{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='https://www.udemy.com/user/joseportilla/'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "<img src=\"mushroom.jpg\" width=\"400\" height=\"400\">\n",
    "\n",
    "### Mushroom Hunting: Edible or Poisonous?\n",
    "\n",
    "Data Source: https://archive.ics.uci.edu/ml/datasets/Mushroom\n",
    "\n",
    "\n",
    "This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like ``leaflets three, let it be'' for Poisonous Oak and Ivy.\n",
    "\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s\n",
    "2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s\n",
    "3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y\n",
    "4. bruises?: bruises=t,no=f\n",
    "5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s\n",
    "6. gill-attachment: attached=a,descending=d,free=f,notched=n\n",
    "7. gill-spacing: close=c,crowded=w,distant=d\n",
    "8. gill-size: broad=b,narrow=n\n",
    "9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y\n",
    "10. stalk-shape: enlarging=e,tapering=t\n",
    "11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?\n",
    "12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
    "15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
    "16. veil-type: partial=p,universal=u\n",
    "17. veil-color: brown=n,orange=o,white=w,yellow=y\n",
    "18. ring-number: none=n,one=o,two=t\n",
    "19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z\n",
    "20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y\n",
    "21. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y\n",
    "22. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal\n",
    "\n",
    "**THIS IS IMPORTANT, THIS IS NOT OUR TYPICAL PREDICTIVE MODEL!**\n",
    "\n",
    "Our general goal here is to see if we can harness the power of machine learning and boosting to help create not just a predictive model, but a general guideline for features people should look out for when picking mushrooms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Teni\\Desktop\\Git-Github\\DATA\\mushrooms.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
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       "      <td>s</td>\n",
       "      <td>u</td>\n",
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       "      <td>e</td>\n",
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       "      <td>e</td>\n",
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       "      <td>t</td>\n",
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       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
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       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
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       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
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       "      <td>w</td>\n",
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       "      <td>o</td>\n",
       "      <td>p</td>\n",
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       "      <td>u</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
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       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='count'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='class')\n",
    "\n",
    "# because it's all categorical and there can't be any statistical analysis ran on the mean and all\n",
    "# Would check to know if the data is balanced or imbalanced."
   ]
  },
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    {
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       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
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       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
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       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
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       "      <td>8124</td>\n",
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       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
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       "      <th>unique</th>\n",
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       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
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       "      <td>4</td>\n",
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       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
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       "      <th>top</th>\n",
       "      <td>e</td>\n",
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       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4208</td>\n",
       "      <td>3656</td>\n",
       "      <td>3244</td>\n",
       "      <td>2284</td>\n",
       "      <td>4748</td>\n",
       "      <td>3528</td>\n",
       "      <td>7914</td>\n",
       "      <td>6812</td>\n",
       "      <td>5612</td>\n",
       "      <td>1728</td>\n",
       "      <td>...</td>\n",
       "      <td>4936</td>\n",
       "      <td>4464</td>\n",
       "      <td>4384</td>\n",
       "      <td>8124</td>\n",
       "      <td>7924</td>\n",
       "      <td>7488</td>\n",
       "      <td>3968</td>\n",
       "      <td>2388</td>\n",
       "      <td>4040</td>\n",
       "      <td>3148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
       "count   8124      8124        8124      8124    8124  8124            8124   \n",
       "unique     2         6           4        10       2     9               2   \n",
       "top        e         x           y         n       f     n               f   \n",
       "freq    4208      3656        3244      2284    4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "count          8124      8124       8124  ...                     8124   \n",
       "unique            2         2         12  ...                        4   \n",
       "top               c         b          b  ...                        s   \n",
       "freq           6812      5612       1728  ...                     4936   \n",
       "\n",
       "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "count                    8124                   8124      8124       8124   \n",
       "unique                      9                      9         1          4   \n",
       "top                         w                      w         p          w   \n",
       "freq                     4464                   4384      8124       7924   \n",
       "\n",
       "       ring-number ring-type spore-print-color population habitat  \n",
       "count         8124      8124              8124       8124    8124  \n",
       "unique           3         5                 9          6       7  \n",
       "top              o         p                 w          v       d  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4208</td>\n",
       "      <td>3656</td>\n",
       "      <td>3244</td>\n",
       "      <td>2284</td>\n",
       "      <td>4748</td>\n",
       "      <td>3528</td>\n",
       "      <td>7914</td>\n",
       "      <td>6812</td>\n",
       "      <td>5612</td>\n",
       "      <td>1728</td>\n",
       "      <td>...</td>\n",
       "      <td>4936</td>\n",
       "      <td>4464</td>\n",
       "      <td>4384</td>\n",
       "      <td>8124</td>\n",
       "      <td>7924</td>\n",
       "      <td>7488</td>\n",
       "      <td>3968</td>\n",
       "      <td>2388</td>\n",
       "      <td>4040</td>\n",
       "      <td>3148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
       "count   8124      8124        8124      8124    8124  8124            8124   \n",
       "unique     2         6           4        10       2     9               2   \n",
       "top        e         x           y         n       f     n               f   \n",
       "freq    4208      3656        3244      2284    4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "count          8124      8124       8124  ...                     8124   \n",
       "unique            2         2         12  ...                        4   \n",
       "top               c         b          b  ...                        s   \n",
       "freq           6812      5612       1728  ...                     4936   \n",
       "\n",
       "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "count                    8124                   8124      8124       8124   \n",
       "unique                      9                      9         1          4   \n",
       "top                         w                      w         p          w   \n",
       "freq                     4464                   4384      8124       7924   \n",
       "\n",
       "       ring-number ring-type spore-print-color population habitat  \n",
       "count         8124      8124              8124       8124    8124  \n",
       "unique           3         5                 9          6       7  \n",
       "top              o         p                 w          v       d  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "      <th>cap-color</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor</th>\n",
       "      <td>8124</td>\n",
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       "      <th>gill-attachment</th>\n",
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       "      <td>7914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-spacing</th>\n",
       "      <td>8124</td>\n",
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       "      <td>c</td>\n",
       "      <td>6812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-size</th>\n",
       "      <td>8124</td>\n",
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       "      <td>5612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-color</th>\n",
       "      <td>8124</td>\n",
       "      <td>12</td>\n",
       "      <td>b</td>\n",
       "      <td>1728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-shape</th>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>4608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-root</th>\n",
       "      <td>8124</td>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>3776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-above-ring</th>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>s</td>\n",
       "      <td>5176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>s</td>\n",
       "      <td>4936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>4464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>4384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-type</th>\n",
       "      <td>8124</td>\n",
       "      <td>1</td>\n",
       "      <td>p</td>\n",
       "      <td>8124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-color</th>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>w</td>\n",
       "      <td>7924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-number</th>\n",
       "      <td>8124</td>\n",
       "      <td>3</td>\n",
       "      <td>o</td>\n",
       "      <td>7488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-type</th>\n",
       "      <td>8124</td>\n",
       "      <td>5</td>\n",
       "      <td>p</td>\n",
       "      <td>3968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color</th>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>8124</td>\n",
       "      <td>6</td>\n",
       "      <td>v</td>\n",
       "      <td>4040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat</th>\n",
       "      <td>8124</td>\n",
       "      <td>7</td>\n",
       "      <td>d</td>\n",
       "      <td>3148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique top  freq\n",
       "class                     8124      2   e  4208\n",
       "cap-shape                 8124      6   x  3656\n",
       "cap-surface               8124      4   y  3244\n",
       "cap-color                 8124     10   n  2284\n",
       "bruises                   8124      2   f  4748\n",
       "odor                      8124      9   n  3528\n",
       "gill-attachment           8124      2   f  7914\n",
       "gill-spacing              8124      2   c  6812\n",
       "gill-size                 8124      2   b  5612\n",
       "gill-color                8124     12   b  1728\n",
       "stalk-shape               8124      2   t  4608\n",
       "stalk-root                8124      5   b  3776\n",
       "stalk-surface-above-ring  8124      4   s  5176\n",
       "stalk-surface-below-ring  8124      4   s  4936\n",
       "stalk-color-above-ring    8124      9   w  4464\n",
       "stalk-color-below-ring    8124      9   w  4384\n",
       "veil-type                 8124      1   p  8124\n",
       "veil-color                8124      4   w  7924\n",
       "ring-number               8124      3   o  7488\n",
       "ring-type                 8124      5   p  3968\n",
       "spore-print-color         8124      9   w  2388\n",
       "population                8124      6   v  4040\n",
       "habitat                   8124      7   d  3148"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()\n",
    "\n",
    "# feedback on the categorical features\n",
    "# the count, and unique count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>veil-type</td>\n",
       "      <td>8124</td>\n",
       "      <td>1</td>\n",
       "      <td>p</td>\n",
       "      <td>8124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>class</td>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>e</td>\n",
       "      <td>4208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bruises</td>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>4748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gill-attachment</td>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>7914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gill-spacing</td>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "      <td>6812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gill-size</td>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>5612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>stalk-shape</td>\n",
       "      <td>8124</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>4608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ring-number</td>\n",
       "      <td>8124</td>\n",
       "      <td>3</td>\n",
       "      <td>o</td>\n",
       "      <td>7488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cap-surface</td>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>y</td>\n",
       "      <td>3244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>veil-color</td>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>w</td>\n",
       "      <td>7924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>stalk-surface-below-ring</td>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>s</td>\n",
       "      <td>4936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>stalk-surface-above-ring</td>\n",
       "      <td>8124</td>\n",
       "      <td>4</td>\n",
       "      <td>s</td>\n",
       "      <td>5176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ring-type</td>\n",
       "      <td>8124</td>\n",
       "      <td>5</td>\n",
       "      <td>p</td>\n",
       "      <td>3968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>stalk-root</td>\n",
       "      <td>8124</td>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>3776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cap-shape</td>\n",
       "      <td>8124</td>\n",
       "      <td>6</td>\n",
       "      <td>x</td>\n",
       "      <td>3656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>population</td>\n",
       "      <td>8124</td>\n",
       "      <td>6</td>\n",
       "      <td>v</td>\n",
       "      <td>4040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>habitat</td>\n",
       "      <td>8124</td>\n",
       "      <td>7</td>\n",
       "      <td>d</td>\n",
       "      <td>3148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>stalk-color-above-ring</td>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>4464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>stalk-color-below-ring</td>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>4384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>odor</td>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>n</td>\n",
       "      <td>3528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>spore-print-color</td>\n",
       "      <td>8124</td>\n",
       "      <td>9</td>\n",
       "      <td>w</td>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cap-color</td>\n",
       "      <td>8124</td>\n",
       "      <td>10</td>\n",
       "      <td>n</td>\n",
       "      <td>2284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gill-color</td>\n",
       "      <td>8124</td>\n",
       "      <td>12</td>\n",
       "      <td>b</td>\n",
       "      <td>1728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       index count unique top  freq\n",
       "16                 veil-type  8124      1   p  8124\n",
       "0                      class  8124      2   e  4208\n",
       "4                    bruises  8124      2   f  4748\n",
       "6            gill-attachment  8124      2   f  7914\n",
       "7               gill-spacing  8124      2   c  6812\n",
       "8                  gill-size  8124      2   b  5612\n",
       "10               stalk-shape  8124      2   t  4608\n",
       "18               ring-number  8124      3   o  7488\n",
       "2                cap-surface  8124      4   y  3244\n",
       "17                veil-color  8124      4   w  7924\n",
       "13  stalk-surface-below-ring  8124      4   s  4936\n",
       "12  stalk-surface-above-ring  8124      4   s  5176\n",
       "19                 ring-type  8124      5   p  3968\n",
       "11                stalk-root  8124      5   b  3776\n",
       "1                  cap-shape  8124      6   x  3656\n",
       "21                population  8124      6   v  4040\n",
       "22                   habitat  8124      7   d  3148\n",
       "14    stalk-color-above-ring  8124      9   w  4464\n",
       "15    stalk-color-below-ring  8124      9   w  4384\n",
       "5                       odor  8124      9   n  3528\n",
       "20         spore-print-color  8124      9   w  2388\n",
       "3                  cap-color  8124     10   n  2284\n",
       "9                 gill-color  8124     12   b  1728"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose().reset_index().sort_values('unique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_desc= df.describe().transpose().reset_index().sort_values('unique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6),dpi=200)\n",
    "sns.barplot(data=feat_desc, x='index',y='unique')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FhIQoLCws0VLXSJ2ePXuqXLlyunjxoho0aKAlS5bIGON63bIsxcfHa926dWrTpo3effddWZalmjVrqm3btjYm9w/PP/+8HnzwQY0bN+6ObswjdXr06KEePXooZ86cdkdJNxgnsw99DwBI9wzgJUOHDjUZMmQwlmUZh8OR5I/ztXfeecfuuECq7d2711SvXt1YluX6qV+/vjl58mSifXfs2OF6/69Zs8aGtEDKrV271nUez58/v/n666/N33//nWi/8+fPm9GjR5v8+fMby7JMhgwZzPr16xPs07ZtW+NwOMzAgQO9Fd+nXb582TRq1CjZz1bn56tlWaZp06bm6tWrdsf2C/Q90pMtW7aY7Nmzu97X5cqVc73369WrZ8qWLZvgPZ8zZ06za9cuu2P7hfj4ePPNN9+YihUr3vI8U7FiRTN+/HgTHx9vd1y/cOzYsQTv9w4dOpiFCxeakydPmpiYGBMTE2NOnjxpFixYYJ544gnX+z5Hjhzm2LFjCY5Vs2ZN43A4zNtvv23Tn8Z30O/2io6ONsWKFTMOh8NUq1bN7N692xhjXOf6PXv2GGOMOX78uBk4cKDJkCGDcTgcpkyZMiYuLs7O6H5h2bJl5tFHH03wb8D5kz17dtOmTRuzfPlyu2P6jUOHDplChQq53t/Zs2d3/X7//fcn+HuwLMsUK1bMHD9+3O7YfuP06dOmTp06JnPmzGbIkCFm+/bt5sqVK3bHAtIU42T2oe/tFxsba+bMmWP69etn6tata8qXL2/Kly9v6tata/r162fmzJljYmNj7Y7plyIiIsyMGTPMW2+9Zfr372/69+9v3nrrLTNjxgwTERFhd7x0ISYmxkRGRprIyEgTExNjd5x0g35PyDLmpscbAA8LDQ3VyJEjtWzZMkVHRyd4LVOmTGrSpInefPPNRFXWgC87cuSIIiIiVKRIEZUoUSLJfXbu3KkdO3ZIkp5++mkFBAR4L6Af2rdvnyZMmKB169bp8OHDunjx4m2fmLQsS7GxsV5K6H/ef/99DR06VJZlSZIcDodKlizpWh87MjJSR44cUXx8vOvJyvfff1+vv/666xhhYWEqU6aM4uPjtXjxYrVq1cr7fxAfFB8fr1GjRunzzz/XqVOnktwnb968evnll/XKK6/I4WDix7RC3/83GGN07tw5RUdHq2jRosqQIYPdkfzSrl271LVrV+3atcu1zXnOv/krZbly5TRz5kxVrFjR6xn9zfnz59WmTRtt3LhRUsJ+vpnz76F27dpatGiRcufO7a2IfuvXX39V+/btFR0d7erfWzHGKGvWrPr555/VtGlT1/awsDA988wzkqRRo0apSpUqnozsF+h3e4WEhKhx48ausZoyZcpo//79sixLderU0ZkzZ/Tnn39KutH/OXLk0IYNGzjfp8L169fVo0cPzZo1S9Ltz/NPPvmkpkyZokyZMnkto7+KiIhQnz59tGTJkmT3a968uSZPnszM5Gnk5ut0Y8xtz/U3Y8wm7Zw/f147d+5UVFSUrly5cstzj1P37t29lMy/ME5mH/rePsuWLdOzzz6rkydPurY5+/jmc/7dd9+tCRMmqEWLFl7P6I9OnTqll156SfPmzbvlZ2WGDBnUoUMHffbZZ1zXpLF9+/Zp7NixWrlypQ4ePJjgPV+qVCk1a9ZM/fr1cy21jLRBv98axT+wRWxsrPbu3avIyEjFxcUpX758qlChgu666y67o6VL165d099//60CBQpwYxI+7/PPP9frr7+u2NjY2w5g3MyyLMXFxXkwmf/76aef9L///U+RkZGubUndGC5YsKC++OILde7c2esZ/VlsbKy2bNmi0NDQBJ+vVatWVd26dZU5c2a7I/ot+t774uLiNG3aNE2ePFkhISG6fv26LMvSH3/8keBL3eLFi7V27VrlypVLb775po2J/ceSJUu0YMGCJN/vbdu21RNPPMH1ZBowxqhBgwZav369JClfvnzq1KmTAgMDVbhwYRljdPr0aW3ZskWzZs1SVFSULMtS3bp1tWbNGpvT+4ewsDC99NJLWrJkyS2LyB0Oh1q3bq3PP/9c999/v5cT+if63V4UenpXhw4dNH/+fBljFBAQoGbNmiV5nv/1118VExMjy7L0xBNPuIqFkHp79uy55XXNY489pho1atgd0a+k5hqRMZvUCw4O1rBhw1zXl+6g6Cp1GCezD33vfdOnT1evXr1kjHH1cYkSJRJc1xw7dsz1msPh0NSpU/X000/bGdvn7dy5U02bNtW5c+duey/Esizly5dPq1atUqVKlbyU0L+9/vrr+vTTTxMUE/6bZVlyOBwaMmSIPvjgAy8n9E/0e/Io/gH82KVLl7R27VpJUv369ZU9e/YEr0dFRalv375avHixYmNjlT17dvXp00cffPABT5LBJy1btsz1JIZlWQoMDFT16tWVN29etwaZhg0b5umIfu/69euaP3++Vq1apd27d+v8+fOSpDx58qhChQpq0qSJ2rdvTzEEgBSLjIxUu3bttHnz5gRf8CzL0q5duxIU/+zZs0eVKlWSZVnaunUrM0DAZ/zwww/q1q2bLMtSly5dNHbsWOXIkSPJfS9duqQBAwZo+vTpsixL33//vZ566ikvJ/Zf4eHhCg4OTvK6plGjRipatKjNCf0T/W4vCj09b8mSJWrTpo0sy1LDhg01adIkFS9ePMl9jx8/rqCgIK1evVqWZWnRokXMQACfNHz48FS1Z8wm5caNG6dBgwYluCnvDoquUo9xMvvQ995z7NgxlS1bVteuXVO2bNn0+uuv65lnnnHNtuR05swZfffdd/rwww916dIlZcmSRfv379e9995rU3LfdvnyZZUpU0bh4eGSpKZNm6pPnz6uYnLpxmyHW7Zs0XfffacVK1ZIujHz0v79+5U1a1bbsvuDQYMGaezYsa7P1XLlyiVZyL93715JNz5TBw4cqC+//NLO2D6Pfr89in8APzZ16lT16tVL9957rw4fPpxgcC4+Pl6BgYHatm1bohtnPEmWtuLj4xUcHKyNGzcqIiJC0dHReu+99xJMr3j9+nXFxsYqQ4YMfOFIhUceeUQrVqxQnjx5tHDhQtWpU8fuSACANBQfH6/atWtry5Ytcjgc6tChg+rXr6+BAwcmWfwjSXXq1NGmTZv01ltvpfqGA+AtrVu31i+//KKGDRtq9erVbrVp1KiR1qxZo5YtW952GRMAgL06duyouXPnqkqVKtq8ebMyZsyY7P4xMTEKDAzUzp079fjjj2v27NleSgrA1+3bt08PPvig4uPjValSJb377rvKmDGjWrduLcuydOjQIZ0/f16hoaGaMGGCtm3bprp162r8+PHKmjXrLQsTAcDppZde0hdffKHs2bNr7dq1t33waufOnapXr54uX76sF198UZ999pl3gvqZjz76SK+//rocDofGjx+v3r17J7v/pEmT1KdPH0nSyJEjNWTIEG/E9EsbNmxQvXr1ZFmWypUrpwkTJqh27dpJ7rtx40b169dPu3btkmVZWrdu3S33RfLod/fwmA687vTp05o5c6aGDh2qAQMGaMCAARo6dKhmzpyp06dP2x3PryxfvlySknwqb+bMmdq6daskqVq1avrf//6natWqyRijuXPnatmyZV7P64+WLFniWl/y7bff1tixYzVlyhTXkwZOEydOVI4cOVSwYEFdvnzZprS+LzQ0VJZl6e2336bwB34vKChIQUFBevfdd91+Ei88PFxBQUG3/TKIO3f69GmtWrVKs2fP1uzZs7Vq1Squazxg2rRp2rJlizJmzKglS5ZoxowZ6t+/f7Jt2rRpI2PMHU1vD9ht27ZtrqeT3DVo0CBJ0vbt2z0VCwCQRjZt2iTLsjR48ODbFv5IUsaMGfXyyy/LGKNNmzZ5IWH6EhsbqzNnzujMmTMscQS/M3r0aMXFxSl//vxat26d2rZtm2CWjZIlS6patWp69tlnFRISoiFDhmj9+vUaNGgQhT8A3LJixQpZlqUhQ4a4NeNy5cqVXdc1zntYuHMLFiyQZVnq2bOnW2O9QUFBrqXZ5s+f74WE/mv8+PGSbnyGbtiwIdmiklq1amnt2rW67777JEnffPONVzL6I/rdPRT/wGv++usvPfnkk7r33nvVpUsXffDBB/rmm2/0zTff6IMPPlCXLl10zz336Mknn9Tx48ftjusXdu/eLcuyVKtWrUSvTZ8+XZJUvXp1bdq0SZ999pk2btyohx56SNKNm2tIne+++05t27bVkSNHZIxRvnz5bjm1bu/evZU7d25dunSJC69UiI6OliTVrVvX5iSA502ZMkVTp07V8OHD1bx580RFhUk5f/68pkyZoilTpng+YDoQHx+vcePGqVKlSipatKiaN2+uzp07q3PnzmrevLmKFi2qSpUqady4cYqPj7c7rl/46aefZFmW+vbtqxYtWrjVpmrVqpKkAwcOeDKa3/rrr7/UuHFjNWnSxDWVdHJOnjypJk2aqEmTJoqMjPRCQv907tw5STcGNNzl3NfZFgDw33XmzBlJSjRjYXLKli0r6cYS7ki9PXv2aNCgQSpXrpyyZMmiwoULq3DhwsqSJYvKlSunQYMGaffu3XbHBFJtzZo1sixLzz///C2XkXWyLEsfffSRGjdurN9++02TJk3yUkoAvsx5P69p06Zut2nWrFmCtrhzf/75pySpc+fObrdxLhHubIuUWbdunSzL0muvvaZcuXLddv9cuXLp1VdflTFG69at80JC/0S/uyfA7gBIH9atW6c2bdro4sWLya4rHBsbqzlz5mj58uVavHgxN/BTyTmY9O+nNGJiYlxf/Pr376+AgBungowZM6pfv37asmWLNm/e7PW8/uTQoUMaMGCAJKlx48b6+uuvVbZs2UQzMDllypRJTzzxhGvt1a5du3ozrt8oVqyYDh8+rOvXr9sdJV27ePGijhw5oosXL7o1I039+vW9kMp/GWMUHByswMBALVy40HVTAJ51+vRptW7d2jXDxq2ub/bu3auBAwdq0qRJWrRokWvNbaTMjh07JElt27Z1u41zjfmzZ896IpLfmz17toKDg1W3bl0VLVr0tvsXK1ZMsbGxWr9+vWbNmnVHM9fg/+TKlUtnz55VeHi4q4DtdpzFWTlz5vRktHTl7Nmz2rhxow4fPuz2dc3bb7/thWT+jX63V1RUlKZOnaqVK1dq9+7droLCvHnzqmLFimratKl69Oih/Pnz25zUt2XLlk1///33HV2fOP8usmbN6qlY6UJ8fLxeeukljRkzRvHx8Ymu440xOnDggP7880998803GjhwoD777LNbjucg5WJiYrRt27YkzzXVqlVza1Ys3N6JEyck3Zj53cmyLNfvMTExifr62Wef1erVq/X9998rKCjIO0H9GONk9qHvvcPZtxkyZHC7jXNfHpZLuUuXLkm68dnprjx58kgSq0+kUkREhCS5PV4j/d/nMDPFpxz97h6Kf+BxJ0+eVJs2bXThwgVJUsuWLRUUFKSHHnpIhQoVknTjH11ISIgmTZqkpUuX6sKFC2rTpo327Nnj1k0GJM35xfnfX+BCQ0N15coVWZalli1bJnitdOnSkv7vJIqU+eKLLxQTE6OKFStq6dKlypQp023b1KtXT999953rxibuXJs2bfTll19qw4YNSc54Bc/69ttvNXbsWP3xxx9ut7Esi2nVUykoKEhTpkzRoUOH9PDDD2vGjBl65JFH7I7l165evarGjRtr//79MsaoQIEC6tSpk+vaxhijyMhIhYSEaNasWYqMjNTWrVvVtGlTbd26VZkzZ7b7j+Cz/v77b0n/V9DjjpiYGEnihk0KLV68WJZlqX379m63efzxx7Vu3TotXLiQ4p8UqlixotasWaPJkyerdevWbrVxPpldsWJFT0ZLFyIjI/W///1Pc+bMuePrFIpQUo5+t98XX3yhoUOHumZUvbko4uTJkwoPD9eKFSv0zjvv6L333tMLL7xgV1SfV6ZMGW3evFkzZ850+yn5GTNmuNoi5Tp37qy5c+e63t8VKlRI8jp+9+7diouL01dffaXw8HDNnDnT5uT+Izo6WiNGjNC33357yxls8+TJo2effVZvvfUWBW+pdPXqVUlKMMaeLVs21+/nz59P9P3qgQcekHTjYRakHONk9qHvvatYsWIKCwvT77//7lpZ4nZ+//13SeL+XyoUKFBA4eHh2rdvX4ICz+Ts27dPkijkT6UsWbLo+vXrd1RE5SzWYlw45eh3NxnAwwYOHGgsyzIBAQFm+vTpt93/hx9+MBkyZDAOh8MMGjTICwn9V65cuYzD4TDLly9PsP3DDz80lmWZUqVKJWqzbds2Y1mWyZo1q7di+qWyZcsah8NhJk6cmGC7ZVnG4XCYPXv2JGqzYcMGY1mWyZUrl5dS+p+TJ0+afPnymcKFC5tTp07ZHSfdiI2NNY899phxOBzG4XAYy7Lc/nE4HHbH91k3n08WLVpkcubM6fq8/eyzz5Jss3v3bvo9DXzwwQeufnzmmWfMpUuXbrnv5cuXzbPPPuva/8MPP/RiUv9TqFAh43A4zOrVqxNsT+7zddq0acayLFO8eHEvpfQvJUuWNA6HwwQHB7vdJjg42FiWZR544AEPJvNvY8aMcb2vhw0bZuLj42+5b3x8vBk2bJhr/7Fjx3oxqf85d+6cKVWq1B1f0zh/kDL0u/3+97//Jej/PHnymMaNG5unnnrKdO7c2TRu3NjkzZs3wXX8//73P7tj+yznuEyGDBnM5MmTb7v/5MmTXd+3PvroI88H9FM//PCD6/1bpUoVs2XLllvuGxISYqpVq+ba/6effvJiUv917Ngxt8/3DofDlC5d2vz11192x/ZphQsXNg6Hw2zYsMG17fLly65zyrp16xK1Wb58ubEsy2TOnNmbUf0G42T2oe/t4Rz3KlSokDl58uRt9//rr79c4zt9+/b1QkL/1KFDB2NZlqlWrZqJiYm57f7Xr183VatWNQ6Hw3To0MELCf2Xsx8HDx7sdpvBgwe7/r6QMvS7e5j5Bx63dOlSWZalPn36uLWUUZcuXbR+/Xp98803WrJkib766isvpPRP999/v3bs2KHg4GA1b97ctX3+/PmyLEsNGjRI1Ma5VNidPFGPxP766y9JUpUqVdxu43zqxvmUJe5c0aJFtWDBArVv3161a9fW119/rVatWtkdy+998803WrhwoSSpUKFC6tWrl6pXr668efMy04aXPProo9qwYYPatGmjY8eOaciQIdqzZ4+++eYbpkr3gJkzZ8qyLDVr1kzffvttsvtmzZpV48eP17Fjx7RixQrNmDFDr732mpeS+p/y5ctrzZo1Wr9+vRo1auRWmx9//FGWZal69eoeTuefTp06JUnKnTu3222c+zqXocKd69Onj77++mvt379fI0aM0Ny5c9WzZ08FBgaqUKFCsixLERER2rx5s6ZOnao9e/ZIksqWLas+ffrYnN63jRw5UocOHZIkNW/eXC+99JLruubmJTKQtuh3ey1btkxffPGFJOnuu+/WZ599pvbt27uWCHeKi4vTvHnzNGTIEB0/flxffvmlHnnkkQTjDXDPoEGDNHr0aEVERKh3796aPXu2goKCXOd56cYs2Zs3b9bEiRO1fPlyGWNUrFgxZtVLBee1e+nSpbV+/foEs5/8W40aNbR27VrVqFFDBw4c0Pjx49W5c2dvRfVLMTExatmypet8X7ZsWfXq1UuBgYEqXLiwjDE6ffq0tmzZoilTpmjv3r06ePCgWrZsqe3btyc6J8E9ZcuWVWRkpA4ePKjatWtLuvE9tVSpUjp48KAWLlyounXrJmjjHOMpUKCA1/P6A8bJ7EPf22PQoEGaOHGizpw5o8DAQH3++ed6/PHHEy0DFhcXp7lz52rw4MGKjIxUhgwZuK5Jhe7du2vu3LnasWOHWrdurcmTJ99yJqWTJ08qKChIO3bskGVZ6tmzp3fD+plWrVppx44d+vrrr9WyZUs1adIk2f1XrVql0aNHy7Is7lelAv3uJrurj+D/smTJkuQT2slZvXq1sSzLZMmSxYPJ/N+rr77qmklm6dKl5uLFi+arr75yVbIvWLAgUZtPPvnEWJZlateubUNi/5E9e3bjcDhMaGhogu3JzUzw66+/GsuyTL58+bwV0+80atTINGrUyJQpU8bV13nz5jWBgYGu127107hxY7vj+6yHHnrIWJZlKlSoYM6dO2d3nHQjqfNJVFSUqVevnuu1unXrmsjISNfrzPyTNrJly3bLz9FbWbBggbEsy2TPnt2DyfzfqFGjjGVZpnDhwubs2bOu7bf6fJ00aZLrNXdmoERi+fLlMw6Hw6xatcrtNqtWrTKWZZncuXN7MJn/O3r0qLnvvvtc7+HkfizLMvfff785duyY3bF9XpkyZYzD4TBt2rSxO0q6Qr/bq1WrVsayLFOsWDETHh5+2/1PnTplihUrZhwOh2nVqpUXEvqnbdu2uWZTcuc8nzdvXrNjxw67Y/u0vHnzGofD4dZsS06TJ092zYaF1Bk7dqzr/f7WW2+ZuLi4W+4bFxdnhg4d6tp/3LhxXkzqX9566y1jWZYJCgpKsP311193ze4zceJEc+nSJRMZGWk++eQTkzFjRuNwOEz37t1tSu3bGCezD31vn5tnyXaOyTdp0sR06dLFPP3006ZJkyauz2HnbEvMjp167du3d/V75syZzaOPPmpGjBhhJkyYYL799lszYsQI8+ijj5rMmTO7+v6JJ56wO7bPO3PmjGvllYwZM5r+/fub0NDQBNc2cXFxJjQ01Dz33HMmY8aMrnGyqKgoG5P7NvrdPRT/wOOKFCliHA6H2bp1q9tttm7daizLMkWKFPFgMv8XHh7uOhH+e9CoQoUKSS4f0LBhwzueNg2JlS5d2jgcDvPjjz8m2J5c8Y/zy3j16tW9FdPv3PwF406mdKUYInVy5MhhHA6HmTFjht1R0pVbnU+uX79ugoKCXK+XKFHC/PHHH8YYin/SinOwYtu2bW63cS6rmTdvXg8m83/R0dGum43VqlUzu3fvNsYk/vdw/PhxM3DgQNdSsmXKlEn25gJurXr16sbhcJihQ4e63cZ5TfPggw96MFn6cOnSJfPyyy+bPHny3PJaJk+ePGbIkCHm4sWLdsf1C3fddZdxOBxm2bJldkdJV+h3exUoUMA4HA7z9ddfu93m66+/NpZlmQIFCngwmf87efKk6dChgwkICLjleT4gIMB07NjRnDhxwu64Ps/5oFZKxigp4k+9Ro0aGYfDYR5//HG32zz++OPGsiwe2EqFTZs2uR42vHLlimt7VFSUq9A/qbHjrFmzmr1799qY3HcxTmYf+t5eY8aMMdmyZUsw7v7vc4tlWSZbtmwsV51Grl69ajp16nTLPv9333fq1MlcvXrV7th+Yfny5SZz5swJ+j1LliymaNGiplixYq6JMZz9nzlzZvPrr7/aHdvn0e+3x1yZ8LgaNWpoyZIl2rVrl6pVq+ZWm127drnaIuWKFCmiRYsWqXPnzq4lGyTpvvvu05w5cxJNnx4WFqZ169ZJkpo1a+bVrP6mfv36OnjwoH788Uc99dRTt90/KipK48ePl2VZaty4sRcS+qf69euzLICNypQpY3cESMqYMaMmTpyoChUq6NVXX9WxY8dUp04dTZ8+XQ888IDd8fxC2bJltWnTJv3111+qWrWqW22cy0GWLVvWk9H83l133aX58+ercePG2rFjhx588MEE555+/frpzJkz+vPPPyVJxhjlyJFDc+bMYYrvFGratKm2bdumMWPG6LnnnlORIkWS3f/kyZMaM2aMLMtS06ZNvZTSf2XLlk2ffPKJ3n//fW3dulW7d+/WuXPnJEl58+ZVxYoVVb16dWXKlMnmpP4je/bsunbtmmvZHXgH/W6vy5cvS5ICAwPdbuPc19kWKVO0aFHNnj1bERER+u2335I8zzds2PC2n79wT/HixbVv3z79888/bre5cOGCqy1SZ/fu3ZKkoKAgt9v07t1b8+fPd40V484FBgZq8uTJio2N1fnz513nk3z58mn58uXq1KmTjhw5kqBNwYIFNW3aNJUrV86OyH6DcTL70Pf26N+/vzp16qTJkydr5cqVSV7XNG3aVL169VL+/PltTusfMmfOrJkzZ6p79+4aO3as1qxZo+jo6AT7ZM2aVQ0aNNCAAQPS19JHHta8eXNt2rRJzz77rEJDQyVJ165dS3Av1qlmzZqaMGGCKleu7O2Yfod+vz3LGGPsDgH/tnLlSjVv3lzlypVTSEiIsmbNmuz+0dHRrvW0ly1bRhFKGrh+/bo2bNigiIgIFSlSRHXr1k1ynez169dr1apVkqRXX31VWbJk8XZUvxESEqLAwEBZlqXvvvtOvXr1kiQ5HA5ZlqVdu3apfPnykqQTJ07o8ccfV2hoqAICArR3715u0MOnVK9eXTt27NCvv/5K8ZoXJXU++bdffvlFTz31lC5cuCCHw6GuXbtq2rRpsixLcXFxXk7sP7799lv17dtXjzzyiJYuXepWm1atWmn58uUaO3as+vbt6+GE/m/Xrl3q2rVrgpsAzuLPm7/elCtXTjNnzlTFihW9ntFfHDt2TKVLl1ZsbKzKlCmjGTNm6MEHH0xy3507d6pz5846cOCAMmbMqL179+r+++/3cmIgdZo0aaLg4GDNmzdPjz32mN1x0g363V5lypTRoUOHtHbtWtWpU8etNhs2bFC9evX0wAMPuIpugf+6YcOGacSIEerfv7++/vprt9oMHDhQ48aN0xtvvKERI0Z4OKF/y5w5s2JjYxUaGur2QxTbt293FTpfvXrVwwnTp5iYGK1evVp79uxRbGysSpUqpRYtWtx2DB+3xjiZfeh7pHdxcXE6fPhwgqKr++67TxkyZLA5mX8LCQlJtuCtZs2aNif0T/R70pj5Bx7XtGlTDRs2TMOHD1fDhg01YcIEValSJcl9d+7cqWeffVYHDhzQsGHDKPxJI5kyZVKjRo1uu1/dunVVt25dLyTyfzVr1lS/fv30zTff6JlnntHSpUvVsWNH1+t//PGHdu3apRUrVmjGjBm6evWqLMvS4MGDKfyBz+ncubO2b9+uxYsX88X6P6Zly5bauHGj2rRpo8OHD2v69Ol2R/ILzzzzjObNm6fly5erf//++vzzz29ZMHvt2jUNHjxYy5YtU4sWLSj8SSOVKlXSzp07tWTJEi1YsEChoaGKjIxUXFyc8uXLp6pVq6pt27Z64oknmPEnlYoXL673339fr7zyig4cOKBq1aqpQYMGql+/vooUKSLLshQeHq61a9dqzZo1MsbIsiy9++67FP7AJ/Xt21e//fabpk+fThGKF9Hv9mrdurW+/PJL/fLLL24X/zgLoFu3bu3JaECaeumll/TDDz9o/Pjxql+/vjp16pTs/nPmzNH48eNVsmRJvfzyy15K6b/y5s2ryMhIHTlyxO3in8OHD7vawjMyZsyoFi1aqEWLFnZH8RuMk9mHvkd6lyFDBpUqVcruGOlOzZo1022hiZ3o96Qx8w887t1335UkLV68WKGhobIsS5UqVVLNmjVVsGBBWZal06dPKyQkJMFyX7cbQHr77bc9nh1Ijbi4OAUFBWn69OnJLkXlPA337NlTEydOZNkq+Jxr164pMDBQBw4c0IoVK1SvXj27I6UL7sz843Tu3Dk98cQTWrNmjSQx808qrV27VteuXdNbb72l0NBQFSpUSJ06dUry2sa5jEONGjX0/vvvJ7s0T/369b34pwDuzIgRIzR8+HDFx8ff8lrFGCOHw6Hhw4frzTff9HJCIO08/fTTmjFjht5//3299tprdsdJN+h3+4SHh6tq1aq6ePGifv3119sWAP3+++9q2rSpcuTIoW3btqlYsWJeSurfTp8+neRTqyyHl7aOHj2qJ598UqGhoWrTpo169uyZ5HX81KlTtXDhQtWoUUOzZs1i2a800Lp1a/3yyy9q1KiRa+bx22ncuLHWrFmjRx55REuWLPFwQv+0du1aSTdukN11111utbl69aq2bNkiie+pKcE4mX3oewBAekfxDzzOeXPSyfk0cFKSe+3fuGkJXzF37lx9+OGH2rZtW5Kvly9fXm+99ZY6d+7s5WTpw8mTJxUREeFaUtDdgQ7cmcjISNfydc8//7y6dOmismXLsnygBzkLeR566CG33texsbEaMWKEjh8/LkmaPHmyR/P5s39f26QFy7IUGxubpscE0tr27dv18ccfa/ny5fr7778TvJYnTx61atVKL7/8crpbSzs1pk2b5pHjdu/e3SPHTQ/Wrl2ruLg4DR06VBs3blT16tVd1zXuLH/BDbKUod/tt3XrVnXs2FHh4eHq16+fevbsqQcffNA1g54xRjt37tTUqVM1btw4FS1aVHPmzFG1atVsTu7bjDGaMGGCvv76a+3duzfJfcqXL69BgwapT58+PCyUSjcveeHOGKQ7+3Ad777vv/9e3bt3l2VZ6tGjh0aPHq1s2bIlue/ly5c1aNAgTZkyRZZlafr06erSpYuXE/sHh8Mhh8OhP/7447YPDjmFhYWpVKlScjgcvL9TiHEy+9D3AID0jOIfeJynllqIj4/3yHH9lTFGO3bs0M6dOxUVFaUrV67odv/8mV0pbYWHhye5LAlLYqS9ixcv6tNPP9WkSZMUHh7u2v7vGVJmzJihefPmKVeuXPr222/tiOoX7nQA9WYMlMIXeeLahtmY4EuMMTpy5IiioqIkSfnz51fJkiW5IZkCFBP+96Tm74S+Tzn63V733XefJCk6OlqRkZGuv4tMmTIpb968sixLZ8+e1fXr1yXd+BwoWLBgsoVZlmUpLCzM8+F92Pnz59WmTRtt3LhRkm45RuP8+6hdu7YWLVqk3Llzeyui3+E63l7GGNWrV0+///67LMtS/vz51alTJwUGBqpQoUKyLEsRERHavHmzZs+erTNnzsgYo7p167pmr8Gdu5NZg52cxT+8v1OGcTL70PeedXP/phX6/faCgoLS/JiWZWnixIlpflx/43yINq3de++9Hjmuv6DfU4fiHyAdmDp1qoYPH65jx47dUTu+3MEXHTp0SC1bttThw4cTDJ4mNdBx7NgxPfDAA4qPj9eaNWtUt25dOyL7vNQMoDKQBF/knHUprTVo0MAjx/VXUVFRmjp1qlauXJnkEhlNmzZVjx49lD9/fpuTArfGTcj/Hq5r7EG/24tzkfcZY9SgQQOtX79ekpQvXz5XEUThwoVljNHp06e1ZcsWzZo1S1FRUbIsS3Xr1vXYtWh6MHz4cI8cd9iwYR45rj86f/68WrdurU2bNknSLW/MO8dzatWqpcWLFytPnjxey+hvUlL8c/DgQZUpU0YBAQGuwk+4j+sa+9D3nsU1oz3S+qEhZ2Ec/X57FLzZg35PnQC7AwDwrDfffFMjR4687Sw/0o2TH/WA8GXXrl1T69atFRYWpmzZsmnAgAGqX7++Hn300ST3L168uGut+YULF1L8k0IMdCK9oUjHfl988YWGDh2q6OhoSQmflD958qTCw8O1YsUKvfPOO3rvvff0wgsv2BUVSNaRI0fsjoB/+e233+yOkC7R7/bq0aOH3RHSnR9//FHr16+XZVnq0qWLxo4dqxw5ciTar3v37ho5cqQGDBig6dOna/369frpp5/01FNP2ZDa9/Hd1X558uTR+vXrNW7cOI0dO1b79u1Lcr9y5cppwIAB6tevn8dmlcetHT16VJKUK1cue4P4KM419qHvPet2/btkyRKFhoZKkipUqKCHHnpIhQoVkjFGkZGRCgkJ0e7du2VZlmrUqKFWrVp5I7bPu/fee5lp2SbcL7UH/Z46zPwD+LHNmzerVq1asixLTZs21SeffKL4+HhVq1bNVeV4/vx5hYaGaty4cVqwYIHq1q2r2bNnq1ChQnbH93s7d+7UnDlzFBUVpZIlS6pr164qWrSo3bF82pdffqn//e9/ypYtm9atW6cqVapISv4pp1GjRmnw4MGqU6eO1q1bZ0NqIHnvvvuu6/ebl2O8eXtKsLQjfNVLL72kL7/80vVFMHfu3KpatWqCAaUdO3bo/Pnzkm4UN7/wwgv6/PPP7YwNAADg0rp1a/3yyy9q2LChVq9e7VabRo0aac2aNWrZsqWWLFni4YSAd5w6dSrJWTyLFCliczLf9e+lMkqUKCHLsrRixQqVKlUq2bbXrl1TWFiYhg4dqm3btqlevXoKDg72YFoA/mLEiBEaNmyYKleurAkTJqhmzZpJ7hcaGqpnn31WO3fu1DvvvKOhQ4d6OSngvqlTp3rkuDx8kTz6PXUo/oHHjRw5Uj169OBLmw169uypadOmqUSJEvrzzz8VEBCgPXv2qFKlSklO6zdu3DgNGDBAlStX1ubNm5UpUyabkvu+kJAQDRgwQAEBAVq6dKly586d4PXx48drwIABCSpYs2fPrnnz5qlJkyZeTus/nNOmv/7663rvvfdc25Mr/lm1apWaNWumggULKiIiwtuRgdu6eWrXm8/bqZ3ylald4YuWLVvmejLs7rvv1meffab27dsrICDhhKZxcXGaN2+ehgwZouPHj8uyLP3yyy9q3ry5HbH9RlhYmBYuXKidO3cqKipKV65cSfZpHMuytGrVKi8mBADANxQpUkSRkZGaPXu2Hn/8cbfazJs3Tx06dFDhwoUVHh7u4YQAfNW/l8pwXq+nZPzg22+/VVBQUJrkAuC/nOPrpUuX1tatW5UtW7Zk9798+bKqVaumQ4cOafny5WratKmXkgKA/2PZL3jcG2+8obffflstWrRQ79699eijjya6QQPP+P3332VZlp5//nm3+vy5557T6tWrNW/ePI0dO1Yvvvii50P6qUWLFik0NFQtW7ZMVPhz5MgRPf/884qPj0+w/eLFi+rUqZMOHDig/PnzezGt/9i7d68k3dHN3Xz58kmS/v77b09EAtLErW6uU8ON9Gb06NGSpKJFi2rz5s23LC7PkCGDOnbsqHr16qlGjRo6deqUvvzyS4p/Uig6Otq13Mi/zzvOteL/vU1K2Q0GAADSA+csJyVLlnS7jXNfZ1sASEpajB9kyZJFzz//PIU/ANzy1VdfybIsvfbaa7ct/JGkbNmy6bXXXlPv3r01evRoin8AIA1RgQGviI2N1dKlS7V06VLlz59f3bp1U69evVShQgW7o/m1U6dOSVKCfr55neyYmBhlzJgxQZtu3bpp7ty5mjlzJsU/qRAcHCzLstSyZctEr40dO1YxMTG666679MMPP6hJkyZavny5evToob///lvffPON3nrrLRtS+76LFy9KurM1ya9evSpJif4tAP8V/y4UvN12eBczoXhXSEiILMvS66+/7taskoULF9brr7+uQYMGKSQkxAsJ/Y8xRu3bt9fKlStljFH+/Pl19913a8eOHbIsS/Xq1dP58+d14MABxcTEyLIslSlTRoULF7Y7ul/ZunWrVq5cmeSyGE2bNlX16tVtTgjAl5UrV05BQUHq3r07S4B7Sa5cuXT27FmFh4eratWqbrVxzvaTM2dOT0ZLN4wx2rFjh9vX8RJLJ8M3TJ48OcF/9+rVS5ZlacSIESpWrNgt21mWpSxZsqhIkSKqWrWqsmfP7umoAPxEaGioJOnBBx90u03lypUlibEaAEhjFP/A43bt2qWJEyfqhx9+0JkzZ3TmzBmNGjVKo0aNUs2aNdW7d2917txZOXLksDuq34mJiZEkFSxY0LXt5i9uZ86cUdGiRRO0ueeeeyRJhw4d8kJC/3Xy5ElJUsWKFRO99vPPP8uyLPXt21ft2rWTJHXo0EEbN27UqFGj9Msvv1D8k0L58uVTRESETp8+7XabXbt2SRKD3G6YNm2a6/fu3bsnuT0lbj4W4CuYCcUely9fliQFBga63ca5r7Mt7szs2bP166+/yrIsDRs2TEOHDtXevXtdg3pr1qyRdKN/v/vuOw0dOlTnzp3Tt99+q7p169oZ3S/s2rVLzz77rLZs2XLLfd544w0FBgZq/PjxqlSpkhfT+Tbnk+yWZWnixImJtqfEv4+FxOj3/6YDBw7otdde05tvvqlWrVopKChIrVu3TrR0DNJOxYoVtWbNGk2ePFmtW7d2q82kSZNcbZE6U6dO1fDhw3Xs2LE7akfxj3veffdd1+8399nN21OC/ndPjx49Evx3r169JEnt2rVT+fLl7Yjktxgnsw99/9/ifEDln3/+cbvNhQsXJEnnz5/3SKb05ty5c5o8eXKyDw316tVLefPmtTmpf4qJidG2bduS7Ptq1arx4LmH0O+3YAAviYmJMfPnzzdt27Y1GTNmNJZlGYfDYRwOh8mWLZvp3r27CQ4OtjumX7n77ruNw+Ewa9eudW27du2aCQgIMA6Hw6xcuTJRmyVLlhjLskzmzJm9GdXv5MiRwzgcDrNz584E20+cOOF67//+++8JXlu2bJmxLMvky5fPm1H9SqtWrYzD4TCvvPJKgu3OPt+zZ0+iNg8//LBxOBymS5cu3orps5z9mCFDhiS3p+Tn38cCfEF8fLxp3ry5cTgcxrIsU6BAAVO1alXXv4X69eubSpUqmUyZMrm2lS1b1jRs2NA0bNjQ7vg+rXTp0sbhcJj169e73Wb9+vXGsixTqlQpDybzX4899pixLMvUqVPHtW337t2u9/a/bdmyxdx1112mQIEC5uTJk96M6nd+/fVXkyVLFte5xrIskylTJlOkSBFTuHBh1znG+ZMlS5Ykr++RtJuvX261/U5+bvVvAgnR7/9N1apVc51LnH1bqFAhM2TIELN371674/mlMWPGuPp72LBhJj4+/pb7xsfHm2HDhrn2Hzt2rBeT+p833ngjwWdrcj//3g/uSetzfVLHgvuCg4NNcHCwiY6OtjuK32GczD70/X/LfffdZxwOhxkwYIDbbQYMGGAsyzIlS5b0YLL04ZtvvjHZs2dP8B3p39czDofDZM+e3YwfP97uuH7l8uXL5rXXXjP58uW75bklX7585vXXXzeXL1+2O67foN+T57h9eRCQNgICAtSuXTstWLBAJ06c0Mcff6xy5crJGKPo6Gh9//33aty4sR544AF98MEHrplTkHLO5b7279/v2pYpUybX9pkzZyZq88MPP0hSohmBcGeuX78uSbp06VKC7evWrZMkZc2aVQ899FCC15wzzziXrsKde/zxx2WM0fjx4916gm/48OHavHmzJOnJJ5/0dDy/YIxJcip05/aU/AC+xjkTiiQNGzZMERERCZ4uW7Nmjf744w+dO3dOo0aNUrZs2XTu3DmNGDFCv/32m12x/YLzyfhffvnF7TZLly5N0BZ3JjQ0VJZlqU+fPm7tX7NmTT333HOKiorSV1995eF0/isqKkodO3bUtWvXZFmWnnnmGW3evFmXL19WeHi4Tp06pejoaG3ZskV9+vRRhgwZdO3aNXXs2FFnz561O75PuPfee10/t9p+Jz/FixdPdCwkRr//N23dulU7d+7UCy+8oPz588sYo8jISH322WeqWLGiateurYkTJyb6fouU69Onj8qWLStjjEaMGKEHH3xQn332mdavX6+DBw/q0KFDWr9+vT777DNVrlxZI0aMkCSVLVvW7c9kJLZ582Z9+OGHkqRmzZppx44d2rZtm6Qbs4jFxcUpKipKy5Yt02OPPSZjjOrWratTp06x7PIdYuzgv6NBgwZq0KCB7rrrLruj+CXe6/ah7/87HnnkEdeY/KxZs267/5w5czR+/HhZlqVWrVp5IaH/GjlypPr376/Lly/LGKOcOXOqYcOG6ty5s5588kk1bNhQOXPmlDFGly9f1nPPPaePP/7Y7th+4fjx46pSpYo+/vhjnTt37pbnlXPnzumjjz5S1apVdeLECbtj+zz6/fYswycabLZlyxZNmjRJM2fOdE0LaFmWHA6HmjVrpt69e6tt27bpd3quVHj//fc1dOhQde7cWT/++KNr+4cffqg333xTDodDb7zxhp588klFR0dr6tSpGjt2rCzLUr9+/TRmzBgb0/u2e+65R+Hh4ZoyZYq6devm2t67d29NnjxZTZo0cd04dtq0aZNq166t/PnzKzIy0tuR/UJcXJwefPBB7d+/X/fcc4/GjBmjVq1aKUOGDLIsS7t371aZMmW0YcMGffzxx64bwjVr1tSmTZtsTv/fd3NBVfHixZPcnhI3Hwvui4mJ0cGDByVJ999/vzJnzpzg9atXr+rNN9/UrFmzFBUVpZIlS6p///4aOHCgHXH9Srt27bRw4ULVrl1b69evlyTt2bNHlSpVct00uFlISIgaNGig7Nmza8eOHRTYpkJ4eLiqVq2qixcv6tdff1WdOnWS3f/3339X06ZNlSNHDm3btk3FihXzUlL/kSVLFsXExCg4OFj16tWTJP35558qW7asLMvSpUuXEt1IWL16tZo2bary5ctr9+7ddsT2eUOHDtX777+vTJkyacGCBWrRokWy+69YsUJt2rRRbGys3nzzzVQvqwEg/YqNjdWiRYs0efJkLVu2TLGxsa5lS7NmzaqOHTuqV69ers8EpNyxY8fUuHFjHTly5LZLwxpjdN9992n16tUUvKVCz549NW3aNJUoUUJ//vmnAgICkr2OHzdunAYMGKDKlStr8+bNypQpk03JAfwXMU5mH/r+v+XkyZOqUKGC66HmNm3aqGfPnqpZs6YKFiwoy7J0+vRphYSEaOrUqVq4cKGrUGXPnj2M1aTQ7t27VbVqVcXFxalIkSL65JNP1LFjx0T3UmNjYzV79mwNGTJE4eHhCggI0Pbt212TBODOxcTEqEqVKtq3b5+kGwX6vXr1UmBgoAoXLixjjE6fPq0tW7ZoypQp2rt3r6QbEzZs375dAQEBdsb3WfS7m9yZHgjwhitXrpjp06ebIkWKJJqOrkCBAubVV1814eHhdsf0Kc4lGXLkyGH++ecf1/bLly+bkiVLJjkNpnPZqb/++svG5L6vXbt2xrIs06BBAxMXF2eMMSYqKsrkzp3bOBwO8+GHHyZq8/333xvLskzFihW9HdevHDp0yBQqVMj1/s6ePbvr9/vvvz/RFJjFihUzx48ftzs2cMdmzpxpHA6HKViwoLl27Vqi1x955JFEU706HA7z/PPP25DWvxQrVsw4HA4zZcoU17bklkEyxpiXXnrJWJZlXn31VW/F9FuhoaGmZMmSJnPmzOaFF14w27dvd33WGnNjaYzt27ebF1980WTOnNmULFnSbN261cbEvi1btmzG4XCY7du3u7aFh4e73u+HDx9O1CY0NNRYlmVy5szpxaT+pWrVqsbhcJjBgwe73Wbw4MHGsixTrVo1DyYDkJ5ERESYjz76yJQvXz7ROE2pUqXMyJEjGadJpUuXLpmXX37Z5MmT55ZLT+XJk8cMGTLEXLx40e64Pq9UqVLG4XCYUaNGubbd7jq+Q4cOidoA/0VTp051/dxqe0p+AMAdwcHBJmfOnG4tveYcLwgODrY7tk/r27evsSzLFCxY0Bw7duy2+x8/ftwUKlTIOBwO069fPy8k9F9jx451vdffeuutBOOS/xYXF2eGDh3q2n/cuHFeTOpf6Hf3MPMP/hOOHj2qqVOnaurUqa7q7H+/NS3LUpYsWTRixAi99NJLdsT0SWvWrFFsbKyqVq2qvHnzurYfO3ZMXbt21YYNGxLsX7FiRU2fPl2VK1f2dlS/Mn/+fD3xxBOyLEuBgYGqXbu2Fi1apIMHDypjxow6dOiQ7rnnngRtBgwYoHHjxql9+/aaO3euTcn9Q0REhPr06aMlS5Yku1/z5s01efJkFSlSxEvJgLTjnEmsb9++GjduXILXlixZojZt2siyLBUrVkw1a9bUli1bdPLkSVmWpfXr16tWrVo2Jfd9zITieffdd1+yr0dHRysyMtL1lHymTJmUN29eWZals2fPupbfNMaoYMGCypo1qyzLUlhYmMez+5vSpUsrLCxMy5YtU7NmzSTd6Nfs2bPr6tWrmjVrlp544okEbWbOnKmnnnpKd911ly5fvmxHbJ+XJ08eXbhwQb/99pvq16/vVpu1a9eqYcOGypUrl86fP+/hhP7LuYxjmTJlFBgYaHOa9IN+/+/bvHmzJk6cqFmzZunChQuSbozTZMiQQS1atFDv3r3Vpk0bZciQweakvun69evaunWrdu/erXPnzkmS8ubNq4oVK6p69erMOJNGcuTIoejo6ATXNfv27VOFChVkWZauXr2a6Gn5hQsXql27dgoMDNTGjRvtiA24xeFwyLIsWZal2NjYRNtT4t/HgnuOHz8uScqePXuC8Xh4Hn1vr2PHjumll17SwoULE82m55QhQwY99thj+uyzz5hpKZWc4zWfffaZXnzxRbfajBo1SoMHD9YDDzygP//807MB/Vjjxo21Zs0atWvXzu17eU888YTmz5+vRo0aadWqVR5O6J/od/ekk/mN8F905coVzZ07V5MnT9aaNWsSrKtarlw59e7dW926ddOuXbs0ceJEzZkzR1euXNGQIUNUsGBBde3a1eY/gW9o0KBBktuLFy+udevW6cCBA9qzZ49iY2NVqlQpVa1a1csJ/VP79u3VoUMHzZkzR5s2bdLmzZtd7+9XXnklUeFPXFyc5s2bJ8uy1KRJEzsi+5XChQtr0aJF2rNnjxYsWKDQ0FBFRkYqLi5O+fLlU9WqVfXYY4+pRo0adkcFUmzbtm2yLCvJm8KTJ0+WdONL4JYtW5QjRw79888/ql27tvbv36/vvvuO4p9UCAgIUExMjHLkyOHadvPvERERKlmyZII2uXLlkiT99ddf3gnp444ePerWfs7P1mvXrunUqVNJ7uNcSjOlA97pXeXKlRUWFqZdu3a5bpI5i5vXrFmjsWPHJij+iY2N1ahRoyRJpUqVsiWzP7h69aokKVu2bG63yZo1q6Qb/x6Qcj179pRlWfrpp58oQvEi+v2/LzAwUIGBgXrsscfUp08fnT59WtKN8/7SpUu1dOlSFSlSRC+//LIGDhyYfqZUTyHn8oyBgYFq0aKFMmXKpFq1anGN7mExMTGSpIIFC7q2Zc+e3fX7mTNnEi3R6xy/OXTokBcS+rfGjRvLsixNmjTJ7Ru+4eHh6tq1qyzLSjc3bFLjVs958/y3d5UoUUKWZWn06NHq37+/3XHSFfreXsWLF9fcuXMVERGh3377Tbt27dL58+dljFHevHlVqVIlNWrUSIULF7Y7ql84efKkJKl27dput6lTp46kG5+vSDnng51BQUFut+ndu7fmz5+vXbt2eSqW36Pf3cM3cXjdxo0bNXnyZM2aNcu1BqgxRlmzZlWnTp30zDPPJPiwaty4sRo3bqz3339fTzzxhLZv365Ro0ZR/JNGypQpozJlytgdwy/NmDFDY8eO1ezZsxUREaEiRYqoR48e6tWrV5L7OgdPnTfWkHoVKlRg7VovuHjxoutG77PPPnvbL3CnTp3St99+K0kaMmRIohlS4B5nQcO/Z0iJj4/XypUrZVmWBg4c6CpKyZUrlwYOHKgBAwbo999/93pef1K0aFGFhYXpzJkzrm2FCxfWXXfdpatXr2rbtm2Jin+cNwt4atI9PXr0sDsC/r/GjRtr7ty5WrZsWYLZN4OCghQcHKzg4GA1aNBAnTp1UnR0tGbMmKHt27fLsix16tTJxuS+rXDhwjp+/Li2b9+u6tWru9Vm+/btkqRChQp5Mprfy5Urly5cuEDxmpfR7/9tx44d09SpUzVlypQEszUHBASoWbNm2rNnj44fP67w8HANHjxY33//vX799VflyZPH5uT/Xe+8844sy9L8+fPtjpKuFChQQOHh4a7Zq6Qbn5sZMmRQfHy89u3bl6j4x1lg7hzDRMoFBwfLsqw7mhnyypUrrnZI3pEjR+5oOzzHOTZQs2ZNu6OkO/T9f0PhwoX11FNP6amnnrI7il9zzrh5J2ONzn0dDodHMqUX//zzjyQlum5MjnP1iZuvQ3Fn6Hf3UPwDrzh16pSmTZumyZMn6+DBg5L+74mD6tWr65lnnlGXLl0SPDX/byVKlNDIkSPVokULpqODT3A4HBo4cKAGDhx4232ffvppPf30015IBaS9n3/+We+8845KlSqlt99++7b7Fy5cWD/88IMOHTqksmXLcnM4haKioiTdWILqZjt27NCFCxdkWZZat26d4LWKFStKYvaZ1GImFM9zzl4F+7Vv314DBw7Ub7/9psOHD7sKDrt27aoff/xRy5Yt0/r167V+/foE7apUqcJSvalQr149ff/99xo5cqQ6deqknDlzJrv/hQsX9NFHH8myLNdyhEiZkiVLaufOnSyd5mX0+3/P1atXXbM1BwcHJ5ituVSpUurdu7d69uzpmkHl119/1SeffKKVK1dq+/btGj58uL744gsb/wT/bfny5dO5c+d077332h0lXalQoYLCw8O1f/9+1+dlpkyZVKFCBe3atUszZ85MNBvzDz/8IOnObjIAdrjVbEosq+N9xYoVU1hY2C2XPYLn0Pf/HadPn05yOVMeVkk79957r/bt26dVq1a5PfuPcxY9rkFTJ2/evIqMjNSRI0fcXk3l8OHDrrZIGfrdPZT2weNat26te++9V2+88Yb+/PNPGWOUK1cu9e/fX9u3b1dISIj69u2bbOGPk/Mp+ujoaE/HBgC4yblknbtFPJZlqXPnzjLGaPbs2R5O578yZcok6f+KgJzWrl0rSbr77rtVokSJBK85P2sZBEmdxo0byxijZcuWJdgeFBQkY4xrJpQxY8bok08+UWBgoLZs2cJMKPBJhQsXVkxMjK5evZpoprH58+frzTffVKFChVw3hXPlyqUBAwbot99+S1ScCPf17dtX0o0ntevXr6+QkJBb7rtlyxbVr1/fNaDhbIuUad++vYwxWrRokd1R0hX6/b9j06ZN6tu3rwoXLqzu3bvrt99+U3x8vDJnzqynn35awcHBOnDggF555ZUESyc1a9ZMK1as0KBBg2SM0cKFC238U/z3PfDAA5JuLBcL76lXr56MMfrtt98SbH/yySdljNGkSZP09ttva8+ePQoJCdHAgQP1008/ybIstWzZ0qbU6ZtzliCuK+FLmjdvLkmJHpCA59H39jLGaPz48apUqZKKFi2q5s2bq3PnzurcubOaN2+uokWLqlKlSpowYQLLEaaBZs2ayRijTz/91K0ljf744w998sknsizL9W8FKVOtWjUZYzRmzBi324wZM0aWZbldtILE6Hf3WIYzLDzs5unj6tevr2eeeUYdOnRI0Ze28PBwdenSRZZlJfqinp5NmzbNI8ft3r27R44LeMvFixd15MgRXbx40a1ih/r163shlf8pV66c/vzzTy1evNjtAdFly5apVatWKleunPbs2ePhhP6pUqVK2rt3r0aMGKE33njDtb1JkyYKDg5Wly5dNH369ARtVq9eraZNm+ruu+/W8ePHvR3Zb0RERKhYsWJyOBw6cOBAgoKIVq1aadmyZYmmpTfGqGrVqtqwYQMD1/BL586dU2xsrAoUKMCyDGlk4MCBGjt2rKs/y5cvr8DAQBUqVEiWZSkiIkKbN2/W3r17Jd04zwwYMECjR4+2M7bPu3DhgipXrqxTp05p6dKlaty4sd2R0gX63V6nTp3S9OnTNXnyZNdMy87hwsqVK+uZZ55R165dlStXrtsey7lcYYYMGRQTE+PR3L7siy++0EsvvaSePXtq0qRJdsdJN/bs2aNKlSope/bsOnHihGtmvejoaFWsWFFHjx5N8jo+b9682rFjh+6++247YvsNh8Mhy7K0a9culS9f3q02H330kV5//XWVKlVKBw4c8HBCIG0cPHhQVatWVfbs2bV161YVK1bM7kjpBn1vn/Pnz6tNmzbauHGjJN2yuMf5OVu7dm0tWrRIuXPn9lZEv3Ps2DGVLVtW169fV/bs2fXWW2+pV69eyp8/f4L9oqKiNGnSJH3wwQe6cOGCsmTJov379zP7Typ8//336t69uyzLUo8ePTR69Ghly5YtyX0vX76sQYMGacqUKbIsS9OnT1eXLl28nNg/0O/uofgHHle4cGH16NFDzzzzDEtdeIjzy3NasizrjtYKBf4rjDH69ttvNW7cOP3xxx9ut+M9n3I5cuRQdHS0tm7dqipVqrjVZseOHapWrZpy5sypv//+26P5/NXzzz+vr7/+WoUKFdLq1atVrlw5LVy4UO3bt5d042L432trf/XVV3rxxRdVs2ZNbd682Y7YfiM+Pl7GGNf62k7Xrl3Te++9p4kTJ7qe5M6dO7eefvppvf/++7ddugewS7Vq1WRZlubMmeOabVOSq1CwWLFiid7v8CxjjF599VV9/vnnio+Pl6Qkb0hKN74PDB48WCNHjqT4Kg0cOnRIHTp00J49e9SrVy916dJFDz74oPLkyUP/ehD9bp+MGTO6rm0kKWfOnOrcubOeeeYZ1ahR446OFRYWplKlSsmyLGabTMb169cVGBioXbt2aeLEierRo4fdkdKNNWvWKDY2VlWrVk0w/f+xY8fUtWtXbdiwIcH+FStW1PTp01W5cmVvR/V5QUFBCf7befPlscceu+3N3mvXriksLMw1+2Hv3r01YcIET0VNV7Zu3aqVK1cmuRxP06ZNVb16dZsT+oeFCxe6Cmc/+ugjdejQwTWDMzyLvvc+Y4waNGjgmnEpX7586tSpkwIDA1W4cGEZY3T69Glt2bJFs2bNUlRUlCzLUt26dbVmzRqb0/u2adOmqVevXq7/tixLJUqUSPDQ0NGjR12zNVuWpSlTpqhbt242pvZ9xhjVq1dPv//+uyzLUv78+V3v+X8/sDV79mydOXNGxhjVrVvXNWs/7hz97h6Kf+BxsbGxCggIsDuGX7t5dqW0wkCde5yzPViWpbCwsETbU+Lfx4L7YmJi1K5dO9dSPHfyEcd7PuVy5cqlS5cuad26dW6vL/z777+rbt26ypo1qy5duuThhP7p4MGDqlSpkuuJ6jx58uj8+fMyxujuu+/WoUOHEg1uPProo/rll1/Ur1+/O5oeEynDTCiedfbsWW3cuFGHDx92e4a3t99+2wvJfNOtnsR2OBxyOBz6448/3H5CG2lr9+7dGjdunFauXKmDBw8meK1UqVJq2rSpnnvuOVWsWNGmhP7l5iI35+CouygmTzn63V7OMYVatWqpT58+6tSpk7JmzZqiY126dElz586VJApaknH8+HGdOXNGvXv31q5du9SkSZMEBW+3K7jlSW3POXDggPbs2aPY2FiVKlUqXS0RkNb+/bCic4zG3XO8c/+8efMqJCQkQYE67lxoaKgGDBig0NDQZPerUaOGxowZc8fFn/g/zhkMjx07piNHjsiyLGXKlEmlSpW67TnesiytWrXKW1H9Dn1vjx9++EHdunWTZVnq0qWLxo4dqxw5ciS576VLlzRgwABNnz5dlmUl+fAi7sySJUvUr18/nTx50rXN+Vl78/2RokWLasKECWrVqpXXM/qj8+fPq3Xr1tq0aZOkW1/fOP8OatWqpcWLFytPnjxey+iP6Pfbo/gHHudckqpdu3ZuP+1+6dIlzZs3TxJLT7nj2LFjHjlu8eLFPXJcf+IcJP134UhqCrIoQkm5kSNHupY/Kl68uHr06KHKlSsrd+7cbv2dNGjQwNMR/VLZsmV18OBBjRo1Ss8//7xbbZwz0Nx33306dOiQhxP6r9mzZysoKEiXL192bcudO7cWLVqkOnXqJNg3IiJCxYsXV2xsrObOnat27dp5OS2QNiIiIvTSSy9p7ty5d3yzl8/XW3MOgu7cuTNBEUlKlmeA51y/fl3nz5+XdKPokydY0x7X8fag3+01ePBgPfPMMypXrpzdUdKNm4siKHiDvypRokSC9/axY8dkWZaKFCmijBkz3rKdZVnKkiWLihQpotq1a+u5555T0aJFvRHZb82aNUvdunVTbGys62ZYpkyZlDdvXhljdP78eV2/ft21f8aMGfX999+rY8eOdkX2af8+x7vDsizX5wHXNSlH39ujdevW+uWXX9SwYUOtXr3arTaNGjXSmjVr1LJlSy1ZssTDCf1fbGys5s+fn+zMbu3bt2eihjQWHx+vcePGaezYsdq3b1+S+5QrV04DBgxQv379PDKRQ3pEvyeP4h94XEpuGDiniXY4HAxo4D/t5ikVJ0+enOT2lLj5WHBfhQoVtG/fPtWqVUsrV67UXXfdZXekdKFPnz6aOHGiSpcurV27diU7iCfdmKGpUqVKOnjwoLp166YpU6Z4J6ifioyM1JIlSxQREaEiRYqobdu2Caavd1qxYoV++uknSdKXX37J8lPwSWfOnFFgYKCOHTt2R7O7OTmXTkJiefPm1T///KPFixerZcuWru0U/yC9GT58eKraDxs2LI2SpC/0O9IbCt6QHnFdaY/9+/eratWqunbtmgICAtSnTx8FBQWpSpUqrgcA4uLitHPnTk2cOFHffvutYmNjlTlzZm3fvl1ly5a1+U/gexo2bJiq2X9/++23NEyTvtD39ihSpIgiIyM1e/ZsPf744261mTdvnjp06KDChQsrPDzcwwkBzzt16lSShVdFihSxOZl/o98To/gHHpea4h8GNADciaxZs+ratWtasGCBHn30UbvjpBvOJbwsy9Ljjz+uqVOn3nKZgOjoaHXv3l3z5s2TZVlavXo1My4BcFv//v31zTffSJI6duyo5557zjXDG0urpU6DBg20fv16Pfzww5o4caJKlSqlDBkyuK7ld+/ezYwQAOCnGjduLMuyNGnSJLdnAA4PD1fXrl1ZIiOFpk6dmqr2LKkGX+S8KT9lyhRmG/eiXr16aerUqcqSJYuWLl2qhg0bJru/cyaOa9euqUePHpo0aZJ3ggLwWZkzZ1ZsbKxCQ0PdXi5z+/btql69ujJlyqSrV696OCEApB/M74X/JOdsP0xBlzoXL17UqFGjJEnPPvusChcunOz+p06d0rfffitJGjJkCLOmwOfkyZNHERERuvvuu+2Okq7Url1bnTt31owZMzRv3jxt3rxZffr0Uf369VWkSBFZlqXw8HCtXbtW3333nU6cOCHLstShQwcKf+DTzp49q++//17r1q3T4cOHdfHixdsWLVuWpbCwMC8l9D+LFy+WZVnMGuYBzzzzjNatW6dNmzapQoUKCV4zxiRYCsxdLEuSclFRUXrllVckSe+9995tl7o4efKkhg4dKsuy9PnnnytXrlzeiAnATwQHB8uyrARLyd7OlStXXO1w5yjesd/OnTvv+Dp+4sSJXkrnn4KDg+2OkC6tWrVKlmXpxRdfvG3hj3TjoYAXX3xRI0eO1MqVKz0fEIDPy5Url86ePavw8HC3i3+cs/0wMzkApC0qK/CfdODAAUlKctkSuO/nn3/WO++8o1KlSuntt9++7f6FCxfWDz/8oEOHDqls2bLq1KmTF1ICaady5cqKiIjQ0aNHVaVKFbvjpCuTJk1SVFSUVq5cqZMnT+qdd95Jcj/nhIPNmjVL9dOuSNqZM2c0btw4SXLr3I+UmT17tp599llduHBB0p2tJY+UO3PmjCQpKCjI5iT+p1u3btq1a5dGjRqV5M0vJoz1rjlz5mjKlCmqUqXKbQt/JKlYsWLauXOnduzYobp166Z6CVoAAPzVgQMHFBQUpE2bNrndxhhD8Q98lvM7VKtWrdxu07p1a40cOdLVFgCSU7FiRa1Zs0aTJ09W69at3WrjnFUsJQ8a4YY///xTjzzyiAICAhQcHOzWQ0MNGjSQMUarV69mFr5UuHr1qmbNmiVJatmypQoUKJDs/mfOnNEvv/wiSerSpQsTX6QQ/e6e9PGnhFetXbs2ye0hISGKiopKtu21a9cUFhamTz/9VJZlcfM+lZzL6rhbxGNZljp37qwRI0Zo9uzZFP/A5wwcOFDLli3TxIkT1a5dO7vjpCtZsmTR8uXL9dVXX+nTTz/VyZMnk9zvnnvu0ZAhQzRgwACKIDwkMjJS77zzjizLovjHQzZv3qwuXbooPj5exhgVLVpUVatWVd68eeVwOOyO59eKFi2qo0ePKlu2bHZH8Usff/yxnn/+ef322286efKkrl27puHDh8uyLPXr108FCxa0O2K6sWDBAtcsee7q1KmTtm/frnnz5lH84wHh4eF66623uPnrZfT7f5dzlqAsWbLYnARw38mTJ1W/fn1FRUW5CpuzZ8+uPHnycB0Pv1WgQAGdPHnyjs7XmTNnliTlz5/fU7HSpX/++UcLFiyQJHXv3t3mNOkLfe9ZHTp0UHBwsObPn6933nlHw4YNu+W4rzFGw4cP1/z582VZljp27OjltP5j5syZOnr0qB555BG3HxoqXbq0li9frhkzZujVV1/1Qkr/tHTpUvXs2VPFihVTly5dbrt/njx59Oabbyo8PFx58+bVo48+6oWU/od+dw/FP0hzzvWbb2aMuaMntJ1P1PTt2zet46Ur+/fvl3RjSR531apVS5K0d+9ej2RKL25VBOeu+vXrp1GS9KVVq1Z6/vnn9dVXX+nll1/WRx99pAwZMtgdK92wLEsvvPCCnn/+ee3YsUPbt293FX3mz59f1apVU+XKlSn6gc/76KOPFBcXp7vuukvffvutW182kDbq16+vo0ePateuXapevbrdcfzS3XffrW7durn+e/jw4ZKkAQMGqHz58nbFSncOHTokSXrooYfcblOjRg1J0sGDBz2SKb07f/68pkyZQhGKl9Hv/13OJyhZcjltHT16VEFBQbIsS6tWrbI7jt95//33debMGVmWpWeeeUYvv/yySpcubXesdOf69ev64Ycf9PPPP2vnzp2KiorSlStXkm3DcrIpV6dOHc2aNUshISGu68Xb2bJliySpbt26noyW7pw4cUI9e/aUw+GgAMXL6HvP6tOnj77++mvt379fI0aM0Ny5c9WzZ08FBgaqUKFCsixLERER2rx5s6ZOnao9e/ZIksqWLav/x959R0V1fW0c/x5sKCr23o0VbAgaQVEJ9liwICrWqFhjQrppGn9JTGJP7F0D9q6xoYJd6YpdYklURBQriJR5//CdiSjiKMyMDPuzVtYyd+6Z9TDizJ179tlnyJAhJk6fde3cuROlFB07dtR7TOfOndmxYwd//fWXFP9kwJo1awDo2bOnXt1kcubMSa9evZg0aRKrV6/ONkUomU1ed/1I8Y8wiLS2BXidrQLKlSvH2LFjpXNHBv37778AlC5dWu8xpUqVAnhp1w6hn7SK4PQlNzQyZtq0aVSoUIFvvvmGtWvX0rVrV6pXr06+fPleOVa+/GUOpRQNGjTQe49nIbKaw4cPo5Tiyy+/lMIfI/v0009ZuXIlkydPxsPDQ7oNGEGFChWwsLAgd+7cpo6SrVy/fh3glS2Mn6VdmS3X8UKIV3nZ4qxvvvmGQoUKpTtW27E5MDAQpRTNmzc3QMLs69GjR/j7+8uCCQPZsWMHSin69evHvHnzTB0nWzp//jxdunTh3Llzsq2skXh7e7Nu3Tp++uknunfv/srry+joaH7++Wdy5crFxx9/bKSU2Yv87puOvPaGkStXLrZv346LiwuXLl3i9OnTfP755y89X6PRUKVKFbZv355ttuExhKtXrwJQt25dvcdot1nTjhVv5uTJkyilXmsRf7NmzZg0aRLh4eEGTGbe5HXXj7yriky3b98+3Z81Gg0uLi66VXqVK1d+6TilFJaWlpQuXZry5csbI6rZ07YsjouL03uM9lwpPsk4+TJhGnFxccTGxpIrVy7++ecfpk+frtc47Q1AIYR4lbt37wLQpk0b0wbJhmxsbFi0aBH9+/enTZs2zJ8/X1ZrG5h226/bt2/zzjvvmDpOtpEnTx4eP37MvXv39B5z//59AJkwFkK8krab0rM0Go1uO4xX0X7XLVKkCF999VWm5xPCULTFtfLd3zQePXpEu3btuHTpEhYWFnTu3JnixYszf/58lFJ88803xMbGEhQUxNGjR1FK0aRJE1q1amXq6Fmag4MDc+fOZdiwYTRu3JgpU6bQqVOnF7a6S0lJYcuWLXh7e3Pr1i1mz579Wl0ohRDZW8WKFTlx4gTjxo1j4cKFuntnzytUqBCDBw/mu+++I3/+/MYNaWaio6MBXut11J4bFRVlkEzZhbbxwuvMZWs7psqCrTcnr7t+pPhHZLqXrfpq1KiRbBVgZKVLl+bChQsEBQXpvfVXUFAQ8F8HIPFmni2Ce5lHjx5x7tw5VqxYofs7mjBhguwznwFxcXG0bt2aI0eOAFKAJYQwjNKlS3P16lWZYDeRXr16Ua1aNTp06EDt2rWpW7euXh3eZMuYNzNw4ECUUqxYsYLGjRubOk62Ua5cOe7du8eRI0f0XtF06NAhAMqWLWvIaEIIM1ChQoVU1zFXrlxBKUXp0qXJlSvXS8c9u2jL0dGR4cOHU6ZMGWNEFiJTFC5cmOjo6Fd2uBKGMWfOHC5dukSOHDnYuXMnLi4unDp1ivnz5wP/bTcLEBYWhqenJ0ePHsXDw4NRo0aZKnaW8bKublq1a9cmPDycbt26UbhwYRo0aECJEiVQSnHz5k3CwsK4c+cOAPXq1ePgwYMcOnRIvkMJIfRmZWXFb7/9xo8//khwcDARERG695UiRYpga2tLw4YNpbNwJrG2tiYmJoaoqCjq1aun1xht0Y8+uySIl9M2T0hISNB7zJMnT4DXa9YgUpPXXT9S/CMM7tKlS4DchDaFZs2acf78eWbNmsXw4cPTvYkHkJiYyKxZs1BKyZ7OGaRv6/P27dvz8ccfM3HiRMaOHcv8+fPx9fU1cDrzNWXKFA4fPgzAu+++i5eXF3Xr1qVQoUJSVGUkGo2GsLAwwsPDiYmJIT4+/pVFWN99952R0gmROVxdXVm0aBHBwcE4ODiYOk62c/78eby9vYmJiQEgPDz8la1bNRqNFP+8IWtra+7fv0+1atVMHSVbadGiBREREfz+++8MHz6cggULpnv+/fv3+eOPP1BK0aJFC+OEFEJkWZcvX071/9rvSrt27ZJFW8Ks2dvb89dff3H+/HnZptoEtmzZglIKd3d3XFxc0j23fv367Nu3j3r16uHt7U2TJk1o2LChkZJmTWl1dXueUgqNRsOdO3fYu3dvqse0926UUqm+Y8l3KCHE68qdOzdNmjShSZMmpo5i1qpVq0ZMTAw7duzQuzv59u3bAahataoho5m9kiVLcvnyZSIiInj33Xf1GnPy5Eng9bZ3F6nJ664fKf4RBlexYkVTR8i2Bg4cyMKFC7lw4QK9e/dm6dKlL63ojYuLo1+/fpw/fx6lFAMHDjRy2uztyy+/5Pjx46xatYqOHTvSq1cvU0fKknx9fVFK0a5dOzZv3iwFP0a2dOlSxo8fz5UrV15rnBT/ZC4rKyucnZ2lK40BffLJJ6xYsYJJkybRp08fChQoYOpI2cbVq1dxdnbm1q1bupvTBQsWxNraWt7zDaRy5cqEh4cTGxtr6ijZipeXF7NmzeLGjRt06NCBNWvWvLQzZ1RUFD169OD69etYWFjg5eVl5LRCiKxOe+1oZWVl6ihCGNSHH37Itm3bmDdvHj179jR1nGzn9OnTALi5uaX5uLZgX6t48eJ4e3vz+eef88cff7B48WKj5Myqnu/qJoQQwry1adOGw4cPM2/ePIYOHUqtWrXSPV/bbU8pRdu2bY2U0jw5Ojpy6dIl5s+fz+DBg/UaM3fuXJRSehetiBfJ664fKf4RJnP58mUGDRqEUoo9e/aYOo5ZcnR0xMPDg5UrV7J+/XqOHTvGkCFDcHZ2pnTp0iiluH79Ovv372fBggX8+++/KKXo3r273p1rROYZMGAAGzduZN68eVL884a0K1jHjBkjk8BG9vXXXzNx4kS9tlrTrjQThlGpUiX8/f1NHcOs1axZk+XLl9OnTx9dFyAbGxtTx8oWfvjhB6Kjo7GwsODTTz9lxIgRUmhuYG5uboSFhbFly5ZXrtAWmcfGxoYPP/yQadOmcfjwYapVq4a7u3ua1/GrV68mLi4OpRQjR46kfv36po5vlgoXLky/fv1kUs3I5HU3Drl2FNlFq1at+Pzzz/n1118ZPnw4M2bMeGWXbJF57t69C6ReKJonTx7dnx8+fPjCwgonJycAAgICDB8wi3u+q5t4++TOnZsKFSrIPUsTkNdemKPhw4fz66+/EhcXh4uLC/PmzaNjx45pnrt582a8vLyIj48nX758jBw50shpzUvv3r3x8fEhKCiIMWPGMG3atJd+Z9VoNHz00UcEBwejlKJ3795GTms+5HXXj9LI7JswkVOnTlGnTh2UUiQnJ5s6jtl6/PgxnTp1ws/PL90bptq3glatWrFp0yYsLS2NFVH8v7CwMOzs7ChatCi3bt0ydZwsqWzZskRFRREUFCQtvI3o2LFjNGnSBKUUrq6u/Pbbb6SkpGBnZ4dSiqSkJGJjYwkKCmL27Nls2rSJpk2bsmbNGkqWLGnq+EK81KBBg9J9PDw8nNDQUJRS1KlTh5o1a75yz2zZeipjKleuzNWrV/noo4+YPHmyqeNkC/fv36devXrcuHGDv/76SwqAjCglJYUhQ4boVrmnd0MDYPDgwboVTUIIkRmSk5O5du0a8LSjhDC8W7duMWvWLAC+//57E6fJupYtW5bu4/PmzePIkSOULl2a7t2763UdD9CvX7/MipgtFSpUiAcPHnDs2DHs7e0BiImJoUSJEiilOHHixAuLKo4ePYqjoyOWlpbExcWZIrYQQgjx1vLx8aFv3766+wCVK1emWbNmqRYNHThwgEuXLuk67C1ZsoS+ffuaOHnW5+rqyt69e1FKYWdnx4cffqhbsAVw48YN9u/fz++//05wcDDwtOPqvn37TBk7y5PX/dWk+EeYjBT/GI9Go2HGjBlMmjRJd+PueeXLl+ezzz5j5MiRMmFgIjt37qRdu3ZyQyMDunTpwpYtW/D19ZUW3kY0YMAAli1bRqVKlTh//jw5c+ZM9z1+9uzZjBw5knr16nHs2DFy585touRCpM/CwkKvz8Tn29O/6jy57nlz+fLlIyEhgQMHDuDo6GjqONnGxYsX6d69O6dOnWLgwIH07t2bunXrUrhwYbluNIJNmzbxyy+/cOzYsRc65ymlcHR05IsvvuD99983UUIhhLnSXtNbWFiQlJRk6jhC6E3f6/jXoV3YIt5cgwYNOHHiBBs2bKBTp06648WKFSM2NpbZs2czdOjQVGNmzpzJ6NGjKViwoK5zkBBCCCH+s2zZMkaMGKGbU3r+Gkh7H8HKyorZs2fj6elp9Izm6M6dO7Ro0YKIiIhXXndqNBrq1KnDvn37KFKkiJESmid53V9Nin+EyUjxj/FpNBrCwsIIDQ0lJiYGePoF287Ojnr16snkjYl16tSJrVu3Ur16dc6ePWvqOFnSvn37cHV1xdHRkf3798vvtJFUr16dyMhIJk+ezEcffQS8+j2+R48erF+/PtUYkbb9+/cb5HmdnZ0N8rzmpFKlSgZ5H7l06VKmP2d2UbVqVS5fvpxqtbAwrBw5cuj+rG+hm5ZMkmWuO3fuEBYWluo6vkGDBhQuXNjEycxLz549ad68OS1atKB27dqmjpNt/f333xw5coSoqCji4uIYPnw4xYoVM3WsbEfu2xjOnj17eO+9995o7IgRI3TdgUTaDLGti/w7yLghQ4awaNEivv76a3744Qfd8Z49e7JmzRqqVq3K0aNHKVq0KPB0GytnZ2euXbtGs2bNZItCkaVdu3ZNd11jb29P3rx5TR3JLM2ePZvmzZvLdbzIdm7cuMGMGTP466+/iIiI0BX8WFhYYGtrS8eOHRk1apR04c9k8fHxjB07lvnz5790Qb+VlRVeXl5MmDBB3vszibzu6ZPiH2EychNJCHRbIU2dOpUdO3aglOKLL77gp59+MnW0LGvixImMHTuWHj16MHfuXAoVKmTqSGavQIECxMXFsWPHDlq1agXAmTNnsLGxQSnF48ePyZUrV6oxmzdvpkuXLjRu3JgjR46YInaWIatWhfjPsGHDmD9/Pn/88QfDhw83dZxsISOTZ3KdL7KiZz93ixUrhrOzs64YyNbW1sTpzF9oaCgfffQRBw8eTHX85MmTqSZxZs6cyfjx47G2tub06dMvXGuKzCH3bQynYMGC7NmzBwcHh9caN3ToUBYuXCh/H69w5coVgzxvxYoVDfK82cXq1avx8PCgbt26hIWF6Y4fOnSIZs2aoZSiUKFCuLi4EBcXx8GDB3nw4AFKKZYvX07v3r1NF97MJCcnExsbS3x8/AudJZ8n2z6+uQcPHjBp0iQWLVrE9evXdcefv65ZuXIl69evx9ramvnz55siqtnQXstrr+NbtGhB8+bN5TpeZCtJSUncuXMHgCJFipAzZ04TJzJ/d+/eZd++fWk2XmjZsiXW1tYmTmie5HVPmxT/CJORm0jCnD27Sl5fGo2G6tWrc+zYsWz7oZRR2pVjf/31F8ePH8fKyopWrVpRvXp18uXL98rx3333naEjmiVLS0sSExMJCQmhXr16APzzzz9UrFgRpRT//PMPZcqUSTUmNDSUhg0bUrRoUW7dumWK2FmGrFoV4j8XL17Ezs6OIkWKEBISkq1atprK+PHjMzT++++/z6QkQhhH/fr1iYiIICUlBUjdMr1IkSKpioHq1q1rqphmadu2bXTv3p0nT56kmohUSr0wSfbw4UNKly5NXFwca9euxc3NzRSRzZ7ctzEcCwsLihYtyv79+6lVq5ZeYwYPHsyiRYvk70NkWXFxcbRv357k5GSWLFlC1apVdY+NGzdOd09H+9mr/SwYNGgQCxYsMH5gMxMTE8Pvv//Oxo0bOX36tO5aJz2ycOjNXbx4kXbt2vH333+/8rrmypUrvPPOO6SkpBAQEEDTpk1NEdksPHsPLa3reG0xkFzHCyGEMFdS/CNMRm4iCXP2upP1OXPmpHv37kybNo0SJUoYKJX5e75DyutuTyLvRW+mfPnyXL9+HX9/f5o1awbAkydPsLKyIiUlhV27dr3Q0v6vv/7i/fffJ3fu3Dx+/NgUsbOMgIAAgzxv8+bNDfK8Qhjanj17cHd3p0SJEsyYMUPXcUyI7ODy5csMGjQIpRR79uwxdRyzdffuXQICAvD39ycgIIATJ06kWQxUqFChVMVA9evXN1HirC8qKorq1avz8OFDbGxsmDRpEk2bNqVAgQJpTpIB9O3bF19fXz744APmzZtnouTmTe7bGE7NmjU5f/48ZcuW5dChQ6/srDFgwACWL1+ORqPB09OTZcuWGSmpEMazZ88eFixYwKlTp0hKSqJatWr069ePbt26mTpalnf48GG6du3KrVu3Xtnp51ny/v9mEhISqFu3LhcuXMDKyoqRI0fi7OzM+++//9LrmtatW7Nnzx4++eQTfv31VxMlz/qevY739/fn5MmTaV7HFy5cmGbNmumKgeQ6XgghhLmQXl/CZEqUKCErgYXZ0ud328LCggIFClC5cmWcnJwoVqyYEZKZv+dvYkiNq+HZ2Nhw/fp1zp49qyv+yZ07NzY2Npw8eZJVq1a9UPzj4+MD8EJHIPEiKdLJuiIjI4mJiaFSpUqyp3YmcXFxAZ62cD137hxt27alUKFCVKtW7ZUd3qRYQpiDR48e4e/vn+nbQYrUChUqROfOnencuTPwdBJh//79umKg8PBwUlJSiI2NZfPmzWzevFk37vbt26aMnmVNnTqVhw8fUrFiRQ4cOKDX1r0tWrTAx8eH4OBgwwcUIpPt3r0bJycn/v33X1q1asWBAwfSXAik0Wjo16+f7vtT//79WbRokbHjCmEU77333gv3DkTG3b59m86dO3P79m3y58/P4MGDKVSoEOPGjUMpxYIFC4iNjSUoKIhNmzbx+PFjnJyc+OCDD0wdPcuaM2eOrvDnwIEDehWWtGvXDj8/P44cOWL4gGYsvet4f39/XVH/nTt32LRpk+463traGmdnZzZu3GjC9EJkDm0Bv4WFhXRvM7Jbt24xe/ZsQHaZMCZ53VOTzj9CCCGEyJAff/yRb7/9Fg8PD3x9fXXHf/75Z77++mssLCwYO3YsPXv2JC4ujqVLlzJr1iyUUgwbNoyZM2eaML0Qr+/WrVusWbMGgD59+rywVePFixfp2bMnYWFhwNOiky5durBgwQK9JjPFyz3b4U3frzFKKV0nOFm1KrI66cLxdrh37x779+9nz549LF26lHv37gGyOj4j6tevz8mTJ5k+fTqjRo3SHde+76e1Qv7gwYM4OztTuHBhKboyEHnPMaxz587RrFkzbt++Td26dfH39091XZmSkoKnpycrV64Enm59NH/+fCkAzYDExEQuXLgAQNWqVcmTJ0+qxx8/fszXX3/N6tWriYmJoXLlyowYMSLV+5IQWc348eMZP348efLkISgoCBsbm5e+v0dFRdG7d28CAgL49NNP+eWXX0yYPOtq3rw5Bw8e5KuvvuJ///uf7nh61zV79uyhVatWlChRgqioKGNHzja01/HaYiBtUT/ItbwwH3INbzry2puGvO6pSecfIYQQQmRIly5d+Pbbb9m6dSv379+nYMGCAIwZM4b58+dz+fJlfvzxR3788cdU4woXLsxXX31lishCZMi6desYNWoUNWrUYMSIEakeS0hIoF27dvz999+64hSNRsOGDRuIiYnB39/fBInNh7Ozs0x4CSFMRqPREBoaqpssOHDgAPfv3zd1LLNw6dIlABo1aqT3mAIFCgDw8OFDg2QSUKNGDd3fjch8NWrUYPv27bi4uHDixAnef/99du/ejaWlJSkpKfTq1UtXcD548GDZ3i4TbNiwgV69elGsWDH++eefFx53c3Nj165duuv4s2fPMmbMGC5cuMD06dONHVeITLF9+3aUUgwaNAgbG5t0zy1VqhTbtm2jXr16TJo0iTZt2ui6rwr9nT59Gni6lZe+ihYtCjztVCMMp2DBgpQtW5ayZctSpkwZLly4wKNHj6RzvBBCCLMhxT9CCGEAFhYWWFhY8NNPP/H555+bOo4QBmVjY8O+fftISkpK1Uo0X7587Nu3D09PTw4dOpRqjK2tLcuXL6dcuXLGjitEhu3atQulFN26dXvhsSVLlhAZGYlSik6dOvHee+/h5+fHli1bOHDgAKtXr8bd3d0Eqc2DFE8Z3w8//JCh8dJuV2Rl6RX7aCcI8ubNS5MmTWjRogUtW7Y0ZdwsLTExEYBcuXLpPUY7OWZlZWWISALImTMnFStWNHUMs9awYUM2bdpE+/btOXz4MN26dWPt2rX06dNHt/WIl5eXro29yJidO3ei0Wjo2rUruXPnTvXYtm3b2LlzJ0opypUrh4ODA8ePH+fatWv88ccfeHh40KRJExMlF+LNXbx4EQBXV1fdsWcXVCQnJ5MjRw7d/+fNm5ePP/6YkSNHMmfOHCn+eQMPHjwAeKFLcHoeP34MvN61kHi117mel2t5IYQQ5kCKf0SmyejEwMvIhIHIinLnzk1iYiLNmjUzdRQhjKJ58+ZpHq9YsSIHDhzg3LlznDp1iqSkJKpVq0aDBg2MnDDrks/Xt8+5c+eAtLsTrFixAgAXFxfdhM3o0aNp3bo1fn5+rFixQop/RJYybty4DHVbkvcakdWEhoayb98+/P39OXjwoG5Lr7SKfVq0aEHjxo1lkiYTlCpViitXrnDp0iW9rxOPHDkCIMXkIstr0aIFK1asoHv37uzYsYPKlStz69YtAIYPHy7bJGeikJAQlFI4Ozu/8NjixYsBqF69OsePH6dAgQLcu3cPR0dHzp49y4IFC6T4R09VqlTJ9OdUShEZGZnpz5sdaAsdni3mtLS01P35wYMHL2xPbW9vD8CxY8cMH9AMFS1alKioKG7evKn3mJMnTwJQsmRJQ8XKFp4v9pHreSGEENmNFP+ITJPRiYGXkQkDkRWVKVOGK1eupFo5I0zj/v37PHjwQK+9PitUqGCERNlTjRo1qFGjhqljZEny+fr20U7GlClTJtXx+Ph4jhw5glKKoUOHpnps0KBB+Pn5ERISYrScQmSW12mBrpSSlukiS2vYsKHuc1ej0cjkgJE4OTlx5coVNmzYQNeuXV95flxcHHPmzHnpJL4QWU3nzp1ZsGABgwYNIjo6GoBRo0YxY8YMEyczL9rX9vnilJSUFPz8/FBKMWrUKN22gtbW1owaNYqRI0dy+PBho+fNqi5fvpzpzylb/765/Pnzc+/evVSdmosUKaL78+XLl6lfv36qMdouNNp/M+L11K9fnx07drBnzx69t/5atGgRSikaN25s4HTmrWjRolLsI4QQIluT4h+RqeRGvxBPOTs7s3z5ckJCQtLsDCEMa/fu3cyaNYsDBw4QGxur1xilVKobIUK8TeTz9e2i3WbEwsIi1fGjR4+SmJiIhYVFqpbqAJUrVwbk5qnIelJSUl55TlxcHOfOncPHx4fff/8dBwcH1q1bJ6tWDaBEiRJ8//33po6RbXTu3JkvvviCxo0by6SjgfXv3x8fHx9WrFhB3759050oe/jwIR4eHly9ehWlFB988IERk5qnN120YmlpibW1NdWqVePdd9+lX79+2NjYZHK6rO3q1at6n9uyZUvGjBnDtGnT6N69O59++ulLx8vClTcTExMDpO56AhAWFsb9+/dRStGhQ4dUj9na2gLwzz//GCekGejfv7+pI4hnvPPOOwQHB3P16lXdPcpChQpRqlQpbt68yb59+14o/tEWu8nWmm+ma9eubN++nblz5zJixIhXbqE5fvx4jh07hlKKnj17Gimlebp7967uur1z5858/PHHNGnSRIp9hBBCZBtKI7NJQgiR6YKDg2nSpAkVKlQgJCSEggULmjpStvHhhx/q2qK/bqcCfboDiVdLTEwkJCSEiIgI7ty5AzxdVWZra4udnZ184RZZXpEiRbh37x5btmyhffv2uuPff/89EyZMwNbWlhMnTqQaExISgr29PVZWVjx48MDYkc3G/v37MzReukMY3v79+2nTpg01atTg6NGjL0yuCfG2y5cvn261u3bioGDBgjg7O9O8eXNatGhBgwYNpBjIALp27crGjRvJnTs3o0ePpkePHrz77rsopQgICCBXrlzs2rWLOXPmEBUVBUC/fv10W/WIN/d8QfOb0P6bGDx4MDNmzCBPnjwZfk5zYIhuwLJw5c0VKFCAuLg4du3axXvvvac7Pm3aNLy9vSlfvjxXrlxJNSYsLAw7Ozvy5MlDfHy8sSMLkWGjR49m1qxZfPrpp/zyyy+644MGDWLJkiWULFmSgIAAqlevDsDx48dp164dd+/epXXr1mzfvt1U0bOs5ORk6taty9mzZylfvjwzZ86kffv25MiRA6UUERER1KhRg0OHDvHrr7/y119/AeDg4MDRo0dNnD5re/aaRimlu45v0aIFzZs3l+t4kS3ExcURGBgIQPPmzU2cJnu5fPkyAwYMQCnFvn37TB0n25DXPTUp/hFCCAOZO3cuo0aNwtbWlpkzZ+Lo6GjqSGbP19cXT09P4OlKvi5dutCwYUOKFCmi1w1tWZ2WMQ8fPmTChAksXLjwpR2XChcuzAcffMA333yja6UuRFbj6OjIsWPHGDlyZKqtGGxtbTlz5gwjRozg999/TzXmr7/+4v3336dKlSpcvHjR2JHNhoWFxRvfqJOJMuP5+OOPmTFjBj/99BNffPGFqeMI8VoSExM5fvw4/v7++Pv7c+TIEeLi4oDUxUDNmjXTbR0gkwiZIy4ujvfffx9/f/90X0/tbaz33nuPrVu3SpFJJhg/fjwA27dv5/jx4wDUq1cPe3t7ihcvDjzd9jQoKIjw8HCUUjg4ONCmTRvu379PREQE+/fvJzExEaUUXbt2Zc2aNSb7ed4mmVFY9TxZuPLm6tSpw+nTp5kwYQJjx47VHX/vvffw9/end+/eLF++PNWYvXv34urqSrly5V6rk5MQb4utW7fSqVMnqlatyoULF3THIyIisLOzIzk5mRw5clCvXj3i4uI4f/48ycnJKKXYtm0bbdu2NWH6rCsyMhInJyeio6NRSpEvXz4ePXqEUorKlStz8+ZN3TWmRqOhTJkyHDlyhPLly5s4edZ26NAhuY4XQgiRrUnxjxBCGMCgQYOAp6tlTp8+jVKK8uXLU7duXQoXLpzu6j+lFAsXLjRWVLPSvHlzDhw4QPny5dm7dy9Vq1Y1daRs48yZM7Rt25Z///33lR2XtP8edu7cSY0aNYyUUIjM8+OPP/Ltt9+SO3dupk6dSrNmzViyZAlTpkxBKYWfnx8tW7ZMNeZ///sf3333Hc7Ozvj7+5smuBnIyASaTJQZj3aSrF69eoSGhpo6jhAZkpSU9EIx0KNHj4D/JhGsra1p2rQpLVu25OOPPzZl3CwvJSWFqVOnMmXKFG7cuJHmOUWKFOHTTz/l888/N0hhRXb1888/8/XXX9OoUSPmzZtH3bp10zwvPDycoUOHEhQUxLhx4/j2228BuH79OgMGDMDPz08mjJ+xdOlSgzyvLFx5Mx9++CF//PEHJUuWZO/evdSqVYvNmzfj5uYGwJ9//kmvXr1SjZkxYwYfffQRDg4OHDt2zBSxhciQxMREhgwZQnJyMj/88INuS2qAhQsXMnz48DQXSYwfP173Hi/eTFRUFEOGDGHbtm3pnte6dWsWL15M6dKljZQse3j+Ov7w4cOvLAays7MzZWQhXouLiwtKKRYtWvTK7QW1rl+/jqenJ0op9uzZY+CEQghTkOIfIYQwgOc7E2jfal+1kkCj0cjkZAYULlyY+/fvM3/+fF0BljC8u3fvYmNjo5ugsbW1pX///jRq1IiSJUui0WiIjo4mMDCQpUuXcvLkSQDKli1LREQE1tbWpowvxGu7d+8etWvX5saNGy+81zs6OnLw4MEXxjRu3JigoCDGjh3LhAkTjBnXrAQEBLzynEePHnHu3DlWrFhBUFAQjo6OTJgwAQsLC2l3bCTa7THy58/P/fv3TR3nrfbDDz8Y5Hm/++47gzyveDqJEBgYqJtEOHToUKpJBLmOzxzayZqgoCCio6NJTk6maNGiNGjQgKZNm0q3n0zm7+/Pe++9R+3atQkMDHzllo2PHz+mYcOGnD17lp07d+Lq6qo7XrduXSIjI3F3d2fFihXGiC+E3i5cuECdOnVITEwEnt5DiI2NRaPRUK5cOS5evEju3LlTjXn//ffZvn07w4YN020xLoQ5OXfuHEuWLOHUqVMkJSVRrVo1+vbti729vamjmY1Tp06xadOmNK9rOnfuLK+1kTx/HX/48GFdNyaQbsEi69HOQZ08eZLatWvrNSYyMpJq1arJd1cDuHnzJhEREdy5cwd4umjF1taWkiVLmjiZyG6k+EcIIQygUqVKGWoZeunSpUxMk33kz5+f+Ph4goKCaNCgganjZBtfffUVv/zyC0opfvjhB8aOHfvS33+NRsPPP//MN998g1KKL774gp9++snIiYXIuDNnztC3b19CQkJ0x5o1a8aKFSsoU6ZMqnPDw8N1raT37duHs7OzseNmWxMnTmTs2LF4eHjg6+tr6jjZxqpVq+jVq5cU/+ghI1vZpUdu4hlOXFwcBw8e1E0aBAcHk5SUJEX8Iktzc3Nj8+bNLF68mH79+uk1ZunSpQwcOJCOHTuyadMm3fFp06bh7e1NxYoV5XuteCutWbOGQYMG6bq4ARQqVIgtW7bg5OSU6tyoqCgqVqxIUlIS69ato0uXLkZOa17279+fofHyPUoIkRni4uI4cOAAu3fvZuHChdy/f1+u5UWWJMU/ppeSksLcuXOZNWsWp0+fTvOc2rVrM2LECLy8vKRzrZ4yes34MtnlWlKKf0SmcXFxAXihXZz2+JuQ1nNCiNdha2vLmTNn8Pf3p1mzZqaOk23UqlWL8+fP07NnT70n13v16sWqVauoUaMGZ86cMXDCrE0+X99uly5dIioqitKlS1OpUqU0zwkPDycsLAyAPn36kDNnTuMFFHTt2pVNmzaluY2DyHx3796ladOmnDlzBgcHB44ePWrqSG81Q934SUlJMcjzZkcvK/bR0t5SyZcvH46OjuzatctUUYV4Y2XLliUqKorAwEC9t7sICQnB3t6eUqVKcf36dd3xgwcP4uzsTN68eVMVVwjxNomOjmbbtm266/hOnTpRpEiRF87btWuXroPV9OnTKViwoLGjmpWMFD1LRw4hxJtK73r+2elRKysrHjx4YKqYQry2Nyn+OXHiBPXr15dr9Uxw8+ZNOnTooNvu/mXlFtprHzs7O7Zs2UKpUqWMljGrMsRCuex0LSkzDyLT+Pv7Ay9ua+Tv749S6qVvfGnRnm+IVbBCCPPVtWtXfvzxR/bs2SPFP0Z05coVAPr376/3mAEDBrBq1SrdWPFy8vn6dqtcuTKVK1dO95x69epRr149IyUSzxswYAAbN25k3rx5UvzzBvRZbZOSkkJsbCxBQUEsXryYmzdvAk9fe5E+KdJ5+7zO5ICjoyPNmzenRYsWNGrUSIo7M8DBwQFPT0969uwpN0NNQNua/nW6tWnPjY2NTXW8QIECwKu3vBbClEqUKMHAgQNfeV7r1q1p3bq1ERJlH7IO2fQePHjApUuXePDggV5dH7LLKvnM9Nlnn+Hp6Sn3AUzkda7nmzRpQosWLWjevDmNGjUyVWQhjGb79u0AlCtXzsRJsrbHjx/j4uLC2bNn0Wg0FC9eHHd3dxo1akTJkiXRaDRER0cTGBjI6tWriY6OJjg4GFdXV4KDg2Ubaz3INeObkztTItM4OzuneXPnZceFyI6SkpJ0N0cLFy4sEwSZ7JNPPmH58uVMmzYNDw8PatasaepI2UKBAgVISEigRIkSeo/Rnps/f35DxTIb8vkqRMZUqFABgIiICBMnyZpatGjxWu812i/nbm5ueHl5GSqWEAZTuHDhNCcH8ufPj5OTk67Yx97eXq7lM1FwcDAhISF8+umntGzZkj59+tC1a1ddIYkwrFKlSnH16lU2bNhAixYt9Bqzfv16AEqXLp3quLaQqHjx4pmaMav64YcfDPK83333nUGeVwhD2rdv3yvPefToEefOnWPFihUEBQXh6OjIhAkTZJuMTDB//nxmzZrFiRMn9B6TnVbJZ6bJkyczZcoUatasiaenJ7169Xppp2CRuZycnAgKCnrp9byjo6Ou2MfBwUGu50WWMmjQoDSPf/PNNxQqVCjdsQkJCURGRhIYGIhSiubNmxsgYfYxdepUzpw5g1KKDz74gGnTpmFlZfXCeX379mXixIl8/PHHzJ8/nzNnzjB16lS+/PJLE6TOOvS5ZhQvJ9t+CSGEgZ06dYo5c+bg5+fHhQsXdF86lFJUq1YNV1dXvLy8sLW1NXFS83Du3Dk6depETEwMEyZMoFevXhQuXNjUscyaq6sr+/btY8WKFbi7u+s1ZvXq1Xh4eNCyZUvZfkqYjcuXLzNo0CDZVu0ts3PnTtq1a4elpSVxcXGmjpPlvO5ES926dRkxYgRDhgyRAkWRJWl/5wsWLJiq2Kdhw4bkyJHDxOnMl42NjW4rWO17h6WlJe+//z59+vShffv2MjljQMOGDWPevHnkzJmTVatW4ebmlu7569evp2fPnqSkpODl5cWsWbN0j02aNInPP/8cJycnDhw4YOjobz1DtKwH9OrWIfR369YtZs+eDUhh1dtk4sSJjB07Fg8PD723GBcvSk5Oplu3bmzZsgV4vZX0Sil5v3kDOXPm1HX41H4GNGnSBE9PT3r06EHRokVNGc+sPfv9tUCBAjg5OemKfezt7eV6XmRpz19XPjvPpA/t+UWKFCEwMPCVnczFy9WvX5+TJ0/SqlUrduzYodeYtm3bsmvXLurWrUtYWJhhA4psTYp/hBDCQFJSUvD29mbmzJmkpKSku+enhYUFo0aNYvLkybKaSQ9VqlRJ9/G4uDiio6NRSqGUolixYuTLly/dMUopIiMjMzNmtrFmzRp69uzJu+++y8GDB1/5O5ySkoKTkxPHjx/H19eXnj17GimpEIZ16tQp6tSpIzdI3zKdOnVi69atVK9enbNnz5o6TpYTEBDwynMsLCwoUKAAlStXxtra2giphDCcSZMm0aJFC+zs7OS63MjCw8Px8fFh5cqV/Pvvv8B/N7ILFSqEu7s7vXv3lu19DeDq1avY2NjoimS7du1Kv379aNiwoa5jZ3R0NEFBQSxbtowNGzag0WjInz8/ERERui57AI0aNSI4OJhvvvmG8ePHm+TneZsY6n1Eto3MXHId//bq2rUrmzZt4s8//5QtfN/QzJkzGT16NAAlS5Zk4MCBNGzYkCJFiuj1HiXdIV7fzZs3WblyJT4+PgQFBQH/XdPkzJmT1q1b06dPHzp37kzevHlNGdXsvP/++7pin4YNG8r1vDArlSpVSlXoc+XKFZRSlC5dmly5cr10nFIKS0tLSpcujaOjI8OHD6dMmTLGiGy28ufPT3x8PBs2bKBTp056jdm8eTNdunTBysqKBw8eGDihyM6k+EcIIQzE3d2ddevW6Yp+bGxs0tzzU7sNiVKK7t27s2rVKlPGzhIM8cVNbvJlzAcffMDixYt5//33mTdvHqVKlUrzvJs3b+Ll5cXmzZsZOHAgCxcuNHJSIQxHJg3eHrGxsQQFBTF16lR27NiBUoovvviCn376ydTRhBBC6CEgIAAfHx/WrVun2zZZe6O7XLly9OnTh969e0v31Ey0e/du3NzciIuLe+XqYY1GQ758+di4cSOurq6645GRkQwePBh42gq/fv36howsRKaR6/i3l3airHnz5rIFxBtq3LgxgYGB1K5dmwMHDkh3bCO7ePEiPj4++Pr6cuHCBeC/axorKyvc3Nzo3bs3rVq1kkIVIcRr0XYCOnnyJLVr1zZ1nGylaNGi3L17l6CgIBo0aKDXmNDQUBo2bEjhwoW5ffu2gROK7EyKf4RJaDQa7ty5Q1xcHGXKlJF2i8Ls+Pr64unpiVKKunXrMm/ePBwcHNI8NygoCC8vL0JDQ1FK4ePjg4eHh5ETZy0DBw40yPMuXrzYIM9r7pYtWwY8XU0WGBiIpaUlrVu3xsHBgRIlSqCU4ubNmwQGBrJr1y4SEhKwt7dn5MiR6T5vv379jBE/y7p69eprj9Gu9LC2tiZ37twGSJW9yaSB4b3JNaNGo6F69eocO3ZMutKIt46LiwvAC9sFao+/Cdl6UJiTxMRE/vrrL3x9fdmyZQuPHz8G/ps0s7GxwdPTk88//9yUMc1GZGQk3t7ebNu27aWdZSwsLOjQoQNTpkyhatWqRk4ohGHIdfzbKywsDDs7O4oWLcqtW7dMHSdLKliwII8ePZLOy2+BoKAgfHx8WLVqFVFRUcB/1zTFixfHw8ODXr160bhxY1PGFEJkES1atEApxZIlS6hYsaKp42QrTk5OHD169I06/zRp0oRDhw4ZOKHIzqT4RxhNcnIyy5YtY/HixQQGBvLkyROUUpw4cSJVVerWrVvZv38/1tbWfP311yZMLMSba9myJQEBAdSoUYOgoCCsrKzSPf/Ro0fY29tz7tw5Wc0kspy09ht+2Wrh9B57llKKpKSkTMtojjJaOFuuXDneffddBgwYQLt27TIpVfYmkwaG97orIXPmzEn37t2ZNm2abtsSId4m2t/p5983tJ+tr/N1XXu+vAdlruDgYPz8/IiIiODOnTsAFClSBFtbW1xdXWnYsKGJE2YfDx48YN26dfj6+rJv3z7d77n8zme+69ev4+/vT0REhK7zUuHChbGxsaFly5ayTYAwO3Id//bauXMn7dq1w9LSUrc1oXg92uKf4OBg6cj2lkhJSWHv3r38+eefbNy4kfv37wNPr2nkfljmSkxMJCQkJM1reTs7u3S3ShJCiJeZP38+Xl5etG3blr/++kuvMe3bt2fnzp3MmjULLy8vAyfM2n744QeDPO93331nkOd920jxjzCK6OhounTpwrFjx1LdwE6rJd2zX7jlS4nIqrRt/xYuXMiAAQP0GrNkyRIGDRpEoUKFdF9GhMgKZBs208iM111biOXq6srKlSul/XcGyaSB4Y0fP/6V51hYWFCgQAEqV66Mk5MTxYoVM0Iy85WYmKhrT1+1alXy5MmT6vHHjx/z9ddfs3r1amJiYqhcuTIjRoxg1KhRpoib5WhX6gGpir+fPf4mpJA8406ePMnQoUM5fvx4uuc1btyYuXPnUqdOHSMlExqNhhUrVjBq1Cju3r0rn7tCiAyT6/i3V6dOndi6dSvVq1fn7Nmzpo6TJTVs2JCwsDB2796doe6SwjASEhLw9fXlk08+keuaTBQXF8eECROYP3++rpD5eYULF2bo0KF888035MuXz8gJhRBZmUajoX379uzatQsvLy+mTJmCpaVlmucmJCTwySefMGvWLNq0acP27duNnDbreX6xeWbJLp+vOU0dQJi/lJQUOnXqxPHjx7GwsKBHjx44Ozu/dELAxsaGJk2a6FqmSfGPyIqePHkCQN26dfUeoz03MTHRIJmEMJRLly6ZOkK2pN2mbs6cORw7dgxLS0vatGmDvb09xYsXB+DWrVsEBQWxc+dOEhISaNSoEUOHDuX+/ftERESwadMmYmJi8PPzo0uXLgQEBJjyRxLilb7//ntTR8h2NmzYQK9evShWrBj//PPPC4+7ubmxa9cuXYH/2bNnGTNmDBcuXGD69OnGjpvl+Pv7v9ZxYRx+fn507NiRJ0+e6H63c+XKRdGiRXVbWGuv2Y8ePUqjRo3YunUr7733niljm72QkBB8fX1ZuXIlN27cMHUcIYQQBhIbG0tQUBBTp05lx44dKKXo2rWrqWNlWR4eHoSGhrJ161Yp/nmLJCUlsWPHDnx8fNiyZQvx8fGmjmQ2rl69iqurK5GRkel2Ur1z5w6//PIL69atY8+ePZQrV86IKYUwjPv37/PgwQO9ihwqVKhghETm6cCBA3h7e3Pnzh3mzp3Lxo0bcXd3x8HBgRIlSqCU4ubNmwQGBrJmzRqioqJwcHDgk08+Yf/+/S99XmdnZyP+FG836V3z5qT4RxjcsmXLOH78OLly5WLz5s20adMGIN3VwB07duTIkSMcPHjQWDGFyFQVK1bkzJkz3Lt3T+8x2havsj9rxly9ehWAkiVLvtCd4HmPHz8mOjoakIvdjJDfWdPo378/w4YN4/jx43Tu3Jm5c+e+dFuj6Ohohg4dypYtW7CxsWHBggUA/P777wwbNoxly5Zx8OBBVq1aRc+ePY35Y5iVEiVKSHGKMDs7d+5Eo9HQtWtXcufOneqxbdu2sXPnTpRSlCtXDgcHB44fP861a9f4448/8PDwoEmTJiZKLsSbiYmJoUePHiQkJGBhYcEHH3zAkCFDaNCgATlzPr2FkpycTGhoKPPnz2fRokUkJCTQo0cPLly4QNGiRU38E5iXyMhIfH198fX15fz588B/NwELFCiAm5sbffr0MWVEITJs3759bNy4kfDwcGJiYoiPj0/3ZrdSisjISCMmNH9WVlY4OzsbZIWx+M+bbl1drVo1vvjii0xOk318+OGH+Pj4MHv2bNzc3GjWrJmpI2VrBw4cwNfXlzVr1ug60mjf8ytWrEjv3r1NGS/LS0xMpF27dly8eBGAmjVrMnDgQBo3bkypUqXQaDTcvHmT48ePs2TJEk6fPs2FCxdo164doaGhuut9IbKS3bt3M2vWLA4cOPDSTlfPky0GM+b5bs03b97k999/T3dMUFCQbn48LfJ38p+UlBRTR8jS5JNMGNyKFStQSuHl5ZXuG9uzGjRoAMC5c+cMGU0Ig+nWrRsTJkxg3bp1tGzZUq8xa9euRSmFm5ubgdOZr4MHD+Ls7EyBAgW4fPnyK4t/4uPjsbW1JS4ujsOHD9OoUSMjJRUi4zZu3Mi8efNwdHRk/fr16d6oLlGiBBs2bKBp06YsXryY1q1b4+7ujqWlJYsWLSIiIoLQ0FBWrlwpxT8ZULx4cSn+EWYnJCQEpVSaq4+0HciqV6/O8ePHKVCgAPfu3cPR0ZGzZ8+yYMECKf4RWc706dO5d+8euXPnZtOmTWl+h82RIwf29vbY29vTrVs3OnbsyL1795g+fbrB9qbPTqKjo1m1ahW+vr66bdee7cDUpk0b+vTpQ+fOnV/aWl28udu3b3PkyBH+/vtvvVcNf/fdd0ZIZn6io6Px8PDQdd98WcGPUirVY1KgkvkqVaokXfeM4HVXcOfMmZPu3bszbdo0rK2tDZTK/OXJk4ddu3bRtWtXWrVqxYcffkjv3r2pWbOmfI4aycmTJ/H19WXFihW6bqrafw9FihShR48eeHp64uTkZMqYZmHBggWcOXMGpRRjx45l/PjxWFhYpDqnRo0aODs74+3tzbhx4/jf//7H6dOnWbBgAcOGDTNRciHezIcffsjMmTMB6ZRibPJ6i7eVFP8IgwsLCwOe7tGsL23ngtu3bxsikhAG5+3tjY+PD3PnzsXZ2Rl3d/d0z1+7di1z586lcuXKfPrpp0ZKaX5WrVoFQJcuXShcuPArzy9cuDDdunVj6dKlrFy5Uop/RJYya9YslFKMGTNGrwkApRQfffQRPXv2ZN68ebr3JQsLC4YMGcLw4cMJCgoydGyzsGfPnjfe2mXEiBHMmjUrkxMJYTjaDnlVqlRJdTwlJQU/Pz+UUowaNYoCBQoAYG1tzahRoxg5ciSHDx82el5zpt1uKi4ujjJlyrzx6nmRvm3btul+r/VZvNK6dWtGjx7NlClT2LZtmxT/vKFHjx6xfv16fHx82Lt3r67gRHtD1dHREU9PT9zd3SlSpIgpo5qt6OhoPv74Y9auXfvaK06l+Of1aTsThIWFodFoaNCgAWXKlNG9B3l6ehIbG0tISAjXr19HKYWdnR22tramji7EG9NnoYSFhQUFChSgcuXKODk5UaxYMSMkMw/6XBtqNBomT57M5MmT9XpO6ULw5q5evarrXnjq1Cngv+uavHnz0rFjR/r06UO7du2k20wmWrNmDUopunTpwoQJE9I918LCgh9++IFTp06xYcMG1qxZI8U/Ikvx9fXljz/+AMDS0pIuXbrQsGFDihQp8kLRm8hc+/btM3UEIV5KriqEwd29exfgpVuRpCUxMRFAPqBElmVtbY2fnx89e/akV69e+Pr6MmDAgDT3/Fy6dCmbN2/G3t6e1atXy2qmDDhy5AhKKVq1aqX3mNatW7N06VLZZjADEhMTuXDhAgBVq1Z9oePS48eP+frrr1m9ejUxMTFUrlyZESNGpLv9o3i1EydOAPDOO+/oPUZ77smTJ1Mdr1u3LiBFt/pyc3Njz549ODg4vNa4oUOHsnDhQin+yQRJSUls27aNAwcO6N2ZQCnFnj17jJTQfMTExAC8sCo4LCyM+/fvo5SiQ4cOqR7TTkxqV7WKN5ecnMyyZctYvHgxgYGBPHnyBKUUJ06coHbt2rrztm7dyv79+7G2tubrr782YeKs79KlS8DrLV7p1KkTU6ZM4e+//zZULLNXsmRJ4uPjgf8mxmrVqkWfPn3o3bs3lSpVMmE68xcbG0vTpk2JjIyUFaxGsmTJEkJDQ1FKsXjxYvr378+pU6fYtm0bAEuXLtWdu2nTJkaOHMnp06f58ssv6datm6lim6WbN28SERHBnTt3gKddOGxtbSlZsqSJk5kf6ZJqWPq+f8v7vOE5Oztz+PBhNBqN7vW2sLDgvffeo0+fPnTt2lW3eEJkroiICAAGDRqk95gPPviADRs2vHCvTIi33dy5cwEoX748e/fupWrVqiZOlH00b97c1BGEeCkp/hEGV7hwYW7duvVaE4ra7b6KFy9uqFhCZAp9V9Vs2bKFLVu2pHtOUFAQVapUkVU1GaCdZKxRo4beY7TFENeuXTNIpuxgw4YN9OrVi2LFiqU50evm5sauXbt0NzzOnj3LmDFjuHDhAtOnTzd2XLNx//59AG7duqX3GO25Dx48SHU8b968wNOtNMSrPXz4kPbt27N//35q1aql15jBgwezaNEi2aYhExw8eJC+ffty9epV3bH0bmBrt8qQ1/7N5M6dm6SkJF0RkNb+/fsBKFeu3AuT8tob2fpsFSNeLjo6mi5dunDs2LFXTtJUrlyZTp066Yqx6tevb5yQZujx48cAWFlZ6T0mX758ACQkJBgkU3YQFxcHQJkyZfDw8KBPnz667cCF4U2cOJGLFy8CTxdHeHt761YNy+enYaxbtw6Atm3b0r9//3TP7dy5M7a2ttjb2zNgwADq1q1LtWrVjBHTbKWkpDB37lxmzZrF6dOn0zyndu3ajBgxAi8vL1mcKLIEKa56ezy7wNDOzo4+ffrQq1cvSpUqZcJU2cO9e/eAp9eU+ipdujTw3302IbKKEydOoJTi+++/l8IfIYSOFP8Ig6tduzYBAQEcPHiQli1b6jXG19cXpRQNGzY0cDohMiYzV9XIypuM037Be51tMLTnSseTN7dz5040Gg1du3Yld+7cqR7btm0bO3fuRClFuXLlcHBw4Pjx41y7do0//vgDDw8PmjRpYqLkWVv58uW5ePEiPj4+tG7dWq8xy5cvB6BChQqpjmu39ZGiW/1Ur16d8+fP07p1aw4dOvTC6/m8AQMG6F77Pn36GCOi2Tp79ixt27YlPj4ejUZD7ty5qVatmrQ0NqBKlSpx+vRpjh07lmq7uy1btqCUwtnZ+YUx2pXz8p7y5lJSUujUqRPHjx/HwsKCHj164Ozs/NKueTY2NjRp0oSjR4+yYcMGKf7JgFKlSnH16lVCQ0P1/j4aGhoKIF0iMmDAgAF4enrSsmVLKTYxgU2bNumKBzdv3mzqONlCeHi4bnuvtDxfuFy1alXGjBnDDz/8wPTp03VbPIjXd/PmTTp06KB7737ZvZjTp08zatQoFi1axJYtW2TSXrz1pPjn7VGlShV69+5Nnz59XmuBosi4IkWKEB0dzaVLl/QuJNd275StZUVWo91BRRZNCHPj4uICvNjFXXv8TWSnjvBS/CMMrlOnTvj7+zNr1ixGjhz5youoxYsX6yaK3dzcjJRSiDcjX6zfLsWKFePGjRv8/fff2NnZ6TVG+wWvcOHChoxm1kJCQl46Abx48WLgabHE8ePHKVCgAPfu3cPR0ZGzZ8+yYMECKf55Q506dWLy5Mn8+eefNGjQgI8++ijd86dOnYqPjw9KqRe2Mjl27BgAFStWNFRcs7J7926cnJz4999/adWqFQcOHEhze1ONRkO/fv3w8fEBoH///ixatMjYcc3KTz/9RFxcHDly5GD8+PF8+OGH5M+f39SxzFrLli05deoUv//+O25ubtSqVYvNmzfj7+8PQPv27V8Yo221rl1BKV7fsmXLOH78OLly5WLz5s20adMGIN0tMzt27MiRI0dkK9MMatasGX/++ScTJ07E3d2dggULpnv+/fv3+eWXX1BK0axZMyOlND/y+Wha2m56I0eONHGS7ENbKFu5cmXdsWcXUsTFxb3Qgey9997jhx9+YPfu3cYJaYYeP36Mi4sLZ8+eRaPRULx4cdzd3WnUqBElS5ZEo9EQHR1NYGAgq1evJjo6muDgYFxdXQkODn5hi2uRMTdv3sTf3z/NbddatGghRbUiy9J20xPGZ2dnx/bt25k5cyZdu3bVa8zMmTNRSkkBhchyKlWqxJkzZ3j48KGpowiRqbT3HJ9fGOTv76/r8K6v7NgRXop/hMF5eXkxadIkbty4QatWrVi2bBk2NjYvnPfPP//w66+/Mnv2bJRSVKtWjd69e5sgsRD6k+Kft0v9+vW5ceMGq1atonv37nqNWblyJQC2traGjGbWtF1jqlSpkup4SkoKfn5+KKUYNWqUbhsYa2trRo0axciRIzl8+LDR85qLL7/8kqVLl3L79m0++eQTfHx86NevHw0bNtQVokRHRxMUFMTy5csJCQkBnnbi+PLLL1M918qVK1FK0apVK6P/HFlR+fLl2b17N82aNePixYu0adMGf39/rK2tdeekpKTg6empe48ZNGgQ8+fPz1ZfNAxh7969KKUYM2YMY8eONXWcbGH06NHMmzeP6OhobG1tKVy4MLGxsWg0GsqVK0e3bt1eGLNr1y6UUtjb25sgsXlYsWIFSim8vLx0hT+vor1Zrd1CWbwZLy8v/vzzTy5duoSzszPz58/HwcEhzXOPHz/O0KFD+fvvv3V/X0JkRfnz5ychIUEm2o1Iu63mswU/zxYbXrt2jerVq6caY2lpqXtMvJmpU6dy5swZlFJ88MEHTJs2Lc1tHvv27cvEiRP5+OOPmT9/PmfOnGHq1KkvfI8Sb+aff/7h008/ZePGjS/d9j5Hjhy4ubnx22+/vbLTqhBCaPXq1Yvt27fj7+/PoEGD+P3331+6ne+jR48YPXq0bjJZOjWLrKZr1678+OOP7NmzRxaimFBkZCSbN28mPDycmJgYXbfyl8lOHWjelLOzc5r30F92XKSmNLLPjDCCwMBAXFxciIuLA6BGjRqcPXsWpRROTk7cunWL8+fPA09XyRcoUIBDhw7JZLwQ4rXMnz8fLy8vlFKsXLmSHj16pHv+6tWr8fDwQCnFjBkzZKXrG8qTJw9JSUmEhIRQr1493fGQkBDs7e1RShEZGUmlSpV0jx04cIDmzZuTL18+WZ2QAeHh4bRt25abN2++8sJXo9FQqlQpduzYQd26dXXH//77byZMmADA2LFjqVatmkEzm5Pg4GBcXFx4+PAhjo6O7N69G0tLS1JSUujVqxdr1qwBYPDgwcybN8/Eac2DpaUliYmJ7N+/HycnJ1PHuzpnlwAApDlJREFUyTbWrFnDoEGDePToke5YoUKF2LJlywt/D1FRUVSsWJGkpCTWrVtHly5djJzWPJQsWZKYmBh27tyJq6ur7riFhQVKKU6ePEnt2rVTjdFuU5UnTx7i4+ONHdmsjBo1ilmzZuk+W2vXrk3jxo0pWbIkSimioqI4duwYp0+fBp5+xo4cOZLff//dlLHNRnJyMhs3bsTPzy/NbhCurq506dLltbb6Fel777338Pf3Z/369XTu3NnUcbIFGxsbzp49y9atW2nXrp3uuLW1NQ8fPmTJkiX07ds31ZglS5YwaNAgrKysePDggbEjm4X69etz8uRJWrVqxY4dO/Qa07ZtW3bt2kXdunUJCwszbMBs4MCBA3Ts2JEHDx68ctW2UooCBQqwdetWmjZtaqSEQmQ+6XJlPBqNhmbNmnH48GGUUhQrVgx3d/c0r+XXrFnDrVu30Gg0NG3alP3795s6vhCv5d69e9SvX5/Y2FiOHj1KzZo1TR0pW4mLi2PkyJEsX778hWuatDrNaM9RSpGcnGy0nCIb0ghhJCdOnNDUrVtXo5TS/WdhYaGxsLBIdax27dqakydPmjquECILevLkiaZy5coapZQmZ86cGm9vb83Vq1dfOO/q1auajz/+WJMzZ06NhYWFpmLFiprHjx+bILF5yJ8/v8bCwkLj5+eX6vjUqVM1SilNhQoVXhgTGhqqUUppLC0tjRXTbMXGxmo+/PBDTaFChVJ9nj77X6FChTQffvih5s6dO6aOa3b27dunyZs3r8bCwkLTvn17TVxcnMbNzU332g8bNszUEc1KuXLlNBYWFprg4GBTR8l2bt68qVm0aJHmp59+0ixevFhz+/btNM/buXOnZsCAAZoBAwZo7t27Z+SU5iN37twaCwsLTXh4eKrj2u9Qp06demHMsWPHNEopTb58+YwV02ylpKRoPvvsM02OHDle+O76/HfYHDlyaD7//HNNSkqKqWObhe3bt2vKly+f5mv97LEKFSpoduzYYeq4ZmPVqlUapZSmW7dupo6SbXh6emosLCw0P/74Y6rj77//vkYppWnYsGGq76h3797V1KpVS2NhYaFp1KiRseOaDSsrK42FhYVm06ZNeo/ZtGmTRimlyZ8/vwGTZQ///vuvxtraWve+3r59e83atWs1V69e1SQkJGgSEhI0V69e1axbt07ToUOHVN9nr127Zur4Qry269evazw8PHTX9mn9lytXLk2vXr00169fN3Vcs3Hnzh1NkyZNXnod//w1pqOjo9wvE1nW2bNnNdWrV9cUKVJEM3PmTPldNpKUlBRN69atde8lxYsX1zRo0ED3nuPs7KypU6eOJnfu3LpjNWvW1LRo0ULTokULU8cXZk46/wij27ZtG5s2bSIoKIjo6GiSk5MpWrQoDRo0oFOnTnTr1g0LCwtTxxRCZFFhYWE4Ozvz8OFDXXV1hQoVKF26NEoprl+/ztWrV4Gn1db58+cnICBA9nXOgDp16nD69GkmTJiQahse7Qri3r17s3z58lRj9u7di6urK+XKldP9fYiMSUhIIDg4mIiICGJjYwEoXLgwNjY22NvbkydPHhMnNF+bNm2ie/fupKSkULx4cd3KseHDhzNz5kxTxzMrvXr1YvXq1SxevJh+/fqZOo4QBlOqVClu3bqFn58fLVu21B1Pr/PP8uXL6d+/PxUqVODy5ctGTmyeIiIimD17Nn5+fly4cCHVY9WqVcPV1ZXhw4dLx9pMsnz5cgYOHIhGo9GtiqxUqRKlSpVCo9Fw8+ZNrly5onvMwsKCpUuXyhYNmaRPnz6sXLmSH3/8UbY2MgJtF58mTZpw6NAh3fFt27bRsWNHlFJUrVqVzp07ExcXx5YtW/j333+la20GFS1alLt37xIUFKT3PQBtZ73ChQtz+/ZtAyc0b6NHj2bmzJnkyJGDxYsX4+npme75vr6+9OvXT9dhb8aMGUZKKkTGhYeH4+rqyp07d/TqclW0aFH27NlDnTp1jJTQvKWkpDB79mxmzZrFmTNn0jynVq1ajBw5kmHDhsl8lMiSqlSpAjztQBMdHY1SStfxKl++fOmO1XbqF2/m2R0lvvvuO7799ltOnz5N3bp1U3X2efToEQsWLODbb78lb968rFu3TroZCoOT4h8hhBBm5/Tp03h6eqZqya0tBHr2Y69hw4YsX75cWmJm0Icffsgff/xByZIl2bt3L7Vq1WLz5s24ubkB8Oeff9KrV69UY2bMmMFHH32Eg4MDx44dM0VsITLV0qVLGTRokO49ZtSoUXJz2gACAwNxcnKiTp06HDt2jJw5c5o6khAG4eLiQkBAAOPGjePbb7/VHU+v+Kddu3bs2rWLLl26sG7dOmNHNntPnjxJVVybO3duEycyL1euXKFmzZokJCRgZWXFV199xeDBgylRokSq827dusWCBQv4+eefefjwIZaWlpw9e5YKFSqYKLl52L9/P8nJyXz77bccOXKEhg0b0rt3b2rWrPnKiQMAZ2dnI6Q0L3fv3qV+/fpoNBr27t1L1apVdY8NHjyYRYsWAS9+j23Tpg3btm2TSco35OTkxNGjR9mwYQOdOnXSa8zmzZvp0qXLC4Va4vVVrVqVy5cv4+XlxaxZs/QaM2LECObMmUPlypVlklJkGY8ePaJGjRpcv34dAFdXV4YMGULjxo0pVaoU8HS75OPHj7NgwQJ27doFQLly5Th79qxen71Cfzdu3Ehzy7XSpUubOJkQGZOR60HZeipjunTpwubNm3F0dOTgwYMAnDp1ijp16qT52gYGBtK8eXPy589PWFgYZcqUMUXsLO9NFpErpbC0tMTa2jrb3MeR4h9hcDNnzqRnz54UK1bM1FGEENnMrl272LZtG6GhocTExABQrFgx7Ozs6NixI++9956JE5qHCxcuUKdOHRITE4GnE2KxsbFoNBrKlSvHxYsXX7iwev/999m+fTvDhg2TzijirfW6XyimTZvGtGnT6N69O5MmTXrpeTJBmTF//PEHY8aMoUOHDixatEiuMU3k1q1bzJ49G4DvvvvOxGnMz7Rp0/D29qZkyZKcOnWKIkWKAC8v/lm8eDEffPABSimWLl36ypX0QrxtvL29mTZtGvnz52f//v3Ur18/3fPDw8Np1qwZjx494qOPPmLy5MnGCWqmtO8tb0IpRVJSUiYnEgsXLmTBggWcOnWKpKQkqlWrRr9+/RgzZowUP2fA/Pnz8fLyom3btvz11196jWnfvj07d+5k1qxZeHl5GTihecubNy9Pnjx5obNhevbt28d7771Hnjx5iI+PN3BCITLHL7/8wldffYWFhQVz587lgw8+SPf8RYsWMWTIEAAmTpzIZ599ZoyYQogsbuDAgRkav3jx4kxKkv2UK1eOGzdusGjRIvr37w+kX/wD8MknnzB16lQ+//xzJk6caOzIZiFHjhwZGl+uXDneffddBgwYQLt27TIp1dtHin+EwVlYWJAzZ05atWpFnz596NKli1SvCyGEmVmzZg2DBg3i0aNHumOFChViy5YtODk5pTo3KiqKihUrkpSUxLp16+jSpYuR0wqhn4x+oUiLTJBlzA8//ADAjh07OHr0KHnz5qVVq1Z6dyaQIpXM86qbGiJj4uPjqVatGjdu3KB+/fosW7YMGxubF4p//vnnH3799Vdmz56NRqOhWrVqnD59WjpCiCzH1taWM2fOvNDtKj0//PAD48aNo3bt2kRERBg4oXmTVcMiu9BoNLRv355du3bh5eXFlClTsLS0TPPchIQEPvnkE2bNmkWbNm3Yvn27kdOanzJlynDz5k0CAwOxs7PTa0xISAj29vaUKlVK10VFiLedo6Mjx44dY+DAgSxYsECvMdqub++++y6HDx82cEIhhBAZYWlpSWJiIv7+/jRr1gyA8+fPU7NmTZRSPHz4kLx586Yas3fvXlxdXeX7awZkxr0u7aIXV1dXVq5cSeHChTP8nG8bKf4RBqf9x6j9B5UvXz46d+5Mnz59aN26tUEm1oQQQhhfdHQ027ZtIyoqitKlS9OpUyddp4Jn7dq1ixUrVgAwffp0ChYsaOyoQujFEJPnMkGWMc93JtBoNK/VqUBe+8wjxT+GFxgYiIuLC3FxcQDUqFGDs2fPopTCycmJW7ducf78eeDpv4UCBQpw6NAhbG1tTRlbiDdSsGBBHj16xMGDB2nSpIleY44cOYKTkxP58+fn/v37Bk5o3gICAjI0vnnz5pmURAjD2r9/PwkJCXzzzTcEBQVRsmRJ3N3dcXBwoESJEiildMUpa9asISoqCnt7e3788cd0twmQre/006lTJ7Zt25ZqlfyrLF26lIEDB/L++++zefNmAycUInMUK1aM2NhYdu7ciaurq15j9uzZQ6tWrShSpIiue7kQQoi3U/78+YmPjyc4OFjXtfbGjRuULVsWpRQXL16kcuXKqcYEBwfj4OBAgQIFuHfvnglSZ31Lly4FYM6cORw7dgxLS0vatGmDvb09xYsXB552Kg8KCmLnzp0kJCTQqFEjhg4dyv3794mIiGDTpk3ExMSglKJp06YZ/i78NpLiH2FwgYGB+Pr6smrVKqKiooD/CoGKFStGz5496d27N++++64pYwohhBBCpKL9QpHZ9L3RLV6U0YKslJSUTEoipPjHOE6ePImnpycnT57UHdN+l3r2q3ytWrVYtWqVFP68BhcXl0x/TqUUe/bsyfTnzQ6srKx4/PgxR44coVGjRnqNOX78OO+++y758uXj4cOHBk4oROZycXFBKcWiRYuoWLGiXmOuX7+Op6envNdkQEa2uHsZ6eypPz8/P1q3bk2tWrUIDAx8ZefOuLg47O3tOXfuHDt27KBVq1ZGSipExmg7QrxJlyvZ4k4/2q7AmU26BQsh9FG9enUiIyNTXZ9oNBry58/P48ePWb16Nd26dUs1ZtWqVfTq1Yu8efOm2j1BvJ5hw4Yxf/58OnXqxNy5cylRokSa50VHRzN06FC2bNmSqhPf48ePGTZsGMuWLUMpha+vLz179jTmj2BwUvwjjCYlJYW9e/fi4+PDhg0bdCvztF+6K1WqhKenJ7169aJmzZqmjCqEMCPJycnExsYSHx/Pqz7yKlSoYKRUQgghRNYmxT/GtW3bNjZt2kRQUBDR0dEkJydTtGhRGjRoQKdOnejWrZts9fWatBPAmXFLRPs88u/hzWlvnk6ePJmPPvpIrzHTpk3D29ubd955R9cFS4is4vltHPURGRlJtWrV5L0mA6Szp+mNHz+e8ePHY29vz7x583Sr5Z8XHh7O0KFDCQoK4vvvv5cJeZGllC9fnuvXr7Ns2TL69Omj1xgfHx/69u1L2bJl+eeffwycMOszRDEnSLdgIYR+evTowfr16/ntt9/w9vbWHXdxcSEgIIAWLVqkKtZPSkqiadOmHD9+nLp16xIWFmaC1Fnfxo0b6dq1K46Ojhw4cOCVnwMajYamTZty9OhRVqxYgbu7O/C0XqFRo0aEhobSqVMnNmzYYIz4RiPFP8IkEhIS2LJlCz4+Pmzfvp0nT54A/xUCNWjQAE9PT3r27Enp0qVNGVUIkQXFxMTw+++/s3HjRk6fPq1XpwdZrZf5bt26xezZswFZOSOEEOZGin9EVteiRQuDTBjs27cv058zO/Dy8mL+/PmUKFGCkJAQypQpk+75//77L/b29ty6dYshQ4YwZ84cIyUVInNI8Y9pGKqtv2x9px9tp46tW7cSFBSEUoo6deqkue2atuuhvb09HTp0SPd55X6DeNv06NGDdevW0aBBA44dO0bOnDnTPT8xMZHGjRsTHh5O165dWbNmjZGSZl2GWvgg3YLF22j//v26Pz+71eizx9+EbFv65mbPns3IkSNxdXVl165duuN//vkn/fr1020p5e7uTlxcHCtXriQ0NBSlFBMmTGDs2LEmTJ91tW7dmj179rBy5Up69Oih15g1a9bQs2dPXFxc8PPz0x2fO3cuw4cPN8uiWyn+ESZ39+5d1q5di4+PDwcOHNBdYCmlyJEjh64wSAgh9HH48GG6du3KrVu3Xmslt9xAzXwyMSyEEOZL3uMNa+bMmfTs2ZNixYqZOooQRhEREUH9+vXRaDSUKVOGKVOm0LVrV3LkyJHqvOTkZNatW8cnn3zCtWvXyJEjB6GhobLlnchy3qT458SJE9SvX1+2ChBZ1vOdOrRd89KS3mPPk2tR8bbZsmULnTt3RimFq6srixcvfmlh87Vr1xg0aBC7d+9GKcXmzZtfWfAmhMhetJ+fzy9ezkgHLFkInTFRUVGULVsWCwsLzp07R5UqVXSPtW/fnh07drzwd6PRaGjQoAGHDh3C0tLS2JHNQqlSpbh16xZBQUE0aNBArzGhoaE0bNiQ4sWLc/PmTd3xI0eO4OTkhKWlJXFxcYaKbBLplxwLYQSFChVi8ODBDB48mGvXruHr68vPP//M3bt35cubEOK13L59m86dO3P79m3y58/P4MGDKVSoEOPGjUMpxYIFC4iNjSUoKIhNmzbx+PFjnJyc+OCDD0wdXQghRAYkJydz7do1QLZwNBYrKyucnZ0N0jlFwOjRo/n4449p1aoVffr0oUuXLuTLl8/UsYQwGFtbWyZMmMDXX3/N9evX8fDwoFChQjRo0ICSJUuilCIqKorQ0FDu3r2rK/KfMGGCFP68hkGDBgFPb/YvXLjwheNv4vnnEoazfft2AMqVK2fiJEK8uecXaaW3aEvWLIusqmPHjnTp0oWNGzfi5+dHlSpVaNWqFY0bN051XXPs2DF2795NYmIiAG5ublL4I4RI08s+E+Wz0jRKlSpFYmIiGo3mhQUrGzZs4H//+x8LFy4kKioKeDoP3qdPH3788Ucp/MmA+/fvA093nNCX9twHDx6kOp43b14AcuXKlUnp3h7S+Ue8NSIiIvDx8WHFihX8888/uhUeUgAkhNCXdu/4PHnyEBQUhI2NzUs7E0RFRdG7d28CAgL49NNP+eWXX0yY3DxJVwiR1Wnb0mc2aUuf+bTvNxYWFrJySZgFbRt7bXFVvnz56Ny5M3369KF169Yv3FwSwlzMmjWLzz//XLfyLq3VkvD038Rvv/3G8OHDjZ4xK3t2dfCz1+dvumpY7tvo7/kCqyVLlqCUonPnzhQqVCjdsQkJCURGRhIYGAjABx98wLx58wwVVQghRCZISEigX79+ui280utyBU+3Clu2bBl58uQxWkYhRNbw7Lalz241mtHtTGXbUsO7c+cOSUlJFC9eXBbPZYIaNWpw8eJFPD09Wbp0qV5j+vbti4+PD9WrV+fs2bO647t27aJt27ZUqVKFixcvGiqySUjxjzCpq1evsmLFCnx8fDh16hSQ+mZe586d8fHxMWVEIUQW8u677xIYGMiwYcOYOXMmkH4BSnx8PPXq1SMyMpLdu3fj4uJiithmS4p/RFaXkfa56ZF/D5lP3m+EuQkMDMTX15dVq1bpVopp34+KFStGz5496d27N++++64pY2YbGo2GO3fuEBcXR5kyZaT4ysBiYmJYvHgxfn5+REREcOfOHQCKFCmCra0trq6uDBw4ULbFewOVKlXSvZdcunQpzeNv4tnnEmlLa7sjePlk8PO05xcpUoTAwEAqV66c+SGFEEJkum3btjFr1iwCAgJe2FYkX758NG/enJEjR9K+fXsTJRRCCCGyhs8++4zJkyejlGLy5Ml89NFH6Z4/depUPvnkE5RSfPLJJ/z666+6xyZMmMD3339Py5Yt2bNnj4GTG5cU/wiju3PnDmvWrMHHx4fDhw+j0Wh0NzFy5MiBq6srnp6edOnSBSsrKxOnFUJkJcWKFSM2Npa1a9fi5uYGwOnTp7G1tUUpxZMnT16YrJk9ezYjR46ke/furF692hSxzZZMxousTtt5I7OlpKQY5HmzM3m/Ma6bN2+mOSFfsmRJEyczPykpKezduxcfHx82bNiga3GsnSyuVKkSnp6e9OrVi5o1a5oyqtlJTk5m2bJlLF68mMDAQJ48eYJSihMnTlC7dm3deVu3bmX//v1YW1vz9ddfmzCxEOJt9nyB1ZUrV1BKUbp06XRbzSulsLS0pHTp0jg6OjJ8+HDKlCljjMhmLzIyks2bNxMeHk5MTAzx8fHpbp2hlDK7iQEhhPEkJyfz999/p/oOVaVKFSkqN6CLFy+ybNkyjhw5QlRUFPHx8ezYsYN33nlHd05ERARXr17FyspKuqAIIfT24MEDpk6dCsDQoUMpVapUuuffuHGD+fPnA08LWLRbTonXc/v2bWrVqsXt27cBsLOzo1+/fjRs2JASJUoAEB0dTVBQEMuXLyckJASNRkOJEiU4ffo0RYoU0T2XjY0NZ8+e5ccff+TLL780yc9jKFL8I4wiPj6eTZs24evry86dO3XbMWh//Ro1akSfPn3w8PCgePHipowqhMjCcufOTXJyMoGBgdjZ2QHw999/884776CU4vbt2y+0VA8MDKRx48aUL1+eK1eumCC1+ZLJeCGEscj7jeGlpKQwd+5cZs2axenTp9M8p3bt2owYMQIvLy+DFc9lZwkJCWzZsgUfHx+2b9/OkydPgP8KgRo0aICnpyc9e/akdOnSpoya5UVHR9OlSxeOHTuWaiJYKcXJkydTFf88+/4THBxM/fr1TZBYiIxZtmwZ8LSNeuPGjU2cJnvQdgJ6/j1FGF5cXBwjR45k+fLlLxT7aLeye/4YINeZQgiRRaSkpPDFF18wbdo0UlJSUr2PP/+5u337djp06EDOnDm5dOkSZcuWNVVsIV6bhYUFFhYW/PTTT3z++eemjpOtLF++nP79+1OtWjXOnTv3yvM1Gg01a9bk4sWLrFixAnd3dyOkNE/h4eG0bduWmzdvvrKLqkajoVSpUuzYsYO6devqjv/9999MmDABgLFjx1KtWjWDZjY2uSMrDK5fv36ULFmSPn36sG3bNhITE9FoNLzzzjt8//33nD9/nqNHjzJ69Ggp/BFCZEj+/PkBdAWGQKpq3suXL78w5vHjx8DTSR6RuaysrHB2dsbZ2dnUUYQQQmTAzZs3adSoEaNGjeL06dO6zp3P/3f69GlGjRpF48aNdVtVicyTJ08eunfvzoYNG4iKimLevHk0b94cpRQajYaQkBA++eQTKlasaOqoWVpKSgqdOnXi6NGjKKVwd3fnjz/+eOn5NjY2NGnSBIANGzYYK6YQmWrAgAEMHDhQFkMYkfZ7knS8Ni6NRoObmxvLli0jJSWFokWLUq9ePd3EcLNmzbCxsSFnzpy6YzVq1KB58+byvTaT3blzh8mTJ9OuXTvKly+PlZUVVlZWlC9fnnbt2jF58mRdhxQhhHgdXl5eTJkyheTkZMqUKUP37t1fem67du2oUqUKycnJrF271ogphci43Llzo9FoaNasmamjZDvr16/X3S/Qh1IKDw8PNBoNa9asMXA681avXj3OnDnD6NGjKViw4EvvURYsWJDRo0dz6tSpVIU/AFWqVGHx4sUsXrzY7Ap/AHKaOoAwf3/++afuzyVKlKBnz554enri4OBgwlRCCHP0zjvvEBwczNWrV2nUqBEAhQoVolSpUty8eZN9+/a9sBr78OHDAHLT1QAqVaqEv7+/qWMIIYTIgMePH+Pi4sLZs2fRaDQUL14cd3d3GjVqRMmSJdFoNERHRxMYGMjq1auJjo4mODgYV1dXgoODyZMnj6l/BLNUqFAhBg8ezODBg7l27Rq+vr78/PPP3L17V7oSZNCyZcs4fvw4uXLlYvPmzbRp0waAUaNGvXRMx44dOXLkCAcPHjRWTLMWExPD0qVL8fPzS3OLQVdXV/r370+xYsVMnNR8WFtbc//+fbO88fm20vd7UkJCAnfv3qV48eLSVS8TrFmzht27d6OU4vvvv+fbb7/l9OnTugmBgIAAAB49esSCBQv49ttvuXPnDvPnz6dp06amjG5W5s6dy6effkpcXBxAqg5M165d4/r16+zatYtx48YxefJkhg4daqqoQmSK4ODgdK9rGjZsaOKE5sPf35+FCxeilGLs2LGMHz+eHDlypPsZ2qNHD3755Rf27dvHmDFjjJhWiIwpU6YMV65cke0DTeDs2bMAODo66j1Gu2joZd20hf4KFSrE9OnT+fXXXwkODiYiIoLY2FgAChcujI2NDfb29tn2nqQU/wiDs7Kyws3NjT59+tCqVSu5WSGEMJjGjRsTHBxMYGBgqlUdbdu2ZcmSJfz666906NCB6tWrA3D8+HF+/fVXlFJSkCiEEEKkYerUqZw5cwalFB988AHTpk1Ls2C2b9++TJw4kY8//pj58+dz5swZpk6danb7Zr9tIiIi8PHxYcWKFdy7d8/UcczCihUrUErh5eWlK/x5lQYNGgDo1e5bpG/atGl8++23ek0I/+9//5MJmkxSuXJlwsPDdTdMheE9fPiQ/fv3A0+7AGm72GrFxMTg5eXF1q1bSUpKIn/+/AwZMoSffvqJ3LlzmyKyWfD19QWeTr58//33AGluF2BlZcWYMWNwdHSkefPmdO3albCwMMqUKWPUvOZo4sSJfP3117r3d2traxo0aECpUqXQaDTcvHmT0NBQ7t27x6NHjxg+fDh3796V7UxElhQUFMTIkSMJCgp66Tljx47F3t6emTNnYm9vb8R05mnOnDkAtG/fnv/97396jdEuID116pTBcglhCM7OzixfvpyQkBDd77Ewjn///RfgtbZcL1WqFPD0e63IHHny5MHR0fG1irCyA6nCEAYXHR3NsmXLaNOmjRT+CCEMqk2bNmg0GtavX5/quLe3Nzlz5iQ6OhpbW1scHBywsbHByclJd4NbJg6EECLrqlGjBpcuXeLvv/82dRSzs2rVKpRStGrVivnz56fbKS9fvnzMnTuX1q1bo9FoWLlypRGTZh9Xr17ll19+oW7dutSrV49ff/2Vq1evotFoyJcvHx4eHqaOmKWFhYUB0KlTJ73HlChRAoDbt28bIlK24e3tzSeffMKjR4/QaDRYW1vTsmVLPDw86NmzJy1btqRQoUJoNBoePXqEt7c33t7epo5tFtzc3NBoNGzZssXUUbKNdevW8f777zNixAjy5cuX6rGUlBTatWvHxo0bSUxMRKPR8ODBA6ZOnYqnp6eJEpuHoKAglFIMGTJEr/MdHBwYPnw4MTExzJgxw8DpzF9ERATffvstGo2G0qVL8+effxIdHc3evXvx9fVlxYoV7N27l1u3buHj40OZMmXQaDR88803MikvspzVq1fj5OREUFCQbguSXLlyUbJkSUqUKEGuXLl0xwMDA3FycpKtYDLBkSNHdAtX9FWuXDkA2bpaZDmjR48mR44cTJo0ifv375s6TrainevWLlrRh/bcpKQkg2QSQks6/wiDy5s3r6kjCCGyiTZt2tCvXz+Sk5O5dOkSlStXBsDW1pbZs2czfPhwkpKSCA4OTjVu3LhxtG3b1hSRsxTtytTM5uzsbJDnFcIY9u3bx8aNGwkPDycmJob4+PhUXQqep5QiMjLSiAmzh5w5c1KxYkVTxzBLFy9eBGDEiBF6jxkxYgS7du2S3/VMdOfOHdasWYOPjw+HDx/WTRQA5MiRA1dXVzw9PenSpYtsZZpBd+/eBf4r6NFHYmIigCx2yYAdO3Ywbdo04OkEzOTJk3FzcyNnztS3rZKTk1m/fj2fffYZV69eZfr06bRt25bWrVubILX5GDNmDIsWLWL27Nl07NgRFxcXU0cyezt37gSgW7duL7x3rFq1iuDgYJRS2NnZ0bx5cwICAggJCWHdunXs2LFDvr++oZiYGACqVKmiO5YrVy7dn+Pj41+4j9mhQwemTp3K1q1bmThxonGCmqk//viD5ORkihcvzpEjR6hQoUKa5+XMmZNevXrRtGlTHBwcuHXrFn/88QezZ882cmIh3szZs2fp378/iYmJ5MyZkyFDhjBo0CDq16+v254nOTmZ8PBwFi5cyPz580lMTKRfv37UqVOHmjVrmvgnyLqio6MBdPeE9aG93tRe0wuRVTRs2JDff/+dUaNG0bx5c2bOnCkdUIykdOnSXLhwgaCgIL1fc20XOG0HICEMRYp/hBBCmI1cuXKxZMmSNB/74IMPaNq0KUuWLOHUqVMkJSVRrVo1+vbtK2119dSiRYs0W6JnhFJKqt1FlhQdHY2HhwcBAQEALy34UUqleiyz/w0JYWh58uQhPj6e8uXL6z1Ge65sS5Ix8fHxbNq0CV9fX3bu3Kn7vNS+pzRq1Ig+ffrg4eFB8eLFTRnVrBQuXJhbt269Vhcf7XZf8vfw5n7//XcAypQpw7Fjx17aPj1Hjhz06NGDZs2aYW9vz40bN5g+fboU/2RQwYIF2b17N927d6dNmzYMHDiQ3r17U7duXQoXLizXLwYQERGBUoomTZq88Njy5cuBpxM6hw8fJmfOnCQmJtKsWTMCAwNZtmyZFP+8Ie1rWaBAAd2xZ/8cFRX1woSxtbU1AP/8849xQpqxvXv3opTiq6++emnhz7PKly/PF198wSeffMKePXuMkFCIzPHLL7+QkJCApaUlf/31Fy1atHjhnBw5cmBnZ4ednR3u7u60a9eOhIQEfv31VxYtWmT80GYib968PHny5LW6cVy9ehV4+j1AiKxk0KBBwNNu2OHh4TRr1ozy5cvrruG1xYZpUUqxcOFCY0U1O82aNeP8+fPMmjWL4cOHpyomT0tiYiKzZs1CKUXTpk2NlFJkV1L8I4QQItuoUaMGP//8s6ljZGnpdTQRIrtITEykXbt2hIWFodFoaNCgAWXKlGHbtm0opfD09CQ2NpaQkBCuX7+uW7Vta2tr6uhCvLaaNWty9OhR/vnnHxo0aKDXGO3kmKxYfXP9+vVj48aNPHr0CPjv87datWr07t2bPn368M4775gyotmqXbs2AQEBHDx4kJYtW+o1xtfXF6UUDRs2NHA68xUYGKibEH5Z4c+zSpUqxVdffcXo0aMJDAw0QkLz9uzEgEajYeHChXpPBkgx/5u5desWwAudCxMTEwkICEApxYgRI3TdCHLlysWwYcM4fvw4x44dM3pec1GmTBkiIyN1rz88fT/Jmzcvjx8/JiQk5IXiH20XRPk9z7hr164BvFZXAicnJwCuX79ukExCGMKePXtQSvHRRx+lWfjzvObNm/PRRx8xceJE/Pz8DB/QjFWuXJmwsDBCQ0PTLLBNy9atW4Gn3wOEyEqWLFmiK9LXLj68evXqKwuWNRqNFP9k0MCBA1m4cCEXLlygd+/eLF269IWtfLXi4uLo168f58+fRynFwIEDjZxWZDdS/COEEEIIvezbt8/UEYR4KyxZsoTQ0FCUUixevJj+/ftz6tQptm3bBsDSpUt1527atImRI0dy+vRpvvzyS7p162aq2GYhvVVL6bG0tMTa2ppq1arx7rvv0q9fP2xsbDI5nXkaMGAAR44cYc6cOXTq1EmvMXPmzEEpRb9+/Qycznz9+eefuj+XKFGCnj174unpiYODgwlTZQ+dOnXC39+fWbNmMXLkSIoUKZLu+YsXL2bnzp0opXBzczNSSvOjLXRr3Lix3mO052rHijf3fIG/FPwb3p07dwBeWCUcFBREfHw8SinatWuX6rHq1asDT7vTiDdTr149IiMjOXnyJK1atQKeTpY1btyYgIAAZs2alep6PSkpialTpwJPC3BFxmiv5V+nkEp7rmytKbISbYFh+/bt9R7ToUMHJk6cmKo4Uby+1q1bExoayrx58xg2bNgr3zuCg4NZvnw5SinpqieynAoVKkiHThNxdHTEw8ODlStXsn79eo4dO8aQIUNwdnamdOnSKKW4fv06+/fvZ8GCBfz7778opejevTvNmzc3dXxh5qT4RwghhNmwsLDAwsKCEydO6L1aIzIykmrVqmFhYSEr+V5BLkyFeGrdunUAtG3blv79+6d7bufOnbG1tcXe3p4BAwZQt25dmTjIgDedjIyPjyc+Pp6oqCgOHjzIpEmTGDx4MDNmzCBPnjyZnNK8DB48mPXr17Nz505GjBjBlClTsLS0TPPchIQEPvnkE3bs2EGbNm3w8vIyclrzYWVlhZubG3369KFVq1Yy4WVEXl5eTJo0iRs3btCqVSuWLVuWZrHgP//8w6+//srs2bNRSum6Mok3U65cOS5evEhCQoLeY7Tnli1b1lCxso3vv//e1BGynbx58/LgwQOio6NTHdduKVu1alVKliz5whiRMS4uLqxbt44dO3bg7e2tOz5o0CD8/f3x9/enefPmuLu7ExcXx8qVK3VF/+7u7iZMbh4qVKjAmTNn2LNnj97df7TbfemzTZgQb4vixYtz7dq1l35vSov2e2mxYsUMFStbGDVqFDNmzODkyZMMGTKEOXPmvHQ7nnXr1jFs2DCePHmCtbU1Q4cONXJaITLm8uXLpo6QrS1atIiYmBj8/Py4du0a48aNS/M87b3MVq1apVowKoShSPGPEEIIs/KmE8OyulUIoa/w8HDd9l5p0bbP1apatSpjxozhhx9+YPr06fzxxx/Gimp2tJOT27dv5/jx48DTFdz29vYUL14ceLrKMigoSPf35ODgQJs2bbh//z4RERHs37+fxMREFixYwJ07d1izZo3Jfp6s4MCBA3h7e3Pnzh3mzp3Lxo0bcXd3x8HBgRIlSqCU4ubNmwQGBrJmzRqioqJwcHDgk08+Yf/+/S99XmdnZyP+FFlPdHS0TPKaSN68edmwYQMuLi6EhYVRt25datSooXt82LBh3Lp1i/PnzwNP3/MLFCjA2rVrpUgrAzp06MD06dPZvn27bouXV/nrr790Y0XGSPGP8VWtWpWwsDD8/f1p3bq17viGDRtQSqW58ELbDaJEiRJGy2lu3NzcGDVqFPv27ePvv/+mSpUqAHh6euLr68uOHTs4ePAgBw8eTDWufv36qYqFxJtp1aoVp0+fZtKkSXTp0oU6deqke/6JEyf47bffUEql+ncixNvOycmJ1atXExgYiL29vV5jtN9vmzZtashoZq9s2bLMmDGDIUOGsGTJEnbt2kXHjh11jy9cuJC4uDj8/Pz4+++/dfdv5s2bh7W1tQmTCyGyGktLS3bu3MmMGTOYNGmSbnvT55UvX57PPvuMkSNHSqcmYRRKI7OdQgghzISFhQVKKU6ePKl355+LFy9SvXp16fwjhNBbnjx5SEpK4tChQ7z77rsAXLhwgRo1aqCU4v79+1hZWaUac+DAAZo3b061atU4d+6cKWKbjZ9//pmvv/6aRo0aMW/ePOrWrZvmeeHh4QwdOpSgoCDGjRvHt99+C8D169cZMGAAfn5+KKXYtm2btPdOh/azNTMppeQzV7z1Tp48iaenJydPntQd0/5bePY2Sq1atVi1ahW2trZGz2hOrl+/ToMGDXjw4AG7d+9+ZQHQ4cOHcXV1pUCBAoSEhEj3H5HlfPnll/z6668ULFiQFStW0KxZMxYvXsyYMWNQSrFhw4YXttucNGkSn3/+OU2aNOHQoUMmSp71paSkoNFoXthONiEhgf/9738sXLhQt7VaoUKF6NOnDz/++CMFCxY0RVyzcuXKFWrWrMmTJ0/Inz8/33zzDQMHDnyh00lMTAyLFi3ip59+4v79+1haWnL27Fnp/iOyjMDAQJycnChZsiQhISG6hSovEx0dTcOGDYmOjubAgQM0atTISEnN16JFi/jwww+Ji4tL8/us9no+T548zJkz55VdnYUQIj0ajYawsDBCQ0OJiYkBnnZys7Ozo169elL0I4xKin+EEEKYjTcp/jl27BhNmjShQIEC3Lt3z8AJhRDmoECBAsTFxREYGIidnR0AN2/e1O3pfObMGapXr55qTGBgII0bNyZfvnw8fPjQFLHNgr+/P++99x61a9cmMDDwlW3UHz9+TMOGDTl79iw7d+7E1dVVd7xu3bpERkbi7u7OihUrjBE/SzJEJxOlFMnJyZn+vEIYwrZt29i0aRNBQUFER0eTnJxM0aJFadCgAZ06daJbt27S8SeTBAcH06NHD65fv86wYcN022VqX1+NRkN4eDhLly5l9uzZlClThrVr1+o+i4XISm7cuEGtWrV48OBBquMajYbatWtz8uTJFyYJWrZsyf79+/n444+ZNGmSMeNmO3fu3CEpKYnixYvLZE0mW7ZsGQMHDtT9v1KKSpUqUbJkSZRSREVFcfnyZTQaja4jx5IlS+jbt68JUwvx+hYvXsywYcMoW7YsU6ZMoVOnTi9cM6akpLBlyxa8vb25du0as2bNYtCgQSZKbH7+/fdfpk2bxubNm7l48WKqx8qWLUunTp347LPPqFSpkmkCCmEgSUlJxMbGAlC4cGFy5pRNgITITqT4RwghhNnQFv9ERERQq1atV57/6NEjvLy88PX1pX79+oSEhBghZdb1ww8/GOR5v/vuO4M8rxCGYmNjw9mzZ9m6dSvt2rXTHbe2tubhw4dp3pxesmQJgwYNwsrK6oVJHqE/Nzc3Nm/ezOLFi+nXr59eY5YuXcrAgQPp2LEjmzZt0h2fNm0a3t7eVKxYkUuXLhkqcpYXEBBgkOdNazsTIYT5026x8zJxcXFER0frJttz585NkSJFUEpx+/Ztnjx5AjwtkChRogT58uVDKUVkZKTBswuR2Q4cOICHhwc3btzQHatSpQpbt26lZs2aqc6NjIykRo0aaDQa/vrrL9q0aWPsuEJkmm3btjFs2LBU22Ok1V2vTJkyzJs3j/bt2xs9oxD6eFWhTmhoqG476sKFC9OgQYNUWyeHhYVx584d4Ol21vXr10cpxcKFC40RP1u5f/9+qkL+5zuOCZHVnTp1ijlz5uDn58eFCxd0n6dKKapVq4arqyteXl7SsVaIbECKf4QQQmRZz08eXL58GaUUZcqUIVeuXOmOTUhIIDo6mpSUFAC++eYbxo8fb7Cs5sAQW78A0v1BZDl9+/bF19eXCRMmMHbsWN3xjh07sm3bNuzs7Dh06BB58uQB4N69ezRp0oRz585hb2/PsWPHTBU9yytbtixRUVGpui69SkhICPb29pQqVYrr16/rjh88eBBnZ2fy5s3Lo0ePDBVZCCHEM6SbmBCpPXnyhEOHDhEVFUXp0qVp2rRpmquzDx48yJ49ewD44osvXtn9UKTtwYMHTJ06FYChQ4dSqlSpdM+/ceMG8+fPB+Czzz4jb968Bs+YXSQlJbFhwwb8/PyIiIjQFUAUKVIEW1tbXF1dcXNzk24F4q2m732yZyfh9Tku1zVCCH2lpKTg7e3NzJkzdVubpkUphYWFBaNGjWLy5MnSvVYIMybFP0IIIbKszLpIfffdd9m9ezdWVlaZ8nzmylBfCrQFWEJkFdouPk2aNOHQoUO649u2baNjx44opahatSqdO3cmLi6OLVu28O+//6KUYsaMGYwcOdKE6bO2vHnz8uTJE/bs2UOLFi30GuPv74+Liwt58uQhPj5edzw8PJwGDRrIVmxCCGFEz271kpkWL15skOcVQpiX5cuX079/f6pVq8a5c+deeb5Go6FmzZpcvHiRFStW4O7uboSUQoisolKlSgZZJCedaYUQ+nJ3d2fdunW6oh8bGxsaNWpEyZIl0Wg0REdHExgYSEREBPC0CKh79+6sWrXKlLGFEAYkpfNCCCGyrP79+6f6/6VLl6KUolOnThQqVOil45RSWFpaUrp0aRwdHXFxcTHIl3VzI0U6QjzVpUsXxo0bx7///ktkZCRVq1YFoEOHDgwaNIhFixZx8eJFpkyZAvy3mq9169YMHz7cZLnNQalSpbh69SobNmzQu/hn/fr1AJQuXTrVce3q4uLFi2dqRiGE+YiJiWHp0qXpdiXo37+/bBvwGqRIRwhhSuvXr0cppXcRj1IKDw8PJkyYwJo1a6T4x0CSkpKIjY0FoHDhwtLtR2QZly9fNnUE8QrBwcHpXss3bNjQxAmFeHO+vr6sXbsWpRT16tVj3rx5ODg4pHluUFAQXl5ehIaGsnbtWlauXImHh4eREwshjEE6/wghhDAb2na7J0+epHbt2qaOI4TIphYuXMiCBQs4deoUSUlJVKtWjX79+jFmzBi5kZ1Bw4YNY968eeTMmZNVq1bh5uaW7vnr16+nZ8+epKSk4OXlxaxZs3SPTZo0ic8//xwnJycOHDhg6OhCiCxm2rRpfPvtt8TFxQG80D5dWzieL18+/ve//zFmzBijZxRCCPF6atWqxfnz59m6dSvt2rXTa8yOHTto3749tWrV4tSpUwZOmH2cOnWKOXPm4Ofnx4ULF1Jtf1StWjVcXV3x8vLC1tbWxEmFEFlRUFAQI0eOJCgoKN3z7O3tmTlzJvb29kZKJkTmadmyJQEBAdSoUYOgoKBX7mrw6NEj7O3tOXfuHM2bN2ffvn1GSiqEMCYp/hFCCGE2xo0bh1KKkSNHygpsIYQwQ1evXsXGxkY3Gd+1a1f69etHw4YNKVGiBADR0dEEBQWxbNkyNmzYgEajIX/+/ERERFChQgXdczVq1Ijg4GC++eYbxo8fb5KfJ6uJjIxk8+bNhIeHExMTQ3x8/Ev3k4enkzd79uwxYkIhMoe3tzfTp0/X/X4XKlSIBg0apGqdHhYWputSoJRizJgxuo5vQggh3k4FChQgLi6O4OBg6tevr9eYsLAw7OzsKFiwIHfv3jVovuwgJSUFb29vZs6cSUpKykuvJZVSWFhYMGrUKCZPnmywbciFEOZn9erV9O3bl6SkJN17TO7cuSlSpAgajYbY2FiePHmiOz9Xrlz8+eef9OjRw1SRhXgjRYsW5e7duyxcuJABAwboNWbJkiUMGjSIQoUK6bphCSHMixT/CCGEMBtXr14FoGzZsuTIkUOvMSkpKfz7778AqSaFhRBCvJ12796Nm5sbcXFxr9yyUaPRkC9fPjZu3Iirq6vueGRkJIMHDwZg6tSpek/+ZFdxcXGMHDmS5cuXvzBBo9FoXvh7eHbldnJystFyCpEZtB0eAMqVK8fkyZNxc3N7oXNbcnIy69ev57PPPuPq1asopdi+fTutW7c2RWwhhBB6sLa25uHDhxw4cABHR0e9xhw+fJimTZuSL18+Hj58aOCE5s/d3Z1169bprhdtbGxo1KhRqgLbwMBAIiIigKfXk927d2fVqlWmjC2EyCLOnj1LgwYNSEhIIGfOnAwZMoRBgwZRv3593b3i5ORkwsPDWbhwIfPnzycpKYk8efIQGhpKzZo1TfwTCKE/bVFzYGAgdnZ2eo0JCQnB3t4eKysrHjx4YOCEQghTkH0HhBBCmI1KlSphYWHBiRMn9N7269KlS1SrVg0LCwuSkpIMnFAIYQ5cXFxQSrFo0SIqVqyo15jr16/j6ekpnVAyQatWrQgPD8fb25tt27aRkpKS5nkWFha8//77TJkyhapVq6Z6rGrVqtLeWE8ajQY3Nzf8/PzQaDQUK1aMcuXKERYWhlKKZs2aERsby7lz50hMTEQpRY0aNShVqpSpowvxRn7//XcAypQpw7FjxyhdunSa5+XIkYMePXrQrFkz7O3tuXHjBtOnT5fin0wSGxurd5cxgH79+hkpmRAiKytdujQXLlwgKChI7+If7ZYxcm2Tcb6+vqxduxalFPXq1WPevHk4ODikeW5QUBBeXl6Ehoaydu1aVq5ciYeHh5ETC5F5kpOTiY2N1eu6RhYnvrlffvmFhIQELC0t+euvv2jRosUL5+TIkQM7Ozvs7Oxwd3enXbt2JCQk8Ouvv7Jo0SLjhxbiDVWsWJEzZ85w7949vcfcv39fN1YIYZ6k+EcIIYRZedOGdtII79VcXFyAF7dx0R5/E1IIIbIif39/lFI8evRI7zHx8fG6cSLjqlatyqZNm7h+/Tr+/v5ERETott8pXLgwNjY2tGzZkjJlypg4ada3Zs0adu/ejVKK77//nm+//ZbTp09Tt25dAAICAoCne8cvWLCAb7/9ljt37jB//nyaNm1qyuhCvJHAwECUUnz11VcvLfx5VqlSpfjqq68YPXo0gYGBRkho3vz9/fn+++85ePCg3mOUUlL8I4TQS7NmzTh//jyzZs1i+PDh5MqVK93zExMTmTVrFkopua7JBPPnzwegevXqHDx4ECsrq5eea29vz/79+7G3t+fcuXPMnTtXin9ElhMTE8Pvv//Oxo0bOX369EsXrjxLKSWLEzNgz549KKX46KOP0iz8eV7z5s356KOPmDhxIn5+foYPKEQm6tatGxMmTGDdunW0bNlSrzHaIlw3NzcDpxNCmIps+yWEEMJsWFhYoJTi5MmTenf+uXjxItWrVydHjhwkJiYaOGHWZmFhAby4jYv2dX+dSwrt+bIljMiK3uS9JjIykmrVqsnvvMhyunTpwubNm3F0dNRNxp86dYo6deqk+fscGBhI8+bNyZ8/P2FhYVKAJbIcKysrHj9+zLFjx7C3t9drTFBQEI0aNSJv3ryvVRgqUps9ezajR49Go9G89nWlfLYKIfSh3cJLKUXXrl1ZunQp+fLlS/PcuLg4+vXrx/r161FKsXfvXpo3b27kxOalaNGi3L17l4ULFzJgwAC9xixZsoRBgwZRqFAh7ty5Y9iAQmSiw4cP07VrV27duiXXNUaUN29enjx5QkBAgN5Fm4cOHaJZs2bkyZOH+Ph4AycUIvPcu3ePhg0bcuXKFXx8fHB3d0/3/LVr19KrVy8qVqxIcHAw1tbWRkoqhDAm6fwjhBAiW7tx4wbwdI9ckT5nZ+c0u5a87LgQ4j/ayWBLS0sTJxHi9QQFBaGUYsiQIXqd7+DgwPDhw5k6dSozZsxg4sSJBk4oROYqV64cFy9eJCEhQe8x2nPLli1rqFhm78yZM3z44YdoNBrq1KnDDz/8QK5cuejQoQNKKS5evEhsbCxBQUHMmzePkJAQmjZtyty5c186cS+EEM9zdHTEw8ODlStXsn79eo4dO8aQIUNwdnamdOnSKKW4fv06+/fvZ8GCBfz7778opejevbsU/mSCJ0+eAOg6SOpDe64s1hJZye3bt+ncuTO3b98mf/78DB48mEKFCjFu3DiUUixYsEB3XbNp0yYeP36Mk5MTH3zwgamjZ3nFixfn2rVrr3XvJU+ePAAUK1bMULGEMAhra2v8/Pzo2bMnvXr1wtfXlwEDBuDg4ECJEiVQSnHz5k0CAwNZunQpmzdvxt7entWrV0vhjxBmTIp/hBBCmB19ClESExOJjIzkxx9/BKBGjRqGjpXl+fv7v9ZxIcR/tm/fDjydVBYiK4mJiQGgSpUqumPPbpERHx9P3rx5U43p0KEDU6dOZevWrVL8I7KcDh06MH36dLZv346Tk5NeY/766y/dWPFmfv/9d5KTkylevDgHDhygQIECnDp1Svd45cqVqVy5MnZ2dgwZMoQvv/yS3377jdGjR8sWDUKI17Jo0SJiYmLw8/Pj2rVrjBs3Ls3ztJ06WrVqxdKlS42Y0HxVrFiRM2fOcO/ePb3H3L9/XzdWiKzijz/+4Pbt2+TJk4cjR45gY2PDqVOndO83AwcO1J0bFRVF7969CQgIoEmTJvzyyy8mSm0enJycWL16NYGBgXp38Tx+/DiAbO8ospwcOXLo/qzRaNiyZQtbtmx56fkajYagoKBU93eeJ1sPCpH1SfGPEEKILOvZC1wtjUaDra3taz2PdiWfEEKkZdCgQWke/+abbyhUqFC6YxMSEoiMjCQwMBCllKwYzkS3b9/myJEj/P333zx48ECv1ujfffedEZKZl5w5c5KYmJiqQ96zf46KiqJy5cqpxmhXkP3zzz/GCSlEJvr000/x8fFhypQptGvX7pUFQIcPH2bq1KkUL16cTz/91EgpzU9AQABKKT788MNXduRUSvHLL78QHBzMvn37WLRo0Us/q4UQ4nmWlpbs3LmTGTNmMGnSJK5du5bmeeXLl+ezzz5j5MiR0uk2k3Tr1o0JEyawbt06WrZsqdeYtWvXopTCzc3NwOmEyDzbt29HKcWgQYOwsbFJ99xSpUqxbds26tWrx6RJk2jTpg0uLi5GSmp+vL29WbduHT/99BPdu3enePHi6Z4fHR3Nzz//TK5cufj444+NlFKIzPH8loL6bDH4OtsQCiGyJqWRf+lCCCGyKAsLi0x5Hnd3d3x8fNIsJhJCCAsLi1Q3/LWXz/pOAmjPL1KkCIGBgS8USojXEx0dzccff8zatWtfezWSPgVCIrXq1asTGRnJjh07aNWqFfD0dzp//vw8fvyY1atX061bt1RjVq1aRa9evcibN69uyzshspLg4GB69OjB9evXGTZsGAMGDKBu3bq6a0+NRkN4eDhLly5l9uzZlClThrVr12JnZ2fi5FmXtbU1Dx8+ZOvWrbRr1w6A06dPY2tri1KKx48fp+o6BrB69Wo8PDxo0aIFe/fuNUVsIUQWp9FoCAsLIzQ0VNftsFixYtjZ2VGvXj0p+slk9+7do2HDhly5cgUfHx/c3d3TPX/t2rX06tWLihUrEhwcLFuUiCyjWLFixMbGsnbtWl3h2rPXNU+ePHnhHuTs2bMZOXIk3bt3Z/Xq1aaIbTYWL17MsGHDKFu2LFOmTKFTp04v3ENOSUlhy5YteHt7c+3aNWbNmiXF5CLLGT9+vEGe9/vvvzfI8wohjEOKf4QQQmRZz1/gjh8/HqUUw4YNo0SJEi8dp5TC0tKS0qVL4+joSNWqVQ0d1axdvXr1tcdo/w6sra3JnTu3AVIJkXkqVaqU6sb/lStXUEpRunTpFyYin/X8e83w4cMpU6aMMSKbrdjYWBo3bkxkZOQbrVZKSUkxQCrz1qNHD9avX89vv/2Gt7e37riLiwsBAQG0aNGCPXv26I4nJSXRtGlTjh8/Tt26dQkLCzNBaiFeLb1W5wBxcXFER0fr3v9z585NkSJFUEpx+/Ztnjx5AjydOC5RogT58uVDKUVkZKTBs5ujPHnykJSUREhICPXq1QOeft5WrlwZpRQ3btx44fo+JCQEe3t7SpQoQVRUlCliCyGEeE2XL1+mZ8+eBAUF0bFjRwYMGICDgwMlSpRAKcXNmzcJDAxk6dKlbN68GXt7e1avXi3bfoksJXfu3CQnJxMYGKgrDv/777955513dNeSz3cRDgwMpHHjxpQvX54rV66YIHXW8qpCndDQUMLDw1FKUbhwYRo0aJDqfSYsLIw7d+4AUK9ePerXr49SioULFxojvhBCCGEwUvwjhBDCbGi7c5w8eZLatWubOk62kdGOSeXKlePdd99lwIABupXeQrzN5L3GdL744gt+++03AFq3bo23tzcNGzbUTciLzKddgerq6squXbt0x//880/69euHUoqmTZvi7u5OXFwcK1euJDQ0FKUUEyZMYOzYsSZML8TLZVYHyWcppaTD2BsqXbo00dHRHDhwAEdHR+BpAZZ2C7CAgACaNm2aasyuXbto27YtuXPn5vHjx0bPLIQQ4vU8e+9Ao9G88vpdn3OUUq/dDVQIQytSpAj/1959R1dV7V0fnyuEEIo0aUEBAUOvIYKUUII0aSogCJgA0kF5sD9eFcSrr9eOBREIgSCKShUQpEhCFZLQgwWQIjUk9J6y3z94cq4xlARyzs45+X7GyBhh77W2E7wXk5zfWfPMmTPasGGDGjZsKEk6ffq04/vW2NhY1atXL92eNWvWqEWLFsqXL58uXbpkQ2r38s8Tmm/kRic33+g6X8sDANydt90BAADILuHh4ZKuDZPAde50jvivv/7SoUOHNHv2bD300EOaNWuWihUrlk3pgOzXvHlzGWNUsGBBu6PkOgsWLJAxRh07dtQPP/xgd5xc4dFHH9XIkSO1atUq/fnnn47TUvr27auvv/5aS5cu1dq1a7V27dp0++rVq5fupCAgpwkNDbU7Av6mWrVqio+P1+7dux3DPwUKFJC/v792796tH374IcPwT9p/B0qWLOnyvACArPvnzw4y87ME3rcMd3T//fcrNjZWBw8edAz/FC1aVGXKlNHx48e1atWqDMM/69evlyR+zpBJ5cuX5w1AAABcB8M/AACPwYs49kgbupo4caI2btwoX19ftWvXToGBgY4XY06cOKGYmBj99NNPunLliho2bKjBgwfr7Nmz2rlzpxYsWKCEhAStWLFCjzzyiKKiouz8LQE3FRkZmal1V65c0enTp1WyZEmnnDCRG6XVDI4YMcLmJLlHmTJllJSUJMuyMpz0Nm/ePP373/9WWFiYo3KnaNGi6tOnj9566y35+vraERnIlLSvX5AzNGvWTFFRUVq9enW6r+kfe+wxvfPOO/rkk09UrVo19ezZUxcvXtT06dM1adIkGWMUHBxsY3IAQGaNGTPG7giASzRq1EixsbGKjo5W9+7dHdfbt2+vadOm6d1331XHjh1VpUoVSdKmTZv07rvvyhijBx54wK7YbmX//v12RwByrCNHjujVV1+lyg7Ipaj9AgAAd2zo0KGaPHmyunTpoi+//FKlSpW67rr4+HgNHjxYCxcuVP/+/TVlyhRJ0uXLlzV06FBFRETIGKOvv/5aPXv2dOVvAci08+fPa/Xq1ZKunQJUqFChdPcTEhI0ZMgQLVq0SMnJySpUqJAGDRqkt99+Wz4+PnZE9hilSpVSYmLidY9Jh71Onjyp5ORklSxZkndgAsiyjRs3qnHjxipevLgOHTrkGB5MTExU1apVderUqQx7LMtS/vz5FRMTo+rVq7s6MgAAwHUtWrRIXbp0UeXKlbV7927H9Z07dyogIEApKSnKkyeP6tatq4sXL+qPP/5QSkqKjDFavHix2rdvb2N6AO4uLi5OtWvXppYayKUY/gEAeKxTp05p27ZtSkhI0KVLl255XHRISIiLknmW+fPn67HHHlOTJk20Zs2aW77oa1mWmjVrpl9++UXffPONHn/8cUlSamqqGjZsqC1btqhLly6aN2+eK+IDWTZ9+nT1799f5cuX159//pnuVJ/U1FQ1atRImzdvTvd3jjFG3bp103fffWdHZI/RunVrRUZGau7cueratavdcQAA2Wj69OlKTk7Www8/LD8/P8f12NhYPf7449q3b1+69aVKlVJERITatm3r6qgAAAA3lJSUpEGDBiklJUXjxo1TxYoVHffCwsI0bNgwJScnZ9j3xhtv6LXXXnNlVAAeiOEfIHdj+AcA4HEiIyM1ZswYrV27NtN7jDHX/cYbt9a2bVutXLlSs2bNUo8ePTK15/vvv1fPnj0VHBysFStWOK5/+eWXGjZsmO655x799ddfzooM3JHevXtr1qxZGj16tD744IN097755hv16dNHxhjVr19fLVq0UFRUlDZv3sy7+LLBd999p169eumxxx7T7Nmz7Y6TK5w7d04fffSRJGnw4MEqU6bMTdcfPXpUkydPliS98MILyp8/v9MzAq5w4sQJffHFF5Kk119/3eY0uU9SUpJ+/vlnxcXFKTk5Wf7+/mrXrp0KFChgdzQAAIAs+f333zVt2rR0X9c8+eSTCgwMtDuax7p48aJiYmIkXTvBGfBkDP8AuRvDPwAAj/LFF1/o6aeflmVZtzzp5+/4Yvj2lSlTRidOnFBMTIzq16+fqT1btmxRgwYNVLJkSR0/ftxxfcOGDWratKl8fX118eJFZ0UG7kidOnUUFxenb7/9Vt27d0937+GHH9bSpUsVGBio9evXy9vbW0lJSQoKClJ0dLR69uypr7/+2qbknqFPnz6aNWuW3nrrLb388st2x/F4M2bMUGhoqPz9/fX777/fcr1lWapWrZr27NmT7nQ3wN3xA1QAAADAPaV9Le/l5cWbP+Hx+N4VyN287Q4AAEB2+fXXX/XMM8/IsizVrl1b48aNU968edWxY0cZY7Rnzx6dOnVKMTExmjRpkjZv3qxmzZrpyy+/5F3Dd+Ds2bOSrr0jPrPS1p47dy7d9bQTIvLmzZtN6YDsl/a/3woVKqS7npSUpKioKBljNHz4cHl7X/tSO2/evBo6dKg2bdqkjRs3ujyvJ1m9erUGDhyoAwcO6F//+pfmzp2r3r17q1q1apn6e5x3+GXd3LlzZYzJ9BCPMUa9evXSm2++qe+//57hHwAAAABAjsBZCAAAT8fwDwDAY3z66adKSUlRyZIltWbNGt11112Ki4tz3K9YsaIqVqyogIAADRo0SC+//LLee+89Pf300+mqp5A15cqV0549ezRz5ky1bds2U3tmzJghSSpfvny66/Hx8ZKkkiVLZm9IIBudPHlSUsYhtZiYGF26dEnGGHXo0CHdvSpVqkiSjh075pqQHqply5Yyxjh+HRsbq9jY2Eztpd7x9vz222+SpCZNmmR6T+PGjSVJu3btckomALkHdWsAAMBTUD0FAACczcvuAAAAZJe0EzeeeeYZ3XXXXTdda4zRf/7zHwUHB2vVqlWaOnWqi1J6ni5dusiyLH311Vf6+OOPb7n+o48+0syZM2WMUZcuXdLdSzsV5Z8nqgA5SdoJVWnDammioqIkSZUrV1bp0qWvuwd3Lq3W8XY+kHWHDh2SJPn5+WV6T5kyZSRJhw8fdkomALlHfHy8xo4dqzfeeMPuKAAAAHdk3759atmypYKDg+2OAsCDFStWTCEhIQoJCbE7CgAbcPIPAMBjpL1AGRAQ4Lj29xMikpKSMpzUMXjwYP3888/66quvNGDAANcE9TAvv/yypk+frsTERD333HOaOXOmQkJC1KBBA5UqVUrStRduYmJiNGPGDG3evFnStdN9Xn755XTPmjVrlowxatOmjct/H0BmVa5cWVu3blVkZGS6067mzZsnY4xatGiRYU9aVVja/ydwe1atWmV3hFzHy+va+0UuXryY6T1pazlpCQAAAADS440pAJypbNmymjZtmt0xANiE4R8AgMe4fPmypGtf4KYpWLCg4/NTp05leOH9/vvvl0Q1yZ24++67tXz5crVv317Hjx/X5s2bHQM+12NZlsqUKaOlS5eqePHijut//vmnGjZsqIYNG6pbt26uiA7cljZt2mjLli2aMGGCgoKCFBQUpPDwcEVHR8sYo86dO2fYs337dknp/35C1l1vsArO5efnp927dysmJibT1V9pR9mnnQAEAAAAAAAAAHAuhn8AAB6jePHiio+P14ULFxzXSpYs6Tj9548//sgw/JOQkCBJOn36tMtyeqK6devq119/1ZgxYxQREaEzZ85cd12RIkUUEhKisWPHqlixYunuVapUSeHh4a6IC9yRUaNGaeLEiTp37pw6deqU7l716tWvO/yzePFiGWPUuHFjV8UEskVQUJD++OMPTZgwQcOGDctwgt4/JSUlacKECTLGqFmzZi5KCThfwYIF1bx583SnSgIAAADIOVavXi3p2ptY/P39bU4DuNaff/6pDRs26NixY7p48aKGDRumEiVK2B0LgIsx/AMA8BjVqlVTfHy8du/e7TidoECBAvL399fu3bv1ww8/ZHgh8ocffpB0bUgId6Zo0aIaP3683n33XcXGxmrnzp06deqUpGtdwzVr1lRgYKDy5ctnc1Lgzvj5+WnhwoXq1auXjh496rheqVIlzZ49O8MLw3v37tWaNWskiUo7uJ3+/fsrLCxMu3fvVu/evTV9+nQVKFDgumsvXryokJAQ/fHHHzLGqH///i5OCzjPfffdp8jISLtjAAAAALiBli1byhijsLCwdMM/NWvWVGpqqo3JAOfZsmWL/ud//kdr165Nd71bt27phn8+//xzvfHGGypSpIh27dp1yzd3AXBPDP8AADxGs2bNFBUVpdWrVys0NNRx/bHHHtM777yjTz75RNWqVVPPnj118eJFTZ8+XZMmTZIxRsHBwTYm9yz58uVTkyZNMl0PA7ijoKAg7du3T+vWrdOxY8fk5+enZs2ayds745fXR48e1WuvvSaJ2iq4nyZNmqhXr16aNWuW5s6dq40bN2rQoEFq3ry5/Pz8ZIzRkSNHtHr1ak2ZMkWHDh2SMUbdu3fnf+8AAAAAAJcpVKiQLly4oNq1a9sdBXCJxYsXq3v37rp69aosy3Jcv96JtaGhoXr55ZeVmJioRYsW6dFHH3VlVAAuYqy//20AAIAb27hxoxo3bqzixYvr0KFD8vX1lSQlJiaqatWqjlNo/s6yLOXPn18xMTGqXr26qyMDAK5jwIABkuR4x94/r9+Ofz4LmXf58mV16dJFK1asuGnlUdq3lm3atNGCBQsc/x0G3Nnly5cVGxvrODq9a9euKly4sN2xco39+/erX79+MsZo1apVdscBAAC4pRtVT8XFxal27doyxiglJcWueB6tVq1a+vXXXxUZGamgoCC74wBOdezYMVWpUkXnz59XzZo19f7776tZs2a66667ZIzRjh07VKNGjXR7nnzySX399dd66qmnNGnSJJuSA3Amhn8AAB5l+vTpSk5O1sMPPyw/Pz/H9djYWD3++OPat29fuvWlSpVSRESE2rZt6+qoAIAb8PLycgyZ/P2Hon+/nhWWZfED1jtkWZY++eQTvf/++zp8+PB115QrV04vvPCCRowYcVv/noCc5K+//tKrr76qb7/9VklJSY7r//wBalhYmL788ksVKVJEy5Yt43/7AAAAuVza961hYWHq16+f3XFylZdeeknvv/++Xn31Vb3xxht2xwGc6qWXXtJ7772nChUqaMuWLSpatKik//4ddL3hn7CwMA0aNEj169dXbGysDakBOBvDPwCAXCMpKUk///yz4uLilJycLH9/f7Vr104FChSwOxoA4G/uu+8+xwvofx/a/Pv12/HPAVBknWVZ2rp1q7Zs2aKEhARJUokSJRQQEKC6desy+ACPsGnTJj388MM6depUhqPT//kD1BMnTqhcuXJKSkrSjz/+qHbt2tkRGQAAADlE4cKFdeHCBW3atEkNGjSwO06ucuzYMdWuXVtXr17VunXrVKtWLbsjAU5Tr1497dixQ+PHj9fIkSMd1282/LN27Vo1b95cxYoVU2JioqsjA3ABb7sDAADgKnnz5lW7du14UQYAcrj9+/dn6Tpcxxij+vXrq379+nZHAZzizJkz6tq1q06ePCk/Pz+99tprCgoKUu3ata+7vmTJkurQoYN++OEHLV68mK8zswl1awAAwF2VL19ev/76qy5evGh3lFynTJkyWrRokbp166amTZvqpZdeUu/evXXffffZHQ3IdmlvcGvYsGGm99x1112SpPPnzzslEwD7MfwDAAAAwC1ERERIkqpWrapGjRrZnAaAJ/r00091/PhxlShRQhs2bFD58uVvuadNmzZasGCBNm3a5IKEno26NQAA4O46duyoX3/9VStWrFBQUJDdcXKVSpUqSZKuXr2qc+fO6bXXXtNrr72mQoUKqWjRosqTJ88N9xpjtHfvXldFBe5Y2vdLefPmzfSe06dPS5IKFizojEgAcgCGfwAAHu/EiRP64osvJEmvv/66zWkAALerX79+Msbom2++YfgHgFMsXLhQxhg9++yzmRr8kaSaNWtKEi8W3KGb1a39U5cuXTRixAglJSVp2bJlnLgEAAByjNGjR2vq1Kn6+OOP1aNHD6qnXOifpwWnfU157tw5nTt37qZ7GSaHuylTpowOHDigffv2Zfp05g0bNkiS7r33XmdGA2Ajhn8AAB4vPj5eY8eOlTGG4R8AcGNFihTR2bNn5e/vb3cUAB5q9+7dkqTmzZtnek/RokUlSWfPnnVGpFyBujUAAOApqJ6yT2hoqN0RAJdp2rSpDhw4oHnz5umxxx675fqLFy9q4sSJMsZk6ftdAO6F4R8AAAAAbqFixYratm2bTp06ZXcUAB7q0qVLkrJ2DPr58+clSb6+vk7JlBtQtwYAADwF1VP2CQ8PtzsC4DKhoaGaOXOmvvnmGz355JNq27btDdeeP39evXr10sGDB2WM0VNPPeXCpABcieEfAAAAAG7h0Ucf1datW7Vw4UIFBwfbHQeABypZsqQOHz6sv/76S3Xr1s3UntjYWEmSn5+fM6N5NOrWAACAp6B6CoArPPTQQ3rkkUc0f/58denSRU8//bR69OjhuH/y5Elt3LhRy5Yt08SJE3Xs2DEZYxQSEpLpmjAA7ofhHwAAAABuYdSoUZo6daq++OILde7cmQEgANmuYcOGmjdvnpYsWaJOnTrdcn1KSoomTZokY4yaNWvmgoSeibo1AADgKaieAuAqX331lTp16qTIyEh9+OGH+vDDDx1DhC1atHCsSxtCbN26tSZOnGhLVgCuwfAPAAAAALdQuHBhLV++XN27d1e7du3Uv39/9e7dW3Xq1FGxYsV4lySAO/bEE09o7ty5mjp1qgYOHHjTd0SmpqZq6NCh2rVrl4wx6tu3rwuTehbq1gAAgKegeipnOX78uHbu3KmTJ09KkooXL65atWqpdOnSNicD7lyBAgW0YsUKffTRR/rwww919OjR664rXry4nn/+eb344ovy8vJycUoArsTwDwAAAAC3kCdPHsfnlmUpLCxMYWFhmdprjFFycrKzogHwEN26dVOTJk20fv16tW7dWm+++Wa6o9ONMTp+/LiWLVumjz76SNu2bZMxRu3bt1fLli3tC+7mqFsDAABAdrEsS5MmTdJnn32mXbt2XXdNjRo19PTTT2vQoEG8kQhuzcvLS88995xGjRqlTZs2KSYmRvHx8UpJSdHdd9+t+vXrq1mzZsqXL5/dUQG4gLHSzvoCAMBD7d+/X/369ZMxRqtWrbI7DgDgNt3Ju5OMMUpJScnGNAA8VUJCgpo3b67ffvstwwsBPj4+unr1quPXlmWpdu3aioqKctRQIeu6d++uefPmaejQofr8888d1728vGSM0Y4dO1SjRg3H9ZSUFNWtW1e//vqr+vfvrylTptgRGwAAADnMqVOn1LlzZ23YsEHSf+uO/int6/wmTZpo4cKFfC0PtxMRESFJqlq1qho1amRzGgA5BcM/AAAAANzCG2+8cUf7x4wZk01JAHi6ixcv6qWXXlJYWJguX7583TV58+ZV//799cEHH2SprgoZzZkzRz169FC+fPm0fv16R93a9YZ/UlNTNWTIEIWFhckYo5UrV3LqEgAAyNGonnINy7LUokULrV27VpJ099136/HHH1ejRo1UpkwZWZal48ePa9OmTfruu++UkJAgY4yaNWumqKgom9MDWZP2vdI333yjxx9/3O44AHIIhn8AAAAAAACuIzExUT/99NN1j07v0KGDypYta3dEj9GsWTOtX79eRYsWddStlSlTRsYY7dy5U8WLF09XtyZJ7du31+LFi21ODgAAkBHVU643c+ZMPfnkkzLGqHfv3powYYLuuuuu6649f/68RowYoRkzZsgYo6+++kpPPPGEixMDt69YsWI6e/asYmJiHG+eAACGfwAAAAAAAGAr6tYAAICnoHrKHh07dtSSJUvUsmVL/fzzz5na06pVK0VFRalDhw4MlcOtBAQEaNu2bVq+fLmCg4PtjgMgh/C2OwAAAFm1evVqpzy3efPmTnkuAAAAgJsrUaKEYmJirlu3duXKFcfn1K0BAICczLIsde3aVevXr5eUueqp9evXq2vXrlRP3aHNmzfLGKORI0dmes/TTz+tqKgobdmyxYnJgOz36KOPauvWrVq4cCHDPwAcOPkHAOB20vpss5MxRsnJydn6TAAAAABZR90aAABwV1RP2SdfvnxKTk7OUg3Sli1b1KBBA/n4+KQbPgdyurNnz6pu3bo6evSofvzxRwaAAEhi+AcA4Ia8vLyy/ZnGGKWkpGT7cwEAAOA+tmzZosDAQPn4+GjPnj265557brr+8OHDqly5spKTk7V9+3bVqFHDRUkBAACQE1E9ZZ9SpUopMTFRP/zwgzp27JipPYsXL1bnzp1VokQJxcfHOzkhkL327Nmj7t27Ky4uTv3791fv3r1Vp04dFStWLNvfPA3APVD7BQBwO6tWrbI7AgAAADzQt99+K8uy1KlTp1sO/kjSPffcoy5dumj27NmaNWuWxo0b54KUAAAAyKmonrJPrVq1FBUVpfDw8EwP/0ydOtWxF3AnefLkcXxuWZbCwsIUFhaWqb20IACei+EfAIDbadGihd0RAAAA4IEiIyNljFGHDh0yvadjx46aPXu2VqxYwfAPAABALnfy5ElJUsWKFTO9J21t2l7cnu7duysyMlLz5s3T2LFjNWbMmBuefmJZlt544w3NmzdPxhj16NHDxWmBO/PPYh+KfgBIDP8AAAAAAABIkv766y9JylJ9V9WqVSVJhw4dckqm3IC6NQAA4CmKFCmixMREHTlyRPXr18/UniNHjkiSChcu7MxoHm/QoEH67LPP9Ntvv+nNN9/UnDlz1K9fPzVq1EilS5eWMUbHjh3Txo0bNX36dMXFxUmSqlWrpkGDBtmcHsiaMWPG2B0BQA7E8A8AAAAAAICkxMRESZKvr2+m9+TLl0+SFB8f75RMuQF1awAAwFNQPWWfvHnzasmSJQoODta+ffu0a9cuvfjiizdcb1mWKlWqpCVLlsjbm5dL4V4Y/gFwPV52BwAAAAAAAMgJihUrJkk6ePBgpveknfjDO7Vv3+3WrUnSihUrnBULAAAgy7p37y7LshzVUzer4rEsS2PHjqV6KhtVqFBB27dv13PPPaciRYrIsqzrfhQ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      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6),dpi=200)\n",
    "sns.barplot(data=df.describe().transpose().reset_index().sort_values('unique'),x='index',y='unique')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('class',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>m</td>\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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>8119</th>\n",
       "      <td>k</td>\n",
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       "      <td>n</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8121</th>\n",
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       "    <tr>\n",
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       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>x</td>\n",
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       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
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       "      <td>l</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8124 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0            x           s         n       t    p               f   \n",
       "1            x           s         y       t    a               f   \n",
       "2            b           s         w       t    l               f   \n",
       "3            x           y         w       t    p               f   \n",
       "4            x           s         g       f    n               f   \n",
       "...        ...         ...       ...     ...  ...             ...   \n",
       "8119         k           s         n       f    n               a   \n",
       "8120         x           s         n       f    n               a   \n",
       "8121         f           s         n       f    n               a   \n",
       "8122         k           y         n       f    y               f   \n",
       "8123         x           s         n       f    n               a   \n",
       "\n",
       "     gill-spacing gill-size gill-color stalk-shape  ...  \\\n",
       "0               c         n          k           e  ...   \n",
       "1               c         b          k           e  ...   \n",
       "2               c         b          n           e  ...   \n",
       "3               c         n          n           e  ...   \n",
       "4               w         b          k           t  ...   \n",
       "...           ...       ...        ...         ...  ...   \n",
       "8119            c         b          y           e  ...   \n",
       "8120            c         b          y           e  ...   \n",
       "8121            c         b          n           e  ...   \n",
       "8122            c         n          b           t  ...   \n",
       "8123            c         b          y           e  ...   \n",
       "\n",
       "     stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring  \\\n",
       "0                           s                      w                      w   \n",
       "1                           s                      w                      w   \n",
       "2                           s                      w                      w   \n",
       "3                           s                      w                      w   \n",
       "4                           s                      w                      w   \n",
       "...                       ...                    ...                    ...   \n",
       "8119                        s                      o                      o   \n",
       "8120                        s                      o                      o   \n",
       "8121                        s                      o                      o   \n",
       "8122                        k                      w                      w   \n",
       "8123                        s                      o                      o   \n",
       "\n",
       "     veil-type veil-color ring-number ring-type spore-print-color population  \\\n",
       "0            p          w           o         p                 k          s   \n",
       "1            p          w           o         p                 n          n   \n",
       "2            p          w           o         p                 n          n   \n",
       "3            p          w           o         p                 k          s   \n",
       "4            p          w           o         e                 n          a   \n",
       "...        ...        ...         ...       ...               ...        ...   \n",
       "8119         p          o           o         p                 b          c   \n",
       "8120         p          n           o         p                 b          v   \n",
       "8121         p          o           o         p                 b          c   \n",
       "8122         p          w           o         e                 w          v   \n",
       "8123         p          o           o         p                 o          c   \n",
       "\n",
       "     habitat  \n",
       "0          u  \n",
       "1          g  \n",
       "2          m  \n",
       "3          u  \n",
       "4          g  \n",
       "...      ...  \n",
       "8119       l  \n",
       "8120       l  \n",
       "8121       l  \n",
       "8122       l  \n",
       "8123       l  \n",
       "\n",
       "[8124 rows x 22 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cap-shape_c</th>\n",
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       "</table>\n",
       "<p>8124 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  cap-shape_x  \\\n",
       "0               0            0            0            0            1   \n",
       "1               0            0            0            0            1   \n",
       "2               0            0            0            0            0   \n",
       "3               0            0            0            0            1   \n",
       "4               0            0            0            0            1   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "8119            0            0            1            0            0   \n",
       "8120            0            0            0            0            1   \n",
       "8121            0            1            0            0            0   \n",
       "8122            0            0            1            0            0   \n",
       "8123            0            0            0            0            1   \n",
       "\n",
       "      cap-surface_g  cap-surface_s  cap-surface_y  cap-color_c  cap-color_e  \\\n",
       "0                 0              1              0            0            0   \n",
       "1                 0              1              0            0            0   \n",
       "2                 0              1              0            0            0   \n",
       "3                 0              0              1            0            0   \n",
       "4                 0              1              0            0            0   \n",
       "...             ...            ...            ...          ...          ...   \n",
       "8119              0              1              0            0            0   \n",
       "8120              0              1              0            0            0   \n",
       "8121              0              1              0            0            0   \n",
       "8122              0              0              1            0            0   \n",
       "8123              0              1              0            0            0   \n",
       "\n",
       "      ...  population_n  population_s  population_v  population_y  habitat_g  \\\n",
       "0     ...             0             1             0             0          0   \n",
       "1     ...             1             0             0             0          1   \n",
       "2     ...             1             0             0             0          0   \n",
       "3     ...             0             1             0             0          0   \n",
       "4     ...             0             0             0             0          1   \n",
       "...   ...           ...           ...           ...           ...        ...   \n",
       "8119  ...             0             0             0             0          0   \n",
       "8120  ...             0             0             1             0          0   \n",
       "8121  ...             0             0             0             0          0   \n",
       "8122  ...             0             0             1             0          0   \n",
       "8123  ...             0             0             0             0          0   \n",
       "\n",
       "      habitat_l  habitat_m  habitat_p  habitat_u  habitat_w  \n",
       "0             0          0          0          1          0  \n",
       "1             0          0          0          0          0  \n",
       "2             0          1          0          0          0  \n",
       "3             0          0          0          1          0  \n",
       "4             0          0          0          0          0  \n",
       "...         ...        ...        ...        ...        ...  \n",
       "8119          1          0          0          0          0  \n",
       "8120          1          0          0          0          0  \n",
       "8121          1          0          0          0          0  \n",
       "8122          1          0          0          0          0  \n",
       "8123          1          0          0          0          0  \n",
       "\n",
       "[8124 rows x 95 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AdaBoostClassifier(n_estimators=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(n_estimators=1)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,plot_confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['p', 'e', 'p', ..., 'p', 'p', 'e'], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           e       0.96      0.81      0.88       655\n",
      "           p       0.81      0.96      0.88       564\n",
      "\n",
      "    accuracy                           0.88      1219\n",
      "   macro avg       0.88      0.88      0.88      1219\n",
      "weighted avg       0.89      0.88      0.88      1219\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_\n",
    "\n",
    "# To identify the features with the greatest importance to the capability of the model to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_.argmax()\n",
    "\n",
    "# to get the index position whish is position 22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'odor_n'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[22]\n",
    "\n",
    "# 'odor_n' is odor is none\n",
    "# the Ada Boost predicts gets the feature with the highest importance or correlation and \n",
    "# predict the feature with the hghest correlation to the Y label- which in this case\n",
    "# helps to know what feature to be looked out for to easiy spot a poisonous mushroom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='odor', ylabel='count'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='odor',hue='class')\n",
    "\n",
    "# mushrooms with no odor are edible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing performance as more weak learners are added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rates = []\n",
    "\n",
    "for n in range(1,96):\n",
    "    \n",
    "    model = AdaBoostClassifier(n_estimators=n)\n",
    "    model.fit(X_train,y_train)\n",
    "    preds = model.predict(X_test)\n",
    "    err = 1 - accuracy_score(y_test,preds)\n",
    "    \n",
    "    error_rates.append(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1329b5a9820>]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,96),error_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(n_estimators=95)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.01052632, 0.        ,\n",
       "       0.        , 0.01052632, 0.        , 0.        , 0.        ,\n",
       "       0.01052632, 0.        , 0.05263158, 0.03157895, 0.03157895,\n",
       "       0.        , 0.        , 0.06315789, 0.02105263, 0.        ,\n",
       "       0.        , 0.        , 0.09473684, 0.09473684, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.01052632, 0.01052632, 0.        , 0.        , 0.        ,\n",
       "       0.06315789, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.03157895, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.06315789, 0.        , 0.        ,\n",
       "       0.01052632, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.01052632, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.05263158, 0.        , 0.16842105, 0.        , 0.10526316,\n",
       "       0.        , 0.        , 0.04210526, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.01052632])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "feats = pd.DataFrame(index=X.columns,data=model.feature_importances_,columns=['Importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cap-shape_c</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape_f</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape_k</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape_s</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape_x</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_l</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_m</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_p</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_u</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_w</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Importance\n",
       "cap-shape_c    0.000000\n",
       "cap-shape_f    0.000000\n",
       "cap-shape_k    0.000000\n",
       "cap-shape_s    0.000000\n",
       "cap-shape_x    0.000000\n",
       "...                 ...\n",
       "habitat_l      0.000000\n",
       "habitat_m      0.000000\n",
       "habitat_p      0.000000\n",
       "habitat_u      0.000000\n",
       "habitat_w      0.010526\n",
       "\n",
       "[95 rows x 1 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats\n",
    "\n",
    "# they are all 0 because they have no sttrong correlation with the label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_feats = feats[feats['Importance']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cap-color_c</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-color_n</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-color_w</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bruises_t</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor_c</th>\n",
       "      <td>0.031579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor_f</th>\n",
       "      <td>0.031579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor_n</th>\n",
       "      <td>0.063158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor_p</th>\n",
       "      <td>0.021053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-spacing_w</th>\n",
       "      <td>0.094737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-size_n</th>\n",
       "      <td>0.094737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-shape_t</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-root_b</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-above-ring_k</th>\n",
       "      <td>0.063158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-below-ring_y</th>\n",
       "      <td>0.031579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring_n</th>\n",
       "      <td>0.063158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring_w</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-number_t</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color_r</th>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color_w</th>\n",
       "      <td>0.168421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_c</th>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_v</th>\n",
       "      <td>0.042105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_w</th>\n",
       "      <td>0.010526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Importance\n",
       "cap-color_c                   0.010526\n",
       "cap-color_n                   0.010526\n",
       "cap-color_w                   0.010526\n",
       "bruises_t                     0.052632\n",
       "odor_c                        0.031579\n",
       "odor_f                        0.031579\n",
       "odor_n                        0.063158\n",
       "odor_p                        0.021053\n",
       "gill-spacing_w                0.094737\n",
       "gill-size_n                   0.094737\n",
       "stalk-shape_t                 0.010526\n",
       "stalk-root_b                  0.010526\n",
       "stalk-surface-above-ring_k    0.063158\n",
       "stalk-surface-below-ring_y    0.031579\n",
       "stalk-color-below-ring_n      0.063158\n",
       "stalk-color-below-ring_w      0.010526\n",
       "ring-number_t                 0.010526\n",
       "spore-print-color_r           0.052632\n",
       "spore-print-color_w           0.168421\n",
       "population_c                  0.105263\n",
       "population_v                  0.042105\n",
       "habitat_w                     0.010526"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_feats = imp_feats.sort_values(\"Importance\")\n",
    "\n",
    "# to get the features greater than zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(14,6),dpi=200)\n",
    "sns.barplot(data=imp_feats.sort_values('Importance'),x=imp_feats.sort_values('Importance').index,y='Importance')\n",
    "\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.countplot(data=df,x='habitat',hue='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting to see how the importance of the features shift as more are allowed to be added in! But remember these are all weak learner stumps, and feature importance is available for all the tree methods!"
   ]
  }
 ],
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