9-XGBSklearn-Synthesis-Copy3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "game_csv = pd.read_csv('./data/Training_dataset_Original.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "gameDf = pd.DataFrame(game_csv)\n",
    "\n",
    "dataDict = pd.read_csv('./data/Data_Dictionary.csv',na_values=\"NA\")\n",
    "\n",
    "#Leaderboard\n",
    "leader_csv = pd.read_csv('./data/Leaderboard_dataset.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "leaderDf = pd.DataFrame(leader_csv)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 49)\n",
      "(49, 2)\n"
     ]
    }
   ],
   "source": [
    "print(gameDf.shape)\n",
    "print(dataDict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = dict(zip(dataDict.values[:, 1], dataDict.values[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mvar25'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['Average tenure of active revolving credit cards (in days)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#remove ID column\n",
    "del gameDf[dic['Application ID (primary key)']]\n",
    "del dic['Application ID (primary key)']\n",
    "\n",
    "leaderAppID = leaderDf['application_key']\n",
    "del leaderDf['application_key']\n",
    "leaderX = leaderDf.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = gameDf.iloc[:,:-1]\n",
    "gameY = gameDf.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 80000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(gameDf[\"mvar47\"].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#one hot encoding C=0 L=1\n",
    "gameX['mvar47'] = pd.get_dummies(gameX['mvar47'])\n",
    "leaderX['mvar47'] = pd.get_dummies(leaderX['mvar47'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameX[\"mvar47\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesis begins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 47)\n",
      "(25000, 47)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX = gameX.assign(var345 = (1+gameX['mvar3'])*(1+gameX['mvar4'])*(1+gameX['mvar5']))\n",
    "leaderX = leaderX.assign(var345 = (1+leaderX['mvar3'])*(1+leaderX['mvar4'])*(1+leaderX['mvar5']) )\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX  = gameX.assign(mvar678 = (gameX['mvar7'] + gameX['mvar8'])/(1+gameX['mvar6']))\n",
    "leaderX = leaderX.assign(mvar678 = (leaderX['mvar7'] + leaderX['mvar8'])/(1+leaderX['mvar6']))\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp16 = df['mvar16'].copy()\n",
    "temp16[temp16.isnull()] = -1\n",
    "temp17 = df['mvar17'].copy()\n",
    "temp17[temp17.isnull()] = -1\n",
    "temp18 = df['mvar18'].copy()\n",
    "temp18[temp18.isnull()] = -1\n",
    "\n",
    "\n",
    "temp16 = temp16 + temp17+ temp18\n",
    "temp16[temp16>0] = 0.5265\n",
    "temp16[temp16==0] = 0.1763\n",
    "temp16[temp16==-1] = 0.2594\n",
    "temp16[temp16==-2] = 0.2594\n",
    "temp16[temp16==-3] = 0.4423\n",
    "\n",
    "df = df.assign(var161718 = temp16)\n",
    "\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp16 = df['mvar16'].copy()\n",
    "temp16[temp16.isnull()] = -1\n",
    "temp17 = df['mvar17'].copy()\n",
    "temp17[temp17.isnull()] = -1\n",
    "temp18 = df['mvar18'].copy()\n",
    "temp18[temp18.isnull()] = -1\n",
    "\n",
    "\n",
    "temp16 = temp16 + temp17+ temp18\n",
    "temp16[temp16>0] = 0.5265\n",
    "temp16[temp16==0] = 0.1763\n",
    "temp16[temp16==-1] = 0.2594\n",
    "temp16[temp16==-2] = 0.2594\n",
    "temp16[temp16==-3] = 0.4423\n",
    "\n",
    "df = df.assign(var161718 = temp16)\n",
    "\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX = gameX.assign(var2223 = gameX['mvar23'] - gameX['mvar22'])\n",
    "leaderX = leaderX.assign(var2223 = leaderX['mvar23'] - leaderX['mvar22'])\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp26 = df['mvar26'].copy()\n",
    "temp26[temp26.isnull()] = -365\n",
    "temp27 = df['mvar27'].copy()\n",
    "temp27[temp27.isnull()] = -365\n",
    "\n",
    "temp26 = (temp26+temp27)/2/365\n",
    "temp26 = temp26.astype('int64')\n",
    "\n",
    "temp26[(temp26>=0) & (temp26<=5)] = 0.3243\n",
    "temp26[temp26>5] = 0.1562\n",
    "temp26[temp26<0] = 0.4026\n",
    "\n",
    "df = df.assign(var2627 = temp26)\n",
    "gameX = df.copy()\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp26 = df['mvar26'].copy()\n",
    "temp26[temp26.isnull()] = -365\n",
    "temp27 = df['mvar27'].copy()\n",
    "temp27[temp27.isnull()] = -365\n",
    "\n",
    "temp26 = (temp26+temp27)/2/365\n",
    "temp26 = temp26.astype('int64')\n",
    "\n",
    "temp26[(temp26>=0) & (temp26<=5)] = 0.3243\n",
    "temp26[temp26>5] = 0.1562\n",
    "temp26[temp26<0] = 0.4026\n",
    "\n",
    "df = df.assign(var2627 = temp26)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp28 = df['mvar28'].copy()\n",
    "temp28[temp28.isnull()] = -365\n",
    "\n",
    "temp28 = temp28/365\n",
    "temp28 = temp28.astype('int64')\n",
    "\n",
    "temp28[temp28!=0] = 0.1231\n",
    "temp28[temp28==0] = 0.3605\n",
    "\n",
    "df = df.assign(var28 = temp28)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp28 = df['mvar28'].copy()\n",
    "temp28[temp28.isnull()] = -365\n",
    "\n",
    "temp28 = temp28/365\n",
    "temp28 = temp28.astype('int64')\n",
    "\n",
    "temp28[temp28!=0] = 0.1231\n",
    "temp28[temp28==0] = 0.3605\n",
    "\n",
    "df = df.assign(var28 = temp28)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "df = df.assign(var3031 = df[[\"mvar30\", \"mvar31\"]].max(axis=1))\n",
    "gameX = df.copy()\n",
    "\n",
    "df = leaderX.copy()\n",
    "df = df.assign(var3031 = df[[\"mvar30\", \"mvar31\"]].max(axis=1))\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp33 = df['mvar33'].copy()\n",
    "temp33[temp33.isnull()] = -5\n",
    "\n",
    "temp33 = temp33/5\n",
    "temp33 = temp33.astype('int64')\n",
    "\n",
    "temp33[temp33<0] = 0.3118\n",
    "temp33[temp33==1] = 0.2138\n",
    "temp33[temp33>1] = 0.1601\n",
    "temp33[temp33==0] = 0.2959\n",
    "\n",
    "df = df.assign(var33 = temp33)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp33 = df['mvar33'].copy()\n",
    "temp33[temp33.isnull()] = -5\n",
    "\n",
    "temp33 = temp33/5\n",
    "temp33 = temp33.astype('int64')\n",
    "\n",
    "temp33[temp33<0] = 0.3118\n",
    "temp33[temp33==1] = 0.2138\n",
    "temp33[temp33>1] = 0.1601\n",
    "temp33[temp33==0] = 0.2959\n",
    "\n",
    "df = df.assign(var33 = temp33)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp34 = df['mvar34'].copy()\n",
    "temp34[temp34.isnull()] = -1\n",
    "\n",
    "temp34[temp34==0] = 0.2151\n",
    "temp34[temp34==-1] = 0.2151\n",
    "temp34[temp34==1] = 0.3589\n",
    "temp34[temp34==2] = 0.4459\n",
    "temp34[temp34>2] = 0.5641\n",
    "\n",
    "df = df.assign(var34 = temp34)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp34 = df['mvar34'].copy()\n",
    "temp34[temp34.isnull()] = -1\n",
    "\n",
    "temp34[temp34==0] = 0.2151\n",
    "temp34[temp34==-1] = 0.2151\n",
    "temp34[temp34==1] = 0.3589\n",
    "temp34[temp34==2] = 0.4459\n",
    "temp34[temp34>2] = 0.5641\n",
    "\n",
    "df = df.assign(var34 = temp34)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp35 = df['mvar35'].copy()\n",
    "temp35[temp35.isnull()] = -1\n",
    "\n",
    "temp35[temp35>0] = 0.5137\n",
    "temp35[temp35==0] = 0.1878\n",
    "temp35[temp35==-1] = 0.2623\n",
    "\n",
    "df = df.assign(var35 = temp35)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp35 = df['mvar35'].copy()\n",
    "temp35[temp35.isnull()] = -1\n",
    "\n",
    "temp35[temp35>0] = 0.5137\n",
    "temp35[temp35==0] = 0.1878\n",
    "temp35[temp35==-1] = 0.2623\n",
    "\n",
    "df = df.assign(var35 = temp35)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "#mvar36, 37, 38\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp36 = df['mvar36'].copy()\n",
    "\n",
    "temp36[temp36.isnull()] = -1\n",
    "\n",
    "temp36[temp36==1] = 0.163769\n",
    "temp36[temp36==2] = 0.170170\n",
    "temp36[temp36==3] = 0.190171\n",
    "temp36[temp36==4] = 0.211005\n",
    "temp36[temp36==5] = 0.226932\n",
    "temp36[temp36==6] = 0.247148\n",
    "temp36[temp36==7] = 0.271086\n",
    "temp36[temp36==8] = 0.296529\n",
    "temp36[temp36==9] = 0.309190\n",
    "temp36[temp36==10] = 0.323132\n",
    "temp36[temp36==11] = 0.330296\n",
    "temp36[temp36==12] = 0.33815\n",
    "temp36[temp36==13] = 0.352103\n",
    "temp36[temp36==14] = 0.370433\n",
    "temp36[temp36==15] = 0.365985\n",
    "temp36[temp36==16] = 0.384926\n",
    "temp36[temp36==17] = 0.395310\n",
    "temp36[temp36==18] = 0.449219\n",
    "temp36[temp36==19] = 0.425926\n",
    "temp36[temp36<0] = 0.176674\n",
    "temp36[temp36>=20] = 0.4647\n",
    "\n",
    "df = df.assign(var36 = temp36)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp36 = df['mvar36'].copy()\n",
    "\n",
    "temp36[temp36.isnull()] = -1\n",
    "\n",
    "temp36[temp36==1] = 0.163769\n",
    "temp36[temp36==2] = 0.170170\n",
    "temp36[temp36==3] = 0.190171\n",
    "temp36[temp36==4] = 0.211005\n",
    "temp36[temp36==5] = 0.226932\n",
    "temp36[temp36==6] = 0.247148\n",
    "temp36[temp36==7] = 0.271086\n",
    "temp36[temp36==8] = 0.296529\n",
    "temp36[temp36==9] = 0.309190\n",
    "temp36[temp36==10] = 0.323132\n",
    "temp36[temp36==11] = 0.330296\n",
    "temp36[temp36==12] = 0.33815\n",
    "temp36[temp36==13] = 0.352103\n",
    "temp36[temp36==14] = 0.370433\n",
    "temp36[temp36==15] = 0.365985\n",
    "temp36[temp36==16] = 0.384926\n",
    "temp36[temp36==17] = 0.395310\n",
    "temp36[temp36==18] = 0.449219\n",
    "temp36[temp36==19] = 0.425926\n",
    "temp36[temp36<0] = 0.176674\n",
    "temp36[temp36>=20] = 0.4647\n",
    "\n",
    "df = df.assign(var36 = temp36)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp37 = df['mvar37'].copy()\n",
    "\n",
    "temp37[temp37.isnull()] = -1\n",
    "\n",
    "temp37[temp37<0] = 0.458344\n",
    "temp37[temp37==0] = 0.381028\n",
    "temp37[temp37==1] = 0.309995\n",
    "temp37[temp37==2] = 0.239528\n",
    "temp37[temp37==3] = 0.186923\n",
    "temp37[temp37==4] = 0.159852\n",
    "temp37[temp37==5] = 0.14485\n",
    "temp37[temp37==6] = 0.131449\n",
    "temp37[temp37==7] = 0.118592\n",
    "temp37[temp37==8] = 0.111612\n",
    "temp37[temp37==9] = 0.102641\n",
    "temp37[temp37>=10] = 0.0904\n",
    "\n",
    "df = df.assign(var37 = temp37)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp37 = df['mvar37'].copy()\n",
    "\n",
    "temp37[temp37.isnull()] = -1\n",
    "\n",
    "temp37[temp37<0] = 0.458344\n",
    "temp37[temp37==0] = 0.381028\n",
    "temp37[temp37==1] = 0.309995\n",
    "temp37[temp37==2] = 0.239528\n",
    "temp37[temp37==3] = 0.186923\n",
    "temp37[temp37==4] = 0.159852\n",
    "temp37[temp37==5] = 0.14485\n",
    "temp37[temp37==6] = 0.131449\n",
    "temp37[temp37==7] = 0.118592\n",
    "temp37[temp37==8] = 0.111612\n",
    "temp37[temp37==9] = 0.102641\n",
    "temp37[temp37>=10] = 0.0904\n",
    "\n",
    "df = df.assign(var37 = temp37)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp38 = df['mvar38'].copy()\n",
    "\n",
    "temp38[temp38.isnull()] = -1\n",
    "\n",
    "temp38[temp38<0] = 0.215139\n",
    "temp38[temp38==0] = 0.145566\n",
    "temp38[temp38==1] = 0.176224\n",
    "temp38[temp38==2] = 0.200382\n",
    "temp38[temp38==3] = 0.232083\n",
    "temp38[temp38==4] = 0.252492\n",
    "temp38[temp38==5] = 0.275282\n",
    "temp38[temp38==6] = 0.297332\n",
    "temp38[temp38==7] = 0.298409\n",
    "temp38[temp38==8] = 0.328731\n",
    "temp38[temp38==9] = 0.342270\n",
    "temp38[temp38>=10] = 0.3907\n",
    "\n",
    "df = df.assign(var38 = temp38)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp38 = df['mvar38'].copy()\n",
    "\n",
    "temp38[temp38.isnull()] = -1\n",
    "\n",
    "temp38[temp38<0] = 0.215139\n",
    "temp38[temp38==0] = 0.145566\n",
    "temp38[temp38==1] = 0.176224\n",
    "temp38[temp38==2] = 0.200382\n",
    "temp38[temp38==3] = 0.232083\n",
    "temp38[temp38==4] = 0.252492\n",
    "temp38[temp38==5] = 0.275282\n",
    "temp38[temp38==6] = 0.297332\n",
    "temp38[temp38==7] = 0.298409\n",
    "temp38[temp38==8] = 0.328731\n",
    "temp38[temp38==9] = 0.342270\n",
    "temp38[temp38>=10] = 0.3907\n",
    "\n",
    "df = df.assign(var38 = temp38)\n",
    "leaderX = df.copy()\n",
    "\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "gameX = gameX.assign(var363738 = (gameX['mvar36']*gameX['mvar37']/(1+gameX['mvar38'])) )\n",
    "leaderX = leaderX.assign(var363738 = (leaderX['mvar36']*leaderX['mvar37']/(1+leaderX['mvar38']) ) )\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "\n",
    "\n",
    "#mvar39\n",
    "df = gameX.copy()\n",
    "\n",
    "temp39 = df['mvar35'].copy()\n",
    "temp39[temp39.isnull()] = -1\n",
    "\n",
    "temp39[temp39>0] = 0.4925\n",
    "temp39[temp39==0] = 0.2109\n",
    "temp39[temp39==-1] = 0.4390\n",
    "\n",
    "df = df.assign(var39 = temp39)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp39 = df['mvar35'].copy()\n",
    "temp39[temp39.isnull()] = -1\n",
    "\n",
    "temp39[temp39>0] = 0.4925\n",
    "temp39[temp39==0] = 0.2109\n",
    "temp39[temp39==-1] = 0.4390\n",
    "\n",
    "df = df.assign(var39 = temp39)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp40 = df['mvar40'].copy()\n",
    "temp40[temp40.isnull()] = -50\n",
    "temp40 = temp40/50\n",
    "temp40 = temp40.round(0)\n",
    "\n",
    "temp40[temp40>=2] = 0.375\n",
    "temp40[temp40==1] = 0.1643\n",
    "temp40[temp40==0] = 0.1540\n",
    "temp40[temp40<0] = 0.2199\n",
    "\n",
    "df = df.assign(var40 = temp40)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp40 = df['mvar40'].copy()\n",
    "temp40[temp40.isnull()] = -50\n",
    "temp40 = temp40/50\n",
    "temp40 = temp40.round(0)\n",
    "\n",
    "temp40[temp40>=2] = 0.375\n",
    "temp40[temp40==1] = 0.1643\n",
    "temp40[temp40==0] = 0.1540\n",
    "temp40[temp40<0] = 0.2199\n",
    "\n",
    "df = df.assign(var40 = temp40)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp41 = df['mvar41'].copy()\n",
    "temp41[temp41.isnull()] = -25\n",
    "temp41 = temp41/25\n",
    "temp41 = temp41.round(0)\n",
    "\n",
    "temp41[temp41>=4] = 0.3000\n",
    "temp41[temp41==3] = 0.2231\n",
    "temp41[ (temp41==2) | (temp41==1) | (temp41==0)] = 0.1610\n",
    "temp41[temp41<0] = 0.2526\n",
    "\n",
    "df = df.assign(var41 = temp41)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp41 = df['mvar41'].copy()\n",
    "temp41[temp41.isnull()] = -25\n",
    "temp41 = temp41/25\n",
    "temp41 = temp41.round(0)\n",
    "\n",
    "temp41[temp41>=4] = 0.3000\n",
    "temp41[temp41==3] = 0.2231\n",
    "temp41[ (temp41==2) | (temp41==1) | (temp41==0)] = 0.1610\n",
    "temp41[temp41<0] = 0.2526\n",
    "\n",
    "df = df.assign(var41 = temp41)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp45 = df['mvar45'].copy()\n",
    "temp45[temp45.isnull()] = -1\n",
    "\n",
    "temp45[temp45>0] = 0.3481\n",
    "temp45[temp45==0] = 0.1268\n",
    "temp45[temp45<0] = 0.3281\n",
    "\n",
    "df = df.assign(var45 = temp45)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp45 = df['mvar45'].copy()\n",
    "temp45[temp45.isnull()] = -1\n",
    "\n",
    "temp45[temp45>0] = 0.3481\n",
    "temp45[temp45==0] = 0.1268\n",
    "temp45[temp45<0] = 0.3281\n",
    "\n",
    "df = df.assign(var45 = temp45)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "df = gameX.copy()\n",
    "\n",
    "temp46 = df['mvar46'].copy()\n",
    "temp46[temp46.isnull()] = -1\n",
    "\n",
    "temp46[temp46>0] = 0.4979\n",
    "temp46[temp46==0] = 0.2010\n",
    "temp46[temp46<0] = 0.2968\n",
    "\n",
    "df = df.assign(var46 = temp46)\n",
    "gameX = df.copy()\n",
    "\n",
    "\n",
    "\n",
    "df = leaderX.copy()\n",
    "\n",
    "temp46 = df['mvar46'].copy()\n",
    "temp46[temp46.isnull()] = -1\n",
    "\n",
    "temp46[temp46>0] = 0.4979\n",
    "temp46[temp46==0] = 0.2010\n",
    "temp46[temp46<0] = 0.2968\n",
    "\n",
    "df = df.assign(var46 = temp46)\n",
    "leaderX = df.copy()\n",
    "\n",
    "print(gameX.shape)\n",
    "print(leaderX.shape)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#drop\n",
    "df = gameX.copy() \n",
    "leader_df = leaderX.copy()\n",
    "drop_target = ['mvar27']\n",
    "#number of misssing values\n",
    "# for i in df.columns:\n",
    "#     if (100*(80000-gameDf[i].describe()['count'])/80000) > "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in drop_target:\n",
    "    del df[i]\n",
    "    del leader_df[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mvar1', 'mvar2', 'mvar3', 'mvar4', 'mvar5', 'mvar6', 'mvar7', 'mvar8',\n",
       "       'mvar9', 'mvar10', 'mvar11', 'mvar12', 'mvar13', 'mvar14', 'mvar15',\n",
       "       'mvar16', 'mvar17', 'mvar18', 'mvar19', 'mvar20', 'mvar21', 'mvar22',\n",
       "       'mvar23', 'mvar24', 'mvar25', 'mvar26', 'mvar28', 'mvar29', 'mvar30',\n",
       "       'mvar31', 'mvar32', 'mvar33', 'mvar34', 'mvar35', 'mvar36', 'mvar37',\n",
       "       'mvar38', 'mvar39', 'mvar40', 'mvar41', 'mvar42', 'mvar43', 'mvar44',\n",
       "       'mvar45', 'mvar46', 'mvar47'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leader_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gameX = df\n",
    "leaderX = leader_df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthesis Ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 66)\n",
      "(25000, 66)\n"
     ]
    }
   ],
   "source": [
    "print(gameX.shape)\n",
    "print(leaderX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>mvar10</th>\n",
       "      <th>...</th>\n",
       "      <th>var35</th>\n",
       "      <th>var36</th>\n",
       "      <th>var37</th>\n",
       "      <th>var38</th>\n",
       "      <th>var363738</th>\n",
       "      <th>var39</th>\n",
       "      <th>var40</th>\n",
       "      <th>var41</th>\n",
       "      <th>var45</th>\n",
       "      <th>var46</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1753.0</td>\n",
       "      <td>0.500100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>19179.000000</td>\n",
       "      <td>206.00000</td>\n",
       "      <td>14221.000000</td>\n",
       "      <td>19438.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1894.0</td>\n",
       "      <td>1.970100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>19820.000000</td>\n",
       "      <td>19820.00000</td>\n",
       "      <td>176895.000000</td>\n",
       "      <td>66346.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1774.0</td>\n",
       "      <td>0.171800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>2563.000000</td>\n",
       "      <td>877.00000</td>\n",
       "      <td>8869.000000</td>\n",
       "      <td>3637.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1586.0</td>\n",
       "      <td>0.112300</td>\n",
       "      <td>5.299</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.163769</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1832.0</td>\n",
       "      <td>1.444200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>7577.000000</td>\n",
       "      <td>179.00000</td>\n",
       "      <td>21059.000000</td>\n",
       "      <td>20306.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.395310</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1838.0</td>\n",
       "      <td>0.054400</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>882.000000</td>\n",
       "      <td>14039.000000</td>\n",
       "      <td>10976.00000</td>\n",
       "      <td>278941.000000</td>\n",
       "      <td>74030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.370433</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.252492</td>\n",
       "      <td>53.200000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.075300</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>16141.000000</td>\n",
       "      <td>3568.00000</td>\n",
       "      <td>69482.000000</td>\n",
       "      <td>56755.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.296529</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.297332</td>\n",
       "      <td>10.285714</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1935.0</td>\n",
       "      <td>0.560100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>44919.000000</td>\n",
       "      <td>17115.00000</td>\n",
       "      <td>124814.000000</td>\n",
       "      <td>124167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.342270</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1885.0</td>\n",
       "      <td>0.081100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7369.000000</td>\n",
       "      <td>19264.000000</td>\n",
       "      <td>19264.00000</td>\n",
       "      <td>59956.000000</td>\n",
       "      <td>26633.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.170170</td>\n",
       "      <td>0.186923</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>0.744400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2103.000000</td>\n",
       "      <td>582.00000</td>\n",
       "      <td>16429.000000</td>\n",
       "      <td>9197.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.342270</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.509700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2433.000000</td>\n",
       "      <td>8673.000000</td>\n",
       "      <td>8673.00000</td>\n",
       "      <td>6739.000000</td>\n",
       "      <td>7388.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.163769</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1755.0</td>\n",
       "      <td>0.301300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>4051.000000</td>\n",
       "      <td>261.00000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>2.290900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "      <td>7620.00000</td>\n",
       "      <td>64983.000000</td>\n",
       "      <td>14067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.323132</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1562.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.611</td>\n",
       "      <td>3.7845</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.370433</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.4979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1798.0</td>\n",
       "      <td>5.082500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>3359.000000</td>\n",
       "      <td>2973.00000</td>\n",
       "      <td>22099.000000</td>\n",
       "      <td>13507.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1684.0</td>\n",
       "      <td>1.547900</td>\n",
       "      <td>1.338</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>13874.000000</td>\n",
       "      <td>13874.00000</td>\n",
       "      <td>98011.000000</td>\n",
       "      <td>56200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.252492</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1862.0</td>\n",
       "      <td>0.240100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1487.000000</td>\n",
       "      <td>12310.000000</td>\n",
       "      <td>3964.00000</td>\n",
       "      <td>7928.000000</td>\n",
       "      <td>26564.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.297332</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1767.0</td>\n",
       "      <td>0.300100</td>\n",
       "      <td>1.514</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>2478.000000</td>\n",
       "      <td>2478.00000</td>\n",
       "      <td>8790.000000</td>\n",
       "      <td>6065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.176674</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1802.0</td>\n",
       "      <td>0.636400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>18759.000000</td>\n",
       "      <td>10905.00000</td>\n",
       "      <td>51037.000000</td>\n",
       "      <td>58544.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.131449</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1780.0</td>\n",
       "      <td>1.955900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>12525.000000</td>\n",
       "      <td>12525.00000</td>\n",
       "      <td>18932.000000</td>\n",
       "      <td>14602.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1714.0</td>\n",
       "      <td>2.266400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1671.000000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.297332</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1923.0</td>\n",
       "      <td>0.088000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5946.000000</td>\n",
       "      <td>53893.000000</td>\n",
       "      <td>24739.00000</td>\n",
       "      <td>128435.000000</td>\n",
       "      <td>125679.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.226932</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1743.0</td>\n",
       "      <td>1.720800</td>\n",
       "      <td>2.295</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>3621.000000</td>\n",
       "      <td>2323.00000</td>\n",
       "      <td>9018.000000</td>\n",
       "      <td>9649.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.226932</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.328731</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1700.0</td>\n",
       "      <td>1.660500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>46.00000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.226932</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1922.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4524.000000</td>\n",
       "      <td>15530.000000</td>\n",
       "      <td>14819.00000</td>\n",
       "      <td>116154.000000</td>\n",
       "      <td>100684.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.118592</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>0.412000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>6201.000000</td>\n",
       "      <td>6201.00000</td>\n",
       "      <td>66477.000000</td>\n",
       "      <td>34053.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.425926</td>\n",
       "      <td>0.144850</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>7.307692</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1811.0</td>\n",
       "      <td>0.911700</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.410</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>27717.000000</td>\n",
       "      <td>9815.00000</td>\n",
       "      <td>110001.000000</td>\n",
       "      <td>69683.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.296529</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1658.0</td>\n",
       "      <td>2.382900</td>\n",
       "      <td>37.127</td>\n",
       "      <td>10.3678</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>396.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6566.000000</td>\n",
       "      <td>4068.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.186923</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.4979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1827.0</td>\n",
       "      <td>0.280100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "      <td>1189.00000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.163769</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>1734.0</td>\n",
       "      <td>0.927900</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>11643.000000</td>\n",
       "      <td>1524.00000</td>\n",
       "      <td>16748.000000</td>\n",
       "      <td>25359.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>1849.0</td>\n",
       "      <td>1.830500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2251.000000</td>\n",
       "      <td>33275.000000</td>\n",
       "      <td>7505.00000</td>\n",
       "      <td>44843.000000</td>\n",
       "      <td>47453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>1.525100</td>\n",
       "      <td>2.727</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.727</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2129.000000</td>\n",
       "      <td>720.00000</td>\n",
       "      <td>5896.000000</td>\n",
       "      <td>5206.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.297332</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>1642.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.353</td>\n",
       "      <td>1.2114</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>6290.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>6290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.458344</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>1709.0</td>\n",
       "      <td>3.680100</td>\n",
       "      <td>7.583</td>\n",
       "      <td>3.1371</td>\n",
       "      <td>0.000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>8132.000000</td>\n",
       "      <td>297.00000</td>\n",
       "      <td>1041.000000</td>\n",
       "      <td>6960.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.464700</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.4979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>1890.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>15162.000000</td>\n",
       "      <td>15162.00000</td>\n",
       "      <td>68181.000000</td>\n",
       "      <td>43334.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>1727.0</td>\n",
       "      <td>1.844800</td>\n",
       "      <td>15.181</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2234.000000</td>\n",
       "      <td>2754.000000</td>\n",
       "      <td>2234.00000</td>\n",
       "      <td>2676.000000</td>\n",
       "      <td>6539.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.186923</td>\n",
       "      <td>0.252492</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>1789.0</td>\n",
       "      <td>1.299400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>17838.000000</td>\n",
       "      <td>17838.00000</td>\n",
       "      <td>8424.000000</td>\n",
       "      <td>22307.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>0.144850</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>1706.0</td>\n",
       "      <td>0.499700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>98.00000</td>\n",
       "      <td>546.000000</td>\n",
       "      <td>643.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>1638.0</td>\n",
       "      <td>4.680200</td>\n",
       "      <td>16.751</td>\n",
       "      <td>2.6642</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>7937.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3032.000000</td>\n",
       "      <td>10489.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>1855.0</td>\n",
       "      <td>1.996500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>30203.000000</td>\n",
       "      <td>22656.00000</td>\n",
       "      <td>168999.000000</td>\n",
       "      <td>166720.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.338150</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>1790.0</td>\n",
       "      <td>0.205000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>18018.000000</td>\n",
       "      <td>13297.00000</td>\n",
       "      <td>3914.000000</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.170170</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>1546.0</td>\n",
       "      <td>0.349100</td>\n",
       "      <td>50.803</td>\n",
       "      <td>4.2695</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>17595.807537</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3330.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.395310</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.328731</td>\n",
       "      <td>1.888889</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.4979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>3.962900</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>23707.000000</td>\n",
       "      <td>23707.00000</td>\n",
       "      <td>33432.000000</td>\n",
       "      <td>19640.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.352103</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>1943.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24775.000000</td>\n",
       "      <td>24775.00000</td>\n",
       "      <td>80772.000000</td>\n",
       "      <td>69483.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.145566</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>1691.0</td>\n",
       "      <td>0.166300</td>\n",
       "      <td>1.979</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9591.000000</td>\n",
       "      <td>2627.00000</td>\n",
       "      <td>46903.000000</td>\n",
       "      <td>30402.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.352103</td>\n",
       "      <td>0.090400</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>28.166667</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>1681.0</td>\n",
       "      <td>0.173900</td>\n",
       "      <td>9.006</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>6.568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3004.000000</td>\n",
       "      <td>2251.00000</td>\n",
       "      <td>6818.000000</td>\n",
       "      <td>7783.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.296529</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>1723.0</td>\n",
       "      <td>1.057000</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>28479.000000</td>\n",
       "      <td>28479.00000</td>\n",
       "      <td>16352.000000</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.298409</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>1693.0</td>\n",
       "      <td>1.214500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>1592.0</td>\n",
       "      <td>6.480500</td>\n",
       "      <td>5.250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.204</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.226932</td>\n",
       "      <td>0.458344</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>1641.0</td>\n",
       "      <td>0.660100</td>\n",
       "      <td>8.614</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>34097.436013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.458344</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>1740.0</td>\n",
       "      <td>0.390100</td>\n",
       "      <td>4.322</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>29460.000000</td>\n",
       "      <td>9449.00000</td>\n",
       "      <td>42047.000000</td>\n",
       "      <td>31583.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.190171</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.232083</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>1651.0</td>\n",
       "      <td>2.490900</td>\n",
       "      <td>1.335</td>\n",
       "      <td>0.9108</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3163.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>3167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.309190</td>\n",
       "      <td>0.381028</td>\n",
       "      <td>0.390700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>1729.0</td>\n",
       "      <td>1.360100</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5675.000000</td>\n",
       "      <td>167.00000</td>\n",
       "      <td>15071.000000</td>\n",
       "      <td>10305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.247148</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>1730.0</td>\n",
       "      <td>1.670100</td>\n",
       "      <td>5.554</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>5692.000000</td>\n",
       "      <td>496.00000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>8125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5137</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.275282</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.4925</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2231</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>1715.0</td>\n",
       "      <td>12.000200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>991.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>8751.000000</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.176674</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.176224</td>\n",
       "      <td>7.256161</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.055100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5946.000000</td>\n",
       "      <td>14867.000000</td>\n",
       "      <td>9613.00000</td>\n",
       "      <td>46490.000000</td>\n",
       "      <td>44722.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.170170</td>\n",
       "      <td>0.309995</td>\n",
       "      <td>0.252492</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.2109</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>1686.0</td>\n",
       "      <td>1.330100</td>\n",
       "      <td>2.025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.025</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>4582.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.211005</td>\n",
       "      <td>0.144850</td>\n",
       "      <td>0.145566</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>1826.0</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3568.000000</td>\n",
       "      <td>26277.000000</td>\n",
       "      <td>9058.00000</td>\n",
       "      <td>72739.000000</td>\n",
       "      <td>86797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.271086</td>\n",
       "      <td>0.102641</td>\n",
       "      <td>0.200382</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.3281</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>1916.0</td>\n",
       "      <td>0.139400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18889.000000</td>\n",
       "      <td>18889.000000</td>\n",
       "      <td>18889.00000</td>\n",
       "      <td>28343.000000</td>\n",
       "      <td>51174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2623</td>\n",
       "      <td>0.170170</td>\n",
       "      <td>0.159852</td>\n",
       "      <td>0.145566</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2199</td>\n",
       "      <td>0.2526</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mvar1      mvar2   mvar3    mvar4  mvar5         mvar6         mvar7  \\\n",
       "0      1753.0   0.500100   0.000   0.0000  0.000    206.000000  19179.000000   \n",
       "1      1894.0   1.970100   0.000   0.0000  0.000    297.000000  19820.000000   \n",
       "2      1774.0   0.171800   0.000   0.0000  0.000    197.000000   2563.000000   \n",
       "3      1586.0   0.112300   5.299   0.0000  0.000   1606.702372  17595.807537   \n",
       "4      1832.0   1.444200   0.000   0.0000  0.000    179.000000   7577.000000   \n",
       "5      1838.0   0.054400   0.241   0.0000  0.000    882.000000  14039.000000   \n",
       "6      1743.0   1.075300   0.272   0.0000  0.000    397.000000  16141.000000   \n",
       "7      1935.0   0.560100   0.000   0.0000  0.000    496.000000  44919.000000   \n",
       "8      1885.0   0.081100   0.000   0.0000  0.000   7369.000000  19264.000000   \n",
       "9      1730.0   0.744400   0.000   0.0000  0.000      0.000000   2103.000000   \n",
       "10     1731.0   0.509700   0.000   0.0000  0.000   2433.000000   8673.000000   \n",
       "11     1755.0   0.301300   0.000   0.0000  0.000    261.000000   4051.000000   \n",
       "12     1739.0   2.290900   0.000   0.0000  0.000      0.000000   7620.000000   \n",
       "13     1562.0   0.000000  34.611   3.7845  0.000   1606.702372     18.000000   \n",
       "14     1798.0   5.082500   0.000   0.0000  0.000    259.000000   3359.000000   \n",
       "15     1684.0   1.547900   1.338   0.0000  0.000   1606.702372  17595.807537   \n",
       "16     1789.0   0.000000   1.428   0.0000  0.000   2478.000000  13874.000000   \n",
       "17     1862.0   0.240100   0.000   0.0000  0.000   1487.000000  12310.000000   \n",
       "18     1767.0   0.300100   1.514   0.0000  0.000    991.000000   2478.000000   \n",
       "19     1802.0   0.636400   0.000   0.0000  0.000     96.000000  18759.000000   \n",
       "20     1780.0   1.955900   0.000   0.0000  0.000    191.000000  12525.000000   \n",
       "21     1714.0   2.266400   0.000   0.0000  0.000     31.000000   1671.000000   \n",
       "22     1923.0   0.088000   0.000   0.0000  0.000   5946.000000  53893.000000   \n",
       "23     1743.0   1.720800   2.295   0.0000  0.000    159.000000   3621.000000   \n",
       "24     1700.0   1.660500   0.000   0.0000  0.000      0.000000   1037.000000   \n",
       "25     1922.0   1.105629   0.000   0.0000  0.000   4524.000000  15530.000000   \n",
       "26     1777.0   0.412000   0.000   0.0000  0.000    369.000000   6201.000000   \n",
       "27     1811.0   0.911700   0.410   0.0000  0.410    297.000000  27717.000000   \n",
       "28     1658.0   2.382900  37.127  10.3678  0.000      0.000000    396.000000   \n",
       "29     1827.0   0.280100   0.000   0.0000  0.000    678.000000   1189.000000   \n",
       "...       ...        ...     ...      ...    ...           ...           ...   \n",
       "24970  1734.0   0.927900   0.520   0.0000  0.000    256.000000  11643.000000   \n",
       "24971  1849.0   1.830500   0.000   0.0000  0.000   2251.000000  33275.000000   \n",
       "24972  1739.0   1.525100   2.727   0.0000  2.727      8.000000   2129.000000   \n",
       "24973  1642.0   0.000000   3.353   1.2114  0.000   1606.702372   6290.000000   \n",
       "24974  1709.0   3.680100   7.583   3.1371  0.000    297.000000   8132.000000   \n",
       "24975  1890.0   0.000000   0.000   0.0000  0.000   1982.000000  15162.000000   \n",
       "24976  1727.0   1.844800  15.181   0.0000  0.000   2234.000000   2754.000000   \n",
       "24977  1789.0   1.299400   0.000   0.0000  0.000    364.000000  17838.000000   \n",
       "24978  1706.0   0.499700   0.000   0.0000  0.000     98.000000    297.000000   \n",
       "24979  1638.0   4.680200  16.751   2.6642  0.000   1606.702372   7937.000000   \n",
       "24980  1855.0   1.996500   0.000   0.0000  0.000    350.000000  30203.000000   \n",
       "24981  1790.0   0.205000   0.000   0.0000  0.000    237.000000  18018.000000   \n",
       "24982  1546.0   0.349100  50.803   4.2695  0.000   1606.702372  17595.807537   \n",
       "24983  1777.0   3.962900   0.968   0.0000  0.000     14.000000  23707.000000   \n",
       "24984  1943.0   1.105629   0.000   0.0000  0.000      0.000000  24775.000000   \n",
       "24985  1691.0   0.166300   1.979   0.0000  0.000      0.000000   9591.000000   \n",
       "24986  1681.0   0.173900   9.006   0.0000  6.568      0.000000   3004.000000   \n",
       "24987  1723.0   1.057000   0.285   0.0000  0.000    248.000000  28479.000000   \n",
       "24988  1693.0   1.214500   0.000   0.0000  0.000     18.000000    100.000000   \n",
       "24989  1592.0   6.480500   5.250   0.0000  1.204   1606.702372      0.000000   \n",
       "24990  1641.0   0.660100   8.614   0.0000  0.000   1606.702372      0.000000   \n",
       "24991  1740.0   0.390100   4.322   0.0000  0.000    490.000000  29460.000000   \n",
       "24992  1651.0   2.490900   1.335   0.9108  0.000      4.000000   3163.000000   \n",
       "24993  1729.0   1.360100   0.218   0.0000  0.000      0.000000   5675.000000   \n",
       "24994  1730.0   1.670100   5.554   0.0000  0.000    104.000000   5692.000000   \n",
       "24995  1715.0  12.000200   0.000   0.0000  0.000   1606.702372    991.000000   \n",
       "24996  1800.0   0.055100   0.000   0.0000  0.000   5946.000000  14867.000000   \n",
       "24997  1686.0   1.330100   2.025   0.0000  2.025   1606.702372      0.000000   \n",
       "24998  1826.0   1.105629   0.428   0.0000  0.000   3568.000000  26277.000000   \n",
       "24999  1916.0   0.139400   0.000   0.0000  0.000  18889.000000  18889.000000   \n",
       "\n",
       "             mvar8          mvar9    mvar10   ...     var35     var36  \\\n",
       "0        206.00000   14221.000000   19438.0   ...    0.1878  0.190171   \n",
       "1      19820.00000  176895.000000   66346.0   ...    0.1878  0.190171   \n",
       "2        877.00000    8869.000000    3637.0   ...    0.2623  0.190171   \n",
       "3       6773.68666   34097.436013       0.0   ...    0.2623  0.163769   \n",
       "4        179.00000   21059.000000   20306.0   ...    0.2623  0.395310   \n",
       "5      10976.00000  278941.000000   74030.0   ...    0.2623  0.370433   \n",
       "6       3568.00000   69482.000000   56755.0   ...    0.1878  0.296529   \n",
       "7      17115.00000  124814.000000  124167.0   ...    0.1878  0.271086   \n",
       "8      19264.00000   59956.000000   26633.0   ...    0.1878  0.170170   \n",
       "9        582.00000   16429.000000    9197.0   ...    0.1878  0.247148   \n",
       "10      8673.00000    6739.000000    7388.0   ...    0.1878  0.163769   \n",
       "11       261.00000     297.000000     261.0   ...    0.1878  0.190171   \n",
       "12      7620.00000   64983.000000   14067.0   ...    0.1878  0.323132   \n",
       "13      6773.68666     198.000000      18.0   ...    0.2623  0.370433   \n",
       "14      2973.00000   22099.000000   13507.0   ...    0.1878  0.309190   \n",
       "15      6773.68666   34097.436013       0.0   ...    0.1878  0.190171   \n",
       "16     13874.00000   98011.000000   56200.0   ...    0.1878  0.190171   \n",
       "17      3964.00000    7928.000000   26564.0   ...    0.1878  0.190171   \n",
       "18      2478.00000    8790.000000    6065.0   ...    0.5137  0.176674   \n",
       "19     10905.00000   51037.000000   58544.0   ...    0.2623  0.271086   \n",
       "20     12525.00000   18932.000000   14602.0   ...    0.1878  0.247148   \n",
       "21        57.00000     396.000000    2582.0   ...    0.1878  0.190171   \n",
       "22     24739.00000  128435.000000  125679.0   ...    0.1878  0.226932   \n",
       "23      2323.00000    9018.000000    9649.0   ...    0.1878  0.226932   \n",
       "24        46.00000     892.000000    1235.0   ...    0.1878  0.226932   \n",
       "25     14819.00000  116154.000000  100684.0   ...    0.1878  0.309190   \n",
       "26      6201.00000   66477.000000   34053.0   ...    0.1878  0.425926   \n",
       "27      9815.00000  110001.000000   69683.0   ...    0.2623  0.296529   \n",
       "28         0.00000    6566.000000    4068.0   ...    0.5137  0.271086   \n",
       "29      1189.00000     892.000000    2065.0   ...    0.1878  0.163769   \n",
       "...            ...            ...       ...   ...       ...       ...   \n",
       "24970   1524.00000   16748.000000   25359.0   ...    0.1878  0.309190   \n",
       "24971   7505.00000   44843.000000   47453.0   ...    0.1878  0.211005   \n",
       "24972    720.00000    5896.000000    5206.0   ...    0.1878  0.309190   \n",
       "24973   6773.68666   34097.436013    6290.0   ...    0.2623  0.211005   \n",
       "24974    297.00000    1041.000000    6960.0   ...    0.1878  0.464700   \n",
       "24975  15162.00000   68181.000000   43334.0   ...    0.1878  0.271086   \n",
       "24976   2234.00000    2676.000000    6539.0   ...    0.1878  0.271086   \n",
       "24977  17838.00000    8424.000000   22307.0   ...    0.1878  0.247148   \n",
       "24978     98.00000     546.000000     643.0   ...    0.1878  0.211005   \n",
       "24979   6773.68666    3032.000000   10489.0   ...    0.2623  0.211005   \n",
       "24980  22656.00000  168999.000000  166720.0   ...    0.1878  0.338150   \n",
       "24981  13297.00000    3914.000000    1305.0   ...    0.1878  0.170170   \n",
       "24982   6773.68666    3330.000000       0.0   ...    0.2623  0.395310   \n",
       "24983  23707.00000   33432.000000   19640.0   ...    0.1878  0.352103   \n",
       "24984  24775.00000   80772.000000   69483.0   ...    0.2623  0.211005   \n",
       "24985   2627.00000   46903.000000   30402.0   ...    0.5137  0.352103   \n",
       "24986   2251.00000    6818.000000    7783.0   ...    0.2623  0.296529   \n",
       "24987  28479.00000   16352.000000    3117.0   ...    0.5137  0.211005   \n",
       "24988    100.00000     991.000000     118.0   ...    0.1878  0.190171   \n",
       "24989   6773.68666   34097.436013       0.0   ...    0.2623  0.226932   \n",
       "24990   6773.68666   34097.436013       0.0   ...    0.2623  0.211005   \n",
       "24991   9449.00000   42047.000000   31583.0   ...    0.1878  0.190171   \n",
       "24992      4.00000    1982.000000    3167.0   ...    0.1878  0.309190   \n",
       "24993    167.00000   15071.000000   10305.0   ...    0.5137  0.247148   \n",
       "24994    496.00000    4410.000000    8125.0   ...    0.5137  0.211005   \n",
       "24995   6773.68666    8751.000000    1814.0   ...    0.2623  0.176674   \n",
       "24996   9613.00000   46490.000000   44722.0   ...    0.1878  0.170170   \n",
       "24997   6773.68666    4582.000000       0.0   ...    0.2623  0.211005   \n",
       "24998   9058.00000   72739.000000   86797.0   ...    0.2623  0.271086   \n",
       "24999  18889.00000   28343.000000   51174.0   ...    0.2623  0.170170   \n",
       "\n",
       "          var37     var38  var363738   var39   var40   var41   var45   var46  \n",
       "0      0.090400  0.176224  16.500000  0.2109  0.2199  0.1610  0.3281  0.2010  \n",
       "1      0.090400  0.232083   9.000000  0.2109  0.2199  0.2231  0.1268  0.2010  \n",
       "2      0.159852  0.176224   6.000000  0.4390  0.2199  0.2231  0.1268  0.2010  \n",
       "3      0.381028  0.176224   0.000000  0.4390  0.2199  0.2526  0.3281  0.2968  \n",
       "4      0.381028  0.232083   0.000000  0.4390  0.3750  0.2231  0.1268  0.2010  \n",
       "5      0.090400  0.252492  53.200000  0.4390  0.2199  0.2231  0.1268  0.2010  \n",
       "6      0.102641  0.297332  10.285714  0.2109  0.2199  0.2231  0.1268  0.2010  \n",
       "7      0.090400  0.342270   7.000000  0.2109  0.2199  0.2526  0.1268  0.2010  \n",
       "8      0.186923  0.176224   3.000000  0.2109  0.2199  0.2526  0.1268  0.2968  \n",
       "9      0.309995  0.342270   0.600000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "10     0.239528  0.200382   0.666667  0.2109  0.2199  0.2526  0.1268  0.2968  \n",
       "11     0.381028  0.232083   0.000000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "12     0.090400  0.390700  15.000000  0.2109  0.2199  0.3000  0.3281  0.2010  \n",
       "13     0.381028  0.298409   0.000000  0.4390  0.3750  0.3000  0.3481  0.4979  \n",
       "14     0.239528  0.275282   3.000000  0.2109  0.2199  0.2231  0.1268  0.2010  \n",
       "15     0.381028  0.232083   0.000000  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "16     0.090400  0.252492   6.600000  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "17     0.309995  0.297332   0.428571  0.2109  0.2199  0.2231  0.1268  0.2010  \n",
       "18     0.381028  0.176224   7.256161  0.4925  0.2199  0.2526  0.3281  0.2968  \n",
       "19     0.131449  0.232083  10.500000  0.4390  0.2199  0.1610  0.1268  0.2010  \n",
       "20     0.381028  0.298409   0.000000  0.2109  0.2199  0.3000  0.1268  0.2010  \n",
       "21     0.309995  0.297332   0.428571  0.2109  0.2199  0.3000  0.3281  0.2010  \n",
       "22     0.090400  0.200382  16.666667  0.2109  0.2199  0.3000  0.1268  0.2010  \n",
       "23     0.309995  0.328731   0.555556  0.2109  0.2199  0.2231  0.3281  0.2010  \n",
       "24     0.381028  0.275282   0.000000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "25     0.118592  0.232083  15.750000  0.2109  0.2199  0.2526  0.1268  0.2010  \n",
       "26     0.144850  0.390700   7.307692  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "27     0.102641  0.232083  18.000000  0.4390  0.2199  0.2231  0.1268  0.2010  \n",
       "28     0.186923  0.390700   1.400000  0.4925  0.2199  0.2526  0.3281  0.4979  \n",
       "29     0.381028  0.232083   0.000000  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "...         ...       ...        ...     ...     ...     ...     ...     ...  \n",
       "24970  0.381028  0.298409   0.000000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "24971  0.159852  0.200382   5.333333  0.2109  0.2199  0.3000  0.1268  0.2010  \n",
       "24972  0.309995  0.297332   1.285714  0.2109  0.3750  0.3000  0.3281  0.2010  \n",
       "24973  0.458344  0.176224   7.256161  0.4390  0.2199  0.1610  0.3281  0.2010  \n",
       "24974  0.381028  0.390700   0.000000  0.2109  0.3750  0.2526  0.3281  0.4979  \n",
       "24975  0.102641  0.200382  21.000000  0.2109  0.3750  0.1610  0.3281  0.2010  \n",
       "24976  0.186923  0.252492   4.200000  0.2109  0.2199  0.2526  0.1268  0.2010  \n",
       "24977  0.144850  0.275282   5.000000  0.2109  0.3750  0.2231  0.1268  0.2010  \n",
       "24978  0.381028  0.298409   0.000000  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "24979  0.381028  0.232083   0.000000  0.4390  0.3750  0.1610  0.3281  0.2010  \n",
       "24980  0.090400  0.200382  40.000000  0.2109  0.2199  0.2526  0.1268  0.2968  \n",
       "24981  0.239528  0.275282   0.666667  0.2109  0.2199  0.2526  0.3281  0.2010  \n",
       "24982  0.309995  0.328731   1.888889  0.4390  0.2199  0.2526  0.3481  0.4979  \n",
       "24983  0.090400  0.390700  11.000000  0.2109  0.2199  0.2231  0.3281  0.2010  \n",
       "24984  0.159852  0.145566  16.000000  0.4390  0.2199  0.2526  0.1268  0.2968  \n",
       "24985  0.090400  0.275282  28.166667  0.4925  0.3750  0.2231  0.3281  0.2010  \n",
       "24986  0.159852  0.176224  16.000000  0.4390  0.3750  0.2526  0.1268  0.2010  \n",
       "24987  0.309995  0.298409   0.500000  0.4925  0.2199  0.2526  0.3281  0.2010  \n",
       "24988  0.381028  0.232083   0.000000  0.2109  0.2199  0.2526  0.3281  0.2968  \n",
       "24989  0.458344  0.232083   7.256161  0.4390  0.3750  0.2526  0.3281  0.2968  \n",
       "24990  0.458344  0.232083   7.256161  0.4390  0.3750  0.2526  0.3281  0.2968  \n",
       "24991  0.102641  0.232083   6.750000  0.2109  0.2199  0.2526  0.3481  0.2010  \n",
       "24992  0.381028  0.390700   0.000000  0.2109  0.2199  0.2231  0.3281  0.2010  \n",
       "24993  0.309995  0.275282   1.000000  0.4925  0.2199  0.2526  0.3281  0.2010  \n",
       "24994  0.239528  0.275282   1.333333  0.4925  0.2199  0.2231  0.3281  0.2010  \n",
       "24995  0.309995  0.176224   7.256161  0.4390  0.2199  0.2526  0.3281  0.2010  \n",
       "24996  0.309995  0.252492   0.400000  0.2109  0.2199  0.2526  0.1268  0.2010  \n",
       "24997  0.144850  0.145566  20.000000  0.4390  0.3750  0.2526  0.3281  0.2968  \n",
       "24998  0.102641  0.200382  21.000000  0.4390  0.2199  0.1610  0.3281  0.2010  \n",
       "24999  0.159852  0.145566   8.000000  0.4390  0.2199  0.2526  0.1268  0.2010  \n",
       "\n",
       "[25000 rows x 66 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values median\n",
    "gameX.fillna(gameX.mean(),inplace = True)\n",
    "leaderX.fillna(gameX.mean(),inplace = True)\n",
    "\n",
    "# gameX.fillna(0,inplace = True)\n",
    "# leaderX.fillna(0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing as prep\n",
    "\n",
    "scaler_list = []\n",
    "for i in gameX.columns:\n",
    "    scaler_list.append(prep.StandardScaler().fit(gameX[i]))\n",
    "    \n",
    "# prep.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "gameX_scaled = gameX.copy()\n",
    "\n",
    "for i in enumerate(gameX_scaled.columns):\n",
    "    gameX_scaled[i[1]] = scaler_list[i[0]].transform(gameX_scaled[i[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "leaderX_scaled = leaderX.copy()\n",
    "\n",
    "for i in enumerate(leaderX_scaled.columns):\n",
    "    leaderX_scaled[i[1]] = scaler_list[i[0]].transform(leaderX_scaled[i[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(random_state=1, solver='lbfgs', multi_class='multinomial').fit(gameX_scaled, gameY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred1 = clf.predict_proba(leaderX_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.17610069,  0.13131418, -0.01216337, -0.02465285, -0.03546972,\n",
       "        -0.05691299,  0.07494432, -0.01944297,  0.05898198, -0.13430697,\n",
       "        -0.00433704,  0.02982313, -0.06892501,  0.00721303, -0.03160209,\n",
       "         0.02256683, -0.05248328,  0.06418566,  0.01047613,  0.07274987,\n",
       "         0.03837505,  0.01571297,  0.04063722, -0.00313355, -0.03930185,\n",
       "        -0.00781791,  0.00568511,  0.0192157 ,  0.00530355, -0.02528498,\n",
       "        -0.02131945, -0.00090044, -0.02231735, -0.04657767, -0.01272075,\n",
       "         0.00466224, -0.02367935,  0.00175587,  0.01191629,  0.00668416,\n",
       "         0.02197075,  0.09335349, -0.11926675, -0.00523355,  0.02073104,\n",
       "         0.01994133,  0.24126443,  0.00724056, -0.01707251,  0.05209715,\n",
       "        -0.00895298,  0.02106636,  0.02488544,  0.0123422 , -0.00742994,\n",
       "         0.11247983,  0.03708031,  0.06869313,  0.11716657,  0.05887681,\n",
       "         0.01707792,  0.00264569,  0.03858961,  0.00203496,  0.04504807,\n",
       "         0.0405418 ]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 66)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.coef_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmSpC6TRZIkSZIkSepqNWeRJElLyXHpkiRJWiweW45mskiSpAHGPZCA+TyYkCRJ0qbF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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0627296320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LR feature imp\n",
    "lr_coef = clf.coef_.reshape(-1, 1)\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(lr_coef)+1), np.abs(lr_coef) )\n",
    "plt.xticks(range(len(gameX.columns)), gameX.columns, rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Method 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/ekka/.local/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn import cross_validation, metrics   #Additional scklearn functions\n",
    "from sklearn.grid_search import GridSearchCV   #Perforing grid search\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def modelfit(alg, dtrainX, dtrainY, predictors, useTrainCV=True, cv_folds=4, early_stopping_rounds=50):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "#         xgb_param['num_class']=2\n",
    "        xgtrain = xgb.DMatrix(dtrainX[predictors].values, label=dtrainY.values)\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "             early_stopping_rounds=early_stopping_rounds,stratified=True)\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(dtrainX[predictors], dtrainY ,eval_metric='error')\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(dtrainX[predictors])\n",
    "    dtrain_predprob = alg.predict_proba(dtrainX[predictors])[:,1]\n",
    "        \n",
    "    #Print model report:\n",
    "    print (\"\\nModel Report\")\n",
    "    print (\"Accuracy : %.4g\" % metrics.accuracy_score(dtrainY.values, dtrain_predictions))\n",
    "    #print (\"AUC Score (Train): %f\" % metrics.roc_auc_score(dtrain[target], dtrain_predprob))\n",
    "                    \n",
    "    #feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)\n",
    "    #feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "    #plt.ylabel('Feature Importance Score')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "Accuracy : 0.8237\n",
      "time 80.02479434013367\n"
     ]
    }
   ],
   "source": [
    "#Choose all predictors except target & IDcols\n",
    "t=time.time()\n",
    "predictors = [x for x in gameX.columns]\n",
    "xgb1 = XGBClassifier(\n",
    " learning_rate =0.1,\n",
    " n_estimators=1000,\n",
    " max_depth=4, #4\n",
    " min_child_weight=1,\n",
    " gamma=0,\n",
    "reg_alpha = 0.05,\n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread=4,\n",
    " scale_pos_weight=1,\n",
    " seed=27)\n",
    "\n",
    "modelfit(xgb1, gameX, gameY, predictors)\n",
    "\n",
    "print('time',time.time()-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+udes4qJFi+jfvz+vvPIKl19+OZ/5zGcKGKGZmXXm6191ZO9nnJSzrRpYKOlt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\nZmZJLhbNzMzMLOn/A0l2PGtem6LMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0627fb83c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgb feature imp\n",
    "from xgboost import plot_importance\n",
    "ax = plot_importance(xgb1, height=0.5)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(10,20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+udes4qJFi+jfvz+vvPIKl19+OZ/5zGcKGKGZmXXm6191ZO9nnJSzrRpYKOlt\nsg+wnJ7S92TgPyXtIltcfvFgDtjj2G405LxHzrIymUzey4/m3PTs2ZMtW7bstW3cuHGMGzfOl4XM\nzIpEhysWI6IJGJGzPilfW0SsANSmbz3Zh1/ajlmd5xhDjlTMZmZmZh1Vp7wMbWZmZmZHhotFMzMz\nM0vlYtHMzMzMUrlYNDMzM7NULhbNzMzMLJWLRTMzMzNL1eFenVPMtr+3k4oZTxQ6jKIzvbKVSc5L\nXl0hN01t3j3a0NDA+PHj96z/6le/4hvf+AY33HAD48ePp6mpiYqKCv76r/+6vUM1M7M8uvTMoqST\nJP1MUrOke3K2Hy/pCUkbJK2TNKuQcZp1JkOGDGH16tWsXr2alStXcvzxxzNu3DhmzZrFxRdfzKZN\nm7j44ot56KGHCh2qmZnRhWcWJXUH/gh8jeyLvEe02eUfI+JnkkqApZI+GxH/0d5xmnVmS5cuZdCg\nQZx22mnU19eTyWQAuPHGGxk9enRhgzMzM6CIZxYlVSQze/MkbZT0oKQxkpZJ2iTpPElNkkpz+myS\nVC5prKTnJa2StERSedJeLWm+pGXA/IhoiYhnyRaNe0TEtoj4WbK8A3gJ6N9+Z2/WNSxYsIAJEyYA\nsHnzZvr27QvAKaecwltvvVXI0MzMLFHsM4uDgWuAKWS/83w9cAFwBXA72U/3jQNqJY0GXo2IzZKe\nBc6PiJA0FbgNmJ6MORy4ICK2H0wASTE6FvinlPZpwDSAPn3KmFnZelgn2pmV98jem2f76gq52T1b\n2NZ7773HY489xuc+9zkymQytra377JvWt6trbm52blI4N+mcm3TOzf4Ve7HYGBFrACStA5YmBeAa\noAKYDcwEaoHrgLqkX3+gTlJfoARozBlz8SEUit2Bh4HvRsSv8u0TEXOBuQADBg6OO9cUe0rb3/TK\nVpyX/LpCbpomVuXdXl9fz+jRo/n85z8PQL9+/RgyZAh9+/blN7/5DSeeeCJVVfn7dnWZTMa5SeHc\npHNu0jk3+1e0l6ET7+Ys78pZ30W20F0ODJZUBlwFPJ601wD3REQl8GXgQznjtBzC8ecCmyLi7sOI\n3cz24+GHH95zCRrgiiuu4P777wfg/vvv50/+5E8KFZqZmeUo9mJxvyIigEXAXcD6iNiSNPUGXk+W\nbzycsSXdkYzz1Q8ap5ntraWlhSeffHLPrCLAjBkzePLJJznjjDNYsmQJ119/fQEjNDOz3TrD9a86\nsvczTsqLNnzIAAAgAElEQVTZVg0slPQ28BRwelpnSU3ACUCJpKuAS4H/H/h7YAPwkiTIzlT+aH+B\n9Di2Gw1t3iln2en9tEuRXV1XzU3Pnj3ZsmXLXttOOukkli5dumfd9w+ZmRWHoi0WI6KJnNfZRMSk\nfG0RsQJQm771ZB9+aTtmdZ5tFSkhKGW7mZmZWZfRoS9Dm5mZmdnR5WLRzMzMzFK5WDQzMzOzVC4W\nzczMzCyVi0UzMzMzS+Vi0czMzMxSFe2rczqi7e/tpGLGE4UOo+hMr2xlkvOSV0fPTVOe94pu3bqV\nqVOnsnbtWiRx3333cffdd9PQ0LCnvbS0lNWrV7d3uGZmdhi6dLEo6STgUeATwLyIuDmn7ZvADcBH\nIqJXgUI063BuueUWLrvsMh599FF27NjBtm3bqKur29M+ffp0evfuXcAIzczsUHTZYlFSd+CPwNfI\nvuB7RJtdfgLcA2xq59DMOqx33nmHp59+mnnz5gFQUlJCSUnJnvaI4JFHHuGpp54qUIRmZnaoivae\nRUkVkjZImidpo6QHJY2RtEzSJknnSWqSVJrTZ5OkckljJT0vaZWkJZLKk/ZqSfMlLQPmR0RLRDxL\ntmjcS0Q8FxG/ab8zNuv4GhsbKSsrY/LkyYwaNYqpU6fS0tKyp/2ZZ56hvLycM844o4BRmpnZoVBE\nFDqGvCRVAL8ARgHryH7/+WXgS8AVwGTgVWB1RNRKGg18MyLGSPoIsDUiQtJUYFhETJdUDYwFLoiI\n7TnHmgScm3sZOqeteX+XoSVNA6YB9OlTds7Mu3/4gc+9synvAZu3H3i/rqij56ay396XkxsaGrjp\nppuoqalh+PDh1NTU0LNnT6ZMmQLAnDlz6NevH9dee+0Bx25ubqZXL98Bko9zk865SefcpOuqufn0\npz+9MiLOPdB+xX4ZujEi1gBIWgcsTQrANUAFMBuYCdQC1wG7b4zqD9RJ6guUAI05Yy7OLRQ/qIiY\nC8wFGDBwcNy5pthT2v6mV7bivOTX0XPTNLFqr/WhQ4fyrW99i5tuugmAbt26MWvWLKqqqmhtbWX8\n+PGsXLmS/v37H3DsTCZDVVXVAffripybdM5NOucmnXOzf0V7GTrxbs7yrpz1XWQL3eXAYEllwFXA\n40l7DXBPRFQCXwY+lDNOC2Z2VJxyyimceuqpe558Xrp0KcOHDwdgyZIlDB069KAKRTMzKx4dd0oD\nSGYZFwF3AesjYkvS1Bt4PVm+sSDBmXVRNTU1TJw4kR07djBw4EBqa2sBWLBgARMmTChwdGZmdqg6\ndLGYqCN7P+OknG3VwEJJbwNPAaendZbUBJwAlEi6Crg0Il6RNBu4Hjhe0q+BH0VE9f4C6XFsNxry\nvHeuq8tkMvtcrrSszpibkSNHsmLFin22735C2szMOpaiLRYjoomc19lExKR8bRGxAlCbvvVAfZ4x\nq/Nsq0g5/m3AbYceuZmZmVnnUez3LJqZmZlZAblYNDMzM7NULhbNzMzMLJWLRTMzMzNL5WLRzMzM\nzFK5WDQzMzOzVEX76hwz65i2bt3K1KlTWbt2LZK47777uPvuu/d81WXr1q2UlpayevXqAkdqZmYH\no0sXi5IuAWaR/X70DuBvI+KpNvssBgZGxIg8Q+xl+3s7qZjxxFGJtSObXtnKJOclr46em6Y8L6G/\n5ZZbuOyyy3j00UfZsWMH27Zto66ubk/79OnT6d27d3uGaWZmH0CXLRYldQd+D4yNiDckjQD+E+iX\ns8/ngeYChWjW4bzzzjs8/fTTe77WUlJSQklJyZ72iOCRRx7hqaeeShnBzMyKTdHesyipQtIGSfMk\nbZT0oKQxkpZJ2iTpPElNkkpz+mySVC5prKTnJa2StERSedJeLWm+pGXA/IhYFRFvJN3XAT0kHZfs\n2wv438Ad7XzqZh1WY2MjZWVlTJ48mVGjRjF16lRaWlr2tD/zzDOUl5dzxhlnFDBKMzM7FEVbLCYG\nA3cCQ5Pf9cAFwK3A7WQ/6TcOQNJo4NWI2Aw8C5wfEaOABez92b7hwJiImNDmWF8AXoqId5P1/yc5\n9rajcF5mnVJraysvvfQSX/nKV1i1ahU9e/Zk1qxZe9offvhhJkxo+5+emZkVs2K/DN0YEWsAJK0D\nlkZESFoDVACzgZlALXAdsPvGqP5AnaS+ZO9HbMwZc3FEbM89iKQzgW8DlybrI4FBEfE3kir2F6Ck\nacA0gD59yphZ2XrYJ9tZlffI3ptn++rouclkMnutv/XWW/Tp04ft27eTyWQYNGgQDz30EBdffDE7\nd+6krq6Oe++9d59++TQ3Nx/Ufl2Rc5POuUnn3KRzbvav2IvFd3OWd+Ws7yIb+3JgsKQy4Crev2Rc\nA9wVEYslVQHVOeO05CwjqT+wCLghIn6ZbP4kcK6kpuQ4J0vKRERV2wAjYi4wF2DAwMFx55piT2n7\nm17ZivOSX0fPTdPEqn22zZkzh759+zJkyBAymQwXXnghVVVV/PSnP6WyspJrrrnmoMbOZDJUVe07\nvjk3++PcpHNu0jk3+9dx/5YCklnGRcBdwPqI2JI09QZeT5ZvTOuf3O/4BDAjIpbljPt94PvJPhXA\nv+UrFM1sXzU1NUycOJEdO3YwcOBAamtrAViwYIEvQZuZdUAdulhM1AEvApNytlUDCyW9DTwFnJ7S\n92ay90XOlDQz2XZpRLx5dEI16/xGjhzJihUr9tm++wlpMzPrWIq2WIyIJmBEzvqkfG0RsQJQm771\nZB9+aTtmdZv1OzjA085t49ifHsd2oyHPe+e6ukwmk/dypTk3ZmZW/Ir9aWgzMzMzKyAXi2ZmZmaW\nysWimZmZmaVysWhmZmZmqVwsmpmZmVkqF4tmZmZmlsrFopl9YFu3buXqq69m6NChDBs2jOXLlwPZ\nF3QPHTqUM888k9tuu+0Ao5iZWTEq2vcstgdJlwCzyH4/egfwtxHxVNI2Hvh7oBvZL7j83YHG2/7e\nTipmPHEUI+6Yple2Msl5yauj5qapzftEb7nlFi677DIeffRRduzYwbZt2/jZz35GfX09L7/8Mscd\ndxxvvul33ZuZdURddmZRUnfg98DYiKgk+1nA+UnbScB3gIsj4kzgFEkXFyxYsyL2zjvv8PTTT/Ol\nL30JgJKSEkpLS/n+97/PjBkzOO644wA4+eSTCxmmmZkdpqItFiVVSNogaZ6kjZIelDRG0jJJmySd\nJ6kp+b7z7j6bJJVLGivpeUmrJC2RVJ60V0uaL2kZMD8iVkXEG0n3dUAPSccBA4FNEfG7pG0J8IV2\nPH2zDqOxsZGysjImT57MqFGjmDp1Ki0tLWzcuJFnnnmG0aNH86lPfYoXX3yx0KGamdlhKNpiMTEY\nuBMYmvyuBy4AbgVuJ/tJv3EAkkYDr0bEZuBZ4PyIGAUsAHJvlhoOjImICW2O9QXgpYh4F/gFMCQp\nWLsDVwGnHp1TNOvYWltbeemll/jKV77CqlWr6NmzJ7NmzaK1tZW33nqL5557ju985ztce+21RESh\nwzUzs0NU7PcsNkbEGgBJ64ClERGS1gAVwGxgJlALXAfUJf36A3WS+pK9H7ExZ8zFEbE99yCSzgS+\nDVwKEBFvS/pKMt4u4L+BQfkClDQNmAbQp08ZMytbP+g5dzrlPbL35tm+OmpuMpnMnuW33nqLPn36\nsH37djKZDIMGDeKhhx7i+OOPZ+DAgfz85z8HYMeOHdTX11NaWpoy6t6am5v3Oo69z7lJ59ykc27S\nOTf7V+zF4rs5y7ty1neRjX05MFhSGdnZvzuS9hrgrohYLKkKqM4ZpyX3AJL6A4uAGyLil7u3R8RP\ngJ8k+0wDduYLMCLmAnMBBgwcHHeuKfaUtr/pla04L/l11Nw0Tazaa33OnDn07duXIUOGkMlkuPDC\nCxk0aBBvvPEGVVVVbNy4kWOOOYYrr7wSSQd1jEwmQ1VV1QH364qcm3TOTTrnJp1zs38d72+pHMks\n4yLgLmB9RGxJmnoDryfLN6b1T+53fAKYERHL2rSdHBFvSvoIcBNw7RE/AbNOoqamhokTJ7Jjxw4G\nDhxIbW0tPXv2ZMqUKYwYMYKSkhLuv//+gy4UzcyseHToYjFRB7wITMrZVg0slPQ28BRwekrfm8ne\nFzlT0sxk26UR8SbwT5LOSrZ9IyI2HunAzTqLkSNHsmLFin22P/DAAwWIxszMjqSiLRYjogkYkbM+\nKV9bRKwA1KZvPdmHX9qOWd1m/Q7ev3Tddt+2D8AcUI9ju9HQ5v1zlp3eb3vZ0rKcGzMzK3bF/jS0\nmZmZmRWQi0UzMzMzS+Vi0czMzMxSuVg0MzMzs1QuFs3MzMwslYtFMzMzM0vlYtHMzMzMUhXtexY7\nou3v7aRixhOFDqPoTK9sZZLzklex56Ypz3tDt27dytSpU1m7di2SuO+++/jkJz8JwJ133smtt97K\n7373O/r06dPe4ZqZ2VHQZYpFSR8H7gVOIPtt6U8AxwLP5OzWH3ggIr4qaQBwP1AKdCP7ScB/b9+o\nzYrPLbfcwmWXXcajjz7Kjh072LZtGwCvvfYa//Vf/8WAAQMKHKGZmR1Jnf4ytKTukroDDwB/GRFn\nAlXAexHxh4gYufsHvAo8nnT9B+CRiBgFXAf8cwHCNysq77zzDk8//TRf+tKXACgpKaG0tBSAv/mb\nv2H27Nn+/rOZWSdT8GJRUoWkDZLmSdoo6UFJYyQtk7RJ0nmSmiSV5vTZJKlc0lhJz0taJWmJpPKk\nvVrSfEnLgPnApcD/RMTLABGxJSJ2tonjY8DJvD/TGGRnIQF6A28c1USYdQCNjY2UlZUxefJkRo0a\nxdSpU2lpaaG+vp5+/fpx1llnHXgQMzPrUBQRhQ1AqgB+AYwC1gEvAi8DXwKuACaTnfFbHRG1kkYD\n34yIMZI+AmyNiJA0FRgWEdMlVQNjgQsiYrukrwLnkC0Gy4AFETG7TRwzgRMi4tZkvS/wX8BHgJ7A\nmIhYmSf+acA0gD59ys6ZefcPj1xyOonyHrB5e6GjKE7FnpvKfr33Wm9oaOCmm26ipqaG4cOHU1NT\nw7HHHsvLL7/Md77zHXr16sV1113HvffeS+/evVNGPTjNzc306tXrA43RWTk36ZybdM5Nuq6am09/\n+tMrI+LcA+1XLPcsNkbEGgBJ64ClSQG4BqgAZgMzgVqyl4Trkn79gbqksCsBGnPGXBwRu/8a7g5c\nQPY+xW3AUkkrI2Jpzv7XAV/MWZ8AzIuIOyV9EpgvaURE7MoNPCLmAnMBBgwcHHeuKZaUFo/pla04\nL/kVe26aJlbttT506FC+9a1vcdNNNwHQrVs3qqur2bJlCzfffDMAv//97/mrv/orXnjhBU455ZTD\nPnYmk6GqquqA+3VFzk065yadc5POudm/gl+GTrybs7wrZ30X2UJvOTBYUhlwFe/fV1gD3BMRlcCX\ngQ/ljNOSs/xr4OmI+H1EbAP+HTh7d6Oks4DubWYOvwQ8AhARy5Ox/XindWmnnHIKp556Kg0NDQAs\nXbqUs88+mzfffJOmpiaampro378/L7300gcqFM3MrHgUS7G4X5G9Vr4IuAtYHxFbkqbewOvJ8o37\nGeI/gUpJxycPu3wKeCWnfQLwcJs+/x9wMYCkYWSLxd99kPMw6wxqamqYOHEiH//4x1m9ejW33357\noUMyM7OjqHivf+2rjuz9jJNytlUDCyW9DTwFnJ6vY0S8LemupH8A/x4RuS+3uxb4szbdpgM/lPQ3\nSZ9JcYAbPHsc242GPO+l6+oymcw+lzMtqyPmZuTIkaxYsSK1vampqf2CMTOzo67gxWJENAEjctYn\n5WuLiBWA2vStB+rzjFmdZ9sDZF+fky+GgXm2vQL8r4M5BzMzM7POqkNchjYzMzOzwnCxaGZmZmap\nXCyamZmZWSoXi2ZmZmaWysWimZmZmaVysWhmZmZmqQr+6pzOZPt7O6mY8cSBd+xiple2Msl5yatY\nctOU5/2gW7duZerUqaxduxZJ3HfffTz++OP85Cc/oaSkhEGDBlFbW0tpaWkBIjYzs/bSpWcWJV0i\naaWkNcmff5rTlpHUIGl18ju5kLGatbdbbrmFyy67jA0bNvDyyy8zbNgwLrnkEtauXcv//M//8LGP\nfYxvfetbhQ7TzMyOsi47s5h89u/3wNiIeEPSCLKfBeyXs9vE5GXgZl3KO++8w9NPP828efMAKCkp\noaSkhEsvvXTPPueffz6PPvpogSI0M7P2UrQzi5IqJG2QNE/SRkkPShojaZmkTZLOk9QkqTSnzyZJ\n5ZLGSnpe0ipJSySVJ+3VkuZLWgbMj4hVEfFG0n0d0EPScQU4XbOi0tjYSFlZGZMnT2bUqFFMnTqV\nlpaWvfa57777+OxnP1ugCM3MrL3oAJ87LhhJFcAvgFFkC7kXgZeBLwFXAJOBV4HVEVEraTTwzYgY\nI+kjwNaICElTgWERMV1SNTAWuCAitrc53tXAX0bEmGQ9A5QB7wGPAXfk+za0pGnANIA+fcrOmXn3\nD49oHjqD8h6wefuB9+uKiiU3lf1677Xe0NDATTfdRE1NDcOHD6empoaePXsyZcoUAB544AEaGhr4\nxje+gaR8Q35gzc3N9OrV66iM3dE5N+mcm3TOTbqumptPf/rTKyPi3APtV+yXoRsjYg2ApHXA0qQA\nXANUALOBmUAtcB1Ql/TrD9RJ6guUAI05Yy7OUyieCXwbuDRn88SIeF3Sh8kWi18E/qVtgBExF5gL\nMGDg4LhzTbGntP1Nr2zFecmvWHLTNLFqr/WhQ4fyrW99i5tuugmAbt26MWvWLKqqqpg3bx7r1q1j\n6dKlHH/88UctpkwmQ1VV1QH364qcm3TOTTrnJp1zs39Fexk68W7O8q6c9V1kC93lwGBJZcBVwONJ\new1wT0RUAl8GPpQzzl7X0iT1BxYBN0TEL3dvj4jXkz//ADwEnHeEzsms6J1yyimceuqpNDQ0ALB0\n6VKGDx/OT3/6U2bPns3ixYuPaqFoZmbFo/BTGh9AMsu4CLgLWB8RW5Km3sDryfKNaf2T+x2fAGZE\nxLKc7d2B0oj4vaRjgc8BS47GOZgVq5qaGiZOnMiOHTsYOHAgtbW1fOITn+Ddd9/lkksuAbIPufzg\nBz8ocKRmZnY0dehiMVFH9n7GSTnbqoGFkt4GngJOT+l7MzAYmClpZrLtUrKzj/+ZFIrdyBaKB7wZ\nscex3WjI8766ri6TyexzmdOyijk3I0eOZMWKvV8G8Itf/KJA0ZiZWaEUbbEYEU3AiJz1Sfnaklfb\nqE3feqA+z5jVbdbvAO5ICeGcw4nbzMzMrDMp9nsWzczMzKyAXCyamZmZWSoXi2ZmZmaWysWimZmZ\nmaVysWhmZmZmqVwsmpmZmVmqon11Tke0/b2dVMx4otBhFJ3pla1Mcl7yKmRumvK8E3Tr1q1MnTqV\ntWvXIon77ruPX//611RXV7N+/XpeeOEFzj33gJ8RNTOzTqRLzyxKOknSzyQ1S7qnTVtGUoOk1cnv\n5ELFadZebrnlFi677DI2bNjAyy+/zLBhwxgxYgSPP/44F110UaHDMzOzAuiyM4vJJ/3+CHyN7Au+\nR+TZbWLy0m+zTu+dd97h6aefZt68eQCUlJRQUlJCaWlpYQMzM7OCKtqZRUkVkjZImidpo6QHJY2R\ntEzSJknnSWpKvu+8u88mSeWSxkp6XtIqSUsklSft1ZLmS1oGzI+Iloh4lmzRaNalNTY2UlZWxuTJ\nkxk1ahRTp06lpaWl0GGZmVmBFW2xmBgM3AkMTX7XAxcAtwK3k/2k3zgASaOBVyNiM/AscH5EjAIW\nALfljDkcGBMREw7i+Pcnl6C/JkkH3t2s42ptbeWll17iK1/5CqtWraJnz57MmjWr0GGZmVmBFftl\n6MaIWAMgaR2wNCJC0hqgApgNzARqgeuAuqRff6BOUl+gBGjMGXNxRGw/iGNPjIjXJX0YeAz4IvAv\nbXeSNA2YBtCnTxkzK1sP/Sw7ufIe2Qc5bF+FzE0mk9lr/a233qJPnz5s376dTCbDoEGDeOihh7j4\n4ouB7MMvK1eupLm5uV3ia25u3idGy3Ju0jk36ZybdM7N/hV7sfhuzvKunPVdZGNfDgyWVAZcBdyR\ntNcAd0XEYklVQHXOOAd1XS0iXk/+/IOkh4DzyFMsRsRcYC7AgIGD4841xZ7S9je9shXnJb9C5qZp\nYtU+2+bMmUPfvn0ZMmQImUyGCy+8kKqq7H6lpaWcc8457fY0dCaT2XNs25tzk865SefcpHNu9q/Y\nL0PvV0QEsAi4C1gfEVuSpt7A68nyjYc6rqTukvoky8cCnwPWfvCIzYpbTU0NEydO5OMf/zirV6/m\n9ttvZ9GiRfTv35/ly5dz+eWX85nPfKbQYZqZWTvqDNM9dcCLwKScbdXAQklvA08Bp6d1ltQEnACU\nSLoKuBR4FfjPpFDsBiwBfngUYjcrKiNHjmTFir1fADBu3DjGjRtXoIjMzKzQirZYjIgmcl5nExGT\n8rUlr7ZRm771ZB9+aTtmdZ5tFSkhnHOoMfc4thsNeV503NVlMpm8lzzNuTEzs+LXoS9Dm5mZmdnR\n5WLRzMzMzFK5WDQzMzOzVC4WzczMzCyVi0UzMzMzS+Vi0czMzMxSuVg0sz22bt3K1VdfzdChQxk2\nbBjLly9n4cKFnHnmmRxzzDH7vIPRzMw6v6J9z2J7kHQS8CjwCf4Pe/cfZVV93/v/+RJESSCQhoHL\nj2tGgokkTJUvBO1dNIwGldZoIblGg6txNBSTNFF7h3D5mlsytUm0VPwRbUxHK1gkMlqkUGiUCByr\nBK1DncmEKJJ+GSveLjQUhRlBZuD9/eNs4DCcPYjCnDNzXo+1WNl7f85n7/d+r7B4+/ns/dmwMCK+\nldP2BDCUbI6eAf40IvZ3dr49bfspn7PqJEbcPVVXtFPlvORVyNw051kT9MYbb2TKlCn8wz/8A/v2\n7eOdd95h4MCBPP7441x//fUFiNLMzAqtZItFSb2BvcCfk13ge0yHn3w5InZJEtmC8gpgSddGadZ1\n3n77bf7lX/6FhQsXAtCnTx/69OnDwIEDCxuYmZkVVNFOQ0sql/SypIWSXpG0WNJkSeslbZE0QVKz\npIE5fbZIGiLpMknPS3pR0lOShiTtNZIWSVoPLIqI1oh4lmzReISI2JVs9gb6ANEFt21WMFu3bqWs\nrIxrr72WsWPHMmPGDFpbWwsdlpmZFVjRFouJUcB84Ozkz3RgIjALuJnsJ/2mAUg6D3g1IrYDzwLn\nR8RYsqOBs3PO+WlgckR85VgXl/Qk8Aawm+zoolmP1d7ezr/927/xjW98gxdffJEPf/jD3HbbbYUO\ny8zMCqzYp6G3RkQTgKRNwJqICElNQDkwD5gLLACuAuqSfiOAOklDyY4Kbs0554qI2PNeLh4Rl0g6\nHVgMXAj8vONvJM0EZgIMGlTG3Ir2477Jnm5I3+yzeXa0QuYmk8kcsf9f//VfDBo0iD179pDJZPjE\nJz7BT3/6Uz7/+c8D2ZdfNm7cSEtLS5fE19LSclSMluXcpHNu0jk36ZybzhV7sfhuzvaBnP0DZGPf\nAIySVAZMBb6ftN8D3BERKyRVAjU55zmuebWI2CtpOfBH5CkWI6IWqAU4Y+SomN9U7CntetUV7Tgv\n+RUyN81XVx517M4772To0KF86lOfIpPJ8Pu///tUVmZ/N3DgQMaNG8f48eO7JL5MJnPo2nYk5yad\nc5POuUnn3HSu2KehOxURASwD7gBeiogdSdMA4PVk+5rjPa+kfsmo5MEXYS4FXv7gEZsVt3vuuYer\nr76a3/3d36WhoYGbb76ZZcuWMWLECDZs2MCll17KJZdcUugwzcysC/WE4Z464AWgKudYDfCYpJ3A\nWuDMtM6SmoGPAH0kTQUuBnYAKySdRragXgf85CTEblZUzj333KPWUpw2bRrTpk0rUERmZlZoRVss\nRkQzOcvZRERVvraIqAfUoe9ysi+/dDxnTZ5j5SkhfPZ4Y+57ai8251m7rtRlMpm8U57m3JiZWfHr\n1tPQZmZmZnZyuVg0MzMzs1QuFs3MzMwslYtFMzMzM0vlYtHMzMzMUrlYNDMzM7NULhbNzMzMLFXR\nrrPYHe1p20/5nFWFDqPoVFe0U+W85HUyc9OcZ83P8vJy+vfvT69evejduzf19fU0NDTw9a9/nb17\n99K7d29+/OMfM2HChJMSk5mZdT8lPbIo6SJJGyU1Jf97YU7bDyS9JqmlkDGanWjr1q2joaHh0Jda\nZs+ezfe+9z0aGhq45ZZbmD17doEjNDOzYlKyxWLyzeffApdFRAXZb0gvyvnJPwEeXrEeTxK7du0C\n4O2332bYsGEFjsjMzIpJ0U5DSyoHngCeA/4H2e8/LwD+AhgMXA08CpwbEW8lfbYAE8kWef8H6EP2\nO89XR8R2STXAJ4CRwH9ExFdyLrkJ6CvptIh4NyKeS855cm/UrAtJYvLkyfTq1Yvrr7+emTNnctdd\nd3HJJZcwa9YsDhw4wC9+8YtCh2lmZkVEEVHoGPJKisXfAGPJFnIvAI3A14DLgWuBV4GGiFgg6Tzg\nBxExWdJHgbciIiTNAEZHRHVSLF4GTIyIPR2u9z+Br0fE5A7HWyKiXydxzgRmAgwaVDZu7l33f/Cb\n72GG9IXte479u1J0MnNTMXzAUcfefPNNysrK2LlzJ7NmzeKGG27g6aef5pxzzmHSpEmsW7eOlStX\nMn/+/JMT1HFoaWmhX7/Uv3olzblJ59ykc27SlWpuLrjggo0RMf5YvyvakcXE1ohoApC0CViTFIBN\nQDkwD5hLdsTxKqAu6TcCqJM0lOzo4tacc67IUyh+Bvgr4OLjDTAiaoFagDNGjor5TcWe0q5XXdGO\n85LfycxN89WVnbY3NjbS1tbGmjVrWLp0KZKYNGkSd955J5WVnfftCplMpijiKEbOTTrnJp1zk865\n6VyxP7P4bs72gZz9A2QL3Q3AKEllwFTg8aT9HuDe5FnE64HTc87TmnsBSSOAZcBXI+LfT/gdmBWJ\n1li+cGEAACAASURBVNZWdu/efWh79erVjBkzhmHDhvH0008DsHbtWs4666xChmlmZkWmWw/3JKOM\ny4A7gJciYkfSNAB4Pdm+Jq2/pIHAKmBORKw/qcGaFdj27duZNm0aAO3t7UyfPp0pU6bQr18/brzx\nRtrb2zn99NOpra0tcKRmZlZMunWxmKgj+zxjVc6xGuAxSTuBtcCZKX2/BYwC5kqamxy7OCLekDQP\nmA58SNI24IGIqOkskL6n9mJznrXtSl0mkznmlGip6srcjBw5ksbGxqOOT5w4kY0bN3ZJDGZm1v0U\nbbEYEc3AmJz9qnxtEVEPqEPf5cDyPOes6bD/feD7KdefDXjBOTMzMytpxf7MopmZmZkVkItFMzMz\nM0vlYtHMzMzMUrlYNDMzM7NULhbNzMzMLJWLRTMzMzNLVbRL53RHe9r2Uz5nVaHDKDrVFe1UOS95\nnejcNOdZ57O8vJz+/fvTq1cvevfuTX19PVdeeSWbN28G4K233mLgwIE0NDScsDjMzKznKOliUdIE\nku86k12rsSYiliVtTwBDyeboGeBPI2J/QQI1+4DWrVvHoEGDDu3X1dUd2q6urmbAgAGFCMvMzLqB\nki0WJfUGfgWMj4h2SUOBRkn/FBHtwJcjYpckAf8AXAEsKWDIZidcRPDoo4+ydu3aQodiZmZFqmif\nWZRULullSQslvSJpsaTJktZL2iJpgqTm5PvOB/tskTRE0mWSnpf0oqSnJA1J2mskLZK0HlgUEe8k\nhSHA6UAcPFdE7Eo2ewN9ctvMuhNJTJ48mXHjxh313ednnnmGIUOGcNZZZxUoOjMzK3aKKM4aSFI5\n8BtgLLCJ7PefG4GvAZcD1wKvAg0RsUDSecAPImKypI8Cb0VESJoBjI6Iakk1wGXAxIjYk1znPOBB\n4OPAHx+chk7angQmAD9L2o6ahpY0E5gJMGhQ2bi5d91/wnPR3Q3pC9v3FDqK4nSic1Mx/Ojp5Dff\nfJOysjJ27tzJrFmzuOGGGzjnnHMAuPPOOxk+fDhf/vKXT1wQJ0hLSwv9+vUrdBhFyblJ59ykc27S\nlWpuLrjggo0RMf5Yvyv2aeitEdEEIGkTsCYpAJuAcmAeMBdYAFwFHHwQawRQl0wt9wG25pxzxcFC\nESAingc+I2k08JCkn0XE3qTtEkmnA4uBC4GfdwwwImpJnns8Y+SomN9U7CntetUV7Tgv+Z3o3DRf\nXdlpe2NjI21tbVRWVtLe3s6VV17Jxo0bGTFixAmL4UTJZDJUVlYWOoyi5Nykc27SOTfpnJvOFe00\ndOLdnO0DOfsHyBa6G4BRksqAqcDjSfs9wL0RUQFcT3aK+aDWfBeKiJeAFmBMh+N7geXAH32gOzEr\ngNbWVnbv3n1oe/Xq1YwZk/2/+FNPPcXZZ59dlIWimZkVj2493JOMMi4D7gBeiogdSdMA4PVk+5q0\n/pLOBF5LXnD5OHA20CypH9A/Iv4zeRHmUrJvRJt1K9u3b2fatGkAtLe3M336dKZMmQLAkiVL+MpX\nvlLI8MzMrBvo1sVioo7s84xVOcdqgMck7QTWAmem9J0IzJHURna08psR8dvkhZgVkk4jO/q6DvjJ\nsQLpe2ovNudZ567UZTKZY06PlqqTnZuRI0fS2NiYt23hwoUn7bpmZtZzFG2xGBHN5EwJR0RVvraI\nqCe7RmJu3+Vkp447nrOmw/4iYFGe320HPvv+ozczMzPrGYr9mUUzMzMzKyAXi2ZmZmaWysWimZmZ\nmaVysWhmZmZmqVwsmpmZmVkqF4tmZmZmlqpol87pjva07ad8zqpCh1F0qivaqXJe8joRuWnOs7Zn\neXk5/fv3p1evXvTu3Zv6+noA7rnnHv7mb/6GXr16cemllzJv3rwPdG0zM+v5XCwCks4Afg3URMTt\nybEngKFkc/QM8KcRsb9wUZodn3Xr1jFo0KAj9pcvX05jYyOnnXYab7zxRgGjMzOz7qJkp6GTz/gd\ndAfwsw4/+XJEnEN28e8y4Iquis3sZLjvvvuYM2cOp512GgCDBw8ucERmZtYdFG2xKKlc0suSFkp6\nRdJiSZMlrZe0RdIESc2SBub02SJpiKTLJD0v6UVJTyWf70NSjaRFktaTfLlF0lRgK7Ap9/oRsSvZ\n7A30AaIr7tvsRJDE5MmTGTduHLW1tQC88sorPPPMM5x33nlMmjSJF154ocBRmplZd6CI4qyBJJUD\nvwHGki3kXgAaga8BlwPXAq8CDRGxQNJ5wA8iYrKkjwJvRURImgGMjohqSTXAZcDEiNgjqR/wc+Ai\nYBbQcnAaOonhSWAC2VHHP843DS1pJjATYNCgsnFz77r/xCejmxvSF7bvKXQUxelE5KZi+ICjjr35\n5puUlZWxc+dOZs2axQ033MBdd93F2LFj+fa3v83LL7/MLbfcwk9/+lMk5Tlr4bW0tNCvX79Ch1GU\nnJt0zk065yZdqebmggsu2BgR44/1u2J/ZnFrRDQBSNoErEkKwCagHJgHzAUWAFcBdUm/EUCdpKFk\nRwW35pxzRUQc/Oe5BrgzIlry/YMZEZdIOh1YDFxItrDs+JtaoBbgjJGjYn5Tsae061VXtOO85Hci\nctN8dWWn7Y2NjbS1tfGpT32Kb3/721xwwQVccMEF3H777YwZM4aysrIPdP2TJZPJUFlZWegwipJz\nk865SefcpHNuOle009CJd3O2D+TsHyBb6G4ARkkqA6YCjyft9wD3RkQFcD1wes55WnO2zwPmSWoG\nbgJulvSt3AAiYi+wHPijE3FDZidba2sru3fvPrS9evVqxowZw9SpU1m3bh2QnZLet2/fES/AmJmZ\n5dOth3uSUcZlZF9QeSkidiRNA4DXk+1rOun/+we3kynqloi4N5me7h8R/5m8CHMp2TeizYre9u3b\nmTZtGgDt7e1Mnz6dKVOmsG/fPq677jrGjBlDnz59eOihh4p2CtrMzIpHty4WE3Vkn2esyjlWAzwm\naSewFjjzOM/5YWCFpNPIjr6uA37ygSM16wIjR46ksbHxqON9+vTh4YcfLkBEZmbWnRVtsRgRzWSX\nrTm4X5WvLSLqAXXou5zs1HHHc9Z0cr2anO3twGePN+a+p/Zic54FkktdJpM55nN1pcq5MTOzYlfs\nzyyamZmZWQG5WDQzMzOzVC4WzczMzCyVi0UzMzMzS+Vi0czMzMxSuVg0MzMzs1QuFs26sfLycioq\nKjj33HMZP/7Iz3vOnz8fSfz2t78tUHRmZtYTFO06i11B0kXAbWS/H70P+E5ErE3a+gD3ApVkPy/4\n3YhY2tn59rTtp3zOqpMac3dUXdFOlfOS1/HmpjnPOp7r1q076rN9r732GqtXr+aMM874wDGamVlp\nK9mRxeQzfr8FLku+IX0NsCjnJ98F3oiITwKfBp7u+ijN3p8/+7M/Y968ef6cn5mZfWBFWyxKKpf0\nsqSFkl6RtFjSZEnrJW2RNEFSs6SBOX22SBoi6TJJz0t6UdJTkoYk7TWSFklaDyyKiBcj4v8m3TcB\nfZNP/AFcB9wKEBEHIsJzeVZ0JDF58mTGjRtHbW0tAMuXL2f48OGcc845BY7OzMx6gmKfhh4FXEG2\ncHsBmA5MBC4Hbib7Sb9pwAJJ5wGvRsR2Sc8C50dESJoBzAaqk3N+GpgYEXs6XOtLwL9FxLs5Behf\nSqoE/h34VvIZQLOi8eyzzzJ8+HDeeOMNLrroIs4++2x++MMfsnr16kKHZmZmPYQiotAx5CWpHPh5\nRJyV7P898GRELJY0Engc+CYwNyKmSLoT+HVE3C+pApgPDCX7POLW5Dc1QETEX3S41meAFcDFEfHv\nkgYBbwJXRMQ/SPpfwNiI+OM8cc4EZgIMGlQ2bu5d95/4ZHRzQ/rC9o6luQHHn5uK4QNS2xYuXMgp\np5zCsmXLOO207AD5m2++yaBBg7jvvvv4nd/5nQ8abpdqaWmhX79+hQ6jKDk36ZybdM5NulLNzQUX\nXLAxIsYf63fFPrL4bs72gZz9A2Rj3wCMklQGTAW+n7TfA9wRESuSkcGanPO05l5A0ghgGfDViPj3\n5PAO4B2yBSnAY8DX8gUYEbVALcAZI0fF/KZiT2nXq65ox3nJ73hz03x15aHt1tZWDhw4QP/+/Wlt\nbeXmm29m7ty5PPjgg4d+U15eTn19/VEvwHQHmUyGysrKQodRlJybdM5NOucmnXPTuW79L3gyzbwM\nuAN4KSJ2JE0DgNeT7WvS+ifTzauAORGxvsN5/4nsm9Brgc8Dvz7xd2D2/m3fvp1p06YB0N7ezvTp\n05kyZUqBozIzs56mWxeLiTqyzzNW5RyrAR6TtJNssXdmSt9vkX0ucq6kucmxiyPiDeB/A4sk3UV2\nSvraEx+62fs3cuRIGhsbO/1Nc3Nz1wRjZmY9VtEWixHRDIzJ2a/K1xYR9YA69F1O9uWXjues6bD/\nfQ5PXXf87avA544n5r6n9mJznnXwSl0mkzli+tQOc27MzKzYFe3SOWZmZmZWeC4WzczMzCyVi0Uz\nMzMzS+Vi0czMzMxSuVg0MzMzs1QuFs3MzMwslYtFMzMzM0tVtOssdkd72vZTPmdVocMoOtUV7VQ5\nL3m9l9w0d1i7s7y8nP79+9OrVy969+5NfX09f/7nf87y5cs55ZRTGDx4MAsXLmTYsGEnM3QzMysR\nHlnMQ9Kpkh6S1CTpJUn/b6FjMsu1bt06GhoaqK+vB+A73/kOv/zlL2loaOALX/gCt9xyS4EjNDOz\nnsIjix1I6g1cAZwWERWSPgT8WtIjyZdjzIrORz7ykUPbra2tSOrk12ZmZu9dtxtZlFQu6WVJCyW9\nImmxpMmS1kvaImmCpGZJA3P6bJE0RNJlkp6X9KKkpyQNSdprJC2StB5YBATw4aRw7AvsA3YV4n7N\nOpLE5MmTGTduHLW1tYeOf/e73+W///f/zuLFiz2yaGZmJ4wiotAxHBdJ5cBvgLHAJuAFoBH4GnA5\ncC3wKtAQEQsknQf8ICImS/oo8FZEhKQZwOiIqJZUA1wGTIyIPZJOJVs0fh74EPBnEVFLHpJmAjMB\nBg0qGzf3rvtP0p13X0P6wvY9hY6iOL2X3FQMH3DE/ptvvklZWRk7d+5k1qxZ3HDDDZxzzjmH2hcv\nXsy+ffu49tprT0bIXaalpYV+/foVOoyi5Nykc27SOTfpSjU3F1xwwcaIGH+s33XXaeitEdEEIGkT\nsCYpAJuAcmAeMBdYAFwF1CX9RgB1koYCfYCtOedcEREH/9meAOwHhgEfBZ6R9FRE/H8dA0mKyFqA\nM0aOivlN3TWlJ091RTvOS37vJTfNV1emtjU2NtLW1kZl5eHfjBw5kj/8wz/koYceOkFRFkYmkzni\nvuww5yadc5POuUnn3HSu201DJ97N2T6Qs3+AbAG8ARglqQyYCjyetN8D3BsRFcD1wOk552nN2Z4O\nPBERbRHxBrAeOGblbXaytba2snv37kPbq1evZsyYMWzZsuXQb5YvX87ZZ59dqBDNzKyH6ZHDPcko\n4zLgDuCliNiRNA0AXk+2r+nkFP8BXAgskvRh4HzgrpMVr9l7tX37dqZNmwZAe3s706dPZ8qUKXzp\nS19i8+bNnHLKKXz84x/nJz/5SYEjNTOznqJHFouJOrLPM1blHKsBHpO0E1gLnJnS92+ABckUt4AF\nEfHLY12w76m92NxhTTzLDu93NpVayo43NyNHjqSxsfGo40uXLj2BUZmZmR3W7YrFZPmaMTn7Vfna\nIqKebKGX23c5sDzPOWs67LeQXT7HzMzMrKR112cWzczMzKwLuFg0MzMzs1QuFs3MzMwslYtFMzMz\nM0vlYtHMzMzMUrlYNDMzM7NU3W7pnGK2p20/5XNWFTqMolNd0U6V85JXWm6a86zXWV5eTv/+/enV\nqxe9e/emvr6e73znO/zTP/0Tffr04ROf+AQLFixg4MCBXRG6mZmVCI8sppD0u5I2SNokqUnS6cfu\nZXZyrVu3joaGBurr6wG46KKL+NWvfsUvf/lLPvnJT3LrrbcWOEIzM+tpXCx2IKm3pN7Aw8DXI+Iz\nQCXQVtDAzPK4+OKL6d07O0Fw/vnns23btgJHZGZmPU23KxYllUt6WdJCSa9IWixpsqT1krZImiCp\nWdLAnD5bJA2RdJmk5yW9KOkpSUOS9hpJiyStBxYBFwO/jIhGgIjYERH7C3LDZglJTJ48mXHjxlFb\nW3tU+4MPPsgf/MEfFCAyMzPryRQRhY7huEgqB34DjAU2kf3+cyPwNeBy4FrgVaAhIhZIOg/4QURM\nlvRR4K2ICEkzgNERUS2pBrgMmBgReyTdBIwDBgNlwJKImJcSz0xgJsCgQWXj5t51/0m68+5rSF/Y\nvqfQURSntNxUDB9w1LE333yTsrIydu7cyaxZs7jhhhs455xzAHj44YfZvHkzt9xyC5KO6tsdtbS0\n0K9fv0KHUZScm3TOTTrnJl2p5uaCCy7YGBHjj/W77vqCy9aIaAKQtAlYkxSATUA5MA+YCywArgLq\nkn4jgDpJQ4E+wNacc66IiIP/bPcGJgKfBd4B1kjaGBFrOgYSEbVALcAZI0fF/KbumtKTp7qiHecl\nv7TcNF9d2Wm/xsZG2traqKysZOHChWzatIk1a9bwoQ996CRF2vUymQyVlZWFDqMoOTfpnJt0zk06\n56Zz3W4aOvFuzvaBnP0DZAu9DcAoSWXAVODxpP0e4N6IqACuB3JfWmnN2d4G/EtE/DYi3gH+Gfh/\nTvhdmL1Hra2t7N69+9D26tWrGTNmDE888QTz5s1jxYoVPapQNDOz4tEjh3uSUcZlwB3ASxGxI2ka\nALyebF/TySmeBGZL+hCwD5gE3Hmy4jU7lu3btzNt2jQA2tvbmT59OlOmTGHUqFG8++67XHTRRUD2\nJZef/OQnhQzVzMx6mB5ZLCbqyD7PWJVzrAZ4TNJOYC1wZr6OEbFT0h1J/wD+OSKOuVBg31N7sTnP\n+nilLpPJHHNatVS919yMHDmSxsbGo47/5je/OQlRmZmZHdbtisWIaAbG5OxX5WuLiHpAHfouB5bn\nOWdNnmMPk10+x8zMzKxkdddnFs3MzMysC7hYNDMzM7NULhbNzMzMLJWLRTMzMzNL5WLRzMzMzFK5\nWDQzMzOzVN1u6ZxitqdtP+VzjrkcY8mprminynnJKzc3zXnW6CwvL6d///706tWL3r17U19fz2OP\nPUZNTQ0vvfQS//qv/8r48cf8rKeZmdn7VtIji5I+JmmdpBZJ93ZoGyepSdJvJP1IktLOY3YyrVu3\njoaGBurr6wEYM2YMjz/+OJ/73OcKHJmZmZWCkh1ZlNQb2Av8OdmFvMd0+Ml9wJ8Az5P9NvQU4Gdd\nGaNZPqNHjy50CGZmVkKKdmRRUrmklyUtlPSKpMWSJktaL2mLpAmSmiUNzOmzRdIQSZdJel7Si5Ke\nkjQkaa+RtEjSemBRRLRGxLNki8bcaw8FPhIRz0VEAH8PTO3C2zcDQBKTJ09m3Lhx1NbWFjocMzMr\nQcU+sjgKuAK4jux3mqcDE4HLgZvJfrpvGrBA0nnAqxGxXdKzwPkREZJmALOB6uScnwYmRsSeTq47\nHNiWs78tOXYUSTOBmQCDBpUxt6L9fd1oTzakb/bZPDtabm4ymcxR7fPmzaOsrIydO3cya9Ys9uzZ\nwznnnAPAW2+9xcaNG2lpaenKkLtMS0tL3pyYc9MZ5yadc5POuelcsReLWyOiCUDSJmBNUgA2AeXA\nPGAusAC4CqhL+o0A6pIRwj7A1pxzrjhGoXhcIqIWqAU4Y+SomN9U7CntetUV7Tgv+eXmpvnqyk5/\n29jYSFtbG5WV2d8NHDiQcePG9dgXXDKZzKF7tSM5N+mcm3TOTTrnpnNFOw2deDdn+0DO/gGyhe4G\nYJSkMrLTxI8n7fcA90ZEBXA9cHrOeVrfw3VfJ1twHjQiOWbWZVpbW9m9e/eh7dWrVzNmTMdHa83M\nzE6uYi8WO5U8T7gMuAN4KSJ2JE0DOFzcXfM+zvufwC5J5ydvQX+V7JS3WZfZvn07EydO5JxzzmHC\nhAlceumlTJkyhWXLljFixAg2bNjApZdeyiWXXFLoUM3MrAfrCXODdWSfZ6zKOVYDPCZpJ7AWODOt\ns6Rm4CNAH0lTgYsj4tfAN4GFQF+yb0Ef803ovqf2YnOetfJKXSaTOeYUa6nqLDcjR46ksbHxqOPT\npk1j2rRpJzkyMzOzrKItFiOimZzlbCKiKl9bRNQD6tB3OXlGAiOiJs+x8pTr13P0cjpmZmZmJaVb\nT0ObmZmZ2cnlYtHMzMzMUrlYNDMzM7NULhbNzMzMLJWLRTMzMzNL5WLRzMzMzFIV7dI5ZqWqvLyc\n/v3706tXL3r37k19fT3/9V//xZVXXklzczPl5eU8+uijfPSjHy10qGZmVgJcLOYhaQLJ957JruFY\nExHLjtVvT9t+yuesOqmxdUfVFe1UOS95LZzy4bzH161bx6BBgw7t33bbbXz+859nzpw53Hbbbdx2\n22381V/9VVeFaWZmJczT0B1I6g38ChgfEecCU4C/TY6bFcTy5cu55prslyuvueYa/vEf/7HAEZmZ\nWanodsWipHJJL0taKOkVSYslTZa0XtIWSRMkNUsamNNni6Qhki6T9LykFyU9JWlI0l4jaZGk9cCi\niHgnItqT7qcDUYBbtRIlicmTJzNu3Dhqa7MD3Nu3b2fo0KEA/Lf/9t/Yvn17IUM0M7MS0l1Hy0YB\nVwDXkf0u9HRgInA5cDPZT/1NAxZIOg94NSK2S3oWOD8iQtIMYDZQnZzz08DEiNgDkPR7EPg48Mc5\nxaPZSfXss88yfPhw3njjDS666CLOPvvsI9olISmlt5mZ2YnVXYvFrRHRBCBpE7AmKQCbgHJgHjAX\nWABcBdQl/UYAdZKGAn2ArTnnXHGwUASIiOeBz0gaDTwk6WcRsbdjIJJmAjMBBg0qY26Fa8qOhvTN\nPrdoR2tpaSGTyRx1fMuWLQCMHTuWRx55hI985CMsXbqUj33sY+zYsYP+/fvn7deTpOXGnJvOODfp\nnJt0zk3numux+G7O9oGc/QNk72kDMEpSGTAV+H7Sfg9wR0SskFQJ1OScpzXfhSLiJUktwBigPk97\nLcnLMGeMHBXzm7prSk+e6op2nJf8Fk75MJWVlYf2W1tbOXDgAP3796e1tZWbb76ZuXPn0q9fP7Zs\n2cKXvvQlbrvtNq666qoj+vVEmUymx9/j++XcpHNu0jk36ZybzvXIf8GTUcZlwB3ASxGxI2kaALye\nbF+T1l/SmcBrEdEu6ePA2UDzSQzZDMg+mzht2jQA2tvbmT59OlOmTOGzn/0sX/7yl/m7v/s7Pv7x\nj/Poo48WOFIzMysVPbJYTNSRfZ6xKudYDfCYpJ3AWuDMlL4TgTmS2siOVn4zIn578kI1yxo5ciSN\njY1HHf/Yxz7GmjVrChCRmZmVum5XLEZEM9kp4YP7VfnaIqKe7BqJuX2Xk335peM5azrsLwIWHW9s\nfU/txebbLj3ebj1eJpOh+erKQodRlPyMjJmZFbtut3SOmZmZmXUdF4tmZmZmlsrFopmZmZmlcrFo\nZmZmZqlcLJqZmZlZKheLZmZmZpbKxaJZkdi/fz9jx47lC1/4AgCNjY383u/9HhUVFVx22WXs2rWr\nwBGamVkp6nbrLJ4Mks4Afg3URMTtHdpWACMjYkzezjn2tO2nfM6qkxRl91Vd0U6V83JIc8panHff\nfTejR48+VBTOmDGD22+/nUmTJvHggw/y13/91/zlX/5lV4ZqZmZWuiOLknIL5TuAn+X5zReBli4L\nykrWtm3bWLVqFTNmzDh07JVXXuFzn/scABdddBFLly4tVHhmZlbCirZYlFQu6WVJCyW9ImmxpMmS\n1kvaImmCpGZJA3P6bJE0RNJlkp6X9KKkpyQNSdprJC2StJ7kCy2SpgJbgU0drt8P+F/A97vspq1k\n3XTTTcybN49TTjn8V/Izn/kMy5dnPzj02GOP8dprrxUqPDMzK2FFWywmRgHzgbOTP9PJfrd5FnAz\n2U/3TQOQdB7wakRsB54Fzo+IscASYHbOOT8NTI6IryQF4f8G/iLPtf8yufY7J+G+zA5ZuXIlgwcP\nZty4cUccf/DBB/nxj3/MuHHj2L17N3369ClQhGZmVsqK/ZnFrRHRBCBpE7AmIkJSE1AOzAPmAguA\nq4C6pN8IoE7SUKAP2ZHDg1ZExJ5kuwa4MyJapMOfkZZ0LvCJiPgzSeWdBShpJjATYNCgMuZWtL/v\nm+2phvTNPrdoWbnfg25paeGRRx5h9erVPP744+zbt4933nmHiy66iO9+97vcfPPNALz22msMHjy4\npL4l3dLSUlL3ezycm3TOTTrnJp1z07liLxbfzdk+kLN/gGzsG4BRksqAqRyeMr4HuCMiVkiqJFsU\nHtSas30e8D8lzQMGAgck7QX2A+MlNSfXGSwpExGVHQOMiFqgFuCMkaNiflOxp7TrVVe047wc1nx1\n5aHtTCbD4sWLj9i//fbbWblyJW+88QaDBw/mwIEDVFVV8Z3vfIfKysqjT9hDZTKZkrrf4+HcpHNu\n0jk36ZybzhX7NHSnIiKAZWRfUHkpInYkTQOA15Ptazrp//sRUR4R5cBdwA8j4t6IuC8ihiXHJwKv\n5CsUzU6mRx55hE9+8pOcffbZDBs2jGuvvbbQIZmZWQnqCcM9dcALQFXOsRrgMUk7gbXAmV0fltnx\nq6ysPPRftzfeeCM33nhjYQMyM7OSV7TFYkQ0A2Ny9qvytUVEPaAOfZeTffml4zlrOrle3raOcXSm\n76m92Jyyhl4py2QyR0y9mpmZWffRraehzczMzOzkcrFoZmZmZqlcLJqZmZlZKheLZmZmZpbKxaKZ\nmZmZpXKxaGZmZmapXCyamZmZWaqiXWexO9rTtp/yOasKHUbRqa5op8p5oTnPGpz79+9n7NixDB8+\nnJUrV9LQ0MDXv/519u7dS+/evfnxj3/MhAkTChCtmZlZVkmPLEr6mKR1klok3ZvymxWSftXVGEKy\njwAAIABJREFUsVlpWLp0KaNHjz60P3v2bL73ve/R0NDALbfcwuzZswsYnZmZWQkXi5J6A3uBPwdm\npfzmi0BLV8ZlpWPbtm0899xzzJgx49AxSezatQuAt99+m2HDhhUqPDMzM6CIp6EllQNPAM8B/4Ps\n958XAH8BDAauBh4Fzo2It5I+W4CJwATg/wB9gB3A1RGxXVIN8AlgJPAfEfEV4FlJo/Jcvx/wv4CZ\nyXXMTqibbrqJ66+/nlNOOfzfbHfddReXXHIJs2bN4sCBA/ziF78oYIRmZmZFXCwmRgFXANeRLRan\nky0GLwduJvv952nAAknnAa8mReGzwPkREZJmALOB6uScnwYmRsSeY1z7L4H5wDud/UjSTLIFJYMG\nlTG3ov3477KHG9I3+9xiqctkMoe2N2zYQFtbG8OHD6ehoYEdO3aQyWT40Y9+xNe+9jUmTZrEunXr\n+OIXv8j8+fMLF3QBtbS0HJEzO8y5SefcpHNu0jk3nSv2YnFrRDQBSNoErEkKwCagHJgHzCU74ngV\nUJf0GwHUSRpKdnRxa845VxyrUJR0LvCJiPizZIQzVUTUArUAZ4wcFfObij2lXa+6oh3nBZqvrjy0\n/eSTT7Jx40aef/55AHbt2sUDDzzAmjVrWLp0KZKYNGkSd955J5WVlflP2MNlMpmSvfdjcW7SOTfp\nnJt0zk3niv2ZxXdztg/k7B8gW+huAEZJKgOmAo8n7fcA90ZEBXA9cHrOeVrfw3V/DxgvqRl4Fvik\npMz7vAezo9x6661s27aNJUuWsGTJEi688EIefvhhhg0bxtNPPw3A2rVrOeusswocqZmZlbpuPdyT\njDIuA+4AXoqIHUnTAOD1ZPua93He+4D74NCzkysjovKDxmt2LPfffz833ngj7e3tnH766dTW1hY6\nJDMzK3HdulhM1JF9nrEq51gN8JikncBa4My0zsno4UeAPpKmAhdHxK/fTyB9T+3F5jxr6ZW6TCZz\nxBSsHamysvLQ9MfEiRPZuHFjYQMyMzPLUbTFYkQ0A2Ny9qvytUVEPaAOfZeTffml4zlr8hwrP544\nzMzMzEpJsT+zaGZmZmYF5GLRzMzMzFK5WDQzMzOzVC4WzczMzCyVi0UzMzMzS+Vi0czMzMxSFe3S\nOd3Rnrb9lM9ZVegwik51RTtVzgvNedbg3L9/P2PHjmX48OGsXLmShoYGvv71r7N371569+7Nj3/8\nYyZMmFCAaM3MzLI8sghIOkNSi6RZOcfGSWqS9BtJP5Kkzs5h9n4sXbqU0aNHH9qfPXs23/ve92ho\naOCWW25h9uzZBYzOzMyshItFSbmjqncAP+vwk/uAPwHOSv5M6aLQrERs27aN5557jhkzZhw6Jold\nu3YB8PbbbzNs2LBChWdmZgYU8TR08k3mJ4DngP9B9pN+C4C/AAYDVwOPAudGxFtJny3ARGAC8H+A\nPsAO4OqI2C6pBvgEMBL4D+ArySf+tgKtOdceCnwkIp5L9v8emMrRBaXZ+3bTTTdx/fXXc8oph/+b\n7a677uKSSy5h1qxZHDhwgF/84hcFjNDMzKyIi8XEKOAK4DqyxeJ0ssXg5cDNZD/pNw1YIOk84NWk\nKHwWOD8iQtIMYDZQnZzz08DEiNgjqR/wv4GLgFk51x0ObMvZ35YcO4qkmcBMgEGDyphb0f7B77qH\nGdI3+9xiqctkMoe2N2zYQFtbG8OHD6ehoYEdO3aQyWT40Y9+xNe+9jUmTZrEunXr+OIXv8j8+fML\nF3QBtbS0HJEzO8y5SefcpHNu0jk3nSv2YnFrRDQBSNoErEkKwCagHJgHzCU74ngVUJf0GwHUJSOE\nfciOHB60IiL2JNs1wJ0R0fJ+H0mMiFqgFuCMkaNiflOxp7TrVVe047xA89WVh7affPJJNm7cyPPP\nPw/Arl27eOCBB1izZg1Lly5FEpMmTeLOO++ksrIy/wl7uEwmU7L3fizOTTrnJp1zk8656VyxP7P4\nbs72gZz9A2QL3Q3AKEllZKeJH0/a7wHujYgK4Hrg9JzztOZsnwfMk9QM3ATcLOlbwOtkC86DRiTH\nzE6IW2+9lW3btrFkyRKWLFnChRdeyMMPP8ywYcN4+umnAVi7di1nnXVWgSM1M7NS162He5JRxmVk\nX1B5KSJ2JE0DOFzcXdNJ/98/uJ08z9gSEfcm+7sknQ88D3yVbAFqdlLdf//93HjjjbS3t3P66adT\nW1tb6JDMzKzEdetiMVFH9nnGqpxjNcBjknYCa4Ez38d5vwksBPqSfbHlmC+39D21F5vzrKVX6jKZ\nzBFTsHakysrKQ9MfEydOZOPGjYUNyMzMLEfRFosR0QyMydmvytcWEfWAOvRdTvbll47nrOnkejUd\n9utzr29mZmZWior9mUUzMzMzKyAXi2ZmZmaWysWimZmZmaVysWhmZmZmqVwsmpmZmVkqF4tmZmZm\nlqpol87pjva07ad8zqpCh1F0qivaqSrRvDTnWXdz//79jB8/nuHDhzNr1iyuvPJKNm/eDMBbb73F\nwIEDaWho6OpQzczM8irpYlHSBJLvOpNdq7EmIpYlbeM4vCj3PwM3RkQUIk7rWe6++25Gjx7Nrl27\nAKirqzvUVl1dzYABAwoVmpmZ2VFKdhpaUm/gV8D4iDgXmAL8bXIc4D7gT4Czkj9TChKo9Sjbtm1j\n1apVzJgx46i2iODRRx/lK1/5SgEiMzMzy69oi0VJ5ZJelrRQ0iuSFkuaLGm9pC2SJkhqljQwp88W\nSUMkXSbpeUkvSnpK0pCkvUbSIknrgUUR8U5EtCfdTwci+d1Q4CMR8Vwymvj3wNSuzYD1RDfddBPz\n5s3jlFOO/qv3zDPPMGTIEM4666wCRGZmZpZfsU9DjwKuAK4j+/3n6cBE4HLgZrKf9JsGLJB0HvBq\nRGyX9CxwfkSEpBnAbKA6OeengYkRsQcg6fcg8HHgjyOiXdJwYFtOHNuA4fkClDQTmAkwaFAZcyva\n8/2spA3pm31usRRlMplD2xs2bKCtrY3du3fT0NDAjh07aGlpOfSbO++8kwkTJhzRp5Tl5saO5Nyk\nc27SOTfpnJvOFXuxuDUimgAkbQLWJAVgE1AOzAPmAguAq4CDD3+NAOqSEcI+wNacc644WCgCRMTz\nwGckjQYekvSz4wkwImpJnns8Y+SomN9U7CntetUV7ZRqXpqvrjy0/eSTT7Jx40aqqqrYu3cvu3bt\n4u677+bnP/857e3tXHnllWzcuJERI0YULuAikslkqKysLHQYRcm5SefcpHNu0jk3nSvaaejEuznb\nB3L2D5AtdDcAoySVkZ0mfjxpvwe4NyIqgOvJTjEf1JrvQhHxEtACjAFeJ1twHjQiOWb2vt16661s\n27aN5uZmlixZwoUXXsh3v/tdAJ566inOPvtsF4pmZlZ0ir1Y7FTyPOEy4A7gpYjYkTQN4HBxd01a\nf0lnHnyhRdLHgbOB5oj4T2CXpPMlCfgq2Slvs5NiyZIlfrHFzMyKUk+YG6wj+zxjVc6xGuAxSTuB\ntcCZKX0nAnMktZEdrfxmRPw2afsmh5fO+Vnyx+yEqKyspLKy8tAzMgsXLixoPGZmZmmKtliMiGay\nU8IH96vytUVEPdk1EnP7LifPSGBE1HTYXwQsSrl+fe7134u+p/Zic55FmEtdJpM54tk9MzMz6z66\n9TS0mZmZmZ1cLhbNzMzMLJWLRTMzMzNL5WLRzMzMzFK5WDQzMzOzVC4WzczMzCyVi0WzLrZ//37G\njh3LF77wBQCuvPJKzj33XM4991zKy8s599xzCxyhmZnZYUW7zmJXkHQRcBvZ70fvA74TEWuTtieA\noWRz9AzwpxGxv7Pz7WnbT/mcVSc36G6ouqKdqhLNS3OedTfvvvtuRo8eza5duwCoq6s71FZdXc2A\nAQO6LD4zM7NjKdmRxeQzf78FLku+IX0NRy7Q/eWIOIfswtxlwBVdH6X1NNu2bWPVqlXMmDHjqLaI\n4NFHH/Vn/8zMrKgUbbEoqVzSy5IWSnpF0mJJkyWtl7RF0gRJzZIG5vTZImmIpMskPS/pRUlPSRqS\ntNdIWiRpPbAoIl6MiP+bdN8E9JV0GkBE7EqO9yY78hhdd/fWU910003MmzePU045+q/eM888w5Ah\nQzjrrLMKEJmZmVl+RVssJkYB84Gzkz/TyX7PeRZwM9lP+k0DkHQe8GpEbAeeBc6PiLHAEmB2zjk/\nDUyOiI7DN18C/i0i3j14QNKTwBvAbuAfTvjdWUlZuXIlgwcPZty4cXnbH3nkEY8qmplZ0Sn2Zxa3\nRkQTgKRNwJqICElNQDkwD5gLLACuAg4+/DUCqJM0lOyo4Nacc66IiD25F5H0GeCvgItzj0fEJZJO\nBxYDFwI/7xigpJnATIBBg8qYW9H+gW64JxrSN/vcYinKZDKHth955BFWr17N448/zr59+3jnnXdo\nbW0Fsi+91NXV8bd/+7dH9CllLS0tzkUK5yadc5POuUnn3HSu2IvFd3O2D+TsHyAb+wZglKQyYCrw\n/aT9HuCOiFghqRKoyTlPa+4FJI0AlgFfjYh/7xhAROyVtBz4I/IUixFRC9QCnDFyVMxvKvaUdr3q\ninZKNS/NV1ce2q6sPLydyWS4/fbbmTVrFpWVlTzxxBNUVFRwxRV+NPagTCZzRM7sMOcmnXOTzrlJ\n59x0rtinoTsVEUG20LsDeCkidiRNA4DXk+1r0vonzzuuAuZExPqc4/2SUcmDL8JcCrx84u/ALGvJ\nkiWegjYzs6LUE4Z76oAXgKqcYzXAY5J2AmuBM1P6fovsc5FzJc1Njl0MCFiRvOxyCrAO+MkJj9xK\nVmVlJZWVlYemPRYuXFjQeMzMzNIUbbEYEc1kl605uF+Vry0i6skWd7l9l5N9+aXjOWs67H+fw1PX\nHX32eGPue2ovNudZV6/UZTKZI6ZjzczMrPvo1tPQZmZmZnZyuVg0MzMzs1QuFs3MzMwslYtFMzMz\nM0vlYtHMzMzMUrlYNDMzM7NULhbNzMzMLFXRrrPYHe1p20/5nFWFDqPoVFe0U1VCeWnOs9bm/v37\nGT9+PMOHD2flypUA3HPPPfz1X/81/fv359JLL2XevHldHaqZmdkxlXSxKOki4DagD7AP+E5ErJXU\nH3gm56cjgIcj4qYChGk9wN13383o0aPZtWsXAOvWrWP58uU88MADXHzxxbzxxhsFjtDMzCy/kp2G\nTr75/FvgsoioIPsN6UUAEbE7Is49+Ad4FXi8cNFad7Zt2zZWrVrFjBkzDh277777mDNnDn369AFg\n8ODBhQrPzMysU0VbLEoql/SypIWSXpG0WNJkSeslbZE0QVKzpIE5fbZIGiLpMknPS3pR0lOShiTt\nNZIWSVoPLIqIFyPi/ybdNwF9k+9B58bxSWAwR440mr1nN910E/PmzeOUUw7/dXvllVd45pln+MY3\nvsGkSZN44YUXChihmZlZumKfhh4FXAFcB7wATAcmApcDN5P9/vM0YIGk84BXI2K7pGeB8yMiJM0A\nZgPVyTk/DUyMiD0drvUl4N8i4t0Ox68C6iIi8gUoaSYwE2DQoDLmVrR/oBvuiYb0zT63WCoymcyh\n7Q0bNtDW1sbu3btpaGhgx44dZDIZ3n77bZqampg3bx7btm3j8ssv56c//SmS0k9cYlpaWo7IpR3m\n3KRzbtI5N+mcm84Ve7G4NSKaACRtAtYkBWATUA7MA+YCC0iKuqTfCKBO0lCyzyNuzTnnio6FoqTP\nAH8FXJwnhquAP04LMCJqgVqAM0aOivlNxZ7Srldd0U4p5aX56spD208++SQbN26kqqqKvXv3smvX\nLh544AE+9alP8e1vfxtJfOMb3+D2229nzJgxlJWVFS7wIpPJZKisrCx0GEXJuUnn3KRzbtI5N50r\n2mnoRO4o34Gc/QNkC90NwChJZcBUDj9XeA9wb/Is4vXA6Tnnac29gKQRwDLgqxHx7x3azgF6R8TG\nE3M7VmpuvfVWtm3bRnNzM0uWLOHCCy/k4YcfZurUqaxbtw7ITknv27ePQYMGFThaMzOzo3Xr4Z5k\nlHEZcAfwUkTsSJoGAK8n29ek9U+ed1wFzImI9Xl+8hXgkRMYshkA1113Hddddx2LFi3iox/9KA89\n9JCnoM3MrCh162IxUUf2ecaqnGM1wGOSdgJrgTNT+n6L7HORcyXNTY5dHBEH1zH5MvCH7zWQvqf2\nYnOeNfZKXSaTOWJqtlRVVlYemubo06cPDz/8sKc+zMys6BVtsRgRzcCYnP2qfG0RUQ+oQ9/lZF9+\n6XjOmg773we+30kMI99H6GZmZmY9RrE/s2hmZmZmBeRi0czMzMxSuVg0MzMzs1QuFs3MzMwslYtF\nMzMzM0vlYtHMzMzMUhXt0jnd0Z62/ZTPWVXoMIpOdUU7VT08L8151tfcv38/48ePZ/jw4axcuZKa\nmhruv//+Q5/0++EPf8iHPvShrg7VzMzsuHhkEZB0hqQWSbNyjv1A0muSWgoZm3Vfd999N/8/e/cf\np1Vd53389Qb8gTuC3vEjwWQkNAgHMQhyIxpULF00vXXJlt2A4qZ23U1cwriXIMRciKQ0rTU0EYE7\nkdBG4U4zYdpdFyyRQSRFbB1jjBuVJBwcYGb43H9cB7wY5gwjzHBdM/N+Ph7Xw3O+53y/53M+D3zw\n4Xt+9evX75C2G2+8kbKyMsrKyrj88ka/793MzCxn2myxKCl7VvV7wC/q7PIYMOT4RWStSUVFBStX\nrmTChAm5DsXMzOyY5G2xKKlQ0kuS7pf0sqQlki6R9LSkLZKGSCpPvu98oM8WSd0lXSHpGUnrJf1K\nUvdk+0xJiyQ9DSxK2q4CXgU2ZR8/ItZGxLbjeMrWikyaNIm5c+fSrt2h/4vdeeedDBgwgC996Uu8\n/fbbOYrOzMys8RQRuY6hXpIKgVeAC8gUcr8FNgBfBq4ExgOvAWURsUDSUODWiLhE0unAzogISROA\nfhExWdJM4ApgWERUSSoAngRGAl8HKiPitjpxVEZEQQNxTgQmAnTp0nXQjNvvabIctBbdO8L2qlxH\n0byKenY+uLxmzRrWrl178JLz0qVLmT17Nn/605/o3LkzkrjvvvvYsWMH119/PQUFqX+82rTKykrn\nJoVzk865SefcpGuruRkxYsS6iBh8pP3y/QGXVyNiI4CkTcBTSQG4ESgE5gIzgAXAdcDSpN+ZwFJJ\nZwAnkpk5PODRiDhQuswEvh8RldIhn5dutIiYD8wHOKt3n5i3Md9TevxNLqqhteelfEzxweUnnniC\ndevWMW7cOPbs2cOuXbu49957Wbx48cF9evfuzahRoygoKKC4uPjwAY3S0lLnJoVzk865SefcpHNu\nGpa3l6ETe7OW92et7ydT6K4B+kjqClwFPJxsvxO4KyKKgK8AJ2eNsztreSgwV1I5MAn4F0n/2NQn\nYW3L7NmzqaiooLy8nAcffJCLLrqIxYsXs23be3c1PPLII5x33nk5jNLMzKxxWvR0TzLL+AiZB1Re\njIgdyabOwOvJ8tgG+n/qwHJyiboyIu5qpnCtjbvpppsoKytDEoWFhfz4xz9m8+bNuQ7LzMysQS26\nWEwsJXM/47istpnAMklvA6uAs9/voJLmAn8DnCKpArg3ImY21KfjCe3ZXM/79tq60tLSQy7TtiXF\nxcUHL20sWrTosO0uFs3MLN/lbbEYEeXAeVnr4+rbFhHPAqrTtwQoqWfMmQ0cb2ad9ZuAm95/5GZm\nZmatR77fs2hmZmZmOeRi0czMzMxSuVg0MzMzs1QuFs3MzMwslYtFMzMzM0vlYtHMzMzMUuXtq3Na\noqrqWgqnrsx1GHlnclEN41pJXspT3qNZW1vL4MGD6dmzJytWrGD69OmUlJTQrl07unXrxv3330+P\nHj2Oc7RmZmbHrk3PLEoaIqks+W2QdHXWtlslbZVUmcsYrWW444476Nev38H1KVOm8Pzzz1NWVsao\nUaOYNWtWDqMzMzM7em22WJTUAXgBGBwRA4HPAj9O2gEeA4bkKj5rOSoqKli5ciUTJkw42NapU6eD\ny7t370ZSfV3NzMzyXt5ehpZUCDwOrAX+kswn/RYANwPdgDHAQ8DAiNiZ9NkCDCNT5H0TOBHYAYyJ\niO3J958/DPQG/hARX8g65MlAHFiJiLXJmM11itZKTJo0iblz5/LOO+8c0j5t2jQeeOABOnfuzOrV\nq3MUnZmZ2bFRRBx5rxxIisVXgAuATWSKxQ3Al4ErgfHAa0BZRCyQNBS4NSIukXQ6sDMiQtIEoF9E\nTE6KxSuAYRFRlRxnKHAf0Av4u4h4pE4clRFR0ECcE4GJAF26dB004/Z7mioFrUb3jrC9KtdRNI2i\nnp0PWV+zZg1r167lxhtvpKysjKVLlzJ79uxD9lmyZAn79u1j/Pjxh41XWVlJQUHqH682zblJ59yk\nc27SOTfp2mpuRowYsS4iBh9pv7ydWUy8GhEbASRtAp5KCsCNQCEwF5hBZsbxOmBp0u9MYKmkM8jM\nLr6aNeajBwpFgIh4BugvqR+wUNIvImJPYwOMiPnAfICzeveJeRvzPaXH3+SiGlpLXsrHFB+y/sQT\nT7Bu3TrGjRvHnj172LVrF/feey+LFy8+uE/v3r25/PLLWbhw4WHjlZaWUlxcfFi7OTcNcW7SOTfp\nnJt0zk3D8v2exb1Zy/uz1veTKXTXAH0kdQWuAh5Ott8J3BURRcBXyFxiPmB3fQeKiBeBSuC8Jove\nWr3Zs2dTUVFBeXk5Dz74IBdddBGLFy9my5YtB/cpKSmhb9++OYzSzMzs6LXo6Z5klvER4HvAixGx\nI9nUGXg9WR6b1l/S2cDWiKiR1AvoC5Q3Y8jWRkydOpXNmzfTrl07evXqxd13353rkMzMzI5Kiy4W\nE0vJ3M84LqttJrBM0tvAKuDslL7DgKmSqsnMVv5DRLwFIGku8DfAKZIqgHsjYmZDgXQ8oT2bU97D\n15aVlpYedvm2NSouLj54GWP58uW5DcbMzKyJ5G2xGBHlZF0Sjohx9W2LiGcB1elbApTUM+bMOuuL\ngEUpx78JuOnoojczMzNrHfL9nkUzMzMzyyEXi2ZmZmaWysWimZmZmaVysWhmZmZmqVwsmpmZmVkq\nF4tmZmZmlsrFolkTqK2t5YILLmDUqFEATJ8+nQEDBjBw4EAuvfRS/vjHP+Y4QjMzs6OTt+9ZPB4k\njQTmkPl+9D5gSkSsknQKsAz4MFALPBYRU480XlV1LYVTVzZnyC3S5KIaxrWSvJSnvHT9jjvuoF+/\nfuzatQuAKVOmcMsttwDwgx/8gFmzZvkrLmZm1iK12ZlFSR2At4Arkm9Ij+XQF3TfFhF9gQuAT0q6\nLAdhWgtQUVHBypUrmTBhwsG2Tp06HVzevXs3kurramZmlvfydmZRUiHwOLAW+Esyn/RbANwMdAPG\nAA8BAyNiZ9JnC5lP+A0BvklmxnAHMCYitkuaSWa2sDfwh4j4QtYhNwEdJZ0UEe8CqwEiYp+k54Az\nm/N8reWaNGkSc+fO5Z133jmkfdq0aTzwwAN07tyZ1atX5yg6MzOzY5PvM4t9gHlA3+T3N2SKwa8D\n/0Lmk35XA0gaCrwWEduB/wQ+EREXAA9y6Gf7PgpcUqdQBLgGeC4i9mY3SjoNuAJ4qmlPzVqDFStW\n0K1bNwYNGnTYtltvvZWtW7cyZswY7rrrrhxEZ2ZmduwUEbmOoV7JzOKTEXFOsv4A8ERELJHUG3gY\n+AdgRkR8VtL3gd9FxD2SisgUmWeQmV18NdlnJhARcXOdY/UHHgUujYjfZ7V3AB5Ljnt7SpwTgYkA\nXbp0HTTj9nuaLAetRfeOsL0q11E0jaKenQ9Zv+eee/jlL39J+/bt2bdvH++++y6f+tSnmDZt2sF9\ntm/fztSpU1mwYMFh41VWVlJQUNDscbdEzk065yadc5POuUnXVnMzYsSIdREx+Ej75e1l6ET2LN/+\nrPX9ZGJfA/SR1BW4Cvh2sv1O4HsR8aikYmBm1ji7sw8g6UzgEeCL2YViYj6wJa1QBIiI+cl+nNW7\nT8zbmO8pPf4mF9XQWvJSPqb4kPXi4vfWS0tLue2221ixYgVbtmzhnHPOAeDOO+9k0KBBh+yb3ae+\ndnNuGuLcpHNu0jk36ZybhrXov8EjIiQ9AnwPeDEidiSbOgOvJ8tj0/onl5hXAlMj4uk6276djDOh\nvr5mDZk6dSqbN2+mXbt29OrVy09Cm5lZi9Wii8XEUjIPv4zLapsJLJP0NrAKODul7z+SuS9yhqQZ\nSdulZC5dTwNeAp5LnmS9KyLubergrfUoLi4++C/T5cuX5zYYMzOzJpK3xWJElAPnZa2Pq29bRDwL\nqE7fEjIPv9Qdc2ad9W/z3qXrut73u046ntCezSnv4WvLSktLD7t8a2ZmZi1Dvj8NbWZmZmY55GLR\nzMzMzFK5WDQzMzOzVC4WzczMzCyVi0UzMzMzS+Vi0czMzMxSuVg0awK1tbVccMEFjBo1CoDp06cz\nYMAABg4cyKWXXsof//jHHEdoZmZ2dPL2PYtNSdLjZL4T3QH4D+D6iKjN2n4N8DPg48l7G5FUC2xM\ndvlDRFx5pONUVddSOHVlU4ff4k0uqmFcK8lLecp7NO+44w769evHrl27AJgyZQq33HILAD/4wQ+Y\nNWuWv+JiZmYtUqueWVRGO2B0RJxP5kXeXYG/ztrnVOAG4Jk63asiYmDyO2KhaG1XRUUFK1euZMKE\n974M2alTp4PLu3fvJvkKkJmZWYvTImYWJc0BtkbED5P1mUANMAI4HTgB+GZElEgqBJ4gU/wNAi6P\niNeSoTqQ+ZRfZA1/C/AdYEqzn4i1SpMmTWLu3Lm88847h7RPmzaNBx54gM6dO7N69eocRWdmZnZs\nWsrM4lJgdNb6aGAhcHVEfIxM0ThP703fnAP8KCL6HygUJT0BvAG8Q+aSM5I+BnwoIuq7RnqypOck\nrZV0VbOclbV4K1asoFu3bgwaNOiwbbfeeitbt25lzJgx3HXXXTmIzszM7NgpIo68Vx6TbxHQAAAg\nAElEQVSQ9CJwMZnLyD8CioHvA8OB/cBHgLOBk4HVEXF2PWOcDCwB7gaeAlYB4yKiXFIp8PWsexZ7\nRsTrknon+10cEb+vZ8yJwESALl26Dppx+z1NedqtQveOsL0q11E0jaKenQ9Zv+eee/jlL39J+/bt\n2bdvH++++y6f+tSnmDZt2sF9tm/fztSpU1mwYMFh41VWVlJQUNDscbdEzk065yadc5POuUnXVnMz\nYsSIdREx+Ej7taRicRbwFvBB4P8Bu4DLgL+NiGpJ5WQKSIAVEXFeyjhfBIYA04DfA5XJpg8CfwKu\nPFAwZvW5PxnzZw3FeFbvPtFu9B3v+9xau8lFNczb2CLueDiitAdcAEpLS7nttttYsWIFW7Zs4Zxz\nzgHgzjvv5Ne//jU/+9nhf3xKS0spLi5urnBbNOcmnXOTzrlJ59yka6u5kdSoYrEl/Q2+FLgH6AJ8\nmsyl6DeSQnEE0Ku+TpIKgFMjYpukDsBfAf8REX9OxjqwXynJzKKk04F3I2KvpC7AJ4G5zXhu1spM\nnTqVzZs3065dO3r16uUnoc3MrMVqMcViRGxKnlx+PSn8lgCPSdoIPAu8lNL1L4BHJZ1E5h7N1WQu\nQzekH/BjSfuTPnMi4ndNciLWahUXFx/8l+ny5ctzG4yZmVkTaTHFIkBEFGUtvwVcmLLreVn7bQc+\n3oixi7OW/wsoSt+7fh1PaM/mBi5TtlWlpaWUjynOdRhmZmZ2FN7309CSTpc0oDmCMTMzM7P80qhi\nUVKppE6S/gfwHHCPpO81b2hmZmZmlmuNnVnsHBG7gP8JPBARQ4FLmi8sMzMzM8sHjS0WO0g6g8wT\nyCuaMR4zMzMzyyONLRZnkfmE3u8j4rfJi6q3NF9YZmZmZpYPGvU0dEQsA5Zlrf83cE1zBWVmZmZm\n+aGxD7icK+kpSS8k6wMkfbN5QzMzMzOzXGvsexbvAaYAPwaIiOcl/R/g280VWEtUVV1L4dSVuQ4j\n70wuqmFcK8lL2uf+amtrGTx4MD179mTFihVMnz6dkpIS2rVrR7du3bj//vvp0aPHcY7WzMzs2DX2\nnsVTIuI3ddpqmjqY403SEEllyW+DpKuT9lMkrZT0kqRNkubkOlbLb3fccQf9+vU7uD5lyhSef/55\nysrKGDVqFLNmzcphdGZmZkevscXiW5I+DASApGuBbc0W1XGQfCf6BWBwRAwEPkvmE38HZltvi4i+\nwAXAJyVdlqNQLc9VVFSwcuVKJkyYcLCtU6dOB5d3796NpFyEZmZmdswaexn6emA+0FfS68CrwJhm\niwqQVAg8DqwF/hL4LbAAuBnolhz/IWBgROxM+mwBhgFDgG8CJwI7gDERsV3STODDQG/gDxHxhaxD\nnkxSDEfEu2S+IU1E7JP0HHBm852ttWSTJk1i7ty5vPPOO4e0T5s2jQceeIDOnTuzevXqHEVnZmZ2\nbBQRDe8gtQOujYiHJP0F0C4i3mmwU1MElikWXyEzs7eJTLG4AfgycCUwHngNKIuIBZKGArdGxCWS\nTgd2RkRImgD0i4jJSbF4BTAsIqqS4wwF7gN6AX8XEY/UieM0Ml+tuSR5CrxunBOBiQBdunQdNOP2\ne5o2Ea1A946wvSrXUTSNop6dD1lfs2YNa9eu5cYbb6SsrIylS5cye/bsQ/ZZsmQJ+/btY/z48YeN\nV1lZSUFBQbPG3FI5N+mcm3TOTTrnJl1bzc2IESPWRcTgI+13xJnFiNgv6SbgoYjY3STRNd6rEbER\nQNIm4KmkANwIFAJzgRlkZhyvA5Ym/c4EliYvEj+RzEzoAY8eKBQBIuIZoL+kfsBCSb+IiD3JMTsA\nPwV+UF+hmPSfT2bWlbN694l5Gxs7Wdt2TC6qobXkpXxM8SHrTzzxBOvWrWPcuHHs2bOHXbt2ce+9\n97J48eKD+/Tu3ZvLL7+chQsXHjZeaWkpxcXFh7Wbc9MQ5yadc5POuUnn3DSssfcs/krS1yV9SNL/\nOPBr1sgy9mYt789a30+m0F0D9JHUFbgKeDjZfidwV0QUAV8hc4n5gHoL3oh4EagEzstqng9siYjb\nj/E8rJWaPXs2FRUVlJeX8+CDD3LRRRexePFitmx57531JSUl9O3bN4dRmpmZHb3GTvd8Pvnv9Vlt\nQebev5xJZhkfAb4HvBgRO5JNnYHXk+Wxaf0lnQ1sjYgaSb2AvkB5su3byTgT0vqbpZk6dSqbN2+m\nXbt29OrVi7vvvjvXIZmZmR2Vxn7B5ezmDuQYLCVzP+O4rLaZwDJJbwOrgLT4hwFTJVWTma38h4h4\nS9KZwDTgJeC55EnWuyLi3oYC6XhCezanvIevLSstLT3s8m1rVFxcfPAyxvLly3MbjJmZWRNpVLEo\n6Yv1tUfEA00bziFjl5N1STgixtW3LSKeBVSnbwlQUs+YM+usLwIW1bNfRd0xzczMzNqixl6G/njW\n8snAxWSeEG62YtHMzMzMcq+xl6H/KXs9eZ3Mg80SkZmZmZnljcY+DV3XbtLvAzQzMzOzVqKx9yw+\nRvJ1EzIF5keBZc0VlJmZmZnlh8bes3hb1nIN8FryEIiZmZmZtWKNvQx9eUT8Ovk9HREVkr7TrJGZ\nmZmZWc41dmZxJPCNOm2X1dPWplVV11I4dWWuw8g7k4tqGNdK8lKe8h7N2tpaBg8eTM+ePVmxYgXT\np0+npKSEdu3a0a1bN+6//3569OhxnKM1MzM7dg3OLEr6++Q7zB+R9HzW71Xg+eMTYvORNERSWfLb\nIOnqevZ5VNILuYjPWo477riDfv36HVyfMmUKzz//PGVlZYwaNYpZs2blMDozM7Ojd6TL0P8HuAJ4\nNPnvgd+giPjbZo6tWUnqALwADI6IgcBngR8n7Qf2+Z9kvhdtlqqiooKVK1cyYcJ7X4bs1KnTweXd\nu3eTfAXIzMysxWmwWIyIP0dEeUR8ISJeA6rIPBVdIOms5gxMUqGklyTdL+llSUskXSLpaUlbklnB\n8uSdjwf6bJHUXdIVkp6RtF7SryR1T7bPlLRI0tPAooh4NyJqku4n894T30gqAP4Z+HZznqe1fJMm\nTWLu3Lm0a3fo/07Tpk3jQx/6EEuWLPHMopmZtViKiCPvJF0BfA/oAbwB9AJejIj+zRaYVAi8AlwA\nbCLz/ecNwJeBK4HxwGtAWUQskDQUuDUiLpF0OrAzIkLSBKBfREyWNJPMzOiwiKhKjjMUuC85p7+L\niEeS9u8D/w6sB1ZExMFPD9aJcyIwEaBLl66DZtx+T5PnoqXr3hG2V+U6iqZR1LPzIetr1qxh7dq1\n3HjjjZSVlbF06VJmz559yD5Llixh3759jB8//rDxKisrKSgoaNaYWyrnJp1zk865SefcpGuruRkx\nYsS6iBh8pP0a+4DLt4FPAL+KiAskjQCOx2XoVyNiI4CkTcBTSQG4ESgE5gIzgAXAdcDSpN+ZwFJJ\nZwAnAq9mjfnogUIRICKeAfpL6gcslPQLoC/w4Yi4MSlaU0XEfGA+wFm9+8S8jY1NadsxuaiG1pKX\n8jHFh6w/8cQTrFu3jnHjxrFnzx527drFvffey+LFiw/u07t3by6//HIWLlx42HilpaUUFxcf1m7O\nTUOcm3TOTTrnJp1z07DGvjqnOiJ2AO0ktYuI1cARK9EmsDdreX/W+n4yhe4aoI+krsBVwMPJ9juB\nuyKiCPgKmUvMB+yu70AR8SKZ+xPPAy4EBksqB/4TOFdSaROcj7Uys2fPpqKigvLych588EEuuugi\nFi9ezJYtWw7uU1JSQt++fXMYpZmZ2dFr7HTPzuQevv8Alkh6g5Si63hKZhkfIXOJ/MWkoAXoDLye\nLI9N6y/pbGBrRNRI6kVmRrE8Ip4F/i3Zp5DMZejiZjkJa5WmTp3K5s2badeuHb169eLuu+/OdUhm\nZmZHpbHF4ufIPNwyCRhDphjLlzv2l5K5n3FcVttMYJmkt4FVpH/HehgwVVI1mdnKf4iIt442kI4n\ntGdzynv42rLS0tLDLt+2RsXFxQcvYyxfvjy3wZiZmTWRRhWLEbE7mXk7JyIWSjoFaN+cgUVEOZlL\nwgfWx9W3LZkFVJ2+JUBJPWPOrLO+CFj0fuIwMzMza0sadc+ipP8F/Az4cdLUE/h5cwVlZmZmZvmh\nsQ+4XA98EtgFEBFbgG7NFZSZmZmZ5YfGFot7I2LfgZXkKydHfkGjmZmZmbVojS0Wfy3pX4COkkYC\ny4DHmi8sMzMzM8sHjS0WpwJvAhvJvLfw/wLfbK6gzMzMzCw/NPg0tKSzIuIPEbEfuCf5mZmZmVkb\ncaRX5/wc+BiApOURcU3zh9RyVVXXUjh1Za7DyDuTi2oY1wLzUl7POzNra2sZPHgwPXv2ZMWKFSxb\ntoyZM2fy4osv8pvf/IbBg4/Hh43MzMyOnyNdhs5+f2Hv5gwkn0gqlFQlqSz5+fMbBsAdd9xBv379\nDq6fd955PPzwwwwfPjyHUZmZmTWfIxWLkbLcaiVPegP8PiIGJr+v5jQoywsVFRWsXLmSCRMmHGzr\n168fH/nIR3IYlZmZWfM6UrF4vqRdkt4BBiTLuyS9I2nX8QiwrmTW7yVJ90t6WdISSZdIelrSFklD\nJJVLOi2rzxZJ3SVdIekZSesl/UpS92T7TEmLJD3NEb7oYm3XpEmTmDt3Lu3aNfa5MDMzs5avwb/1\nIqJ9RHSKiFMjokOyfGC90/EKsh59gHlA3+T3N2S+8/x14F/IfOrvagBJQ4HXImI78J/AJyLiAuBB\n4KasMT8KXBIRX0jWz04uQf9a0qeOwzlZHluxYgXdunVj0KBBuQ7FzMzsuGrUt6Hz0KsRsRFA0ibg\nqYgISRuBQmAuMANYAFwHLE36nQkslXQGcCLwataYj0ZEVbK8DTgrInZIGgT8XFL/iDhsNlXSRGAi\nQJcuXZlRVNPEp9ryde+YecilpSktLT24/NOf/pRf/vKXPPzww+zbt493332XkSNHMm3aNAB27tzJ\nunXrqKysfF/HqKysPOQ49h7nJp1zk865SefcpHNuGtZSi8W9Wcv7s9b3kzmnNUAfSV2Bq4BvJ9vv\nBL4XEY9KKgZmZo2z+8BCROw9MGZErJP0e+Bc4Nm6gUTEfGA+wFm9+8S8jS01pc1nclENLTEv5WOK\nDy4XF7+3XFpaym233caKFSsOtp122mkMGjTofT8NXVpaesjY9h7nJp1zk865SefcpHNuGtYqb76K\niAAeAb4HvBgRO5JNnYHXk+Wxaf0ldZXUPlnuDZwD/HfzRWwt1SOPPMKZZ57JmjVr+Ku/+is+85nP\n5DokMzOzJtXypnsabynwW2BcVttMYJmkt4FVwNkpfYcDsyRVk5mt/GpE/Kn5QrWWpLi4+OC/QK++\n+mquvvrq3AZkZmbWjFpcsRgR5cB5Wevj6tsWEc9y6HsiiYgSMg+/1B1zZp315cDy9xtbxxPas7me\nFzm3daWlpYdc0jUzM7OWo1VehjYzMzOzpuFi0czMzMxSuVg0MzMzs1QuFs3MzMwslYtFMzMzM0vl\nYtHMzMzMUrlYNKtjz549DBkyhPPPP5/+/fvzrW99C4ANGzZw4YUXUlRUxBVXXMGuXYd9/dHMzKzV\nycv3LEp6HDiDTHz/AVwfEbXJttFkXq4dwIaI+BtJvch8saUdcAJwZ0Tcnez/H8CpydDdgN9ExFWS\nPgfcQual2zXApIj4z6TPjcCE5BgbgfERsedIcVdV11I4dWUTZKB1mVxUw7g8zkt5nXdjnnTSSaxa\ntYqCggKqq6sZNmwYl112Gf/0T//Ebbfdxqc//Wnuu+8+vvvd73LLLbfkKGozM7PjI69mFpXRDhgd\nEeeTecF2V+Cvk+3nAP8b+GRE9AcmJV23ARdGxEBgKDBVUg+AiPhURAxMtq0BHk76PAWcn7R/Cbg3\nOUZP4GvA4Ig4D2gPXNfMp255RBIFBQUAVFdXU11djSRefvllhg8fDsDIkSNZvvx9v7fdzMysxWmW\nYlHSHEnXZ63PlPRNSU9Jek7SxmRmD0mFkjZLegB4AfhQRBy4vtcBOJHMDB/A/wJ+GBFvA0TEG8l/\n90XE3mSfk+o7L0mdgIuAnyd9KpNvSAP8RdYxDhy3o6QOwCnAH48pIdbi1NbWMnDgQLp168bIkSMZ\nOnQo/fv3p6Qk8wGgZcuWsXXr1hxHaWZm1vyaa2ZxKTA6a300sBC4OiI+BowA5kk68Dm+c4AfRUT/\niHgNQNITwBvAO8DPkv3OBc6V9LSktZI+e+AAkj4k6XlgK/CdiKhb4F0FPJVViCLpakkvASvJzC4S\nEa8DtwF/IDNj+eeI+OUx5sNamPbt21NWVkZFRQW/+c1veOGFF7jvvvv40Y9+xKBBg3jnnXc48cQT\ncx2mmZlZs9N7k2tNPLD0InAxmcvIPwKKge8Dw8ncJ/gR4GzgZGB1RJxdzxgnA0uAuyPiSUkrgGoy\nxeeZwL8DRRGxM6tPDzKzh1dExPas9l8A9ybffa57nOHAjIi4RNLpZL4L/XlgJ7AM+FlELE45z4nA\nRIAuXboOmnH7PY3OUVvRvSNsr8p1FOmKenZucPvChQs5+eST+fznP3+wbevWrfzrv/4r//Zv/3ZM\nx66srDx4ydsO5dykc27SOTfpnJt0bTU3I0aMWBcRg4+0X3M+4LIMuBb4IJmZxjFkCsdBEVEtqZxM\noQiwu74BImKPpBLgc8CTQAXwTERUA69KepnMrORvs/r8UdILwKdIZiQldQGGAFenHOffJfVO9hsB\nvBoRbyZ9Hwb+Eqi3WIyI+cB8gLN694l5G/PymaGcmlxUQz7npXxM8SHrb775JieccAKnnXYaVVVV\nTJ8+nW984xt89KMfpVu3buzfv59x48YxZcoUiouL6x2zsUpLS495jNbKuUnn3KRzbtI5N+mcm4Y1\n5wMuS8k8GHItmcKxM/BGUiiOAHrV10lSgaQzkuUOwF8BLyWbf05mhvJAAXgu8N+SzpTUMWk/HRgG\nbM4a9lpgRfYTzZL6HLgMLuljZO513EHm8vMnJJ2SbL8YePEYc2EtyLZt2xgxYgQDBgzg4x//OCNH\njmTUqFH89Kc/5dxzz6Vv37706NGD8ePH5zpUMzOzZtds0z0RsUnSqcDrEbFN0hLgMUkbgWd5rwCs\n6y+ARyUdeFBlNXB3su0J4FJJvwNqgSkRsUPSSDL3QAYg4LaI2Jg15nXAnDrHuQb4oqRqoAr4fPLA\nyzOSfgY8R+aVOutJZg6tbRgwYADr168/rP2GG27ghhtuyEFEZmZmudOs1wYjoihr+S3gwpRdz8va\nbzvw8ZTxAvjn5Jfd/iQwoIE4iutp+w7wnZT9vwV8K228NB1PaM/mOu/ss8z0ft1LvWZmZtYy5NV7\nFs3MzMwsv7hYNDMzM7NULhbNzMzMLJWLRTMzMzNL5WLRzMzMzFK5WDQzMzOzVC4WzczMzCxV/n6D\nrQWqqq6lcOrKXIeRdyYX1TAuz/JSXud9mHv27GH48OHs3buXmpoarr32Wm6++WbKysr46le/yp49\ne+jQoQM/+tGPGDJkSI6iNjMzO/5azcyipMclbZC0SdLdkton7SdJWirpFUnPSCpM2ntJek5SWdLn\nq1lj/WOyfySfFbRW7qSTTmLVqlVs2LCBsrIyHn/8cdauXctNN93Et771LcrKypg1axY33XRTrkM1\nMzM7rlp8saiMdsDoiDifzNdgugJ/nezyZeDtiOgDfJ/3vtqyDbgwIgYCQ4Gpknok254GLgFeO06n\nYTkmiYKCAgCqq6uprq5GEpLYtWsXAH/+85/p0aNHQ8OYmZm1OnlzGVrSHGBrRPwwWZ9J5tvMI4DT\ngROAb0ZESTI7+ATwDDAIuDwiDhR2HYATgUjWPwfMTJZ/BtwlSRGxL+vwB75DDUBErE9iaNJztPxW\nW1vLoEGDeOWVV7j++usZOnQot99+O5/5zGf4+te/zv79+/mv//qvXIdpZmZ2XCnzueXck3QBcHtE\nfDpZ/x3wGeDPEbEruRy8FjgH6AX8N/CXEbE2a4wngCHAL4C/i4haSS8An42IimSf3wNDI+ItSR8C\nVgJ9gCkHCtWs8cqBwcl3rdPinghMBOjSpeugGbff0wTZaF26d4TtVbmO4lBFPTunbqusrGT69Ol8\n7Wtf47HHHuP888/n05/+NKtXr2bFihXMmzevyeKorKw8OKNph3Ju0jk36ZybdM5NuraamxEjRqyL\niMFH2i9vikUASS8CF5O5jPwjoJjMpePhwH7gI8DZwMnA6og4u54xTgaWAHdHxJMNFYtZfXoAPweu\niIjtWe3lHKFYzHZW7z7RbvQd7/e0W73JRTXM25g3k9jA4Q+41DVr1ixOOeUUbrnlFnbu3IkkIoLO\nnTsfvCzdFEpLSykuLm6y8VoT5yadc5POuUnn3KRrq7mR1KhiMd/uWVwGXAt8HlgKjCFTOA5K7i3c\nTqZQBNhd3wARsQcoIXP5GeB14EMAkjoAnYEddfr8EXgB+FQTnou1IG+++SY7d+4EoKqqiieffJK+\nffvSo0cPfv3rXwOwatUqzjnnnFyGaWZmdtzl13RPpkC8B+gCfBoYDbwREdWSRpC5/HwYSQXAqRGx\nLSkI/wr4j2Tzo8BYYA2ZQnRVRISkM4EdEVEl6XRgGJlZTGuDtm3bxtixY6mtrWX//v2MHj2aUaNG\ncdppp3HDDTdQU1PDySefzPz583MdqpmZ2XGVV8ViRGySdCrwelL4LQEek7QReBZ4KaXrXwCPSjrw\noMpq4O5k20+ARZJeAf4EXJe09wPmSQpAwG0RsRFA0teAm4APAs9L+r8RMeFI8Xc8oT2bj3B5sy0q\nLS2lfExxrsNo0IABA1i/fv1h7cOGDWPdunU5iMjMzCw/5FWxCBARRVnLbwEXpux6XtZ+24GPp4y3\nh/deo5Pd/iQwIKXPD4AfND5qMzMzs9Yp3+5ZNDMzM7M84mLRzMzMzFK5WDQzMzOzVC4WzczMzCyV\ni0UzMzMzS+Vi0czMzMxS5d2rc1qyqupaCqeuzHUYeWdyUQ3jmikvdT/bt2fPHoYPH87evXupqanh\n2muv5eabb2b69OmUlJTQrl07unXrxv3330+PHj2aJSYzM7PWxDOLDZB0lqRKSV/PdSzWOCeddBKr\nVq1iw4YNlJWV8fjjj7N27VqmTJnC888/T1lZGaNGjWLWrFm5DtXMzKxFcLFYR/K5wAO+B/wiV7HY\n+yeJgoICAKqrq6murkYSnTp1OrjP7t27kZSrEM3MzFqUFlcsSiqU9JKk+yW9LGmJpEskPS1pi6Qh\nksolnZbVZ4uk7pKukPSMpPWSfiWpe7J9pqRFkp4GFiVtVwGvAptycqJ21Gpraxk4cCDdunVj5MiR\nDB06FIBp06bxoQ99iCVLlnhm0czMrJEUEbmO4X2RVAi8AlxAppD7LbAB+DJwJTAeeA0oi4gFkoYC\nt0bEJZJOB3ZGREiaAPSLiMmSZgJXAMMiokpSAfAkMBL4OlAZEbelxDMRmAjQpUvXQTNuv6eZzrzl\n6t4Rtlc1z9hFPTunbqusrGT69Ol87Wtf4+yzzz7YvmTJEvbt28f48eObJ6j3obKy8uBMqB3KuUnn\n3KRzbtI5N+naam5GjBixLiIGH2m/lvqAy6sRsRFA0ibgqaQA3AgUAnOBGcAC4DpgadLvTGCppDOA\nE8nMHB7waEQcKGlmAt+PiMojXa6MiPnAfICzeveJeRtbakqbz+SiGporL+Vjihvc/txzz7Fjx45D\nCsPevXtz+eWXs3DhwmaJ6f0oLS2luLg412HkJecmnXOTzrlJ59ykc24a1uIuQyf2Zi3vz1rfT6YA\nXgP0kdQVuAp4ONl+J3BXRBQBXwFOzhpnd9byUGCupHJgEvAvkv6xqU/Cmt6bb77Jzp07AaiqquLJ\nJ5+kb9++bNmy5eA+JSUl9O3bN1chmpmZtSitchosmWV8hMwDKi9GxI5kU2fg9WR5bAP9P3VgOblE\nXRkRdzVTuNaEtm3bxtixY6mtrWX//v2MHj2aUaNGcc0117B582batWtHr169uPvuu3MdqpmZWYvQ\nKovFxFIy9zOOy2qbCSyT9DawCjj78G5Hr+MJ7dlc571/lpneP9Ll4qYyYMAA1q9ff1j78uXLj8vx\nzczMWpsWVyxGRDlwXtb6uPq2RcSzgOr0LQFK6hlzZgPHS91mZmZm1tq11HsWzczMzOw4cLFoZmZm\nZqlcLJqZmZlZKheLZmZmZpbKxaKZmZmZpXKxaGZmZmapWtyrc/JZVXUthVNX5jqMvDO5qIZxTZiX\n8jrvstyzZw/Dhw9n79691NTUcO2113LzzTczZcoUHnvsMU488UQ+/OEPs2DBAk477bQmi8PMzKwt\naNMzi5KGSCpLfhskXZ217QuSNkp6XtLjkrrkMlZLd9JJJ7Fq1So2bNhAWVkZjz/+OGvXrmXkyJG8\n8MILPP/885x77rnMnj0716GamZm1OG22WJTUAXgBGBwRA4HPAj+W1CHZdgcwIiIGAM8D/jZ0npJE\nQUEBANXV1VRXVyOJSy+9lA4dMpPnn/jEJ6ioqMhlmGZmZi1S3haLkgolvSTpfkkvS1oi6RJJT0va\nkswKlks6LavPFkndJV0h6RlJ6yX9SlL3ZPtMSYskPQ0sioh3I6Im6X4yEAeGSn5/IUlAJ+CPx+/s\n7f2qra1l4MCBdOvWjZEjRzJ06NBDtt93331cdtllOYrOzMys5VJEHHmvHJBUCLwCXABsIvOd5w3A\nl4ErgfHAa0BZRCyQNBS4NSIukXQ6sDMiQtIEoF9ETJY0E7gCGBYRVclxhgL3Ab2Av4uIR5L2a5P2\n3cAWMrOMtfXEORGYCNClS9dBM26/pznS0aJ17wjbq5puvKKenVO3VVZWMn36dL72ta9x9tmZT38v\nXryYzZs3M2vWLDK1f/6orKw8OCtqh3Ju0jk36ZybdM5NuraamxEjRqyLiMFH2i/fH3B5NSI2Akja\nBDyVFIAbgUJgLjADWABcByxN+p0JLJV0BnAi8GrWmI8eKBQBIuIZoL+kfsBCSVSZwEUAACAASURB\nVL8AaoG/J1Oo/jdwJ/C/gW/XDTAi5gPzAc7q3Sfmbcz3lB5/k4tqaMq8lI8pbnD7c889x44dOxg/\nfjz3338/mzZt4qmnnuKUU05pshiaSmlpKcXFxbkOIy85N+mcm3TOTTrnJp1z07C8vQyd2Ju1vD9r\nfT+ZQncN0EdSV+Aq4OFk+53AXRFRBHyFzCXmA3bXd6CIeBGoBM4DBiZtv4/M1OtDwF82xQlZ03vz\nzTfZuXMnAFVVVTz55JP07duXxx9/nLlz5/Loo4/mZaFoZmbWErToabBklvER4HvAixGxI9nUGXg9\nWR6b1l/S2cDWiKiR1AvoC5STmY38qKSuEfEmMBJ4sZlOw47Rtm3bGDt2LLW1tezfv5/Ro0czatQo\n+vTpw969exk5ciSQecjl7rvvznG0ZmZmLUuLLhYTS8nczzguq20msEzS28Aq4OyUvsOAqZKqycxW\n/kNEvAUg6Wbg35Ntr9UZ3/LIgAEDWL9+/WHtr7zySg6iMTMza13ytliMiHIyl4QPrI+rb1tEPEvm\nyeXsviVAST1jzqyzvghYlHL8u4H3NQ3V8YT2bK7zwmjL3AtypPsMzczMLD/l+z2LZmZmZpZDLhbN\nzMzMLJWLRTMzMzNL5WLRzMzMzFK5WDQzMzOzVC4WzczMzCyVi0VrMfbs2cOQIUM4//zz6d+/P9/6\n1rcA+NOf/sTIkSM555xzGDlyJG+//XaOIzUzM2s98vY9i++HpFOAZcCHyXzX+bGImJps+2dgAlAD\nvAl8KSJekzQQ+DegU9Ln1ohYmvT5CTCYzPsbXwbGRUTlkeKoqq6lcOrKpj69Fm9yUQ3jjjIv5Vnv\nrTzppJNYtWoVBQUFVFdXM2zYMC677DIefvhhLr74YqZOncqcOXOYM2cO3/nOd5oqfDMzszatxc8s\nSjrwQu7bIqIvcAHwSUmXJe3rgcERMQD4GTA3aX8X+GJE9Ac+C9wu6bRk240RcX7S5w/APx6Pc7GG\nSaKgoACA6upqqqurkURJSQljx2a+6jh27Fh+/vOf5zJMMzOzViVvikVJcyRdn7U+U9I3JT0l6TlJ\nGyV9LtlWKGmzpAeAF4CuEbEaICL2Ac8BZybrqyPi3WTYtVntL0fElmT5j8AbQNdkfVdyHAEdgWju\n87fGqa2tZeDAgXTr1o2RI0cydOhQtm/fzhlnnAHABz/4QbZv357jKM3MzFqPvCkWyXzjeXTW+mhg\nIXB1RHwMGAHMy5pJPAf4UUT0j4jXDnRKZgevAJ6q5xhfBn5Rt1HSEOBE4PdZbQuA/wf0Be48hvOy\nJtS+fXvKysqoqKjgN7/5DS+88MIh2yXx3h8RMzMzO1Z5c89iRKyX1E1SDzIzfG+TKda+L2k4sB/o\nCXRPurwWEWuzx5DUAfgp8IOI+O862/6WzH2In67TfgaZ70OPjYj9WfGMl9SeTKH4eWBBfXFLmghM\nBOjSpSszimqO5vRbte4dM/ctHo3S0tLUbYWFhfzwhz+kU6dOLF++nA984APs2LGDU089tcF++aSy\nsrLFxHq8OTfpnJt0zk065yadc9OwvCkWE8uAa4EPkplpHEOmcBwUEdWSyoGTk31319N/PrAlIm7P\nbpR0CTAN+HRE7M1q7wSsBKbVLTwBIqJW0oPATaQUixExPzkuZ/XuE/M25ltKc29yUQ1Hm5fyMcUH\nl998801OOOEETjvtNKqqqpg+fTrf+MY3KCgoYMuWLVxzzTXMmTOH6667juLi4tQx80lpaWmLifV4\nc27SOTfpnJt0zk0656Zh+VbZLAXuAbqQmQEcDbyRFIojgF5pHSV9G+hM5snn7PYLgB8Dn42IN7La\nTwQeAR6IiJ9ltQv4cES8kixfCbzUROdnx2Dbtm2MHTuW2tpa9u/fz+jRoxk1ahQXXngho0eP5ic/\n+Qm9evXioYceynWoZmZmrUZeFYsRsUnSqcDrEbFN0hLgMUkbgWdJKdoknUlm5vAl4LnknrW7IuJe\n4LtAAbAsaf9DRFxJphAdDnxA0rhkqHHA88DCZNZRwAbg75vhdO19GjBgAOvXrz+s/QMf+ABPPVXf\nLapmZmZ2rPKqWASIiKKs5beAC1N2PS9rvwoyhV19412S0r4YWJwy9icbFWwdHU9oz+as9wJaRmlp\n6SGXk83MzKzlyKenoc3MzMwsz7hYNDMzM7NULhbNzMzMLJWLRTMzMzNL5WLRzMzMzFK5WDQzMzOz\nVC4WzczMzCxV3r1nsSWrqq6lcOrKXIeRdyYX1TDuKPNSnvXeyj179jB8+HD27t1LTU0N1157LTff\nfDN/+tOf+PznP095eTmFhYU89NBDnH766U0VvpmZWZvWpmcWJX1A0mpJlZLuqrPt85Kel7RJ0ndy\nFaO956STTmLVqlVs2LCBsrIyHn/8cdauXcucOXO4+OKL2bJlCxdffDFz5szJdahmZmatRpstFiV1\nAPYA04Gv19n2ATKfCbw4IvoDH5R08fGP0rJJoqCgAIDq6mqqq6uRRElJCWPHjgVg7Nix/PznP89l\nmGZmZq1K3haLkgolvSTpfkkvS1oi6RJJT0vaImmIpHJJp2X12SKpu6QrJD0jab2kX0nqnmyfKWmR\npKeBRRGxOyL+k0zRmK03sCUi3kzWfwVcczzO2xpWW1vLwIED6datGyNHjmTo0KFs376dM844A4AP\nfvCDbN++PcdRmpmZtR75fs9iH+CvgS8BvwX+BhgGXAn8C1ACXA0skDQUeC0itkv6T+ATERGSJgA3\nAZOTMT8KDIuIqgaO+wrwEUmFQAVwFXBifTtKmghMBOjSpSszimqO/mxbqe4dM/ctHo3S0tLD2m6/\n/XYqKyuZPn06ffv2paam5pD9amtr6+2XjyorK1tMrMebc5POuUnn3KRzbtI5Nw3L92Lx1YjYCCBp\nE/BUUgBuBAqBucAMYAFwHbA06XcmsFTSGWSKvFezxnz0CIUiEfG2pL9PxtsP/Bfw4ZR95wPzAc7q\n3Sfmbcz3lB5/k4tqONq8lI8pTt323HPPsWPHDnr27MlHPvIRzjjjDLZt20aPHj0oLk7vl09KS0tb\nTKzHm3OTzrlJ59ykc27SOTcNy9vL0Im9Wcv7s9b3kyl01wB9JHUlM/v3cLL9TuCuiCgCvgKcnDXO\n7sYcOCIei4ihEXEhsBl4+ajPwprEm2++yc6dOwGoqqriySefpG/fvlx55ZUsXLgQgIULF/K5z30u\nl2GamZm1Ki16GiyZZXwE+B7wYkTsSDZ1Bl5PlscezdiSukXEG5JOB/4BGH3MAdsx2bZtG2PHjqW2\ntpb9+/czevRoRo0axYUXXsjo0aP5yU9+Qq9evXjooYdyHaqZmVmr0aKLxcRSMvczjstqmwksk/Q2\nsAo4O62zpHKgE3CipKuASyPid8Adks5PdpsVEUecWex4Qns2Z70X0DJKS0sbvJzcWAMGDGD9+vWH\ntX/gAx/gqaeeOubxzczM7HB5WyxGRDlwXtb6uPq2RcSzgOr0LSHz8EvdMWfW01aYcvwvHEXYZmZm\nZq1Kvt+zaGZmZmY55GLRzMzMzFK5WDQzMzOzVC4WzczMzCyVi0UzMzMzS+Vi0czMzMxS5e2rc1qi\nqupaCqeuzHUYeWdyUQ3j3kdeyrPeVbl161a++MUvsn37diQxceJEbrjhBjZs2MBXv/pVKisrKSws\nZMmSJXTq1Kk5wjczM2vT2vTMoqQhksqS3wZJV2dtO1HSfEkvS3pJ0jW5jLWt6tChA/PmzeN3v/sd\na9eu5Yc//CG/+93vmDBhAnPmzGHjxo1cffXVfPe73811qGZmZq1Smy0WJXUAXgAGR8RA4LPAj5N2\ngGnAGxFxLvBR4Ne5ibRtO+OMM/jYxz4GwKmnnkq/fv14/fXXefnllxk+fDgAI0eOZPny5bkM08zM\nrNXK22JRUmEyo3d/Mru3RNIlkp6WtCWZFSyXdFpWny2Suku6QtIzktZL+pWk7sn2mZIWSXoaWBQR\n70ZETdL9ZCCyQvgSMBsgIvZHxFvH6dQtRXl5OevXr2fo0KH079+fkpLMR3qWLVvG1q1bcxydmZlZ\n66SIOPJeOSCpEHgFuADYROb7zxuALwNXAuOB14CyiFggaShwa0RcIul0YGdEhKQJQL+ImCxpJnAF\nMCwiqpLjDAXuA3oBfxcRjyQF6EZgGVAM/B74x/j/7N17fFXlmff/z7dGC0Mw1uFggMHIgCVKKoKV\nUnkw+Snag7Zaj4hiFIfytD7DFKlFp0Njx7bWNkpr61QYBZufRWSqg6fSUTEdS9UpFJwIinbqtpRW\nqNYDQfQJ5Hr+2AvchKyAmmTvJN/365UXa933uu91reuvi3WvvVbE5lbinA5MB+jXr//YufMWdEQ6\nurSBvWHz9v0/vmJwyV5t27dvZ+bMmVx44YVMnDiR3//+99x00028/vrrnHDCCdx99927i8eupLGx\nkeLi4nyHUZCcm3TOTTrnJp1zk66n5qaqqmp1RBy3r+MK/QcuL0REA4CkdcAjSQHYAJQB1wNzgYXA\n+cCSZNwQYImkUuAg4IWcOe/dVSgCRMSTwNGSyoHbJf2MbF6GAL+KiFmSZgHfBS5qGWBEzAfmAwwd\nNjxqGwo9pZ3vioodvJu8ZKZU7rHf1NTEaaedxowZM5g1a9bu9qlTpwLw3HPPsW7dOior9xzXFdTX\n13fJuDuDc5POuUnn3KRzbtI5N20r2GXoxNs52805+81kC7rHgeGS+gNnAHcn/TcBP4iICuDzZJeY\nd9nW2oki4hmgERgFvAK8mTPfUmDM+70Ye/cigmnTplFeXr5HobhlyxYAmpubufbaa5kxY0a+QjQz\nM+vWCr1YbFNk19DvAW4AnomIV5KuEmBTsn1x2nhJR+z6QYukw4GRQCaZ9z6yS9AAJwHr2/0CbJ9W\nrlxJXV0dK1asYPTo0YwePZoHH3yQxYsXc+SRRzJy5EgGDRrEJZdcku9QzczMuqXusGa6hOzzjNU5\nbTXAUkmvAiuAI1LGTgDmSGoie7fyCzk/ZPkKUCdpHvBnss9Itqn3gQewIecdgZZVX1+/19Ly/pow\nYQJpz9XOnDnzfURlZmZm+6Ngi8WIyJBdEt61X91aX0SsAtRi7DJgr187RERNi/06oC7l/C8CE99b\n9GZmZmbdQ5dehjYzMzOzjuVi0czMzMxSuVg0MzMzs1QuFs3MzMwslYtFMzMzM0vlYtHMzMzMUhXs\nq3O6ou1NOymb80C+wyg4V1TsoHofecm0eD/lxo0bmTp1Kps3b0YS06dPZ+bMmaxdu5YZM2bw1ltv\nUVRUxM0338zxxx/fkeGbmZn1aD26WJS0HCglm4fHgC9GxE5JNwJVyWF/BQyIiEPyFGaPVFRURG1t\nLWPGjGHr1q2MHTuWSZMmceWVV/K1r32NT37ykzz44INceeWV1NfX5ztcMzOzbqtHFouSRPZF3udG\nxBvJ/r8B5wB3RsSXco79P8Cx+Ym05yotLaW0tBSAvn37Ul5ezqZNm5DEG2+8AcDrr7/OoEGD8hmm\nmZlZt9eli0VJ1wEbI+KHyX4NsIPsXcEPAQcCX42IZZLKgJ8DTwJjgU8lX2mBbB4OAlr7rtxk4Gsd\ndxW2L5lMhjVr1jBu3DjmzZvHqaeeyuzZs2lubuZXv/pVvsMzMzPr1pT23d2uQNKxwLyIODHZXw+c\nCrye3DHsBzwBjAAOB34HfDwinsiZ4+fA8cDPgIsiYmdO3+HJ+CG57S1imA5MB+jXr//YufMWtP+F\ndnEDe8Pm7W0fUzG4pNX27du3M3PmTC688EImTpzI97//fY455hhOPPFEHn30Ue6//35qa2s7IOrO\n0djYSHFxcb7DKEjOTTrnJp1zk865SddTc1NVVbU6Io7b13FdulgEkPQMcBLQH7gZqARuJPtd52bg\nw8ARQC/g0Yg4opU5egF3AD+KiIdy2r9CtlD8P/sTy9Bhw+MD537vfV1Pd3RFxQ5qG9q+id3yBy4A\nTU1NnHbaaZx66qnMmjULgJKSEl577TUkERGUlJTsXpbuiurr66msrMx3GAXJuUnn3KRzbtI5N+l6\nam4k7Vex2B1enbMUOBs4D1gCTCFbOI6NiNHAZrKFIsC21iaIiLeAZcBnW3SdDyzugJhtHyKCadOm\nUV5evrtQBBg0aBC/+MUvAFixYgUjRozIV4hmZmY9Qpd+ZjGxBFgA9ANOBM4FtkREk6QqssvPe5FU\nDPSNiD9JKgI+TfYX0bv6R5J97vHxDo7fWrFy5Urq6uqoqKhg9OjRAHzzm99kwYIFzJw5kx07dtCr\nVy/mz5+f50jNzMy6ty5fLEbEOkl9gU1J4XcHcJ+kBmAV8GzK0D7AvZI+SPYO66PAj3L6zyf7y+j9\nXqfvfeABbGhlObWnq6+vJzOl8l2NmTBhAmmpX716dTtEZWZmZvujyxeLABFRkbP9MjA+5dBROcdt\nBj7axpw17RWfmZmZWVfVHZ5ZNDMzM7MO4mLRzMzMzFK5WDQzMzOzVC4WzczMzCyVi0UzMzMzS+Vi\n0czMzMxSuVi0grFx40aqqqo46qijOProo/ne97KfTjzvvPMYPXo0o0ePpqysbPdLus3MzKzjdYv3\nLL5XkiYB1wEHAf8X+HJErEj6JgNXAwH8EbgweYdjqu1NOymb80DHBt0FXVGxg+pW8tLye9BFRUXU\n1tYyZswYtm7dytixY5k0aRJLlix5Z64rrqCkpKTDYzYzM7OsHntnMfnE38vA6clLvS8G6nL6vgdU\nRcRHgP8GLs9XrD1FaWkpY8aMAaBv376Ul5ezadOm3f0RwV133cXkyZPzFaKZmVmPU7DFoqQySc9K\nWiTpOUl3SDpZ0kpJz0s6XlJG0iE5Y56XNFDS6ZKelLRG0sOSBib9NZLqJK0E6iJiTUT8MRm+Duid\nfP5PyV8fSQIOJnt30TpJJpNhzZo1jBs3bnfbY489xsCBAxkxYkQeIzMzM+tZCrZYTAwHaoGRyd8F\nwARgNtkl4mXAmQCSxgEvJp/x+yXwsYg4FrgTuDJnzqOAkyOi5e2ps4DfRMTbEdEE/G+ggWyReBRw\na4dcoe2lsbGRs846i3nz5nHwwQfvbl+8eLHvKpqZmXUyRUS+Y2iVpDLgoYgYkez/GPh5RNwhaRhw\nN/AFYG5EfELSjcD6iFggqYJskVlK9nnEF5JjaoCIiGtanOto4F7glIj4H0kHAsuB6cDvgJuAlyLi\n2lbinJ4cR79+/cfOnbegvVPR5Q3sDZu3791eMXjvZw937NjBVVddxUc/+lHOPffc3e07d+7knHPO\n4ZZbbqF///4dGW6namxspLi4ON9hFCTnJp1zk865SefcpOupuamqqlodEcft67hC/4HL2znbzTn7\nzWRjfxwYLqk/cAawq5i7CbghIu6VVAnU5MyzLfcEkoYA9wBTI+J/kubRALv2Jd0FzGktwIiYD8wH\nGDpseNQ2FHpKO98VFTtoLS+ZKZV77EcEF198MSeccALz5s3bo2/58uVUVFRwzjnndGSona6+vp7K\nysp8h1GQnJt0zk065yadc5POuWlboS9Dtymyt0XvAW4AnomIV5KuEmDXLyMuThufPO/4ADAnIlbm\ndG0CjkqKUIBJwDPtGbvtbeXKldTV1bFixYrdr8p58MEHAbjzzju9BG1mZpYH3eE22BLg10B1TlsN\nsFTSq8AK4IiUsZeTfS5yrqS5SdspEfFHSdcA/ympCXixxfzWASZMmEDaYxGLFi3q3GDMzMwMKOBi\nMSIywKic/erW+iJiFdlfLueOXUb2xy8t56xpsX8t7yxdtzz2R8CP3k3MvQ88gA0t3h1o2dv7LZec\nzczMrGvo0svQZmZmZtaxXCyamZmZWSoXi2ZmZmaWysWimZmZmaVysWhmZmZmqVwsmpmZmVkqF4tW\nMDZu3EhVVRVHHXUURx99NN/73vcAOO+883a/pLusrIzRo0fnOVIzM7Oeo2Dfs9gZJE0CriP7/ej/\nC3w5IlYkfWOBRUBv4EFgZuzjQ9rbm3ZSNueBDo25K7qiYgfVreQl0+KdlEVFRdTW1jJmzBi2bt3K\n2LFjmTRpEkuWLHlnriuuoKRk729Km5mZWcfosXcWJRUBLwOnR0QF2c8C1uUc8i/A3wEjkr9PdHqQ\nPUxpaSljxowBoG/fvpSXl7Np06bd/RHBXXfd5c/+mZmZdaKCLRYllUl6VtIiSc9JukPSyZJWSnpe\n0vGSMsn3nXeNeV7SQEmnS3pS0hpJD0samPTXSKqTtBKoi4g1EfHHZPg6oLekD0oqBQ6OiCeSu4k/\nBs7o7Bz0ZJlMhjVr1jBu3LjdbY899hgDBw5kxIgReYzMzMysZynYYjExHKgFRiZ/FwATgNnA1WQ/\n6XcmgKRxwIsRsRn4JfCxiDgWuBO4MmfOo4CTI6Ll7amzgN9ExNvAYOAPOX1/SNqsEzQ2NnLWWWcx\nb948Dj744N3tixcv9l1FMzOzTlbozyy+EBENAJLWAY9EREhqAMqA64G5wELgfGDXw21DgCXJHcKD\ngBdy5rw3IrbnnkTS0cC3gVPebYCSpgPTAfr168/cih3vdopub2Dv7HOLLdXX1+/VtmPHDq666irG\njRvHoYceuvuYnTt3smTJEm655ZZWx3VVjY2N3ep62pNzk865SefcpHNu0jk3bSv0YvHtnO3mnP1m\nsrE/DgyX1J/sMvG1Sf9NwA0Rca+kSqAmZ55tuSeQNAS4B5gaEf+TNG8iW3DuMiRp20tEzAfmAwwd\nNjxqGwo9pZ3vioodtJaXzJTKPfYjgosvvpgTTjiBefPm7dG3fPlyKioqOOecczoy1E5XX19PZWVl\nvsMoSM5NOucmnXOTzrlJ59y0rdCXoduUPE94D3AD8ExEvJJ0lfBOcXdx2vjkeccHgDkRsTJn3j8B\nb0j6mCQBU8kueVsHWrlyJXV1daxYsWL3q3IefPBBAO68804vQZuZmeVBd7gNtgT4NVCd01YDLJX0\nKrACOCJl7OVkn4ucK2lu0nZKRGwBvsA7r875WfJnHWjChAmkvZ1o0aJFnRuMmZmZAQVcLEZEBhiV\ns1/dWl9ErALUYuwyWrkTGBE1Lfav5Z2l65bHrso9//7ofeABbGjx7kDL3t5vueRsZmZmXUOXXoY2\nMzMzs47lYtHMzMzMUrlYNDMzM7NULhbNzMzMLJWLRTMzMzNL5WLRzMzMzFK5WDQzMzOzVAX7nsWu\naHvTTsrmPJDvMArOok/02WN/48aNTJ06lc2bNyOJ6dOnM3PmTGpqaliwYAH9+/cH4Jvf/Caf+tSn\n8hGymZmZJVwsApKGAuuBmoj4btJWD5QC25PDdn3Zxd6noqIiamtrGTNmDFu3bmXs2LFMmjQJgC99\n6UvMnj07zxGamZnZLj22WJRUFBE7kt0baP1zflOSL7lYOyotLaW0tBSAvn37Ul5ezqZNm/YxyszM\nzPKhYJ9ZlFQm6VlJiyQ9J+kOSSdLWinpeUnHS8pIOiRnzPOSBko6XdKTktZIeljSwKS/RlKdpJVA\nXdJ2BvACsC4vF9rDZTIZ1qxZw7hx4wC46aab+MhHPsKll17Kq6++mufozMzMTBGR7xhaJakM+C1w\nLNlC7tfAU8A04DPAJcCLwNqIWChpHPCNiDhZ0oeA1yIiJF0GlEfEFZJqgNOBCRGxXVIx8BAwCZgN\nNLZYhu4PNAE/Ba6NVpIlaTowHaBfv/5j585b0BHp6NKOKDmA4uLivdq3b9/OzJkzufDCC5k4cSJ/\n+ctfKCkpQRK33XYbr7zyCl/5ylfyEHHnaWxsbDU35ty0xblJ59ykc27S9dTcVFVVrY6I4/Z1XKEv\nQ78QEQ0AktYBjyQFYANQBlwPzAUWAucDS5JxQ4AlkkqBg8jeOdzl3ojY9RxiDXBjRDRKannuKRGx\nSVJfssXiRcCPWx4UEfOB+QBDhw2P2oZCT2nnW/SJPlRWVu7R1tTUxGmnncaMGTOYNWvWXmOGDRvG\naaedtte47qa+vr7bX+N75dykc27SOTfpnJt0zk3bCnYZOvF2znZzzn4z2UL3cWC4pP7AGcDdSf9N\nwA8iogL4PNArZ55tOdvjgOslZYB/AK6WdDlARGxK/t0K/AQ4vv0uq2eLCKZNm0Z5efkeheKf/vSn\n3dv33HMPo0aNykd4ZmZmlqNL3wZL7jLeQ/YHKs9ExCtJVwmw6xcTF7cx/n/t2k6WqBsj4geSioBD\nIuJlSQcCpwEPd8Q19EQrV66krq6OiooKRo8eDWRfk7N48WLWrl2LJMrKyrjlllvyHKmZmZl16WIx\nsYTs84zVOW01wFJJrwIrgCPe5ZwfBH6eFIoHkC0U9/kwYu8DD2DDdZ9+l6fq/urr6/fYnzBhAq09\nK+t3KpqZmRWegi0WIyIDjMrZr26tL3m1jVqMXQYsa2XOmjbOV5OzvQ0Y+94iNzMzM+s+Cv2ZRTMz\nMzPLIxeLZmZmZpbKxaKZmZmZpXKxaGZmZmapXCyamZmZWSoXi2ZmZmaWqmBfndMVbW/aSdmcB/Id\nRsHI5LxzcuPGjUydOpXNmzcjienTpzNz5szd/bW1tcyePZs///nP9OvXLx/hmpmZWSt69J1FScsl\nPSVpnaQfSTogp+9cSeuTvp/kM87uoKioiNraWtavX88TTzzBD3/4Q9avXw9kC8n/+I//YOjQoXmO\n0szMzFrqkcWisj4AnBsRx5B9wXd/4JykfwRwFXBCRBxN9rvR9j6UlpYyZswYAPr27Ut5eTmbNmW/\nyPilL32J66+/HkltTWFmZmZ50KWLRUnXSfpizn6NpK9KekTSbyQ1SPps0lcmaYOkHwNPA38TEW8k\nQ4uAg4Bd36D7O+CHEfEqQERs6bSL6gEymQxr1qxh3LhxLFu2jMGDB3PMMcfkOywzMzNrhVr7Rm9X\nIelYYF5EnJjsrwdOBV6PiDck9QOeAEYAhwO/Az4eEU/kzPFz4HjgZ8BFEbFT0r8DzwEnkP02dE1E\nLE+JYTowHaBfv/5j587b5yeke4yKwSUANDY2UlxcDMD27duZOXMmF154qwm+kAAAIABJREFUIccf\nfzxf+tKX+M53vkNxcTHnn38+t9xyCyUlJfkMu1Pl5sb25Nykc27SOTfpnJt0PTU3VVVVqyPiuH0d\n16WLRQBJzwAnkV1GvhmoBG4EJgLNwIeBI4BewKMRcUQrc/QC7gB+FBEPSbofaALOBYYA/wlURMRr\nbcUydNjw+MC532unK+v6dv3Apb6+nsrKSpqamjjttNM49dRTmTVrFg0NDZx00kn81V/9FQB/+MMf\nGDRoEP/1X//FYYcdls/QO82u3NjenJt0zk065yadc5Oup+ZG0n4Vi93h19BLgbOBw4AlwBSyhePY\niGiSlCFbKAJsa22CiHhL0jLgs8BDwB+AJyOiCXhB0nNk707+uiMvpDuLCKZNm0Z5eTmzZs0CoKKi\ngi1b3lnhLysrY9WqVf41tJmZWQHp0s8sJpYA55MtGJcCJcCWpFCsIrv8vBdJxZJKk+0i4NPAs0n3\nv5O9Q0mylH0k2SVse49WrlxJXV0dK1asYPTo0YwePZoHH3ww32GZmZnZPnT5O4sRsU5SX2BTRPxJ\n0h3AfZIagFW8UwC21Ae4V9IHyRbNjwI/Svp+DpySPAO5E/hyRLyyr1h6H3gAG3LeLWjvmDBhAvt6\n5CGTyXROMGZmZrbfunyxCBARFTnbLwPjUw4dlXPcZuCjKfMFMCv5MzMzM+uxusMytJmZmZl1EBeL\nZmZmZpbKxaKZmZmZpXKxaGZmZmapXCyamZmZWSoXi2ZmZmaWqlu8OqdQbG/aSdmcB/IdRofJtHiH\n5MaNG5k6dSqbN29GEtOnT2fmzJn85S9/4bzzziOTyVBWVsbf//3f5yliMzMze7969J1FScdLWpv8\nPSXpzJy+ekkbcvoH5DPWQlRUVERtbS3r16/niSee4Ic//CHr16/nuuuu46STTuL555/npJNO4ic/\n+Um+QzUzM7P3qMfeWUw+8fc0cFxE7Eg+/feUpPsiYkdy2JSIWJW/KAtbaWkppaWlAPTt25fy8nI2\nbdrEsmXLqK+vB+Diiy9m3LhxeYzSzMzM3o+CvbMoqUzSs5IWSXpO0h2STpa0UtLzyV3BjKRDcsY8\nL2mgpNMlPSlpjaSHJQ1M+msk1UlaCdRFxJs5hWEvoO3v0VmqTCbDmjVrGDduHJs3b95dRB522GH8\n5S9/yXN0ZmZm9l4V+p3F4cA5wKXAr4ELgAnAZ4CrgWXAmcBCSeOAFyNis6RfAh+LiJB0GXAlcEUy\n51HAhIjYDpCMuw04HLgop3gEuF1SE/BT4Npo5ePGkqYD0wH69evP3IodLQ/pNnbdLWxp+/btzJw5\nk8suu4zf/OY37NixY69j08b2dI2Njc5NCucmnXOTzrlJ59ykc27aVujF4gsR0QAgaR3wSFIANgBl\nwPXAXGAhcD6wJBk3BFiSLC0fBLyQM+e9uwpFgIh4EjhaUjnZ4vBnEfEW2SXoTZL6ki0WLwJ+3DLA\niJgPzAcYOmx41DYUekrfu8yUyr3ampqaOO2005gxYwazZmU/pT148GA+/OEPU1payp/+9CcOPfRQ\nKiv3HmvZItq5aZ1zk865SefcpHNu0jk3bSvYZejE2znbzTn7zWQL3ceB4ZL6A2cAdyf9NwE/iIgK\n4PNkl5h32dbaiSLiGaARGJXsb0r+3Qr8BDi+Ha6nW4kIpk2bRnl5+e5CEeAzn/kMt99+OwC33347\nH//4x/MVopmZmb1PhV4stilZFr4HuAF4JiJeSbpKgE3J9sVp4yUdkfzQBUmHAyOBjKQiSf2S9gOB\n08j+GMZyrFy5krq6OlasWMHo0aMZPXo0Dz74IHPmzOGhhx5ixIgRPPzww1xwwQX5DtXMzMzeo+6w\nZrqE7POM1TltNcBSSa8CK4AjUsZOAOYkzyU2A1+IiJcl9QF+nhSKBwAPAws6Jvyua8KECbTyGCcA\njzzyyO5tPwdiZmbWdRVssRgRGZIl4WS/urW+5NU2ajF2Gdkfv7Scs6bFfh1Q18px24Cx7zbm3gce\nwIYWL642MzMz68q69DK0mZmZmXUsF4tmZmZmlsrFopmZmZmlcrFoZmZmZqlcLJqZmZlZKheLZmZm\nZpbKxaK9K5deeikDBgxg1KjdbzXiqaeeYvz48VRUVHD66afzxhtv5DFCMzMza08F+57FziDpr4F/\nAz4KLIqIy3P6DgJ+AFSSfWH3P0bET9uab3vTTsrmPNBxAedBpsV7I6urq7n88suZOnXq7rbLLruM\n7373u5x44oncdtttfOc73+Gf//mfOztUMzMz6wA99s5i8pm/t4B/Ama3csg/Alsi4kjgKOAXnRhe\nwZo4cSKHHnroHm3PPfccEydOBGDSpEn89Kdt1tRmZmbWhRRssSipTNKzkhZJek7SHZJOlrRS0vOS\njpeUkXRIzpjnJQ2UdLqkJyWtkfSwpIFJf42kOkkrgbqI2BYRvyRbNLZ0KfAtgIhojoiXO+O6u6Kj\njz6aZcuyH8xZunQpGzduzHNEZmZm1l4KtlhMDAdqgZHJ3wVkv+c8G7ia7Cf9zgSQNA54MSI2A78E\nPhYRxwJ3AlfmzHkUcHJETE47aU4B+s+SfiNp6a6C0/Z22223cfPNNzN27Fi2bt3KQQcdlO+QzMzM\nrJ0U+jOLL0REA4CkdcAjERGSGoAy4HpgLrAQOB9YkowbAiyRVAocBLyQM+e9EbF9H+ctSub4VUTM\nkjQL+C5wUcsDJU0HpgP069efuRU73tOFFqr6+vq92l566SW2bdu2R9/VV18NwMaNGxkwYMAefY2N\nja3OY85NW5ybdM5NOucmnXOTzrlpW6EXi2/nbDfn7DeTjf1xYLik/sAZwLVJ/03ADRFxr6RKoCZn\nnm37cd5XgDeBu5P9pcC01g6MiPnAfIChw4ZHbUOhp/TdyUyp3Lstk6FPnz5UVmb7tmzZwoABA2hu\nbqa6upovf/nLu/sgW3Dm7ts7nJt0zk065yadc5POuUnn3LSt0Jeh2xQRAdwD3AA8ExGvJF0lwKZk\n++L3OO99ZH8JDXASsP59BdtNTJ48mfHjx7NhwwaGDBnCrbfeyuLFiznyyCMZOXIkgwYN4pJLLsl3\nmGZmZtZOusNtsCXAr4HqnLYaYKmkV4EVwBFpgyVlgIOBgySdAZwSEeuBrwB1kuYBfwZcAQGLFy9u\ntX3mzJmdHImZmZl1hoItFiMiA4zK2a9urS8iVgFqMXYZ2R+/tJyzppW2spTzvwhMfDcx9z7wADa0\neC+hmZmZWVfWpZehzczMzKxjuVg0MzMzs1QuFs3MzMwslYtFMzMzM0vlYtHMzMzMUrlYNDMzM7NU\nLhbNzMzMLFXBvmexK9retJOyOQ/kO4x2lWnx3shLL72U+++/nwEDBvD0008D8NRTTzFjxgwaGxsp\nKyvjjjvu4OCDD85HuGZmZtbOevSdRUnHS1qb/D0l6cykvW9O+1pJLydfcunxqqurWb58+R5tl112\nGddddx0NDQ2ceeaZfOc738lTdGZmZtbeemyxKKkIeBo4LiJGA58AbpFUFBFbI2L0rj/gReDufMZb\nKCZOnMihhx66R9tzzz3HxInZj91MmjSJn/70p/kIzczMzDpAwRaLksokPStpkaTnJN0h6WRJKyU9\nn9wVzEg6JGfM85IGSjpd0pOS1kh6WNLApL9GUp2klUBdRLwZETuS4b2AaCWOI4EBwGOdcNld0tFH\nH82yZdmvKy5dupSNGzfmOSIzMzNrL4rYqz4qCJLKgN8CxwLrgF8DTwHTgM8Al5C947c2IhZKGgd8\nIyJOlvQh4LWICEmXAeURcYWkGuB0YEJEbE/OMw64DTgcuCgi7mkRx1zg4IiYnRLndGA6QL9+/cfO\nnbegHbOQfxWDS/Zqe+mll7jqqqtYuHAhAL///e+56aabeP311znhhBO4++67dxePAI2NjRQXF3da\nzF2Jc5POuUnn3KRzbtI5N+l6am6qqqpWR8Rx+zqu0H/g8kJENABIWgc8khSADUAZcD0wF1gInA8s\nScYNAZZIKgUOAl7ImfPeXYUiQEQ8CRwtqRy4XdLPIuKtnOPPBy5KCzAi5gPzAYYOGx61DYWe0ncn\nM6Vy77ZMhj59+lBZ+U7f1KlTgeyS9Lp16/boq6+v32Pf3uHcpHNu0jk36ZybdM5NOuembQW7DJ14\nO2e7OWe/mWyh+zgwXFJ/4Azeea7wJuAHEVEBfJ7sEvMu21o7UUQ8AzQCo3a1SToGKIqI1e//Urqv\nLVu2ANDc3My1117LjBkz8hyRmZmZtZdCLxbbFNk19HuAG4BnIuKVpKsE2JRsX5w2XtIRyQ9dkHQ4\nMBLI5BwyGVjczmF3aZMnT2b8+PFs2LCBIUOGcOutt7J48WKOPPJIRo4cyaBBg7jkkkvyHaaZmZm1\nk+6wZrqE7POM1TltNcBSSa8CK4AjUsZOAOZIaiJ7t/ILEfFyTv+5wKf2N5DeBx7AhhbvJexuFi9u\nvXaeOXNmJ0diZmZmnaFgi8WIyJCzJBwR1a31RcQqQC3GLgOW0UJE1LTYrwPq2ohh2HsI3czMzKzb\n6NLL0GZmZmbWsVwsmpmZmVkqF4tmZmZmlsrFopmZmZmlcrFoZmZmZqlcLJqZmZlZqoJ9dU5XtL1p\nJ2VzHsh3GO0i0+J9kZdeein3338/AwYM4OmnnwZg7dq1zJgxg7feeouioiJuvvlmjj/++HyEa2Zm\nZh2kR99ZlPTXkh6V1CjpBy36JktqkPTfkpZL6pevOAtBdXU1y5cv36Ptyiuv5Gtf+xpr167l61//\nOldeeWWeojMzM7OO0mOLxeQzf28B/wTMbqXve0BVRHwE+G/g8k4PsoBMnDiRQw89dI82SbzxxhsA\nvP766wwaNCgfoZmZmVkHKthlaEllwHLgCeDjZD/ptxC4BhgATAHuAkZHxGvJmOfJfsLveOCrwEHA\nK8CUiNgsqQb4W2AY8PuImAz8UtLwlqdP/vpIegU4GPhtR11rVzVv3jxOPfVUZs+eTXNzM7/61a/y\nHZKZmZm1M0VEvmNoVVIs/hY4FlhHtlh8CpgGfAa4BHgRWBsRCyWNA74RESdL+hDwWkSEpMuA8oi4\nIikWTwcmRMT2nHNVA8dFxOU5bWcDtwHbgOfJ3mXc2Uqc04HpAP369R87d96Cds1DvlQMLtmr7aWX\nXuKqq65i4cKFAHz/+9/nmGOO4cQTT+TRRx/l/vvvp7a2dq9xjY2NFBcXd3jMXZFzk865SefcpHNu\n0jk36XpqbqqqqlZHxHH7Oq7Qi8WHImJEsv9j4OcRcYekYcDdwBeAuRHxCUk3AusjYoGkCqAWKCV7\nd/GF5JgaICLimhbnqianWJR0INm7mtOB3wE3AS9FxLVtxTx02PD4wLnfa5frz7eWP3AByGQynHba\nabt/4FJSUsJrr72GJCKCkpKS3cvSuerr66msrOzokLsk5yadc5POuUnn3KRzbtL11NxI2q9isdCf\nWXw7Z7s5Z7+Z7BL648BwSf2BM8gWkJAt7n4QERXA54FeOfNs24/zjgaIiP+JbDV9F9mlcMsxaNAg\nfvGLXwCwYsUKRowYkeeIzMzMrL0V7DOL+yNZZr4HuAF4JiJeSbpKgE3J9sXvYepNwFGS+kfEn4FJ\nwDPvO+AubPLkydTX1/Pyyy8zZMgQrrnmGhYsWMDMmTPZsWMHvXr1Yv78+fkO08zMzNpZly4WE0vI\nPs9YndNWAyyV9CqwAjgibbCkDNkfsBwk6QzglIhYL+ka4D8lNZF9NrI6bY5deh94ABtaWb7tDhYv\nXtxq++rVqzs5EjMzM+tMBVssRkQGGJWzX91aX0SsIvvL5dyxy4BlrcxZ00pbWcr5fwT86N1HbmZm\nZtZ9FPozi2ZmZmaWRy4WzczMzCyVi0UzMzMzS+Vi0czMzMxSuVg0MzMzs1QuFs3MzMwsVcG+Oqcr\n2t60k7I5D+Q7jPes5Sf+Lr30Uu6//34GDBiw+xN/5513Hhs2bADgtdde45BDDmHt2rWdHquZmZl1\nDt9ZBCQNldQoaXZO20GS5kt6TtKzks7KZ4z5UF1dzfLly/doW7JkCWvXrmXt2rWcddZZfO5zn8tT\ndGZmZtYZeuydRUlFEbEj2b0B+FmLQ/4R2BIRR0r6AHBopwZYACZOnEgmk2m1LyK46667WLFiRecG\nZWZmZp2qYO8sSipL7ugtSu7u3SHpZEkrJT0v6XhJGUmH5Ix5XtJASadLelLSGkkPSxqY9NdIqpO0\nEqhL2s4AXgDWtQjhUuBbABHRHBEvd8qFdxGPPfYYAwcOZMSIEfkOxczMzDqQIiLfMbRKUhnwW+BY\nsoXcr4GngGnAZ4BLyH6zeW1ELJQ0DvhGRJws6UPAaxERki4DyiPiCkk1wOnAhIjYLqkYeAiYBMwG\nGiPiu0kB2gAsBSqB/wEuj4jNrcQ5HZgO0K9f/7Fz5y3okHx0horBJXu1vfTSS1x11VUsXLhwj/Yb\nb7yRwYMHc+655+5z3sbGRoqLi9stzu7EuUnn3KRzbtI5N+mcm3Q9NTdVVVWrI+K4fR1X6MvQL0RE\nA4CkdcAjSQHYAJQB1wNzgYXA+cCSZNwQYImkUuAgsncOd7k3IrYn2zXAjRHRKO3xeemiZI5fRcQs\nSbOA7wIXtQwwIuYD8wGGDhsetQ2FntJ0mSmVe7dlMvTp04fKynf6duzYwXnnncfq1asZMmTIPuet\nr6/fY7y9w7lJ59ykc27SOTfpnJt0zk3bCnYZOvF2znZzzn4z2YLucWC4pP7AGcDdSf9NwA8iogL4\nPNArZ55tOdvjgOslZYB/AK6WdDnwCvBmznxLgTHtdE1d3sMPP8zIkSP3q1A0MzOzrq3Qi8U2RXYN\n/R6yP1B5JiJeSbpKgE3J9sVtjP9fEVEWEWXAPOCbEfGDZN77yC5BA5wErG//KyhskydPZvz48WzY\nsIEhQ4Zw6623AnDnnXcyefLkPEdnZmZmnaHrrpm+YwnZ5xmrc9pqgKWSXgVWAEe8h3m/AtRJmgf8\nmewzkm3qfeABbGjxrsKubPHixa22L1q0qHMDMTMzs7wp2GIxIjLAqJz96tb6ImIVoBZjlwHLWpmz\npo3z1bTYfxGY+O4jNzMzM+s+uvQytJmZmZl1LBeLZmZmZpbKxaKZmZmZpXKxaGZmZmapXCyamZmZ\nWSoXi2ZmZmaWysViD3DppZcyYMAARo3a/SYivvzlLzNy5Eg+8pGPcOaZZ/Laa6/lMUIzMzMrVAX7\nnsXOIGk5UEo2D48BX4yInTn9ZwH/Bnw0eZ9jm7Y37aRszgMdFe5+y7R4MXh1dTWXX345U6dO3d02\nadIkvvWtb1FUVMRXvvIVvvWtb/Htb3+7s0M1MzOzAtcj7ywq6wPAuRFxDNkXfPcHzsk5pi8wE3gy\nP1G2n4kTJ3LooYfu0XbKKadQVJT9v8LHPvYx/vCHP+QjNDMzMytwXbpYlHSdpC/m7NdI+qqkRyT9\nRlKDpM8mfWWSNkj6MfA08DcR8UYytAg4CIic6f8Z+DbwVudcTf7cdtttfPKTn8x3GGZmZlaAunSx\nSPa70Ofm7J8L3A6cGRFjgCqgVtKuzwGOAG6OiKOTz/kh6efAFmAr2SVnJI0hW0zmf025g33jG9+g\nqKiIKVOm5DsUMzMzK0CKiH0fVcAkPQOcRHYZ+WagEriR7Hedm4EPA0cAvYBHI+KIVuboBdwB/Ah4\nBFgBVEdERlI9MDvtmUVJ04HpAP369R87d96C9ry896RicMlebS+99BJXXXUVCxcu3N22fPly7rvv\nPmpra+nVq1eHxdPY2EhxcXGHzd+VOTfpnJt0zk065yadc5Oup+amqqpqdUQct6/jusMPXJYCZwOH\nkb3TOIVs4Tg2IpokZcgWigDbWpsgIt6StAz4LPBfZJ9hrE9uSB4G3CvpM60VjBExH5gPMHTY8Kht\nyH9KM1Mq927LZOjTpw+Vldm+5cuXc++99/KLX/yC/v37d2g89fX1u89re3Ju0jk36ZybdM5NOucm\nnXPTtq6+DA3ZAvF8sgXjUqAE2JIUilXA4a0NklQsqTTZLgI+DTwbEa9HRL+IKIuIMuAJoNVCsauY\nPHky48ePZ8OGDQwZMoRbb72Vyy+/nK1btzJp0iRGjx7NjBkz8h2mmZmZFaD83wZ7nyJiXfLL5U0R\n8SdJdwD3SWoAVgHPpgztQ/aO4QfJFs2Pkl2G7nYWL168V9u0adPyEImZmZl1NV2+WASIiIqc7ZeB\n8SmHjso5bjPw0f2Yu3J/4+h94AFsaPGOQzMzM7OurDssQ5uZmZlZB3GxaGZmZmapXCyamZmZWSoX\ni2ZmZmaWysWimZmZmaVysWhmZmZmqVwsdlOXXnopAwYMYNSo3W8LYunSpRx99NF84AMfYNWqLvuO\ncTMzM+tE3eI9i++VpL8G/o3s+xYXRcTlSXtf4LGcQ4cA/39E/ENb821v2knZnAc6Ktw2ZVq837G6\nuprLL7+cqVOn7m4bNWoUd999N5///Oc7OzwzMzPronpssZh84u8t4J/Ivqw794XdW4HROceuBu7u\n7Bjfj4kTJ5LJZPZoKy8vz08wZmZm1mUV7DK0pDJJz0paJOk5SXdIOlnSSknPSzpeUkbSITljnpc0\nUNLpkp6UtEbSw5IGJv01kuokrQTqImJbRPySbNGYFseRwAD2vNNoZmZm1iMUbLGYGA7UAiOTvwuA\nCcBs4GpgGXAmgKRxwIvJZ/x+CXwsIo4F7gSuzJnzKODkiJi8nzGcDyyJiHj/l2NmZmbWtRT6MvQL\nEdEAIGkd8EhEhKQGoAy4HpgLLCQp6pJxQ4AlkkqBg4AXcua8NyK2v4sYzgcuSuuUNB2YDtCvX3/m\nVux4F1O3n/r6+r3aXnrpJbZt27ZX32uvvcbq1atpbGzslNgaGxtbjc+cm7Y4N+mcm3TOTTrnJp1z\n07ZCLxbfztluztlvJhv748BwSf2BM4Brk/6bgBsi4l5JlUBNzjzb9vfkko4BiiJiddoxETEfmA8w\ndNjwqG3IT0ozUyr3bstk6NOnD5WVe/YdcsghjB07luOOO65TYquvr98rBstybtI5N+mcm3TOTTrn\nJp1z07ZCX4ZuU7I0fA9wA/BMRLySdJUAm5Lti9/HKSYDi9/H+LyZPHky48ePZ8OGDQwZMoRbb72V\ne+65hyFDhvD444/z6U9/mlNPPTXfYZqZmVmBK/Q7i/tjCfBroDqnrQZYKulVYAVwRNpgSRngYOAg\nSWcAp0TE+qT7XOBT7R9yx1u8uPUa98wzz+zkSMzMzKwrK9hiMSIy7Pk6m+rW+iJiFaAWY5eR/fFL\nyzlrWmkrayOGYe8m5t4HHsCGFu87NDMzM+vKuvQytJmZmZl1LBeLZmZmZpbKxaKZmZmZpXKxaGZm\nZmapXCyamZmZWSoXi2ZmZmaWysWimZmZmaUq2PcsdkXbm3ZSNueBdp8308q7G2+88Ub+9V//FUlU\nVFSwcOFCevXq1e7nNjMzs56tR99ZlHS8pLXJ31OSzszpO0/Sf0taJ+nb+YyzpU2bNvH973+fVatW\n8fTTT7Nz507uvPPOfIdlZmZm3VCPLRYlFQFPA8dFxGjgE8Atkook/TXwHeCkiDgaOEzSSXkMdy87\nduxg+/bt7NixgzfffJNBgwblOyQzMzPrhgq2WJRUJulZSYskPSfpDkknS1op6fnkrmBG0iE5Y56X\nNFDS6ZKelLRG0sOSBib9NZLqJK0E6iLizYjYkQzvBUSyPQx4PiL+nOw/DJzVSZe+T4MHD2b27NkM\nHTqU0tJSSkpKOOWUU/IdlpmZmXVDioh9H5UHksqA3wLHAuuAXwNPAdOAzwCXAC8CayNioaRxwDci\n4mRJHwJei4iQdBlQHhFXSKoBTgcmRMT25DzjgNuAw4GLIuKeZHwDMAH4A7AEOCgiTm8lzunAdIB+\n/fqPnTtvQbvnomJwyR77W7du5Wtf+xpz586luLiYmpoaTjzxRCZNmtTu524PjY2NFBcX5zuMguTc\npHNu0jk36ZybdM5Nup6am6qqqtURcdy+jiv0H7i8EBENAJLWAY8kBWADUAZcD8wFFgLnky3qAIYA\nSySVAgcBL+TMee+uQhEgIp4EjpZUDtwu6WcR8aqk/53M1wz8Cvjb1gKMiPnAfIChw4ZHbUP7pzQz\npXKP/aVLl3LsscdyxhlnAPDHP/6RJ554gsrKyr0HF4D6+vqCjS3fnJt0zk065yadc5POuUnn3LSt\nYJehE2/nbDfn7DeTLXQfB4ZL6g+cAdyd9N8E/CAiKoDPk11i3mVbayeKiGeARmBUsn9fRIyLiPHA\nBuC5drmidjB06FCeeOIJ3nzzTSKCRx55hPLy8nyHZWZmZt1QoReLbYrsGvo9wA3AMxHxStJVAmxK\nti9OGy/piOSHLkg6HBgJZJL9Acm/HwK+APxrB1zCezJu3DjOPvtsxowZQ0VFBc3NzUyfPj3fYZmZ\nmVk3VOjL0PtjCdnnGatz2mqApZJeBVYAR6SMnQDMkdRE9m7lFyLi5aTve5KOSba/HhH7vLPY+8AD\n2NDKOxE7wjXXXMM111zTKecyMzOznqtgi8WIyJAsCSf71a31RcQqQC3GLgOWtTJnTYv9OqAu5fyT\n32PoZmZmZt1Gl16GNjMzM7OO5WLRzMzMzFK5WDQzMzOzVC4WzczMzCyVi0UzMzMzS+Vi0czMzMxS\nFeyrc7qi7U07KZvzQLvMlWnxvsYNGzZw3nnn7d7/3e9+x9e//nX+4R/+oV3OZ2ZmZtaaHl0sSloO\nlJLNw2PAFyNip6ShwO3AIcABwJyIeDB/kcKHP/xh1q5dC8DOnTsZPHgwZ555Zj5DMjMzsx6gRy5D\nK+sDwLkRcQzZF3z3B85JDvkqcFdEHAucD9ycn0hb98gjj/C3f/u3HH744fkOxczMzLq5Ll0sSrpO\n0hdz9mskfVXSI5J+I6lB0meTvjJJGyT9GHga+JuIeCMZWgQcBESyH8DByXYJ8MdOuaD9dOeddzJ5\nsj8wY2ZmZh1PEbHvowqUpGOBeRFxYrK/HjgVeD0i3pDUD3gCGAG0e79iAAAgAElEQVQcDvwO+HhE\nPJEzx8+B44GfARcly9ClwH8AHwL6ACdHxOqUGKYD0wH69es/du68Be1ybRWDS1ptb2pq4uyzz2bh\nwoUceuih7XKujtbY2EhxcXG+wyhIzk065yadc5POuUnn3KTrqbmpqqpaHRHH7eu4Lv3MYkSskTRA\n0iCyy8ivAi8BN0qaCDQDg4GByZAXcwvFZI5TJfUC7gD+P+AhYDKwKCJqJY0H6iSNiojmVmKYD8wH\nGDpseNQ2tE9KM1MqW21ftmwZ48aN43Of+1y7nKcz1NfXU1lZme8wCpJzk865SefcpHNu0jk36Zyb\ntnXpYjGxFDgbOAxYAkwhWziOjYgmSRmgV3LsttYmiIi3JC0DPku2WJwGfCLpezwpJvsBWzrwOvbL\n4sWLvQRtZmZmnaZLP7OYWEL2Ryhnky0cS4AtSaFYRXb5eS+SipPlZiQVAZ8Gnk26fw+clPSVky02\n/9yRF7E/tm3bxkMPPdSl7iqamZlZ19bl7yxGxDpJfYFNEfEnSXcA90lqAFbxTgHYUh/gXkkfJFs0\nPwr8KOm7Algg6Utkf+xSHfvxcGfvAw9gQ4v3I7anPn368Morr3TY/GZmZmYtdfliESAiKnK2XwbG\npxw6Kue4zcBHU+ZbD5zQnjGamZmZdUXdYRnazMzMzDqIi0UzMzMzS+Vi0czMzMxSuVg0MzMzs1Qu\nFs3MzMwslYtFMzMzM0vVLV6dUyi2N+2kbM4D73pcpsW7GcvKyujbty8HHHAARUVFrFq1qr1CNDMz\nM3tXXCwCku4FhkXEqJy2c4Easi/lfioiLujMmB599FH69evXmac0MzMz20uPLBYlCVBENEv6HNDY\non8EcBVwQkS8KmlAPuI0MzMzy7cu/cyipOskfTFnv0bSVyU9Iuk3khokfTbpK5O0QdKPgaeBv5FU\nDMwCrm0x9d8BP4yIVwEiYkvnXNHu6+Dkk09m7NixzJ8/vzNPbWZmZraHrn5ncQkwD/hhsn8ucCrw\n/Yh4Q1I/4IlkmRlgBHBxRDwBIOlGoBZ4s8W8Ryb9K4EDgJqIWN6hV5Ljl7/8JYMHD2bLli1MmjSJ\nkSNHMnHixM46vZmZmdluioh8x/C+SHoGOAnoD9wMVAI3AhOBZuDDwBFAL+DRiDgiGTca+HpEfEZS\nGXD/rmcWJd0PNJEtPocA/wlURMRrrZx/OjAdoF+//mPnzlvwrq+hYnBJat+iRYvo3bs355133rue\nt1A0NjZSXFyc7zAKknOTzrlJ59ykc27SOTfpempuqqqqVkfEcfs6rqvfWQRYCpwNHEb2TuMUsoXj\n2IhokpQhWygCbMsZNx44LukvAgZIqo+ISuAPwJMR0QS8IOk5snclf93y5BExH5gPMHTY8KhtePcp\nzUyp3L29bds2mpub6du3L9u2bePqq69m7ty5VFZWpo4vdPX19V06/o7k3KRzbtI5N+mcm3TOTTrn\npm3doVhcAiwA+gEnkr0buCUpFKuAw1sbFBH/AvwLZJ9nJHtnsTLp/ndgMrAwWco+Evhdx13COzZv\n3syZZ54JwI4dO7jgggv4xCc+0RmnNjMzM9tLly8WI2KdpL7Apoj4k6Q7gPskNQCrgGffw7Q/B06R\ntB7YCXw5Iv5fe/cfZVV533v8/eFHxDiGxAwkij+AK1oUDArRkCg5gzVqpaKGoBYTqaG0d9lW9K42\ncrWGpumCUqnQ1RgXYqw/CFJSkyAkYqrMNXVJoiAGUCc/dKxDEASNOApxHL73j73Rw3geGGSGc87M\n57XWLPbez7Of/d1f9rC+7Oecvbd1XNRpgwcP5umnnz4YhzIzMzPbp6ovFgEiYnjR8layKeZShpXa\nGBGNxW2RfZDzuvyn3Q7t3ZOGNg/YNjMzM6tmVf3oHDMzMzPrXC4WzczMzCzJxaKZmZmZJblYNDMz\nM7MkF4tmZmZmluRi0czMzMySXCxWkNbWVk499VTGjRtX7lDMzMzMgC7ynMUDJWkpMLjo3dCHAHcD\nI4FtwKX5sxj3akdLKwOvX96uYzaWeB7jvHnzGDp0KNu3b29/8GZmZmadqFveWVSmR758CdDcpstX\ngdci4njgFuCfOjumpqYmli9fzpQpUzr7UGZmZmbtVtXFoqRZkq4uWp8h6UZJD0taI2mdpPF520BJ\nDZLuBtYDx0iqIXtLyzfbDD0euCtf/h5wtiR15rlMmzaN2bNn06NHVf+VmJmZWRdT7ZXJYmBi0fpE\nsiLv4og4DagD5hQVekOAWyPi5Ih4EfgHYA7wVptxBwAvAUTEO8DrwMc76ySWLVtG//79GTlyZGcd\nwszMzOwDUfYa5Ool6VngbKAfcCtQIJs6HgPsAk4EBgF9gJURMSjfbwTwjYi4UNJAYFnRZxbXA+dF\nRFO+/hvgjPy9022PPxWYClBb22/kTXNvb1fcwwf0fXf59ttv56GHHqJnz568/fbbvPXWW5x11lnc\ncMMN+5mNytTc3ExNTU25w6hIzk2ac5Pm3KQ5N2nOTVp3zU1dXd3qiBi1r35d4QsuS4AJwCfJ7jRO\nIiscR0ZEi6RGskIR4M2i/UYDo/L2XkB/SfURUQA2AscATZJ6AX3JvujyPhExH5gPcOzg42POuval\ntHFS4d3lQuG95fr6em6++WaWLVvWrnGqQX19/R7naO9xbtKcmzTnJs25SXNu0pybvav2aWjICsTL\nyArGJWSF3Za8UKwDjiu1U0R8OyKOioiBwJnAL/NCEWApcGW+PAF4JKr9FqyZmZnZB1D1dxYjYoOk\nw4GNEbFJ0kLgAUnrgCeB5z7AsHcA90j6NfAqWTF6UBQKBf/vxszMzCpG1ReLABExvGh5K9kUcynD\nEvs3FrdFxE7gS/sbx6G9e9JQ4vmJZmZmZtWqK0xDm5mZmVkncbFoZmZmZkkuFs3MzMwsycWimZmZ\nmSW5WDQzMzOzJBeLZmZmZpbkYtHMzMzMkrrEcxYrxY6WVgZev7xdfRtLPI+xtbWVUaNGMWDAgC71\nuj8zMzOrXr6zCEhaKml90fpkSa9IWpv/TDkYccybN4+hQ4cejEOZmZmZtUu3LBaV6ZEvXwI0l+i2\nOCJG5D8LOjumpqYmli9fzpQpB6UuNTMzM2uXqi4WJc2SdHXR+gxJN0p6WNIaSeskjc/bBkpqkHQ3\nsB44RlINcB3wzfKcwXumTZvG7Nmz6dGjqv9KzMzMrIup9s8sLgbmAt/K1ycC5wL/GhHbJdUCqyQt\nzduHAFdGxCoASbcAc4C3Soz9RUmfBxqAayPipVIBSJoKTAWore3HTcPfaVfg9fX17y4//vjjtLS0\n8MYbb7B27Vq2bdu2R3u1a25u7lLn05GcmzTnJs25SXNu0pybNOdm76q6WIyIpyT1l3QU0A94DXgZ\nuEXSGGAXMAD4RL7Li0WF4gjgf0XEtZIGthn6AWBRRPxe0p8DdwFjEzHMB+YDHDv4+Jizrn0pbZxU\neHd5xYoVrF69msmTJ7Nz5062b9/OggULuPfee9s1VqWrr6+nUCiUO4yK5NykOTdpzk2ac5Pm3KQ5\nN3vXFeY8lwATgEvJ7jROIiscR0bECGAz0Cfv+2bRfqOBUZIagf8GTpBUDxAR2yLi93m/BcDIzjyB\nmTNn0tTURGNjI/fddx9jx47tMoWimZmZVbeuUCwuBi4jKxiXAH2BLRHRIqkOOK7UThHx7Yg4KiIG\nAmcCv4yIAoCkI4u6Xgg823nhm5mZmVWuqp6GBoiIDZIOBzZGxCZJC4EHJK0DngSe+wDD/rWkC4F3\ngFeBye3Z6dDePWko8fzE/VEoFHwr3MzMzCpG1ReLABExvGh5K9kUcynDEvs3FrdFxHRgegeGaGZm\nZlaVusI0tJmZmZl1EheLZmZmZpbkYtHMzMzMklwsmpmZmVmSi0UzMzMzS3KxaGZmZmZJXeLROZVi\nR0srA69f3q6+jSWex9ja2sqoUaMYMGAAy5Yt6+jwzMzMzPZbl7izKOnDkpZLek7SBkmz2rRPlPRM\n3vbdfNsISY/n234h6dKi/j+VtDb/+a2kHxyM85g3bx5Dhw49GIcyMzMza5eqLxYlKV+8OSL+ADgV\n+Jyk8/P2IWQP2P5cRJwMTMv7vwV8Jd92HjBX0kcBIuKsiBiRv1v6ceD+zj6PpqYmli9fzpQpUzr7\nUGZmZmbtVjHFoqRZkq4uWp8h6UZJD0taI2mdpPF520BJDZLuBtYD/SJiJUBEvA2sAY7Oh/oz4FsR\n8VreviX/85cR8at8+bfAFqBfm5g+AowFOv3O4rRp05g9ezY9elTMX4mZmZlZRX1mcTEwF/hWvj4R\nOBf414jYLqkWWCVpad4+BLgyIlYVD5LfHfxjYF6+6YR8+2NAT2BGRDzYZp/TgQ8Bv2kT00XAwxGx\nPRW0pKnAVIDa2n7cNPyddp1sfX39u8uPP/44LS0tvPHGG6xdu5Zt27bt0V7tmpubu9T5dCTnJs25\nSXNu0pybNOcmzbnZu4opFiPiKUn9JR1FdofvNeBl4BZJY4BdwADgE/kuL5YoFHsBi8gKzOfzzb3I\nCssC2d3GRyUNj4jf5fscCdxDVnjuahPW5cCCfcQ9H5gPcOzg42POuvaltHFS4d3lFStWsHr1aiZP\nnszOnTvZvn07CxYs4N57723XWJWuvr6eQqFQ7jAqknOT5tykOTdpzk2ac5Pm3Oxdpc15LgEmAJeS\n3WmcRFY4jsw/P7gZ6JP3fbPE/vOBX0XE3KJtTcDSiGiJiBeAX5IVj7unmZcDN5QoPGuB0/P2TjVz\n5kyamppobGzkvvvuY+zYsV2mUDQzM7PqVmnF4mLgMrKCcQnQF9gSES2S6oDjUjtK+mbef1qbph+Q\n3VXcXQCeADwv6UPA94G7I+J7JYacACyLiJ0HdEZmZmZmVaxipqEBImKDpMOBjRGxSdJC4AFJ64An\ngedK7SfpaOCGvH1N/gXpf4uIBcAK4AuSngFagb+JiG2SrgDGAB+XNDkfanJErM2XLwP2eATPvhza\nuycNJZ6fuD8KhYJvhZuZmVnFqKhiESAihhctbwVGJ7oOK+rXBKhUp4gI4Lr8p3j7vUByrjciCu0O\n2szMzKyLqrRpaDMzMzOrIC4WzczMzCzJxaKZmZmZJblYNDMzM7MkF4tmZmZmluRi0czMzMySKu7R\nOdVsR0srA69v3wtfGks8j7G1tZVRo0YxYMAAli1b1tHhmZmZme23bn1nUdKDkp6WtEHSbZJ65tsn\nS3pF0tr8Z8rBiGfevHkMHTr0YBzKzMzMrF26ZbGoTA9gYkR8iuwB3/2ALxV1WxwRI/KfBZ0dU1NT\nE8uXL2fKlINSl5qZmZm1S1UXi5JmSbq6aH2GpBslPSxpjaR1ksbnbQMlNUi6G1gPHBMR2/NdewEf\nAuKgn0Ru2rRpzJ49mx49qvqvxMzMzLoYZW/Dq06STgXmRsTn8/VngHOB1yNiu6RaYBUwBDgOeB74\nbESsKhpjBXA68GPgyxHRmr8reiawFWgAro2IlxIxTAWmAtTW9ht509zb2xX78AF9311+/PHHWbVq\nFddeey1r165l8eLFzJw5cz8yUdmam5upqakpdxgVyblJc27SnJs05ybNuUnrrrmpq6tbHRGj9tWv\nqotFAEnPAmeTTSPfChSAW4AxwC7gRGAQ0AdYGRGDSozRB1gI3BYRP5H0caA5In4v6c+BSyNi7L5i\nOXbw8dFj4rx2xV38BZfp06dzzz330KtXL3bu3Mn27du55JJLuPfe5Kurq0p9fT2FQqHcYVQk5ybN\nuUlzbtKcmzTnJq275kZSu4rFrjDnuQSYAFwKLAYmkRWOIyNiBLCZrFAEeLPUABGxE/ghMD5f3xYR\nv8+bFwAjOy16YObMmTQ1NdHY2Mh9993H2LFju0yhaGZmZtWtKxSLi4HLyArGJUBfYEtEtEiqI5t+\nfh9JNZKOzJd7ARcAz+XrRxZ1vRB4tvPCNzMzM6tcVf+cxYjYIOlwYGNEbJK0EHhA0jrgSfICsITD\ngKWSDiErmlcCt+Vtfy3pQuAd4FVgcntiObR3TxpKPD9xfxQKhW55K9zMzMwqU9UXiwARMbxoeSsw\nOtF1WFG/zcCnE+NNB6Z3ZIxmZmZm1agrTEObmZmZWSdxsWhmZmZmSS4WzczMzCzJxaKZmZmZJblY\nNDMzM7MkF4tmZmZmluRisYK0trZy6qmnMm7cuHKHYmZmZgZU6HMWJf0j8BXgYxFR06ZtIjADCODp\niPiTfPuDwGeA/46IcUX9fwocnq/2B34eERdJ+gPgTuA04IaIuLlon2uBKfkx1gF/mr8ScK92tLQy\n8Prl7TrHxhIP7543bx5Dhw5l+/bt7RrDzMzMrLNV1J1FZXoADwCnl2gfQvaw7M9FxMnAtKLmfwa+\n3HafiDgrIkbk74l+HLg/b3oV+Gvg5uL+kgbk20dFxDCgJ9nrBDtVU1MTy5cvZ8qUKZ19KDMzM7N2\n65RiUdIsSVcXrc+QdKOkhyWtkbRO0vi8baCkBkl3A+uBYyJiVURsKjH0nwHfiojXACJiy+6GiHgY\neGMvMX0EGAv8YPe+EfEE0FKiey/g0Pyd0R8Gfrt/Gdh/06ZNY/bs2fToUVH1u5mZmXVznVWZLAYm\nFq1PBO4CLo6I04A6YI4k5e1DgFsj4uSIeHEv454AnCDpMUmrJJ23HzFdBDwcEXud442IjWR3G/8H\n2AS8HhEP7cdx9tuyZcvo378/I0eO7MzDmJmZme23TvnMYkQ8Jam/pKOAfsBrwMvALZLGALuAAcAn\n8l1ejIhV7Yx3CFAAjgYelTQ8In7Xjn0vBxbsq5OkjwHjgUHA74Alkq6IiHsT/acCUwFqa/tx0/B3\n2hEK1NfXv7u8aNEiHnroIe6//37efvtt3nrrLc455xxuuOGGdo1V6Zqbm/c4X3uPc5Pm3KQ5N2nO\nTZpzk+bc7F1nfsFlCTAB+CTZncZJZIXjyIhokdQI9Mn7vtnOMZuAn0VEC/CCpF+SFY9P7G0nSbVk\nn4G8uB3H+EPghYh4Jd/3fuCzQMliMSLmA/MBjh18fMxZ176UNk4qvLtcKLy3XF9fz80338yyZcva\nNU41qK+v3+Mc7T3OTZpzk+bcpDk3ac5NmnOzd535AbnFZF8MmUBWOPYFtuSFYh1w3AcY8wdkdxV3\nF4AnAM+3Y78JwLL2fKOZbPr5M5I+nE+Tnw08+wFiNTMzM6t6nVYsRsQGskfWbMy/rLIQGCVpHdlj\ncZ5L7StptqQm4MOSmiTNyJtWANskPQOsBP4mIrbl+/yUrCg9O9/n3KIhLwMWtTnGJ/NjXAfcmO/z\nkYj4GfA9YA3ZY3N6kN85PBgKhUKXuqtoZmZm1a1Tn7MYEcOLlrcCoxNdh7XZ72+Bvy0xXpAVd9eV\naDtrL3EUSmx7mexzj6X6fx34emq8lEN796ShxPMTzczMzKqVn9NiZmZmZkkuFs3MzMwsycWimZmZ\nmSW5WDQzMzOzJBeLZmZmZpbkYtHMzMzMklwsltHOnTs5/fTT+dSnPsXJJ5/M17++30/rMTMzM+tU\nnfqcxWohaSkwOCKG5etjgLnAKcBlEfG99oyzo6WVgdcvT7Y3tnkG4yGHHMIjjzxCTU0NLS0tnHnm\nmZx//vl85jOf+aCnYmZmZtahuuWdRWV65MuXAM1tuvwPMBn4bifHQU1NDQAtLS20tLSQvWHQzMzM\nrDJUdbEoaZakq4vWZ0i6UdLDktZIWidpfN42UFKDpLuB9cAxkmrI3gbzzeJxI6IxIn4B7Orsc2ht\nbWXEiBH079+fc845hzPOOKOzD2lmZmbWblVdLAKLgYlF6xOBu4CLI+I0oA6Yo/du1w0Bbo2IkyPi\nReAfgDnAWwcx5j307NmTtWvX0tTUxM9//nPWr19frlDMzMzM3kfZ65arl6RngbOBfsCtQAG4BRhD\ndmfwRGAQ0AdYGRGD8v1GAN+IiAslDQSW7f7MYtHY/55vT35mUdJUYCpAbW2/kTfNvT0Z6/ABffd6\nLnfddRd9+vTh0ksv3Wu/atPc3PzudLvtyblJc27SnJs05ybNuUnrrrmpq6tbHRGj9tWvK3zBZQkw\nAfgk2Z3GSWSF48iIaJHUSFYoArxZtN9oYFTe3gvoL6k+Igr7c/CImA/MBzh28PExZ106pY2T9hz6\nlVdeoXfv3nz0ox9lx44d/N3f/R1f+9rXKBT2K4SKV19f3+XOqaM4N2nOTZpzk+bcpDk3ac7N3nWF\nYnExcDtQC3yebCp6S14o1gHHldopIr4NfBuyzzOS3UEsHIR437Vp0yauvPJKWltb2bVrFxMnTmTc\nuHEHMwQzMzOzvar6YjEiNkg6HNgYEZskLQQekLQOeBJ4bn/HlPRp4PvAx4A/lvT3EXFyhwYOnHLK\nKTz11FMdPayZmZlZh6n6YhEgIoYXLW8lm2IuZVipjRHRWNwWEU8AR+9vHIf27klDm2cpmpmZmVWz\nav82tJmZmZl1IheLZmZmZpbkYtHMzMzMklwsmpmZmVmSi0UzMzMzS3KxaGZmZmZJLhbNzMzMLKlL\nPGexUuxoaWXg9cv32NbY5rmLL730El/5ylfYvHkzkpg6dSrXXHPNwQzTzMzMrN26dbEo6UHgSLI8\n/BS4OiJaJR0HfIfsHdOvAldERFNHHLNXr17MmTOH0047jTfeeIORI0dyzjnncNJJJ3XE8GZmZmYd\nqltOQyvTA5gYEZ8ie3tLP+BLeZebgbsj4hTgG8DMjjr2kUceyWmnnQbA4YcfztChQ9m4cWNHDW9m\nZmbWoaq6WJQ0S9LVReszJN0o6WFJayStkzQ+bxsoqUHS3cB64JiI2J7v2gv4EBD5+knAI/nySmB8\nZ8Tf2NjIU089xRlnnNEZw5uZmZkdMEXEvntVKEmnAnMj4vP5+jPAucDrEbFdUi2wChgCHAc8D3w2\nIlYVjbECOB34MfDlfBr6u8DPImKepEuA/wRqI2JbiRimAlMBamv7jbxp7u17tA8f0Ldk7Dt27OCa\na67hiiuuYMyYMQeUh0rX3NxMTU1NucOoSM5NmnOT5tykOTdpzk1ad81NXV3d6ogYta9+VV0sAkh6\nFjibbBr5VqAA3AKMAXYBJwKDgD7AyogYVGKMPsBC4LaI+Imko4B/y/d7FPgiMCwifre3WI4dfHz0\nmDhvj21tv+AC0NLSwrhx4zj33HO57rrr9ut8q1F9fT2FQqHcYVQk5ybNuUlzbtKcmzTnJq275kZS\nu4rFrvAFlyXABOCTwGJgElnhODIiWiQ1khWKAG+WGiAidkr6Idl0808i4rfAJQCSaoAv7qtQbK+I\n4Ktf/SpDhw7tFoWimZmZVbeq/sxibjFwGVnBuAToC2zJC8U6sunn95FUI+nIfLkXcAHwXL5em38B\nBmA62TejO8Rjjz3GPffcwyOPPMKIESMYMWIEP/rRjzpqeDMzM7MOVfV3FiNig6TDgY0RsUnSQuAB\nSeuAJ8kLwBIOA5ZKOoSsaF4J3Ja3FYCZkoJsGvrqkiO0cWjvnjSUmHYuduaZZ1LtU/9mZmbWfVR9\nsQgQEcOLlrcCoxNdhxX12wx8OjHe94DvdWSMZmZmZtWoK0xDm5mZmVkncbFoZmZmZkkuFs3MzMws\nycWimZmZmSW5WDQzMzOzJBeLZmZmZpbkYrED7WhpZeD1y9+3/aqrrqJ///4MGzasxF5mZmZmlatb\nF4uSHpT0tKQNkm6T1DPf/heS1klaK+m/JZ10IMeZPHkyDz74YMcEbWZmZnYQdctiUZkewMSI+BTZ\nw7r7AV/Ku3w3IoZHxAhgNvAvB3K8MWPGcMQRRxxQzGZmZmblUNXFoqRZkq4uWp8h6UZJD0tak98d\nHJ+3DZTUIOluYD1wTERsz3ftBXwICICi7ZC9FtDv5zMzM7NuSdX8nmJJpwJzI+Lz+fozwLnA6xGx\nXVItsAoYAhwHPA98NiJWFY2xAjgd+DHw5YhozbdfDVxHVkSOjYhfJWKYCkwFqK3tN/KmubczfEDf\n9/V7+eWXmT59OnfeeWfHnHwVaW5upqamptxhVCTnJs25SXNu0pybNOcmrbvmpq6ubnVEjNpXv6ou\nFgEkPQucTTaNfCtQAG4BxgC7gBOBQUAfYGVEDCoxRh9gIXBbRPykTdufAOdGxJX7iuXYwcdHj4nz\naJx1wfvaGhsbGTduHOvXr9+/E+wC6uvrKRQK5Q6jIjk3ac5NmnOT5tykOTdp3TU3ktpVLFb1NHRu\nCTABuBRYDEwiKxxH5p853ExWKAK8WWqAiNgJ/BAYX6L5PuCiDo7ZzMzMrCp0hWJxMXAZWcG4BOgL\nbImIFkl1ZNPP7yOpRtKR+XIv4ALguXx9SFHXC4CSU9DtdfnllzN69GgaGho4+uijueOOOw5kODMz\nM7ODple5AzhQEbFB0uHAxojYJGkh8ICkdcCT5AVgCYcBSyUdQlY0rwRuy9v+UtIfAi3Aa8A+p6AB\nDu3dk4YSU9CLFi3an1MyMzMzqxhVXywCRMTwouWtwOhE12FF/TYDn06Md02HBmhmZmZWpbrCNLSZ\nmZmZdRIXi2ZmZmaW5GLRzMzMzJJcLJqZmZlZkotFMzMzM0tysWhmZmZmSS4WD4KrrrqK/v37M2zY\nsH13NjMzM6sg3bZYlPRhScslPSdpg6RZRW3HSlop6SlJv5D0R+0Zc0dLKwOvX/6+7ZMnT+bBBx/s\nwOjNzMzMDo5uWSxKUr54c0T8AXAq8DlJ5+fbbwT+IyJOJXuV4K0HcrwxY8ZwxBFHHMgQZmZmZmVR\n1W9wye8GvhQR38rXZwDvAHXAx4DewI0R8UNJA4EVwM+AkcAfRcRKgIh4W9Ia4Oh86AA+ki/3BX57\nMM7HzMzMrNJU+53FxcDEovWJwF3AxRFxGlnROKfoTuIQ4NaIODkiXty9k6SPAn8MPJxvmgFcIakJ\n+BHwV516FmZmZmYVShFR7hgOiKRngbOBfmTTxQXgFmAMsAs4ERgE9AFWRsSgNvv3Ah4AVkTE3Hzb\ndWS5mSNpNHAHMCwidpU4/lRgKkBtbb+RN829neED+r4vzuIPpKQAAAiSSURBVJdffpnp06dz5513\ndsh5V5Pm5mZqamrKHUZFcm7SnJs05ybNuUlzbtK6a27q6upWR8SoffWr6mno3BJgAvBJsjuNk8gK\nx5ER0SKpkaxQBHizxP7zgV/tLhRzXwXOA4iIxyX1AWqBLW13joj5+RgcO/j4mLOuF42TCu87SGNj\nI4cddhiFwvvburr6+vpued7t4dykOTdpzk2ac5Pm3KQ5N3tX7dPQkBWIl5EVjEvIPmO4JS8U64Dj\nUjtK+mbef1qbpv8hu1uJpKFkxeYrHzTAyy+/nNGjR9PQ0MDRRx/NHXfc8UGHMjMzMzuoqv7OYkRs\nkHQ4sDEiNklaCDwgaR3wJPBcqf0kHQ3ckLevyT/W+G8RsQD4P8Dtkq4l+7LL5DiA+fpFixZ90F3N\nzMzMyqrqi0WAiBhetLwVGJ3oOqyoXxOgUp0i4hngc/sbx6G9e9Iw64L93c3MzMysYnWFaWgzMzMz\n6yQuFs3MzMwsycWimZmZmSW5WDQzMzOzJBeLZmZmZpbkYtHMzMzMklwsmpmZmVmSi0UzMzMzS3Kx\naGZmZmZJLhbNzMzMLMnFopmZmZklKSLKHUOXIekNoKHccVSgWmBruYOoUM5NmnOT5tykOTdpzk1a\nd83NcRHRb1+deh2MSLqRhogYVe4gKo2kJ52X0pybNOcmzblJc27SnJs052bvPA1tZmZmZkkuFs3M\nzMwsycVix5pf7gAqlPOS5tykOTdpzk2ac5Pm3KQ5N3vhL7iYmZmZWZLvLJqZmZlZkovFDiDpPEkN\nkn4t6fpyx1NOko6RtFLSM5I2SLom3z5D0kZJa/OfPyp3rOUgqVHSujwHT+bbjpD0E0m/yv/8WLnj\nPNgknVh0bayVtF3StO563Uj6jqQtktYXbUteJ5Km5//+NEg6tzxRd75EXv5Z0nOSfiHp+5I+mm8f\nKGlH0bVzW/ki73yJ3CR/f7rLNQPJ3CwuykujpLX59m513bSXp6EPkKSewC+Bc4Am4Ang8oh4pqyB\nlYmkI4EjI2KNpMOB1cBFwESgOSJuLmuAZSapERgVEVuLts0GXo2IWfl/Nj4WEV8rV4zllv9ObQTO\nAP6UbnjdSBoDNAN3R8SwfFvJ60TSScAi4HTgKOC/gBMiorVM4XeaRF6+ADwSEe9I+ieAPC8DgWW7\n+3V1idzMoMTvT3e6ZqB0btq0zwFej4hvdLfrpr18Z/HAnQ78OiKej4i3gfuA8WWOqWwiYlNErMmX\n3wCeBQaUN6qKNx64K1++i6y47s7OBn4TES+WO5ByiYhHgVfbbE5dJ+OB+yLi9xHxAvBrsn+XupxS\neYmIhyLinXx1FXD0QQ+sAiSumZRuc83A3nMjSWQ3MxYd1KCqjIvFAzcAeKlovQkXR0B2Ox84FfhZ\nvumv8qmi73THqdZcAP8labWkqfm2T0TEpnz5ZeAT5QmtYlzGnv9w+7rJpK4T/xv0nquAHxetD8qn\nEv+fpLPKFVSZlfr98TXznrOAzRHxq6Jtvm7acLFonUJSDfCfwLSI2A58GxgMjAA2AXPKGF45nRkR\nI4Dzgavz6ZF3Rfa5kG772RBJHwIuBJbkm3zdlNDdr5NSJN0AvAMszDdtAo7Nf9+uA74r6SPliq9M\n/Puzb5ez539Ofd2U4GLxwG0EjilaPzrf1m1J6k1WKC6MiPsBImJzRLRGxC7gdrrwlMfeRMTG/M8t\nwPfJ8rA5/6zn7s98bilfhGV3PrAmIjaDr5s2UtdJt/83SNJkYBwwKS+kyadYt+XLq4HfACeULcgy\n2MvvT7e/ZgAk9QIuARbv3ubrpjQXiwfuCWCIpEH5XZHLgKVljqls8s9/3AE8GxH/UrT9yKJuFwPr\n2+7b1Uk6LP/SD5IOA75AloelwJV5tyuBH5Ynwoqwx//yfd3sIXWdLAUuk3SIpEHAEODnZYivLCSd\nB/wtcGFEvFW0vV/+ZSkkDSbLy/PlibI89vL7062vmSJ/CDwXEU27N/i6Ka1XuQOodvk38P4SWAH0\nBL4TERvKHFY5fQ74MrBu96MIgP8LXC5pBNnUWSPw5+UJr6w+AXw/q6fpBXw3Ih6U9ATwH5K+CrxI\n9mHrbicvoM9hz2tjdne8biQtAgpAraQm4OvALEpcJxGxQdJ/AM+QTcNe3YW/1VoqL9OBQ4Cf5L9b\nqyLiL4AxwDcktQC7gL+IiPZ+AaTqJHJTKPX7052uGSidm4i4g/d/Phq62XXTXn50jpmZmZkleRra\nzMzMzJJcLJqZmZlZkotFMzMzM0tysWhmZmZmSS4WzczMzCzJj84xM+tEklqBdUWbLoqIxjKFY2a2\n3/zoHDOzTiSpOSJqDuLxekXEOwfreGbW9Xka2sysjCQdKelRSWslrZd0Vr79PElrJD0t6eF82xGS\nfiDpF5JWSTol3z5D0j2SHgPukdRT0j9LeiLv2y0eZm5mncPT0GZmnevQorcZvRARF7dp/xNgRUT8\nY/6asQ9L6kf2Lt8xEfGCpCPyvn8PPBURF0kaC9wNjMjbTgLOjIgdkqYCr0fEpyUdAjwm6aGIeKEz\nT9TMuiYXi2ZmnWtHRIzYS/sTwHck9QZ+EBFrJRWAR3cXd0WvGzsT+GK+7RFJH5f0kbxtaUTsyJe/\nAJwiaUK+3pfsHbcuFs1sv7lYNDMro4h4VNIY4ALg3yX9C/DaBxjqzaJlAX8VESs6IkYz6978mUUz\nszKSdBywOSJuBxYApwGrgDGSBuV9dk9D/xSYlG8rAFsjYnuJYVcA/zu/W4mkEyQd1qknYmZdlu8s\nmpmVVwH4G0ktQDPwlYh4Jf/c4f2SegBbgHOAGWRT1r8A3gKuTIy5ABgIrJEk4BXgos48CTPruvzo\nHDMzMzNL8jS0mZmZmSW5WDQzMzOzJBeLZmZmZpbkYtHMzMzMklwsmpmZmVmSi0UzMzMzS3KxaGZm\nZmZJLhbNzMzMLOn/A0l2PGtem6LMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0627fb83c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xgb feature imp\n",
    "from xgboost import plot_importance\n",
    "ax = plot_importance(xgb1, height=0.5)\n",
    "fig = ax.figure\n",
    "fig.set_size_inches(10,20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IkrTuLFnoCkiSJEmSJGnDYc8iSdIGzx4Es8NhMpIkSbPPZJEkSRsgky6SJElaKCaLJEmS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\nSZIkSZIkdf1/mqNLdL62lLoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06292af0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGB feature imp\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(xgb1.feature_importances_)+1), xgb1.feature_importances_)\n",
    "plt.xticks(range(1, len(xgb1.feature_importances_)), gameX.columns, rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IkrTuLFnoCkiSJEmSJGnDYc8iSdIGzx4Es8NhMpIkSbPPZJEkSRsgky6SJElaKCaLJEmS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\nSZIkSZIkdf1/mqNLdL62lLoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06292af0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGB feature imp\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(xgb1.feature_importances_)+1), xgb1.feature_importances_)\n",
    "plt.xticks(range(1, len(xgb1.feature_importances_)), gameX.columns, rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FmP1Jrq2Jy5M83Vp7ovuUtLckebi19tOjzhwAAACA0a15ZFFr7bmqujHJ3Um2JLm9tXao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\nAAAA6GkWAQAAAND7/85sLAccanVdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd7f7231c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGB feature imp\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(xgb1.feature_importances_)+1), xgb1.feature_importances_)\n",
    "plt.xticks(range(1, len(xgb1.feature_importances_)+1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred2 = xgb1.predict_proba(leaderX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(25000, 3)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1 = pd.DataFrame(pred1)\n",
    "pred1['application_key'] = leaderAppID\n",
    "print(pred1.shape)\n",
    "\n",
    "pred2= pd.DataFrame(pred2)\n",
    "pred2['application_key'] = leaderAppID\n",
    "pred2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>application_key</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.736320</td>\n",
       "      <td>0.263680</td>\n",
       "      <td>350053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.891561</td>\n",
       "      <td>0.108439</td>\n",
       "      <td>350054</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.827006</td>\n",
       "      <td>0.172994</td>\n",
       "      <td>350055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.582630</td>\n",
       "      <td>0.417370</td>\n",
       "      <td>350056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.700264</td>\n",
       "      <td>0.299736</td>\n",
       "      <td>350057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.836657</td>\n",
       "      <td>0.163343</td>\n",
       "      <td>350058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.731447</td>\n",
       "      <td>0.268553</td>\n",
       "      <td>350059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.868201</td>\n",
       "      <td>0.131799</td>\n",
       "      <td>350060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.850819</td>\n",
       "      <td>0.149181</td>\n",
       "      <td>350061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.731821</td>\n",
       "      <td>0.268179</td>\n",
       "      <td>350062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.831919</td>\n",
       "      <td>0.168081</td>\n",
       "      <td>350063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.774888</td>\n",
       "      <td>0.225112</td>\n",
       "      <td>350064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.665181</td>\n",
       "      <td>0.334819</td>\n",
       "      <td>350065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.456064</td>\n",
       "      <td>0.543936</td>\n",
       "      <td>350066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.772278</td>\n",
       "      <td>0.227722</td>\n",
       "      <td>350067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.581122</td>\n",
       "      <td>0.418878</td>\n",
       "      <td>350068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.819206</td>\n",
       "      <td>0.180794</td>\n",
       "      <td>350069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.856365</td>\n",
       "      <td>0.143635</td>\n",
       "      <td>350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.774268</td>\n",
       "      <td>0.225732</td>\n",
       "      <td>350071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.844969</td>\n",
       "      <td>0.155031</td>\n",
       "      <td>350072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.758503</td>\n",
       "      <td>0.241497</td>\n",
       "      <td>350073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.704869</td>\n",
       "      <td>0.295131</td>\n",
       "      <td>350074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.927523</td>\n",
       "      <td>0.072477</td>\n",
       "      <td>350075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.744548</td>\n",
       "      <td>0.255452</td>\n",
       "      <td>350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.525343</td>\n",
       "      <td>0.474657</td>\n",
       "      <td>350077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.865328</td>\n",
       "      <td>0.134672</td>\n",
       "      <td>350078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.802702</td>\n",
       "      <td>0.197298</td>\n",
       "      <td>350079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.876717</td>\n",
       "      <td>0.123283</td>\n",
       "      <td>350080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.347898</td>\n",
       "      <td>0.652102</td>\n",
       "      <td>350081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.807136</td>\n",
       "      <td>0.192864</td>\n",
       "      <td>350082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>0.584130</td>\n",
       "      <td>0.415870</td>\n",
       "      <td>375023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>0.759250</td>\n",
       "      <td>0.240750</td>\n",
       "      <td>375024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>0.655995</td>\n",
       "      <td>0.344005</td>\n",
       "      <td>375025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>0.627158</td>\n",
       "      <td>0.372842</td>\n",
       "      <td>375026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>0.488270</td>\n",
       "      <td>0.511730</td>\n",
       "      <td>375027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>0.883951</td>\n",
       "      <td>0.116049</td>\n",
       "      <td>375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>0.791308</td>\n",
       "      <td>0.208692</td>\n",
       "      <td>375029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>0.752425</td>\n",
       "      <td>0.247575</td>\n",
       "      <td>375030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>0.708044</td>\n",
       "      <td>0.291956</td>\n",
       "      <td>375031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>0.551767</td>\n",
       "      <td>0.448233</td>\n",
       "      <td>375032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>0.844798</td>\n",
       "      <td>0.155202</td>\n",
       "      <td>375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>0.819892</td>\n",
       "      <td>0.180108</td>\n",
       "      <td>375034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>0.539901</td>\n",
       "      <td>0.460099</td>\n",
       "      <td>375035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>0.596525</td>\n",
       "      <td>0.403475</td>\n",
       "      <td>375036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>0.905327</td>\n",
       "      <td>0.094673</td>\n",
       "      <td>375037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>0.601548</td>\n",
       "      <td>0.398452</td>\n",
       "      <td>375038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>0.683953</td>\n",
       "      <td>0.316047</td>\n",
       "      <td>375039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>0.718676</td>\n",
       "      <td>0.281324</td>\n",
       "      <td>375040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.613941</td>\n",
       "      <td>0.386059</td>\n",
       "      <td>375041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>0.428096</td>\n",
       "      <td>0.571904</td>\n",
       "      <td>375042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>0.458119</td>\n",
       "      <td>0.541881</td>\n",
       "      <td>375043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>0.754428</td>\n",
       "      <td>0.245572</td>\n",
       "      <td>375044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.426730</td>\n",
       "      <td>0.573270</td>\n",
       "      <td>375045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>0.681187</td>\n",
       "      <td>0.318813</td>\n",
       "      <td>375046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>0.756655</td>\n",
       "      <td>0.243345</td>\n",
       "      <td>375047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.582586</td>\n",
       "      <td>0.417414</td>\n",
       "      <td>375048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.802148</td>\n",
       "      <td>0.197852</td>\n",
       "      <td>375049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.614587</td>\n",
       "      <td>0.385413</td>\n",
       "      <td>375050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.851357</td>\n",
       "      <td>0.148643</td>\n",
       "      <td>375051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.934327</td>\n",
       "      <td>0.065673</td>\n",
       "      <td>375052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1  application_key\n",
       "0      0.736320  0.263680           350053\n",
       "1      0.891561  0.108439           350054\n",
       "2      0.827006  0.172994           350055\n",
       "3      0.582630  0.417370           350056\n",
       "4      0.700264  0.299736           350057\n",
       "5      0.836657  0.163343           350058\n",
       "6      0.731447  0.268553           350059\n",
       "7      0.868201  0.131799           350060\n",
       "8      0.850819  0.149181           350061\n",
       "9      0.731821  0.268179           350062\n",
       "10     0.831919  0.168081           350063\n",
       "11     0.774888  0.225112           350064\n",
       "12     0.665181  0.334819           350065\n",
       "13     0.456064  0.543936           350066\n",
       "14     0.772278  0.227722           350067\n",
       "15     0.581122  0.418878           350068\n",
       "16     0.819206  0.180794           350069\n",
       "17     0.856365  0.143635           350070\n",
       "18     0.774268  0.225732           350071\n",
       "19     0.844969  0.155031           350072\n",
       "20     0.758503  0.241497           350073\n",
       "21     0.704869  0.295131           350074\n",
       "22     0.927523  0.072477           350075\n",
       "23     0.744548  0.255452           350076\n",
       "24     0.525343  0.474657           350077\n",
       "25     0.865328  0.134672           350078\n",
       "26     0.802702  0.197298           350079\n",
       "27     0.876717  0.123283           350080\n",
       "28     0.347898  0.652102           350081\n",
       "29     0.807136  0.192864           350082\n",
       "...         ...       ...              ...\n",
       "24970  0.584130  0.415870           375023\n",
       "24971  0.759250  0.240750           375024\n",
       "24972  0.655995  0.344005           375025\n",
       "24973  0.627158  0.372842           375026\n",
       "24974  0.488270  0.511730           375027\n",
       "24975  0.883951  0.116049           375028\n",
       "24976  0.791308  0.208692           375029\n",
       "24977  0.752425  0.247575           375030\n",
       "24978  0.708044  0.291956           375031\n",
       "24979  0.551767  0.448233           375032\n",
       "24980  0.844798  0.155202           375033\n",
       "24981  0.819892  0.180108           375034\n",
       "24982  0.539901  0.460099           375035\n",
       "24983  0.596525  0.403475           375036\n",
       "24984  0.905327  0.094673           375037\n",
       "24985  0.601548  0.398452           375038\n",
       "24986  0.683953  0.316047           375039\n",
       "24987  0.718676  0.281324           375040\n",
       "24988  0.613941  0.386059           375041\n",
       "24989  0.428096  0.571904           375042\n",
       "24990  0.458119  0.541881           375043\n",
       "24991  0.754428  0.245572           375044\n",
       "24992  0.426730  0.573270           375045\n",
       "24993  0.681187  0.318813           375046\n",
       "24994  0.756655  0.243345           375047\n",
       "24995  0.582586  0.417414           375048\n",
       "24996  0.802148  0.197852           375049\n",
       "24997  0.614587  0.385413           375050\n",
       "24998  0.851357  0.148643           375051\n",
       "24999  0.934327  0.065673           375052\n",
       "\n",
       "[25000 rows x 3 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>application_key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.846230</td>\n",
       "      <td>0.153770</td>\n",
       "      <td>350053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.983193</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>350054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.955055</td>\n",
       "      <td>0.044945</td>\n",
       "      <td>350055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.718546</td>\n",
       "      <td>0.281454</td>\n",
       "      <td>350056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.766788</td>\n",
       "      <td>0.233212</td>\n",
       "      <td>350057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.986447</td>\n",
       "      <td>0.013553</td>\n",
       "      <td>350058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.849426</td>\n",
       "      <td>0.150574</td>\n",
       "      <td>350059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.986866</td>\n",
       "      <td>0.013134</td>\n",
       "      <td>350060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.984936</td>\n",
       "      <td>0.015064</td>\n",
       "      <td>350061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.863061</td>\n",
       "      <td>0.136939</td>\n",
       "      <td>350062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.971778</td>\n",
       "      <td>0.028222</td>\n",
       "      <td>350063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.901904</td>\n",
       "      <td>0.098096</td>\n",
       "      <td>350064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.679052</td>\n",
       "      <td>0.320948</td>\n",
       "      <td>350065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.392530</td>\n",
       "      <td>0.607470</td>\n",
       "      <td>350066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.892257</td>\n",
       "      <td>0.107743</td>\n",
       "      <td>350067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.754118</td>\n",
       "      <td>0.245882</td>\n",
       "      <td>350068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.948483</td>\n",
       "      <td>0.051517</td>\n",
       "      <td>350069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.964349</td>\n",
       "      <td>0.035651</td>\n",
       "      <td>350070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.930863</td>\n",
       "      <td>0.069137</td>\n",
       "      <td>350071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.946247</td>\n",
       "      <td>0.053753</td>\n",
       "      <td>350072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.865846</td>\n",
       "      <td>0.134154</td>\n",
       "      <td>350073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.817822</td>\n",
       "      <td>0.182179</td>\n",
       "      <td>350074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.994584</td>\n",
       "      <td>0.005416</td>\n",
       "      <td>350075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.881760</td>\n",
       "      <td>0.118240</td>\n",
       "      <td>350076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.502137</td>\n",
       "      <td>0.497863</td>\n",
       "      <td>350077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.989851</td>\n",
       "      <td>0.010149</td>\n",
       "      <td>350078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.953572</td>\n",
       "      <td>0.046428</td>\n",
       "      <td>350079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.976223</td>\n",
       "      <td>0.023777</td>\n",
       "      <td>350080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.109634</td>\n",
       "      <td>0.890366</td>\n",
       "      <td>350081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.950250</td>\n",
       "      <td>0.049750</td>\n",
       "      <td>350082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24970</th>\n",
       "      <td>0.755948</td>\n",
       "      <td>0.244052</td>\n",
       "      <td>375023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24971</th>\n",
       "      <td>0.950474</td>\n",
       "      <td>0.049526</td>\n",
       "      <td>375024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24972</th>\n",
       "      <td>0.735333</td>\n",
       "      <td>0.264667</td>\n",
       "      <td>375025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24973</th>\n",
       "      <td>0.557199</td>\n",
       "      <td>0.442801</td>\n",
       "      <td>375026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24974</th>\n",
       "      <td>0.508161</td>\n",
       "      <td>0.491839</td>\n",
       "      <td>375027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24975</th>\n",
       "      <td>0.988933</td>\n",
       "      <td>0.011067</td>\n",
       "      <td>375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24976</th>\n",
       "      <td>0.877805</td>\n",
       "      <td>0.122195</td>\n",
       "      <td>375029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24977</th>\n",
       "      <td>0.879750</td>\n",
       "      <td>0.120250</td>\n",
       "      <td>375030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24978</th>\n",
       "      <td>0.834880</td>\n",
       "      <td>0.165120</td>\n",
       "      <td>375031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24979</th>\n",
       "      <td>0.655017</td>\n",
       "      <td>0.344983</td>\n",
       "      <td>375032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24980</th>\n",
       "      <td>0.976508</td>\n",
       "      <td>0.023492</td>\n",
       "      <td>375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24981</th>\n",
       "      <td>0.951195</td>\n",
       "      <td>0.048805</td>\n",
       "      <td>375034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24982</th>\n",
       "      <td>0.645134</td>\n",
       "      <td>0.354866</td>\n",
       "      <td>375035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24983</th>\n",
       "      <td>0.517419</td>\n",
       "      <td>0.482581</td>\n",
       "      <td>375036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24984</th>\n",
       "      <td>0.990834</td>\n",
       "      <td>0.009166</td>\n",
       "      <td>375037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24985</th>\n",
       "      <td>0.758348</td>\n",
       "      <td>0.241652</td>\n",
       "      <td>375038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24986</th>\n",
       "      <td>0.885973</td>\n",
       "      <td>0.114027</td>\n",
       "      <td>375039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24987</th>\n",
       "      <td>0.909279</td>\n",
       "      <td>0.090721</td>\n",
       "      <td>375040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.748471</td>\n",
       "      <td>0.251529</td>\n",
       "      <td>375041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24989</th>\n",
       "      <td>0.247596</td>\n",
       "      <td>0.752404</td>\n",
       "      <td>375042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>0.300912</td>\n",
       "      <td>0.699088</td>\n",
       "      <td>375043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24991</th>\n",
       "      <td>0.903719</td>\n",
       "      <td>0.096281</td>\n",
       "      <td>375044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.346349</td>\n",
       "      <td>0.653651</td>\n",
       "      <td>375045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>0.824963</td>\n",
       "      <td>0.175037</td>\n",
       "      <td>375046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24994</th>\n",
       "      <td>0.801910</td>\n",
       "      <td>0.198090</td>\n",
       "      <td>375047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.685873</td>\n",
       "      <td>0.314127</td>\n",
       "      <td>375048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.940385</td>\n",
       "      <td>0.059615</td>\n",
       "      <td>375049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.739733</td>\n",
       "      <td>0.260267</td>\n",
       "      <td>375050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.988584</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>375051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.995870</td>\n",
       "      <td>0.004130</td>\n",
       "      <td>375052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1  application_key\n",
       "0      0.846230  0.153770           350053\n",
       "1      0.983193  0.016807           350054\n",
       "2      0.955055  0.044945           350055\n",
       "3      0.718546  0.281454           350056\n",
       "4      0.766788  0.233212           350057\n",
       "5      0.986447  0.013553           350058\n",
       "6      0.849426  0.150574           350059\n",
       "7      0.986866  0.013134           350060\n",
       "8      0.984936  0.015064           350061\n",
       "9      0.863061  0.136939           350062\n",
       "10     0.971778  0.028222           350063\n",
       "11     0.901904  0.098096           350064\n",
       "12     0.679052  0.320948           350065\n",
       "13     0.392530  0.607470           350066\n",
       "14     0.892257  0.107743           350067\n",
       "15     0.754118  0.245882           350068\n",
       "16     0.948483  0.051517           350069\n",
       "17     0.964349  0.035651           350070\n",
       "18     0.930863  0.069137           350071\n",
       "19     0.946247  0.053753           350072\n",
       "20     0.865846  0.134154           350073\n",
       "21     0.817822  0.182179           350074\n",
       "22     0.994584  0.005416           350075\n",
       "23     0.881760  0.118240           350076\n",
       "24     0.502137  0.497863           350077\n",
       "25     0.989851  0.010149           350078\n",
       "26     0.953572  0.046428           350079\n",
       "27     0.976223  0.023777           350080\n",
       "28     0.109634  0.890366           350081\n",
       "29     0.950250  0.049750           350082\n",
       "...         ...       ...              ...\n",
       "24970  0.755948  0.244052           375023\n",
       "24971  0.950474  0.049526           375024\n",
       "24972  0.735333  0.264667           375025\n",
       "24973  0.557199  0.442801           375026\n",
       "24974  0.508161  0.491839           375027\n",
       "24975  0.988933  0.011067           375028\n",
       "24976  0.877805  0.122195           375029\n",
       "24977  0.879750  0.120250           375030\n",
       "24978  0.834880  0.165120           375031\n",
       "24979  0.655017  0.344983           375032\n",
       "24980  0.976508  0.023492           375033\n",
       "24981  0.951195  0.048805           375034\n",
       "24982  0.645134  0.354866           375035\n",
       "24983  0.517419  0.482581           375036\n",
       "24984  0.990834  0.009166           375037\n",
       "24985  0.758348  0.241652           375038\n",
       "24986  0.885973  0.114027           375039\n",
       "24987  0.909279  0.090721           375040\n",
       "24988  0.748471  0.251529           375041\n",
       "24989  0.247596  0.752404           375042\n",
       "24990  0.300912  0.699088           375043\n",
       "24991  0.903719  0.096281           375044\n",
       "24992  0.346349  0.653651           375045\n",
       "24993  0.824963  0.175037           375046\n",
       "24994  0.801910  0.198090           375047\n",
       "24995  0.685873  0.314127           375048\n",
       "24996  0.940385  0.059615           375049\n",
       "24997  0.739733  0.260267           375050\n",
       "24998  0.988584  0.011416           375051\n",
       "24999  0.995870  0.004130           375052\n",
       "\n",
       "[25000 rows x 3 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred1 = pred1.sort_values(by = 0,kind = 'mergesort',ascending = False)\n",
    "\n",
    "pred2 = pred2.sort_values(by = 0,kind = 'mergesort',ascending = False)\n",
    "# pred2 = pred2.sort_values(by = 0,kind = 'mergesort',ascending = True) #true for xgb method3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = pred1.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred1 = pred1[cols]\n",
    "\n",
    "cols = pred2.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "pred2 = pred2[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred1 = pred1.loc[:,'application_key':]\n",
    "\n",
    "pred2 = pred2.loc[:,'application_key':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submit1 = pred1['application_key'][:10000]\n",
    "submit1 = pd.DataFrame(submit1)\n",
    "submit1[0] = 0\n",
    "\n",
    "submit2 = pred2['application_key'][:10000]\n",
    "submit2 = pd.DataFrame(submit2)\n",
    "submit2[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# submit.iloc[-400:, 1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_key</th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>21128</th>\n",
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       "      <th>15911</th>\n",
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       "    <tr>\n",
       "      <th>23607</th>\n",
       "      <td>373660</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>353695</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19140</th>\n",
       "      <td>369193</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23338</th>\n",
       "      <td>373391</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21731</th>\n",
       "      <td>371784</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22990</th>\n",
       "      <td>373043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>358611</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19883</th>\n",
       "      <td>369936</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24868</th>\n",
       "      <td>374921</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22647</th>\n",
       "      <td>372700</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22465</th>\n",
       "      <td>372518</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>351446</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21040</th>\n",
       "      <td>371093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13848</th>\n",
       "      <td>363901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>365041</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11401</th>\n",
       "      <td>361454</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24175</th>\n",
       "      <td>374228</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3747</th>\n",
       "      <td>353800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17352</th>\n",
       "      <td>367405</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3051</th>\n",
       "      <td>353104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18127</th>\n",
       "      <td>368180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17113</th>\n",
       "      <td>367166</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13860</th>\n",
       "      <td>363913</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16123</th>\n",
       "      <td>366176</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20378</th>\n",
       "      <td>370431</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>350975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5673</th>\n",
       "      <td>355726</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>354229</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15183</th>\n",
       "      <td>365236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24344</th>\n",
       "      <td>374397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13898</th>\n",
       "      <td>363951</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14606</th>\n",
       "      <td>364659</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4670</th>\n",
       "      <td>354723</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16086</th>\n",
       "      <td>366139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20453</th>\n",
       "      <td>370506</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3897</th>\n",
       "      <td>353950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22762</th>\n",
       "      <td>372815</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9010</th>\n",
       "      <td>359063</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4436</th>\n",
       "      <td>354489</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7988</th>\n",
       "      <td>358041</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3792</th>\n",
       "      <td>353845</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>350349</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6542</th>\n",
       "      <td>356595</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16828</th>\n",
       "      <td>366881</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17965</th>\n",
       "      <td>368018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>357803</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8404</th>\n",
       "      <td>358457</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9865</th>\n",
       "      <td>359918</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>351383</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>350807</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16700</th>\n",
       "      <td>366753</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>350328</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4439</th>\n",
       "      <td>354492</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15128</th>\n",
       "      <td>365181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24029</th>\n",
       "      <td>374082</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12497</th>\n",
       "      <td>362550</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21266</th>\n",
       "      <td>371319</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key  0\n",
       "21128           371181  0\n",
       "7820            357873  0\n",
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       "3642            353695  0\n",
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       "22990           373043  0\n",
       "8558            358611  0\n",
       "19883           369936  0\n",
       "24868           374921  0\n",
       "22647           372700  0\n",
       "22465           372518  0\n",
       "1393            351446  0\n",
       "21040           371093  0\n",
       "13848           363901  0\n",
       "14988           365041  0\n",
       "11401           361454  0\n",
       "24175           374228  0\n",
       "3747            353800  0\n",
       "17352           367405  0\n",
       "3051            353104  0\n",
       "18127           368180  0\n",
       "17113           367166  0\n",
       "13860           363913  0\n",
       "16123           366176  0\n",
       "20378           370431  0\n",
       "922             350975  0\n",
       "5673            355726  0\n",
       "...                ... ..\n",
       "4176            354229  0\n",
       "15183           365236  0\n",
       "24344           374397  0\n",
       "13898           363951  0\n",
       "14606           364659  0\n",
       "4670            354723  0\n",
       "16086           366139  0\n",
       "20453           370506  0\n",
       "3897            353950  0\n",
       "22762           372815  0\n",
       "9010            359063  0\n",
       "4436            354489  0\n",
       "7988            358041  0\n",
       "3792            353845  0\n",
       "296             350349  0\n",
       "6542            356595  0\n",
       "16828           366881  0\n",
       "17965           368018  0\n",
       "7750            357803  0\n",
       "8404            358457  0\n",
       "9865            359918  0\n",
       "1330            351383  0\n",
       "754             350807  0\n",
       "16700           366753  0\n",
       "275             350328  0\n",
       "4439            354492  0\n",
       "15128           365181  0\n",
       "24029           374082  0\n",
       "12497           362550  0\n",
       "21266           371319  0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>15263</th>\n",
       "      <td>365316</td>\n",
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       "      <th>21478</th>\n",
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       "      <th>5667</th>\n",
       "      <td>355720</td>\n",
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       "      <th>17578</th>\n",
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       "      <th>21119</th>\n",
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       "      <td>371675</td>\n",
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       "      <th>24535</th>\n",
       "      <td>374588</td>\n",
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       "      <th>17492</th>\n",
       "      <td>367545</td>\n",
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       "      <th>14078</th>\n",
       "      <td>364131</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12113</th>\n",
       "      <td>362166</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5522</th>\n",
       "      <td>355575</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11407</th>\n",
       "      <td>361460</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>16233</th>\n",
       "      <td>366286</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>352125</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24108</th>\n",
       "      <td>374161</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>20481</th>\n",
       "      <td>370534</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7231</th>\n",
       "      <td>357284</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19448</th>\n",
       "      <td>369501</td>\n",
       "      <td>0</td>\n",
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       "      <th>20192</th>\n",
       "      <td>370245</td>\n",
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       "      <th>19930</th>\n",
       "      <td>369983</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6958</th>\n",
       "      <td>357011</td>\n",
       "      <td>0</td>\n",
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       "      <th>7204</th>\n",
       "      <td>357257</td>\n",
       "      <td>0</td>\n",
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       "      <th>13223</th>\n",
       "      <td>363276</td>\n",
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       "      <th>5359</th>\n",
       "      <td>355412</td>\n",
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       "      <th>11172</th>\n",
       "      <td>361225</td>\n",
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       "      <th>1794</th>\n",
       "      <td>351847</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6146</th>\n",
       "      <td>356199</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5422</th>\n",
       "      <td>355475</td>\n",
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       "      <th>14520</th>\n",
       "      <td>364573</td>\n",
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       "      <th>7022</th>\n",
       "      <td>357075</td>\n",
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       "    <tr>\n",
       "      <th>6797</th>\n",
       "      <td>356850</td>\n",
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       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>350944</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9861</th>\n",
       "      <td>359914</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5639</th>\n",
       "      <td>355692</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>351582</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20356</th>\n",
       "      <td>370409</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7131</th>\n",
       "      <td>357184</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10451</th>\n",
       "      <td>360504</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13887</th>\n",
       "      <td>363940</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13207</th>\n",
       "      <td>363260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21857</th>\n",
       "      <td>371910</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22990</th>\n",
       "      <td>373043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11648</th>\n",
       "      <td>361701</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2757</th>\n",
       "      <td>352810</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15449</th>\n",
       "      <td>365502</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18567</th>\n",
       "      <td>368620</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13632</th>\n",
       "      <td>363685</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23837</th>\n",
       "      <td>373890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22086</th>\n",
       "      <td>372139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24313</th>\n",
       "      <td>374366</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key  0\n",
       "11322           361375  0\n",
       "24638           374691  0\n",
       "17393           367446  0\n",
       "16492           366545  0\n",
       "3302            353355  0\n",
       "24532           374585  0\n",
       "24282           374335  0\n",
       "21597           371650  0\n",
       "16771           366824  0\n",
       "15263           365316  0\n",
       "22627           372680  0\n",
       "21478           371531  0\n",
       "5667            355720  0\n",
       "17578           367631  0\n",
       "21119           371172  0\n",
       "21622           371675  0\n",
       "24535           374588  0\n",
       "17492           367545  0\n",
       "14078           364131  0\n",
       "12113           362166  0\n",
       "5522            355575  0\n",
       "11407           361460  0\n",
       "16233           366286  0\n",
       "2072            352125  0\n",
       "24108           374161  0\n",
       "20481           370534  0\n",
       "7231            357284  0\n",
       "19448           369501  0\n",
       "20192           370245  0\n",
       "19930           369983  0\n",
       "...                ... ..\n",
       "6958            357011  0\n",
       "7204            357257  0\n",
       "13223           363276  0\n",
       "5359            355412  0\n",
       "11172           361225  0\n",
       "1794            351847  0\n",
       "6146            356199  0\n",
       "5422            355475  0\n",
       "14520           364573  0\n",
       "7022            357075  0\n",
       "6797            356850  0\n",
       "891             350944  0\n",
       "9861            359914  0\n",
       "5639            355692  0\n",
       "1529            351582  0\n",
       "20356           370409  0\n",
       "7131            357184  0\n",
       "10451           360504  0\n",
       "13887           363940  0\n",
       "13207           363260  0\n",
       "21857           371910  0\n",
       "22990           373043  0\n",
       "11648           361701  0\n",
       "2757            352810  0\n",
       "15449           365502  0\n",
       "18567           368620  0\n",
       "13632           363685  0\n",
       "23837           373890  0\n",
       "22086           372139  0\n",
       "24313           374366  0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# no = 270\n",
    "\n",
    "submit1.to_csv('flabbergasted_IITGuwahati_'+str(no)+'.csv',header = False, index = False)\n",
    "submit2.to_csv('flabbergasted_IITGuwahati_'+str(no+1)+'.csv',header = False, index = False)\n",
    "\n",
    "no+=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "270"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wh6G11p6uqhuT3JVkW5LbW2uHq+r6bvn+JAeTXJXkSJInk1zXPfxLk3xjko9U1X3dfd/f\nWjs47pcBAAAAwBgWOmdRN9w5uOG+/VPXW5IbNnncbyepgTUCAACckZx/EDgdLXIYGgAAAABnCcMi\nAAAAAHoLHYYGAADAehjj0LbE4W1wNrNnEQAAAAA9exaxUv6qAbC+nHQVAODsZFgEAACwBQN04Gxi\nWAQArD17sgIAjMewCKb4ixEAfCrbRgA4+xgWAQAAzwrDSIDTk09DAwAAAKBnWAQAAABAz7AIAAAA\ngJ5zFrGWHN8OAAAAq2HPIgAAAAB6hkUAAAAA9ByGBgCnkMNoAQA43dmzCAAAAICeYREAAAAAPcMi\nAAAAAHrOWQQAAAAsxPkXzw6GRQCw5sZ405Z44wYAwMRCh6FV1ZVV9VBVHamqmzZZXlV1c7f8/qq6\nbGrZ7VX1eFU9MGbhAAAAAIxvy2FRVW1LckuSPUkuTXJNVV26YbU9SXZ1l31Jbp1a9jNJrhyjWAAA\nAABWa5E9iy5PcqS19nBr7akkdybZu2GdvUnuaBN3Jzm/qi5Iktba+5P86ZhFAwAAALAaiwyLLkzy\n6NTto919J7sOAAAAAKe50+YE11W1L5ND2HLxxRc/y9UAALBKPk0HAE5fi+xZ9FiSi6Zu7+juO9l1\n5mqt3dZa291a2719+/aTeSgAAAAAI1lkWHRPkl1VdUlVnZvk6iQHNqxzIMm13aeiXZHkidbasZFr\nBQAAAGDFtjwMrbX2dFXdmOSuJNuS3N5aO1xV13fL9yc5mOSqJEeSPJnkuhOPr6q3JvnKJC+sqqNJ\n/kNr7S1jfyEAMCaHyAAAcLZa6JxFrbWDmQyEpu/bP3W9JblhxmOvGVIgAADAosYY9icG/sDZbZHD\n0AAAAAA4SxgWAQAAANAzLAIAAACgZ1gEAAAAQM+wCAAAAIDeQp+GBgAAALAqY3ySoU8xHI89iwAA\nAADoGRYBAAAA0DMsAgAAAKDnnEUAAABsyTll4OxhWATAKTHGG8zEm0wAAFg1h6EBAAAA0DMsAgAA\nAKDnMDQA1przJwAAwLjsWQQAAABAz7AIAAAAgJ5hEQAAAAA9wyIAAAAAeoZFAAAAAPQMiwAAAADo\nGRYBAAAA0FtoWFRVV1bVQ1V1pKpu2mR5VdXN3fL7q+qyRR8LAAAAwOljy2FRVW1LckuSPUkuTXJN\nVV26YbU9SXZ1l31Jbj2JxwIAAABwmlhkz6LLkxxprT3cWnsqyZ1J9m5YZ2+SO9rE3UnOr6oLFnws\nAAAAAKeJRYZFFyZ5dOr20e6+RdZZ5LEAAAAAnCaqtTZ/hap/luTK1tq3dre/MckrWms3Tq3zjiRv\naq39dnf715P82yQ7t3rsVMa+TA5hS5KXJHlo2Je2Nl6Y5BNrkrtOta4qd51qXVXuOtW6qtx1qnVV\nuetU67rlrlOtq8pdp1pXlbtOta4qd51qXVXuOtW6qtx1qnVVuetU66py16nWVeWuU62ryl2nWk9n\nn99a277VSucsEPRYkoumbu/o7ltknecs8NgkSWvttiS3LVDPGaWqDrXWdq9D7jrVuqrcdap1Vbnr\nVOuqctep1lXlrlOt65a7TrWuKnedal1V7jrVuqrcdap1VbnrVOuqctep1lXlrlOtq8pdp1pXlbtO\nta4qd51qPRMschjaPUl2VdUlVXVukquTHNiwzoEk13afinZFkidaa8cWfCwAAAAAp4kt9yxqrT1d\nVTcmuSvJtiS3t9YOV9X13fL9SQ4muSrJkSRPJrlu3mNX8pUAAAAAMNgih6GltXYwk4HQ9H37p663\nJDcs+lg+xaoOvVtF7jrVuqrcdap1VbnrVOuqctep1lXlrlOt65a7TrWuKnedal1V7jrVuqrcdap1\nVbnrVOuqctep1lXlrlOtq8pdp1pXlbtOta4qd51qXXtbnuAaAAAAgLPHIucsAgAAAOAsYVj0LKmq\n26vq8ap6YMTMi6rqN6vqo1V1uKpeP1Lup1fV71bVh7vcHxojt8veVlUfqqp3jJj5SFV9pKruq6pD\nI+aeX1W/UlUfq6oHq+pLRsh8SVfnicsnq+oNI+R+Z/e9eqCq3lpVnz40s8t9fZd5eEidm73+q+qz\nquo9VfX73b+fOVLu13f1/k1VnfSnHMzI/LHudXB/Vb29qs4fKfeHu8z7qurdVfV5Y+ROLfvuqmpV\n9cIRav3Bqnps6rV71Vi1VtV3dM/v4ar60TFyq+q/T9X6SFXdN0Lmy6rq7hM/a6rq8pFqfWlV/U73\nc+zXqurvnmTmptuCoT02J3doj83KXbrP5mQO6rFZuVPLl+2xWfUu3Wfzah3SY3NqHdpjs3KX7rM5\nmUN7bNP3RSP02KzcpXtsTuag7dic3KE9Nvc95zI9NqfWQduxebUO7LFZ9S7dY3MyB23H5uQO6rEu\n41N+RxjaX3NyB23D5uQOfq84I3eM94qb/v61TH9tUevg94qz6h3SYzNqHbQNO2O11lyehUuSL09y\nWZIHRsy8IMll3fXnJ/m9JJeOkFtJPqO7/pwkH0hyxUg1f1eSX0zyjhGfh0eSvHAF37OfTfKt3fVz\nk5w/cv62JB9P8vkDcy5M8n+SPLe7/UtJvnmE+r4oyQNJnpfJ+c7em+QLlsx6xus/yY8muam7flOS\nHxkp9wuTvCTJ+5LsHinzq5Oc013/kRFr/btT1/91kv1j5Hb3X5TJhw384cn2x4xafzDJ9wx8TW2W\n+1Xda+vvdLdfNNZzMLX8J5K8cYRa351kT3f9qiTvG+k5uCfJV3TXvyXJD59k5qbbgqE9Nid3aI/N\nyl26z+ZkDuqxWbnd7SE9NqvepftsTuagHpv3HEyts0yPzap36T6bkzm0xzZ9XzRCj83KXbrH5mQO\n2o7NyR3aYzPfcy7bY3NqXbq/tsgd2mNbvu8+2R6bU+ug7dic3EE91j3uU35HGNpfc3IHbcPm5A5+\nrzgjd4z3is/4/WvZ/tqi1kE9Nid3jPeKM38HPdn+OpMv9ix6lrTW3p/kT0fOPNZa+2B3/c+TPJjJ\n4GBobmut/UV38zndZfDJrqpqR5LXJnnz0KxVq6oXZPJL3VuSpLX2VGvtz0b+b16d5A9aa384QtY5\nSZ5bVedkMtz54xEyvzDJB1prT7bWnk7yW0n+6TJBM17/ezMZyKX795+Mkdtae7C19tAydc7JfHf3\nHCTJ3Ul2jJT7yamb52WJPpvzs+W/JPnekTMHmZH7bUne1Fr7q26dx0fKTZJUVSX550neOkJmS3Li\nr6UvyBJ9NiP3xUne311/T5KvO8nMWduCQT02K3eEHpuVu3Sfzckc1GNbbGeH9Njo2+85mYN6bKta\nB/TYrNyl+2xO5tAem/W+aGiPbZo7pMfmZA7ajs3JHdpj895zLtVjq3ofOyd3aI/NrXeZHpuTOWg7\nNid3UI/N+B1h8PvEzXKHbsPm5A5+rzgjd1CPzfn9a+lt2Ba5g8zIHdRj82pddht2pjIsOkNV1c4k\nL89kwj9G3rZud7zHk7yntTZG7k9l8kPpb0bImtaSvLeq7q2qfSNlXpLkeJKf7nZZfHNVnTdS9glX\nZ4QfTK21x5L8eJI/SnIsyROttXcPzc1kr6Ivq6rPrqrnZfIXqItGyD3hc1prx7rrH0/yOSNmr9K3\nJPlfY4VV1X+sqkeT/Iskbxwpc2+Sx1prHx4jb8p3dLtC377s7uCbeHEmr7MPVNVvVdU/Gin3hC9L\n8iettd8fIesNSX6s+379eJLvGyEzSQ5n8qY4Sb4+A/psw7ZgtB4bexuzQO7SfbYxc6wem84ds8c2\neQ4G99mGzNF6bMb3a3CPbcgdpc82ZA7usRnviwb32Creby2QuVR/zcod2mOb5Q7tsTnPwaD+mpE7\nuMe2+J4t1WMzMgf314zcoT222e8IY2zDVvW7x1a5y27DNs0d2GPPyBxpGzbrORi6Ddssd2iPzft+\njfk+ce0ZFp2BquozkrwtyRs2TJ+X1lr769bayzKZil9eVV80sMavSfJ4a+3eMerb4JVdrXuS3FBV\nXz5C5jmZHCpya2vt5Un+MpNdYEdRVecmeV2SXx4h6zMz2UBfkuTzkpxXVd8wNLe19mAmu9G+O8m7\nktyX5K+H5s74v078teu0VlU/kOTpJL8wVmZr7Qdaaxd1mTcOzesGe9+fkQZPU25N8veSvCyToeRP\njJR7TpLPymQ39n+T5Je6v/KM5ZqM99eib0vynd336zvT7Xk4gm9J8u1VdW8mh848tUzIvG3BkB5b\nxTZmXu6QPtssc4wem87tahulxzapd3CfbZI5So/NeR0M6rFNcgf32SaZg3tsq/dFy/bY2O+3tsoc\n0l+zcof22Ca5X5yBPTaj1sH9NSN3cI9t8TpYqsdmZA7urxm5S/fYIr8jLNNfq/rdY6vcZXtsXu6y\nPbZZ5hjvE+fUOqjH5uQu3WMLvA7GfJ+4/tppcCzc2XpJsjMjnrOoy3xOJsebftcK635jhp+r5D8n\nOZrJ+YU+nuTJJD+/glp/cGitXc7nJnlk6vaXJXnniHXuTfLukbK+Pslbpm5fm+S/reC5/U9Jvn3A\n4z/l9Z/koSQXdNcvSPLQGLlT978vSx6Lvllmkm9O8jtJnjfWc7Bh2cXL/nyYzk3yDzL5a98j3eXp\nTPY6+9wRa136Z9kmr4N3Jfmqqdt/kGT7SN+zc5L8SZIdI9X6RJLqrleST67gdfDiJL+7ROYztgVj\n9NhmuVPLhvTYprlD+mxerd3ypXpsY+6IPbZVvSfdZzNeB4N7bM73a2iPbVbvoD5b4Hldqsc2ZLwx\nyfeM0WOb5U7dXrrHNssc0l9b1drdt/R2bEPuvx+jx7ao9aT7a87rYJTt2Izv2aAe26TWUbZjWzy3\nJ9VjmfE7wtD+mpU7tXyp/pqXO6THtqq3W+ekemxG5tuG9teCtZ50j815LSzdY1t8v0bprzPpYs+i\nM0g3UX1Lkgdbaz85Yu726s7gX1XPTfKaJB8bktla+77W2o7W2s5MDr/6jdba4L1fquq8qnr+ieuZ\nnFxu8CfOtdY+nuTRqnpJd9erk3x0aO6UMafYf5Tkiqp6XveaeHUm52kYrKpe1P17cSbnK/rFMXI7\nB5J8U3f9m5L8zxGzR1VVV2ay++rrWmtPjpi7a+rm3gzssyRprX2ktfai1trOrt+OZnLC148Pya2q\nC6Zufm1G6LPOr2Zy4sJU1YszOZn8J0bK/sdJPtZaOzpS3h8n+Yru+quSjLLL8lSffVqSf5dk/0k+\nfta2YFCPrXAbs2nukD6bkzmoxzbLHaPH5tS7dJ/N+X4N6rEtXgdL99ic3KX7bM7zOrTHZr0vGtpj\no7/fmpU5dDs2J3doj22W+6EhPTan1kHbsTnfr6E9Nu91sFSPzckctB2b89wu3WNzfkcY1F+r+t1j\nVu7QHpuTu3SPzcj8uqHbsDm1DuqxOd+zpXtsi9fB2O8T19+zPa06Wy+ZDAaOJfl/mTTlvxwh85WZ\n7JJ5fyaHCN2X5KoRcr84yYe63Acy8tnhk3xlRvo0tEx2dfxwdzmc5AdGrPNlSQ51z8OvJvnMkXLP\nS/J/k7xgxFp/KJONxwNJfi7dpwWMkPu/MxmSfTjJqwfkPOP1n+Szk/x6Jm9U3pvks0bK/dru+l9l\n8teCu0bIPJLk0ak+W+aTKDbLfVv3Pbs/ya9lckLewbkblj+Sk/+kps1q/bkkH+lqPZDur30j5J6b\nyV+NHkjywSSvGus5SPIzSa4f8TX7yiT3dv3wgST/cKTc12fyqU2/l+RN6f7qexKZm24LhvbYnNyh\nPTYrd+k+m5M5qMdm5Y7QY7PqXbrP5mQO6rF5z8HAHptV79J9NidzaI9t+r5ohB6blbt0j83JHLQd\nm5M7tMe2fM95sj02p9ZB27E5uUN7bOZzsGyPzal10HZsTu6gHpvK/8r87SdgDX6fOCN30DZsTu7g\n94ozcge/V9yYueH+k+qvLWod/F5xRu7g94qbPQfL9teZfDmx2yEAAAAAOAwNAAAAgL9lWAQAAABA\nz7AIAAAAgJ5hEQAAAAA9wyIAAAAAeoZFAAAAAPQMiwAAAADoGRYBAAAA0Pv/GOoroE6l+FYAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4c2f9f5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGB feature imp\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.bar(range(1, len(xgb1.feature_importances_)+1), xgb1.feature_importances_)\n",
    "plt.xticks(range(1, len(xgb1.feature_importances_)+1))\n",
    "plt.show()"
   ]
  }
 ],
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