8_regression_model_luther.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Lasso, LassoCV, Ridge, RidgeCV, ElasticNet, ElasticNetCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.style as style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('result_data_2.0.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(519, 113)"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.loc[:,[\n",
    " 'Yrs',\n",
    " 'GP',\n",
    " 'MPG',\n",
    " 'FG_Perc',\n",
    " '3P_Perc',\n",
    " 'FT_Perc',\n",
    " 'APG',\n",
    " 'SPG',\n",
    " 'BPG',\n",
    " 'PPG',\n",
    " 'Duke University',\n",
    " 'Georgia Institute of Technology',\n",
    " 'Indiana University',\n",
    " 'Louisiana State University',\n",
    " 'Ohio State University',\n",
    " 'Stanford University',\n",
    " 'Syracuse University',\n",
    " 'University of Kentucky',\n",
    " 'University of Memphis',\n",
    " 'University of Southern California',\n",
    " 'Total_Height_in',\n",
    " 'PPG_top3',\n",
    " 'PPG_top5',\n",
    " 'PPG_top7',\n",
    " 'PPG_top13',\n",
    " 'top3/top5',\n",
    " 'top3/top7',\n",
    " 'top3/top13',\n",
    " 'top5/top7',\n",
    " 'top5/top13',\n",
    " 'top7/top13']]\n",
    "\n",
    "y = df['PTS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = StandardScaler()\n",
    "std.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_tr = std.transform(X_train)\n",
    "X_te = std.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1300c8160>"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X/QVioXvK87z/uNsDu677w8BLwD/3PO99rut+FnCJjd4LwKc8z8t3ev/GxkZ9YBcuXOj0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\ngK8Cnwf+LvBZ4HXgHa7rOp7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08CZ2I9Aw51hUa1milSj28IZa12tqnpiymx6o80VFUWnU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1zZFO8h77Ba75ktG56cjZhI2/zs2RrfXIm5kXnI5/ilgfPU8i5FFg9PRe/47t69lCQP4q2LN4Pc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\nMBh6yv8CT+DY9eteYVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fb3eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(10,4))\n",
    "\n",
    "ax[0].scatter(X_train['top3/top5'],y_train, alpha = .5)\n",
    "ax[0].set_xlabel('PPG Top3/Top5')\n",
    "ax[0].set_ylabel('PPG Rookie Yr')\n",
    "\n",
    "ax[1].scatter(X_train['top7/top13'],y_train, alpha = .5)\n",
    "ax[1].set_xlabel('PPG Top7/Top13')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model1 = LinearRegression()\n",
    "lr_model1.fit(X_tr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Yrs', -0.012615236364467016),\n",
       " ('GP', 0.20093237380296336),\n",
       " ('MPG', -0.81976992002779114),\n",
       " ('FG_Perc', 0.5435788987952167),\n",
       " ('3P_Perc', 0.18124506158306475),\n",
       " ('FT_Perc', 0.061755210015261852),\n",
       " ('APG', 1.2437466029396114),\n",
       " ('SPG', 0.60134679853041828),\n",
       " ('BPG', 0.10442238823197925),\n",
       " ('PPG', 1.3568893320822584),\n",
       " ('Duke University', 0.26471141685146016),\n",
       " ('Georgia Institute of Technology', 0.15073523159619601),\n",
       " ('Indiana University', 0.43690357229443921),\n",
       " ('Louisiana State University', 0.10589647476910688),\n",
       " ('Ohio State University', 0.14432611424671415),\n",
       " ('Stanford University', 0.48803119294472819),\n",
       " ('Syracuse University', 0.47401577202238016),\n",
       " ('University of Kentucky', 0.58323982320735024),\n",
       " ('University of Memphis', 0.0091073048545513291),\n",
       " ('University of Southern California', 0.45275020055984266),\n",
       " ('Total_Height_in', 0.46045546238503232),\n",
       " ('PPG_top3', 7.9023135363661519),\n",
       " ('PPG_top5', -10.129400568309659),\n",
       " ('PPG_top7', -1.557637644663288),\n",
       " ('PPG_top13', 6.2778810636062481),\n",
       " ('top3/top5', 16.357251312469263),\n",
       " ('top3/top7', -29.201130055684686),\n",
       " ('top3/top13', -1.9950471087853296),\n",
       " ('top5/top7', 6.7218683900075886),\n",
       " ('top5/top13', 20.000529305082672),\n",
       " ('top7/top13', -9.2971353495066253)]"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,lr_model1.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_pred1 = lr_model1.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x130d1cda0>]"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PH2bcuHGxr5999lkmTJjAZZddxuc+9zluvvlmzGYzV1xxBXV1dTkJVohsqrVK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\nJIQQoigkAQkhhCiK/x8OpREbFgjMQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130d1cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "\n",
    "plt.scatter(lr_test_pred1,y_test,alpha=0.5)\n",
    "plt.plot(np.linspace(0,12,1000),np.linspace(0,12,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinReg MAD:  3.2521702580196843\n",
      "LinReg RMSE:  4.02173875643\n",
      "LinReg MSE:  3.25217025802\n",
      "LinReg r2:  0.0962422054315\n"
     ]
    }
   ],
   "source": [
    "print('LinReg MAD: ',np.mean(np.abs(lr_test_pred1 - y_test)))\n",
    "print('LinReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred1)))\n",
    "print('LinReg MSE: ', mean_absolute_error(y_test,lr_test_pred1))\n",
    "print('LinReg r2: ',r2_score(y_test, lr_test_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LassoCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alphavec = 10**np.linspace(-3,9,27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha:  0.070170382867\n",
      "cv:  5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "lr_model2 = LassoCV(alphas = alphavec, cv=5)\n",
    "lr_model2.fit(X_tr,y_train)\n",
    "\n",
    "print('alpha: ',lr_model2.alpha_)\n",
    "print('cv: ',lr_model2.cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model3 = Lasso(alpha=0.1)\n",
    "lr_model3.fit(X_tr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Yrs', -0.090642158839793624),\n",
       " ('GP', 0.19253829415003601),\n",
       " ('MPG', -0.17863071423267043),\n",
       " ('FG_Perc', 0.42681818974925179),\n",
       " ('3P_Perc', 0.0),\n",
       " ('FT_Perc', 0.0035232463916359228),\n",
       " ('APG', 0.56467012161665719),\n",
       " ('SPG', 0.38204215959609039),\n",
       " ('BPG', 0.048175822856854962),\n",
       " ('PPG', 0.99977186316989775),\n",
       " ('Duke University', 0.11592759232632072),\n",
       " ('Georgia Institute of Technology', 0.07409118018681389),\n",
       " ('Indiana University', 0.27153327196489774),\n",
       " ('Louisiana State University', 0.0),\n",
       " ('Ohio State University', 0.10518606219754439),\n",
       " ('Stanford University', 0.27167721630354141),\n",
       " ('Syracuse University', 0.35853537538232794),\n",
       " ('University of Kentucky', 0.42577296406008119),\n",
       " ('University of Memphis', 0.0),\n",
       " ('University of Southern California', 0.30308923554262918),\n",
       " ('Total_Height_in', 0.0),\n",
       " ('PPG_top3', -0.078244680406691941),\n",
       " ('PPG_top5', -0.0),\n",
       " ('PPG_top7', -0.0),\n",
       " ('PPG_top13', 0.0),\n",
       " ('top3/top5', -0.060432264332225108),\n",
       " ('top3/top7', -0.0),\n",
       " ('top3/top13', -0.0),\n",
       " ('top5/top7', 0.0),\n",
       " ('top5/top13', -0.0),\n",
       " ('top7/top13', -0.38541231092155681)]"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,lr_model3.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_pred2 = lr_model3.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x130388e48>]"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aRQKQ6ChBAkGldTNAzWto1ltav9Sesws2E/MOQ8t6iGfSDocn/I3bHjuXw/Hi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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130388f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "\n",
    "plt.scatter(lr_test_pred2,y_test,alpha=0.5)\n",
    "plt.plot(np.linspace(0,11,1000),np.linspace(0,11,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LassoReg MAD:  3.1024060888752087\n",
      "LassoReg RMSE:  3.88804133701\n",
      "LassoReg MSE:  3.10240608888\n",
      "LassoReg r2:  0.155331904406\n"
     ]
    }
   ],
   "source": [
    "print('LassoReg MAD: ',np.mean(np.abs(lr_test_pred2 - y_test)))\n",
    "print('LassoReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred2)))\n",
    "print('LassoReg MSE: ', mean_absolute_error(y_test,lr_test_pred2))\n",
    "print('LassoReg r2: ',r2_score(y_test, lr_test_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha:  119.377664171\n",
      "cv:  5\n"
     ]
    }
   ],
   "source": [
    "lr_model3 = RidgeCV(alphas = alphavec, cv=5)\n",
    "lr_model3.fit(X_tr,y_train)\n",
    "\n",
    "print('alpha: ',lr_model3.alpha_)\n",
    "print('cv: ',lr_model3.cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=119.37766417144383, copy_X=True, fit_intercept=True,\n",
       "   max_iter=None, normalize=False, random_state=None, solver='auto',\n",
       "   tol=0.001)"
      ]
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model4 = Ridge(alpha=lr_model3.alpha_)\n",
    "lr_model4.fit(X_tr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Yrs', -0.15525917078230689),\n",
       " ('GP', 0.21069522333982077),\n",
       " ('MPG', -0.12544298446223348),\n",
       " ('FG_Perc', 0.40748218056478192),\n",
       " ('3P_Perc', 0.066630218684927078),\n",
       " ('FT_Perc', 0.17861720915582557),\n",
       " ('APG', 0.50339328515697335),\n",
       " ('SPG', 0.44065781230637996),\n",
       " ('BPG', 0.10715619560921087),\n",
       " ('PPG', 0.74056293869656054),\n",
       " ('Duke University', 0.1627107975096268),\n",
       " ('Georgia Institute of Technology', 0.13258160050946369),\n",
       " ('Indiana University', 0.29724124307419514),\n",
       " ('Louisiana State University', 0.047646219801875896),\n",
       " ('Ohio State University', 0.15990368211141645),\n",
       " ('Stanford University', 0.28462383888290188),\n",
       " ('Syracuse University', 0.34771602682312019),\n",
       " ('University of Kentucky', 0.38551974800646899),\n",
       " ('University of Memphis', 0.021071053430279093),\n",
       " ('University of Southern California', 0.32336935884801399),\n",
       " ('Total_Height_in', 0.095286993262857778),\n",
       " ('PPG_top3', -0.10004542455106902),\n",
       " ('PPG_top5', -0.049422078389546605),\n",
       " ('PPG_top7', -0.11314905403850233),\n",
       " ('PPG_top13', 0.18706544391618507),\n",
       " ('top3/top5', -0.084820627018331804),\n",
       " ('top3/top7', -0.024815049273517881),\n",
       " ('top3/top13', -0.10231501464428056),\n",
       " ('top5/top7', 0.15660609411670354),\n",
       " ('top5/top13', -0.054543573294334763),\n",
       " ('top7/top13', -0.20723762555609779)]"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,lr_model4.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_pred3 = lr_model4.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x131ff8048>"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f5gpUdGexu2jRIk6/bJPJhNHRUc6+EBERQQuLNpuNnrsc50Cz2Uyfu96BiEFBQUhJSWEd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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131dd88d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "style.use('fivethirtyeight')\n",
    "plt.figure(figsize = (10,6))\n",
    "\n",
    "plt.scatter(lr_test_pred3,y_test,alpha=0.5,)\n",
    "plt.plot(np.linspace(0,12,1000),np.linspace(0,12,1000))\n",
    "plt.xlabel('Predicted PPG')\n",
    "plt.ylabel('Actual PPG')\n",
    "\n",
    "\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize = 18)\n",
    "plt.axvline(x = 0, color = 'black', linewidth = 1.3, alpha = .7)\n",
    "plt.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7)\n",
    "\n",
    "plt.text(x = -0.8, y = 26, s = \"Predicted Rookie Year PPG vs Actual\",\n",
    "               fontsize = 34, weight = 'bold', alpha = .75,color = '#f0f0f0', backgroundcolor = 'grey')\n",
    "plt.text(x = -0.8, y = 22, s = \"Each point represents a drafted rookie in the test set since 2003\\nwho played at least one game in the NBA.\",\n",
    "               fontsize = 19, weight = 'bold', alpha = .75)\n",
    "\n",
    "plt.text(x = 5.6, y = 20, s = 'Kevin Durant', weight = 'bold', rotation = 0,\n",
    "             backgroundcolor = '#f0f0f0')\n",
    "#plt.text(x = -2, y = 5, s = 'James Augustine', weight = 'bold', rotation = 0,\n",
    "#              backgroundcolor = '#f0f0f0')\n",
    "plt.text(x = 9.4, y = 1, s = 'Ridge Regression\\nr^2 = 0.162', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x130451400>]"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s0zT5a0+IHcM1ddqGkged6uGS1Wsa3Fw+2yEn/0XaJACJjBt9TsKvm9GkkroZ\n/jqaryzL5yYyceg0lwdXU/leFVYmvY71bm+keqiXQwPJg06lVeHq+eGS1VfOceKQoCMyQAKQyLjR\nu7ucFoXIzIxFCX8dke2dZ5k4dJrLg6upfq901o4O9J2aXtuXQslql0UJl6xucPGxuU5cVgk6IrMk\nAImMG727y2FRqHWo9Ph1pjktMdmas7nzLBMpU3KZdmUi3yvVtaP3BzR2tPjY3uLlnd7kQceiwPxK\nC2dMsbJitpMvDO+oEyIbJACJrBh9h76w2kKXXWWaU6U/aORk51kmUqbkMu1KOt8r3jpW26DGM8OF\n3N7qTq1k9eIaK/MqLJxZa4tOrx3z6pJcU2SVBCCRFYnu0HO58ywTh04j7xHQTfy6iXN4RDeR98hF\ne496dJ5pDa/p/DmFktVWBS6fHS5Z/bezHfzi3SFJrilyTgKQyKrRd+jZOjeRaJfbZBfrHapCp9fg\n4ICGQrjsc61D5YwaK401mZ0+nGh7j3vDJat3tPh49UQw6furClw6XLL6ugVOpg6XrG4f0iS5psgL\nCUCiqGU7Jc0TB73McKn0B8NbyHUTuvw6U+wq3zkv89OHydp70qfz9DErfzx4kleOB0mWfU0BLp5p\nHw46Lk5zja0eWkjpiUR5kQAkilo2U9JENwXYVJbV2QmOmIbTTZOAYWZ8gT5ee3sDBv/3nUFeOhpg\nV2cQ3bQD4494PnTaqZLVs9yJS1ZDYaUnEuVFApAoWtlOSTN6U0Dk8CxAf9CMOzWVqcOqhgn/0xHg\nZ+8Msb9PI4XioZxXZ2N1g4tV9S7mTjCjdyGlJxLlQwKQKFqZTkkzOnikOjU1GDI44dN58Yifox49\n7cOqgyGDne3hktUvHU2tZPUHhktWr2pwUV+V/p9zIaUnEuVDApAoWplKSTPeOtJ4U1MOVWHbfg+H\nBkK81RViSDOY7rRwzlQbFlVJ6bCqJ2Tw/JFw0HnhiB9/8qQETLGarD27ik83VtBYk9k/4UJKrhlv\nNJnvbOoisyQAiaKViZQ0MP460nhTU5HXqYqCTzdxWlT6ggZ7e0Isq7Mn3Mbs00xeOBIuWf27I368\nKZSsnupQOWuKlTNrrXj6+/iHxRU5K5yXa/FuCBZUWjlxwoKvbyDr6ZBE7pTmb7AoG5Ndu0iWfSBg\nmAnPM0Ve1x80ornuLIoSk3A1MhVoVxVeOhoOOr9t9zOUQtCpsSssqbVx1hQrdU41WsgtMKiU9M60\neDcEO9uvY9uNAAAgAElEQVR9eLwWLqvOfjokkTsSgERRm+zaxciNBkHdxKebuIY3G4xcRxo9NTXy\ndSNz3cGphKsWFTo8Gve8McDzR/wMpFCyev5wyerVDS72dAVpG9LHjO7mO0v3XE68G4KgbjIQMvHr\nCgHdPHUQWA7KFj0JQKIkpLt2UetQUYG3uoLRcz4WJTzldXq1NeFIY/QGBZdFwaOFRzpDIYP/PRbg\n4ICW0prOHLeFVQ3hmjrn1p0qWb2k1hZ3dHdBVQpvWqTipSPy6Sa6Gd4ZODIAgRyULXYSgERZq7Kp\ndPsNuv06douKbfjfu/06NfbEQa3KprKg0srOwz76AwZ9QYPjXgOPRkpbpmcMl6xe0+Dig6fFL1md\naHTX3Jz+9aYrV4v/8XYeuiwKFiWcyWF0GQg5KFvcJACJgpSrDm8wZFDnVOgLWugdOQJyWqhzKgmn\ndwzT5P0Bjf19Gsd9BqEUok6dMxx0Vje4uOi01EtW53NnWi5rIUH8Q7F2i4LbApoSO4UpB2WLX9oB\n6KabbqKiIrz4N3v2bO66667oY8888wzbt2/HarXyxS9+kUsvvXTyLRVlIdcdXm/AQEdheZ2dgG5G\np3gcFoX+oBEzvWOaJm90hdje4mVHi49j3uRRx2GBhdVW/naWg++cW427yDrLXNZCihi5scSnmxzq\n1wAFFXi5w0+1TeWcqVYWT7HLQdkil1YACgQCmKbJli1bxjzW1dXFk08+ybZt2wgGg6xdu5YPfehD\n2O32STdWlL5cd3gjp3wigSfCroZ3ou3pCoZLVrf6aE+hZLVFgdOrLCyvs7OgyoJFUQjqJk+97yuq\nHVu5rIU0+v0jU4+PvDeEXVGotKv0dHuonOJkMGQwv8paVD9LEV9aAai5uRm/38+GDRvQNI2vfe1r\nLF26FIB3332XD3zgA9jtdux2O3PnzuXgwYMsWbIk5feejMm+vliVwnV7NNh92IYrTp+2uweWGR1U\njPqNzcR1V/osdPhVIn2pacKJIHQFFS45OMhhf/JO1qaYLHCFd6h1+BU+VBXCHvLT35P8GtKRi8/7\nmF/hRI+Fqjgjz0HNZM++LmY6k+/sS5dHg0PHw78PkR/jUF8vCvD2IOxRjmfkZ1kMivnve9GiRQkf\nS+vjczqdfO5zn2PlypW0t7dzyy238NRTT2G1WvF4PFRWVkaf63a7GRoaykhjk2lubp7U64tVqVx3\n+5BG5cAQNXHSG/QHDabNq4w5fJmp677l9PC03+snAuzr03h/UKM3kLxjtatQX2Xl7ForDdVWbKpC\nl19H79OYOd2R0jWkI1ef98yQwYv+gTEjIACXZrD8zOqsrr+M/n3o6e5h6rSpQOZ+lsWgVP6+40nr\n05s/fz5z585FURQWLFhATU0N3d3dzJgxg4qKCrxeb/S5Xq83JiAJkUg+ygIc6teiazrv9iUvWe20\nwMfmOlnd4OLyWQ6ebfNzaCCEVzOxqyaLa6w4J3ANhZxaJt9ZsqVMROlLKwA9++yzHDp0iNtvv52T\nJ0/i8XiYNm0aAEuWLOFnP/sZgUCAUChEa2srjY2NGW20KE2Z7vASde6tgxrPtIRLVv+1J3n1ULsK\nV85xsqbBxVXznTHvNXqbNMDW94bo9hlUjug9R19Dos0W1y1wMqSZBROQ8pklO98BUGRfWgFo5cqV\n3HPPPaxduxaA7373uzz55JPMmzePyy67jE996lN85StfwTRN1q9fj8MxdjpCiHgy0eHF69ynOsI1\ncZ5t8/GXrtRKVl8xXLL6mvmuce+2q2wqDlWJu3OrsdqKyzr2GkZvttANk9+0efl1q4/FU6xJd//l\nauSU7yzZI38fBjUTl2ZImYgSovT19WVvFTHHSnmudDyleN2pdHiJrnvbfg+tgxoBw2R/n8a+vhBH\nPcm3TFsUuGxWOOhct8BF7QSmeCLfc2SwGAoa1LksfPmsiphrGAwZ3Lsndm1lT1eQ/oCBAVwy0xFO\nBaSb1I/a7RXUTR58pYUh19SySso5GDLYs+99lp95etmNfErx7zui9FfwRFFK9/Bly2CIHS1eDg3o\nHPYk3zKtAJfMtLOmwc319U7qnONXD40n0XblSrtKb2BsG0anmwnoJr0BA5uqEBquuhopfjd6u/MT\nB710+FVmVJVXUs4qm8pMp1l2wafUSQASRa/Hr/ObtnBNnf89FkgpFc5sdzjX28JqC39T55hU5x0v\nf1lEvFxloxfX/bo5Ipt2OLlpvNdHAt3oPjiXSTkLedOEKD4SgERR6gsYPNtpYVdLF7/vCKCnMJFs\nVcI52CqsCjV2BZ9m8m6vRvuQwXULnExNY/QDE9+tNXpxPZJNWzdNpjrUmKm0ka+PBLp4sp2UM9cZ\nKkR5kAAkisZA0GDn4VMlq0OGAwiM+5pqm8IUu0KlVWFIN7Ep4WzVuqky0z3csfsNth3w8s0PVKXV\nrkS7tYaCRsIpvdGbLSpt4VQz50y1RZ8zerdXJND54rxfNrclD4aMmJ195TT1J7JLApAoaEMhg98N\nB50Xj/qJs6Qyxtm1VuqrrFTZ4b0eDYdFocoWLhTX6TOwqOEKppppYlUUbBbo9GoxU1gTnWpKlL9s\nYTXcu2dgzGghsrvsmFejfVBnpruC33cEwwFJM+Pu/osEujd7Y793trYlR0Y97/UG+VNnEIdFYapD\njZYcl3o8YrIkAImC49NMno+UrD7sx5fC/NqZU6ysbnCxut7F4inhUcR7vUEefmeI6U4LDovCq8cD\ndHh0LBYF0wTdAEUJn7lBUegLGDHbqUdPNQUMM2FQSpS/LGL0aCHRlNY3llbh1cyEu/9uXOims9Ng\nSDOyfi4nslUcRcGmhv83suQ4SD0eMTkSgERBCOgmLx7xs6PVx852P54USlZXWUzOnGpnxWwnty6r\nGrMWMbfSyjSHGk0wumyajbYhjaARLqdgADUOlaVTbQSNcKcfLxnqof4QG/7Yywy3mtL6R28g9hAq\njN0okCjp6nNt/nGntOwWhdWzdGbWV2f1XM7InX0mRCu+ji45LhkJxGRIABJ5E9RNft8RYHuLl9+2\n+xkIJQ860xwqp1dbOLvWhtXbz7S66uhoYnTHPXptxmVTOXOKjRNejUq7lXOn23EMn7dprA6PmuJt\npz7Qr3HSbzCrwkGNPdwTJ1r/SGVHXKLvM5EprWzXCBp5HQ6LQq1DpT8Qnr6MlBwHJCOBmBQJQCKn\nNMPkD8cCbG/x8Vybj75g8qAzt8LCqnoXH5/n4PnDPty28MJ+z/Bq/Hgd9+jF/tOrrdTYVeqcyvD2\nZzM6mjnu08cEj6Bu0jMcNEaWg070PSMbBQLD53mcI0o8REYLE922nQ+jd/YtnWrj7Z4QvQEDzTAx\nMKmvkowEYnIkAIms0w2TVzqD7Gjx8myrn+5A8pM6s9ynSlafPz1csrp9SCNkxt/yO3J0MXKdJlEq\nmXiZFuJtp/bpZrRK6uhy0PGChUNV6PQaHBzQUAhPXdU6VM6osdJYc2q0UOhJNkePHi1quGhfouwO\nQqRDApDICsM0ebUzXMjt120+TviSB53pI0tWzwgHnZHGO2+jAr9t93PUo8Vdpxk9ZRVvCivedmqX\nRQFMah2WMQEoXrB44qCXGS6V/qBKz3CJ7y6/zhS7ynfOcyf8PlB4STbj5eVbPEXO/ojMkQAkMsY0\nTXafHA46rT46UihZPdWhcv0CJ6sb3Fwy045VTdyxjddxd/sNHBZ90pVU43W6C6ttzBhVJW9ksIhs\n2bapw2s7NpVldXaCI6bhdNMkYJjRduczyzSkts0834lIRemTACQmxTRN9nSH2N7iY0eLjyMp5F+r\nsStcuyA8vXbZLAe2cYLOaPGyI8+psBDUjTF35emcUxnd6VpV8GrhHXpHPXpMsLihwcW2/Z5oEPFr\nBi2DOhdMt0fPyUTa1B80Y6brxpsaPO7TYwJDJtPfpJPRINsbHkT5kgAkJsw0Tfb2auxo8bK9xUfr\nYPKgU2VTuGa+kzUNbq6Y7Uh7Cmdkx71nXxfLz6ymN2Dwb+/Er7qb7qK+Q1V46WggpqOeU2Hlo3Mc\nTHdbqLKp0QzYkVGX06Iw1KPFnJOJtjvB2k6kcw/qZkwws6uwYLjaZ2TreCRYXDChK4mVaPu3ZDQQ\n+SABSKTsvd7wSOeZVh/N/cmrh1ZYFa6aF64e+pE5TpzWzK0bjM6OnOlF/Xgd9XGvzv8eD0YD4Oit\n1A6LQp1T5aRfj56TgXBKnmmu8dsQ7/vtbPeBAudNd8QEi06vhSVnTPiSEmbtlowGIl8kAIlxNfeH\n2DE8vfZeCiWrXRaFj81zsKbBzUfnOnBbs9+hZXJRfzBksL8vxOsn/Ex3xf55jOyoE22lXjrVxu4T\nJr1BA5uicGhAA0waTWvclDyR7zk6MASHSzRoJgwGDarsp6bu2v1qWsGiGLZ/i/IiAUiM0TqosX24\nZPXeFEtWf2TucMnqeU4q89CJTWRRP96aSlA3+Y8DHv6r3c8xr85Jn06lLciiGivLpoXXdOBUR51o\nR55FVVhWZ2P92ZX8v4M+7CrjpuSBsYFBN0z+cjLIEY+OAbxyPMBMt4WlwznYNMNMK1hMNGu3ENkm\nAUgA0D6k8UyLjx2tPt5MoWS1TYUVsx2sbnBz9XwnNYl6thxJZcfWeAvwTxz0srPdh1czqbEr9AcV\nQobJwX4NVQmfgYFTHXWyUVelLVyMLllKHhgbGN7uCeHVwlkHLIDTqtAfMHi7J8TyuvBOwXSCRbFs\n/xblI60ApGkamzZtoqOjg1AoxM0338xll10Wffzxxx/n17/+NbW1tQDccccdLFiwIDMtFhnT4dF5\nptXHjhYvu08mDzoWBf52RMnqQrxjTrRja7ySAv++38P7gyEGQmZ0R57LouDXIWBAl08noJsoxKae\nGW/UFS+rQsTo6a6RgcEkPCKyW1QcqgEKWBUFlPC/DwUN5jvTnyrL9/ZvIUZKKwDt3LmTmpoa7rnn\nHvr7+/nc5z4XE4D27dvHxo0bOeusszLWUJEZnV6dZ9vC02uvdgZJlghHVeDDMx2saXBx3QIn09Is\n2pYvqZQUeK83iFcLZzyIVOM5zaVywmcwFDIY1Ez6AzrL6xwxHfV4o66JTndFAsObXYFo9u9FNeHW\n9AXDB1o1w6TOZeHjU5LvOky0dVvO9ohCklYAuvLKK1mxYgUQ3pJrscR2Svv27WPbtm10d3dzySWX\n8IUvfGHSDRXp6/brPNvqZ3uLl1c6g9Hyz4kowIUz7KxpcHH9Ahcz3MUVdEZKpaSAqijY1PAIL0JV\nFGa6Lfh1lTNrrdz2N9XMcsf/c0k1qwIknu6KBIaPzXOwec8gtfZTlVEjB1oNTL58VgXHWxNfb6rn\nfORsjygESl9fXwrFjOPzeDx861vfYuXKlVx11VXRf9+6dSuf/OQnqaio4LbbbmPNmjVceumlKb1n\nc3Nzus0RI/SH4PfdFl7ssrK7T0Un+RbopVU6H63TubJO5zRH2r8WBcOjwSOHbbjU8LTXmwMqkZ3g\nIRPOrTbCFUYNmGE32NVrwasr0UCkm+C2mFwxTWf1rBQq4Y0SMuC5TgvtfhXNMLGqCvOdBtfO0Bmv\n799xzEKHX415TsiA2U4jaTsm81ohsmHRokUJH0t7E0JnZyff/va3+bu/+7uY4GOaJjfeeCOVlZUA\nXHLJJRw4cCDlADReY5Npbm6e1OuLVeS6+4MGv233s6PFy/90BAglz4TDuXU2Vte7WNXgYl5lce1J\nSfZ5tw9pVA4MRTdIzFKC0ZICim5irbJhAEtrrHzxjMroLrhuf7ijnuZU+cR8Fzctrkj54Ozoqa8l\nZzDh6a5bTh87ijlnxCgm0XUPhgyG+gaYUTX2ewxpBjPrq4t61DPedWcqU0QhKuV+La0ep7u7mw0b\nNvCtb32LD37wgzGPeTwePv3pT/Of//mfuFwu/vznP3PddddlpLFirKGQwe9OWri7vZsXj/gJphB0\nlk61saYhnPSzvqq4gs5EJCopEC7NrfPnEwZT7Bacaniq7qbFFfz9QjdHPTqYJnMqrSl3aONNfU10\nuivddZpyO+eTTlohUVjS6n0effRRBgYGaGpqoqmpCYBVq1bh8/lYvXo1X/va11i/fj12u50LLriA\nSy65JKONLndezeD5w+FCbs8f8ePXHYB/3NcsmWJlVUM4/9rCGtu4zy0ViUoKvHbcT32lhfNPc0Qz\nXI88n3PmlPQyJxzoC6ET3kFntygc6Avx8DuDrD2rMq2Of6KBq9zO+UhaoeKXVgC69dZbufXWWxM+\nfs0113DNNdek3Sgxll8zeeGon2dafPz34dRKVi+qsbK6wcXqehdn1ZZH0Blt9LZjExNVVbiw7tTh\nUphcOpoev86vW30MhcK71VRMvBq4rOGzRCe8OmfV2rN+Z15O53wkrVBpKN35lwI1kfnqoG7yUoef\n7S0+drb7GUyhZHVDlYU1DS5WNbg5p9aKopT3VMTo6ayhkMG2A96Y4BMxepoq1c9q234vvQEDt1XB\nBhz3Gvg0A7+uMsWhoihKzu7My+WcT7lNN5YqCUA5kup8dcgwebkjwI7WcMnq/hRKVs9yGHxyUTVr\nGlwsm2Yr+6ATT2Q6azBkJJ2mmsjawmDIoNOnRXedaaaJTzexqAo+3WQK4SSlE70zT3dhvVzO+ZTb\ndGOpkgCUI+PNV392kZtXjgfY3uLjN21+elIoWT07WrLaTU1fG4sXz8vuBZSIRNNUQ0GDuuFDthNZ\nW+gNGKAo1DpU+oeTh5omoIRzulValeg6Uyp35plaWE/nnE8x7SYrp+nGUiYBKAfizVcbpslxn86L\nR/1s+ssAXf7kQec0VyTouPjQaadKVjf3Z63pYxRTJ5XIyGkqn25yqF8DFBZWw7/8pZ/mPo1zR9fz\niTOCGQwZeEIGmGZ0h91Jv4FhmuimgsuqsGzaqbW3VO7M87GwXqy7ycplurGUSQDKgch8tdM0Oeo1\n2NcbYn+fxlAKGwmmOcJBZ1WDi0tm2OOuXeRCsXZS8YycpnrkvSHsihJNGtofNOgZkfhzpMgIxqEq\nMT+LA/06qqKxfJodzYS/nFQYCuqc5rbiGpFtO9mdeb4W1ot1N1m5TDeWMglAWWaaJq2DGq91Bjg4\noKe0kWCKXeG64ZLVl85yYM1T0Bkp0klZFAUwseRwYT2begNGTMZql0XBrir0BgwCuhmdPoNTI5h/\n3z/EgX6NaptKjV3h3Dobe7qC/KUryOIpNpZOtdLltzDNqdIfNFK+M8/Hwnop7CaTtELFSwJQFpim\nyV97ThVyaxtKngKlekTJ6ssnUbI6GyJF2t4f0OgJhLcaWxSY6lAJ6GZRdFLxxOvw7cPJSju9sQEo\nqJssqLTyeLOX/3fQi6oo0Z/BOVNtnHeag76gzhcWu6MHWCd6Z56PhXXZTSbySQJQhpimybu9GjuG\nyxscGkgedBwqfGKBi7873cWVc5wxd9uFpDdg8E5viIAeLlcQWdXoCxrs7Q0VbSeVqMM/Z6qNoBHC\nNE36g+E1nhkuK37d5P2B8Cgw8ln1BQ32dAdZWGND06FixN34RO/M87GwLrvJRD5JAJqkA30htg+P\ndPb3Jy9Z7bSEa+qsOd3F9QvcuKyFGXRGsqkwEDTGlNe2KAqDQYMcVN2etNGbJyJfz6mwcNxrxHT4\nugkr611cu8DJYwc8HPfpHPFo/KUryFSHSmSXu2GanPQZtA4adPkNFOC37X6+eEbqueNGt++6BU5+\n0+bP2cK67CYT+SQBKA3vD2jDQcfLO73Jg47DAh+dEy5Z/fF5TiqK7I86ZEC1LTzdNnIThG6YVNtU\ntBTyz+XL6M0TKtDtN6hzKugoWDDp8ptMc6oYjK2SOhA0mWK30B80UBUFT8jEEzKw2FRO+g38uomC\nggKc5rJw3KtPaF0s0eaObyytwquZOVlYl91kIl8kAKWobVDjmdZwIbe3ulMsWT0cdK6e56Q6zyWr\nJ6PWoXLOVCsHB3R6R6wB1TpUFlZbCnqaZvQOr7e6gnT7dfqCllNlti0mM90WrpnvjHb4oxfnXZbw\nmo9FVXBbFZwWBY9moKBgYjLVqbJ0uMjdRBbvE+1Ae67Nn7PNHbKbTOSLBKBxHB1RsvrPKZSstipw\n+exwyepPzC/MktXpqLKpLJ5ix24Jl4yOLM4rQH1V6hmjc210EAnqJj3D5a5H7nKzWxSOerSYjnf0\n4nxkc0Jf0MBEYU6Vhb5geNptqlPl/OmO6PdNdfG+0HagyW4ykWsSgEY57g0nltzR4uPVE8Gkz1cV\nuGyWg9X14ZLVU4usZHWqRk7TmITXPwp9mmZ0EPHpp8pu6yaxu9xGBY14i/PnTLWxtydEp09HBRQl\nPO22dGpsotdUF+9lB5oodxKAgJM+nWfbwkHnleNBkp3UUYCLZ54qWT3dVZpBZ6RinKYZHUQi02gQ\nnkKMd8YnIt7ivEVVOKvWxhVzHFwz38Vv2/0c9+ox62ITWbyXHWi5UwoZPEpR2Qag3oDBb9rCazp/\nOBbASKEC9YdOs7O6wcXKehez3KUfdOIppmma0UEkMo3W7deZ6rTEnPGJFzTGW5y3WxS+eEbFpBbv\nZQda9pVSBo9SVFYBqC9g8F/tPp5p8fE/HQFSyITDeXU2Vje4WFXvYm6RlawWY4PI6dVWauwqdU4l\naZaCZKO+TIwKZQdadhVrmqFyUfI96mDIYGd7uKbOS0dTK1m9bJqN1cP510q5ZHU5SBQkJhI0ko36\nJjMqLMapzWJRaJs8xFgl2bt6Qga/O+xnR6uPF4748SdPSsCSWitrGtysrnfRWFOSP5ayNjpIFNpU\n4sh6Re1DmqxVZIBs8ih8afW0hmGwefNmmpubsdvt3Hnnncybd6oezTPPPMP27duxWq188Ytf5NJL\nL81YgxPxaSYvdVn4wZEefnfEjzeF+bUzIiWrG1ycMaU8S1aLwiBrFZknmzwKX1oB6OWXXyYYDNLU\n1MTbb7/NQw89xH333QdAV1cXTz75JNu2bSMYDLJ27Vo+9KEPYbfbk7zr5Hz+f7p5/ogD8I37vNOr\nLOGRToOLJVKyWhQIWavIPNnkUfjSCkB79uzhoosuAmDp0qW899570cfeffddPvCBD2C327Hb7cyd\nO5eDBw+yZMmSzLQ4gavnuXj+SCDuY/MrLayuD490pGS1KDSyVpE9ssmjsKUVgDweD5WVldGvVVVF\n0zSsVuuYx9xuN0NDQym/d3NzczpN4mwDLLjQCQeX0+wGH6nT+dh0nSWVBooyCL1wsDetty946f7c\nil0pXPcxv8KJHgtVcabaBjWTPfu6mOmMnVIuhetORzrXfbEKyyphUFOosppUqND2fhYal0XF/Hkv\nWrQo4WNpBaCKigo8Hk/0a9M0sVqt0ce8Xm/0Ma/XGxOQkhmvscl8/P025tbVsKbBxQdHlKwudc3N\nzZP6uRWrUrnumSGDF/0DY0ZAAC7NYPmZ1TEjoFK57omS6y49aY3rly1bxq5duwB4++23aWxsjD62\nZMkS9uzZQyAQYGhoiNbW1pjHs+meM4L864VTuHCGo2yCjyh+kbWKoB47ypG1ClHq0hoBXX755bz2\n2mt86UtfwjRN7rrrLn71q18xb948LrvsMj71qU/xla98BdM0Wb9+PQ6HI/mbClHGZK1ClKO0ApCq\nqtxxxx0x/1ZfXx/971WrVrFq1apJNUyIciIHUkU5khOXQhSQQjsgK0Q2yW+6EEKIvJAAJIQQIi8k\nAAkhhMgLCUBCCCHyQunr60uhKo4QQgiRWTICEkIIkRcSgIQQQuSFBCAhhBB5IQFICCFEXkgAEkII\nkRcSgIQQQuSFBCAhhBB5UfTJSA3DYPPmzTQ3N2O327nzzjuZN29evpuVdZqmsWnTJjo6OgiFQtx8\n881cdtll+W5WzvT09PAP//APPPzwwzGZ2Evdo48+yh/+8Ac0TeOGG25g5cqV+W5S1mmaxsaNGzl2\n7BiqqnLnnXeW/Ge+d+9eHn74YbZs2cLhw4f53ve+B0BjYyO33XYbqloaY4eiv4qXX36ZYDBIU1MT\nX//613nooYfy3aSc2LlzJzU1NWzdupWHHnqIe++9N99NyhlN0/jhD39YdnWm3njjDf7617/yyCOP\nsGXLFjo7O/PdpJx45ZVX0HWdX/7yl3z5y1/mZz/7Wb6blFWPPfYY3//+9wkGgwA8+OCDrFu3jq1b\nt2KaJi+//HKeW5g5RR+A9uzZw0UXXQTA0qVLee+99/Lcoty48sor+epXvwqES6JbLJY8tyh3Hnro\nIdasWcP06dPz3ZScevXVV1m4cCG33XYbt956Kx/+8Ifz3aScmD9/PrquYxgGHo8Hq7XoJ27GNXfu\nXDZv3hz9et++fZx77rkAXHzxxezevTtfTcu4ov8kPR4PlZWV0a9VVUXTtJL/JXW7w5UyPR4Pd9xx\nB+vWrctzi3LjueeeY8qUKVx00UVs27Yt383Jqb6+Po4fP84DDzxAR0cHt956K0899RRKiZefd7vd\nHDt2jE9+8pP09/fzwAMP5LtJWbVixQo6OjqiX5umGf2M3W43Q0ND+WpaxhX9CKiiogKPxxP92jTN\nkg8+EZ2dnaxfv56rr76aq666Kt/NyYlnn32W119/nXXr1nHgwAE2btxIV1dXvpuVEzU1NVx44YXY\nbDYWLFiA3W6nt7c3383Kuscff5wLL7yQp59+ml/96lfcc889BAKBfDcrZ0au93i9XqqqqvLYmswq\n+gC0bNkydu3aBcDbb79NY2NjnluUG93d3WzYsIF//Md/5Prrr893c3LmF7/4BT//+c/ZsmULixcv\nZuPGjdTV1eW7WTmxbNky/vSnP2GaJidPnsTv91NTU5PvZmVddXV1dJajuroaTdMwDCPPrcqdxYsX\n88YbbwCwa9culi9fnucWZU7RDxUuv/xyXnvtNb70pS9hmiZ33XVXvpuUE48++igDAwM0NTXR1NQE\nhBcrnU5nnlsmsuXSSy/lzTff5Atf+AKmafLtb3+7LNb+brzxRjZt2sTatWvRNI3169fjcrny3ayc\n+SXHyrAAAABbSURBVMY3vsEPfvADQqEQDQ0NrFixIt9NyhgpxyCEECIvin4KTgghRHGSACSEECIv\nJAAJIYTICwlAQggh8kICkBBCiLyQACSEECIvJAAJIYTICwlAQggh8uL/B8OJnfyrKZrYAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130451be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "\n",
    "plt.scatter(lr_test_pred3,y_test,alpha=0.5)\n",
    "plt.plot(np.linspace(0,11,1000),np.linspace(0,11,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RidgeReg MAD:  3.077059835301955\n",
      "RidgeReg RMSE:  3.87243782989\n",
      "RidgeReg MSE:  3.0770598353\n",
      "RidgeReg r2:  0.162097952867\n"
     ]
    }
   ],
   "source": [
    "print('RidgeReg MAD: ',np.mean(np.abs(lr_test_pred3 - y_test)))\n",
    "print('RidgeReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred3)))\n",
    "print('RidgeReg MSE: ', mean_absolute_error(y_test,lr_test_pred3))\n",
    "print('RidgeReg r2: ',r2_score(y_test, lr_test_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression on Entire Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_modelX = Ridge()\n",
    "lr_modelX.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_predX = lr_modelX.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x131757a20>"
      ]
     },
     "execution_count": 600,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+KyuLM3DKuY6p7RmdzUKxsbGcQQ11dXWMv/v6+liTg0wmw5o1axjfnEqM7+wz\nZbfbUV9f7+YNp3FsC5OTk6zcrVKpdNY7u/iy78033d3dLOEyJSWFtf1rUlISLeBSUcUqlcqtT6hQ\nKEROTg5jYRobG4vCwkLWufOd2ul64eLFi6z+6+fnh3Xr1mHhwoUsa45IJIJarcaGDRsYuw36EovF\nwkqh5qgwoDIxcDExMQGlUomgoCC6DajVamRkZLDKGhMTw1p0zyYaXyqVsu6j0+m8DvbkyvrgPK63\nt7ezlCBpaWlYsGABo25iY2MZm5QA0/XpnEHHuY8GBgbS/cp5Pna2RvH5fOTl5THqU6lUznp72dTU\nVEawL+UixGU5u977NdG4+hiDwYDW1lZGh+Hz+cjJyUFmZiaMRiOkUimnmaC6upp1rK6uDnl5eYxj\nkZGR2Lp1K92hr4SWpKGhAbGxsYxyBgcHY9OmTTAYDLDb7ZBKpZyrPmdhhMvvMTMzE/Hx8ZBIJGhv\nb0dlZSWA6XRBzgm6o6Ojaf8sgUDAadLv6elhBfa0tLQgKSmJYSrx8/PDypUrMTk5CZPJxBho5hOj\n0Yiqqirk5OQwjovFYixatIil6aB2aHJ+txUrVmBqaorWNnOZifr6+jiFvEuXLrGEF4VCgU2bNsFo\nNMJisbg0v9fU1Hh0E+CisbERiYmJjEE+LS0N7e3tM06Q7eu+N5+YzWbU19ezFggZGRlITU2FXq93\n+X1ra2sZk/PIyAhr8kpISEBsbCzdTlwJTldCoLoRMRgMKCkpwYoVKxj9g9qZLy0tDSaTCZOTk7Tb\nh6dAW18EpLW2trr0Ge3v73fpk26xWNDQ0MDavY3adc5oNMJut7tsO7MJjObStnraqMP53OzsbEa9\nRkVFQSKR0O9pMBjQ1tbGGCN4PB6ys7OxcOFCGAwGl/2qqamJtbg1mUwMs76/vz+2bt0Ks9mMgIAA\nfP7557RwOzQ0xOqH0dHRUCqVMBgMkEgk9HPtdrvb9kFtv+3Y1vz8/LB+/XoYDAbweDy3rm/Xe7++\nvsXua5TLly9zmqNFIhHkcjnnxNnc3MyZD7W9vd3l7imBgYEMoXVwcNBn+RSNRiMuXLjAqS0NCAjg\nFGDsdjvKy8tZAsfIyAjnfQIDAyEUChnaY61Wy+nzyePxIJPJODujwWBARUUF6zi1vSuXq4Kfnx/k\ncjkttFosljltN+oLWltbOd0l1Go1SxMxOTmJ4uJiTi2hWCyGXC7nHHy1Wi3Onz/P+fz+/n5cunSJ\n8zepVMp+7w5gAAAgAElEQVTQvjiX2zkIwlusVitrxyqhUDjrKFhf9r35pqGhgbNcAoHA5fft6elh\n+Vy2trZyavOFQiFkMpnbSWwmKbhudvr7+3H69GmXwqBEIoFcLoe/v79boWR0dBRFRUU+Ce7SarUu\nTduegrLq6+sZZnYKKr7AVdvRarUz3gWPx+MhPj6eddxdcn1nJicnWf66PB6PpXWtrKzkHCOEQqHL\nfjU0NMSyUgHcucHFYjHdbxznttbWVs7FuEAgQFBQEP1cm83mcSFtsVhcfr+AgACP8RrXe78mgusV\nwGq14syZM17tJkNtOUppHLkoLS1FS0uL2/toNBrWVrBzpa+vD6dOnfLK7DM5OYlz585xDjRWq9Wt\nYOPcyZqamlBeXu5Vjr2hoSGcPHnS5bl9fX0oLi52e6+JiQmvfNGuBmVlZZxCRm5uLkszrNVqceLE\nCa+CK+x2O5qbm1FUVOQ2U0BLSwtOnz7tlXluamoKFRUVXu0E5Y729naWb258fLzLwDx3+LrvzSd2\nux0lJSVoaWnxql+3tLRwLkpMJhNKSko8picyGAysCVMkEs0oDdHNztDQEI4cOYKmpqYZp4Pq7+/H\nuXPncOzYMZ9umcsVPDY5OelxsWa321FcXDyjvKzd3d04ffr0jOchSjPqyEzcBCi4lDzOAjEVnOit\noqKrqwtnzpzhfKeWlha3c4uj4ErtMunu/MnJSZw9e9arRcvly5c5FxbONDQ0sOY2XwVyzxfXt774\nGsZiseDChQtoampCbGwswsPDaROE1WqFXq+nI6u96ZyXLl1CR0cHFixYgPDwcPj5+WFqagparRbt\n7e1XTGM0PDyMr7/+GnFxcYiKioJCoYBYLAaPx6OTrPf29qKjo8PtQF1bW4uxsTEkJiYiODgYAoGA\nvp7LHNTe3g6NRgO1Wk37WlHPNZlM0Gq16Ozs9Oq9+/v78fXXXyM5ORlRUVG0lnp8fBzd3d20Rupa\nmKBduQwEBAQgPT2dJVjo9XqcPn0aCoUCarUaYWFhtPsDFeU/MDCA9vZ2r035AwMDOHLkCKKiohAT\nE4OQkBBaS0R9s76+PrS3t/ss8XxVVRXDt4sy380mz6av+958YrPZcOnSJbS0tECtViMyMpJ+F5vN\nBqPRiMHBQbS1tbm1tvT39+Po0aNYsGABlEolpFIpeDwepqamMDo6Co1Gg46ODtjtdqSlpbF2XboW\nFnXXCxaLBZcvX0ZtbS2ioqIQGRkJuVwOqVRK16vFYoHRaMTo6CiGhobQ29t7xfaO7+joQFZWFsNa\nQn1rT9jtdlRWVtLtLzw8HDKZjLZcUJs4UOPxbPMwz9VNgKKnp4e1yPf394dKpWIEj9psNpSXl6Op\nqQnx8fEIDw+n3YgsFgsMBgOGh4fR0dHh9p2MRiOOHTuGtLQ0KJVKSCQSWCwWTExMYHBwkDXmDg0N\n4ejRo0hKSoJKpYJUKoXdboder0d3dzfa2tpgNps5dzJ0xmaz4cyZM4iNjUVcXBwUCgVEIhGsViu9\neUZraytGR0dZ81tUVJRXeW6vVXg6ne7KZa33Mfv375+R6YBAIBAIBAKB4Duio6Oxa9eueXs+cRUg\nEAgEAoFAIFwXEMGVQCAQCAQCgXBdQARXAoFAIBAIBMJ1ARFcCQQCgUAgEAjXBURwJRAIBAKBQCBc\nFxDBlUAgEAgEAoFwXUAEVwKBQCAQCATCdQERXAkEAoFAIBAI1wVEcCUQCAQCgUAgXBcQwZVAIBAI\nBAKBcF0g9HzKtYM3+/cSCAQCgUAgEK4M8y2L8XQ6nX1eS0AgEAgEAoFAIHgBcRUgEAgEAoFAIFwX\nEMGVQCAQCAQCgXBdQARXAoFAIBAIBMJ1ARFcCQQCgUAgEAjXBURwJRAIBAKBQCBcFxDBlUAgEAgE\nAoFwXUAEVwKBQCAQCATCdQERXAkEAoFAIBAI1wVEcCUQCAQCgUAgXBdcV1u+cvHOO++gpKTE43l3\n3XUXNmzYcMXK8dxzz0Gr1UKtVuPZZ5+9Ys+ZLS+//DKampoQEBCAl156aU736unpQXR0tI9KRvDE\n+Pg4bDYb5HL5fBfFLY2NjfjDH/4AALj77ruxbt06n95/amoKn376KSoqKmA0GhEUFIQ777wTS5Ys\nAQAMDQ0hMDAQEonEZ8+80u/kC4aGhvDzn/8cALBhwwbcddddLs89d+4c9u3bBwB49NFHsWjRoqtS\nRgrH8foPf/iDT7/VlWBychInTpxAWVkZBgcHIRKJkJycjB07drDGwOHhYXz22Weoq6uD2WxGbGws\nbrvtNqSmprLue/r0aXz77bcYHBxEQEAA8vLysGPHDlZ9dHZ24ssvv0RzczMsFgtiY2OxY8cOpKWl\nXdH3fvTRRwEAOTk5ePjhh6/os2bDtT7fOnIl+5zJZIJer0dYWJjP7nk9QDSuBK8ZHx/H+++/j//6\nr/+a76LcFFgsFnzzzTf4xS9+gf7+/vkuzrzz1VdfoaioCOPj47BarRgZGYFAIIDJZMLnn3+OX/3q\nV9Dr9fNdTMINgtlsxksvvYQDBw5Ao9HAbDbDaDSisrISL774Ijo6OuhzDQYDXnrpJZSXl8NoNMJs\nNqO1tRWvvvoqGhoaGPc9evQoPvjgA/T29sJisWB0dBQnTpzA66+/zjivvb0dv/vd71BVVYWJiQnG\nPaurq69KHRCuTex2O4qLi/HLX/4STU1N812cq851r3F15Pnnn4dYLOb8zd/f/yqX5trioYcegsVi\nAY/Hm/U9/vnPf+LixYs+LBXBHWfOnMFnn30238W4ZmhtbQUACAQCPPnkk5BIJAgNDcWhQ4dw7Nix\neS7d9UFeXh6trQsMDLzqz7/rrrtw2223AQD8/Pyu+vNnwpkzZ9Dd3Q1gWpNdWFiItrY2vPfeezCb\nzfjkk0+wd+9eANPCqE6nA4/Hw/3334+IiAi88cYb0Gq12L9/P372s58BmBZwDx48CABITU3F7t27\nUVpaisOHD6OhoQGXLl3C4sWLAQBffPEFLBYLAgMD8cMf/hACgQB/+9vfoNfr8eGHHxIFwk1MdXU1\n3nvvvfkuxrxxQ2lc5XI5FAoF53/XuknqSiOTyaBQKBAcHDzfRSF4id1un+8iXFNMTk4CAIKCgpCU\nlITo6GhIJBJSTzNALBbTY6JIJLrqzw8ICKCfP5dF9NWA0mpKpVLceeedUCqVWL58OTIyMgAAzc3N\nsNlsAICysjIAQFpaGvLz8xEXF4f169cDmHatoiwmVVVVMJvNAIAdO3YgKioK27ZtQ0BAAACgvLwc\nwLS1pa+vD8C00Jyeno6UlBQsXboUwLRbgk6nu+J1QLg2udnHvBtK4+otU1NT+Oqrr3Dx4kWMjIwA\nABQKBfLy8rBlyxaG1nZqagpHjhxBWVkZtFot/P39ERMTgy1btiAlJYXz/j09Pfjkk0/Q3NwMPz8/\n5OXl4fbbb3crPDv6qd17770wm804fvw4tFotIiIicMstt9CDliMXLlzA6dOnaVNWZGQkli1bhrVr\n10IgENDncfm4OvrevPDCCygpKcHZs2cxPj6OqKgobN++nfbHoXyKKB599FEkJyfTGgdnHH0DH3jg\nARQVFaGtrQ1BQUH42c9+BqlUip6eHhw4cABNTU0wm82IiorCqlWrsGrVqlnXy5dffolDhw6Bx+Ph\nySefxLvvvgutVou0tDQ89thjAIBLly7hm2++QXd3NwQCAeLj43HrrbeyvmdxcTFOnz6Nvr4+mM1m\nBAYGIjk5GbfffjvLp6ipqQmHDh1Ce3s77HY7YmJisHHjRuTk5HC+yw9+8APweDwcOXIEAwMDkMvl\nKCwsxC233AIej8fy3abq8rXXXqPf4fjx4+ju7sbk5CSkUikSExOxfft2qNVqzm/i/H2+/vprdHd3\nw2g0QiKR0PWQlJTk8fqWlhYcOHAAbW1t8PPzQ35+Pj2hO+L4znv27EFdXR1qamrg7++PvXv3QqlU\n0u/S29sLk8mEgIAAJCUlYfv27VCpVIx7AMDIyAgeffRRhISEIDQ0lGEq+/nPf46QkBD8+te/BjAt\n7B4+fBhlZWXQ6XSQyWRYtGgRtm/fDplMNqt3cgXVR1asWAGZTIaioiIAwO7du7Fs2TIA08LQt99+\ni46ODkxOTiIsLAx5eXnYtGkTSwM5NTWFEydOoLS0FIODgxAIBFCr1diwYYNHP7mpqSn8/ve/R2dn\nJwDg/vvvx9KlS9362505cwYnT55Ef38/xGIxFixYgB07diAmJsbju3vbHrl8XB3Hir1796K7uxsn\nT57E8PAwwsLCsGnTJqxcuZLxvOHhYRw6dAi1tbXQ6/WQy+VISUnB1q1bER4eTp9ntVpx7NgxnDt3\nDkNDQ/D390dGRgZ27Njh0S9wz549GBoagtlsBp//Lx0PJaxSwsPExASGh4cBgFFXcXFx9L87OjoQ\nGRmJrq4u+lhsbCyAaQtCTEwMGhoa6O8lFArx29/+FjqdDkLhv6Zpk8lE/9txfHfm2WefhU6nQ35+\nPn74wx8CAHQ6He0P6ui7qtfr8fTTTwMAbrvtNtx6662MezU1NeHzzz9HZ2cnAgMDsXz5cmzbto31\nfG/nIi6odhESEoK9e/fi448/Rn19Pfh8PrKysnDHHXd45ePvaSwZHx/Hs88+C6vViqysLPzoRz+i\nr7VYLHj66adhMpmwcOFCer7Q6/U4ePAgKisrodfrERwcjCVLlmDLli2sOf3y5cv46quv0NPTg4CA\nABQWFkKhUHgsN4U3sgk1x1Hs27cP+/btwwsvvHDT+LreUILr6OgoJiYmWMd5PB5D0/jmm2+iqqqK\ncc7AwAAOHz6MkZER3HfffQCmG9HLL7/M8GUaHx9HXV0d6uvr8cgjj7AmkJGREbz00kv0AGM2m1FU\nVASDwYAHHnjAq/c4duwYNBoN/bdGo8Hbb78Ng8HACA75xz/+gfPnzzOu7e7uxscff4yqqio89thj\njEHPHW+//TZtigWArq4uvPbaa/jFL36BqKgor+7hig8++ID+LiEhIZBKpWhpacErr7xCax+oZ37w\nwQfQaDTYvXs36z7e1gswPan87//+L/0dqMnz+PHj+Pjjj+nzzGYz6uvr0dDQgAcffBC5ubkAgKKi\nInz44YeMe46NjaG8vBwtLS346U9/Sgs+FRUVePPNNxmr4NbWVrzxxhvYuXMnNm7cyHqXU6dOMep7\neHgYX3zxBQICAhiCOxfV1dX461//yjhmMBhQVVWFpqYm/OQnP4FSqXR5fUtLC1599VVYrVbG9TU1\nNWhoaMDPfvYzREZGury+oaEBf/rTn+jrzWYzvv32W1RUVLgt9xdffEG3A6FQiMjISFRUVOBvf/sb\n47yxsTFcvHgRDQ0N+M///E+393SHxWLBH//4R7S3t9PHdDodTp06hZqaGvy///f/aHP5bN+Ji4sX\nLzLGIWohcPDgQXz11VeMc/v6+nDo0CFcvHgRe/fupTVvExMTeOmllxjt3Ww2o7GxEY2Njbj11ltp\nk7szdrsdb775Ji0E3X333ZyLXkc++ugjfPvtt/TfFosFly9fRl1dHZ544gm3i5m5tkdHPv/8c0a/\n6O/vx3vvvQeFQkEvIvr7+/G73/0OBoOBPk+r1aKkpAQ1NTV45plnEBYWRtfDpUuX6PP0ej0uXLiA\nmpoaPP30027beXh4OEMIBoDBwUHaZzU6Ohp8Pp+xoHd0v6C+JVU+x/+LRCKGgoQ61/FeAOh5y2w2\no66uDqWlpQCAhQsXshZejmRnZ6OoqAh1dXWw2+3g8XhobGykf29qaqKP19bW0scpNwWKzs5OvPLK\nK3S/0Ol0OHz4MGw2G26//Xb6PF/NRSaTCb/73e8wOjpKHzt//jxaWlrw7LPPQiqVurzWm7FEJpMh\nIyMDVVVVqKurg8lkooXP2tpaer6gFpp6vR4vvvgihoaG6HsODw/jyJEjqK+vx1NPPUW/V0lJCd55\n5x36vNHRURw6dGhGQbXeyiY3OzeU4Pr8889zHpdIJPSKvr+/H3V1dQCA9evXY82aNZiYmMD777+P\nrq4uRqM5evQoLbQWFhZi7dq1GB8fxz/+8Q+Mjo7io48+QmZmJmM1Pj4+jtzcXGzduhXj4+P461//\nCpPJhIsXL8JqtXpceQLTAtn69euxcuVK9Pb24oMPPoDRaMRnn32G/Px8BAYG4vz58/RAsWDBAtx+\n++0QiUQ4evQoysrK0NDQgIMHD+KOO+7wqu40Gg1++MMfQq1W48CBA/SEff78edx+++145pln8O67\n76KmpgYA8Jvf/MZroXhqagoPP/wwwsPDMTk5CbvdjnfffRdmsxkhISG45557EBYWhrNnz+Kbb77B\nyZMnsXTpUiQkJMy4XhwJCAjAE088QWu2tFot7TOakpKCO++8E3w+H59//jlqa2vx/vvvIzMzE2Kx\nGGfPnqXrdteuXZBKpaipqcGHH36Iqakp1NTUYNmyZZicnMT7778Pu92O6Oho7N69G4GBgThy5AjO\nnz+Pzz//HLm5uQgJCWGUrbW1Fdu2bUN+fj4qKyvpcpWUlGDVqlW46667EB4eTvvDPfjgg0hMTAQA\numxKpRL33nsvZDIZ2tvbsW/fPtjtdly6dAlbtmxx+T2Ki4thtVohl8vxwAMPIDg4GNXV1fjoo49g\nsVhQW1vrdkLfv38/PZHddtttWLx4Mdra2liCvjMTExO45557sGDBAtofkNJKqlQq3HvvvfD390dx\ncTG++eYbGI1GtLS0IDMzE7/5zW/w5z//GRqNBsHBwXjmmWfA5/PB5/Nx4MABnDlzBgDw9NNPIzQ0\nFMD0QocSWr/zne8gJycH/f392LdvH4aHh3Hw4EF897vfndM7uXrPlStXYtOmTdBoNAgPD0dTUxMt\ntKpUKuzcuRNyuRxnz57FiRMnoNFo8M9//hMPPvggAODjjz+mhdbVq1dj9erV0Ov1+OSTT9DV1YXD\nhw8jKSkJCxcuZD3/o48+osexrVu3esyE0NLSQguteXl5uPXWW2EymbB//350dHTgvffewy9/+UuX\n18+1PTrS0dGBPXv2ID09Hd9++y1OnjwJYNo6RAmuH3zwAQwGA/h8Pu666y5kZGSgubkZH3zwAcbH\nx/Hll1/iBz/4AcrKymihdd26dVi1ahVGR0fx/vvvY2hoCPv376e1at5gMBjw+uuv0+1kzZo1AJha\nUEf3C8d/U+dQ7i7ObhrU3xaLBRaLhTW2/uUvf6EF5oSEBI8CDCW46vV6dHZ2Ii4uDvX19fTver0e\nGo0G0dHRdFuJiIhgKSmGh4exdu1arF27Ft3d3Xj77bdhtVpRVlZGC66+nIuMRiNCQ0Px4x//GAqF\nAocPH0Z5eTmGhobw9ddf484773R5rTdjyaJFi7B8+XJUVVXBYrGgsrISBQUFAEDHb0gkEloh9cUX\nX2BoaAgCgQDf/e53sWDBArS1teH9999HR0cHTpw4gc2bN8NiseCTTz4BML0ov+uuu5CSkoKamhp8\n+umnHt8b8F422bhxI0JDQ2nLyc6dO5GXl3fNZ53xJTeU4OoNkZGReOWVV9Df34/w8HAIhUIMDw8j\nNDQUXV1djFU8JbwpFAp897vfpQXUe++9F4ODg4iJiWH5mgiFQtx777202S83Nxdnz56F1WrF+Pi4\nVz6m8fHx2LVrF4DpTjgyMoJPPvmE1g4uWbIEp06dAjAddPbII4/QK/b7778fPT096O3txenTp3Hb\nbbd5JSxv2LAB+fn5AIDbb7+dfncqSlsulzM0BDMxfyxatIhhMu/q6qJ9vgoLC6FSqQAAa9euxfnz\n5zE6OooLFy6wBFdv6sX5neLj4+m/jx8/Tk86W7duRVBQEP3v2tpaGI1GVFdXIzc3l35XrVaLtrY2\npKWlYc2aNcjLy2MIyHV1dTAajQCmBxTKVLN9+3aUlpbCarWivLwcmzZtYpQtOTkZ27dvBwBs3rwZ\n3377LXQ6HcbHxwFMC92OAYWUjzIAumxjY2Nobm5GRkYGHXQTEBDg0Xfw3nvvxc6dO2EymRAaGgqL\nxcLQiFHvw4VOp0NPTw+A6cmRMitGRUVhYGAAR44ccXmtWq2mtcnUBPnkk09idHQUdrsdwcHBMJlM\nrLLw+XwoFAp6Mqf+pnA0sQcFBdEDOOV3GB0dTU9OsbGxKCgowIkTJ3DhwgXs2bMHo6Ojs34nLng8\nHnbu3Al/f396AUD1Vx6Ph3//939HREQEAGDXrl3o6+tDbW0tysvLsXv3bohEIly4cAHAtCBACdcA\n8Mgjj+AXv/gFrFYrioqKWIJrRUUFbWLMycnBjh07PJaX8qsEpv0uxWIxpFIpNm3ahDfffBN9fX3o\n6OhgmL4dmWt7dCQ/P58WCHfu3ImioiLY7XZ6HNLr9bTmMDc3lxbKIyMjYbPZIBKJaBM89f0DAgLo\n/ieRSLB69Wp8+umnqKmpwfj4uFvNJYXBYMArr7xCLyYSExNZ7gveMFvfRMoVAZiuA2rh7IqUlBT4\n+/tjYmICNTU1iIuLo+vNz88Pk5OTaGxshEqlooUlZ20rMD3O33333eDxeIiMjMTp06fR0NDA8K/1\n9Vy0Z88eZGZmAgDuu+8+NDQ0QK/X4/Lly24FV2/GEgDIyspCQEAADAYDysvLUVBQAKvVisuXLwOY\n7jdisRh2u52eB9PT0+mFU2pqKrKysnDx4kWcP38emzdvRkdHB91GV69eTbfhqKgodHR00G3RHd7K\nJhKJhKHNp/zGbyZuKMHV27yAJpMJ9fX1+Oyzz9De3k4LC84MDAwAmG58jlpVd35vYWFhjInUUfhw\nNM26w1lgc/ybGsCoiTY2NpbRiPl8PlJTU9Hb24uJiQmMjIx45ffiuNJ2FMy8LbM7KMGUgqpXADhw\n4AAOHDjAusbRF4zCm3pxxDnPouNz//jHP3KWtbu7G7m5ubjjjjvwl7/8BVqtlta6BQYGIi0tDYWF\nhXRuRsd7OpqJnO/pjLNmIzAwEDqdzqv63rp1KxobGzEyMoJPP/0Un376Kfz9/ZGSkoKCggLGIsEV\nvb29KCsrQ0tLC3p7exnPdTe5OpoyHRcFAFwKNhTO7YCiq6sLFy9eRGtrK/r7+xnPn0sQAvVtenp6\n8NOf/pT1u8lkwtDQEKP/z/SduFAoFKwsJpTAExISQgutFOnp6bS5VqPRQCKRwGKxAAArX2dISAgi\nIyOh0Wg42xUltALT6ZTMZrPHICzHNuzKatXd3e2yLnzRHikc+4VQKIREIsHExATdPgcHB+nfnX1v\nnV1sqPcyGAyc3x+YbhuecqJSQis1JoWFheHhhx+m5wTH8Z76bgAYblDUvESd63ie47lCoZDTkvXg\ngw9CLBbj66+/xoULF/DOO+9AKpW69HUWCATIzMxEaWkpampqsHTpUgwPD4PP52Pt2rU4cuQImpqa\noFaraYEoOzubdR+VSsVYeFDt2nG88PVcRFmWANALkbq6Os4x3hlvxhKhUIj8/HycPHmSdhdobW2l\nBVtHNwHqWHV1NWcbolKaeRoXvRFcAe9lk5udG0pw9QadTocXX3wROp0OcrkceXl5SEhIQHNzM06f\nPs15zUyEN1fpuADvJ2HnQc0RahBxFKTd4a22w3Fy83W0r/Niwpuyc3VYb+rFV89NTEzECy+8gLKy\nMlRXV6OlpQV6vR5lZWUoKyvDrl27sH79+lm/i7Mw4e33BKZNes8//zwqKipoP8Lx8XFUVlaisrIS\n69evpzXTXBw6dAhffvklgGlfufz8fCQmJuL3v/+9x2e7axuetClci8p9+/bh3Llz4PP5yM7OxurV\nqxEcHIw33njDY1k8IRAIGMIDF+Pj43N6Jy643tPd93UcF3g8ntfPdHVPlUoFjUaDkZERHD16FFu3\nbp3VfRxxN4HOtT06MpN+4Wlcnut7AdOm/T//+c+00BoaGoonn3ySYZZ1tKI5WuwccwpTrkKUZmxq\naoqxqKDOdXYpoqAWDd///vdRXl4Oq9WK8+fPuw3SW7x4MUpLS9He3k67TMTHx2Px4sW04EpZBORy\nOUsxALDnM66+4uu5yHmcp/qHp+tnMpYsX74cJ0+epH25qQBPhUKB5ORkAN69l91uh8FgYJTNeZ73\ntj/PRja5WbnpBNfTp0/TZo4nn3yS7rgtLS2sc8PDw9Hb2wuNRsPwOzp//jyKi4uhVCqxbds2n/uW\ntLS00I7zABjBYdSKValUoq2tDV1dXTAajbTTut1up01CUqn0ipkQHMvnCeeO66hxuueeexiakra2\nNiiVSs68u97Ui7fPff755+lvbzQaodVqoVQqIRQKYbFYoNFo0NfXh5iYGKxevRp2ux0ajQZvvPEG\nBgYGcPLkSaxfv55xz8cff5zWxlssFvT09ECpVM46XyXXYEiVo7+/H0FBQXjooYcATGui9u3bh+bm\nZpw6dQo7d+7kHHinpqbw9ddfAwCWLFlCBwx6u7J3rOe2tjbGb45BNVw4f4+hoSGcO3cOALBp0yba\nZ84xmMobXLXD8PBwdHV1ITExkY6aBqZ9yYRCIUJCQsDj8RjvPtN34oJLY6ZUKtHd3Q2tVovBwUFG\n0I+j76FKpYJQKIRAIIDVakVDQwO2bdtG/67Vamk3G67d62JjY7F371689dZbqKqqwpEjR7BixQq3\nLkpUG+bz+Xj55Zfp9jo6Ogqj0YiIiAiXk+9c2+NMcWx/zlaZ/fv3o7+/HzExMbj99tsREREBjUYD\nuVyO3/72t3Q7GRoags1mQ3h4uMcx7J133qHbY1hYGPbu3csaUwMDAxEcHAydTkcHxAFMSwsleDpq\niTs7O5GUlASbzUZrLakg0qGhIXz11VcYGRnBpk2b6HHFarXSWQ08LcoyMjLo8Yzq8ykpKYiLi4NU\nKoVer6cFouzs7FkrLHw9FzU3N9NBstQ4CoAVKOfITMcStVpNL/DKysro85YuXUrXQ0BAAKRSKYxG\nI2OsBEBnDaD6lWO7bG9vZwRDejuGzEQ2cSco3wzcUIKrq6wCwPRkIpPJGI70paWlyM/PR3NzM93o\nAaOFcIYAACAASURBVNBBVHl5efjyyy8xPj6O9957D5s2bYLBYMAXX3yBkZERDAwMYM+ePT5/D41G\ngw8++ABr167FwMAAHdTh5+eH9PR0ANMrxra2NkxMTOD111/Hd77zHYjFYhw9epQ2S65Zs8YnkwWF\n44Tc3NxMpwabKSqVih40Dh06hKCgIKhUKpSVldFuA1zR+N7UiztycnLw6aefwmKx4N1338Udd9yB\ngIAAHDhwABcvXgSPx8MzzzwDpVKJP/zhDzCZTFAoFPje976HyMhIjI6O0sEV1ESelpaGwMBA6PV6\n7N+/H3fffTdCQkLw7bff0sECDz30ED0QzwTH+u7s7IRYLEZ8fDxee+01DA8Pw9/fH9///vehVquh\n0+los5ZAIHA5CVmtVlqj0d7eTg/YlAaWOscVMpkMiYmJaG1txeXLl/Hll18iNzcX7e3tOHHixIze\nz7EvNjQ0oKenB0ajkQ5y8FQWCsd6amtrg8FgQFxcHPLz89HV1YXW1lYcPHgQ+fn5GB0dxd///neM\nj48jOjoazz33nE/fyRXLly9HWVkZ7HY7Xn/9dezatQsymQznzp2j3QSWLl1Ku+nk5OSgrKwMTU1N\n+PDDD7Fq1SoYDAZ88skndJ2sXbuW9ZyUlBRIJBLceeedqKmpwdTUFL744gu3wTz5+fk4fvw4bDYb\n3nrrLTrV0Ycffojm5maIRCL86le/cil4zKU9zhSZTIaUlBQ0Njbi0qVLOHr0KBYtWoS2tjYUFRXB\narXSGu/8/HxcunQJo6Oj9LhhNpvxj3/8gxa0X3jhBZdWsoqKCjpgRyAQ4Pvf/z4ApjtGcHAweDwe\ncnNzceLECdTX16O0tBSRkZF024mJiaGFkKysLFqYPHjwIHbv3o2KigpaU0v56cvlclRUVGBychJa\nrRbf+973EBAQgEOHDtGCSlZWltu6kkgkSEtLQ3V1NX3/tLQ08Hg8pKSk4NKlS27dBLzF13PRRx99\nBKFQiNDQUBw9epReWLor42zGkuXLl+OTTz5hBGRTvvAU+fn5KCoqQkVFBRITE5GRkYG+vj78/e9/\nh9lsplNqqdVqhISEQKvV4tSpU4iIiEBqaiqqqqpm5CZA4Uk2cRzzurq60NXVhYiIiGt+Uw9fcUMJ\nrq78swDQOUcXL15MDyiHDh1i5EOjGBsbg0KhwKZNm1BZWYmuri5G5CSFY8CWLwkODsaZM2foSGkK\nKuADmA5qqqurw8WLF9HU1ETnZqVIT0/3aCKcKdHR0XQ6lpdffhlKpdJttLEreDwe7r77bvzpT3/C\n6Ogoa6tDlUqFwsJC1nXe1Is7goODsXXrVhw4cAAtLS2sOisoKKD9k3bt2oV3330XIyMj+POf/8y6\nFxXAIxaLsXPnTrzzzjvo6+vDq6++yjgvJSWFM+jBGxw1alRk6v/8z//gnnvuwWuvvYaJiQlW+hcA\n2LJli0tBwd/fH6mpqWhoaMDQ0BBefPFF1jljY2Nuy7V79278/ve/x9TUFKMPxcbGcvomu0KpVCIi\nIgIDAwNob2/n3AnIU1kAZj299dZbEIlEePXVV7FmzRqUlJRAo9Hgq6++YqSiEggEuPPOO+l68tU7\nuSIjIwMbNmzA8ePHodFo8MorrzB+j4mJYaSA2717Nzo7OzEwMICioiJ6EUSxbds2t4s1pVKJlStX\n4vTp0ygpKcG6detc5veNi4vDypUrUVxcjMuXL9NBKhSbN292KbTyeLw5tcfZsGfPHrz00kswGo20\nTy1FYGAgrW3LyclBeno66urqOMcNSsByxfHjx+l/W61WTr94Kq5iy5YtqKiogE6nw1tvvUX/TmU+\noAgKCsK2bdvwxRdfsNK9paam0sKZSCTCPffcg7fffhsDAwN0VhyKjIwMrFixwm09AdPCHrWRglAo\npNOapaWl0e4DlD/ybPH1XMTn8+l81RQRERHYvHmzy2tmM5YUFBTgs88+ozXYarWaFXuwZcsWXL58\nGSMjI/joo48Yv0kkEjr4kc/nY8+ePXjttddgtVrxf//3f/R53o4hM5FNHMt58uRJnDx5Ej/5yU9Y\n/rU3KjfUzlnekJycjIceegjR0dEQiUSQy+XIzs7G448/Tp9DDdxisRh79+7FLbfcgvDwcAgEAgQF\nBWHhwoXYu3fvnFap7sjLy8N9991Hm+hUKhUefPBBhkmdx+Ph4Ycfxn333Yfk5GT4+/tDKBQiJiYG\nu3btwo9//GOv01V5y6pVq5CTkwOpVAqJROLSH8sbUlNT8fTTTyM7OxsBAQEQCAQIDQ3Fxo0bsXfv\nXk4/QW/qxRO33norHnjgASQlJUEikUAsFiM6Ohp333037r33Xvq8FStW4D/+4z+QlZUFuVwOPp8P\nqVSK9PR0PPbYYwxT0LJly/D4448jPT0dUqmUzlG6Y8cOPPbYY7Ne3CQkJNBCg1AohEqlgtlsRkZG\nBp5++mksWbIEISEhEAgEkEgkSEpKwv333+8x9dADDzyAZcuWQSaTQSwWQ6VSYdeuXXRQQk1NDT2Y\nc6FWq/HUU08hPT0dIpEIgYGBWLduHR555JEZvZ9QKMTjjz+OxYsXIyAgABKJBGq1Gvfffz/tZ+Ys\nRHGxePFiOum/SCRCVFQUpqamIBaL8dRTT2HTpk10/w0MDERWVhb27t3LCLL01Tu546677sKjjz6K\nhQsX0m0+MjIS27dvx9NPP83IURkYGIjnnnsOO3bsgEqlgkgkgkQiQUpKCn70ox/RGSncsX37dloD\ns3//frfnfu9738OePXugVqvpZ8XHx+P+++/3+Ky5tseZEhUVhWeffRYFBQUICgqix47ly5fjJz/5\nCW225fF4+NGPfoTvfOc7iIqKglAohFQqRUpKCh577DG3gt/U1NSMXFZkMhldB9QYkJiYiMcff5wO\n5KTYsmUL/u3f/g0qlQoCgQByuZyzrS1duhRPPvkk0tPTIZPJ6DFv586d+PGPf+yV76TjHJWYmEjP\nCY4BaZmZmXOaK3w9Fz3xxBN0ZhepVIply5bhqaeecht4PZuxRCaTMbJyOGtbAdCp91atWgWFQkF/\nryVLluCZZ56hM1gA0xrwJ554gq5nhUKBbdu24Z577vHqvWcimwQHB2PXrl0ICwuDUChkBXze6PB0\nOt3N5yBxDeK4O9CGDRsYq/SbGVIvBAKBcGPDtaPa1eC1117D5cuXwefz8d///d9epUYjzD83lKsA\ngUAgEAgEgivGxsZgs9lQVVVFazA97URGuLYggiuBQCAQCISbguPHj+Obb76h/xYIBD6PByFcWYjg\nSiAQCAQC4aYgKioKEokEdrsdUVFR2LZt200T1HSjQHxcCQQCgUAgEAjXBTddVgECgUAgEAgEwvUJ\nEVwJBAKBQCAQCNcF172P63yl0fCGoaEhBAYGXlNlIvyLRx99FMB0ovKHH37Y5/fv6elhJMc/d+4c\n9u3bRz/b3T7jvuC5556DVquFRCLBL3/5S9a2n1TfCQkJwa9//WsAQGNjIyvZOYVYLIZCocDixYux\ndetWl8nb7XY7nnvuOXqHIedtfQkEAoFAmC1E43oFMJlM+Pzzz/GrX/0Ker1+votDuMqMj4/j/fff\n59y9ZT4wmUyMrQ9ny9TUFPr7+3HkyBG88cYbLvfIrqurY2yLefbs2Tk/m0AgEAgE4AbQuF6LHDp0\nCMeOHZvvYhA88Jvf/AYA3G77OBv++c9/0nucXyuUlZWhsLCQtYuPO3bs2IHly5cDmN7ycnx8HB//\nf+zdeXRU5fkH8O/sM0kmJCGQjSzsoIAGJNQNBEEJhE3qgha1Vm2xarWKPVXr1kprtYJLf6iIuNNF\nEAmrQEVEDdSGVSEkkAQISwjZJpl97v39Mcw4ycwkM8ns+X7O8Ry5c2fmnXtvJk+e97nv88knOHbs\nGL7//nscPnzYY9tR197aAFBVVeWWfSYiIuoKZlyDwFsmiiJLcnIykpOTER8fH+6hhMQ//vEPWK1W\nn/fXaDTOY5Samor+/ftj5syZzsc99d/W6/XOHuh9+vRxbmfWlYiIAiFmM64mkwkbN27Ed999h8bG\nRmi1WowaNQpFRUVuHTK+/vpr7NixA3V1dTCbzUhISMCwYcMwY8YMpKSkOPfbu3cvtm3bhpMnT8Jk\nMiEuLg4DBgxAUVERcnJyAAAvv/wyysvLnc/5wx/+0KaG0JvTp0/j888/x+HDh6HT6Zx9lAsLC9G7\nd2/nfq51kn/84x9RUlKCb775BjqdDhkZGSgqKnKrnSwvL8f69etRVVUFURTRr18/TJ48Gfn5+T4d\ny++++w6bN2/GmTNnkJiYiAkTJiA3NxdLlixxjsPRH/zkyZNYv349Kisr0dLSApVKhaysLEyePLnN\nuBz1pbNmzUJqaio2btyI2tpapKamYsaMGcjPz8e2bduwfft2NDU1ITMzEzfccINbxnDv3r34/PPP\ncfLkSchkMuTl5aGwsBBDhgzp9HN5qnF11H2mpqbisccew+rVq7F//35YLBYMGTIEc+fORUZGhtfX\ndNSVur7H4MGD8dvf/rbNfqIoYtOmTdixYwd0Oh2ysrIwc+ZMXHTRRW326+65c3XmzBn85z//wXXX\nXef3cx0kEonz/xMSEtwe/+6775zB8ezZs/HJJ5+goaEBu3btwpw5c7rVE52IiCgmM65WqxVLlizB\n5s2bcf78edhsNjQ2NmLHjh144YUX2tSdbtmyBR9++CGOHz8OvV4Pq9WKxsZGlJSUYPHixbDZbACA\ngwcP4s0330RFRQWMRiNEUURraysOHDiAxYsX48yZM10e78GDB7Fo0SKUlJSgsbHROd6vv/4aixYt\nQlVVlcfnrVixAuvXr0dDQwOsVitOnDiBpUuX4vTp0859SktLsXjxYpSVlcFkMsFsNuPYsWN46623\nfCpn+OKLL7B8+XKcPHkSVqsV9fX1+PTTT7FmzRq3fc+dO4eXX34Ze/fuRVNTE2w2G/R6PcrLy7F0\n6VKUlZW5PWf37t1Yvnw5Tp06BavVijNnzmDZsmX4+9//jlWrVuH8+fOwWq04fvw4/v73v7cJCrdt\n24Y333wTlZWVsFgsMBqNOHz4MJYsWYLS0lIfjrx3JpMJL774IkpKSqDX62GxWPD999/jtdde8ytr\n6c3q1avx2WefOc9ddXU1/u///i+g585h5MiRzqzyhg0b0NjY6Pd4BUHAuXPnUFxcDACIj4/HpZde\n6rafI7OqVqsxcuRIXHbZZQCA1tZW7Nu3z+/3JSIichWT6Y+tW7c6g71Zs2YhPz8fZ8+exfvvv4/z\n58+juLgY8+bNgyiK+OqrrwAAQ4YMwU033QSZTIZNmzZh165dqKurw5kzZ5CVleX8hZyeno7bb78d\nWq0WVVVVeP/99yGKIvbu3YupU6finnvuwdq1a7Fz504AwMKFC9tkTNszGAxYsWIFrFYr4uLi8NOf\n/hR5eXk4cuQIVq1aBb1ej2XLluHZZ591y1adOnUKd911F3JycrB27VpnsLZr1y7Mnj0bJpMJH330\nEURRRFZWFm6++WYkJCRg8+bN2LVrF9asWYPRo0e3ySq7am1txaeffgoAiIuLw80334zs7Gzs2bPH\nGcC42rVrFwwGA1QqFe666y5kZmaiuroab7/9NgDgwIEDbhnT06dP4/rrr8fll1+Or776Ctu2bQMA\nfP/995g+fTouu+wybN68GSUlJc7g8eqrr3YG0I5zd8MNN0AqlWLNmjX44Ycf8NFHH2HEiBFdrl/V\n6XRISEjAww8/jLi4OLz99ts4e/YsGhoacOTIEbfMqMNjjz2GDz74AN9//z0Aex2tpyxjc3Mz7r77\nbvTr1w+bNm1CSUkJbDYbSktLMX369G6fO1fx8fGYM2cOPvzwQ5hMJvz73//GPffc0+nz/vWvf+Ff\n//qX2/akpCTcc889iIuLa7P99OnTqK6uBgCMGjUKCoUCBQUF2LJlCwD7zMaYMWM6fV8iIiJvYjLj\n+t133wEAsrKyMG7cOCiVSmRnZ2PcuHEA7Fk+URQhkUjw3HPP4fnnn8c999yDrKwsaLVapKenO19L\nr9cD+PEGnubmZlRUVMBkMmHMmDFYtGgRFi9ejKlTpwIAtFotVCqV8/mJiYno1atXh2N1vMfcuXNx\n+eWXIyMjAxMmTMD06dMBAPX19di/f7/bc6+99lqMHTsWaWlpmD17tnO7I6N86NAh52tPnjwZqamp\nUKvVKCoqglQqhc1mw//+9z+vYzt8+DAsFgsAoLCwEAUFBcjIyMC0adM8TlUXFRVh8eLFeOKJJzBq\n1ChnfaRGo2lzLF2lpqZi9uzZSEtLw+TJk53bHWUP6enpuOqqq9w+2549e5zZ8GnTpiExMREJCQnO\nntN6vR4HDx70+tl8ccstt2DIkCHo168fpkyZ4jYGT3r16tUmWE5OTnYrTQGAiRMnYsyYMUhLS2tT\nN+rIhnb33LV3xRVXYMCAAQDsmdzDhw/7/Nz2mpubsWPHDphMpjbbXW/KcmRa+/Xr5/x5OnToEM6f\nP9/l9yUiIorJjGttbS0A+zqajz/+uNvjRqMRdXV16NOnDwRBwLFjx7B//35UVlairq6uzb6OG62m\nTZuGI0eOoKGhAatXr8bq1auh0WgwZMgQjBs3rks1h4A9a+owbNiwNo8NHz7cOSVfU1OD0aNHt3nc\ntdbStd7QEdA5jgNgr9305OTJk17H5nos+vfv3+axAQMGeLxzvq6uDrt27UJFRQVqamqcgS/g+aa1\ntLQ05/+7rnfr+tlct3v6bI5a2/ZOnjzpdsz80dnx7Q7XO+wdgT0AZxlCd89dexKJBLfccgv+/Oc/\nQxRF/OMf/0B2dnaHz2m/qkBraytKSkqwfft27Nq1CzabDb/4xS8A2EsJdu3aBQCQyWQQRdGZdc7M\nzHSW0nz77bcoKiryedxERESuYjJwlclkbQImT3Q6HVJTU/Haa6/h8OHDUCgUyM/Px3XXXQdRFLFy\n5co2+/ft2xfPPPMMSktLceDAAZSXl0On02Hfvn3Yt28fJk2ahBtvvNHvsUql3pPeroGe600xDgqF\nosPHO3ptB51O5/UxmUzm9TFP7+d649igQYMwdepUDBgwAO+++y6ampo8vo63qfzOpvi7+9l80dnx\nDdRrexKMz5ednY0JEyZg+/btOHv2bJt6YU8cqwo4pKamIjc3Fz/88ANqa2vx3Xff4fbbb4dCocDB\ngwfR3NwMwB7kLl261ONrfvPNN5g+fXrAjycREfUMMRm49unTBydOnMCAAQOwcOFC5/azZ89CLpcj\nJSUFEokEZWVlzinTG2+80dndp/0UrCiKOHXqFM6ePYvExERnfeC5c+fw/vvvo6KiAjt27MDcuXMh\nlUr9+qXsWpZQVlbmzHABaDOd269fPz+OgF3fvn2d///AAw846zKtVitqamqQnp7epqyhPcdKAQBQ\nWVmJgQMHOv995MgRt/0dda8DBgzAI488AsCeiWs/pRwIrp/tmWeecWZu9Xo96uvrkZ6eHhF3sDtK\nUvzV3XPnzaxZs7Bnzx40NTV1+sedN65BtclkgkKhcFu71ZuGhgYcOnTIa40wERFRR8L/mz0Ixo4d\nixMnTuDYsWMoLi7G2LFj0dTUhOXLlzuXHnriiSdgNBqdzzlw4AAGDx6M8+fPY+3atc7tjmnhpUuX\n4vz589BoNPjZz36GnJwcNDY2OusQZTKZM0BxDZgqKyvR2tqK3Nxcj2PNz8/HqlWrYDKZ8Mknn0Ai\nkSAvLw/l5eXYsGEDAKB3794YMWKE38dh2LBhSEhIQEtLC/7973/jpptuQkpKCr744gt8+eWXAIB7\n7rnH63T68OHDER8fj9bWVmzcuBFJSUnIysrCf//7X481t47jeebMGZSVlSEhIQFbtmxxbg/EFLtD\nfn4+Vq9eDavVig8++ABz5sxBfHw81q5diz179kAikeCxxx5DXl5ewN7TV67nv6KiAhqNxu8/PLp7\n7rxRq9W44YYbsGLFik73NRgMzg5YoijCYrFg9+7dzmn/tLQ05xgPHDgAwB5wP/vss26v9b///c95\nk97OnTudgWtTUxMEQYBcLvdYC0xEROQqJgPXCRMmoKSkBKdOncKGDRucASBgDzBvuOEGSCQSDBo0\nqM0vXscvX1fNzc2QSCS49dZbsXTpUhgMBixbtsxtv6lTpzoDV9f6xXfeeQcKhQKvvvqqx7EmJCTg\n9ttvxzvvvAO9Xu9WzxgXF4d77723S9lDpVKJuXPn4r333sOZM2fcxjBkyBCPSxo5qFQqzJw5EytX\nroRer8fy5cudj6WlpeHs2bMAfpxGz8/PxzfffAO9Xu+x7tQxlRwISUlJmDZtGtauXYujR4/ipZde\navP4uHHjwhK0AnAG94B9Xd/09HQ8/fTTfr1Gd89dRwoKCvD11197zJq7Ki4u9rh6hMMNN9wAwH6z\no+OPEtcZA1eXXHKJ82dt//79aGlpQUJCAv7617+ivr4eOTk5+P3vf9+lz0NERD1HTAauSqUSjz76\nKDZu3Ii9e/eivr4eGo0G/fv3d9ZdAvZlgh588EGsXr0aVVVVkEqlyMjIQGFhIVauXInz589j//79\nGDduHC666CIsXLgQW7ZswbFjx9DU1ASFQoGsrCyMHz8eBQUFzve/9NJLccUVV+DAgQMwGo3IyMiA\n2Wz2Wrc5evRopKWlYcuWLSgrK3MuxTRy5EgUFhb6tOSRNz/5yU+QmJiIrVu3orq6GmazGb1790ZB\nQQGmTJnSaS3l+PHjoVQq8fnnn6O2thbJycmYPHkyBEFwLpXkCKpvuukmqNVqlJaWorW1FcnJybjs\nsstgs9mwefNmHD16FHq93m0Zpa4qLCxEnz59sH37dtTU1EAQBPTp0wdXXnklrrnmmoC8R1dcffXV\nqK6uRllZGQRB6PL56+6568i8efPwpz/9ya8suEwmg0qlQk5ODq677jpnu1fXrliuPweu5HI5fvKT\nn2Dr1q2w2WwoKSlps4oEERGRLySNjY3sT0oeOZoHJCcnIyUlpc2d9Zs2bcJnn30GAHjllVe6vF4q\nERERka9iMuNKgWGxWPDGG28AsGfMFixYgL59++LcuXP4+uuvAdiXOmLQSkRERKHAwJW86tWrF/Lz\n87Fnzx5YrVa89tprbvtcf/31YRgZERER9UQsFaAOWSwWbNmyBaWlpTh37hwsFgs0Gg2ys7MxadIk\njBo1KtxDJCIioh6CgSsRERERRYWu35ZMRERERBRCDFyJiIiIKCowcCUiIiKiqMDAlYiIiIiiAgNX\nIiIiIooKDFyJiIiIKCowcCUiIiKiqMDAlYiIiIiiAgNXIiIiIooKDFyJiIiIKCowcCUiIiKiqMDA\nlYiIiIiiAgNXIiIiIooKDFyJiIiIKCowcCUiIiKiqMDAlYiIiIiiAgPXADh16hSKiopw6tSpcA8l\nJpWXl4d7CDGNx5eiFa/d4OLxDR4e265j4EpEREREUYGBKxERERFFBQauRBRRCgoK2vx3+eWXY+rU\nqfjzn/8Mq9Xq3G/WrFkoKChAXV1dp6+5bt06FBQU4M9//nPAxmmz2fDBBx/glltuwdVXX43rrrsO\nv/zlL/HVV1/5PR6bzYY//vGPmDBhAiZNmoStW7fi2LFjmD9/Pq688krMnj0b3377LQoKCvDggw8G\n7DMQEUUbebgHQETUnlQqxVVXXQXAHtQdPHgQn376KRISEvDAAw+EeXSAIAh49NFH8fXXX0OlUmHY\nsGGwWCzYs2cP9uzZg6eeegpFRUVen5+Wlobx48djyJAhAIC9e/eiuLgYKpUKI0eORFpaGj744AOU\nlZUhMzMTw4YNQ9++fTF+/HgMGzYsVB+TiCjiMHAloogjl8vx0ksvOf/9ww8/4M4778TOnTsjInBd\ntWoVvv76a2RlZeH1119HVlYWAGDTpk146qmn8MYbb6CwsBAymczj88eOHYuxY8c6/11fXw8AmDhx\nIp577jkAwPLlywEAjz/+OAoKCgCgzTEhIuqJGLgSUcQzm80AgIyMDK/77Nu3D6+88goqKiogk8kw\nYsQI/O53v0O/fv3c9l28eDFWrlyJ/Px8vP7661AoFFizZg3effdd1NXVYeDAgfjNb36D0aNHe3yv\nTz/9FACwYMECZ9AKAFOnTkViYiIGDhzoDFoLCgrQv39/XHLJJdiyZQvmzp2LvLw8PPfcc5gzZw5G\njhzpDFY3bdqETZs2YfTo0SgtLQUA3H///Zg+fTqKioqwYMEC/OQnP8Grr76KdevW4bnnnsMvf/lL\nnD17Fp9//jkSEhJw3333Ydq0aV04ykREkY+BKxFFHKvVikcffRSAPWg9cOAAUlJScN9993ncX6/X\n45FHHkFraytGjRqF+vp67Nq1C4sXL8bf/va3Nvtu2rQJK1euRHZ2Nl544QUoFArs3LkTixYtQmJi\nIvLz87F//3785je/wUcffYScnJw2zzebzaioqAAA5Ofnu43liiuucNtWVVWFhoYGDBo0CBdddBH0\ner3zsbS0NAwfPhyHDh1CWloahg4dioEDB6Kqqgr19fW45JJLOiwP+PDDD5GSkoKMjAwcPXoUf/nL\nXzB+/HgkJCR4fQ4RUbRi4EpEEUcQBOzYsaPNNpVKhZqaGmddqCuz2Yx77rkHKSkpmDJlCpqamjBl\nyhScOHGizX5Hjx7Fhg0bkJCQgJdffhlJSUkAgPfffx+AfXo+NzcXJSUlePDBB7Fq1So8/PDDbV6j\nubnZ+f+9evUCAOh0Olx77bVt9lu+fDlGjhwJABBFEc8//7yzPGDdunXO/caOHYvbbrsNTz75JC67\n7DI8/fTTAIBDhw6hpKQEv/rVrzBmzBj873//83isEhMT8dFHH0Eul2Pu3Lk4ffo0Kisrne9NRBRL\nGLgSUcRRKpXYuXMnAMBoNOLbb7/F448/jieffBKffvop+vbt22b/pKQkTJw4EZs2bcLDDz+Mffv2\nAQBMJlOb/fbv3w8AkMlkSExMdG4/duwYAODGG29ss/+hQ4fcxub6vHPnziErKwtyuRzjx48HcuRt\nHAAAIABJREFUYC9ZaGpqcntesALJSy+9FGq1GgDQr18/nD59GhaLJSjvRUQUblwOi4gimlqtxsSJ\nEzFw4EBYLBZn8OmqpqYGt9xyC1auXIlLLrkES5Ys8fp6l156KfR6PVasWOHcZrPZAADjx49v89/Q\noUPdnq9UKp1T9//9738BABqNBi+99BJeeuklDBw40O05MpnMGVwGmuvrOupqRVH0+3V0FgHHW6zQ\nWYSAjY2IKNAYuBJRxDMajc71WrVardvjX3zxBVpaWjB16lTceeedXu/mnzhxIl555RUkJydj9erV\nOHv2LACgf//+AIC77roLL730Em699Vbk5ORgwoQJHl9n9uzZAIC3334bVVVVzu2lpaWorKx029/b\neCKB2SbivbJWvLi3Gf/3fQte3NuM98paYbb5H/wSEQUbSwWIKOK43pxls9lQUVGB+vp6ZGRkeLwh\nKj09HQDwr3/9C+Xl5Th48CAAtLkJCrCXFGg0GsyfPx+vvvoq3n77bTzxxBO46aabcPDgQdx33324\n6KKL8MMPP0Cv13tdVWDOnDn49ttv8eWXX+JnP/sZhg8fDovFgkOHDkEURaSkpCA1NTWQhyRoVlbo\nUaWzQiOXQnNhW5XOipUVetwxND6sYyMiao+BKxFFHNebsyQSCVQqFcaMGYOFCxdCqVS67X/ttdfi\npptuwsaNG1FeXo7JkyejqqoK+/fvx/Hjx932/+lPf4qPPvoI69atw/z58zF16lTodDqsXLkS+/bt\nQ0ZGBu68805nE4T2JBIJXnjhBaxevRrFxcWoqKiAKIoYNmwYJk6ciBtuuKFNLWyk0lkEHG22QCNv\nO/mmlElwtNkCnUWAVsGJOSKKHJLGxkbOB3XTqVOncO+99+Ktt95CZmZmuIcTc8rLyzF48OBwDyNm\n8fj2XMdbrPi/71vQS+kenDaZBfz64gRkJ0RufoPXbnDx+AYPj23X8U9pIqIeKlklhYeYFQCglAJJ\nKv6KIKLIwm8lIqIeSquQYmCiwu1GLLNNxMBEBcsEiCji8FuJiKgHmzcoDnlaOQxWAU1mAQargDyt\nHPMGxYV7aEREbiK3eImIiIJOKZPgjqHx0FkENJoEJKmkzLQSUcRi4EpERNAqGLASUeTjtxQRERER\nRQUGrkREQdK+AQIREXUPA1cioiCpqakJ9xCIiGIKA1ciIiIiigoMXImIiIgoKjBwJSIiIqKowMCV\niIiIiKICA1ciIiIiigoMXImIiIgoKjBwJSIiIqKowMCViIiIiKICA1ciIqIQ0VkEHG+xQmcRwj0U\noqgkD/cAiIiIYp3ZJmJlhR5Hmy0wC4BSCgxMVGDeoDgoZZJwD48oajDjSkREFGQrK/So0lmhkUvR\nSymFRi5Flc6KlRX6cA+NKKowcCUiIgoinUXA0WaLW2ZVKZPgaLOFZQNEfmDgSkREFEQNJgFmL7Gp\nWQAaTQxciXzFwJWIiCiIklVSKL38tlVKgSQVfxUT+Yo/LUREREGkVUgxMFEBs01ss91sEzEwUQGt\ngr+KiXzFnxYiIqIgmzcoDnlaOQxWAU1mAQargDytHPMGxYV7aERRhcthERERBZlSJsEdQ+Ohswho\nNAlIUkmZaSXqAgauREREIaJVMGAl6g7+9BAREVFAsDMYBVtYMq7ffvst3nnnHRw+fBhSqRQjRozA\nr371K4wcOdK5T01NDV555RWUlpYCAK688ko89NBDSE5ODseQiYjITzqLgAaTgGROi8c8dgajUAl5\n4FpaWoqHHnoIAwYMwIIFC2Cz2fDJJ5/gV7/6Fd566y1cfPHFaGxsxH333QeLxYL58+fDZrPhww8/\nREVFBd59910oFIpQD5uIiHzEIKbnce0MprmwzdEZ7I6h8WEdG8WWkAeuL7/8MtLS0rBixQqo1WoA\nwLRp03DzzTdj6dKleP311/Hxxx+jtrYWH3/8Mfr37w8AGDFiBO6//36sX78es2fPDvWwiYjIRwxi\nYpenLLqjM5hG3jar7toZjBl3CpSQBq7Nzc0oLy/Hbbfd5gxaAaB3797Iz8/Hrl27AABbtmzB6NGj\nnUErABQUFCA3Nxeff/45A1ciogjFICY2dZRFd3QG03h63oXOYDznFCghDVzj4+Px73//GxqN++Xd\n2NgImUyG5uZm1NTUYNKkSW77DB06FN98800ohkpERF3AICY2dZRFv2GAhp3BKGRCejXJZDLk5OSg\nT58+bbaXl5dj//79GDVqFM6dOwcA6Nu3r9vzU1NT0dLSgpaWlpCMl4iI/MP2prHHkUVvX5/syKID\nYGcwCpmwX016vR7PPPMMAOCOO+5Aa2srAEClUrnt69hmMBhCNj4iIvId25vGHkcW3RNHFp2dwShU\nwtqAwGg04pFHHkF5eTnuvPNOjB49Gvv37wcASCTe7zzt6DFX5eXlARlnZ2prawEAlZWVzsCbAitU\n57Kn4vENnp54bMcCOKuX4bhRCqsgQi6VIEctYKzWhkAfjp54fEOpvLwcrVagpUkBm4e/OQwCcP7E\nORjlwBVS4JIEQGeVQCsXES8Fqo+FfszRgteud4MHD/b6WNgCV51Oh9/+9rfYt28fZsyYgQULFgCA\ns/7VZDK5PcexLT7et7tSO/rggeQYT//+/ZGZmRmS9+xJysvLQ3YueyIe3+Dpycf2oqEIenvTnnx8\nQ8H1+I6VtqJKZ21TLmC2iRihleNSrhThN167XReWOZv6+nosWLAA+/btw5w5c/Dkk086s6jp6ekA\ngLq6Orfn1dXVQavVery5i4iIIotWIUV2gpzlATGApQAUKUKecW1tbcWDDz6II0eOYN68eXj44Yfb\nPK7VapGZmYmysjK355aVlWH48OGhGioRERHBfiPWHUPjg55FJ+pMyK+6v/71rzhy5AhuueUWt6DV\nYdKkSdi9ezeqqqqc23bv3o3q6mpMmTIlRCMlIiIiV8yiU7iFNONaWVmJjRs3QqvVYsiQIdi4caPb\nPoWFhZg/fz42bNiAX//617j11lthNpvxwQcfYNiwYSgsLAzlkImIiIgoQoQ0cC0tLQVgvzHrueee\n87hPYWEhkpOT8eabb2Lx4sV46623oFarMWHCBDz44INQKpWhHDIRERERRYiQBq5z587F3Llzfdo3\nNzcXS5YsCfKIiIiIiChasEiFiIiIiKICA1ciIiIiigoMXImIfKCzCDjeYoXO4qX3JRERBV1YW74S\nEUU6s03Eygo9jjZbYBYApRQYmKjAvEFxbboIERFR8DHjSkTUgZUVelTprNDIpeillEIjl6JKZ8XK\nCn24h0ZE1OMwcCUi8kJnEXC02eKWWVXKJDjabGHZABFRiDFwJSLyosEkwOwlNjULQKOJgSsRUSgx\ncCUi8iJZJYXSy7ekUgokqfgVSkQUSvzWJSLyQquQYmCiAmab2Ga72SZiYKKC/dqJiEKM37pERB2Y\nNygOeVo5DFYBTWYBBquAPK0c8wbFhXtoREHHZeAo0nA5LCKiDihlEtwxNB46i4BGk4AklZSZVop5\nZpuIT0/L0NLYzGXgKKLw25eIyAdahRTZCXIGrdQjrKzQ45RRymXgKOLwG5iIiAKK08vRzbEMXPu/\n0bgMHEUClgoQEVFAsMtYbPBlGTjOPFC48MojIqKAYJex2MBl4CiS8eojIqJuY5ex2OFYBq79KeMy\ncBQJePUREVG3sctYbJk3KA6ZaoHLwFHEYY0rERF1G6eXY4tSJsGcDBvS8xK5DBxFFF6FRETUbewy\nFpu4DBxFGl6JREQUEOwyRkTBxlIBIiIKCHYZI6JgY+BKREQBpVUwYCWi4OA3CxERERFFBQauRERE\nRBQVGLgSEREFgc4i4HiLlc0XyCe8XnzDGlciIqIAMttErKzQ42izBWbBvo7twEQF5g2Kc+ssRsTr\nxT/MuBIREQXQygo9qnRWaORS9FJKoZFLUaWzYmWFPtxDowjE68U/DFyJiIgCRGcRcLTZ4pYpU8ok\nONps4TQwtcHrxX8MXImIiAKkwSTA7CXWMAtAo4mBCP2I14v/GLgSEREFSLJKCqWX36xKKZCk4q9d\n+hGvF//xiBAREQWIViHFwEQFzDaxzXazTcTARAUbM1AbvF78xyNCREQUQPMGxSFPK4fBKqDJLMBg\nFZCnlWPeoLhwD40iEK8X/3A5LCIiCiqdRUCDSUCyqme0glXKJLhjaDx0FgGNJgFJPeRzU9fwevEP\nA1cioggWzUFfT1+fUqvw/ZxF83mmwPDneunJGLgSEUWgWAj6XNen1FzY5lif8o6h8WEdW6SIhfNM\nFEoM7YmIIlC0L0rO9Sl9E+3n2RdsZUqBxIwrEVGEcQR9Gnnb3IJr0BfpU4qO9Sk1Hh5zrE8Z6Z8h\n2GLhPHeE2WQKhuj9iSAiilGxsCg516fsXCyc5470hGwyhR6/OYiIIkwsBH1cn7JzsXCevWGpCAVL\n9P5UEBHFqFgJ+rg+Zcdi5Tx7EuvZ5O7QWQScNkoYvHcRa1yJiCLQvEFxXusDowXXp+xcLJxnT2I5\nm9xVrjW/tfUybDU2s+a3Cxi4EhFFoFgK+rg+pXexdJ5dObLJVTprm6AsFrLJXeVa86uVSdrU/HJ5\nON/1vCuHiCiKaBVSZCfIe+Qv+p4k0s9zV5a0YqnIj1jzGzjMuBIREYVBNHTL6s6SVrGQTQ7UOeLy\ncIHDwJWIiCiEoml900B0P4vGUpFAnyPW/AYOjxQREVEIRcv6pq1W9Njp7UCfo1heQSLUeKSIiIhC\nJJpqHZutkh65pFWwzpFrza/OKvbomt/uYKkAERFRiERTrWOiXOyR09vBOkeuNb97D9fh0mGJEXOu\nowmPGBERUYhEU61jvBw9cno72OdIq5AiXS3G7PELNh41IiKiEIm2WseeuKRVtJ2jnoalAkRERCEU\nTd2yYmFJq66IpnPU0zBwJSIiCqFoDAajcUmr7ojGc9RTMHAlIiIKg54WDEYjnqPIw7NBRERERFGB\ngSsRERERRQUGrkREYaCzCDjeYo2oBeeJiCJd2GtcFy1ahOPHj+ONN95os/3OO+/EDz/84Lb/pEmT\n8Je//CVUwyMiCqho6lNPRBRpwhq4fvbZZ1izZg1Gjx7dZrsoiqisrMSECRMwadKkNo+lp6eHcohE\nRAHl2gPd0ZnH0QP9jqHxYR1bJNBZBDSYBCQH8C7uYLwmEYVHWAJXm82GFStWYNmyZR4fP3XqFAwG\nAyZMmIDCwsIQj46IKDgcPdA18rbBk2sP9J4aWAUjE83sNlHsCfk3pMlkwvz58/HWW2+hsLAQffv2\nddvn2LFjAIC8vLwQj46IKHgcPdA9cfRA76lcM9G9lFJo5FJnJjqSXpOIwivkgavZbEZrayuef/55\nPPPMM5DJZG77tA9cDQZDKIdIRBQU0dSnPpQcmej2WVDXTHQkvCYRhV/ISwXi4+OxatUqyOXe3/rY\nsWOIj4/HkiVLsHXrVuj1emRlZWHBggW47rrrQjhaIqLAcfRAr9JZ2wRUPb0HuiMTrfHwmCMT7e+x\nCcZrElH4hfynViqVdhi0AvbAtbW1FS0tLXj66afxhz/8AXFxcXjyySexYcOGEI2UiCjw5g2KQ55W\nDoNVQJNZgMEqIE8r79E90IORiWZ2myg2hX05LE9mz54NQRBw4403OrdNmTIF8+bNw6uvvorrr7/e\nY4lBe+Xl5cEcplNtbS0AoLKyEq2trSF5z54mVOeyp+LxDR5Px/YKKXBJAqCzSqCVi4iXAtXHwjC4\nCJJgkOGUUQrXJKhFADLVAs5UncMZL8/r6Nr19TVbrUCzVYJEuYj4iPytGD78bggeHlvvBg8e7PWx\niPwRnTt3rts2tVqNwsJCvP3226isrMSgQYM6fZ2OPnggxcfbl7Dp378/MjMzQ/KePUl5eXnIzmVP\nxOMbPDy2vntogPsKACM6WQGgs+Pb2Ws6Vx1o4aoDnvD6DR4e266LyMDVm5SUFACAXs87QomIYolS\nJsEdQ+OhswhoNAlICsCaq529JtfUJYo+EVfkU1tbi5tvvhlvv/2222NVVVUAwKwmEcW003orSs6a\ncFpvDfdQQk6rkCI7QR7QG6c8vSZXHSCKThGXce3bty9aWlqwZs0a3HLLLUhISAAAnDlzBuvXr8eY\nMWOQmpoa5lESEQVei1nAI9824kiTBVYRkEuAIb0U+NvlSUjwdqcRdQlXHSCKThH5U7lw4ULU1tbi\n7rvvxj/+8Q8sX74cd955J2QyGR577LFwD4+IKCge+bYRx5otiJNLkaiQIk4uxbFmCx75tjHcQ4s5\nXHWAKDpF5E/mNddcgxdffBEajQavv/46Pv74Y4wcORJvv/02+vfvH+7hEREF3Gm9FUeaLFDK2reD\nleJIk6VHlg0Ek2NNXbNNbLO9p6+pSxTpwl4q8Nlnn3ncPmHCBEyYMCHEoyEiCo9qnQ1W0fNjFhE4\n0WJDRlzYv7JjyrxBcW6rDjhWFSCiyMRvQSKiCJCrlUHuZQUmhQTITuh87WryTzBWMiCi4OJPKBFR\nBMiIk2NILwXMtrZ3s5ttAob0UjDbGkTBWMmA7HQWAcdbrFylgQKG34RERBHib5cnOVcVsIj2TKtj\nVQGiaOJs7uChDIPNHag7Og1ca2tr0djYiCFDhkAURfzpT39q8/hVV12FiRMnBm2ARESxQGcR0GAS\nkNzBdHSCUoo3J6TgtN6KEy02ZCfImGntgXy5ViIdmztQsHT4jfjPf/4Tr7/+OsaNG4eXXnoJgiBg\n3bp1SE5Ohlwuh8FgwI4dOzB27FjneqtERPSjrmSeMuLkDFh7oFjJUjqaO2jk7VfI+LG5Q7QG5BR+\nXq+c//73v3j55ZdRVFSExx9/vM1jS5Yswbp16/DBBx/AaDSiuLg46AMlIopGrpmnXkopNHKpM/Pk\nC9YI9hzdvVbaC9e142ju4ImjuQNRV3n9k/6f//wnrrjiCvzud7/z+uTMzExMnToVO3bswLx584Iy\nQCKiaNVqBY62dC3zFCvZN/JNILOU4b522NyBgsnr1XPw4EFMmzat0xe44oorUFFREdBBERHFgmar\npMuZp0Bn3yiyBTJLGe5rh80dKJi8Xj0tLS3o27dvm20ymQwPPvgg0tLSnNuSkpJgNBqDN0IioiiV\nKBe7lHlyZN/aZ8dcs28UWwKVpYyUa2feoDjkaeUwWAU0mQUYrALytHI2d6Bu81oqkJycjPr6erft\nt912W5t/nz17FikpKYEfGRFRlIuX26doq3TWNoFEZ5knR/ZN4+ExR/aNWauORcOd+e3H2JVrpb1I\nuXbY3IGCxWvgOnToUHz55ZedLnW1fft2jBo1KuADIyKKBV1pK8oawa4Ld32nL7yNcW5/DVZVGrrV\ngjbSrh2tggErBZbXwLWoqAi///3vcfXVV+Paa6/1uM+2bduwfft2vP7660EbIBFRNOtK5ilQ2bee\nKBrWD/U2xlWVhm5nKXntUKzzGrhec801mDx5Mp544gls2bIF119/PXJzcyGRSHD69Gls27YNGzZs\nwKxZs3DZZZeFcsxERFHH38xTVzK1PV00rB/q6xi7M05eOxTLOlzh+tlnn0X//v3x4YcfYvv27c7t\noihCrVbjrrvuwt133x3sMRIR9TisEfRfpNR3diQUY3RcO6f1VhzX2ZCjZQc2ih0dXslSqRR33XUX\nbrvtNuzevRs1NTWw2WzIzMxEQUEB4uMjY9qFiChWsUbQd5FW3+lJKMYYDXW+RF3l059gKpUKV199\ndbDHQkRE1GXRUN8ZijFGQ50vUVd1GLh+++23+Oc//4nTp08jOzsbN954I8aNGxeqsRFRF0TDMkAU\nvSL9+oqG+s5gjjEa6nyJusNr4PrVV19h4cKFiI+PR25uLg4ePIidO3fi0UcfxU9/+tNQjpGIfMDp\nQQqmaLm+oqE2OJhjjIY6X6Lu8Hr1vv/++xgzZgyKi4vxzjvvYN26dZg8eTKWL18eyvERkY/C3eaR\nYlu0XV9ahRTZCfKIDtKCMcZoqPOl6HCs2YpXDujwygFduIfShtcruKKiArfddhvi4uxTF3K5HL/4\nxS/Q0NCAs2fPhmyARNS5SGnzSL7TWQQcb7F2+9wE6nU6ew9eX9HBUUNrtolttkdSnS9FJlEU8UOD\nBX/Z04wr15zF6FVn8fR3zXjtYAtsgtj5C4SI11IBg8GAxMTENtsyMzMhiiKampqQlpYW9MERkW9i\neXow0msq/RWoKfdQTt3H8vUVi6KhzpcigyiK2HvegrVVBqytNuBos81tnzqjgG/OmnF1hioMI3Tn\nNXAVRRESSdsvP7ncvrsg8K9rokgSi9OD0VJT6a9A3fEdyjvHY/H6imXRUOdL4WMTROyqNaO42oDi\naiNOtroHq+2trTZEfuBKRNEjGpYB8lcsLunj7Y5vEcCeOhOuy1b5tFB8qO8cj8XrqyfgGsDkYBFE\n7DxtwtpqA9YfN6LW0HkCUioBrkhTYmauBtNzPc23hEeH35CHDh2CXv9j4b0jC/vDDz9Ap2tbrDt2\n7NjgjJCIfBJL04ORvqRPV8sX2k+52wQRB+otaDAJMNhEvLBXh/zeyk6zyuGYuo+l64uoJzBaRfzn\nlBHF1UZsPG5Ao7nzOlWFFLgmQ4UZeRpMy1EjVS0LwUj902Hg+tJLL0EU3T/oCy+8AACQSCTOYLak\npCQ4IyQin8TS9GCk1lR2t3yh/ZT7gXoLmkwCFFL7c5OVUp+yyuGYuo+l64soVrVYBGw5aQ9WPz9h\nRIu182BVI5Pg2iwVZuZpcF0/dcSX/ngNXJcuXRrKcRBRgMTC9GCk1VQ6MqwbjxtwRi90uXzBdcpd\nhD1AV0glsIkiUlRSZ/DbWVY5nFP3sXB9EcWSRpOAjSeMWFtlwH9OGWHqvGQVWoUE12erMSNXg8lZ\nKsRH0c+018B19OjRoRwHEZFTpNRUumZYWywi9tSZkaaRYUSKArILWVJ/yxccU+67ao1oMgtIUEjQ\nR21/Tef7+pBV5tR95Im1FTAoctUabFhfbURxtQE7TpvgQ2IVKSoppuXYg9VrMlVQRelNrh2WChw9\nehSffPIJzpw5g379+mH27NkYOHBgqMZGRD1YJARmrjeImQUBMokEjWYBB+stuCRV6dyvK+ULaqkE\nUi+/N3zJKnPqPnLE6goYFFlOtlhRXG3E2moDSs6a4cvKqukaKYpyNZiRq8aV6SrIvX3pRBGvgev+\n/ftx3333wWazoVevXigpKcHq1avx3HPP4dprrw3lGImoBwp3YNb+BjG1zB5oyiQS1JsEmG2iMyjx\np3zBEQz3iZMjJ0FAk0loEwz7m1Xm1H34xeIKGBQZjjZZUVxtX2O1tM7i03NyEmSYkavBzFw1xvZV\nQiqJ/mDVldfAddmyZcjLy8OLL76IjIwMNDY24sknn8Rrr73GwJWIQiZcgVn7G8RUMgmSVVI0mQTY\nRMB4IXD1J9BsHwyPTFE4VxU4rbch12zDRUnKqJru7+nT45G+AgZFF3v3qh+D1R8arD49b0gvOWbm\nalCUq8YlvRVu6/DHEq+B66FDh/Dkk08iIyMDAJCUlIQHHngAd9xxB+rq6pCamhqyQRIRhZqnG8Qc\ngWatwQazTYTBKvhVvtA+GJZJJbg0VQmTTcQ5ow0/HxKPYcnKDl8jUnB63O5kixXnTQL6SiRun5td\nxcgXoihiT50Fa6sNKPbSvcqTUSkKzMhVY2aeBkOTFJ0/IUZ4DVxbW1vRu3fvNttycnIgiiIaGxsZ\nuBJRTPN0g5hMKsFFyQpMylJjWo7a7/IFb6slqGQS9FZJkZUQPT1hevr0uCNwP9RgxsHzFqhkVqSo\npG1v3GNXMfLCJgJfnzGhuNqAdT52rwKAgj5KzMhVY0aeBnna6Pm+CCSvn1oQBEil7aY+lPZMgNXq\nW+qaiCiadXSDWFeyir6slhANU+/RMj0ezGPpCNx7qWRIj5N1u1aZYp9FEPHVaRPWVhmwtlKDektd\np8+RSoAr05SYmafB9BwNMuMjryFAqPXMcJ2IyAfBuEHMWzA8t78G75W1BiRIdgRsCilgERDwwC1S\nG0Q4xxDkMoZYrFWm4DBYRXxxyr7G6sYTRjQ5u1d5vw6joXtVOHUYuNbW1iIpKcn5b5vNnso+d+4c\ntFptm32zsrKCMDwiovAL5A1i3oLh98pauz317gjYjjSacbDeimaLgESlFBcnKzA0KXCBm2vJg9km\nwmAToZHZazwjYXo82GUMsVSrTIGnswjYcuJC96qTRrT62L1qcj8VZuRqcH22Gr28dWChjgPX3//+\n9x63L1y40G0bW74SEfnONRgO1NS7I2CraLbBZBMRJ5fCZBNxrNkKlUwSsMBNq5AiN0GOjccNaLaI\nsImATAIkKiQozNGENdsaijKGWKpVpsBoNAnYcNyA4mqjz92rEl26V10bZd2rwsnrT9cf/vCHUI6D\niKjHCsTUuyNgk0okzlaywI/rzgKdt5L1W/vkbQQsJhCKMoZI6exG4XVWb8OG4/aGAF/50b1qeo4a\nYxT1mHfZgKjtXhVOXgPXoqKiUI6DiCjmebtZyFsGD/B+Z3r713IEbIAIod0vUMe6syICE7jpLAKq\nW6wY00cFk02EySZCJZNAJZOgusUa1puzunIsuyISOrtR6J240L2q2M/uVTNyNSjK1eDKdCXkUgnK\ny+sYtHYR5zOIiIKss5uF/MngeXutGblqKKWAVOLeSlYmsXf+soliQAI316ymI2B1ji/MN2eFKhsa\n7s5uFDoVTRZnq9U9fnSvmpmrwcw8NS7rE3vdq8KJgSsRUZD5crOQrxk8b69VXG10BmyODl8yqT1Y\nTbkQrAYqcAtVVrOrQpkNZcvd2COKIr6/0L2quMqAHxp9WwJ0aC85ZuRpMCNXjVEpsd29KpwYuBIR\nBZGvNwv5ksHr7LV+M1KLddVGmG0CDtaLaLYI0CqlGJAoR55WHrDALdJrPJkNJX+JoojSOgvWVtm7\nVx3T+d69auaFYLUnda8KJwauRERB5O/NQh1l8Dp7Lb1VvBCwadBoEiCXAlYBQQncoqGOrYN2AAAg\nAElEQVTGk9lQ6ohNEFFSa8baKnv3qhq9b8HquL5KFOXaVwPoqd2rwolHnIgoiAI5re7ra4UiYGNW\nk6KR2SbiqzMmFFcZsP64EeeMQqfPkUmAK9NVmJmrxvRcDTLi2BAgnLwGrm+++abPLyKRSHDvvfcG\nZEBERLFAZxFw2ihBOhCwafVwT9F7WhWBWU2KdAariP/U2G+u2tSme5V3CikwMdPeEGBajhq92b0q\nYngNXN955x2fX4SBKxGRnetd/7X1Mmw1NiM3QY5+8TJUt1i7Pa0ejin6YLdQJQo0R/eqtdVGbPGx\ne1WcXILJWfZg9Tp2r4pYXgPXXbt2hXIcREQxwfWuf61MAo1cipOtNuRp5Vh4aWK3p9XDMUUf7Baq\nRIHQ4NK96gs/uldNzVZjRp69e1WcnMFqpAtIjasgCJBKebKJqGfr7K5/QIPsALUDDdUUfShaqBJ1\n1Vm9DetdulfZfOgI0FslxfQLN1dNyFBx1iDK+PQNKooiNm/ejNLSUpjNZoii6NxuMBhw8OBBbNy4\nMagDJaLY4K17VDTx9hlC0W401GLxM1F0O36he9U6P7pXZcRJUZSrwYxcDa5Is3evoujkU+C6bNky\nLF++HAkJCbDZbJDL5ZDL5WhoaIBUKsWsWbOCPU4iinKxUCfZ2WeI9IX5uyIWPxNFn4omC9ZeaLXq\na/eq3AQZZuZpMDNXgzF9FOxeFSN8Clw3bNiAadOm4amnnsKbb76J06dP49lnn8WhQ4fw8MMPY8CA\nAcEeJxFFuViok+zsM4T7rv9giMXPRJFPFEUcdOledcjH7lXDkuQoytVgZq4aI9m9Kib5FLieO3cO\nU6dOhUQiwdChQ7FlyxYAwPDhw/Hzn/8cn332GW666aagDpSIolcs1En6+hlc7/rXWUVorELELczv\nr2hoNkDRT7jQvaq4yoC11QZU+ti96pLeCszMtXevGsLuVTHPp8BVo9E4/2rJzs7GqVOnYDQaoVar\nMXjwYJw6dSqogySi6OZPnWSk1sD6+hlc7/rfe7gOlw5LjKjP0RVsNkDBYhNEfHuhe9V6H7tXSdC2\ne1Uuu1f1KD6d7Ysuugjr169HQUEBcnJyIJPJsHv3bowfPx5VVVVQKpXBHicRRTFf6iQjvQbW31pP\nrUKKdLUYUwEemw1QIJhtInacNqG42t69qs7H7lVXpaswI1eNolwN0tm9qsfyKXC98847cf/996O5\nuRmLFy/G1KlT8eyzzyI/Px+7d+/GxIkTgz1OIopivtRJvlfWGtE1sKz1JOo6g1XENpfuVc0+dK9S\nOrpX5WlQmM3uVWTnU+A6evRovPfee6ioqAAALFy4EFKpFPv27cPkyZPx0EMPBXWQRBQdOprm76hO\nMlpqYFnrSeS7ZrOALSftweqWkybofexeNaXfhe5V/dRIZPcqasfnwpDBgwdj8ODBAACVSoXHH388\naIMioujiyzR/R3WSZwy2qFgrlLWeRB2rN9qw4YTR3r2qxghz51UASFTau1fNzNVgErtXUSd8ClxL\nS0s73Wf06NFdGsCiRYtw/PhxvPHGG22219TU4JVXXnG+95VXXomHHnoIycnJXXofolgRiTcv+bPU\nlac6yWhbK5S1noERidcy+e+s3oZ1F1qt+tu9amauBuPZvYr84FPgumDBgk7XQispKfH7zT/77DOs\nWbPGLehtbGzEfffdB4vFgvnz58Nms+HDDz9ERUUF3n33XSgUXO6Cep5w37zkLcgIxDR/IOpHwxkE\nMQDzj+u13GoRYQMwPEmOnw9NYAATJap1Vqw7bkRxlQG7an3rXpXp0r3qcnavoi7yKXB97bXX3LYZ\nDAbs27cPmzdvxqJFi/x6U5vNhhUrVmDZsmUeH//4449RW1uLjz/+GP379wcAjBgxAvfffz/Wr1+P\n2bNn+/V+RLEgXAv4dxYwB6olaFfrR8MZ0If7j4lgCEUQvrJCj6NNFhxpsqLeJMAmAgfOm1F6zoLX\nrkqO2mMX68qbLFhbZe9etfe8b92r8rSyC2ussnsVBYZPgWtBQYHH7RMmTEB8fDzee+89vPzyyz69\noclkws9//nNUVFRg2rRp+O6779z22bJlC0aPHu0MWh1jyM3Nxeeff87AlXqccN681FnAHKhp/q7W\nj4azI1csdANzCFUQ7riWjzRZ0WgWoJBKYJ9Dk6Ci2YoVZa345UUJAXs/6jpRBPafN6P4QqvVwz52\nrxru6F6Vp8GIZDm7V1FAdXvV3vz8fLz//vs+7282m9Ha2ornn38eU6ZMwaxZs9o83tzcjJqaGkya\nNMntuUOHDsU333zT3SETRZ1AZTX95WvAHMhlovypHw1nQO/Le0eT9kG4ySZi/3kzjDYxoIFkg0lA\nq0VEvcketLZ3uMEcMatI9ESCKOJ/5yworjZgVYUaNcZzPj3v0t4KzMyzd68a3IvlfBQ83Q5ct2/f\njvh43zML8fHxWLVqFeRyz2997pz9h6Rv375uj6WmpqKlpQUtLS1ISOBf5NRzhOvmJV8D5nAtExWu\ngN7X944WrkG4TRBxoN6CBpMAQQRK68wAxIDVnyarpLABsIlA+/BGJgEkEknErCLRU9gEEd+cNWNt\ntQHrqw04pXdcu97PgQTAT9KUF2pW1chJYPcqCg2frrR7773XbZsgCKitrUVtbS1uv/12n99QKpVC\nKvX+w9Da2grAvuRWe45tBoPBp8C1vLzc53F1R21tLQCgsrLSOX4KrFCdy0iWYJDhlFEK19/nFgHI\nVAs4U3UOZ7rx2t6Ob6sVaGlSwObhR9YgAOdPnIPxwrfIFVLgkgRAZ5VAKxcRLwWqj3VjUD7wZ3zh\neO94eXRcu6eNEtTWy6CVSXBIJ4HOJoEjRjULwH+rDGiqO4c5Gb71ju9Mb7MMBqMcosuxs4mAVibC\n3KLH+RN1Pp+3aDi+kcgiAP9tkuI/dXLsqJehwdL5HyUyiBiTJGBibxuu6W1FqlIPoBGm0wDPgv94\n7XrnWH7VE5++GqRSqVuNikwmw5AhQ3DXXXdh5syZ3RuhBx3VxPhaL9PRBw8kR8a5f//+yMzMDMl7\n9iTl5eUhO5eR7KEB7jWIIwJQg+jt+Dpu0hkpGnBGL7iVAYzQynFpBNRxjpW2eixTCMX4OnvvaLl2\n0y0CthqbIZVIYDaakKD68fPIBRHZfVVoEUWk5yUGJBP6uwEiTu5sQEWzvWZSJgH6qKQY2kuOgb0U\nPp03nUXAnsPHkD9sALOzPtJbBWyrsbda9at7VZYaM3PVKMxWI4XdqwIiWr4bIpFPgWv7NVaDSaOx\nT7yZTCa3xxzb/ClNIIoVoVr8vv1NOjKIqDOK6K2WQkDkdYsKVpmCL3fXx0onLUed8v7zZggusYxN\nFJGikkIpk6DJLAZsCl8pk+C1q5KxoqwVhxvMkEgkSFBI/F5ForZehq3G5qhfySGYms0CPj9pxNoq\nA7bW+Ne9qkDVhPmX5bF7FUUUn9dx/d3vfoe8vDy3x8rLy/H000/j448/DsiA0tPTAQB1dXVuj9XV\n1UGr1TqDW6KeKNiL33u6U14pE5EeJ8O0HHXEdYsKdEDvz931sdRJa96gOBhtIkrrzDDa7FnQFJUU\nI1LslaiBrqVWyiT45UUJ3VpFQiuTQCOXRu1KDsHi7F5VZcAXp0w+d68qzFZjRq4G12apoZFLUF5e\nz6CVIo7XwHXv3r0QBPvVXlpaitLSUtTX17vtt3PnTpw8eTJgA9JqtcjMzERZWZnbY2VlZRg+fHjA\n3ouI2uroTvmaVmuHwUW4F+EPVEDflSWuYqGTliOQBESUN1mhVUidgXowG0FEyyoSke6M3oZ11fbu\nVTvP+Na9KlUtxfQcNWbmaXB1OrtXUXTwGrh++umn2LRpEyQSCSQSCf7617+67SOK9p+MKVOmBHRQ\nkyZNwsqVK1FVVeXM8u7evRvV1dX42c9+FtD3IqIfdeUu/WhYhN/XYCqWAyNfj8HPhyY4z2eTWQx7\nIwjXcYdzFQlfxhfq967WWVF8IVjd7Wf3qpl5GlzeVwkZu1dRlPEauD7yyCOYPn06AOCBBx7AI488\n4lYqIJPJoNVqA15gPH/+fGzYsAG//vWvceutt8JsNuODDz7AsGHDUFhYGND3IqIfdWXZrUhehN/f\nYCoSA6P2/A2U/D0GkdIIwtO4s+JlkHkJz4K5LJyv4wvFH2xHGi0orjZibbUB+3zsXtXf0b0qT4PR\nqexeRdHNa+CamJjo7Ji1dOlSDBs2DFKpFGq1GgCg1+uh1+uRmpoa8EElJyfjzTffxOLFi/HWW29B\nrVZjwoQJePDBB6FUKgP+fkRk528zgUjPUPobTIVrvVxfdDVQ6mpAGe4pfE/jPqMXUGcUoZSJAWl2\n0R2h+oNNFO3r6q6tNmKdH92rLkqSoyhPg5m5GlzM7lUUQ3y6Oeviiy/GokWLUF5e7rwJ68CBA3jo\noYcwc+ZMLFy40GtDgc589tlnHrfn5uZiyZIlXXpNIuq6ju6Ub5/ti+QMZVeCqUB3AfNljL5mT7sS\nKIXqD4tAXwcdjbu3Wor0OBlqWq3QWUVorELIV3II9nEVRBHfnfux1WqVzrf1c/NTFfbMaq4ag9i9\nimKUz8th7dixA7/+9a+d2y6++GI89thj+Pvf/44+ffrg7rvvDtogiSh0PE0Vq6QSj8FsUa46YjOU\nXQ2mQrHElb/Z064GSqH6wyLQmeqOxi0AztUt9h6uw6XDArO2bKDG19Xjar3QvarYrXuVd47uVTNy\nNShi9yrqIXy6yrdu3Yrf/OY3mD17tnNbQkIC5syZA0EQ8OGHHzJwJYoxrlPF75W1esz2ras2hjRD\n6Y+uBlOhWOLK3+xpVwOlUJU+BDpT7cu4tQop0tViWK6xQB1Xk03EjtMmrK0yYMNxI8770CZYJgHG\nZ6gwI1eD6TlqpMWxIQD1LD4Frk1NTcjIyPD4WL9+/TyuuUpEsaGzbN9DI7UorjZ2K0PZ3TuzPT2/\nu8FUsJa46kr2tDuBUpJKivMGAQkuLxCMPyw8Zaqz4uW4Kl3p99S5p3NntolotggY0kse9hvkunNt\n6a0Ctp40YZ2je5Wl87UAVDJgYqYaM3LVmJajQXIYZzKIws2nwLV///7YunUrxo0b5/bYF198gezs\n7IAPjIgiQ2fZvlar2OUMZXfvzPZ857kck7NU6Bsni8jOVp0dz5pWG+Lk3QvCXY+LwSbiaJMVgAQD\nE+XQyL3XLHeHa6b6nMGGLSeNqGm1Ytlha5fuuHecu7JGCw42WKAzC0hUSKGW2mcAgnEO/Tke/lxb\nzWYBm08YsbrSgC9OGWH0oWQ1Xi7BlH72VqtTstVhD9aJIoVPgeutt96Kp556Cg0NDbjmmmuQkpKC\nhoYGfPnll9ixYweefvrpYI+TiMLEke0z20QYbCI0Mokz+HDN9nUlQ+mYMrcvzyNCKpH4dWe265S7\nUrDfff31GTM2HDdgeJIcaRo57hgaB4VMEzGdrbxlT22CiCONVrx7uAWQSNyCPX8CpTalCHIgpa8M\nLWYBqRoZ7h4e36ZmucUiQhBFDE9W4udD4zsNLDsL7rQKKVYfM+CMXmhTCnGk0YLXv9fhnuEJPp0D\nRyD85g8tMNlEaJVSqC6MzXGNXBGgU9mVP6A6Kyk5b7Rhw3H7zVXbfexe1cule9WkC92riKgtnwLX\n66+/Hnq9HsuWLcNXX33l3J6UlISFCxdi4sSJQRsgEYWXSirBWb2AimYLAImzFeiQXnIM7NX16Wad\nRcCRRjMqmm1oMAkQREAqsQd2ZpsAnUXjV7OAA/UWNJkEKKX2hdntAZkFX50xYVaeJmIaInjLnu6t\nM0MqAXqpfqxZdA3ifa299VaKkKCUosFkT/WtrNDjaJMFZU1W57E/UG9F6TkzXrsq2eNx8jW4a//+\nNkHEwXoL6k0CTDYRtXobhicrfTofOouAmlYrUjVt6zgdZRWXJHT4dJ91Z2kr1z/YTrt0r/rax+5V\nfVy6V13F7lVEnfL5FsQ5c+Zgzpw5qK6uRlNTExISEmCz2fDpp59i+vTp2LZtWzDHSURhsrJCjzSN\nFI1mGRpMAmwicM4ooJdSwJNjfJuu9ZSlazAJOFhvhckmQuHSvafJJOBgvdjpndmuU+4mm4gGkwCF\nVIIzeisMVhESAHFyCVosAo40WiKiIYJD++ypCBECgNG9265T7anutbPMdqelCC1WHG22B61NF46Z\nQ0WzBSvKWvDLi7Ruz/U1uGv//gfrLWg029/HJgISP7LqnX0WndU+9u6UPHR3aauqC92r1lUbsavW\n7NN7ahUSDOklR55WhmcvS4QAiT0Tz6CVqFN+r52RlZWFQ4cOYdWqVThw4ABEUcSwYcOCMTYiCjPn\nL3WFFJemKmGyiTDZRKhkEgiiCJMgdvjLtqMsnUIKNFsExLULGGRSCZotAuSdxB+uU+5GmwhBtC8p\nZLDZX8PxfJtoX0IpEhoiOLTPnrZYBLx3RO+x/aa/yyt1VtohAmix/BjotyXBoUar23FqH9yZbCKM\nNhFqD8FdskoKESKazAJkEqDe5X1kEkB1YTy+nI/ObkpTS0W8V9bare5VXVmxoczRvarKgP31vnWv\nSlZKMCRJgSG95MiIk0IQgd3nzPjbPh1UcmlEtkomikQ+B641NTVYvXr1/7P35kFyZXed7+fcLdfa\nS6Vdpe6ullqy2q1uu2EMNm08tpteJPxgYOiJ8Bhm/MJhCL8wb8Dz/gD85j2I4A0DAQwMATZhevzA\n8wbswb2Oo40xeAPabbetbqm1tKQqbSXVmpX7Xc55f5zMrMysXKuySlWl+43woqrMe88959663/P7\nfX/fH8899xypVIrh4WH+1b/6VzzxxBPcc8896znGECFC3CbUv9QjpqjoDFNu+6hoqyjdj+6N0O8Y\nFAOFWdXVJ1Baz+i30QRWp9yjpsAQ4Ctt3p6wjMoxTQFRU5AP2o93o1GOnqY92TPbqnbSjn1JC6k0\n0a+HKcBkJVkr3wdlHXG1tCNmCmbyAX22gRsovnAxz/lFn/miRClYdCV74iZKaSJavn86IeTtitL+\n9qZJPr627lWdODYopfj+vMezl7Vm9Wyq8+5Vj+6PcjMfsC9h1nSvem3OJespBiLmCu3uZskMhAix\nGdGSuCql+Pu//3s+//nP8/LLL2OaJj/4gz/IN77xDX7913+dhx56aKPGGSJET9HLaurtjLXYMLVL\nwb5/f4S3DNlcXNIkJ1BUSNbd/VZHZK065R4zBSk3IGoajMVK+kqlGC6lYAOlbmtDhFbopQ9qO2mH\nYwqODDmcmq8lX+W5SthixTyV74Oyjrg6UpvxJS9dLfCRo3Zlo/LQqMOpeY+ZgiTnK27kJIcHLe4f\nXu7m5BhgGTCV8Vs+h82K0k6MR/m1SYO9fbXRyW67VzWb+6IvMYTg//lummcn80xmOute9dCozYm6\n7lVlH2SnJNV1A8VMIWA0ukxaVzP2ECHuRDQlrp/+9Kf54he/yK1bt7j33nv5+Mc/zo/92I9hGAbv\nfe97N3KMIUL0DGu1X7rT0CmhaqZhbZWC9SUcHrQrL+5y6hngYF9nXp2NLJi+dKXAfKlIazhicGzY\n3hQNEdphtdZd1XMPdCTt+LnDCb4z4zaOyjaYpz7bYG/C4hvTbmWNQJPdHVGTa9mAGzm/ZqNSPv93\nZgRLbsDRIbsihch7kpt5yR+9nmn7HDYrSpvK+PiqcfVTt/KK8tyfT7lMZiSTaZ/JTEDKbV9dVe5e\ndbLUvWp/g+5V9WtbCBRJy6gh8qsde4gQdxqaEtdPfepTTExM8Bu/8Ru89a1vrfw8k8lsyMBChFgP\nrKV6+E5FK0LVaiPQSbS2+tgKTYRW47NaTrl/5KjNv7g7zn89l2U6HyAQuHJ1x9xodNuxq9HcDzkm\nBR9ipb/szaQdjin4z+8c4jNnM5xZ9DGBhC1aztN790Z4YSpP3lc10fFjwzYZXzGVCVZsVCKm4AfG\nHF6+5bLgSqKmtvm6mZfsjBldPYf1RWlDEQNLNN5sdtu96u+uF/n2jMvzU3rT0w5Wdfeq8Shjsdbd\nq+rX1jLgj17PNNQ03+5WySFCbHY0Ja5PPvkkX/nKV/j5n/95HnroIZ544gne/e53b+DQQoToLdZa\nPdyrMWw1iUIrQtWsFWyZgHQSre11e9WRqMkvvrV/XVu2rieauQbU3zuNNmGzhYALSz4PR50V368n\nRI4p+MjRvo7naSxu8sCIjSlEJTq+XPSlOJA0G25UTEPwwKjNR9+SxJdUSNtan8M+2+BAVJIPVNfy\niqwn+ZtrRZ6dzPOlLrpXvafUveqxVXavql7bzdoqOUSIzY6mxPVXf/VX+aVf+iVeeuklnn32WX7t\n136NZDLJu971LoQQNSLzECG2Albb770X2A4ShXpC1clGoNP0d7W8oPrfvRhv2pNtdZTV6GX72V6g\ncXcwk6m0T9KpjfTptq6KjNt5i9dOG0dUy0b6Gxx7d9xqScZ2x/XrZirj9+w5fHJnwMtYbe+v8j3w\nyozHS1cLfPlqkXwHJqsJS/D+fVFOHozy3n297V61Gbu6hQixFdCyOCsWi3Hy5ElOnjzJ5OQkzzzz\nDC+++CJKKT75yU/y6KOP8uijjzIxMbFR4w0RYtVYS6HRWrEREoV6wrXe0d1ONgL7k1bbiOp6kfpu\nj7se7WeTeZOP393aMqwa9aR3oSh5capBF6qUz4VFn3+2a2WK+p4Bi9GYsewE0ENC1I5sdULGevkc\n2gZ86N7m99f1rM//9coS37rpciUT0EHzKgYdwWMHdHHVj+5Zv+5V3UpDQoQIodGxHdb4+Dgf+9jH\n+Pmf/3m++c1v8sUvfpE///M/57Of/Sx33303f/EXf7Ge4wwRYs3oZeV2N1hviUI9YTJRzBYUI1ED\nyfpFd7shIK2ieutF6rs97lrH0ej71wtGR9+vXsOCDxeWfECxP2ny/TmPnTGTY8PLxU39tsGSJyuF\nV9WImYIPH9EtpXpNiNqRrU7I2Ho8h9X31/VswPNTeZ65nOfr0y4dNK9iR9TgyfEoJ8djvHN3pIG/\n7fphNa2SQ4S4k9F1AwLTNHnXu97Fu971Lubn53n++ed57rnn1mNsIUL0HLcjPbfeEoV6wvTqrMt8\nISDlmjwwqrWO61GA1gsCsl6kvtvjrnYc5QipbdDw++Wft7uO6jU8t+iS8yUouLwUYArBoit5bd6r\nrKdjCvocg7QriVQVBtXP/WqkEp2gHdlq9/teP4eX0z7PXtatVv9pprPuVXviBicPxjg5HuMHx5yG\nhVIhQoTYfOiauFZjeHiYD37wg3zwgx/s1XhChFhX3I703HpKFBp1NFooShzTYL4ocUuFK+tVgLZW\nAtIpqe9W9tDtZqHbz9dHuQuB4lLK5+EGBKjd5qR6Dd1ALXeaEpD2FUIpTLG8ngD5QHF4wGJiwOZa\n1l/hbzqV8UlYgucmC2uWYKyH5KT6ObyWDVBKsS9pdTWus4sen5my+ObpW113r9qbMPg/39bPgb6V\ndlQhQoTY3FgTcQ0RYqtiI9Nz6ylRqCdc5danoFudFqoqrtejAG2tG4F2pD5uiVW19Ox2s9Dt5+uj\n3LFAccqXnJr3OD7qtP1+NarXMB9oq6kynRJAotSRyleK78y65H2FK1XJisrh4/f3kfUV8RJR/d1T\naVwJ5xY9DLSfalmn2U3kfb0LCstdtjo9vlKK7815PDdZ4JnJPOdSPuAArUnrjqjBoUGLwwMWo1ED\nIQR5XzIUbW1hFSJEiM2JkLiGCLEBWC+JQj3hKrc+heVWp2WsZwHaajcC7Uj9c5OFVelOV9M4odPN\nRSNZgWMKdkRNZgq1ulNPwrE2m5PqNYyZuhlAGaaA4yM2Z1M+pxc8fAm20BrXY0N6vM9OFvjQ4QR/\nfDrNuZRPv20QNSHjKQzBColBp5H39S4o7OT4UilevuXyzKRutTrVYfeqt5W6V3lSkfO7t8sKsXZs\nReu/EFsDIXENEWIDsF4ShXqCFjEFQxGD+ULASNRcjrZu4pd1q5aev3sqvSr9a9qTPDhqcSPns+Dq\nRgTtGieMJy32JUwmM37LzUUzWcGxYZt/mnFJFQMiliaje6Ky7eakfg2HIwaLrta4DkUMYrbBRL9F\n1pOYQpD2NRn7h1suQxGDvB/w+6cC/selPIbQxDdmCnwJMUvUSEags8j7ehcUtjr++ZTLi1MGf3Ot\nyHOTeabz7b0ABPCOnQ4nD8Z48kCUfaXuVa2ixiHWB9vB+i/E5kZIXEOE2ECsh0ShnvhN9JvMOgYj\nUYOUKzf9y7pVS89ui9rcQPHZc1men8ozV5AgYMA2+JHdEf7NfQmGS+nhRo0TrmYDDvZZ/PLx/pab\ni2ayAtMQHBmy+Km7YiQdk70Jk+nLMx29rKvXcH/Swi25ChzoM8n7ktGoiRCCYqCIVR0vVZR8JeXx\n4JjCFMtdsnK+ZL6o2GuZKyQjnUTeq8l5MVCVhgMRU/REclJP/n2pmEwHnE35nE95/MHrubbHsAS8\nfSDgZ46O8PiBxt2rQsupjcdm7E4YRn+3F0LiGiLEFkezl/NWe1k3aunZbVHb5y7kePFKnpynSJaO\n5UrFP9wssiNmVuapVTQRYg37zVePs15WEEjFq7MuEvirS4XKZuHhDq+90RrCsp1VxpM8M5kjXjdm\nBSx4in7bwBBB1fEMwMcNdJQ22mXkfShiYKKvaaEokQoMoX8+0W+uWXIyFDEQKM4uepxd9HlzSW9S\n2iFqwnv2RjkxHuOx/VFmpt7k3nvbk6HQcmpjsBm6E1YjjP5uT4TENUSIbYL6l/NWf1l3W9SW9iRn\nFlyWXFXjw2kKwZKnOL3okvZiPbEnq49yn1v0MQQ8NLLsKnA57XMzZ3L0cHfXXL+GoCOU/Y5BMdCR\n1eXx6mu1DC0RSRVl5fwDjkChZQO66Et1HHnvsw1mC4r5QlAiwRrzhYBZZ/X3VcqV/M8rBZ69nOdL\nVwt4HZDVhCV4dL/2WH3vvkhlQwIws6pRhFgv3M7uhI2wGaO/IdaOkLiGCLHB2Ex7FKYAACAASURB\nVKi01XZIj3VT1LZQlMwXJXlfISywqshroHSx0mJpPhxDE+B8KfXeTRodVto5/dkbGQYiy6nqcnr9\nes7oSZRpKGLwliGbi0s+80VJoHTh1kjEQCldhHf/sM2peY/5ouRWPiDnKw4NWBweMNkZM/nQoXhF\nKtEOaU+WpCZm7fmiJiPR7q5pthDwwpQmq1+9UeyIrMYtwXjSZLzP5O4+k8ODDo8fiG75KNl2eCZb\n4XZ2J6zHZov+hugdQuIaIsQGYaPSVtspPdapRtENFC9O5Tm76HErr6OOMRPGYmalYClpCwYjBhFD\ncDMnubDkAfp3wxGDQwMW9wx0V8DWZxvELQliWTJwat6rpNfTOcGnz2T4hbf0rWnu+2yDw4N2RcNa\n1pyC7hQFWmN7fNThlVtFir5got/i7WMRAJZcWXEf6AQLRYkEHhh1cKs0ro4pSLmybeTsejbguck8\nz0zm+eZNt2LR1go7ogYnxmNETN24oZpwbPUo2XZ6JlvhdnUnbITNFv0N0TuEqxYixBpR7kyUbhNK\nqk5bDTgGMcuovJB7iY06z0aizzbYn7Rato2dzkl2xy3itkCgyd2tfECgFP224OigQ5+tW7DujBmM\nRk1MoaOxMwXJzXx7B4BGGIoYoBQpV/LdOZdUqYFApET0ZvOyJ3P/1EScg30WgVIoIFCKg30Wv/2O\nQQ72WeR9yWw+YK4o2Z2weLDKT7Y6ytTpNZUjZ44p6HeMtlHpy2mf3z+V5n3P3eLof5/mE/+Y4uvT\nrUnrvoTJR48mePHxUd74l7v4Dw/3ryCtqxl/GZ0+m+uN7fhMNkP5Ps37kpQryfuSg33WhheHbqbo\nb4jeIoy4hgjRIerTfN1EUTYqbbVd02OtUqzV13xs2EYqOJfyyPuwFCj2JgSPHYjx1ER8+bO2wfFR\nh2KgKr6rUimKUnUVASub6J9LBcwWAm7lAxKWwVjMQAEDliLpGD2Z+1bR5/LPTy94KGBHgwr7ZlGm\nRnPbSeRMKcXZlM8zpVarpzrsXnVPv8nJ8RgnD8Y4PmIjqjS7C7mgq05qWb/xOTZThDPtSU4vuujt\nFMsbgC3+TDbDZnFy2EzR3xC9RUhcQ4Rog2YvQV8qrmaDjoT/G5W22ojzbKROrxMCUn3NpiF4cIfD\nW4ZtllxJxlf84lv7uG9Q96KaztcSo4i5bCGVclXX81OOpD00avPyLcXNXEDGlwQ5xX2DNnscHW7s\n5Ro3K7rrsw2ODtkk7cbErD7K1G5uG+mL7+6zODpk8X+/kuKZyQLnU02YYx3eMmRx8mCME+Mxjgxa\nNWS1Gt12UsukbB42sisIaSdFORtxH7uB4tOns/zDTRdLLMtSjg3bmEZvrMU2KzZDceh6NX4JcXsR\nEtcQIdqg0Uvw3KLHmUWPh0sawjKaRVE2Km21nue5HVGsTghIo2t2TMFozCThS/YmlqOPvZyf+uj2\ngzscMr5CAArFkSGbzOLqjr1adBNlaje35chZyg34u+tF/v6Gy2+fynClw+5Vb9+hu1edGI9xd39n\nr5puO6kFRmNC2irrMF8IeHaysCH38ecu5JgtBEQMUXG6WHRlpZtZmLJeX2yW6G+I3iIkriFCtECz\nl2AAzNW19yyjURRlo9JW63mejbaWaUVAziy4nFmw2VfSvXZ6zb2cn/rodsQUjEa1JZWnBIVArfrY\n5etfTUTwqYk4nzmb5cyCiyEESVusiDK1I3cLxYDvz3k8M1nguck8NzvoXmWIUveq8RhPVHWv6hZr\n7aTWLuvw9LkcS65c9/u4PMdJx6ixKjOF7maWcSWHBsOU9UZgM0R/Q/QOIXENEaIFmr0Ey92LGhHX\nZlGUjUpbVZ8n6ykC4Mjg2oojbod2ttHcl6v2p3MBaV8xEtFE9CfvivH5S/mO5rZX69Aoelu2pLqV\nD3ADRVHCsS4LU9YS2S5/91rWxxSCANibMFd8t9Hc+lJxOR3w2oLH0+dypNz2VgCWgEf2RDg5HuPx\nA9GG2tpusdZOaq2i6grFzZxfY11WPmev7+PqOS7fFwslazFfKkZjRpiyDhFiFQiJa4gQLdDsJeiY\ngpGoucKWo1V0baPSVmV94mfOZnmjFHW7lg343IXcqtOht8NaptHcn5r3SBUlEVMwFjVxTMHltM/n\nL+U7ntterUOj6K1pCI4O2bxnb5THD0SZuzLL8S6jeGuJbNd8t/TXfTonV3y32sv2YtrnXJfdq/55\nqXvVj+2Prluqe7Wd1FpF1XfGTG41iR73+j6uHm/ZqqxcDKiU4sNHktvKCitEiI1CSFxDbDt8b7bA\nt2c93j5q88BodMW/u0Grl+ATB6JYhug6crcRaSttDxUwFl9+xNeSDr0d1jL1c18MFLP5AIk23m9W\nnV2e23ap9l6sQ6vorWMKCl3+hV1LZLvT7y4WJV+6WuBvr7ucXvAIOvBYTZa6V51o0L1qo9DoWXSl\nbnBwaKDWKq3Zujw5HuX3TqUbHr/X93Gj8UZM7S1wsC+UCIQIsVqExDXEtsF0xufRF2a4VdDm71Jq\nLapjgir1Wh+LGnzp8R3s6kJ/146c9CKC2ssK5/VI6/dCG7qaayzP/blFl2/PeExlA+KWbnH66qzL\n/Q2qszeyiKzXUfS1RLZbfTflKj59JsM3pl3+rsPuVYOO4PEDMU4ejPLu3VGilr7X54sSBbeFeJXv\nh7OLHq8teMwuGewIfKIGPH122V2gvC43cj5TmYADSZPdpU3cRlokhVXtIUL0HiFxDbFt8OgLM8zk\nJXZJvrZUejkXA+jTbkjM5CWPvjDD9356d8fHbUdO1hK5Ww+StV5p/dW+hNdyjeW5/+PTkrSn8JUi\nauqxp4qSU/Mex+uqs29Hf/JeRdHXEtmu/+6SKzmf8jm76HM1G9BBYJWdMYMnx2OcGI/yw7silUp4\nN1A1NlS3yxd1+X7IUAwUdxmSXTu0s0f1Gre65zaSTIZV7SFC9B4hcQ2xLfC92QK3Csuk1a+yl1RA\nIME0wDLhVkHyvdnCqmQDvX7prAfJ6pb8ZH2YyvhtI6GrfQnXX2MxUHx/zqUQKD5yNNn2+2lPci0b\nsDthcSsvWXQlphCYhmA2H3A943NsxKkY09+uBgy9iJqvJbLdZ+uuTF+9XuTCks+NXGfdovYnTU6M\nRzk5HuPhHQ6msZKIrtdmYDVzpu8Hn9GYyXxV46nqNf7CxXzL8W40mQyr2kOE6B1C4hpiW+Dbs15N\na8l6p8lAQbmOWCr47pzPA6MbNbrGWC+S1Sn5KUelXr5ik1zKdBxF6+YlXH2NZUeAhaKWcnxn1gUU\nP3e4dZFKdQT52LDNa/NeqUuVJO8rLMOgz/F5+myWd+5yeh5tbkeuWkX3VoNuIoJKKd5Y9HlmUnev\neq3D7lUT/RYnD2qy+kBd96p6rMd9upYofLuMwrWM39F4QzIZIsTWREhcQ2wLvH3UpjpQVG/KU/0u\nNAQ8OHL7b/31rNTvhPxUomgGDJRCtL1OqVdfY9kRoJx+LgRwPtX+fPXV2Q+MOrwyUyQo6ZZ/aJdT\ncRcoBKpnRWStyFVRqgqZbRXd+6FVLF+7yLZSiu/NeRWy2mn3qmPDNifHdYHVfS26V0EtWV+P+3Qt\nEdx2GQVVGler8QIb1v0tRIgQvcXtf3uHCNEDPDAaZSxqMJOXWCZYFlAKPgm0TADAD2AsZnQtE1gP\nrGelfjvys1Ep9fI1FgNN9Oyq3YUpdPS23fkauQssudo/d9CpdRe4lvXZmzCZzsk1F980Ildvpjw+\n9vUFdsYNXKl9Qc8v+jw06tR8tzyPD7RXQjRFdURQKsU/3nJ5tkRWO+1e9XBV96q7Ouhe1Yis702Y\nmE0Usqu5T9d671XfD/Vjv6dfN6Vo9lyZKF6YynMtG9xWrW6IECFWj3CrGWLb4EuP72BHzMCTuiDL\nRt/gEVP/25OwI6ZdBTYDyi9gt86PqJcVzn22wf6kteJY5ShaI1RHpdKeZCrjk+6kDL3J+e/pt0m7\nslbKoRTDJUur6vM1w1MTcQ72WeR9yUw+oCgVg47u+V4/9vfti1Y+m3IleV9ysMsmAGVyVU9mzqZ8\nLix5mEIw4BgIdBekUw1S9K6EtL96MuRJxVevF/jfv7nIkf9vmsdemOW/vJ5tSVoFcCBp8u7dDv/h\nbf289OQY/9v9fS1Ja/UaV5P1AccgZhlM5ySzBbWm+7T6HJ3ee61QuR8kK9a41XM1W1BM52TN9ZUj\nvSFChNgaCCOuIbYNdiUtvvfTu/nebIHvzvk8OGJVfFyr/72ZcLvsctpFexOW6FkV+VMTcQqB4juz\nLoVAR1qHI8uks5OoXXUE+VrG5zPnsgw6K7s0OQbsiJlrLr5plB5Pu5LpXIAQaEmCKYiZAscQLBRX\ntv91DOizGkcqm+lmC77ib68XeHaywItX8iwU23sBGALGkyaHBy0m+i0SpePNFYOW0cv66CpKcS4V\n8NBo7WZAN9sw2BU3uZb11+wo0YsIbvl+eEBeZ2R/csUaN3qu9iYsiqV1qz/WehfuhQgRoncIiWuI\nbYcHRqM1hVf1/95MuF12Oe3Src9OFnpWRe6YouQeoDif8umzl9P73UaX+2yD+4Ycjg56bYvP1lJ8\nU03sq9vM3sgFGALOpzyOj2ht7XDE4GaulriWx5KoO30jIrcvYTEcMXjxSoEvXSmQ8duT1XL3qh/e\n5XA+5TPWoNVqO/1pvRQi5Woy/dq8xwN10gcJPH5Ad8lai6MEUIngOqZas5wjYcH+Bp7MjZ6rhaLk\nv7yeaXic9er+FiJEiN4jJK4hQmwC3I4K53JU6uV5TVrquwv1Wv/6c4eTFdKWclXTqF3ak1zNaEK9\nr4HMoXrs6xWprib2pxc85goBAi09SVgGqaLk5VsuD+5wODZs40oPpVTNPD41EWfyYu1xy0ROILiS\n0a1WL6azHXWv6rOrulftjZAo2X/91qtLDT/fKnrZSGcaNQW2AfNFiVsXmXQMsIzuCprSnuTMggtC\noKBC6tcSwe0W9c/VRnd/CxEiRO8REtcQIe5QNEu3TmX8dXE7aBdddgPFZ89leX4qz1xBgoCRiMkT\nB6J88FCilkjVSQcUmuT2ssDmqYk4nzqT4fSCp/WSArxA4cmAnC+QKiDjK4YigicOxPjpiXjLaOSl\ntMcXL+e4lA6YTOvWte0wFCl1rxqP8cjuCFGr9vpW6/vaSAoRMQVDEYOb+aAihQDIe5KbeckfvZ7p\nWDbiBoo/fC3N390oEjUNbENHscudzlYbwYVamUU36EX3txC3F73sMBhi6yIkriFC3MFIe5IlX3BP\n1YtgPd0OoHl0+XMXcrx4JU/OUyRLv8/5khen8liGWCFRcAPFFy7m29pVrfYF55gCNyjPh8Ay4FY+\nYK4g8X3dSthXCpSgEDR+od4sCv7mdIZnJ/N8a9rtiKzuqupe9dYRm7SnGIoYK0hrGauJPtsGFHxJ\n1BQ1utz7h228WYVkOXp8My/ZGTM6lo24geJjX1/gbMplviAxDUXMFKAUp+ap6XTWTaahkcwimTf5\n+N0rdavNELZg3ZpoppV+774oYzEzJLF3GELiGiLEHYjqF8GteZMvF5YqL/DbEZkqp5WXXFVnmSVY\n8hSnF13SXqzm3J3YVa2lqCztSW7m/VIKXeBLRSGAfsfAl4qRmME7xhwupwP+6mKeN5cCkrag3zYQ\nAl6YKvDKbAxItT1Xny34mYk4/+KuGA+POfhSX9/fXCu0vY5udNLV634pHZCZ9xmNLkdCAwU/fjDO\nT9wdY7EosQz4o9czXclGPnM2y4Uln4RlkrR1IVshUMwVASHJuJJDg93fR43W+3rB6Ep3HbZg3Zqo\nXntHKl6b9/jGdJEXJgs8MGqHlmZ3GELiGiLEHYjqF0GfKWpsgT50ONGkKtvknbuddam+XihK0p6q\nsczypcJXujgq46kaiUIzL9CzKZ/ZQsCeRJQBR7/EVltUtlCUIHT6PFWU+AqUAoQuVhqJmFxOBywU\nA4oBfH/eYzIdMFPozDpsOGJweNDirj6TH9jh8LP3LZu+fuZshnOLHn2O0fF1dBK9rF73B0ZsXp3z\nuJn3KdxSKwjAamQj5Q1ImT6MxQzd4SxQZDxJzBaMxsyuI5zN1ts2WJXuOuyatXVQv/avzXssupKo\naZAPFIYQPW+cEmJzIySuIUJsc9Trwjo1gC9HpmZyAS9dK3It6/OpM9l1MW3XYxMYQpvt38oH5ANN\nFBWK/rQgXpUqb6TRLDc5AFGj0VxtUdlQxEChONBnckkpZvMSV2rmGjVgJCL41k2PBVdS6KwfAMeG\nLA4kLYYigj7HWJGqni8E/MmZDC9M5XEMA0Msa0PXWhxX8aY1BN+bdZkvSt0KWQhcBR++L8GBvlor\nrG5lIwtFiSFEpYudIQS74ibFQG9Mjg5afPhIouv7Zj27zIXY3KheezdQzFc1MgmUfu77nfaNTEJs\nH4TENUSIbYpmLUvfucvpmAT02QZfmM4znQt6Yo3VDH22wZEhhwtLPm+mfFypMIUu4nEM7ZX63GSh\ncr5GhKoQ6IitKXSFfKvraoeyfvb8os9cQZJyFYHU7HTRVfgSJi8XOzrWwztsTo7HeLKqe1V9qtoN\nFE+fzfLFy3lu5gNm85KkrRiLmaRKDQ6OjzprImllAnBuUUesbEOgaaog5yk+f6nAL761lrh2KxsZ\nihgk7eUotRBUNiGe1Ob/X7iY73rTs9666xCbF9Vrnw8UgYLyXWqKZbeKcANz5yAkriG2BbZ7telq\nrq9ZP/hCoDomAb1qDdvJ+J+aiJPxJGcXdfW6Uoq4Jbh3wOa+IYt/ulXkLUMmh4echoQqamrbpXJH\nrlbX1Q6fu5Dj3KLHXX0m5xc9ZouKYkBHxVUC2Jsw+MjRJA9xkx8+tnfFZ+pT1eXzZTxZiSznfB15\n3hW3Kg0O1kLSyhHk6ohVGbYJN3N+w/Usy0bOLLikPUWfLTgy5DRM95fXBaVlG2cXPfK+TueORU1+\nYMxZ1aanGYH2JBwLHQG2NarXPmYKyssfSF20WLFZCzcwdwxC4hpiS6NZVHErCfVbkbrVXl8rwnkt\n67M3YTKdk22jaGtN0XYzfscU/Nj+KC9MFVjyJFLqdP/VrM/ZlEfG0923dsctnjgQ5V/eE+fzl2pd\nBSb6LXbGVs5hN0Vl17M+f/pGhuvZgAVXdeSxagg4mDQ5NGgxnrQ4OmTzocMJzp+fbvvd8lp5UnEz\nH+BJHT0OlCavIxFJgCDtSt46ohsDTGX8rjdpfbbBzpiJK90a4lomAAixQke8UJQkS0RaCUCU/rcF\nykTXlYprWZ9Bx2A0ZnJ8RBeAmaxOl9pId70nKtfkCNBuQ7XdN8RbBdVrH7UEWU9VigohtDS70xAS\n1xBbGs2iiltBqN8JqVvt9bUjnO/bF+VrN1xNIHxFzJcNbYFWk6Ktftl/4WK+q/F/+VqRQqDoL72A\npnMBsyXz/7ht0G8bKyyyqtPuEUOsyu4o40leuqpbrT43mdctUNtAAIcGLH5gzCFugRBiVfZK5bW6\nmA5wA23032cbZH1FMVBcywaMxU3u7rfwpeI3XkmtiHx2ukn70KEEX79RJOOpkr51WUPrSsVgxFhx\nX55b9DGE4viIw2AHhWLlyv0zCzZpXzEWNVeMbzVp3UaOANOXZ1a1QW337G2HDfF2QvXaV2vuM77C\nMVRoaXaHISSuIbYsepXGvl1oR0rXcn3tCOeOmFk5x6tvzHL8vv6Gx+pG41j/slcozi/6PFTXPrTZ\n+NOe5FpW2zOliroBQc7XBURKQcQAqxQprLfIqj5Op3ZHi0XJi1cKPDuZ5yvXCh0VWAlgOCIYjWqr\np7ePOfzKQwOV463GXqmcwl9ytUygECgMAUlbEDGhPyr4wF0xoqbgxSt5llyt5TUEXFjy8aXi3x5J\ntj8RMBI1+fGDcc4teki0PjBSImrl9Xz6bLZyX5olNwADKjpb6Owe3Je0GGkg24C1pXWr17t9PLsx\n2j17W3lDvJ3RZxv0DRh8ZMAOLc3uYISrHWLLohypaoRyRGezolLh3SBdXiYEa7m+MuF06/Lc9YSz\nzzbYFVUt//A/NRHnYJ9F3pekXEnelxzss1ZEOKpf9gOOgUAwX5S8OueScmXNWBqNv3y99w/bDEQM\n8n5JV6rANKjplBQoKhZZza5/f4N2sbfyAX92NstPfGmWic/d4KNfW+CFqdak1QAsoYnzvrjgvkGb\noYhgR9REsJxeb3S+TrCcwleMxQyipvZTdQNF1BSMRSz+2ZjNX13MsVjSp0ZK3rIZV/H8VJ601/m9\n/tREnEODtm5CEKia9ay/L8vFMKYhKjrbMlwJ1zI+Uxm/4fk7vQc3Gu2evRs5v+2zGeL2Yy3PXIit\njU0dcf3Zn/1ZTp8+veLn73nPe/jN3/zN2zCiEJsJW7nSuBPt6Fqvr1ddgjoxbW/Ul94xYLGomM55\nzOa1nnY4YnBs2G44/vL1mobg+KhDxpX8/Y0CN/MSAVTXE5mliGQna3w14/PspI6sfuumSweSVRKW\nYCSiLbgcAy5nNLMdjpp4UlWuo5xeXyuqU/iDEYMBoM8SvGXI4o2Uz5++keN8Ske9Y6YmuIYQmIZg\nrqDb3t435LQ9D7Rez+l8UHNf1hTDlKyHIqYgkIpzix5/ciZLUSqStuDo4ErZwmbsVNXu2ZvKBG2f\nzZAshQhx+7BpiatSikuXLvHII4/wnve8p+Z3u3btuk2jCrGZsJV7j3dCStd6fb3uEtTMtN0NFJ86\nk+FbN3XRT9l7VCmFEAqUTvHbhmDRlbw663LiYHzFseqvN2YJpBLkfU01r+ckMVMxEjXoLxGlZtdz\nccnnmct5npnM851Zr6PrO5A0OTkeQ9ciKSKW1nsWAsXwvAdCcWTIIWqKig6yV/dZsxT+KzNFjNLc\nmEIT9kKguJWX7IqbywcQ3esuG61n/X1Z3mwsurLGeug7My7Xc0GNVvbNxZWyhc3Yqards3cgaW7Z\nDXGIEHcCNi1xvX79Ovl8nkceeYTHHnvsdg8nxCbFZozodIJOSWkvrm+9uwR97kKOubyspK9BG+nP\nFCS74yY3lEQp3S7VLHWdenI82vBY1df76pyHY8K+pEHO0wRySSosQ7clre7iteQG/NOMyzenXf7n\nlQKnF/yOxn5owOLkeIwTB6O8ddhGiMaFOScO6vjbZMYvpc87KwjJ+p07AFRfeyFQFHyJVIKHRm18\nBTHLwJNa/5oPFL5SCDTp3ZswWx67UzS6L48N27w66yKhMq5ruYABx6jygoWMr3h+qsBPTzTelNxu\nwlpGu2dvd9zashviECHuBGxa4nrx4kUADh48eHsHEmJTYzNGdDpB2pO8c7dDIZBcywZNSelmv76y\nXjDpGFWROYFER0r9QHF40OLIkF1JMxcCpW2eGhyvfL03cj7/8btLDEScSvHQkiuRSvHmUsCVTMCf\nnM4wVwh4bT7gRj4g5XYiAoC3DtucGI/yz/dFGI2aK0hlozkHnWK2DfAlbdehTH5fvmKTXMp0VJVe\nf96MJ3n6XK5iIXXvgMm5lEchACmh6GtLoMcORHt6TzTaLJ04GOfJ8Sg5X1t2fWPaJVAglMIqRXtN\nIZgrBFzLBtw32Nt7tNe2VO02hFt1QxwixJ2ALUNc8/k8sVgj1VGIEJsrotMKjaJ5exMW79sbYUfc\nbHoNm/X6qvWCx4ZtXpv3mC9qghkoiNnaasksFRSBbunaLt3qSYhYVebipu5x/+pMkRvZgAVXcjkd\nkPY6I6vjSZMPH0lwYjzGnrjJ5y7k+OtL+ZZWR3326u21KoVqBgyU8s6dVqWX1zrtyUrKOpA6uioQ\npblVJGyDxw7E+OCh3la5t9os9QWK//d8lulcgGUIhNA62LLmFtAWED3CetlStdsQbvYNY4gQdzI2\nNXFNJBL87u/+Ll/+8pfJ5XLs3buXj370o7z//e+/3cMLEWJVaGSzM50L+Nq0u2ltdlpFu6r1gqYh\neGDUqehC+2yvQlrL6DTdWn3cQCmm0gFnFj3eWPDpkKvSbwtGowaDjmAwYvKzhxMr7J7aWR2txhZp\ntTZm9fNcndI+s+Cx5Cn2JixcX1Y2BJYh1s1XtNFm6XMXcmRcRcIWJdmCqGhud0QNRqIGe5O9e62s\nty1Vuw1htxvGsGFBiBDrj01NXLPZLJlMhk9+8pNkMhn+23/7b/zKr/wKvu/z+OOP3+4hhgjRFW7k\nfL47W2QwUqtH3Ky+s51EuxrpBcv/++MHY1iGWFW61RKCnA8vXc1zMe1T7MBj1UCb9ydKTgD7k1Yl\nYltOYe9N0DGpXC0B7bbbWKt5fmoizmfOZvjq9QBDCKRQDMfMyoZgI++balnIoQGL80t6XZSCrC/Z\nb5o8cSDWs7F0Mv+bBWHDghAhNg6blrh+4AMfQErJT/3UT1V+9r73vY+nnnqK3//93+fRRx/FNFsX\nJJw/f369hwnArVu3ALh06RLZbHZDznmnYaPWcj3gSXjupsmZjMHrGUHUEPSbikNJVbEaSvuKV9+Y\nZVe0d2nWbtBofv/HDZPrBYMyD8kD312Amzcl/8vuZSb5MHAzZzJVMPClwjIEB6KSt8YC8r7g3pgi\nUII+S5EwYPIizBThesFgT1SyI6KPk/XhGwsmfztn8o15k7xs/8IXKAYs6LMU9ycD/jFlYqM1oF7R\np3yIgq+YvDzJLRNuzZv0NSAT9WtwoyA6/mzWhyVf0G/pf2dSNkFp3ubn5ivfy0uYuzJDoeovb7t5\nPhII7rZNnFJnLkdAaqHxONYT1fOxT0DOEswrQZk/vsUp8A5jkV49qp3N/+b429Dps7IVsRnmd7si\nnNvmuPfee5v+btMS15/8yZ9c8bNoNMpjjz3Gpz/9aS5dusTExETLY7S68F4ikdApq7vuuos9e/Zs\nyDnvJJw/f37D1nI98PTZLPm4z8E4XJNFbEPgS8U1DI6Xes/HfNm0e9V6o9H8pj1JZnGJnX0rx5Px\nJbsO1o716GEqesCEJbRvaoPokxso/t23FjmX8vAVoBQxy2AwYvB3N4odJSSz/wAAIABJREFURVYd\nAyYGLA4NWGRc3RRhJGpyeMjmdCFHMVD0WYJkXP95C6RiMCn4obfocrAvF5ZWRPFg5Rrs8mTbz1Y0\nsJnaa31wv+JqNiCzuMDwyDCgo3LH+iyOV6W4O5nnXV2MeT1RPx87RqnIQiSK/+OhgZ6Oo5P5n778\n5m3/29Dts7KVsNX/9m5mhHO7emxa4toMw8P6JZDL5W7zSEKEaI/6dGel8r6qE5GANdnstNPVrUZ3\n1226G5b1gM00pJ85m+Vr1wtMpj1ygWC2IEl5CmjPVocjBk8ciPLY/ig3cgGTGR9Xghk3mC1ob9d8\noDiYtLhZ8ElYBoVAR7T7HcFj+5dT2J1YHZXnbG/CZDonG34W4A9fyzBbCEg6tde6L2FysM/i5XmY\nzQcEwJHBld3GOpnn/cnNYc/UShZysK/34+jEMm61LV97idU8KyFChFg9NiVxvXXrFh/72Md43/ve\nx4c//OGa312+fBkgjGyG2BKof6lVV97nAkWqGHB8NLIqm512urpWvy9KVSGzjdBN165qYgwrNaSB\nVLwy43Iu5TFbUPgdZrV3xw2ePBDjxMEY9w9bpD1VId/11d710d4zCy5pX0dejww5NfPbyuqofs5M\nVIUYy9K1jyctfKn49e+k+IebLhFDMBRZdk9wTMFkxufj9/cxdUMyr3QR07VswOcu5Gp0j53Oczf2\nTJ1uVBp9rn4t63+/0TZRW8GWait38AsRYitiUxLXsbExMpkMf/3Xf83P/MzPkEzqTizT09M8//zz\nvO1tb2N0dPQ2jzJEiPaof6lVV94vuJJPHO9jd3x1j2G7iutGv38z5fGxry+wM25UiEAyb/Lxu9UK\nK6h20a5GxHgwoiOfMQvmi5Jzix7fndUV8Z1gPGly8mCME+NR3r7DwZf6Or98daV11f6q6vXq6m9t\nYxRramPUyuqoUbTYMRW74iaPH4gyGDH4wsU8l9M+AoEldNOFVFFyat7j+KiWfrgSnj6XY8412Dmw\nPM76ivhOG1F0Ys/UaYFQo8+Nl+ZyMuNT8OHCkg8o7hmwiJmi5jgbaRO1FWyptnIHvxAhtiI2JXEF\n+OVf/mU+8YlP8OEPf5gPfOADZLNZ/vIv/xLTNPnEJz5xu4cXIkRHaPZSA3hwxFkVaU17kqsZnzML\nLgNNHApu5PyGFdlnUz6zhYA9iSgDjh7P9YLR0F6oXbSrnhgrpTi74POPt4pkfJgtdFb1bQmImPDe\nPRH+87uG6HeWr+lzFzSRzHmKmULAjqhZkR08diDaNKrYiY1R/WeqZR1uoDQBL7V3vZb1K5GzN5c8\nTCHwAgUoQNRIPyKmQKG4mfOpH0IjR4JuooqtrqtT66hGn3txKg8C3rYjwrlFl5wvQcFUOuD4qNOQ\ncG8kIdusPsZlrDUyHNpohQjROTYtcX33u9/Nb/3Wb/Fnf/Zn/MEf/AGRSISHHnqIX/iFXwi7aYXY\nUuhVurM6UjZXlLw257Erbq70SpWacNTr7oqBlgeA9t4sE2nboEJ2PUnl5dkq2lUmeVFTcD0bcC7l\nc27RY6HD7lWidN5+G+xSqn130iLlKvp10JK0J3ltvsCzk0WWXFWiiGAacDBpcG7RI2HraOCT41Gy\nvuoq7V1GeSNwI6cbGpxbdJkvSgKlW9QORwz2Jy0WixI3UHxvziPv6wYLcwWFED67YiaBoqJZ3hkz\nuZVvTNzrdY+9iCp2at3V6HNuoCoR8YwrmS9K3bpXUEPGN6Nl22bBatdwu9lohQQ8xEZg0xJXgEce\neYRHHnnkdg8jxB2A9fyD26t0Z3WkbEwIIqa/IkUN+uV3oM9cobsrBApZImPRqpdioOCVGY9ffTnF\ngG1UyGD55Vkf7Qqk4qWrBb5yvchkF92r+mzBgCOYyUkCtP+nITRRfnRflIBlPWDak7w+7/E/LhXI\n+9qfFaAYgOvBhUXJAyNKa1ov5/ji5RyHBu2u0t4Anz2X5fmpAnPFgEAqrmUDBhyD3XFTkzdg0ZW4\nSzri+hfnc2Q8RdQU2Ggd7nQu4EZOf6/oS8aiNj9xV5Q/fSNLvsE8NNM9Nooqdnpfdlog1Ohz+UCT\ncIBFTxN2u/S7MhmPmCIsNOoA3UaG17vBwkZhuxHwEJsbm5q4hgix3tjIP7hrSXfWR8ocU1QcCqqj\nYmVd3e74ykr0qClQ6Ahi+WeBVPz9nEFaeWQ9s3JcFDUvT08qvnajyDOX8zw/VWCmAxmAKeDBUZvF\nYkC/ZRArXXvECEi72lD/xHiM4Zipx91nETEET5/N8uaSx7Wcz0JRR2aNErGSEiSQV/DKjEs+ACEU\nIxGDqKnbyr54JQ8K3jbWOu0NOkWe8xVJyyBQCl8FLBQkphDsipckC0r/V8aTXMv6jEYNUkXtDGEI\nwZ6ERdrz2RM3sC3BzULAn76R5WZO4tQZJnSqe+z2vuy0QKjR52KmqPgJD9oG1Yc3Bcttd8NCo55i\ntQ0uNiO2CwEPsTUQEtcQdzTW+w9uOWJmG9Sk4btFo0hZ2aHgRi5gphAwEjFqoomNJAoT/RY7Y8vn\nf3XOY9ETDMQEidK4Fl3JuZSPrxR/dVHw5asFXrxSINWBDMAUcHjQ4qNHkzx+IMqtfMDvnUozU5Ck\nXR3ZG44aREz92ZxUxHxZGXf1eviBjsgGCpA66hqgeaQB+ApcqRDolqPFUthwqTTOjNc87X1mwcWV\nsOSpSmTVk5AwBflAk9SsZ+CYes0O9JkV+cX9wzan5j0WquQEBQ92JUwGq/S5O2NwfgmGfNm1RKQ8\nD6YQgMIUou19OeSYFWuuMuqJcjNLq35bz1HSMSobIpS+9ogpyLiS0Wjrhi8husN2sdHaTgQ8xNZA\nSFxD3LFYzz+45YjZ2UWP1xc8llxJv21wbNji0KDTdUS3UaSs7FAw7gb83KEEe5NWzXirJQrXsgFK\nKcZiJs9NFnhzySPrKW7kfBxD/xzAl9rt4FzK58vXFP7p9n7JloADSZN7ByzesTPCiXFdff/cZIHT\niy5nFn0ihiBmwp6EyVDEpM8xSBUDfva+JHsTZkP95VjUxDZAKk1ehVwmrUKAY0LG0/OgCeyyHEIB\nKbd52jvtKbK+wpWaTFuGjuxKyt6vcGzEYjRqEjEFeV9W5BemITg+6lAMVEXT+q2bReKmQcqVlchv\nzDYYsODkeJSkbaxYn2ZIe5Kzix4Xl/wVWttioGruy+rIbDNZxJPjUaYyfmXT1GhD89gBTZ8mMz77\nkxZu6Th7kwYv3yoCgol++K1Xl9pmJEKdY2fYLjZa24WAh9g6CIlriDsW6/kHtxwxu7jkUwwUcUuT\njgtLAY7ZfUS3leXO0UGH+4acht9zA8UXLuZXpJw/fn8f55d8XKl4xXOZKUjmCpLFUhFUO/Tbgh/b\nH+XEwRg/OOaQ8SQvXS1wLRvwqTeynFv0MIDjow47owbnl3zyvuJSJmBXzKw0Bbhv0K4cs349hmMm\nA45gydVkMmpB4Gm5QNSkpNkMkEqTdIWWQ5Tr1Aac5mnvmKkjmNO5AIFAKUna01FXqTQB/vZMkcf2\nxXADGsovIqX/3Mz5ZD3JyzMusqTbHXAEAphMG3zmXK4mGt5uw7JQlLy+4FEMdDS4PEOLruS1Ba/m\nvqzJGFjwcNTR0dGYwYcOJXh2ssDvnUqviPi2Kror/wzg02cyxAyjJorbLPIb6hy7w3ax0douBDzE\n1kF4R4W4Y7Fef3DLkUPQXqY63UvFMklBJaLbDZ6aiHOwzyLv6zaneV9ysG9lN6ZqVBObAccgZhlc\nTvv8+fkcr8y4fPV6kTMZgwtLAQttSGvCEvzrQ3H+8n0jXHhqN3/yyDAnxmOMxUy+dsNlOieJWVpr\nmvEUGV/x2rxXlohiCEHRVwSqFBKtmq+pjI8bSAr+csof4KfuitHvCCSasJoCEjYcGrDwJDiGwDF1\nFX+ZSMZNgW3o3w1HtHY1kKqS9nYDRdpTxCxBrER0U64u/BICbFNHX+cKkheu5GvmuNEaLHmKhG1g\nG/r8tiG4kPI5v+QTMWBH1KzMe1lb2wq2AUvu8n1ThikEaVdWCtXK91k9KUw6ugjrL0t+s/VrXx5D\nn63dEqoJUv3PFoqyhrSC3iScWXB1k4eqe7jZvdbJNd+pWM0zvdlQJuBuUPvXY6sR8BBbB2HENcQd\ni/WKeJQjh6BqUtWwnK5WdB/R7dadoD71nnIl5xZ9zqV8rmarq4aaR8OStuDQgMXhAYuRqODfP7iy\nH339ecrpetsQzOjqKfYkLAKl0/PHRx1GoyZvLvn88ekMU2mP1+Z9ljxJ1lMkbMFYTNt8xRyTn5lI\nELfgyJDNmQWPnE/lPLYBp+c9coHSNmF5iWPqK/rq9YJ2T1BaVnCgzyRVDBiMGPTbsD/pIIAzCzpC\naOiPsjNmMBYzkQpcKXn//kiNHrR6DSwD/uj1DDlP6Va+QhAoRVGCVIqdjloubmpgTXU1oy9mXxVZ\n9CT02zpCX21zFkhFv23gl7hifYS62ns26ym+P1ekzzFR0HQMrdAoIxFIxal5j+lcQNpXlUjyifFo\nx3Zcrbp13Qkkp/56N3uDhU6wFTqchdg+CIlriDsa6/EHtxzJNYWgPkNaTldLpVYV0e3mJX8143Mp\n7TNfULy55DPdxFe0HgZas/pDOx32JU1EKfKXcmVDsl1PcKrT9UWpq/6jpsAUWufaX/r+a6V0+NVs\nUJFT2IYi6ylmCwEv31I8MGrXpJvfv285Ha3QZG3RVRSDgDdTPrKk433XLodTC35prkwOD1pMZwN2\nJgyuZIOSB67kgRGHoYjBl68WKddVjUaXrbAKUnAlE6xoFFF2iJjK+LiytpVvPtAR3oglOBCri0JJ\nmMkF/PfrOZ6fyjNXkCBgJGLyxIEoHzyUYCiitdAXloKaArChiMFEv7nCISCQqnLuQIGBYr6oiJoQ\ns4KK5djhAQtXleaszaYp7UmyntSeZVU4Ne+RKkoipmAsql0oLqd9nj6baym7mckFfGG6VrJSbVt2\nJ0gLWkkpNnuDhXbYCh3OQmwfhMQ1xB2N9fiDWx3JLVdom0JU0tUCuo7odqofVErx6pzHb353iZdn\nXOaLnXmsRk0YcgyGI4LZQsCOqMF8UbK/b/lPRDP5RL3kImLqhgKpoiRSrqQCAqUqVlxuoEi7mgAt\nlCv/0VHahA0PjzlkfcVH35KsIY316/XJl1MIFHHbxCwG2MCVbMBnz+dJ2rqjVc73iZiQdjXBPjpk\n13jg3tNv4Zj63OVCrTJsAfuTzavpK5uUqla+S57k1Vm3QtSr4Rjw0rUiL13Nk/MUydI9kPMlL07l\nsQx9fYcGHRzTR7FcUCaAg33Lkdnyffbs5RwZf1kPez0bECgIlGAwojdJ5xY9zi5qK69AKV6YyvNz\nh5MrCGL9fXYuFWAIn+MjDr7SmxRDwLBj1EShb+Z9VBOuWb7m6VxQ262ryrbsTrBQuhMso7Y6AQ+x\nNRDeYSFC0FjvtxY8NRFnV9xgR8zAFIKcr0naRL+5Kg1bK/2gUopvz7h88uUUb/v8TX702Rm+dLXY\nlrTGTME9fSaH4gFvG3W4u99iMGISMQ18pZgvdYqC1vKJRhq3+4dtko5gIGKStASFQDLoGBwb1sKJ\nJU+7LEh0MVQ13ECRKkptedUkSNxnG1gGXEr7OKZOnysFGV8hpY70liPeOV9xLqU/V9YYD0eMikVW\n2bvWC2TJ01QzsLwv2ZswK+Sy2Tiqr90xBaNRkyHHoN8RNYTeDRQjUYPvz+m1qZYBmEKw5ClOL2rd\nqL5/TGbyPnOFgEIT7eOT49HKHBZKUgEF7E2YgMINFLfyAa5UFeeFnTGT6ZxsqD2tv88eGrVBwXdm\nXWbyAUWpataxAiHYVfLjrV/LvQmTa9laOU4xUCy5umNX9XeqpQXbCc30yNv1ekOEWE+EEdcQIXqM\nctTqWjbANgSHBkzu6o/w5HiMHTGzJ+08pVJM5wO+cq3Ab766xI1cZy++hCXYlzB4x5jD6UXteODW\nfXUsZjAYMZjJS2418IdtNL537nYoBNp2qxwRft++KO/Y6SCE4JvTRa5lAzK+wjEUhwYsogbYVbIC\nqTTJyvoKsaD9S5tFBgEm0wF+ifNYBigUge5oCwp8pTDRkUo3UPhKVTTG9R64P7Lb4dszPr5UpFxJ\nylXELcVY3G5rAdXKXuq7V7KkXE3C5wqS2SJ8e0Y7AyRtg7GYgVGJSEPGU8zkA/72WpEvXSkwUwjw\npWJH1ODeAXvFubO+4tCgTdQUFXuu78/pQrjhqPahzfoKgY68xm3BsWGbQMF351zen4tUItqN7jPT\nELxtLMKiG/DTd8X475fyNV61ZTgG/OtDiYrVWnVW4J27HT51JlsjI6i2LatuPwzb00IptIwKEaJ3\nCIlriBA9Rr1FEcBCUfG1G+6qUoLll54jFZOZgHMpn/Mpn5zfXgYggD0JA6F0odUP74owWspf38hL\nruf8GhljoBSjUZMHRp0VPqv1aCRf2JuwePfuCF+9UeRa1ucvLuSrLLjiZH1VkWM8fTbL5bRfkRXM\nFAJyviJh6QYFg85yZLDRvI33mVglvmMJgWMI0iidRhL6Z1JB3DbIeZJALmuMm3ng3sj5/PHpLFlX\nMlyV52+V0m0lN3mVG4zsT/LCVIGIqQvikpYg7QkKgW6cUO7QZQq9Ri9dLfDSlQI5X1X0wG6gePHK\nspSgjLJUoeyoUAxUZSNgCcF9gzY5Xx9boXhgZFmLmwsU//G7SxwfjfDURLwluRIIxuIWRwedpsWM\nI1Gz4TykPbnCvaNaBx2tj0JuQwul0DIqRIjeISSuIUL0EL1uapD3Ff94S9tWXUr7FIP23xHAaNTg\n7Tsc7h2wMAV8Y7oIQJ+jq9ULgSpZSul0bdaXFfuoY8M69X1kyKnxWa1HI83edC7gd76f1lpKqJjx\nX077PDtZqCFd5UilG0hendW2UjFL62PL6WjTaD5vu+MWhwZsLi55OKbBrphuAOBLcIRuJpAwdVTz\nhtIR3ZFSQwFo7IGbtA2kUjWkFTpbv0b6voSlScm1rF+5J3bETOZdWXIB0JFhgfbGvbvP5mLaq+no\nBTryueSqkgVVrGknrLK+eL4QMBLVjR5sQ2ufE5bB6XmPjK/PZ0KN5OQn7o61JVdPTcT5zNksZxZc\nDCFI2mJFNL5+Hhq5d0RMQb9TdnzYOA/T2+VgsF08W0OE2AwIiWuI24ZWL5Hq320lNItauYFirqhT\n6XsTtHx5LrnazP+ZyTwvXS12FFm1Dbi7z+LQoEXOkyy6kvsGrcpLst8WSBRnFnSr0nKadqLf4pf2\nuvyjHGI6r834XanaOis0I+i+VHzzZpGxmImASkX7/cP2CuK3HKmMcXre48/OZjANwYBj1OhKW6VS\nf/sdg/y7by1yLuXhKeh3dJT7h3dGuJINWHIVBR8OJk12xk1Go4KUK5u6R7SKOmY8xel5j6PD3RGN\n+mMeG7Z1wVTKJ+3pIrVdcYvHDkR5954Iv3PKXWGjBlpKkPZXOgLUSxUm+k1mHYORqEHWl6TcgIIP\ng1GYzgYESjd0iFgG355xGYoYuIEEYi3JVcQQJQmMbkUboLW0nbgANJRT7F/u1rXeFkqboTlCaBkV\nIkRvEBLXEBuOVi8RYMXvknmTj9+tNuwFsxbUpwSrrYryvuTff2sB0zCY6LeIWsvXnfEkL14p8Mxk\ngb+9VlihO22EiAn7E9rv9K5+qxKhC6Ti5VsuC6X2o2XN5T/cLHIx7QOi0kJ0Z8zg6/Mmn/iR/q6c\nFZoRvO/PeeR9iWA5slmu3r+r32pIQCOG4NU5jzcWfQyxPLZyxLVVKjXpGPzxI8PcyPlcyQTsjBl8\n9brLm0seE5YBSrEzbvGhQ3GGo2bba2yU0i17l97KBwggMSW6Ij31xzQNwYM7Irxl2OFWIeDfHE5w\naNCupNWT9kobNdDp/j5LrJiLZlKFtCf59JkMP7onyuV0wI2SflgqiFuCvXETQwhSRclr85oQtyJX\njSQwraQcnYwR2BALpc1Q0R9aRoUI0RuExDXEhqPVS6T8/8u/KwaKcxmDz5zN8pGjyds25k5RnxJ8\nbd5jsVScowuDAKk7Rd0zYPHFy3n+8PUMby75BO0Dq4xEDN69J8KP3xXlh3dG+L1T6RVRT63ftPnX\nh+LMFRUHkroyfjLjsy9pUQi0r2qZdE0tGpVIaKcv0noypqvEdaTXMkSNpVS5Y9hdTbxrP3chx3Qu\nYCxmkipKTEPo9qbzHkeG7I5SqbvjVqXI6EOH7YYkbiqj9bT7k1bNv5ultQHygeJCSnuX7oyZFX1w\nN6SnWZoY4Ad2RHjbjkjNZ48OOry56JPxVcXhIJCKfkdwZMhpauTfaP0WipKBiMkDEZP9hYDr2QDH\n0O1xy+1pTUOw5OlmCq1IcC8kMI3GuN4WSr2W76wVoWVUiBBrQ0hcQ2woWr1ETi+6CAUDEbMS5Voo\nSnJ5gzcvZAHVtMK82zF0IlFY7culHJ06vehytZRWHXQECQw8BXN5HYH8n1eLHR1vT9zgyfEYJw/G\neMeYU2Oj1IgQ5T3Jzbzkv57LVaJmQ45JwYehqFgxf75sbEhfPxf1/76n3+bNlMfZlDb6zweK6VxQ\nKopSNS1LvQB2xq2aKJv2b6VyP9w/bFfWPFBwMx/wo3sjlUh8N2tTJgduoHj6bLYSQTRRzBa0LZWk\nccr4J++KVeQHBamYzUvGoibvHFpO3ndLerpJEz81EceXiuenCswVtKh5JGrw2P4YP3lXrOZ6WqW8\n66PipiGIW6JiHRYo/QIIlKLPMWqsx+rJ1e2oiu+VHjWs6A8RYnshJK4hNhTtNIQAA5HlDj12KVUs\nhOB8am2pvW4lCqvVwDmm4KmJOL/zfR1F9RWcTQXaF7VDu8a7+kxOjsc4cTDGQ6N2xTKpHo0I0dVs\nQL8tMIRgwNHfmy0EXFjyeTjqrDiGZdSmn+vnqRnZ0wSvyGwhAPQ6JSyDHVFRat2qPToVMOAYfOhQ\nfMWxC4HiUsrn4RIhPz7qVGydioHi8ZKtVKdkrR710f1XZ13mCwEp1+TIkE0+0Ob85eKkhaJuBLAn\nYbI7YTKbD3htwSdq6tawD4w6lTkqa5bvG2xPerpJEzum4N8eSfLTE3GuZQNQquJ6UHZiiFkGZsm3\n9bV5lz94PeB/PZKsOWY5Kl5uBWsK2BE1WShKsr7El9qBYDhilDx8m1/HRlbF91qPGlb0hwixvRAS\n1xAbilYvkaQtECWfzepuSlDS99nGmlJ73UgUqn/XLVFWSvGfvpfmK9eKXE4H5DtwAgA4Mmhx4mCM\nE+Mxjg1ZlVarrVBNiGbyAc9O5vnajSKmEBjCrxRGJR0DUGRcWfr/Gm6gOBCtnc9WZK9M3C6nff78\nQo6dcYM9iWhFfnBmQUsjYhbES/ZMCk2On50s4Evd4rV87FigOOXrCPTx0rHL1k55XxO81eoT66P7\n5fvKMgRvLP7/7J13fBzlnf/fU7ZqV92Wu9xww8btkEPAEHzg4HDGmLuEM8FAypFgYzq/AAehHCEJ\nhCMQqsMRimnHEQhuHC25mBKbYFyx5YIt27JlW2UlrbbNzszvj9ld7UoraSXLXkl+3q8Xr8Q7o2e+\n++zsPp/5Pt+icTQmuCXgg8oQW2rC6Eh8WR2hxKUwsdBGsUvBXm95zeNxyjt8UWrDBmHd5PntfsYX\n2DMWVZ3ZJvba5BRRnChiL0tsrI5QE9I5EjQIGyaqJHHQrzOpqNkWhyxxOGCwq0EjHtccjJrkOyRG\n5No4Jc+WKEWV3JGrLVtOVFZ8d8ejiox+QU9k8ODB2Tah1yK+sYITSrouS9Bcmmh8gZ3GiJHSTUk3\naW4VGtva6yztda7ZVhfhK1/kmLraGKaVEPXzz+uZ8j+HeXBjI3+v1joUrTmqxCm5CgtPcfHeP/Xj\njqm5TCq0ZSRak/HaZN4/EGJrrYaJJf5sspRIjAIYladS7JIJRg3qI5YIG+5V+aeSZiNbzlNjxKAq\noCPHhFtyh6htdRGaNCtpLjfWAnRMnopDljgY0DkS1nEoEoPcCtOKbezwaazcF0qZZ7si0c+pUB2y\nhGCcuKgAEvaEdas5QFwMf1kd5lAg2uac1IUN/Frz34RizRYONln1YiUknLGWs3Vhg/J6HXusc1Y8\nxjZeXioSNQhETb44GknELA9wK+Q5lJSHn3TE42mPtTtSfLdiQ43G4aBOddggalqtaqOmyc76aIot\nr+4KUOKSKXYqKJL1PbIrllQfm6diYoUJZNrJbcFoN8O9aqv7pzuz4o9Xh6l4J7vDQZ3qoH5cbBcI\nOoPbLe69riI8roITTkfxfiHdZH11hJAez6Q2Ey0mu7q1116IQqNmggT5rXfR242B0w2TTw9HWF4R\nZEVFkIMZdq+yy9DPKVPslBnotjx7/jRljprtaz/WL6Kb/KG8iddigqUqoJOjmvR3yYnEqLBu4lIk\nfjzeg18z2OfXGeZRGOhW2bmz9TzZYzHGVQGdQwHdSriSsOq9KlaCkhwriRTWrTqwXzdEaYyYaLpJ\nOGridktMK7aR57DO1zGpCetEWnRKmlho49PDYSr9Gl67klIbdHeDlc1/NKTRGDGJGlbYQ9Qwcaug\nGVDW39HK4xnRTVbvC/JldSQWa2tSH9GpDZsEoyBJUBPW6edUCOomqizRpFmCVJZo9rBqBoZpcjRs\n0qjpHApIeG0Sp+SpTIrfk23Eu0Z0k7cOKfh9Dd2y5e1RJb6q1dhRr0GsNJZdlshRrbAQf0zU7W7Q\nOBSIWt5mm5wSfuFQrPjja071EDXoVGb7iciKPx7xqMmd7GSsh8zBOSe2FJZAIOg+hHAVnHA6WgCt\n6gGWB8lrk/H7AiiydExbe+2FKHhtEmYb61dLoRzRTf56KMw7FUFW7QtRHepYrErAUI/CkByZurDO\nrMEuDJOUzH673DrjPtNYv1d3Bdjh01Aka4s9RzUJRg2OBC2voG5antMJBTb++HWw1Xinp5mneIyx\nU5VQZWubOWKYfN0QZXo/S4i6FKv71uYajepYH3u3IhE2QDOtWNvc+JwVAAAgAElEQVTqUJix+ZbI\nc1rtm1JafMaT8IJRE6fNiqEdnKPwzyNciQS3NVUR9NjY9REDv2Z5DyUJIkaYQ01Wq9YfjfekzElV\nwKoEUBvW+bohSiBqxRvrhlVKLKzD4YCObph4bBJIVrZ9vJOXbsLGGo2wbjLALTNYkmnQTByyJdqT\nk+TSiapXdwU4GJIp8XbPlvfyihA6JqZpXV/CJGqY+DUr/hjJ6sZlAvsa9RQBGA+/AKiPPQAM9XTt\n5/94ZsUfj3jU9GW89BNaCksgEHQfQrgKskZ7C+APxnoSoq0xauKKGp0u1p0uCz5dnNv4gua4zXQx\ncIoEKyqCvFMR5N19IRq0jutWqRIM8yiMy7cxOk/FpVrCO16OqL1Yu7jdq/eFqAroaWP94olE8ax8\nr11OtNAsckhUGRKNmk6OJqGbJqVeNfH3Lcc7HFCYMNb6t9cmMzhH5ZOqSCL+0RWLN81RZRoiludO\nAqpDJkPcCg1hgwN+SzAdDRvYJKsDVVzs1oZ0NtfClGI7RU45JT5pc61GbUinxKUwKFbOqipgcPNn\nPgblKLgUGZskgQQ1YUu0KpLlFZUk0AyTPX6dlftCfG+0u1XppomFNlZUaASiMU+qCbJsPUz4NQNX\nTND1d1kC36FIieoGhwJRjoZMbLJMP6fMhEIbfzscwZbkxXYkHjxSRVXchpa3d1dLMMXHm15kp9Kv\nE9KteqwS1n+FThkJ62FIN02GeZVemZDU3fGoPa0UlkAgOHaEcBX0SJK9shu2VzNlXG7GC0xbnsp/\nHuHizT2tPY7pqgqYprWlvKshxO3r6jPqXpWjSpw/xMlFpVYHpOUVodh4VmmBUbk2bj7N26YNyXY3\naVa4RH+X1WAg7t1TJPjTXssTKSGlZOXn2SV21UcJG9Z2aDAKRwJRRuSqVAai7PRFmVacGg9hVyT2\nheSUBfy8wQ5W7QsSjJqx+GKJRlnGJks0Rg2qg1GGeGx4bZYA1wwwsOZMN0COVYLQYmWXTCyh548Y\nXDjMhRpr4+rXTI4E9UQiVDI76jUG5iiEdJNCh+WpjsSuI8fmQZEBJMI6HAlGExn+ydvNURMcikyu\n3YppNTFxyBIRw0A3JUrcMgUOhYBmUJjUDnZsnoqE1dVJliQaNJNyX5R8u/X/9VgSoUNJvxMQtyHt\n/dnBlne60JD4eHl2mTF5KuW+KDIm0VgHtKqAwfh86+d8VK6NgW611yYkdaXDVFvhNKIUVt+irKws\n5d+KouD1epk0aRI33XRTIuFp3rx5HDp0iFWrVlFcXNxqnM2bN/OjH/2IadOm8fTTT2fV1t7I0qVL\nefbZZ1m8eDFXXnnlCb++EK6CHo3XJjPAaXZqcWkrK/nNPcF2QxTmljr5416Td/YG+dvhSEalq1wK\nXDTcxUWlLmYNduJSm0XClWNzOBSIpsSTxl9PZ0NyqaOIYSS6GiVn3G+OdeGSsRKikrPykwnEOiQ5\nVAmPTUbCitlMHitOyzqu/d0Kk4tsKJKUiF2VJMvT6gsZfN2oo5sSX9VpRA0Tp2IlCFlb2Nb2tWFa\niU9NUQPdNNEMKHYpLByTg12RaNQMvqrVkCBR1D9OUDfRYsLQqVjNDAqdCg2aGYuPtYRrvIC+Edv+\nx7QeLpK3m0O6GYtblWIJSpZYrQlZ5dc0w0ooa4iYKe1gDwcNhnsVapOqW9SHDbx2iXy7leQT1q2Q\njHSiKm5DvQH1sQ5mbXln47QXGpL8ngwTmjQj0UQgGgsFCempiVa9tcVoZ2JpNaP9UmmiFFbfQ5Zl\nzjrrLMB6WC4vL2fNmjVUVVXx8ssvAzBjxgxqa2txOBztDXXcycTW3sjw4cM5++yzGTZsWFauL4Sr\noE+R6dZgfCGsCuisqAiyvCLEx1XhjLpXuVUrOWdMnkp/p8Rt0/JaLawdxae2DJNoabdLkWJexeZt\nabA8SHZZSmzjx7PyDwd0ZAkG5aiEdZN9fpMch8SgHJX6iIkC2NNscUPrOq7J27UVjTr+iIlNljga\n0nHKEoYJR0NWp6XDQYMcVcKlEMvUB2Jb+CUuhRG5KqfkqRiY/Hh8TsL757VZW+85+1oHF7sUCZvU\nHJdZ4JCpDerYZMvLKmFpVJtsCVfdNOnnUhgci9lMtt+pSNiUZvvcqoxNlhnglmnSdC4Y5mTxRG8i\nxMAXtt7XU1v9uFSZQoeOL2KgxGJa6yMmp/ezce5gB98Z5mpTVMVLUW3xybjCEWTJErNj81RG5aX3\neHZUBmpUrlWZYVeDjkOVcajWe3coEoUOGZC4ZKSrOW66l7cYzSSWdsVhhaC77TkTpbD6Hqqq8pvf\n/Cbx7+rqai666CJ27tzJ3r17GT58OHfccUcWLWwmE1t7I7Nnz2b27NlZu7741gp6PcnlhjLZoq1o\njPL4lkYuWHmU8a9Xccvf6vm/Q+2L1hxVYnqxjctGu1h8ag4XDHUyMldFR0pbnitZhOTZZVyq3G7Z\npJZ222NiRDebt6VDuuW5jJcGizOx0IZdkQkkzjFxqRID3LFsftPaYi90yGg6rcpOtazjCvHyQQpH\ngjqaCaGogWlaYyqSRF1YxzAtgdoUNSlyyLhjotuMeQHdNokpRVZS1oR8e9pWn+lKowGMybMRf4eT\nCm0UuhTssoRbaY5xdSgSmmFS4JCYN9ydMn68dJNhWtUUcu3WfObZLa9qk2YwNt/OTaflpnQFy3fI\naAaJz2JioY18u4xmWHMb1k2KXQo/GOthqKft2qfxUlS5NhPNtD6T6pDO4aCR1uOZSRmoBaPdeO0y\nfs0qF2diNXwY5FZwqzL1EashQku8NrldW3srjZrBvpDc7pzBiSnjJcgexcXFidJSwWAQsEIFysrK\nqK6uBqCqqorrrruOmTNnctlll7F9+/ZW43z88cf8y7/8CzNnzuT222/nxRdfpKysjBUrVgCgaRqP\nPPIIs2fP5uyzz+aWW26hqqrqmG31+/3ce++9nHvuucyaNYu7776bhoaGxN/U1NRw8803M3PmTC69\n9FLWrVtHWVkZP/3pTwFr276srIzf/e53XHzxxVxwwQVUV1d3OO7KlSu59NJLmTlzJhdccAEPPPAA\noVAIAF3Xeeyxx5g7dy5nnXUW8+fPT/EQx6/5wgsvJF774IMPuPzyyznrrLOYO3cuzz77LLpu/R4d\nPHiQsrIybrzxRl577TXmzJnDueeeyyOPPIJpZuAtaoHwuAp6NI2awaGQxIA0SRTpvJqDc9S0T2M1\nIZ2ttRqXfVjD5tq2a38mE+9e9Y9DHLy/P4jbprQ6J912Y1cSQlp2OXIpEhMLbWyp1RLb0nbZOq9l\nPKgiS0zppxLRFZyKlaT1+ZFIotuWEhN5EwttRAwN0zQTW+Kjcm2c7m0tduyKxJxhTnb4LDEV0U02\n1ljhCIcCUY4GrcSwRg0kTCSspLN8u7UdrxlW5nogajI2v+3t6ba2s1vGAo/NV5nR3051yGBDdSQW\nZ2rS3ylz0XA3C8ekZoe3bMywel+QDyvDHAlagjvXoVDW3x4rJeZnmy+KAuTYpJR7SJElJhfb8WtG\nbM6kFM9xOho1g3KfxtcNUfy6hKJYYr7AqVDklAkbJmHDTMRkAmytteKa41nv8QcVZ1Lt4qEelXnD\nnaysCOBQZFSZlLa6QCJc4mSgLmwQbeP9Jsev9nbPs6BtIpEI77//Pg0NDbjd7ja3ru+66y42btxI\nSUkJLpeLhx9+OOX4oUOH+NnPfoamaUyaNImvvvqKv/3tbynnPPXUU7z66qsMGTKEESNGsGbNGvbv\n38/LL7+MqnYspdqy9f777+ejjz5i1KhRuFwuVq9ezdGjR3nyyScBuOeee1i7di0lJSXk5ubys5/9\nLO34y5YtY+LEiTidToqLi7ntttvaHHfnzp3cd999eDwepk+fTkVFBW+//TY2m41bb72VN998k2XL\nljFo0CD+4R/+gS1btvDoo48ycOBAZs2a1era77//Pv/+7/+O3W5n0qRJ7N69m6VLl1JTU5Ni74YN\nG9i8eTMjRoxg06ZNvPrqq3zzm99kxowZHc5fMkK4CnokyaL0SK3CB6GGVqWg0m2tVgV0akKWKPNF\nTHbURymvj1KTQdkqgAlJ3atOTepetadBz3i7sSsJIem6HBU6rESc5G3pP34dZG+jJbKS7ZiQn1oZ\nodAh44sYYFpiN55ENG+4i0tGulIW8OQ6rskUOGRybBIuVSasW+1BjwQNasMGBuBSZUwsL2RQM7E7\nYJjXjm6aeFSJUq9KqVdtt+RQe6IiWXi+fyBEZZOOAUwqtlHoUPjOUAfDO9jujW83e2wRxufbGJtv\nS5Qhq2iMcvH/VmPVQ2iec0yoCRk4YuEaW2JxxRHDShT749fBdmuA1oUNttZZZbRsEokHGL9mxRg/\nu62JurBOKAq7GqKAyVCPwqYajX5OGROrZFU8htdjk3DHYqeHeFT6uRQCmpkiWnXDKok1uIslrnoj\nBQ4ZtY1GHekeKI9nGS/BiSMSiaRNfLrxxhvJyWn9W7Nr1y42btxI//79ef3113G73Tz00EO88cYb\niXPefPNNNE3ju9/9LrfeeiuhUIiFCxfS1NQEQCgU4r//+78ZOHAgr7/+OjabjSeffJLnn3+eTz/9\nlLPPPrtLth44cICPPvqI0047jd///vdIksSdd97Je++9x86dO7HZbKxdu5bi4mJee+01cnJyEolR\nLZk8eTJLly4F6HDcQ4cOYZomZ599dkKwv/POO0yYMAGAyspKAK644gouvvhidu3axaZNmxg1alTa\n9xlPcHv44YeZMWMGNTU1XHrppbz11ltcccUVifOCwSDLli1j9OjR/PznP+fdd99l+/btnRau4lss\n6JEki1KvIrXaaj8UiPJldZhkf4tpmhwM6GyujfDCzgDP7wjw6eFIh6J1arGNu6fn8vdL+vPp/BJu\nn5rLxBbdqzrabkwOV+hKQki6LkdHQwaHg0bKtnR7dsSP1Yd18h2ytbVukxjmVVLOy3TrOHkr3xHb\nbvdrOpphxcvKkpWc5lIgYkJAt0IK8u1W0ftil0JlU3PHqHQdpOKvAWlt8tpk1hyKUBUwEmEX+XaF\nUNRkfXU0IyES94B77HKiwxfAV3Ua+/1R1FjMsE22OmZ95YtiVyHfIbG+WuNwrLFEiUthWrG9w05Z\nNhkaYnGxySiSxN7GKAf91n29zx8lEDUIaCZVAYP+LoVyX5Rd9VFsshXfK0vWj/SKilBiPi4c5sJj\nlxLhC5ph4rFLXDjMdVIJM69NZpjTaBVqIuJX+zayLDNz5kwGDhwIQP/+/Xn55ZeZN29e2vMPHDgA\nwPTp0xPb9PGEqTj79u0DSAhQp9PJGWeckTJGJBLh0KFDnHnmmZSVlfH8888DsG3bti7bumfPHgA2\nbdrEjBkzKCsr47333kuMG7dr+vTpCVH+rW99K+21Jk2alPj/HY07ffp0Ro0axcqVKzn//PP52c9+\nhqIojB8/HoALL7yQnJwcfvWrXzF79myee+458vPz03q0fT4f+/fvp7CwMCFAi4qKmDFjBoZhsHXr\n1sS5xcXFjB49GoDS0lLAEved5eR5PBf0Gtrbat/hi/DMVwbbfFG+qNZwyhomElHDZFt9lHAHLVbB\nSu75RomduaUu5pY6MyrE3pZnMKKbabOaSz0qB5r0jDy0iffbRpejsGFmlHATX8DNWKmo8YUqI70q\n5w9x0s+ldGkhT97KL3ZZ1QlkycShWOLapUqUuBX2+3UKHc2CFazt7pqQwd4GjfXV0ZQ5Ko3NeYU/\n2m5zhe6ow5nOAx7RTY6GLM+xboCqWCXEjgYN9jZGOdSkoJsmUcPg9P423DY5kdCmQLvX1gzItckp\nscTWfFixqWrM+52oWCBZNk4rslHuixLUDQJR61hhLDQkOWbzW4McRE3YE6tx7FUlxhfYT8qYzX8q\n0fkctUdUTuiow52ge1BVlYcffphwOMwNN9zAF198wS9/+UuefPJJbDZbq/PjDojkWEpZTv184rGY\nbcVbRqPWw3V+fj6nnXZayrGSkpIu2xofd8CAAYwZMyblb/Py8jq0K5lkb3NH4+bk5PCHP/yBP//5\nz3zyySd8+eWX/Od//ierVq3ixRdfZMyYMbzxxhu8//77rF27lrVr1/LRRx+xYMECbrzxxpTxWs5l\nS5IdQE6nM2VuMn1vLRHCVdDjaG+rfUttlIBu4gubVAV06sJWLcuOUCWYO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uhelQ2Su0e1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13157bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "style.use('fivethirtyeight')\n",
    "plt.figure(figsize = (10,6))\n",
    "\n",
    "plt.scatter(lr_test_predX,y,alpha=0.5,)\n",
    "plt.plot(np.linspace(0,13,1000),np.linspace(0,13,1000))\n",
    "plt.xlabel('Predicted PPG')\n",
    "plt.ylabel('Actual PPG')\n",
    "\n",
    "\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize = 18)\n",
    "plt.axvline(x = 0, color = 'black', linewidth = 1.3, alpha = .7)\n",
    "plt.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7)\n",
    "\n",
    "plt.text(x = -0.8, y = 29, s = \"Predicted Rookie Year PPG vs Actual\",\n",
    "               fontsize = 34, weight = 'bold', alpha = .75,color = '#f0f0f0', backgroundcolor = 'grey')\n",
    "plt.text(x = -0.8, y = 25, s = \"Each point represents a drafted rookie since 2003 who played at\\nleast one game in the NBA.\",\n",
    "               fontsize = 19, weight = 'bold', alpha = .75)\n",
    "\n",
    "plt.text(x = 4.8, y = 22.5, s = 'Blake Griffin', weight = 'bold', rotation = 0,\n",
    "             backgroundcolor = '#f0f0f0')\n",
    "#plt.text(x = -2, y = 5, s = 'James Augustine', weight = 'bold', rotation = 0,\n",
    "#              backgroundcolor = '#f0f0f0')\n",
    "plt.text(x = 10.8, y = 1, s = 'Ridge Regression\\nr^2 = 0.230', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinReg MAD:  2.7188076359905144\n",
      "LinReg RMSE:  3.53405620354\n",
      "LinReg MSE:  2.71880763599\n",
      "LinReg r2:  0.230018626199\n"
     ]
    }
   ],
   "source": [
    "print('LinReg MAD: ',np.mean(np.abs(lr_test_predX - y)))\n",
    "print('LinReg RMSE: ',np.sqrt(mean_squared_error(y, lr_test_predX)))\n",
    "print('LinReg MSE: ', mean_absolute_error(y,lr_test_predX))\n",
    "print('LinReg r2: ',r2_score(y, lr_test_predX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elasticnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=5, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elasticnet\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:747: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:747: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: 0.04231, std: 0.13473, params: {'alpha': 0.0001, 'l1_ratio': 0.0},\n",
       " mean: 0.04232, std: 0.13487, params: {'alpha': 0.0001, 'l1_ratio': 0.25},\n",
       " mean: 0.04232, std: 0.13502, params: {'alpha': 0.0001, 'l1_ratio': 0.5},\n",
       " mean: 0.04232, std: 0.13517, params: {'alpha': 0.0001, 'l1_ratio': 0.75},\n",
       " mean: 0.04233, std: 0.13430, params: {'alpha': 0.00016042171611153241, 'l1_ratio': 0.0},\n",
       " mean: 0.04237, std: 0.13447, params: {'alpha': 0.00016042171611153241, 'l1_ratio': 0.25},\n",
       " mean: 0.04239, std: 0.13468, params: {'alpha': 0.00016042171611153241, 'l1_ratio': 0.5},\n",
       " mean: 0.04240, std: 0.13490, params: {'alpha': 0.00016042171611153241, 'l1_ratio': 0.75},\n",
       " mean: 0.04228, std: 0.13375, params: {'alpha': 0.00025735127000169099, 'l1_ratio': 0.0},\n",
       " mean: 0.04238, std: 0.13394, params: {'alpha': 0.00025735127000169099, 'l1_ratio': 0.25},\n",
       " mean: 0.04245, std: 0.13418, params: {'alpha': 0.00025735127000169099, 'l1_ratio': 0.5},\n",
       " mean: 0.04251, std: 0.13449, params: {'alpha': 0.00025735127000169099, 'l1_ratio': 0.75},\n",
       " mean: 0.04201, std: 0.13321, params: {'alpha': 0.00041284732377153599, 'l1_ratio': 0.0},\n",
       " mean: 0.04227, std: 0.13332, params: {'alpha': 0.00041284732377153599, 'l1_ratio': 0.25},\n",
       " mean: 0.04249, std: 0.13354, params: {'alpha': 0.00041284732377153599, 'l1_ratio': 0.5},\n",
       " mean: 0.04263, std: 0.13389, params: {'alpha': 0.00041284732377153599, 'l1_ratio': 0.75},\n",
       " mean: 0.04125, std: 0.13300, params: {'alpha': 0.00066229676171483253, 'l1_ratio': 0.0},\n",
       " mean: 0.04185, std: 0.13281, params: {'alpha': 0.00066229676171483253, 'l1_ratio': 0.25},\n",
       " mean: 0.04236, std: 0.13281, params: {'alpha': 0.00066229676171483253, 'l1_ratio': 0.5},\n",
       " mean: 0.04276, std: 0.13306, params: {'alpha': 0.00066229676171483253, 'l1_ratio': 0.75},\n",
       " mean: 0.03948, std: 0.13367, params: {'alpha': 0.001062467830894041, 'l1_ratio': 0.0},\n",
       " mean: 0.04066, std: 0.13286, params: {'alpha': 0.001062467830894041, 'l1_ratio': 0.25},\n",
       " mean: 0.04175, std: 0.13227, params: {'alpha': 0.001062467830894041, 'l1_ratio': 0.5},\n",
       " mean: 0.04272, std: 0.13207, params: {'alpha': 0.001062467830894041, 'l1_ratio': 0.75},\n",
       " mean: 0.03624, std: 0.13573, params: {'alpha': 0.0017044291274531946, 'l1_ratio': 0.0},\n",
       " mean: 0.03800, std: 0.13436, params: {'alpha': 0.0017044291274531946, 'l1_ratio': 0.25},\n",
       " mean: 0.03997, std: 0.13283, params: {'alpha': 0.0017044291274531946, 'l1_ratio': 0.5},\n",
       " mean: 0.04213, std: 0.13118, params: {'alpha': 0.0017044291274531946, 'l1_ratio': 0.75},\n",
       " mean: 0.03190, std: 0.13882, params: {'alpha': 0.0027342744561652328, 'l1_ratio': 0.0},\n",
       " mean: 0.03368, std: 0.13741, params: {'alpha': 0.0027342744561652328, 'l1_ratio': 0.25},\n",
       " mean: 0.03588, std: 0.13553, params: {'alpha': 0.0027342744561652328, 'l1_ratio': 0.5},\n",
       " mean: 0.03877, std: 0.13304, params: {'alpha': 0.0027342744561652328, 'l1_ratio': 0.75},\n",
       " mean: 0.02817, std: 0.14165, params: {'alpha': 0.0043863700057795363, 'l1_ratio': 0.0},\n",
       " mean: 0.02939, std: 0.14064, params: {'alpha': 0.0043863700057795363, 'l1_ratio': 0.25},\n",
       " mean: 0.03093, std: 0.13925, params: {'alpha': 0.0043863700057795363, 'l1_ratio': 0.5},\n",
       " mean: 0.03346, std: 0.13636, params: {'alpha': 0.0043863700057795363, 'l1_ratio': 0.75},\n",
       " mean: 0.02575, std: 0.14392, params: {'alpha': 0.0070366900382730557, 'l1_ratio': 0.0},\n",
       " mean: 0.02635, std: 0.14299, params: {'alpha': 0.0070366900382730557, 'l1_ratio': 0.25},\n",
       " mean: 0.02699, std: 0.14218, params: {'alpha': 0.0070366900382730557, 'l1_ratio': 0.5},\n",
       " mean: 0.02861, std: 0.13981, params: {'alpha': 0.0070366900382730557, 'l1_ratio': 0.75},\n",
       " mean: 0.02452, std: 0.14537, params: {'alpha': 0.011288378916846895, 'l1_ratio': 0.0},\n",
       " mean: 0.02463, std: 0.14469, params: {'alpha': 0.011288378916846895, 'l1_ratio': 0.25},\n",
       " mean: 0.02467, std: 0.14409, params: {'alpha': 0.011288378916846895, 'l1_ratio': 0.5},\n",
       " mean: 0.02497, std: 0.14324, params: {'alpha': 0.011288378916846895, 'l1_ratio': 0.75},\n",
       " mean: 0.02496, std: 0.14549, params: {'alpha': 0.018109011179578204, 'l1_ratio': 0.0},\n",
       " mean: 0.02499, std: 0.14465, params: {'alpha': 0.018109011179578204, 'l1_ratio': 0.25},\n",
       " mean: 0.02453, std: 0.14412, params: {'alpha': 0.018109011179578204, 'l1_ratio': 0.5},\n",
       " mean: 0.02411, std: 0.14408, params: {'alpha': 0.018109011179578204, 'l1_ratio': 0.75},\n",
       " mean: 0.02745, std: 0.14398, params: {'alpha': 0.029050786505108613, 'l1_ratio': 0.0},\n",
       " mean: 0.02706, std: 0.14293, params: {'alpha': 0.029050786505108613, 'l1_ratio': 0.25},\n",
       " mean: 0.02705, std: 0.14193, params: {'alpha': 0.029050786505108613, 'l1_ratio': 0.5},\n",
       " mean: 0.02645, std: 0.14102, params: {'alpha': 0.029050786505108613, 'l1_ratio': 0.75},\n",
       " mean: 0.03214, std: 0.14080, params: {'alpha': 0.04660377025539271, 'l1_ratio': 0.0},\n",
       " mean: 0.03154, std: 0.13901, params: {'alpha': 0.04660377025539271, 'l1_ratio': 0.25},\n",
       " mean: 0.03091, std: 0.13775, params: {'alpha': 0.04660377025539271, 'l1_ratio': 0.5},\n",
       " mean: 0.02914, std: 0.13835, params: {'alpha': 0.04660377025539271, 'l1_ratio': 0.75},\n",
       " mean: 0.03881, std: 0.13617, params: {'alpha': 0.074762568016376874, 'l1_ratio': 0.0},\n",
       " mean: 0.03816, std: 0.13314, params: {'alpha': 0.074762568016376874, 'l1_ratio': 0.25},\n",
       " mean: 0.03712, std: 0.13181, params: {'alpha': 0.074762568016376874, 'l1_ratio': 0.5},\n",
       " mean: 0.03930, std: 0.12713, params: {'alpha': 0.074762568016376874, 'l1_ratio': 0.75},\n",
       " mean: 0.04678, std: 0.13053, params: {'alpha': 0.11993539462092344, 'l1_ratio': 0.0},\n",
       " mean: 0.04607, std: 0.12655, params: {'alpha': 0.11993539462092344, 'l1_ratio': 0.25},\n",
       " mean: 0.04735, std: 0.12082, params: {'alpha': 0.11993539462092344, 'l1_ratio': 0.5},\n",
       " mean: 0.04936, std: 0.11184, params: {'alpha': 0.11993539462092344, 'l1_ratio': 0.75},\n",
       " mean: 0.05490, std: 0.12434, params: {'alpha': 0.19240241827602392, 'l1_ratio': 0.0},\n",
       " mean: 0.05471, std: 0.11836, params: {'alpha': 0.19240241827602392, 'l1_ratio': 0.25},\n",
       " mean: 0.05681, std: 0.10664, params: {'alpha': 0.19240241827602392, 'l1_ratio': 0.5},\n",
       " mean: 0.05708, std: 0.09332, params: {'alpha': 0.19240241827602392, 'l1_ratio': 0.75},\n",
       " mean: 0.06162, std: 0.11790, params: {'alpha': 0.30865526123848624, 'l1_ratio': 0.0},\n",
       " mean: 0.06183, std: 0.10719, params: {'alpha': 0.30865526123848624, 'l1_ratio': 0.25},\n",
       " mean: 0.05810, std: 0.09006, params: {'alpha': 0.30865526123848624, 'l1_ratio': 0.5},\n",
       " mean: 0.04459, std: 0.07340, params: {'alpha': 0.30865526123848624, 'l1_ratio': 0.75},\n",
       " mean: 0.06519, std: 0.11144, params: {'alpha': 0.49515006694731312, 'l1_ratio': 0.0},\n",
       " mean: 0.05822, std: 0.09448, params: {'alpha': 0.49515006694731312, 'l1_ratio': 0.25},\n",
       " mean: 0.03928, std: 0.07338, params: {'alpha': 0.49515006694731312, 'l1_ratio': 0.5},\n",
       " mean: 0.01597, std: 0.06079, params: {'alpha': 0.49515006694731312, 'l1_ratio': 0.75},\n",
       " mean: 0.06394, std: 0.10524, params: {'alpha': 0.79432823472428149, 'l1_ratio': 0.0},\n",
       " mean: 0.03890, std: 0.08169, params: {'alpha': 0.79432823472428149, 'l1_ratio': 0.25},\n",
       " mean: 0.00525, std: 0.06518, params: {'alpha': 0.79432823472428149, 'l1_ratio': 0.5},\n",
       " mean: -0.02364, std: 0.06826, params: {'alpha': 0.79432823472428149, 'l1_ratio': 0.75}]"
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uvt/W/VXQJUcp8eGtiT6r5K4qMGPe1/Xh5L0E+JaiOoz45wX8YU8lSmqbnmmi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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1307b7780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Elasticnet\")\n",
    "params = {\n",
    "    \"alpha\": np.logspace(-4, -.1, 20),\n",
    "    \"l1_ratio\": np.arange(0,1,0.25)\n",
    "}\n",
    "\n",
    "grid_est = GridSearchCV(ElasticNet(), param_grid=params, cv=kfold)\n",
    "grid_est.fit(X_tr, y_train)\n",
    "df_en = pd.DataFrame(grid_est.grid_scores_)\n",
    "df_en[\"alpha\"] = df_en.parameters.apply(lambda val: val[\"alpha\"])\n",
    "plt.plot(np.log(df_en.alpha), df_en.mean_validation_score);\n",
    "grid_est.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNet(alpha=0.49515006694731312, copy_X=True, fit_intercept=True,\n",
       "      l1_ratio=0.0, max_iter=1000, normalize=False, positive=False,\n",
       "      precompute=False, random_state=None, selection='cyclic', tol=0.0001,\n",
       "      warm_start=False)"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_est.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0,\n",
       "      max_iter=1000, normalize=False, positive=False, precompute=False,\n",
       "      random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model5 = ElasticNet(alpha=0.1, l1_ratio=0)\n",
    "lr_model5.fit(X_tr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', -0.10278799147170127),\n",
       " ('Weight', 0.2203717649008258),\n",
       " ('Yrs', -0.40073519273685348),\n",
       " ('FG_Perc', 0.50503404669438168),\n",
       " ('FT_Perc', 0.10411079072283642),\n",
       " ('RPG', 0.15925190503882652),\n",
       " ('APG', 0.75401903016010963),\n",
       " ('SPG', 0.53869062425868708),\n",
       " ('BPG', 0.11408361136169901),\n",
       " ('PPG', 1.0160174878608375),\n",
       " ('Age_x', 0.20676354457680196),\n",
       " ('College_PPG', 0.15234726548168803),\n",
       " ('PPG_top3', 0.35451832687011819),\n",
       " ('PPG_top5', 0.079132791669018432),\n",
       " ('PPG_top7', 0.1658704487781382),\n",
       " ('PPG_top13', 0.36556740443776914),\n",
       " ('top3/top5', 0.43613100911988517),\n",
       " ('top3/top7', 0.49463702824457845),\n",
       " ('top3/top13', 0.030111308064683248),\n",
       " ('top5/top7', 0.3994009197259536),\n",
       " ('top5/top13', 0.21746188714183287),\n",
       " ('top7/top13', -0.16500329252555573)]"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,lr_model5.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_pred4 = lr_model5.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13070ef98>]"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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491T8oOOwwCemO7lxhouuoMFhj0Zjt8bje3rzdsQoCpcEIBE36yofMsMSFWtK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\nauMiGiYrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13070ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "\n",
    "plt.scatter(lr_test_pred4,y_test,alpha=0.5)\n",
    "plt.plot(np.linspace(0,11,1000),np.linspace(0,11,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasReg MAD:  3.1068650410565857\n",
      "ElasReg RMSE:  3.8797458196\n",
      "ElasReg MSE:  3.10686504106\n",
      "ElasReg r2:  0.15893242367\n"
     ]
    }
   ],
   "source": [
    "print('ElasReg MAD: ',np.mean(np.abs(lr_test_pred4 - y_test)))\n",
    "print('ElasReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred4)))\n",
    "print('ElasReg MSE: ', mean_absolute_error(y_test,lr_test_pred4))\n",
    "print('ElasReg r2: ',r2_score(y_test, lr_test_pred4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sorted_PPG = df.sort_values(['PTS'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['University of Kentucky',\n",
       " 'Duke University',\n",
       " 'University of California, Los Angeles',\n",
       " 'University of Kansas',\n",
       " 'University of North Carolina',\n",
       " 'University of Arizona',\n",
       " 'University of Connecticut',\n",
       " 'University of Texas at Austin',\n",
       " 'Syracuse University',\n",
       " 'University of Florida']"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top colleges by number of drafted NBA players\n",
    "list(sorted_PPG.groupby(['College']).count()['PTS'].sort_values(ascending=False).head(10).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_college = df.groupby(['College']).mean().sort_values(by='PTS',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# top colleges by rookie PPG\n",
    "good_colleges = ['Duke University',\n",
    " 'Georgia Institute of Technology',\n",
    " 'Indiana University',\n",
    " 'Louisiana State University',\n",
    " 'Ohio State University',\n",
    " 'Stanford University',\n",
    " 'Syracuse University',\n",
    " 'University of Kentucky',\n",
    " 'University of Memphis',\n",
    " 'University of Southern California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PPG_Rookie = list(df_college.filter(items=good_colleges,axis=0)['PTS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colleges_PPG = list(zip(good_colleges,PPG_Rookie))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colleges_PPG.sort(key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Duke University', 6.6200000000000001),\n",
       " ('Louisiana State University', 6.9874999999999998),\n",
       " ('University of Memphis', 7.0777777777777793),\n",
       " ('Stanford University', 7.1333333333333329),\n",
       " ('Georgia Institute of Technology', 7.1799999999999997),\n",
       " ('Ohio State University', 7.4124999999999996),\n",
       " ('University of Kentucky', 7.5600000000000023),\n",
       " ('Syracuse University', 7.6846153846153848),\n",
       " ('University of Southern California', 8.5333333333333332),\n",
       " ('Indiana University', 10.74)]"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colleges_PPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rank1 = [i[0] for i in colleges_PPG]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = dict(enumerate(rank1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = dict (zip(a.values(),a.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Duke University': 1,\n",
       " 'Georgia Institute of Technology': 5,\n",
       " 'Indiana University': 10,\n",
       " 'Louisiana State University': 2,\n",
       " 'Ohio State University': 6,\n",
       " 'Stanford University': 4,\n",
       " 'Syracuse University': 8,\n",
       " 'University of Kentucky': 7,\n",
       " 'University of Memphis': 3,\n",
       " 'University of Southern California': 9}"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def CollegePPG(college):\n",
    "    '''\n",
    "    Returns a ranking from 0-10 based on what college the player played for. Player receives a 0 if they did\n",
    "    not play for the top 10 colleges based on BPM average.\n",
    "    '''\n",
    "    if college in good_colleges:\n",
    "        return res[college]\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['College_PPG'] = df['College'].apply(CollegePPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Birthday', 'Height_ft', 'Height_in', 'Weight', 'Yrs', 'GP',\n",
       "       'MPG', 'FGM', 'FGA_x',\n",
       "       ...\n",
       "       'PPG_top5', 'PPG_top7', 'PPG_top13', 'top3/top5', 'top3/top7',\n",
       "       'top3/top13', 'top5/top7', 'top5/top13', 'top7/top13', 'College_PPG'],\n",
       "      dtype='object', length=114)"
      ]
     },
     "execution_count": 576,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_columns = ['Total_Height_in',\n",
    " 'Weight',\n",
    " 'Yrs',\n",
    " 'FG_Perc',\n",
    " 'FT_Perc',\n",
    " 'RPG',\n",
    " 'APG',\n",
    " 'SPG',\n",
    " 'BPG',\n",
    " 'PPG',\n",
    " 'Age_x',\n",
    " 'College_PPG',\n",
    " 'PPG_top3',\n",
    " 'PPG_top5',\n",
    " 'PPG_top7',\n",
    " 'PPG_top13',\n",
    " 'top3/top5',\n",
    " 'top3/top7', 'top3/top13', 'top5/top7', 'top5/top13', 'top7/top13']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.loc[:,feature_columns]\n",
    "y = df.loc[:,\"PTS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_testrf = train_test_split(X, y, test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=9, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=5,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=500, n_jobs=-1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmodel1 = RandomForestRegressor(n_estimators = 500, min_samples_leaf=5, max_features=9, n_jobs=-1)\n",
    "rfmodel1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_test_pred1 = rfmodel1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12ef3a048>]"
      ]
     },
     "execution_count": 582,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G3126UviVWgJrpNF8o11h4BQGGCqrQg8ExnUTPztpzVT+fTCJqJY/qARcEj6+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ef3a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs predicted\n",
    "\n",
    "plt.scatter(rf_test_pred1,y_testrf,alpha=.5)\n",
    "plt.plot(np.linspace(0,11,1000),np.linspace(0,11,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', 0.019736089342787411),\n",
       " ('Weight', 0.026849469914518167),\n",
       " ('Yrs', 0.012377595770031318),\n",
       " ('FG_Perc', 0.040349616728754048),\n",
       " ('FT_Perc', 0.039595016435732523),\n",
       " ('RPG', 0.062218441222242564),\n",
       " ('APG', 0.049781268405553435),\n",
       " ('SPG', 0.051538357739740311),\n",
       " ('BPG', 0.034512892354301591),\n",
       " ('PPG', 0.132135533687881),\n",
       " ('Age_x', 0.076700954173855959),\n",
       " ('College_PPG', 0.060339884991613343),\n",
       " ('PPG_top3', 0.034295537417477741),\n",
       " ('PPG_top5', 0.032019188936601529),\n",
       " ('PPG_top7', 0.027798124645609351),\n",
       " ('PPG_top13', 0.042264173378772606),\n",
       " ('top3/top5', 0.027511572123150665),\n",
       " ('top3/top7', 0.029737254020336432),\n",
       " ('top3/top13', 0.034707947498671295),\n",
       " ('top5/top7', 0.0318196720844987),\n",
       " ('top5/top13', 0.055263666056799839),\n",
       " ('top7/top13', 0.078447743071070178)]"
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfmodel1.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest1 MAD:  3.068863036130999\n",
      "RandomForest1 RMSE:  3.82418387153\n",
      "RandomForest1 MSE:  3.06886303613\n",
      "RandomForest1 r2:  0.140790249668\n"
     ]
    }
   ],
   "source": [
    "print('RandomForest1 MAD: ',np.mean(np.abs(rf_test_pred1 - y_testrf)))\n",
    "print('RandomForest1 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred1)))\n",
    "print('RandomForest1 MSE: ', mean_absolute_error(y_testrf,rf_test_pred1))\n",
    "print('RandomForest1 r2: ',r2_score(y_testrf, rf_test_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=9, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=1000, n_jobs=-1, oob_score=False,\n",
       "           random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 585,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmodel2 = RandomForestRegressor(n_estimators = 1000, min_samples_leaf=1, max_features=9, n_jobs=-1)\n",
    "rfmodel2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_test_pred2 = rfmodel2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x130d66940>]"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AeEzH0REdw0kTpgAODun43fkU/vn6BfApEhb4ZXjmOQPh9NSVT5HgkSVc1uyFCSCgSPAp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\nRETkiv8P9LC9Nm/MI10AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130d66b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs predicted\n",
    "\n",
    "plt.scatter(rf_test_pred2,y_testrf,alpha=.5)\n",
    "plt.plot(np.linspace(0,11,1000),np.linspace(0,11,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', 0.027129827517952128),\n",
       " ('Weight', 0.031357064648769209),\n",
       " ('Yrs', 0.012456748839653134),\n",
       " ('FG_Perc', 0.052751292922932715),\n",
       " ('FT_Perc', 0.049692614723643176),\n",
       " ('RPG', 0.062120892905500359),\n",
       " ('APG', 0.049716466853922101),\n",
       " ('SPG', 0.05643123657989043),\n",
       " ('BPG', 0.037479927123520183),\n",
       " ('PPG', 0.11411281708287244),\n",
       " ('Age_x', 0.058518133986568674),\n",
       " ('College_PPG', 0.052339844948893717),\n",
       " ('PPG_top3', 0.035209500360367775),\n",
       " ('PPG_top5', 0.031578672190395604),\n",
       " ('PPG_top7', 0.032805211127458683),\n",
       " ('PPG_top13', 0.047474132435761887),\n",
       " ('top3/top5', 0.030992412064632782),\n",
       " ('top3/top7', 0.029867496048000056),\n",
       " ('top3/top13', 0.036005198606783681),\n",
       " ('top5/top7', 0.038278448970520927),\n",
       " ('top5/top13', 0.050340901461120767),\n",
       " ('top7/top13', 0.063341158600839095)]"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfmodel2.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest2 MAD:  3.053213461538464\n",
      "RandomForest2 RMSE:  3.83461289449\n",
      "RandomForest2 MSE:  3.05321346154\n",
      "RandomForest2 r2:  0.136097516783\n"
     ]
    }
   ],
   "source": [
    "print('RandomForest2 MAD: ',np.mean(np.abs(rf_test_pred2 - y_testrf)))\n",
    "print('RandomForest2 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred2)))\n",
    "print('RandomForest2 MSE: ', mean_absolute_error(y_testrf,rf_test_pred2))\n",
    "print('RandomForest2 r2: ',r2_score(y_testrf, rf_test_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='sqrt', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=600, n_jobs=-1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmodel3 = RandomForestRegressor(n_estimators = 600, min_samples_leaf=1, max_features='sqrt', n_jobs=-1)\n",
    "rfmodel3.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_test_pred3 = rfmodel3.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1310031d0>]"
      ]
     },
     "execution_count": 592,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZBbFg5kyrT28I4pejdtn7vYNxnItnDyqqBPwfK7zY2RmAYQmcj1uzApXTGmVO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\ng/CuqIwsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131003208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs predicted\n",
    "\n",
    "plt.scatter(rf_test_pred3,y_testrf,alpha=.5)\n",
    "plt.plot(np.linspace(0,11,1000),np.linspace(0,11,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', 0.030770892989347817),\n",
       " ('Weight', 0.034568876445073421),\n",
       " ('Yrs', 0.017070769484401834),\n",
       " ('FG_Perc', 0.050531054478827962),\n",
       " ('FT_Perc', 0.047211306750661271),\n",
       " ('RPG', 0.058293034069768754),\n",
       " ('APG', 0.046688575148654488),\n",
       " ('SPG', 0.054704457677720426),\n",
       " ('BPG', 0.039194743726582756),\n",
       " ('PPG', 0.09011592099713496),\n",
       " ('Age_x', 0.0531555262270774),\n",
       " ('College_PPG', 0.044003289222391093),\n",
       " ('PPG_top3', 0.039078690734597715),\n",
       " ('PPG_top5', 0.037121076394133437),\n",
       " ('PPG_top7', 0.03780709612592905),\n",
       " ('PPG_top13', 0.048925467170437553),\n",
       " ('top3/top5', 0.035092503263084017),\n",
       " ('top3/top7', 0.037992912597467648),\n",
       " ('top3/top13', 0.044061575672038826),\n",
       " ('top5/top7', 0.041626910338174374),\n",
       " ('top5/top13', 0.051831635325767601),\n",
       " ('top7/top13', 0.060153685160727832)]"
      ]
     },
     "execution_count": 593,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfmodel3.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest3 MAD:  3.097278846153844\n",
      "RandomForest3 RMSE:  3.8516452127\n",
      "RandomForest3 MSE:  3.09727884615\n",
      "RandomForest3 r2:  0.128406028331\n"
     ]
    }
   ],
   "source": [
    "print('RandomForest3 MAD: ',np.mean(np.abs(rf_test_pred3 - y_testrf)))\n",
    "print('RandomForest3 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred3)))\n",
    "print('RandomForest3 MSE: ', mean_absolute_error(y_testrf,rf_test_pred3))\n",
    "print('RandomForest3 r2: ',r2_score(y_testrf, rf_test_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosted Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gbmodel1 = GradientBoostingRegressor(n_estimators = 1000, learning_rate = .01, max_depth = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.01, loss='ls', max_depth=1, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=1000, presort='auto', random_state=None,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbmodel1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_vals_gb1 = gbmodel1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13222d0f0>]"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D/3o8jedPZzGlCRgCUCTgZEKHbgrctCl2wXO40bHONd2lmdbG1fmer5CgUGjbsUkdk3mz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\nEBGRKxhgiIjIFQwwRETkCgYYIiJyxf8H1Gm5diVUhmYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13222d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs predicted\n",
    "\n",
    "plt.scatter(pred_vals_gb1,y_testrf,alpha=.5)\n",
    "plt.plot(np.linspace(2,11,1000),np.linspace(2,11,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradBoost MAD:  3.0966903663538754\n",
      "GradBoost RMSE:  3.85125930757\n",
      "GradBoost MSE:  3.09669036635\n",
      "GradBoost r2:  0.128580673564\n"
     ]
    }
   ],
   "source": [
    "print('GradBoost MAD: ',np.mean(np.abs(pred_vals_gb1 - y_testrf)))\n",
    "print('GradBoost RMSE: ',np.sqrt(mean_squared_error(y_testrf, pred_vals_gb1)))\n",
    "print('GradBoost MSE: ', mean_absolute_error(y_testrf,pred_vals_gb1))\n",
    "print('GradBoost r2: ',r2_score(y_testrf, pred_vals_gb1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinReg MAD:  3.2521702580196843\n",
      "LinReg RMSE:  4.02173875643\n",
      "LinReg MSE:  3.25217025802\n",
      "LinReg r2:  0.0962422054315\n",
      "\n",
      "\n",
      "LassoReg MAD:  3.1024060888752087\n",
      "LassoReg RMSE:  3.88804133701\n",
      "LassoReg MSE:  3.10240608888\n",
      "LassoReg r2:  0.155331904406\n",
      "\n",
      "\n",
      "RidgeReg MAD:  3.077059835301955\n",
      "RidgeReg RMSE:  3.87243782989\n",
      "RidgeReg MSE:  3.0770598353\n",
      "RidgeReg r2:  0.162097952867\n",
      "\n",
      "\n",
      "ElasReg MAD:  3.1068650410565857\n",
      "ElasReg RMSE:  3.8797458196\n",
      "ElasReg MSE:  3.10686504106\n",
      "ElasReg r2:  0.15893242367\n",
      "\n",
      "\n",
      "RandomForest1 MAD:  3.068863036130999\n",
      "RandomForest1 RMSE:  3.82418387153\n",
      "RandomForest1 MSE:  3.06886303613\n",
      "RandomForest1 r2:  0.140790249668\n",
      "\n",
      "\n",
      "RandomForest2 MAD:  3.053213461538464\n",
      "RandomForest2 RMSE:  3.83461289449\n",
      "RandomForest2 MSE:  3.05321346154\n",
      "RandomForest2 r2:  0.136097516783\n",
      "\n",
      "\n",
      "RandomForest3 MAD:  3.097278846153844\n",
      "RandomForest3 RMSE:  3.8516452127\n",
      "RandomForest3 MSE:  3.09727884615\n",
      "RandomForest3 r2:  0.128406028331\n",
      "\n",
      "\n",
      "GradBoost MAD:  3.0966903663538754\n",
      "GradBoost RMSE:  3.85125930757\n",
      "GradBoost MSE:  3.09669036635\n",
      "GradBoost r2:  0.128580673564\n"
     ]
    }
   ],
   "source": [
    "print('LinReg MAD: ',np.mean(np.abs(lr_test_pred1 - y_test)))\n",
    "print('LinReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred1)))\n",
    "print('LinReg MSE: ', mean_absolute_error(y_test,lr_test_pred1))\n",
    "print('LinReg r2: ',r2_score(y_test, lr_test_pred1))\n",
    "print('\\n')\n",
    "print('LassoReg MAD: ',np.mean(np.abs(lr_test_pred2 - y_test)))\n",
    "print('LassoReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred2)))\n",
    "print('LassoReg MSE: ', mean_absolute_error(y_test,lr_test_pred2))\n",
    "print('LassoReg r2: ',r2_score(y_test, lr_test_pred2))\n",
    "print('\\n')\n",
    "print('RidgeReg MAD: ',np.mean(np.abs(lr_test_pred3 - y_test)))\n",
    "print('RidgeReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred3)))\n",
    "print('RidgeReg MSE: ', mean_absolute_error(y_test,lr_test_pred3))\n",
    "print('RidgeReg r2: ',r2_score(y_test, lr_test_pred3))\n",
    "print('\\n')\n",
    "print('ElasReg MAD: ',np.mean(np.abs(lr_test_pred4 - y_test)))\n",
    "print('ElasReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred4)))\n",
    "print('ElasReg MSE: ', mean_absolute_error(y_test,lr_test_pred4))\n",
    "print('ElasReg r2: ',r2_score(y_test, lr_test_pred4))\n",
    "print('\\n')\n",
    "print('RandomForest1 MAD: ',np.mean(np.abs(rf_test_pred1 - y_testrf)))\n",
    "print('RandomForest1 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred1)))\n",
    "print('RandomForest1 MSE: ', mean_absolute_error(y_testrf,rf_test_pred1))\n",
    "print('RandomForest1 r2: ',r2_score(y_testrf, rf_test_pred1))\n",
    "print('\\n')\n",
    "print('RandomForest2 MAD: ',np.mean(np.abs(rf_test_pred2 - y_testrf)))\n",
    "print('RandomForest2 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred2)))\n",
    "print('RandomForest2 MSE: ', mean_absolute_error(y_testrf,rf_test_pred2))\n",
    "print('RandomForest2 r2: ',r2_score(y_testrf, rf_test_pred2))\n",
    "print('\\n')\n",
    "print('RandomForest3 MAD: ',np.mean(np.abs(rf_test_pred3 - y_testrf)))\n",
    "print('RandomForest3 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred3)))\n",
    "print('RandomForest3 MSE: ', mean_absolute_error(y_testrf,rf_test_pred3))\n",
    "print('RandomForest3 r2: ',r2_score(y_testrf, rf_test_pred3))\n",
    "print('\\n')\n",
    "print('GradBoost MAD: ',np.mean(np.abs(pred_vals_gb1 - y_testrf)))\n",
    "print('GradBoost RMSE: ',np.sqrt(mean_squared_error(y_testrf, pred_vals_gb1)))\n",
    "print('GradBoost MSE: ', mean_absolute_error(y_testrf,pred_vals_gb1))\n",
    "print('GradBoost r2: ',r2_score(y_testrf, pred_vals_gb1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x112a3a438>"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dMrkyKrU6XmwkcH/Oia39DHLFKiidArOjZqyHMkjnytAq5Rhy6CCVSLC5l8HE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20EABu0lrIRZLE0+lyqocHdhjZH+3gYmFALFY/ehtRKNb77VUgwDy77WenBX5KiFawcyVDot2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E8XsbSexWi/H+MDXbi/tIX1VAEjUR+GIl70drlwdz6NhbVqPhw9fkrI9Hz1F+pH+/yI9rof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\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zeCeQ+s7xMuFJtVKOQi4UGA75JEBoPc5jvRbJNpLltU3am8VF/+tUim3FNB1NOmTk2MDL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355P2TSAG5MePG5Yy1wrKxtaEgoAIiwFktjNZyESAS89owd/nACI7MBkLDgjgB5wBtMQFsr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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138a2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(pd.concat([X_train.iloc[:,0:5], y_train], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}