diff --git a/Introduction to XGBoost.ipynb b/Introduction to XGBoost.ipynb
new file mode 100644
index 0000000..cf92cab
--- /dev/null
+++ b/Introduction to XGBoost.ipynb	
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The idea of boosting came out of the idea of whether a weak learner can be modified to become better.\n",
+    "\n",
+    "XGBoost is an implementation of gradient boosted decision trees designed for speed and performance.XGBoost stands for eXtreme Gradient Boosting.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from matplotlib import pyplot\n",
+    "from xgboost import plot_importance\n",
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "768 datasets in the file.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pre-times</th>\n",
+       "      <th>pgc</th>\n",
+       "      <th>blood-pres</th>\n",
+       "      <th>skin-fold-thk</th>\n",
+       "      <th>serum-insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>dpf</th>\n",
+       "      <th>age</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pre-times  pgc  blood-pres  skin-fold-thk  serum-insulin   BMI    dpf  age  \\\n",
+       "0          6  148          72             35              0  33.6  0.627   50   \n",
+       "1          1   85          66             29              0  26.6  0.351   31   \n",
+       "2          8  183          64              0              0  23.3  0.672   32   \n",
+       "3          1   89          66             23             94  28.1  0.167   21   \n",
+       "4          0  137          40             35            168  43.1  2.288   33   \n",
+       "\n",
+       "   class  \n",
+       "0      1  \n",
+       "1      0  \n",
+       "2      1  \n",
+       "3      0  \n",
+       "4      1  "
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset=pd.read_csv('pima-indians-diabetes.csv',header=None)\n",
+    "col=['pre-times','pgc','blood-pres','skin-fold-thk','serum-insulin','BMI','dpf','age','class']\n",
+    "dataset.columns=col\n",
+    "print('\\n{} datasets in the file.\\n'.format(len(dataset)))\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pre-times</th>\n",
+       "      <th>pgc</th>\n",
+       "      <th>blood-pres</th>\n",
+       "      <th>skin-fold-thk</th>\n",
+       "      <th>serum-insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>dpf</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pre-times  pgc  blood-pres  skin-fold-thk  serum-insulin   BMI    dpf  age\n",
+       "0          6  148          72             35              0  33.6  0.627   50\n",
+       "1          1   85          66             29              0  26.6  0.351   31\n",
+       "2          8  183          64              0              0  23.3  0.672   32\n",
+       "3          1   89          66             23             94  28.1  0.167   21\n",
+       "4          0  137          40             35            168  43.1  2.288   33"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X=dataset.iloc[:,0:8]\n",
+    "y=dataset.iloc[:,8]\n",
+    "\n",
+    "X.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# seed=7\n",
+    "# test_size=0.33\n",
+    "# X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=test_size,random_state=seed)\n",
+    "# print('Length of training file:',len(X_train))\n",
+    "# print('\\nLength of test file:',len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model=xgb.XGBClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learning_rate=[0.0001,0.001,0.01,0.1,0.2,0.3]\n",
+    "n_estimators=[50,100,150,200]\n",
+    "max_depth=[2,4,6,8]\n",
+    "param_grid=dict(learning_rate=learning_rate,max_depth=max_depth,n_estimators=n_estimators)\n",
+    "kfold=StratifiedKFold(n_splits=10,shuffle=True,random_state=7)\n",
+    "grid_search=GridSearchCV(model,param_grid,scoring='neg_log_loss',n_jobs=-1,cv=kfold)\n",
+    "grid_result=grid_search.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.47074444845566177"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_result.best_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 100}"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_result.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.69162089, -0.69009738, -0.68858821, -0.68710134, -0.69133961,\n",
+       "       -0.68955236, -0.68777435, -0.68600801, -0.69118773, -0.689255  ,\n",
+       "       -0.68736031, -0.68549177, -0.69122552, -0.689334  , -0.68747736,\n",
+       "       -0.68564466, -0.67850626, -0.66518522, -0.65295043, -0.64176016,\n",
+       "       -0.67569275, -0.65975569, -0.64542428, -0.63247297, -0.67483639,\n",
+       "       -0.65874177, -0.64415445, -0.63092161, -0.67518059, -0.65949156,\n",
+       "       -0.64529838, -0.63254504, -0.59069683, -0.54430262, -0.51827081,\n",
+       "       -0.50359575, -0.57439682, -0.52255152, -0.50068321, -0.48714479,\n",
+       "       -0.57344986, -0.51996773, -0.49764159, -0.49128547, -0.57590686,\n",
+       "       -0.5284672 , -0.50311422, -0.4971906 , -0.47711087, -0.47074445,\n",
+       "       -0.47209334, -0.48254166, -0.4812954 , -0.50301247, -0.53285258,\n",
+       "       -0.55537264, -0.51389836, -0.5485174 , -0.58889106, -0.62335212,\n",
+       "       -0.53978194, -0.59231645, -0.63989095, -0.67855694, -0.47326392,\n",
+       "       -0.48720479, -0.50373835, -0.52175705, -0.5150052 , -0.56579447,\n",
+       "       -0.61761096, -0.66755765, -0.56427527, -0.64892386, -0.70226656,\n",
+       "       -0.74908777, -0.60055966, -0.69209373, -0.74238237, -0.77967524,\n",
+       "       -0.48245416, -0.51143422, -0.54181415, -0.5657652 , -0.54599718,\n",
+       "       -0.61914378, -0.68443438, -0.74787357, -0.61906703, -0.72211034,\n",
+       "       -0.78726603, -0.83206644, -0.65006573, -0.73610538, -0.79049532,\n",
+       "       -0.82646305])"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "means = grid_result.cv_results_['mean_test_score']\n",
+    "means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.03167222e-04,   1.95012187e-04,   2.75850482e-04,\n",
+       "         3.42224525e-04,   1.46168785e-04,   2.91442427e-04,\n",
+       "         4.27869904e-04,   5.68203861e-04,   2.08215247e-04,\n",
+       "         4.22456468e-04,   6.40818582e-04,   8.55673177e-04,\n",
+       "         2.83061665e-04,   5.71132393e-04,   8.69753710e-04,\n",
+       "         1.16578480e-03,   7.62115683e-04,   1.43177155e-03,\n",
+       "         2.43713484e-03,   3.54163816e-03,   1.31518079e-03,\n",
+       "         2.62358265e-03,   3.91956047e-03,   5.19978738e-03,\n",
+       "         2.05691210e-03,   3.80230375e-03,   5.56579191e-03,\n",
+       "         7.18550524e-03,   2.83197092e-03,   4.97694764e-03,\n",
+       "         6.96729367e-03,   8.59464981e-03,   1.15849995e-02,\n",
+       "         2.10954687e-02,   2.77067217e-02,   3.33906610e-02,\n",
+       "         1.17885860e-02,   2.36550710e-02,   3.15090706e-02,\n",
+       "         3.70510841e-02,   1.20157686e-02,   1.93489268e-02,\n",
+       "         2.84556809e-02,   3.68902897e-02,   1.43308737e-02,\n",
+       "         1.87678260e-02,   2.52184925e-02,   3.44629598e-02,\n",
+       "         4.61433400e-02,   5.31169285e-02,   5.93635665e-02,\n",
+       "         6.26299703e-02,   5.41974614e-02,   6.74361730e-02,\n",
+       "         7.78974220e-02,   8.52701441e-02,   5.90203992e-02,\n",
+       "         7.35432314e-02,   9.12843067e-02,   1.05370788e-01,\n",
+       "         6.66893977e-02,   8.96940197e-02,   1.08227848e-01,\n",
+       "         1.20470310e-01,   5.75024619e-02,   6.94109232e-02,\n",
+       "         7.46077122e-02,   7.51800644e-02,   5.90329768e-02,\n",
+       "         8.53181188e-02,   1.01741080e-01,   1.11418852e-01,\n",
+       "         8.61164630e-02,   1.14875723e-01,   1.35573479e-01,\n",
+       "         1.48641956e-01,   9.16753258e-02,   1.31024565e-01,\n",
+       "         1.46512261e-01,   1.59256734e-01,   6.32735178e-02,\n",
+       "         7.25936093e-02,   8.10139784e-02,   7.99926360e-02,\n",
+       "         8.49191347e-02,   1.06155574e-01,   1.25813931e-01,\n",
+       "         1.39603566e-01,   9.36178792e-02,   1.22244718e-01,\n",
+       "         1.45174862e-01,   1.63851246e-01,   1.12672818e-01,\n",
+       "         1.39994377e-01,   1.56314443e-01,   1.64718570e-01])"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stds = grid_result.cv_results_['std_test_score']\n",
+    "stds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "({'learning_rate': 0.0001, 'max_depth': 2, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 2, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 2, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 2, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 4, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 4, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 4, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 4, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 6, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 6, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 6, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 6, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 8, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 8, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 8, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.0001, 'max_depth': 8, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.001, 'max_depth': 2, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.001, 'max_depth': 2, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.001, 'max_depth': 2, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.001, 'max_depth': 2, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.001, 'max_depth': 4, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.001, 'max_depth': 4, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.001, 'max_depth': 4, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.001, 'max_depth': 4, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.001, 'max_depth': 6, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.001, 'max_depth': 6, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.001, 'max_depth': 6, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.001, 'max_depth': 6, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.001, 'max_depth': 8, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.001, 'max_depth': 8, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.001, 'max_depth': 8, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.001, 'max_depth': 8, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.01, 'max_depth': 2, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.01, 'max_depth': 2, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.01, 'max_depth': 2, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.01, 'max_depth': 2, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.01, 'max_depth': 4, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.01, 'max_depth': 6, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.01, 'max_depth': 6, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.01, 'max_depth': 6, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.01, 'max_depth': 6, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.01, 'max_depth': 8, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.01, 'max_depth': 8, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.01, 'max_depth': 8, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.01, 'max_depth': 8, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.1, 'max_depth': 2, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.1, 'max_depth': 6, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.1, 'max_depth': 6, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.1, 'max_depth': 6, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.1, 'max_depth': 6, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.1, 'max_depth': 8, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.1, 'max_depth': 8, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.1, 'max_depth': 8, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.1, 'max_depth': 8, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.2, 'max_depth': 2, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.2, 'max_depth': 2, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.2, 'max_depth': 2, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.2, 'max_depth': 2, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.2, 'max_depth': 4, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.2, 'max_depth': 4, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.2, 'max_depth': 4, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.2, 'max_depth': 4, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.2, 'max_depth': 6, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.2, 'max_depth': 6, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.2, 'max_depth': 6, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.2, 'max_depth': 6, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.2, 'max_depth': 8, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.2, 'max_depth': 8, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.2, 'max_depth': 8, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.2, 'max_depth': 8, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.3, 'max_depth': 2, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.3, 'max_depth': 2, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.3, 'max_depth': 2, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.3, 'max_depth': 2, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.3, 'max_depth': 4, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.3, 'max_depth': 4, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.3, 'max_depth': 4, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.3, 'max_depth': 4, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.3, 'max_depth': 6, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.3, 'max_depth': 6, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.3, 'max_depth': 6, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.3, 'max_depth': 6, 'n_estimators': 200},\n",
+       " {'learning_rate': 0.3, 'max_depth': 8, 'n_estimators': 50},\n",
+       " {'learning_rate': 0.3, 'max_depth': 8, 'n_estimators': 100},\n",
+       " {'learning_rate': 0.3, 'max_depth': 8, 'n_estimators': 150},\n",
+       " {'learning_rate': 0.3, 'max_depth': 8, 'n_estimators': 200})"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = grid_result.cv_results_['params']\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.691621 (0.000103) with: {'n_estimators': 50, 'max_depth': 2, 'learning_rate': 0.0001}\n",
+      "-0.690097 (0.000195) with: {'n_estimators': 100, 'max_depth': 2, 'learning_rate': 0.0001}\n",
+      "-0.688588 (0.000276) with: {'n_estimators': 150, 'max_depth': 2, 'learning_rate': 0.0001}\n",
+      "-0.687101 (0.000342) with: {'n_estimators': 200, 'max_depth': 2, 'learning_rate': 0.0001}\n",
+      "-0.691340 (0.000146) with: {'n_estimators': 50, 'max_depth': 4, 'learning_rate': 0.0001}\n",
+      "-0.689552 (0.000291) with: {'n_estimators': 100, 'max_depth': 4, 'learning_rate': 0.0001}\n",
+      "-0.687774 (0.000428) with: {'n_estimators': 150, 'max_depth': 4, 'learning_rate': 0.0001}\n",
+      "-0.686008 (0.000568) with: {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.0001}\n",
+      "-0.691188 (0.000208) with: {'n_estimators': 50, 'max_depth': 6, 'learning_rate': 0.0001}\n",
+      "-0.689255 (0.000422) with: {'n_estimators': 100, 'max_depth': 6, 'learning_rate': 0.0001}\n",
+      "-0.687360 (0.000641) with: {'n_estimators': 150, 'max_depth': 6, 'learning_rate': 0.0001}\n",
+      "-0.685492 (0.000856) with: {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.0001}\n",
+      "-0.691226 (0.000283) with: {'n_estimators': 50, 'max_depth': 8, 'learning_rate': 0.0001}\n",
+      "-0.689334 (0.000571) with: {'n_estimators': 100, 'max_depth': 8, 'learning_rate': 0.0001}\n",
+      "-0.687477 (0.000870) with: {'n_estimators': 150, 'max_depth': 8, 'learning_rate': 0.0001}\n",
+      "-0.685645 (0.001166) with: {'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.0001}\n",
+      "-0.678506 (0.000762) with: {'n_estimators': 50, 'max_depth': 2, 'learning_rate': 0.001}\n",
+      "-0.665185 (0.001432) with: {'n_estimators': 100, 'max_depth': 2, 'learning_rate': 0.001}\n",
+      "-0.652950 (0.002437) with: {'n_estimators': 150, 'max_depth': 2, 'learning_rate': 0.001}\n",
+      "-0.641760 (0.003542) with: {'n_estimators': 200, 'max_depth': 2, 'learning_rate': 0.001}\n",
+      "-0.675693 (0.001315) with: {'n_estimators': 50, 'max_depth': 4, 'learning_rate': 0.001}\n",
+      "-0.659756 (0.002624) with: {'n_estimators': 100, 'max_depth': 4, 'learning_rate': 0.001}\n",
+      "-0.645424 (0.003920) with: {'n_estimators': 150, 'max_depth': 4, 'learning_rate': 0.001}\n",
+      "-0.632473 (0.005200) with: {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.001}\n",
+      "-0.674836 (0.002057) with: {'n_estimators': 50, 'max_depth': 6, 'learning_rate': 0.001}\n",
+      "-0.658742 (0.003802) with: {'n_estimators': 100, 'max_depth': 6, 'learning_rate': 0.001}\n",
+      "-0.644154 (0.005566) with: {'n_estimators': 150, 'max_depth': 6, 'learning_rate': 0.001}\n",
+      "-0.630922 (0.007186) with: {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.001}\n",
+      "-0.675181 (0.002832) with: {'n_estimators': 50, 'max_depth': 8, 'learning_rate': 0.001}\n",
+      "-0.659492 (0.004977) with: {'n_estimators': 100, 'max_depth': 8, 'learning_rate': 0.001}\n",
+      "-0.645298 (0.006967) with: {'n_estimators': 150, 'max_depth': 8, 'learning_rate': 0.001}\n",
+      "-0.632545 (0.008595) with: {'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.001}\n",
+      "-0.590697 (0.011585) with: {'n_estimators': 50, 'max_depth': 2, 'learning_rate': 0.01}\n",
+      "-0.544303 (0.021095) with: {'n_estimators': 100, 'max_depth': 2, 'learning_rate': 0.01}\n",
+      "-0.518271 (0.027707) with: {'n_estimators': 150, 'max_depth': 2, 'learning_rate': 0.01}\n",
+      "-0.503596 (0.033391) with: {'n_estimators': 200, 'max_depth': 2, 'learning_rate': 0.01}\n",
+      "-0.574397 (0.011789) with: {'n_estimators': 50, 'max_depth': 4, 'learning_rate': 0.01}\n",
+      "-0.522552 (0.023655) with: {'n_estimators': 100, 'max_depth': 4, 'learning_rate': 0.01}\n",
+      "-0.500683 (0.031509) with: {'n_estimators': 150, 'max_depth': 4, 'learning_rate': 0.01}\n",
+      "-0.487145 (0.037051) with: {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.01}\n",
+      "-0.573450 (0.012016) with: {'n_estimators': 50, 'max_depth': 6, 'learning_rate': 0.01}\n",
+      "-0.519968 (0.019349) with: {'n_estimators': 100, 'max_depth': 6, 'learning_rate': 0.01}\n",
+      "-0.497642 (0.028456) with: {'n_estimators': 150, 'max_depth': 6, 'learning_rate': 0.01}\n",
+      "-0.491285 (0.036890) with: {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.01}\n",
+      "-0.575907 (0.014331) with: {'n_estimators': 50, 'max_depth': 8, 'learning_rate': 0.01}\n",
+      "-0.528467 (0.018768) with: {'n_estimators': 100, 'max_depth': 8, 'learning_rate': 0.01}\n",
+      "-0.503114 (0.025218) with: {'n_estimators': 150, 'max_depth': 8, 'learning_rate': 0.01}\n",
+      "-0.497191 (0.034463) with: {'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.01}\n",
+      "-0.477111 (0.046143) with: {'n_estimators': 50, 'max_depth': 2, 'learning_rate': 0.1}\n",
+      "-0.470744 (0.053117) with: {'n_estimators': 100, 'max_depth': 2, 'learning_rate': 0.1}\n",
+      "-0.472093 (0.059364) with: {'n_estimators': 150, 'max_depth': 2, 'learning_rate': 0.1}\n",
+      "-0.482542 (0.062630) with: {'n_estimators': 200, 'max_depth': 2, 'learning_rate': 0.1}\n",
+      "-0.481295 (0.054197) with: {'n_estimators': 50, 'max_depth': 4, 'learning_rate': 0.1}\n",
+      "-0.503012 (0.067436) with: {'n_estimators': 100, 'max_depth': 4, 'learning_rate': 0.1}\n",
+      "-0.532853 (0.077897) with: {'n_estimators': 150, 'max_depth': 4, 'learning_rate': 0.1}\n",
+      "-0.555373 (0.085270) with: {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.1}\n",
+      "-0.513898 (0.059020) with: {'n_estimators': 50, 'max_depth': 6, 'learning_rate': 0.1}\n",
+      "-0.548517 (0.073543) with: {'n_estimators': 100, 'max_depth': 6, 'learning_rate': 0.1}\n",
+      "-0.588891 (0.091284) with: {'n_estimators': 150, 'max_depth': 6, 'learning_rate': 0.1}\n",
+      "-0.623352 (0.105371) with: {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.1}\n",
+      "-0.539782 (0.066689) with: {'n_estimators': 50, 'max_depth': 8, 'learning_rate': 0.1}\n",
+      "-0.592316 (0.089694) with: {'n_estimators': 100, 'max_depth': 8, 'learning_rate': 0.1}\n",
+      "-0.639891 (0.108228) with: {'n_estimators': 150, 'max_depth': 8, 'learning_rate': 0.1}\n",
+      "-0.678557 (0.120470) with: {'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.1}\n",
+      "-0.473264 (0.057502) with: {'n_estimators': 50, 'max_depth': 2, 'learning_rate': 0.2}\n",
+      "-0.487205 (0.069411) with: {'n_estimators': 100, 'max_depth': 2, 'learning_rate': 0.2}\n",
+      "-0.503738 (0.074608) with: {'n_estimators': 150, 'max_depth': 2, 'learning_rate': 0.2}\n",
+      "-0.521757 (0.075180) with: {'n_estimators': 200, 'max_depth': 2, 'learning_rate': 0.2}\n",
+      "-0.515005 (0.059033) with: {'n_estimators': 50, 'max_depth': 4, 'learning_rate': 0.2}\n",
+      "-0.565794 (0.085318) with: {'n_estimators': 100, 'max_depth': 4, 'learning_rate': 0.2}\n",
+      "-0.617611 (0.101741) with: {'n_estimators': 150, 'max_depth': 4, 'learning_rate': 0.2}\n",
+      "-0.667558 (0.111419) with: {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.2}\n",
+      "-0.564275 (0.086116) with: {'n_estimators': 50, 'max_depth': 6, 'learning_rate': 0.2}\n",
+      "-0.648924 (0.114876) with: {'n_estimators': 100, 'max_depth': 6, 'learning_rate': 0.2}\n",
+      "-0.702267 (0.135573) with: {'n_estimators': 150, 'max_depth': 6, 'learning_rate': 0.2}\n",
+      "-0.749088 (0.148642) with: {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.2}\n",
+      "-0.600560 (0.091675) with: {'n_estimators': 50, 'max_depth': 8, 'learning_rate': 0.2}\n",
+      "-0.692094 (0.131025) with: {'n_estimators': 100, 'max_depth': 8, 'learning_rate': 0.2}\n",
+      "-0.742382 (0.146512) with: {'n_estimators': 150, 'max_depth': 8, 'learning_rate': 0.2}\n",
+      "-0.779675 (0.159257) with: {'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.2}\n",
+      "-0.482454 (0.063274) with: {'n_estimators': 50, 'max_depth': 2, 'learning_rate': 0.3}\n",
+      "-0.511434 (0.072594) with: {'n_estimators': 100, 'max_depth': 2, 'learning_rate': 0.3}\n",
+      "-0.541814 (0.081014) with: {'n_estimators': 150, 'max_depth': 2, 'learning_rate': 0.3}\n",
+      "-0.565765 (0.079993) with: {'n_estimators': 200, 'max_depth': 2, 'learning_rate': 0.3}\n",
+      "-0.545997 (0.084919) with: {'n_estimators': 50, 'max_depth': 4, 'learning_rate': 0.3}\n",
+      "-0.619144 (0.106156) with: {'n_estimators': 100, 'max_depth': 4, 'learning_rate': 0.3}\n",
+      "-0.684434 (0.125814) with: {'n_estimators': 150, 'max_depth': 4, 'learning_rate': 0.3}\n",
+      "-0.747874 (0.139604) with: {'n_estimators': 200, 'max_depth': 4, 'learning_rate': 0.3}\n",
+      "-0.619067 (0.093618) with: {'n_estimators': 50, 'max_depth': 6, 'learning_rate': 0.3}\n",
+      "-0.722110 (0.122245) with: {'n_estimators': 100, 'max_depth': 6, 'learning_rate': 0.3}\n",
+      "-0.787266 (0.145175) with: {'n_estimators': 150, 'max_depth': 6, 'learning_rate': 0.3}\n",
+      "-0.832066 (0.163851) with: {'n_estimators': 200, 'max_depth': 6, 'learning_rate': 0.3}\n",
+      "-0.650066 (0.112673) with: {'n_estimators': 50, 'max_depth': 8, 'learning_rate': 0.3}\n",
+      "-0.736105 (0.139994) with: {'n_estimators': 100, 'max_depth': 8, 'learning_rate': 0.3}\n",
+      "-0.790495 (0.156314) with: {'n_estimators': 150, 'max_depth': 8, 'learning_rate': 0.3}\n",
+      "-0.826463 (0.164719) with: {'n_estimators': 200, 'max_depth': 8, 'learning_rate': 0.3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for mean, stdev, param in zip(means, stds, params):\n",
+    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
+   ]
+  },
+  {
+   "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": "3.5.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pima-indians-diabetes.csv b/pima-indians-diabetes.csv
new file mode 100644
index 0000000..982aab8
--- /dev/null
+++ b/pima-indians-diabetes.csv
@@ -0,0 +1,768 @@
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31.0,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0.0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38.0,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30.0,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
+1,115,70,30,96,34.6,0.529,32,1
+3,126,88,41,235,39.3,0.704,27,0
+8,99,84,0,0,35.4,0.388,50,0
+7,196,90,0,0,39.8,0.451,41,1
+9,119,80,35,0,29.0,0.263,29,1
+11,143,94,33,146,36.6,0.254,51,1
+10,125,70,26,115,31.1,0.205,41,1
+7,147,76,0,0,39.4,0.257,43,1
+1,97,66,15,140,23.2,0.487,22,0
+13,145,82,19,110,22.2,0.245,57,0
+5,117,92,0,0,34.1,0.337,38,0
+5,109,75,26,0,36.0,0.546,60,0
+3,158,76,36,245,31.6,0.851,28,1
+3,88,58,11,54,24.8,0.267,22,0
+6,92,92,0,0,19.9,0.188,28,0
+10,122,78,31,0,27.6,0.512,45,0
+4,103,60,33,192,24.0,0.966,33,0
+11,138,76,0,0,33.2,0.420,35,0
+9,102,76,37,0,32.9,0.665,46,1
+2,90,68,42,0,38.2,0.503,27,1
+4,111,72,47,207,37.1,1.390,56,1
+3,180,64,25,70,34.0,0.271,26,0
+7,133,84,0,0,40.2,0.696,37,0
+7,106,92,18,0,22.7,0.235,48,0
+9,171,110,24,240,45.4,0.721,54,1
+7,159,64,0,0,27.4,0.294,40,0
+0,180,66,39,0,42.0,1.893,25,1
+1,146,56,0,0,29.7,0.564,29,0
+2,71,70,27,0,28.0,0.586,22,0
+7,103,66,32,0,39.1,0.344,31,1
+7,105,0,0,0,0.0,0.305,24,0
+1,103,80,11,82,19.4,0.491,22,0
+1,101,50,15,36,24.2,0.526,26,0
+5,88,66,21,23,24.4,0.342,30,0
+8,176,90,34,300,33.7,0.467,58,1
+7,150,66,42,342,34.7,0.718,42,0
+1,73,50,10,0,23.0,0.248,21,0
+7,187,68,39,304,37.7,0.254,41,1
+0,100,88,60,110,46.8,0.962,31,0
+0,146,82,0,0,40.5,1.781,44,0
+0,105,64,41,142,41.5,0.173,22,0
+2,84,0,0,0,0.0,0.304,21,0
+8,133,72,0,0,32.9,0.270,39,1
+5,44,62,0,0,25.0,0.587,36,0
+2,141,58,34,128,25.4,0.699,24,0
+7,114,66,0,0,32.8,0.258,42,1
+5,99,74,27,0,29.0,0.203,32,0
+0,109,88,30,0,32.5,0.855,38,1
+2,109,92,0,0,42.7,0.845,54,0
+1,95,66,13,38,19.6,0.334,25,0
+4,146,85,27,100,28.9,0.189,27,0
+2,100,66,20,90,32.9,0.867,28,1
+5,139,64,35,140,28.6,0.411,26,0
+13,126,90,0,0,43.4,0.583,42,1
+4,129,86,20,270,35.1,0.231,23,0
+1,79,75,30,0,32.0,0.396,22,0
+1,0,48,20,0,24.7,0.140,22,0
+7,62,78,0,0,32.6,0.391,41,0
+5,95,72,33,0,37.7,0.370,27,0
+0,131,0,0,0,43.2,0.270,26,1
+2,112,66,22,0,25.0,0.307,24,0
+3,113,44,13,0,22.4,0.140,22,0
+2,74,0,0,0,0.0,0.102,22,0
+7,83,78,26,71,29.3,0.767,36,0
+0,101,65,28,0,24.6,0.237,22,0
+5,137,108,0,0,48.8,0.227,37,1
+2,110,74,29,125,32.4,0.698,27,0
+13,106,72,54,0,36.6,0.178,45,0
+2,100,68,25,71,38.5,0.324,26,0
+15,136,70,32,110,37.1,0.153,43,1
+1,107,68,19,0,26.5,0.165,24,0
+1,80,55,0,0,19.1,0.258,21,0
+4,123,80,15,176,32.0,0.443,34,0
+7,81,78,40,48,46.7,0.261,42,0
+4,134,72,0,0,23.8,0.277,60,1
+2,142,82,18,64,24.7,0.761,21,0
+6,144,72,27,228,33.9,0.255,40,0
+2,92,62,28,0,31.6,0.130,24,0
+1,71,48,18,76,20.4,0.323,22,0
+6,93,50,30,64,28.7,0.356,23,0
+1,122,90,51,220,49.7,0.325,31,1
+1,163,72,0,0,39.0,1.222,33,1
+1,151,60,0,0,26.1,0.179,22,0
+0,125,96,0,0,22.5,0.262,21,0
+1,81,72,18,40,26.6,0.283,24,0
+2,85,65,0,0,39.6,0.930,27,0
+1,126,56,29,152,28.7,0.801,21,0
+1,96,122,0,0,22.4,0.207,27,0
+4,144,58,28,140,29.5,0.287,37,0
+3,83,58,31,18,34.3,0.336,25,0
+0,95,85,25,36,37.4,0.247,24,1
+3,171,72,33,135,33.3,0.199,24,1
+8,155,62,26,495,34.0,0.543,46,1
+1,89,76,34,37,31.2,0.192,23,0
+4,76,62,0,0,34.0,0.391,25,0
+7,160,54,32,175,30.5,0.588,39,1
+4,146,92,0,0,31.2,0.539,61,1
+5,124,74,0,0,34.0,0.220,38,1
+5,78,48,0,0,33.7,0.654,25,0
+4,97,60,23,0,28.2,0.443,22,0
+4,99,76,15,51,23.2,0.223,21,0
+0,162,76,56,100,53.2,0.759,25,1
+6,111,64,39,0,34.2,0.260,24,0
+2,107,74,30,100,33.6,0.404,23,0
+5,132,80,0,0,26.8,0.186,69,0
+0,113,76,0,0,33.3,0.278,23,1
+1,88,30,42,99,55.0,0.496,26,1
+3,120,70,30,135,42.9,0.452,30,0
+1,118,58,36,94,33.3,0.261,23,0
+1,117,88,24,145,34.5,0.403,40,1
+0,105,84,0,0,27.9,0.741,62,1
+4,173,70,14,168,29.7,0.361,33,1
+9,122,56,0,0,33.3,1.114,33,1
+3,170,64,37,225,34.5,0.356,30,1
+8,84,74,31,0,38.3,0.457,39,0
+2,96,68,13,49,21.1,0.647,26,0
+2,125,60,20,140,33.8,0.088,31,0
+0,100,70,26,50,30.8,0.597,21,0
+0,93,60,25,92,28.7,0.532,22,0
+0,129,80,0,0,31.2,0.703,29,0
+5,105,72,29,325,36.9,0.159,28,0
+3,128,78,0,0,21.1,0.268,55,0
+5,106,82,30,0,39.5,0.286,38,0
+2,108,52,26,63,32.5,0.318,22,0
+10,108,66,0,0,32.4,0.272,42,1
+4,154,62,31,284,32.8,0.237,23,0
+0,102,75,23,0,0.0,0.572,21,0
+9,57,80,37,0,32.8,0.096,41,0
+2,106,64,35,119,30.5,1.400,34,0
+5,147,78,0,0,33.7,0.218,65,0
+2,90,70,17,0,27.3,0.085,22,0
+1,136,74,50,204,37.4,0.399,24,0
+4,114,65,0,0,21.9,0.432,37,0
+9,156,86,28,155,34.3,1.189,42,1
+1,153,82,42,485,40.6,0.687,23,0
+8,188,78,0,0,47.9,0.137,43,1
+7,152,88,44,0,50.0,0.337,36,1
+2,99,52,15,94,24.6,0.637,21,0
+1,109,56,21,135,25.2,0.833,23,0
+2,88,74,19,53,29.0,0.229,22,0
+17,163,72,41,114,40.9,0.817,47,1
+4,151,90,38,0,29.7,0.294,36,0
+7,102,74,40,105,37.2,0.204,45,0
+0,114,80,34,285,44.2,0.167,27,0
+2,100,64,23,0,29.7,0.368,21,0
+0,131,88,0,0,31.6,0.743,32,1
+6,104,74,18,156,29.9,0.722,41,1
+3,148,66,25,0,32.5,0.256,22,0
+4,120,68,0,0,29.6,0.709,34,0
+4,110,66,0,0,31.9,0.471,29,0
+3,111,90,12,78,28.4,0.495,29,0
+6,102,82,0,0,30.8,0.180,36,1
+6,134,70,23,130,35.4,0.542,29,1
+2,87,0,23,0,28.9,0.773,25,0
+1,79,60,42,48,43.5,0.678,23,0
+2,75,64,24,55,29.7,0.370,33,0
+8,179,72,42,130,32.7,0.719,36,1
+6,85,78,0,0,31.2,0.382,42,0
+0,129,110,46,130,67.1,0.319,26,1
+5,143,78,0,0,45.0,0.190,47,0
+5,130,82,0,0,39.1,0.956,37,1
+6,87,80,0,0,23.2,0.084,32,0
+0,119,64,18,92,34.9,0.725,23,0
+1,0,74,20,23,27.7,0.299,21,0
+5,73,60,0,0,26.8,0.268,27,0
+4,141,74,0,0,27.6,0.244,40,0
+7,194,68,28,0,35.9,0.745,41,1
+8,181,68,36,495,30.1,0.615,60,1
+1,128,98,41,58,32.0,1.321,33,1
+8,109,76,39,114,27.9,0.640,31,1
+5,139,80,35,160,31.6,0.361,25,1
+3,111,62,0,0,22.6,0.142,21,0
+9,123,70,44,94,33.1,0.374,40,0
+7,159,66,0,0,30.4,0.383,36,1
+11,135,0,0,0,52.3,0.578,40,1
+8,85,55,20,0,24.4,0.136,42,0
+5,158,84,41,210,39.4,0.395,29,1
+1,105,58,0,0,24.3,0.187,21,0
+3,107,62,13,48,22.9,0.678,23,1
+4,109,64,44,99,34.8,0.905,26,1
+4,148,60,27,318,30.9,0.150,29,1
+0,113,80,16,0,31.0,0.874,21,0
+1,138,82,0,0,40.1,0.236,28,0
+0,108,68,20,0,27.3,0.787,32,0
+2,99,70,16,44,20.4,0.235,27,0
+6,103,72,32,190,37.7,0.324,55,0
+5,111,72,28,0,23.9,0.407,27,0
+8,196,76,29,280,37.5,0.605,57,1
+5,162,104,0,0,37.7,0.151,52,1
+1,96,64,27,87,33.2,0.289,21,0
+7,184,84,33,0,35.5,0.355,41,1
+2,81,60,22,0,27.7,0.290,25,0
+0,147,85,54,0,42.8,0.375,24,0
+7,179,95,31,0,34.2,0.164,60,0
+0,140,65,26,130,42.6,0.431,24,1
+9,112,82,32,175,34.2,0.260,36,1
+12,151,70,40,271,41.8,0.742,38,1
+5,109,62,41,129,35.8,0.514,25,1
+6,125,68,30,120,30.0,0.464,32,0
+5,85,74,22,0,29.0,1.224,32,1
+5,112,66,0,0,37.8,0.261,41,1
+0,177,60,29,478,34.6,1.072,21,1
+2,158,90,0,0,31.6,0.805,66,1
+7,119,0,0,0,25.2,0.209,37,0
+7,142,60,33,190,28.8,0.687,61,0
+1,100,66,15,56,23.6,0.666,26,0
+1,87,78,27,32,34.6,0.101,22,0
+0,101,76,0,0,35.7,0.198,26,0
+3,162,52,38,0,37.2,0.652,24,1
+4,197,70,39,744,36.7,2.329,31,0
+0,117,80,31,53,45.2,0.089,24,0
+4,142,86,0,0,44.0,0.645,22,1
+6,134,80,37,370,46.2,0.238,46,1
+1,79,80,25,37,25.4,0.583,22,0
+4,122,68,0,0,35.0,0.394,29,0
+3,74,68,28,45,29.7,0.293,23,0
+4,171,72,0,0,43.6,0.479,26,1
+7,181,84,21,192,35.9,0.586,51,1
+0,179,90,27,0,44.1,0.686,23,1
+9,164,84,21,0,30.8,0.831,32,1
+0,104,76,0,0,18.4,0.582,27,0
+1,91,64,24,0,29.2,0.192,21,0
+4,91,70,32,88,33.1,0.446,22,0
+3,139,54,0,0,25.6,0.402,22,1
+6,119,50,22,176,27.1,1.318,33,1
+2,146,76,35,194,38.2,0.329,29,0
+9,184,85,15,0,30.0,1.213,49,1
+10,122,68,0,0,31.2,0.258,41,0
+0,165,90,33,680,52.3,0.427,23,0
+9,124,70,33,402,35.4,0.282,34,0
+1,111,86,19,0,30.1,0.143,23,0
+9,106,52,0,0,31.2,0.380,42,0
+2,129,84,0,0,28.0,0.284,27,0
+2,90,80,14,55,24.4,0.249,24,0
+0,86,68,32,0,35.8,0.238,25,0
+12,92,62,7,258,27.6,0.926,44,1
+1,113,64,35,0,33.6,0.543,21,1
+3,111,56,39,0,30.1,0.557,30,0
+2,114,68,22,0,28.7,0.092,25,0
+1,193,50,16,375,25.9,0.655,24,0
+11,155,76,28,150,33.3,1.353,51,1
+3,191,68,15,130,30.9,0.299,34,0
+3,141,0,0,0,30.0,0.761,27,1
+4,95,70,32,0,32.1,0.612,24,0
+3,142,80,15,0,32.4,0.200,63,0
+4,123,62,0,0,32.0,0.226,35,1
+5,96,74,18,67,33.6,0.997,43,0
+0,138,0,0,0,36.3,0.933,25,1
+2,128,64,42,0,40.0,1.101,24,0
+0,102,52,0,0,25.1,0.078,21,0
+2,146,0,0,0,27.5,0.240,28,1
+10,101,86,37,0,45.6,1.136,38,1
+2,108,62,32,56,25.2,0.128,21,0
+3,122,78,0,0,23.0,0.254,40,0
+1,71,78,50,45,33.2,0.422,21,0
+13,106,70,0,0,34.2,0.251,52,0
+2,100,70,52,57,40.5,0.677,25,0
+7,106,60,24,0,26.5,0.296,29,1
+0,104,64,23,116,27.8,0.454,23,0
+5,114,74,0,0,24.9,0.744,57,0
+2,108,62,10,278,25.3,0.881,22,0
+0,146,70,0,0,37.9,0.334,28,1
+10,129,76,28,122,35.9,0.280,39,0
+7,133,88,15,155,32.4,0.262,37,0
+7,161,86,0,0,30.4,0.165,47,1
+2,108,80,0,0,27.0,0.259,52,1
+7,136,74,26,135,26.0,0.647,51,0
+5,155,84,44,545,38.7,0.619,34,0
+1,119,86,39,220,45.6,0.808,29,1
+4,96,56,17,49,20.8,0.340,26,0
+5,108,72,43,75,36.1,0.263,33,0
+0,78,88,29,40,36.9,0.434,21,0
+0,107,62,30,74,36.6,0.757,25,1
+2,128,78,37,182,43.3,1.224,31,1
+1,128,48,45,194,40.5,0.613,24,1
+0,161,50,0,0,21.9,0.254,65,0
+6,151,62,31,120,35.5,0.692,28,0
+2,146,70,38,360,28.0,0.337,29,1
+0,126,84,29,215,30.7,0.520,24,0
+14,100,78,25,184,36.6,0.412,46,1
+8,112,72,0,0,23.6,0.840,58,0
+0,167,0,0,0,32.3,0.839,30,1
+2,144,58,33,135,31.6,0.422,25,1
+5,77,82,41,42,35.8,0.156,35,0
+5,115,98,0,0,52.9,0.209,28,1
+3,150,76,0,0,21.0,0.207,37,0
+2,120,76,37,105,39.7,0.215,29,0
+10,161,68,23,132,25.5,0.326,47,1
+0,137,68,14,148,24.8,0.143,21,0
+0,128,68,19,180,30.5,1.391,25,1
+2,124,68,28,205,32.9,0.875,30,1
+6,80,66,30,0,26.2,0.313,41,0
+0,106,70,37,148,39.4,0.605,22,0
+2,155,74,17,96,26.6,0.433,27,1
+3,113,50,10,85,29.5,0.626,25,0
+7,109,80,31,0,35.9,1.127,43,1
+2,112,68,22,94,34.1,0.315,26,0
+3,99,80,11,64,19.3,0.284,30,0
+3,182,74,0,0,30.5,0.345,29,1
+3,115,66,39,140,38.1,0.150,28,0
+6,194,78,0,0,23.5,0.129,59,1
+4,129,60,12,231,27.5,0.527,31,0
+3,112,74,30,0,31.6,0.197,25,1
+0,124,70,20,0,27.4,0.254,36,1
+13,152,90,33,29,26.8,0.731,43,1
+2,112,75,32,0,35.7,0.148,21,0
+1,157,72,21,168,25.6,0.123,24,0
+1,122,64,32,156,35.1,0.692,30,1
+10,179,70,0,0,35.1,0.200,37,0
+2,102,86,36,120,45.5,0.127,23,1
+6,105,70,32,68,30.8,0.122,37,0
+8,118,72,19,0,23.1,1.476,46,0
+2,87,58,16,52,32.7,0.166,25,0
+1,180,0,0,0,43.3,0.282,41,1
+12,106,80,0,0,23.6,0.137,44,0
+1,95,60,18,58,23.9,0.260,22,0
+0,165,76,43,255,47.9,0.259,26,0
+0,117,0,0,0,33.8,0.932,44,0
+5,115,76,0,0,31.2,0.343,44,1
+9,152,78,34,171,34.2,0.893,33,1
+7,178,84,0,0,39.9,0.331,41,1
+1,130,70,13,105,25.9,0.472,22,0
+1,95,74,21,73,25.9,0.673,36,0
+1,0,68,35,0,32.0,0.389,22,0
+5,122,86,0,0,34.7,0.290,33,0
+8,95,72,0,0,36.8,0.485,57,0
+8,126,88,36,108,38.5,0.349,49,0
+1,139,46,19,83,28.7,0.654,22,0
+3,116,0,0,0,23.5,0.187,23,0
+3,99,62,19,74,21.8,0.279,26,0
+5,0,80,32,0,41.0,0.346,37,1
+4,92,80,0,0,42.2,0.237,29,0
+4,137,84,0,0,31.2,0.252,30,0
+3,61,82,28,0,34.4,0.243,46,0
+1,90,62,12,43,27.2,0.580,24,0
+3,90,78,0,0,42.7,0.559,21,0
+9,165,88,0,0,30.4,0.302,49,1
+1,125,50,40,167,33.3,0.962,28,1
+13,129,0,30,0,39.9,0.569,44,1
+12,88,74,40,54,35.3,0.378,48,0
+1,196,76,36,249,36.5,0.875,29,1
+5,189,64,33,325,31.2,0.583,29,1
+5,158,70,0,0,29.8,0.207,63,0
+5,103,108,37,0,39.2,0.305,65,0
+4,146,78,0,0,38.5,0.520,67,1
+4,147,74,25,293,34.9,0.385,30,0
+5,99,54,28,83,34.0,0.499,30,0
+6,124,72,0,0,27.6,0.368,29,1
+0,101,64,17,0,21.0,0.252,21,0
+3,81,86,16,66,27.5,0.306,22,0
+1,133,102,28,140,32.8,0.234,45,1
+3,173,82,48,465,38.4,2.137,25,1
+0,118,64,23,89,0.0,1.731,21,0
+0,84,64,22,66,35.8,0.545,21,0
+2,105,58,40,94,34.9,0.225,25,0
+2,122,52,43,158,36.2,0.816,28,0
+12,140,82,43,325,39.2,0.528,58,1
+0,98,82,15,84,25.2,0.299,22,0
+1,87,60,37,75,37.2,0.509,22,0
+4,156,75,0,0,48.3,0.238,32,1
+0,93,100,39,72,43.4,1.021,35,0
+1,107,72,30,82,30.8,0.821,24,0
+0,105,68,22,0,20.0,0.236,22,0
+1,109,60,8,182,25.4,0.947,21,0
+1,90,62,18,59,25.1,1.268,25,0
+1,125,70,24,110,24.3,0.221,25,0
+1,119,54,13,50,22.3,0.205,24,0
+5,116,74,29,0,32.3,0.660,35,1
+8,105,100,36,0,43.3,0.239,45,1
+5,144,82,26,285,32.0,0.452,58,1
+3,100,68,23,81,31.6,0.949,28,0
+1,100,66,29,196,32.0,0.444,42,0
+5,166,76,0,0,45.7,0.340,27,1
+1,131,64,14,415,23.7,0.389,21,0
+4,116,72,12,87,22.1,0.463,37,0
+4,158,78,0,0,32.9,0.803,31,1
+2,127,58,24,275,27.7,1.600,25,0
+3,96,56,34,115,24.7,0.944,39,0
+0,131,66,40,0,34.3,0.196,22,1
+3,82,70,0,0,21.1,0.389,25,0
+3,193,70,31,0,34.9,0.241,25,1
+4,95,64,0,0,32.0,0.161,31,1
+6,137,61,0,0,24.2,0.151,55,0
+5,136,84,41,88,35.0,0.286,35,1
+9,72,78,25,0,31.6,0.280,38,0
+5,168,64,0,0,32.9,0.135,41,1
+2,123,48,32,165,42.1,0.520,26,0
+4,115,72,0,0,28.9,0.376,46,1
+0,101,62,0,0,21.9,0.336,25,0
+8,197,74,0,0,25.9,1.191,39,1
+1,172,68,49,579,42.4,0.702,28,1
+6,102,90,39,0,35.7,0.674,28,0
+1,112,72,30,176,34.4,0.528,25,0
+1,143,84,23,310,42.4,1.076,22,0
+1,143,74,22,61,26.2,0.256,21,0
+0,138,60,35,167,34.6,0.534,21,1
+3,173,84,33,474,35.7,0.258,22,1
+1,97,68,21,0,27.2,1.095,22,0
+4,144,82,32,0,38.5,0.554,37,1
+1,83,68,0,0,18.2,0.624,27,0
+3,129,64,29,115,26.4,0.219,28,1
+1,119,88,41,170,45.3,0.507,26,0
+2,94,68,18,76,26.0,0.561,21,0
+0,102,64,46,78,40.6,0.496,21,0
+2,115,64,22,0,30.8,0.421,21,0
+8,151,78,32,210,42.9,0.516,36,1
+4,184,78,39,277,37.0,0.264,31,1
+0,94,0,0,0,0.0,0.256,25,0
+1,181,64,30,180,34.1,0.328,38,1
+0,135,94,46,145,40.6,0.284,26,0
+1,95,82,25,180,35.0,0.233,43,1
+2,99,0,0,0,22.2,0.108,23,0
+3,89,74,16,85,30.4,0.551,38,0
+1,80,74,11,60,30.0,0.527,22,0
+2,139,75,0,0,25.6,0.167,29,0
+1,90,68,8,0,24.5,1.138,36,0
+0,141,0,0,0,42.4,0.205,29,1
+12,140,85,33,0,37.4,0.244,41,0
+5,147,75,0,0,29.9,0.434,28,0
+1,97,70,15,0,18.2,0.147,21,0
+6,107,88,0,0,36.8,0.727,31,0
+0,189,104,25,0,34.3,0.435,41,1
+2,83,66,23,50,32.2,0.497,22,0
+4,117,64,27,120,33.2,0.230,24,0
+8,108,70,0,0,30.5,0.955,33,1
+4,117,62,12,0,29.7,0.380,30,1
+0,180,78,63,14,59.4,2.420,25,1
+1,100,72,12,70,25.3,0.658,28,0
+0,95,80,45,92,36.5,0.330,26,0
+0,104,64,37,64,33.6,0.510,22,1
+0,120,74,18,63,30.5,0.285,26,0
+1,82,64,13,95,21.2,0.415,23,0
+2,134,70,0,0,28.9,0.542,23,1
+0,91,68,32,210,39.9,0.381,25,0
+2,119,0,0,0,19.6,0.832,72,0
+2,100,54,28,105,37.8,0.498,24,0
+14,175,62,30,0,33.6,0.212,38,1
+1,135,54,0,0,26.7,0.687,62,0
+5,86,68,28,71,30.2,0.364,24,0
+10,148,84,48,237,37.6,1.001,51,1
+9,134,74,33,60,25.9,0.460,81,0
+9,120,72,22,56,20.8,0.733,48,0
+1,71,62,0,0,21.8,0.416,26,0
+8,74,70,40,49,35.3,0.705,39,0
+5,88,78,30,0,27.6,0.258,37,0
+10,115,98,0,0,24.0,1.022,34,0
+0,124,56,13,105,21.8,0.452,21,0
+0,74,52,10,36,27.8,0.269,22,0
+0,97,64,36,100,36.8,0.600,25,0
+8,120,0,0,0,30.0,0.183,38,1
+6,154,78,41,140,46.1,0.571,27,0
+1,144,82,40,0,41.3,0.607,28,0
+0,137,70,38,0,33.2,0.170,22,0
+0,119,66,27,0,38.8,0.259,22,0
+7,136,90,0,0,29.9,0.210,50,0
+4,114,64,0,0,28.9,0.126,24,0
+0,137,84,27,0,27.3,0.231,59,0
+2,105,80,45,191,33.7,0.711,29,1
+7,114,76,17,110,23.8,0.466,31,0
+8,126,74,38,75,25.9,0.162,39,0
+4,132,86,31,0,28.0,0.419,63,0
+3,158,70,30,328,35.5,0.344,35,1
+0,123,88,37,0,35.2,0.197,29,0
+4,85,58,22,49,27.8,0.306,28,0
+0,84,82,31,125,38.2,0.233,23,0
+0,145,0,0,0,44.2,0.630,31,1
+0,135,68,42,250,42.3,0.365,24,1
+1,139,62,41,480,40.7,0.536,21,0
+0,173,78,32,265,46.5,1.159,58,0
+4,99,72,17,0,25.6,0.294,28,0
+8,194,80,0,0,26.1,0.551,67,0
+2,83,65,28,66,36.8,0.629,24,0
+2,89,90,30,0,33.5,0.292,42,0
+4,99,68,38,0,32.8,0.145,33,0
+4,125,70,18,122,28.9,1.144,45,1
+3,80,0,0,0,0.0,0.174,22,0
+6,166,74,0,0,26.6,0.304,66,0
+5,110,68,0,0,26.0,0.292,30,0
+2,81,72,15,76,30.1,0.547,25,0
+7,195,70,33,145,25.1,0.163,55,1
+6,154,74,32,193,29.3,0.839,39,0
+2,117,90,19,71,25.2,0.313,21,0
+3,84,72,32,0,37.2,0.267,28,0
+6,0,68,41,0,39.0,0.727,41,1
+7,94,64,25,79,33.3,0.738,41,0
+3,96,78,39,0,37.3,0.238,40,0
+10,75,82,0,0,33.3,0.263,38,0
+0,180,90,26,90,36.5,0.314,35,1
+1,130,60,23,170,28.6,0.692,21,0
+2,84,50,23,76,30.4,0.968,21,0
+8,120,78,0,0,25.0,0.409,64,0
+12,84,72,31,0,29.7,0.297,46,1
+0,139,62,17,210,22.1,0.207,21,0
+9,91,68,0,0,24.2,0.200,58,0
+2,91,62,0,0,27.3,0.525,22,0
+3,99,54,19,86,25.6,0.154,24,0
+3,163,70,18,105,31.6,0.268,28,1
+9,145,88,34,165,30.3,0.771,53,1
+7,125,86,0,0,37.6,0.304,51,0
+13,76,60,0,0,32.8,0.180,41,0
+6,129,90,7,326,19.6,0.582,60,0
+2,68,70,32,66,25.0,0.187,25,0
+3,124,80,33,130,33.2,0.305,26,0
+6,114,0,0,0,0.0,0.189,26,0
+9,130,70,0,0,34.2,0.652,45,1
+3,125,58,0,0,31.6,0.151,24,0
+3,87,60,18,0,21.8,0.444,21,0
+1,97,64,19,82,18.2,0.299,21,0
+3,116,74,15,105,26.3,0.107,24,0
+0,117,66,31,188,30.8,0.493,22,0
+0,111,65,0,0,24.6,0.660,31,0
+2,122,60,18,106,29.8,0.717,22,0
+0,107,76,0,0,45.3,0.686,24,0
+1,86,66,52,65,41.3,0.917,29,0
+6,91,0,0,0,29.8,0.501,31,0
+1,77,56,30,56,33.3,1.251,24,0
+4,132,0,0,0,32.9,0.302,23,1
+0,105,90,0,0,29.6,0.197,46,0
+0,57,60,0,0,21.7,0.735,67,0
+0,127,80,37,210,36.3,0.804,23,0
+3,129,92,49,155,36.4,0.968,32,1
+8,100,74,40,215,39.4,0.661,43,1
+3,128,72,25,190,32.4,0.549,27,1
+10,90,85,32,0,34.9,0.825,56,1
+4,84,90,23,56,39.5,0.159,25,0
+1,88,78,29,76,32.0,0.365,29,0
+8,186,90,35,225,34.5,0.423,37,1
+5,187,76,27,207,43.6,1.034,53,1
+4,131,68,21,166,33.1,0.160,28,0
+1,164,82,43,67,32.8,0.341,50,0
+4,189,110,31,0,28.5,0.680,37,0
+1,116,70,28,0,27.4,0.204,21,0
+3,84,68,30,106,31.9,0.591,25,0
+6,114,88,0,0,27.8,0.247,66,0
+1,88,62,24,44,29.9,0.422,23,0
+1,84,64,23,115,36.9,0.471,28,0
+7,124,70,33,215,25.5,0.161,37,0
+1,97,70,40,0,38.1,0.218,30,0
+8,110,76,0,0,27.8,0.237,58,0
+11,103,68,40,0,46.2,0.126,42,0
+11,85,74,0,0,30.1,0.300,35,0
+6,125,76,0,0,33.8,0.121,54,1
+0,198,66,32,274,41.3,0.502,28,1
+1,87,68,34,77,37.6,0.401,24,0
+6,99,60,19,54,26.9,0.497,32,0
+0,91,80,0,0,32.4,0.601,27,0
+2,95,54,14,88,26.1,0.748,22,0
+1,99,72,30,18,38.6,0.412,21,0
+6,92,62,32,126,32.0,0.085,46,0
+4,154,72,29,126,31.3,0.338,37,0
+0,121,66,30,165,34.3,0.203,33,1
+3,78,70,0,0,32.5,0.270,39,0
+2,130,96,0,0,22.6,0.268,21,0
+3,111,58,31,44,29.5,0.430,22,0
+2,98,60,17,120,34.7,0.198,22,0
+1,143,86,30,330,30.1,0.892,23,0
+1,119,44,47,63,35.5,0.280,25,0
+6,108,44,20,130,24.0,0.813,35,0
+2,118,80,0,0,42.9,0.693,21,1
+10,133,68,0,0,27.0,0.245,36,0
+2,197,70,99,0,34.7,0.575,62,1
+0,151,90,46,0,42.1,0.371,21,1
+6,109,60,27,0,25.0,0.206,27,0
+12,121,78,17,0,26.5,0.259,62,0
+8,100,76,0,0,38.7,0.190,42,0
+8,124,76,24,600,28.7,0.687,52,1
+1,93,56,11,0,22.5,0.417,22,0
+8,143,66,0,0,34.9,0.129,41,1
+6,103,66,0,0,24.3,0.249,29,0
+3,176,86,27,156,33.3,1.154,52,1
+0,73,0,0,0,21.1,0.342,25,0
+11,111,84,40,0,46.8,0.925,45,1
+2,112,78,50,140,39.4,0.175,24,0
+3,132,80,0,0,34.4,0.402,44,1
+2,82,52,22,115,28.5,1.699,25,0
+6,123,72,45,230,33.6,0.733,34,0
+0,188,82,14,185,32.0,0.682,22,1
+0,67,76,0,0,45.3,0.194,46,0
+1,89,24,19,25,27.8,0.559,21,0
+1,173,74,0,0,36.8,0.088,38,1
+1,109,38,18,120,23.1,0.407,26,0
+1,108,88,19,0,27.1,0.400,24,0
+6,96,0,0,0,23.7,0.190,28,0
+1,124,74,36,0,27.8,0.100,30,0
+7,150,78,29,126,35.2,0.692,54,1
+4,183,0,0,0,28.4,0.212,36,1
+1,124,60,32,0,35.8,0.514,21,0
+1,181,78,42,293,40.0,1.258,22,1
+1,92,62,25,41,19.5,0.482,25,0
+0,152,82,39,272,41.5,0.270,27,0
+1,111,62,13,182,24.0,0.138,23,0
+3,106,54,21,158,30.9,0.292,24,0
+3,174,58,22,194,32.9,0.593,36,1
+7,168,88,42,321,38.2,0.787,40,1
+6,105,80,28,0,32.5,0.878,26,0
+11,138,74,26,144,36.1,0.557,50,1
+3,106,72,0,0,25.8,0.207,27,0
+6,117,96,0,0,28.7,0.157,30,0
+2,68,62,13,15,20.1,0.257,23,0
+9,112,82,24,0,28.2,1.282,50,1
+0,119,0,0,0,32.4,0.141,24,1
+2,112,86,42,160,38.4,0.246,28,0
+2,92,76,20,0,24.2,1.698,28,0
+6,183,94,0,0,40.8,1.461,45,0
+0,94,70,27,115,43.5,0.347,21,0
+2,108,64,0,0,30.8,0.158,21,0
+4,90,88,47,54,37.7,0.362,29,0
+0,125,68,0,0,24.7,0.206,21,0
+0,132,78,0,0,32.4,0.393,21,0
+5,128,80,0,0,34.6,0.144,45,0
+4,94,65,22,0,24.7,0.148,21,0
+7,114,64,0,0,27.4,0.732,34,1
+0,102,78,40,90,34.5,0.238,24,0
+2,111,60,0,0,26.2,0.343,23,0
+1,128,82,17,183,27.5,0.115,22,0
+10,92,62,0,0,25.9,0.167,31,0
+13,104,72,0,0,31.2,0.465,38,1
+5,104,74,0,0,28.8,0.153,48,0
+2,94,76,18,66,31.6,0.649,23,0
+7,97,76,32,91,40.9,0.871,32,1
+1,100,74,12,46,19.5,0.149,28,0
+0,102,86,17,105,29.3,0.695,27,0
+4,128,70,0,0,34.3,0.303,24,0
+6,147,80,0,0,29.5,0.178,50,1
+4,90,0,0,0,28.0,0.610,31,0
+3,103,72,30,152,27.6,0.730,27,0
+2,157,74,35,440,39.4,0.134,30,0
+1,167,74,17,144,23.4,0.447,33,1
+0,179,50,36,159,37.8,0.455,22,1
+11,136,84,35,130,28.3,0.260,42,1
+0,107,60,25,0,26.4,0.133,23,0
+1,91,54,25,100,25.2,0.234,23,0
+1,117,60,23,106,33.8,0.466,27,0
+5,123,74,40,77,34.1,0.269,28,0
+2,120,54,0,0,26.8,0.455,27,0
+1,106,70,28,135,34.2,0.142,22,0
+2,155,52,27,540,38.7,0.240,25,1
+2,101,58,35,90,21.8,0.155,22,0
+1,120,80,48,200,38.9,1.162,41,0
+11,127,106,0,0,39.0,0.190,51,0
+3,80,82,31,70,34.2,1.292,27,1
+10,162,84,0,0,27.7,0.182,54,0
+1,199,76,43,0,42.9,1.394,22,1
+8,167,106,46,231,37.6,0.165,43,1
+9,145,80,46,130,37.9,0.637,40,1
+6,115,60,39,0,33.7,0.245,40,1
+1,112,80,45,132,34.8,0.217,24,0
+4,145,82,18,0,32.5,0.235,70,1
+10,111,70,27,0,27.5,0.141,40,1
+6,98,58,33,190,34.0,0.430,43,0
+9,154,78,30,100,30.9,0.164,45,0
+6,165,68,26,168,33.6,0.631,49,0
+1,99,58,10,0,25.4,0.551,21,0
+10,68,106,23,49,35.5,0.285,47,0
+3,123,100,35,240,57.3,0.880,22,0
+8,91,82,0,0,35.6,0.587,68,0
+6,195,70,0,0,30.9,0.328,31,1
+9,156,86,0,0,24.8,0.230,53,1
+0,93,60,0,0,35.3,0.263,25,0
+3,121,52,0,0,36.0,0.127,25,1
+2,101,58,17,265,24.2,0.614,23,0
+2,56,56,28,45,24.2,0.332,22,0
+0,162,76,36,0,49.6,0.364,26,1
+0,95,64,39,105,44.6,0.366,22,0
+4,125,80,0,0,32.3,0.536,27,1
+5,136,82,0,0,0.0,0.640,69,0
+2,129,74,26,205,33.2,0.591,25,0
+3,130,64,0,0,23.1,0.314,22,0
+1,107,50,19,0,28.3,0.181,29,0
+1,140,74,26,180,24.1,0.828,23,0
+1,144,82,46,180,46.1,0.335,46,1
+8,107,80,0,0,24.6,0.856,34,0
+13,158,114,0,0,42.3,0.257,44,1
+2,121,70,32,95,39.1,0.886,23,0
+7,129,68,49,125,38.5,0.439,43,1
+2,90,60,0,0,23.5,0.191,25,0
+7,142,90,24,480,30.4,0.128,43,1
+3,169,74,19,125,29.9,0.268,31,1
+0,99,0,0,0,25.0,0.253,22,0
+4,127,88,11,155,34.5,0.598,28,0
+4,118,70,0,0,44.5,0.904,26,0
+2,122,76,27,200,35.9,0.483,26,0
+6,125,78,31,0,27.6,0.565,49,1
+1,168,88,29,0,35.0,0.905,52,1
+2,129,0,0,0,38.5,0.304,41,0
+4,110,76,20,100,28.4,0.118,27,0
+6,80,80,36,0,39.8,0.177,28,0
+10,115,0,0,0,0.0,0.261,30,1
+2,127,46,21,335,34.4,0.176,22,0
+9,164,78,0,0,32.8,0.148,45,1
+2,93,64,32,160,38.0,0.674,23,1
+3,158,64,13,387,31.2,0.295,24,0
+5,126,78,27,22,29.6,0.439,40,0
+10,129,62,36,0,41.2,0.441,38,1
+0,134,58,20,291,26.4,0.352,21,0
+3,102,74,0,0,29.5,0.121,32,0
+7,187,50,33,392,33.9,0.826,34,1
+3,173,78,39,185,33.8,0.970,31,1
+10,94,72,18,0,23.1,0.595,56,0
+1,108,60,46,178,35.5,0.415,24,0
+5,97,76,27,0,35.6,0.378,52,1
+4,83,86,19,0,29.3,0.317,34,0
+1,114,66,36,200,38.1,0.289,21,0
+1,149,68,29,127,29.3,0.349,42,1
+5,117,86,30,105,39.1,0.251,42,0
+1,111,94,0,0,32.8,0.265,45,0
+4,112,78,40,0,39.4,0.236,38,0
+1,116,78,29,180,36.1,0.496,25,0
+0,141,84,26,0,32.4,0.433,22,0
+2,175,88,0,0,22.9,0.326,22,0
+2,92,52,0,0,30.1,0.141,22,0
+3,130,78,23,79,28.4,0.323,34,1
+8,120,86,0,0,28.4,0.259,22,1
+2,174,88,37,120,44.5,0.646,24,1
+2,106,56,27,165,29.0,0.426,22,0
+2,105,75,0,0,23.3,0.560,53,0
+4,95,60,32,0,35.4,0.284,28,0
+0,126,86,27,120,27.4,0.515,21,0
+8,65,72,23,0,32.0,0.600,42,0
+2,99,60,17,160,36.6,0.453,21,0
+1,102,74,0,0,39.5,0.293,42,1
+11,120,80,37,150,42.3,0.785,48,1
+3,102,44,20,94,30.8,0.400,26,0
+1,109,58,18,116,28.5,0.219,22,0
+9,140,94,0,0,32.7,0.734,45,1
+13,153,88,37,140,40.6,1.174,39,0
+12,100,84,33,105,30.0,0.488,46,0
+1,147,94,41,0,49.3,0.358,27,1
+1,81,74,41,57,46.3,1.096,32,0
+3,187,70,22,200,36.4,0.408,36,1
+6,162,62,0,0,24.3,0.178,50,1
+4,136,70,0,0,31.2,1.182,22,1
+1,121,78,39,74,39.0,0.261,28,0
+3,108,62,24,0,26.0,0.223,25,0
+0,181,88,44,510,43.3,0.222,26,1
+8,154,78,32,0,32.4,0.443,45,1
+1,128,88,39,110,36.5,1.057,37,1
+7,137,90,41,0,32.0,0.391,39,0
+0,123,72,0,0,36.3,0.258,52,1
+1,106,76,0,0,37.5,0.197,26,0
+6,190,92,0,0,35.5,0.278,66,1
+2,88,58,26,16,28.4,0.766,22,0
+9,170,74,31,0,44.0,0.403,43,1
+9,89,62,0,0,22.5,0.142,33,0
+10,101,76,48,180,32.9,0.171,63,0
+2,122,70,27,0,36.8,0.340,27,0
+5,121,72,23,112,26.2,0.245,30,0
+1,126,60,0,0,30.1,0.349,47,1
+1,93,70,31,0,30.4,0.315,23,0