact on the overall result of the models since their proportion within the dataset is low."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training\n",
    "\n",
    "The following are the models that will be utilized for training, hyperparameter tuning and testing.\n",
    "\n",
    "1. Multinomial Regression\n",
    "2. Random Forest\n",
    "3. K\\-Nearest Neighbors\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first model featured is the Logistic/Multinomial Regression since it can be seen that there are linearly/polynomial-separable features within the normalized dataset which could help with the classification.\n",
    "\n",
    "In order to begin, the Stochastic Gradient Descent Classifier was imported from the Scikit-learn library for usage, as well as defining the hyperparameters to be used for the hyperparameter tuning portion, which consist of the learning rate for the model, spanning ranges within a=0.001 up to a=0.100 in 0.001 steps.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "alpha = np.arange(0.001, 0.101, 0.001)\n",
    "logis_hyperparameters = [\n",
    "    {\n",
    "        'eta0': alpha\n",
    "    }\n",
    "\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before setting up the logistic regression model, we need to vectorize the labels, otherwise we will not be able to compute the RMSE later on since RMSE can only compute continuous values. Our labels contain strings which we need to vectorize first:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, ..., 1, 0, 2])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_MLR = y_train.copy()\n",
    "\n",
    "labels = {'GALAXY':0,'STAR':1,'QSO':2}\n",
    "\n",
    "label_set = np.vectorize(lambda x: labels[x])\n",
    "y_train_MLR = label_set(y_train_MLR)\n",
    "\n",
    "y_train_MLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_MLR = y_test.copy()\n",
    "\n",
    "labels = {'GALAXY':0,'STAR':1,'QSO':2}\n",
    "\n",
    "label_set = np.vectorize(lambda x: labels[x])\n",
    "y_test_MLR = label_set(y_test_MLR)\n",
    "\n",
    "y_test_MLR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SGDClassifier features several parameters, one of which is the regularization methods, of which we will use the default L2 regularization parameter. We will utilize a learning rate of 0.001 for the meantime, as well as a constant learning rate.\n",
    "\n",
    "For now, we will verify the regularization method for the model by predicting both the training data and the test data, and comparing whether they are sufficiently similar to each other through their prediction accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "logistic_model = SGDClassifier(loss='log_loss', eta0=0.001, learning_rate='constant', random_state=seed_num, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(eta0=0.001, learning_rate=&#x27;constant&#x27;, loss=&#x27;log_loss&#x27;,\n",
       "              random_state=12)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(eta0=0.001, learning_rate=&#x27;constant&#x27;, loss=&#x27;log_loss&#x27;,\n",
       "              random_state=12)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SGDClassifier(eta0=0.001, learning_rate='constant', loss='log_loss',\n",
       "              random_state=12)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model.fit(X_train, y_train_MLR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model prediction for the training data:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, ..., 1, 2, 2])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_predictions = logistic_model.predict(X_train)\n",
    "logistic_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 94.8840%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train Accuracy: {metrics.compute_accuracy(logistic_predictions, y_train_MLR):.4f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Root Mean Squared Error: 0.4089\n"
     ]
    }
   ],
   "source": [
    "rmse = metrics.compute_rmse(logistic_predictions, y_train_MLR)\n",
    "print(f\"Training Root Mean Squared Error: {rmse:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model prediction for the test data:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_predictions = logistic_model.predict(X_test)\n",
    "logistic_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 94.5930%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Test Accuracy: {metrics.compute_accuracy(logistic_predictions, y_test_MLR):.4f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Root Mean Squared Error: 0.4227\n"
     ]
    }
   ],
   "source": [
    "logis_rmse = metrics.compute_rmse(logistic_predictions, y_test_MLR)\n",
    "print(f\"Test Root Mean Squared Error: {logis_rmse:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based from the results above, the training and test accuracy for the logistic model is very similar to each other, which implies a model that is sufficiently low-bias and low-variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "### ------END OF LOGISTIC REGRESSION------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the EDA, we saw distinct differences between the features of the three classes. From that, we theorized that, since the features are distinct, there would be little overlapping instances which makes the data a good fit for Random Forest.\n",
    "\n",
    "Since sklearn's Random Forest implementation can only work with numerical values, we must convert our labels from strings to integer labels. To do this, we can simply run a lambda function across all the values to convert them.\n",
    "\n",
    "The mapping goes as follows: Galaxy: 0 | Star: 1 | QSO: 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, ..., 1, 0, 2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_RF = y_train.copy()\n",
    "\n",
    "labels = {'GALAXY':0,'STAR':1,'QSO':2}\n",
    "\n",
    "label_set = np.vectorize(lambda x: labels[x])\n",
    "y_train_RF = label_set(y_train_RF)\n",
    "\n",
    "y_train_RF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to do the same for the test set as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_RF = y_test.copy()\n",
    "\n",
    "labels = {'GALAXY':0,'STAR':1,'QSO':2}\n",
    "\n",
    "label_set = np.vectorize(lambda x: labels[x])\n",
    "y_test_RF = label_set(y_test_RF)\n",
    "\n",
    "y_test_RF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can proceed with the training and fitting of our initial model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=12)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=12)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=12)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=seed_num)\n",
    "rfc.fit(X_train, y_train_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can attempt a prediction with our model to test its performance prior to any Hyperparameter tuning. First, let's check the accuracy and RMSE in the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy:  99.997\n",
      "Train RMSE:  0.008451602916093027\n"
     ]
    }
   ],
   "source": [
    "predictions_train_RF = rfc.predict(X_train)\n",
    "\n",
    "\n",
    "accuracy_train_RF = metrics.compute_accuracy(predictions_train_RF, y_train_RF)\n",
    "rmse_train_RF = metrics.compute_rmse(predictions_train_RF, y_train_RF)\n",
    "\n",
    "print(\"Train Accuracy: \", accuracy_train_RF)\n",
    "print(\"Train RMSE: \", rmse_train_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy:  97.13\n",
      "Test RMSE:  0.3043024810940588\n"
     ]
    }
   ],
   "source": [
    "predictions_test_RF = rfc.predict(X_test)\n",
    "\n",
    "accuracy_test_RF = metrics.compute_accuracy(predictions_test_RF, y_test_RF)\n",
    "rmse_test_RF = metrics.compute_rmse(predictions_test_RF, y_test_RF)\n",
    "\n",
    "print(\"Test Accuracy: \", accuracy_test_RF)\n",
    "print(\"Test RMSE: \", rmse_test_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now, the model shows no signs of under or overfitting due to the fact that it returned high accuracy and low RMSE for both the train and the test set. It also shows that our initial assumption based on our EDA is correct in that there are very little overlapping instances in the dataset which makes it a good set for Random Forest. Now, let us see if we can further increase the model's performance with hyperparameter tuning in the next section.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors\n",
    "\n",
    "KNN is a model that searches for the nearest neighbors and bases on the prediction on them. KNN is utilized here because it properly fits the goal which is classification. While KNN does not need to train since it only needs to find the nearest neighbors and get the majority class specifically on this task that identifies whether the celestial body is a star, galaxy or quasar. Instead of training the model, what happens is that the data will be become neighbors that will aid in predicting new instances "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 97.684\n",
      "Test Score: 96.9\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "train_results = knn.predict(X_train)\n",
    "test_results = knn.predict(X_test)\n",
    "\n",
    "print(\"Train Score:\",metrics.compute_accuracy(train_results, y_train))\n",
    "print(\"Test Score:\",metrics.compute_accuracy(test_results, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The KNN model does not show any signs of overfitting due to having a high accuracy for predicting on both train and test dataset and their difference is not significantly high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Logistic Regression\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the multinomial logistic regression, we will utilize GridSearchCV for its hyperparameter tuning. For this logistic regression model, we are only looking for the best \"eta0\" or the alpha learning rate. Hence, GridSearchCV can work because it can exhaust the best possible hyperparameters for the model without major performance issues since we are only tuning the alpha learning rate of the model.\n",
    "\n",
    "The hyperparameter dictionary for the GridSearchCV is shown below:\n",
    "\n",
    "- 'eta0': float, default=1\n",
    "  - Constant which the updates are multiplied \\(alpha learning rate\\)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a new logistic regression model using the defaults with a random\\_state of 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "logistic_model = SGDClassifier(loss='log_loss', learning_rate='constant', random_state=seed_num, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now call GridSearchCV to pass the logistic regression model, the hyperparameter dictionary, with n\\_jobs of 12 and 10 cross validation folds. Afterwards, we set it into the new logistic regression model that will be used to train with the later best hyperparameters. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "best_logis_model = GridSearchCV(logistic_model, logis_hyperparameters, n_jobs=12, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "             estimator=SGDClassifier(learning_rate=&#x27;constant&#x27;, loss=&#x27;log_loss&#x27;,\n",
       "                                     random_state=12),\n",
       "             n_jobs=12,\n",
       "             param_grid=[{&#x27;eta0&#x27;: array([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009,\n",
       "       0.01 , 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018,\n",
       "       0.019, 0.02 , 0.021, 0.022, 0.023, 0.024, 0.025, 0.026, 0.027,\n",
       "       0.028, 0.029, 0.03 , 0.031, 0.032, 0.033, 0.034, 0.035, 0.036,\n",
       "       0.037, 0.038, 0.039, 0.04 , 0.041, 0.042, 0.043, 0.044, 0.045,\n",
       "       0.046, 0.047, 0.048, 0.049, 0.05 , 0.051, 0.052, 0.053, 0.054,\n",
       "       0.055, 0.056, 0.057, 0.058, 0.059, 0.06 , 0.061, 0.062, 0.063,\n",
       "       0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07 , 0.071, 0.072,\n",
       "       0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08 , 0.081,\n",
       "       0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09 ,\n",
       "       0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099,\n",
       "       0.1  ])}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10,\n",
       "             estimator=SGDClassifier(learning_rate=&#x27;constant&#x27;, loss=&#x27;log_loss&#x27;,\n",
       "                                     random_state=12),\n",
       "             n_jobs=12,\n",
       "             param_grid=[{&#x27;eta0&#x27;: array([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009,\n",
       "       0.01 , 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018,\n",
       "       0.019, 0.02 , 0.021, 0.022, 0.023, 0.024, 0.025, 0.026, 0.027,\n",
       "       0.028, 0.029, 0.03 , 0.031, 0.032, 0.033, 0.034, 0.035, 0.036,\n",
       "       0.037, 0.038, 0.039, 0.04 , 0.041, 0.042, 0.043, 0.044, 0.045,\n",
       "       0.046, 0.047, 0.048, 0.049, 0.05 , 0.051, 0.052, 0.053, 0.054,\n",
       "       0.055, 0.056, 0.057, 0.058, 0.059, 0.06 , 0.061, 0.062, 0.063,\n",
       "       0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07 , 0.071, 0.072,\n",
       "       0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08 , 0.081,\n",
       "       0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09 ,\n",
       "       0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099,\n",
       "       0.1  ])}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(learning_rate=&#x27;constant&#x27;, loss=&#x27;log_loss&#x27;, random_state=12)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(learning_rate=&#x27;constant&#x27;, loss=&#x27;log_loss&#x27;, random_state=12)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10,\n",
       "             estimator=SGDClassifier(learning_rate='constant', loss='log_loss',\n",
       "                                     random_state=12),\n",
       "             n_jobs=12,\n",
       "             param_grid=[{'eta0': array([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009,\n",
       "       0.01 , 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018,\n",
       "       0.019, 0.02 , 0.021, 0.022, 0.023, 0.024, 0.025, 0.026, 0.027,\n",
       "       0.028, 0.029, 0.03 , 0.031, 0.032, 0.033, 0.034, 0.035, 0.036,\n",
       "       0.037, 0.038, 0.039, 0.04 , 0.041, 0.042, 0.043, 0.044, 0.045,\n",
       "       0.046, 0.047, 0.048, 0.049, 0.05 , 0.051, 0.052, 0.053, 0.054,\n",
       "       0.055, 0.056, 0.057, 0.058, 0.059, 0.06 , 0.061, 0.062, 0.063,\n",
       "       0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07 , 0.071, 0.072,\n",
       "       0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08 , 0.081,\n",
       "       0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089, 0.09 ,\n",
       "       0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098, 0.099,\n",
       "       0.1  ])}])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_logis_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training the new model, we get the best possible parameter and in our case, we are looking for the best possible \"eta0\" or the alpha learning rate of the model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eta0': 0.098}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_alpha = best_logis_model.best_params_[\"eta0\"]\n",
    "best_logis_model.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the cross\\-validation results of the new model to see how the GridSearchCV got the best parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "cv_results = best_logis_model.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check the accuracy of each of the parameters as well.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters: {'eta0': 0.001}, Accuracy: 0.9476278713285572\n",
      "Parameters: {'eta0': 0.002}, Accuracy: 0.9508993611332232\n",
      "Parameters: {'eta0': 0.003}, Accuracy: 0.952070783581328\n",
      "Parameters: {'eta0': 0.004}, Accuracy: 0.9524993407221439\n",
      "Parameters: {'eta0': 0.005}, Accuracy: 0.9531850692956138\n",
      "Parameters: {'eta0': 0.006}, Accuracy: 0.9540850692956138\n",
      "Parameters: {'eta0': 0.007}, Accuracy: 0.9547279264384709\n",
      "Parameters: {'eta0': 0.008}, Accuracy: 0.9547565448125243\n",
      "Parameters: {'eta0': 0.009000000000000001}, Accuracy: 0.9545279795072764\n",
      "Parameters: {'eta0': 0.010000000000000002}, Accuracy: 0.9546279795072765\n",
      "Parameters: {'eta0': 0.011}, Accuracy: 0.9547565529769558\n",
      "Parameters: {'eta0': 0.012}, Accuracy: 0.9547851244055273\n",
      "Parameters: {'eta0': 0.013000000000000001}, Accuracy: 0.954899414202029\n",
      "Parameters: {'eta0': 0.014000000000000002}, Accuracy: 0.9550422774682099\n",
      "Parameters: {'eta0': 0.015}, Accuracy: 0.9550994223664604\n",
      "Parameters: {'eta0': 0.016}, Accuracy: 0.9549851386932826\n",
      "Parameters: {'eta0': 0.017}, Accuracy: 0.9552565672647113\n",
      "Parameters: {'eta0': 0.018000000000000002}, Accuracy: 0.9564280019594638\n",
      "Parameters: {'eta0': 0.019000000000000003}, Accuracy: 0.9569851448166066\n",
      "Parameters: {'eta0': 0.02}, Accuracy: 0.9570279978772478\n",
      "Parameters: {'eta0': 0.021}, Accuracy: 0.957013712162962\n",
      "Parameters: {'eta0': 0.022000000000000002}, Accuracy: 0.9571851407343906\n",
      "Parameters: {'eta0': 0.023}, Accuracy: 0.9572708570612128\n",
      "Parameters: {'eta0': 0.024}, Accuracy: 0.9572994284897842\n",
      "Parameters: {'eta0': 0.025}, Accuracy: 0.9573708591023207\n",
      "Parameters: {'eta0': 0.026000000000000002}, Accuracy: 0.9573851366521747\n",
      "Parameters: {'eta0': 0.027000000000000003}, Accuracy: 0.9574422795093177\n",
      "Parameters: {'eta0': 0.028}, Accuracy: 0.9573708509378891\n",
      "Parameters: {'eta0': 0.029}, Accuracy: 0.9573137101218541\n",
      "Parameters: {'eta0': 0.030000000000000002}, Accuracy: 0.957370852978997\n",
      "Parameters: {'eta0': 0.031}, Accuracy: 0.9574565713469271\n",
      "Parameters: {'eta0': 0.032}, Accuracy: 0.95751371420407\n",
      "Parameters: {'eta0': 0.033}, Accuracy: 0.9575422856326414\n",
      "Parameters: {'eta0': 0.034}, Accuracy: 0.9576137162451781\n",
      "Parameters: {'eta0': 0.035}, Accuracy: 0.9576279999183557\n",
      "Parameters: {'eta0': 0.036000000000000004}, Accuracy: 0.9577280019594637\n",
      "Parameters: {'eta0': 0.037000000000000005}, Accuracy: 0.9577280019594637\n",
      "Parameters: {'eta0': 0.038}, Accuracy: 0.9577994325720001\n",
      "Parameters: {'eta0': 0.039}, Accuracy: 0.9577851468577144\n",
      "Parameters: {'eta0': 0.04}, Accuracy: 0.9577851468577144\n",
      "Parameters: {'eta0': 0.041}, Accuracy: 0.9577708611434286\n",
      "Parameters: {'eta0': 0.042}, Accuracy: 0.9577422897148573\n",
      "Parameters: {'eta0': 0.043000000000000003}, Accuracy: 0.9577708631845365\n",
      "Parameters: {'eta0': 0.044000000000000004}, Accuracy: 0.9578565774702508\n",
      "Parameters: {'eta0': 0.045}, Accuracy: 0.9579994407364316\n",
      "Parameters: {'eta0': 0.046}, Accuracy: 0.9580422978792889\n",
      "Parameters: {'eta0': 0.047}, Accuracy: 0.9581137264507174\n",
      "Parameters: {'eta0': 0.048}, Accuracy: 0.9581708693078601\n",
      "Parameters: {'eta0': 0.049}, Accuracy: 0.958228012165003\n",
      "Parameters: {'eta0': 0.05}, Accuracy: 0.9582280121650031\n",
      "Parameters: {'eta0': 0.051000000000000004}, Accuracy: 0.9581708693078601\n",
      "Parameters: {'eta0': 0.052000000000000005}, Accuracy: 0.958385155022146\n",
      "Parameters: {'eta0': 0.053000000000000005}, Accuracy: 0.958485155022146\n",
      "Parameters: {'eta0': 0.054}, Accuracy: 0.9590851509399302\n",
      "Parameters: {'eta0': 0.055}, Accuracy: 0.9591137223685017\n",
      "Parameters: {'eta0': 0.056}, Accuracy: 0.9591708652256447\n",
      "Parameters: {'eta0': 0.057}, Accuracy: 0.9591565815524667\n",
      "Parameters: {'eta0': 0.058}, Accuracy: 0.9591565815524667\n",
      "Parameters: {'eta0': 0.059000000000000004}, Accuracy: 0.959185152981038\n",
      "Parameters: {'eta0': 0.060000000000000005}, Accuracy: 0.9591565815524667\n",
      "Parameters: {'eta0': 0.061}, Accuracy: 0.959185155022146\n",
      "Parameters: {'eta0': 0.062}, Accuracy: 0.9592280121650031\n",
      "Parameters: {'eta0': 0.063}, Accuracy: 0.959185155022146\n",
      "Parameters: {'eta0': 0.064}, Accuracy: 0.9591994386953239\n",
      "Parameters: {'eta0': 0.065}, Accuracy: 0.959342295838181\n",
      "Parameters: {'eta0': 0.066}, Accuracy: 0.9593708672667522\n",
      "Parameters: {'eta0': 0.067}, Accuracy: 0.9594280101238952\n",
      "Parameters: {'eta0': 0.068}, Accuracy: 0.9594280101238952\n",
      "Parameters: {'eta0': 0.069}, Accuracy: 0.9594565815524667\n",
      "Parameters: {'eta0': 0.07}, Accuracy: 0.9594280121650032\n",
      "Parameters: {'eta0': 0.07100000000000001}, Accuracy: 0.9594565815524667\n",
      "Parameters: {'eta0': 0.07200000000000001}, Accuracy: 0.9594708672667525\n",
      "Parameters: {'eta0': 0.07300000000000001}, Accuracy: 0.9594994386953237\n",
      "Parameters: {'eta0': 0.074}, Accuracy: 0.9594280101238952\n",
      "Parameters: {'eta0': 0.075}, Accuracy: 0.9594137244096095\n",
      "Parameters: {'eta0': 0.076}, Accuracy: 0.9594422978792888\n",
      "Parameters: {'eta0': 0.077}, Accuracy: 0.9594565835935744\n",
      "Parameters: {'eta0': 0.078}, Accuracy: 0.9594851570632541\n",
      "Parameters: {'eta0': 0.079}, Accuracy: 0.9595280142061112\n",
      "Parameters: {'eta0': 0.08}, Accuracy: 0.9596137305329332\n",
      "Parameters: {'eta0': 0.081}, Accuracy: 0.9596423040026127\n",
      "Parameters: {'eta0': 0.082}, Accuracy: 0.9596565897168985\n",
      "Parameters: {'eta0': 0.083}, Accuracy: 0.9596423040026127\n",
      "Parameters: {'eta0': 0.084}, Accuracy: 0.9596994468597556\n",
      "Parameters: {'eta0': 0.085}, Accuracy: 0.9597137325740412\n",
      "Parameters: {'eta0': 0.08600000000000001}, Accuracy: 0.9596994468597556\n",
      "Parameters: {'eta0': 0.08700000000000001}, Accuracy: 0.9596994468597556\n",
      "Parameters: {'eta0': 0.08800000000000001}, Accuracy: 0.9595423040026126\n",
      "Parameters: {'eta0': 0.089}, Accuracy: 0.9595708754311841\n",
      "Parameters: {'eta0': 0.09}, Accuracy: 0.9595565897168983\n",
      "Parameters: {'eta0': 0.091}, Accuracy: 0.9595851611454698\n",
      "Parameters: {'eta0': 0.092}, Accuracy: 0.9597994468597555\n",
      "Parameters: {'eta0': 0.093}, Accuracy: 0.9597851611454699\n",
      "Parameters: {'eta0': 0.094}, Accuracy: 0.9597851611454699\n",
      "Parameters: {'eta0': 0.095}, Accuracy: 0.9597423080848284\n",
      "Parameters: {'eta0': 0.096}, Accuracy: 0.9597708795133999\n",
      "Parameters: {'eta0': 0.097}, Accuracy: 0.9597994509419714\n",
      "Parameters: {'eta0': 0.098}, Accuracy: 0.9598708795133998\n",
      "Parameters: {'eta0': 0.099}, Accuracy: 0.9598137366562568\n",
      "Parameters: {'eta0': 0.1}, Accuracy: 0.9598565937991141\n"
     ]
    }
   ],
   "source": [
    "for params, loss in zip(cv_results['params'], cv_results['mean_test_score']):\n",
    "    print(f\"Parameters: {params}, Accuracy: {loss}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's recreate the model with the new best alpha learning rate value and train it again to check the predictions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "best_logistic_model = SGDClassifier(loss='log_loss', eta0=best_alpha, max_iter=200, learning_rate='constant', random_state=seed_num, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier(eta0=0.098, learning_rate=&#x27;constant&#x27;, loss=&#x27;log_loss&#x27;,\n",
       "              max_iter=200, random_state=12)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier(eta0=0.098, learning_rate=&#x27;constant&#x27;, loss=&#x27;log_loss&#x27;,\n",
       "              max_iter=200, random_state=12)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SGDClassifier(eta0=0.098, learning_rate='constant', loss='log_loss',\n",
       "              max_iter=200, random_state=12)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_logistic_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['QSO', 'GALAXY', 'STAR', ..., 'GALAXY', 'GALAXY', 'GALAXY'],\n",
       "      dtype='<U6')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_predictions = best_logistic_model.predict(X_test)\n",
    "logistic_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the maximized accuracy of the new model with the best hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.7%\n"
     ]
    }
   ],
   "source": [
    "print(f\"{metrics.compute_accuracy(logistic_predictions, y_test)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy is "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of our Random Forest's hyperparameter tuning, we'll be using RandomizedSearchCV. This method, from the name, itself, randomly passes a set of hyper parameters given to it and returns the best set of hyper parameters found. This was primarily considered in contrast to the previously-used GridSearchCV due to the massive computational costs of trying every permutation of each feature for the given hyperparameter values, as well as the long training time of random forest models relative to other models such as the previously-tested multinomial/logistic regression model.\n",
    "\n",
    "Before we can initialize we must construct a dictionary of the hyperparameters we want to optimize.\n",
    "\n",
    "The hyperparameters of Random Forest include:\n",
    "\n",
    "- **n_estimators**: int, default=100\n",
    "  - The number of trees in the forest. \n",
    "<br><br>\n",
    "\n",
    "- **criterion**: {“gini”, “entropy”, “log_loss”}, default=”gini”\n",
    "  - The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical formulation. \n",
    "  - Note: This parameter is tree-specific.\n",
    "  <br><br>\n",
    "\n",
    "- **max_depth**: int, default=None\n",
    "  - The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain fewer than min_samples_split samples.\n",
    "<br><br>\n",
    "\n",
    "- **min_samples_split**: int or float, default=2\n",
    "  - The minimum number of samples required to split an internal node:\n",
    "    - If int, then consider min_samples_split as the minimum number.\n",
    "    - If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.\n",
    "<br><br>\n",
    "\n",
    "- **min_samples_leaf**: int or float, default=1\n",
    "  - The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.\n",
    "    - If int, then consider min_samples_leaf as the minimum number.\n",
    "    - If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) is the minimum number of samples for each node.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When it comes to looking for values for these hyperparameters, we looked to a paper by Zhu, et. al. for a valid baseline wherein they optimized their Random Forest model's hyperparameters for power industrial control systems intrusion detection. Though what they're predicting is different, the range of values they used may may allow us to find our model's optimal hyperparemeters.\n",
    "\n",
    "They're range of values are as follows:\n",
    "\n",
    "- n_estimators: Multiples of 10 up to 300\n",
    "\n",
    "- criterion: gini or entropy\n",
    "\n",
    "- max_depth: Multiples of 2 to 48\n",
    "\n",
    "- min_samples_split: 2 incremented until 10\n",
    "\n",
    "- min_samples_leaf: 1 incremented until 10\n",
    "\n",
    "Due to hardware limitations, we kept the range the same, but chose higher increments to minimize the time it takes to optimize the hyper parameters.\n",
    "\n",
    "With this in mind, let's set up the dictionary for our hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300], 'criterion': ['gini', 'entropy'], 'max_depth': [16, 32, 48], 'min_samples_split': [5, 10], 'min_samples_leaf': [5, 10]}\n"
     ]
    }
   ],
   "source": [
    "hyperparameters_RF = {\n",
    "    'n_estimators': [i for i in range(100,301,100)],\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [i for i in range(16, 49, 16)],\n",
    "    'min_samples_split': [i for i in range(5, 11, 5)],\n",
    "    'min_samples_leaf': [i for i in range (5, 11, 5)]\n",
    "}\n",
    "\n",
    "print(hyperparameters_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's initialize our Randomized Search CV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "rsc = RandomizedSearchCV(rfc, hyperparameters_RF, n_iter=10, cv=3, random_state=seed_num, verbose=3, n_jobs=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's use it to find the best hyper parameters of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=12),\n",
       "                   n_jobs=12,\n",
       "                   param_distributions={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: [16, 32, 48],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [5, 10],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
       "                   random_state=12, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=12),\n",
       "                   n_jobs=12,\n",
       "                   param_distributions={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: [16, 32, 48],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [5, 10],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
       "                   random_state=12, verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=12)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=12)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(random_state=12),\n",
       "                   n_jobs=12,\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [16, 32, 48],\n",
       "                                        'min_samples_leaf': [5, 10],\n",
       "                                        'min_samples_split': [5, 10],\n",
       "                                        'n_estimators': [100, 200, 300]},\n",
       "                   random_state=12, verbose=3)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsc.fit(X_train, y_train_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the RandomizedSearchCV runs this course, we're left with these results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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",
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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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.493978</td>\n",
       "      <td>2.863873</td>\n",
       "      <td>0.483932</td>\n",
       "      <td>0.023037</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 10,...</td>\n",
       "      <td>0.979343</td>\n",
       "      <td>0.975614</td>\n",
       "      <td>0.978957</td>\n",
       "      <td>0.977971</td>\n",
       "      <td>0.001674</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122.406920</td>\n",
       "      <td>6.125966</td>\n",
       "      <td>1.605507</td>\n",
       "      <td>0.673780</td>\n",
       "      <td>200</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.978657</td>\n",
       "      <td>0.974800</td>\n",
       "      <td>0.978185</td>\n",
       "      <td>0.977214</td>\n",
       "      <td>0.001718</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>184.246461</td>\n",
       "      <td>20.034880</td>\n",
       "      <td>2.492190</td>\n",
       "      <td>0.215426</td>\n",
       "      <td>300</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 300, 'min_samples_split': 10,...</td>\n",
       "      <td>0.978485</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>0.978143</td>\n",
       "      <td>0.977228</td>\n",
       "      <td>0.001542</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63.505482</td>\n",
       "      <td>12.373727</td>\n",
       "      <td>0.890804</td>\n",
       "      <td>0.297336</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 10,...</td>\n",
       "      <td>0.979557</td>\n",
       "      <td>0.975485</td>\n",
       "      <td>0.979257</td>\n",
       "      <td>0.978100</td>\n",
       "      <td>0.001853</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>145.360502</td>\n",
       "      <td>25.803949</td>\n",
       "      <td>1.336485</td>\n",
       "      <td>0.448242</td>\n",
       "      <td>200</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.979814</td>\n",
       "      <td>0.975571</td>\n",
       "      <td>0.979085</td>\n",
       "      <td>0.978157</td>\n",
       "      <td>0.001852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>185.582373</td>\n",
       "      <td>7.654314</td>\n",
       "      <td>1.481266</td>\n",
       "      <td>0.305403</td>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 300, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.979214</td>\n",
       "      <td>0.975657</td>\n",
       "      <td>0.979043</td>\n",
       "      <td>0.977971</td>\n",
       "      <td>0.001638</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>150.099143</td>\n",
       "      <td>5.841059</td>\n",
       "      <td>1.138023</td>\n",
       "      <td>0.016585</td>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 300, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.978485</td>\n",
       "      <td>0.974842</td>\n",
       "      <td>0.978314</td>\n",
       "      <td>0.977214</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54.922605</td>\n",
       "      <td>8.381377</td>\n",
       "      <td>0.505230</td>\n",
       "      <td>0.083549</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.978443</td>\n",
       "      <td>0.974800</td>\n",
       "      <td>0.978785</td>\n",
       "      <td>0.977343</td>\n",
       "      <td>0.001804</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58.761011</td>\n",
       "      <td>6.524199</td>\n",
       "      <td>0.369972</td>\n",
       "      <td>0.019703</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 10,...</td>\n",
       "      <td>0.978957</td>\n",
       "      <td>0.974714</td>\n",
       "      <td>0.978614</td>\n",
       "      <td>0.977428</td>\n",
       "      <td>0.001924</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>81.087777</td>\n",
       "      <td>4.307194</td>\n",
       "      <td>0.741373</td>\n",
       "      <td>0.079842</td>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 300, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.978871</td>\n",
       "      <td>0.974842</td>\n",
       "      <td>0.978743</td>\n",
       "      <td>0.977485</td>\n",
       "      <td>0.001870</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0      80.493978      2.863873         0.483932        0.023037   \n",
       "1     122.406920      6.125966         1.605507        0.673780   \n",
       "2     184.246461     20.034880         2.492190        0.215426   \n",
       "3      63.505482     12.373727         0.890804        0.297336   \n",
       "4     145.360502     25.803949         1.336485        0.448242   \n",
       "5     185.582373      7.654314         1.481266        0.305403   \n",
       "6     150.099143      5.841059         1.138023        0.016585   \n",
       "7      54.922605      8.381377         0.505230        0.083549   \n",
       "8      58.761011      6.524199         0.369972        0.019703   \n",
       "9      81.087777      4.307194         0.741373        0.079842   \n",
       "\n",
       "  param_n_estimators param_min_samples_split param_min_samples_leaf  \\\n",
       "0                100                      10                      5   \n",
       "1                200                       5                     10   \n",
       "2                300                      10                     10   \n",
       "3                100                      10                      5   \n",
       "4                200                       5                      5   \n",
       "5                300                       5                      5   \n",
       "6                300                       5                     10   \n",
       "7                100                       5                     10   \n",
       "8                100                      10                     10   \n",
       "9                300                       5                     10   \n",
       "\n",
       "  param_max_depth param_criterion  \\\n",
       "0              16         entropy   \n",
       "1              16            gini   \n",
       "2              32            gini   \n",
       "3              32         entropy   \n",
       "4              16         entropy   \n",
       "5              32            gini   \n",
       "6              16            gini   \n",
       "7              16         entropy   \n",
       "8              48         entropy   \n",
       "9              48         entropy   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0  {'n_estimators': 100, 'min_samples_split': 10,...           0.979343   \n",
       "1  {'n_estimators': 200, 'min_samples_split': 5, ...           0.978657   \n",
       "2  {'n_estimators': 300, 'min_samples_split': 10,...           0.978485   \n",
       "3  {'n_estimators': 100, 'min_samples_split': 10,...           0.979557   \n",
       "4  {'n_estimators': 200, 'min_samples_split': 5, ...           0.979814   \n",
       "5  {'n_estimators': 300, 'min_samples_split': 5, ...           0.979214   \n",
       "6  {'n_estimators': 300, 'min_samples_split': 5, ...           0.978485   \n",
       "7  {'n_estimators': 100, 'min_samples_split': 5, ...           0.978443   \n",
       "8  {'n_estimators': 100, 'min_samples_split': 10,...           0.978957   \n",
       "9  {'n_estimators': 300, 'min_samples_split': 5, ...           0.978871   \n",
       "\n",
       "   split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
       "0           0.975614           0.978957         0.977971        0.001674   \n",
       "1           0.974800           0.978185         0.977214        0.001718   \n",
       "2           0.975057           0.978143         0.977228        0.001542   \n",
       "3           0.975485           0.979257         0.978100        0.001853   \n",
       "4           0.975571           0.979085         0.978157        0.001852   \n",
       "5           0.975657           0.979043         0.977971        0.001638   \n",
       "6           0.974842           0.978314         0.977214        0.001678   \n",
       "7           0.974800           0.978785         0.977343        0.001804   \n",
       "8           0.974714           0.978614         0.977428        0.001924   \n",
       "9           0.974842           0.978743         0.977485        0.001870   \n",
       "\n",
       "   rank_test_score  \n",
       "0                3  \n",
       "1                9  \n",
       "2                8  \n",
       "3                2  \n",
       "4                1  \n",
       "5                3  \n",
       "6                9  \n",
       "7                7  \n",
       "8                6  \n",
       "9                5  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsc_results = pd.DataFrame(rsc.cv_results_)\n",
    "rsc_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a better look, these are the mean test scores in decending order, which shows that index 4 has the best hyper parameters and, by extension, performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0.978157\n",
       "3    0.978100\n",
       "0    0.977971\n",
       "5    0.977971\n",
       "9    0.977485\n",
       "8    0.977428\n",
       "7    0.977343\n",
       "2    0.977228\n",
       "1    0.977214\n",
       "6    0.977214\n",
       "Name: mean_test_score, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsc_results['mean_test_score'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through this, we find that the best hyper parameters for the model are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 200, 'min_samples_split': 5, 'min_samples_leaf': 5, 'max_depth': 16, 'criterion': 'entropy'}\n"
     ]
    }
   ],
   "source": [
    "print(rsc.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, using this, we can then perform the tests once more:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy:  98.529\n",
      "Train RMSE:  0.23679575503644495\n"
     ]
    }
   ],
   "source": [
    "predictions_train_RF = rsc.best_estimator_.predict(X_train)\n",
    "\n",
    "\n",
    "accuracy_train_RF = metrics.compute_accuracy(predictions_train_RF, y_train_RF)\n",
    "rmse_train_RF = metrics.compute_rmse(predictions_train_RF, y_train_RF)\n",
    "\n",
    "print(\"Train Accuracy: \", accuracy_train_RF)\n",
    "print(\"Train RMSE: \", rmse_train_RF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy:  97.263\n",
      "Test RMSE:  0.30276503540974914\n"
     ]
    }
   ],
   "source": [
    "predictions_test_RF = rsc.best_estimator_.predict(X_test)\n",
    "\n",
    "accuracy_test_RF = metrics.compute_accuracy(predictions_test_RF, y_test_RF)\n",
    "rmse_test_RF = metrics.compute_rmse(predictions_test_RF, y_test_RF)\n",
    "\n",
    "print(\"Test Accuracy: \", accuracy_test_RF)\n",
    "print(\"Test RMSE: \", rmse_test_RF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (4203267786.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[62], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Though the training accuracy and scores lessened, the test accuracy increased by ~0.1, showing marginal improvements over the original, unoptimized model.\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Though the training accuracy and scores lessened, the test accuracy increased by ~0.1, showing marginal improvements over the original, unoptimized model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For hyperparameter tuning in KNN, we will be utilizing grid search to test different sets of hyperparameters and obtain the best set of hyperparameters and display their score for train and test. Grid search can be utilized in this instance since there is only one hyperparameter for the model.\n",
    "\n",
    "The following are the hyperparameters that will be needing for tuning, the naming convention of hyperparameters are based on their documentation of sklearn.neighbors.KNeighborsClassifier\n",
    "- **n_neighbors**: int, default=5\n",
    "  - Number of neighbors to use by default.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They're range of values are as follows will allow us to find our model's optimal hyperparameters.\n",
    "1. n_neighbors: from 5 then adding 20 until 245"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code below will contain the dictionary of our hyperparameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "hyperparameters_KNN = {\n",
    "    'n_neighbors': np.arange(5,246,20),\n",
    "}\n",
    "hyperparameters_KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is time to find the best hyperparameter for our KNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn_cv = GridSearchCV(knn,hyperparameters_KNN,cv=5)\n",
    "knn_cv.fit(X_train,y_train)\n",
    "\n",
    "print(\"Hyperparameters:\",knn_cv.best_params_)\n",
    "print(\"Train Score:\",knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a model with the best hyperparameter and display the accuracy when using X_train and X_test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "knn_train_results = knn.predict(X_train)\n",
    "knn_test_results = knn.predict(X_test)\n",
    "\n",
    "print(\"Train Score:\",metrics.compute_accuracy(knn_train_results, y_train))\n",
    "print(\"Test Score:\",metrics.compute_accuracy(knn_test_results, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference of accuracy when using the best hyperparameter for KNN model when predicting train and test data is not significantly high thus the model is neither overfitting nor underfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "rsc.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "predictions_train_RF = rsc.best_estimator_.predict(X_train)\n",
    "\n",
    "\n",
    "accuracy_train_RF = metrics.compute_accuracy(predictions_train_RF, y_train_RF)\n",
    "rmse_train_RF = metrics.compute_rmse(predictions_train_RF, y_train_RF)\n",
    "\n",
    "print(\"Train Accuracy: \", accuracy_train_RF)\n",
    "print(\"Train RMSE: \", rmse_train_RF)\n",
    "      \n",
    "predictions_test_RF = rsc.best_estimator_.predict(X_test)\n",
    "\n",
    "accuracy_test_RF = metrics.compute_accuracy(predictions_test_RF, y_test_RF)\n",
    "rmse_test_RF = metrics.compute_rmse(predictions_test_RF, y_test_RF)\n",
    "\n",
    "print(\"Test Accuracy: \", accuracy_test_RF)\n",
    "print(\"Test RMSE: \", rmse_test_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insight and conclusion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest model performed really well due to the fact that the data set contained no overlapping instances due to the nature of photometric data and how it is classified. Because of this, it was able to grasp the trends in how photometric data relates to Stars, Galaxies and Quasars and was able to differentiate and classify the instances of the three classes. However, the training and hyper parameter optimization of the model is very resource intensive, and as students, we lacked a good enough PC to do more robust optimization on it.\n",
    "\n",
    "So, a recommendation is, if one has the required hardware, is to perform more robust hyper parameter testing with either: Wider ranges of hyper parameters to test, or more iterations in the RandomizedSearchCV to find better results. Or,use GridSearchCV, to search through all the possible configurations, instead.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "- Zhu, N., Zhu, C., Zhou, L., Zhu, Y., &amp; Zhang, X. (2022, October 17). Optimization of the random forest hyperparameters for power industrial control systems intrusion detection using an improved grid search algorithm. MDPI. https://doi.org/10.3390/app122010456 "
   ]
  }
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