RandomForest modelling 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import time\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.tree import export_graphviz, DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, precision_score, recall_score\n",
    "from sklearn.datasets import load_breast_cancer, load_boston\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn import utils\n",
    "\n",
    "\n",
    "# Always make it pretty.\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# We get some interger -> float typecast warning from sklean below, this keeps them out of our hair.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import itertools\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv('data/cleaned_data_together.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_cleaner(df):\n",
    "    df['DateTime'] = pd.to_datetime(df['DateTime'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_cleaner(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Henry Hub Price $/MMBtu</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Production MMCF</th>\n",
       "      <th>Storage BCFE</th>\n",
       "      <th>Reserves BCFE</th>\n",
       "      <th>Consumption MMCF</th>\n",
       "      <th>DOY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5.82</td>\n",
       "      <td>26.0317</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5.82</td>\n",
       "      <td>24.2249</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>5.82</td>\n",
       "      <td>23.5537</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6.09</td>\n",
       "      <td>23.2831</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>6.19</td>\n",
       "      <td>24.5469</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DateTime  Henry Hub Price $/MMBtu     Temp  Production MMCF  Storage BCFE  \\\n",
       "0 2010-01-01                     5.82  26.0317        2197157.0        3117.0   \n",
       "1 2010-01-02                     5.82  24.2249        2197157.0        3117.0   \n",
       "2 2010-01-03                     5.82  23.5537        2197157.0        3117.0   \n",
       "3 2010-01-04                     6.09  23.2831        2197157.0        3117.0   \n",
       "4 2010-01-05                     6.19  24.5469        2197157.0        3117.0   \n",
       "\n",
       "   Reserves BCFE  Consumption MMCF  DOY  \n",
       "0       255035.0         1666520.0    1  \n",
       "1       255035.0         1666520.0    2  \n",
       "2       255035.0         1666520.0    3  \n",
       "3       255035.0         1666520.0    4  \n",
       "4       255035.0         1666520.0    5  "
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.82, 5.82, 5.82, ..., 2.9 , 2.9 , 2.86])"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.pop('Henry Hub Price $/MMBtu').values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3357, numpy.ndarray)"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = np.array([0]+[(b - a)  for a, b in zip(y[::1], y[1::1])])\n",
    "len(test), type(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.  ,  0.  ,  0.27,  0.1 ,  0.28,  1.04, -0.95,  0.  ,\n",
       "        0.  ])"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize= (12,6))\n",
    "ax.hist(test, bins = 80, alpha =0.5)\n",
    "ax.set_title(\"Fights per Event\", fontsize = 15)\n",
    "ax.set_ylabel(\"Event Count\", fontsize = 15)\n",
    "ax.set_xlabel(\"Fights\", fontsize = 15)\n",
    "#plt.xticks(n.index, rotation='vertical')\n",
    "ax.legend()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "X_train, X_test, y_train, y_test = X[0:size], X[size:len(X)], test[0:size], test[size:len(y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: -0.5052703205419347\n"
     ]
    }
   ],
   "source": [
    "\n",
    "rfr = RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=90,\n",
    "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=3,\n",
    "           min_samples_split=12, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
    "           verbose=0, warm_start=False)\n",
    "\n",
    "rfr.fit(X_train, y_train)\n",
    "print(\"score:\", rfr.score(X_test, y_test, sample_weight=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: -0.08780765775558175\n"
     ]
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
    "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=3,\n",
    "           min_samples_split=12, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
    "           verbose=0, warm_start=False)\n",
    "\n",
    "rfr.fit(X_train, y_train)\n",
    "print(\"score:\", rfr.score(X_test, y_test, sample_weight=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4],\n",
       "       [7, 6, 5]])"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rr = np.array([[2,3,4],[7,6,5]])\n",
    "rr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt =np.array([[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 3), (2, 3))"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt.shape, rr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 7, 6, 5, 4, 5, 6])"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.append(rr, np.array([[4,5,6]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.21])"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.08,  0.03,  0.  ,  0.  ,  0.  ,  0.  ,  0.2 ,  0.06,  0.3 ,\n",
       "        0.  ,  0.12,  0.  ,  0.  ,  0.  ,  0.31,  0.05, -0.12,  0.07,\n",
       "        0.  ,  0.  , -0.15,  0.05, -0.1 ,  0.01, -0.05,  0.  ,  0.  ,\n",
       "        0.04, -0.16,  0.11,  0.  ,  0.1 ,  0.  ,  0.  ,  0.  ,  0.1 ,\n",
       "        0.  ,  0.01,  0.  ,  0.  ,  0.  ,  0.  , -0.3 ,  0.01,  0.  ,\n",
       "       -0.04,  0.  ,  0.  , -0.24,  0.07,  0.06,  0.09,  0.  ,  0.  ,\n",
       "        0.  ,  0.  ,  0.01, -0.11, -0.01, -0.02,  0.  ,  0.  , -0.07,\n",
       "        0.  ,  0.1 ,  0.18, -0.13,  0.  ,  0.  , -0.09, -0.22,  0.15,\n",
       "       -0.02,  0.  ,  0.  ,  0.  , -0.21,  0.11,  0.02,  0.06,  0.  ,\n",
       "        0.  ,  0.  , -0.18, -0.08,  0.1 , -0.07, -0.13,  0.  ,  0.  ,\n",
       "        0.  , -0.19, -0.04,  0.11,  0.  ,  0.  ,  0.  , -0.19,  0.1 ,\n",
       "        0.  ,  0.05, -0.09,  0.  ,  0.  ,  0.18,  0.  , -0.09,  0.24,\n",
       "        0.15,  0.  ,  0.  ,  0.06, -0.02,  0.  , -0.18,  0.01,  0.  ,\n",
       "        0.  ,  0.08,  0.16, -0.07, -0.09,  0.04,  0.  ,  0.  , -0.03,\n",
       "        0.01,  0.08,  0.05,  0.05,  0.  ,  0.  ,  0.  , -0.07,  0.16,\n",
       "        0.01,  0.  ,  0.  ,  0.  , -0.08, -0.04, -0.14,  0.03,  0.  ,\n",
       "        0.  ,  0.  ,  0.09,  0.02,  0.  ,  0.02, -0.07,  0.  ,  0.  ,\n",
       "        0.  , -0.07,  0.07,  0.07,  0.07,  0.  ,  0.  ,  0.  , -0.07,\n",
       "        0.01, -0.03, -0.01,  0.  ,  0.  , -0.04, -0.03,  0.  ,  0.2 ,\n",
       "        0.01,  0.  ,  0.  ,  0.02,  0.01,  0.  , -0.14, -0.02,  0.  ,\n",
       "        0.  ,  0.13, -0.02,  0.  , -0.12,  0.  ,  0.  ,  0.  ,  0.  ,\n",
       "        0.  , -0.05,  0.  , -0.2 ,  0.  ,  0.  ,  0.07,  0.  ,  0.14,\n",
       "       -0.08,  0.05,  0.  ,  0.  ,  0.09,  0.  , -0.17, -0.01,  0.07,\n",
       "        0.  ,  0.  , -0.14,  0.04,  0.  , -0.03,  0.01,  0.  ,  0.  ,\n",
       "        0.09,  0.01,  0.04,  0.04, -0.09,  0.  ,  0.  ,  0.  ,  0.  ,\n",
       "        0.  , -0.09,  0.02,  0.  ,  0.  ,  0.01,  0.06,  0.03, -0.05,\n",
       "       -0.02,  0.  ,  0.  ,  0.1 ,  0.07,  0.  ,  0.01, -0.06,  0.  ,\n",
       "        0.  , -0.07,  0.  , -0.07,  0.03,  0.01,  0.  ,  0.  , -0.09,\n",
       "       -0.07,  0.  , -0.04,  0.  ,  0.  ,  0.  ,  0.04,  0.01,  0.  ,\n",
       "        0.08,  0.07,  0.  ,  0.  ,  0.06, -0.06, -0.02, -0.03, -0.01,\n",
       "        0.  ,  0.  ,  0.1 ,  0.  , -0.06,  0.03, -0.04,  0.  ,  0.  ,\n",
       "        0.02,  0.  ,  0.  , -0.03, -0.04,  0.  ,  0.  ,  0.  ,  0.  ,\n",
       "        0.1 , -0.06, -0.04,  0.  ,  0.  ,  0.  ,  0.  ,  0.14,  0.01,\n",
       "       -0.03,  0.  ,  0.  ,  0.11,  0.07, -0.05, -0.02, -0.18,  0.  ,\n",
       "        0.  ,  0.03,  0.  ,  0.02, -0.02, -0.02,  0.  ,  0.  ,  0.  ,\n",
       "       -0.21,  0.07,  0.  ,  0.13,  0.  ,  0.  , -0.06,  0.02,  0.  ,\n",
       "        0.06,  0.04,  0.  ,  0.  , -0.12,  0.01, -0.07,  0.02, -0.03,\n",
       "        0.  ,  0.  ,  0.15,  0.  ,  0.03, -0.07, -0.13,  0.  ,  0.  ,\n",
       "        0.16, -0.1 , -0.16,  0.  ,  0.07,  0.  ,  0.  ,  0.26,  0.06,\n",
       "        0.09,  0.02, -0.01,  0.  ,  0.  , -0.04, -0.07,  0.1 , -0.07,\n",
       "       -0.05,  0.  ,  0.  ,  0.  ,  0.01,  0.  ,  0.  ,  0.  ,  0.  ,\n",
       "        0.  , -0.17,  0.  ,  0.18,  0.  , -0.22,  0.  ,  0.  ,  0.07,\n",
       "       -0.05,  0.  , -0.05, -0.03,  0.  ,  0.  ,  0.03,  0.06,  0.  ,\n",
       "       -0.18, -0.09,  0.  ,  0.  ,  0.11,  0.04, -0.03, -0.08, -0.01,\n",
       "        0.  ,  0.  ,  0.  ,  0.05,  0.08,  0.21,  0.72,  0.  ,  0.  ,\n",
       "        0.  ,  2.55,  0.  , -1.59,  0.  ,  0.  ,  0.  , -1.76,  0.04,\n",
       "        0.23,  0.  ,  0.9 ,  0.  ,  0.  ,  0.  ,  1.4 , -1.54,  0.  ,\n",
       "       -0.72,  0.  ,  0.  , -0.07,  0.22,  0.19,  0.  ,  0.04,  0.  ,\n",
       "        0.  ,  0.02,  0.  , -0.26, -0.28, -0.15,  0.  ,  0.  , -0.06,\n",
       "       -0.1 , -0.01,  0.  , -0.08,  0.  ,  0.  , -0.06, -0.02, -0.05,\n",
       "       -0.04,  0.  ,  0.  ,  0.  ,  0.  ,  0.12,  0.04,  0.  , -0.07,\n",
       "        0.  ,  0.  ,  0.02, -0.01,  0.07,  0.01,  0.  ,  0.  ,  0.  ,\n",
       "        0.03,  0.  ,  0.1 ,  0.  , -0.09,  0.  ,  0.  ,  0.07, -0.02,\n",
       "       -0.07,  0.01, -0.08,  0.  ,  0.  ,  0.  ,  0.09,  0.03, -0.12,\n",
       "       -0.04,  0.  ,  0.  ,  0.05, -0.03,  0.04,  0.17,  0.  ,  0.  ,\n",
       "        0.  , -0.06,  0.  ,  0.06,  0.  ,  0.  ,  0.  ,  0.  , -0.03,\n",
       "        0.  , -0.04,  0.02,  0.06,  0.  ,  0.  ,  0.06, -0.04,  0.01,\n",
       "       -0.05, -0.02,  0.  ,  0.  ,  0.  ,  0.01,  0.02,  0.  ,  0.01,\n",
       "        0.  ,  0.  , -0.07,  0.  ,  0.02, -0.02,  0.  ,  0.  ,  0.  ,\n",
       "       -0.01,  0.03, -0.02,  0.03, -0.03,  0.  ,  0.  ,  0.09, -0.01,\n",
       "        0.02, -0.1 ,  0.  ,  0.  ,  0.  ,  0.02, -0.01,  0.13, -0.01,\n",
       "        0.  ,  0.  ,  0.  ,  0.  , -0.04, -0.01,  0.11, -0.03,  0.  ,\n",
       "        0.  , -0.02,  0.  ,  0.  ,  0.13, -0.11,  0.  ,  0.  ,  0.09,\n",
       "       -0.02,  0.  ,  0.01,  0.03,  0.  ,  0.  , -0.03, -0.04,  0.01,\n",
       "        0.12, -0.13,  0.  ,  0.  ,  0.01,  0.02,  0.03, -0.02, -0.03,\n",
       "        0.  ,  0.  , -0.06,  0.  ,  0.  ,  0.01,  0.  ,  0.  ,  0.  ,\n",
       "       -0.01,  0.  , -0.01,  0.  , -0.03,  0.  ,  0.  , -0.1 ,  0.05,\n",
       "       -0.01, -0.05,  0.04,  0.  ,  0.  ,  0.  , -0.06,  0.09,  0.  ,\n",
       "       -0.04,  0.  ,  0.  , -0.03,  0.07, -0.02, -0.03,  0.09,  0.  ,\n",
       "        0.  , -0.01,  0.1 ,  0.04,  0.03, -0.06,  0.  ,  0.  , -0.04,\n",
       "        0.1 ,  0.  ,  0.  , -0.01,  0.  ,  0.  ,  0.03, -0.03,  0.01,\n",
       "       -0.01, -0.02,  0.  ,  0.  , -0.01,  0.  , -0.02,  0.  ,  0.02,\n",
       "        0.  ,  0.  ,  0.  , -0.02,  0.04, -0.07, -0.05,  0.  ,  0.  ,\n",
       "        0.02,  0.04,  0.04, -0.04, -0.03,  0.  ,  0.  ,  0.04,  0.05,\n",
       "        0.11, -0.04, -0.05,  0.  ,  0.  ,  0.02,  0.08, -0.02, -0.05,\n",
       "       -0.04,  0.  ,  0.  ,  0.12,  0.01,  0.12,  0.13, -0.14,  0.  ,\n",
       "        0.  ,  0.03,  0.12,  0.05, -0.29,  0.03,  0.  ,  0.  ,  0.07,\n",
       "        0.01,  0.03, -0.02, -0.08,  0.  ,  0.  ,  0.  ,  0.08,  0.14,\n",
       "        0.  , -0.15,  0.  ,  0.  , -0.07,  0.07,  0.04,  0.05, -0.1 ,\n",
       "        0.  ,  0.  ,  0.27,  0.  ,  0.01,  0.  ,  0.2 ,  0.  ,  0.  ,\n",
       "        0.22,  0.14,  0.  ,  0.59, -0.39,  0.  ,  0.  ,  0.35,  0.  ,\n",
       "        0.05,  0.  ,  0.  ,  0.  ,  0.  , -0.42,  0.  ,  0.22,  0.  ,\n",
       "        0.11,  0.  ,  0.  , -0.21,  0.  ,  0.29, -0.25,  0.07,  0.  ,\n",
       "        0.  ,  0.03, -0.01,  0.  , -0.22, -0.32,  0.  ,  0.  , -0.19,\n",
       "        0.  , -0.16,  0.06,  0.  ,  0.  ,  0.  ,  0.  ,  0.  , -0.28,\n",
       "       -0.32,  0.15,  0.  ,  0.  ,  0.  ,  0.  ,  0.  , -0.53,  0.08,\n",
       "        0.  ,  0.  , -0.06,  0.15,  0.03,  0.03,  0.  ,  0.  ,  0.  ,\n",
       "        0.41,  0.18,  0.04,  0.  , -0.15,  0.  ,  0.  ,  0.  ,  0.  ,\n",
       "       -0.3 ,  0.  , -0.06,  0.  ,  0.  , -0.02, -0.11,  0.06, -0.18,\n",
       "       -0.12,  0.  ,  0.  , -0.13, -0.03,  0.04,  0.04,  0.04,  0.  ,\n",
       "        0.  ,  0.1 , -0.05, -0.1 ,  0.  , -0.02,  0.  ,  0.  ,  0.  ,\n",
       "        0.1 ,  0.  ,  0.04,  0.01,  0.  ,  0.  ,  0.1 , -0.1 ,  0.15,\n",
       "        0.  ,  0.3 ,  0.  ,  0.  ,  1.06, -1.07,  0.  , -0.28,  0.  ,\n",
       "        0.  ,  0.  , -0.04])"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.07292\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test \n",
    "history_X = X_train\n",
    "history_y = y_train\n",
    "predictions = list()\n",
    "for t in range(len(y_test)):\n",
    "    rfr = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
    "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=3,\n",
    "           min_samples_split=12, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
    "           verbose=0, warm_start=False)\n",
    "    rfr_fit = rfr.fit(history_X, history_y)\n",
    "    output = rfr_fit.predict(X_test[t].reshape((1,6)))\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = y_test[t]\n",
    "    history_X = np.append(history_X, X_test[t].reshape((1,6)), axis=0)\n",
    "    history_y = np.append(history_y,obs)\n",
    "    #print('{} of {} predicted={}, expected={}'.format(t, len(y_test), yhat, obs))\n",
    "pred = np.array(predictions)\n",
    "error = mean_squared_error(y_test, predictions)\n",
    "print('Test MSE: %.5f' % error)\n",
    "\n",
    "# plt.plot(test)\n",
    "# plt.plot(predictions, color='red')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with bootstrap off, Test MSE: 0.04656"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid search work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: -0.00144\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor()\n",
    "depth_param = np.arange(1, 12, 1)\n",
    "num_samples_param = np.arange(5,95,10)\n",
    "n_trees = np.arange(1,70, 10)\n",
    "param_grid = {\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth': [90, 100], #, 110\n",
    "    'max_features': [2, 3, 4, 5],\n",
    "    'min_samples_leaf': [3, 4, 5, 10], #, 50, 90\n",
    "    'min_samples_split': [8, 10, 12],\n",
    "    'n_estimators': [100, 200] #, 300, 1000, 2000\n",
    "}\n",
    "clf = GridSearchCV(model, param_grid, cv=3, n_jobs=-1)\n",
    "clf.fit(X_train,y_train)\n",
    "print('Accuracy score: {:.3}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "param_grid = {\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth': [80, 90], #, 100, 110\n",
    "    'max_features': [2, 3, 4, 5],\n",
    "    'min_samples_leaf': [3, 4, 5, 10], #, 50, 90\n",
    "    'min_samples_split': [8, 10, 12],\n",
    "    'n_estimators': [100, 200] #, 300, 1000, 2000\n",
    "}\n",
    "Accuracy score: 0.00025\n",
    "\n",
    "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
    "           max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=10,\n",
    "           min_samples_split=8, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=None, oob_score=False,\n",
    "           random_state=None, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "param_grid = {\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth': [80, 90], #, 100, 110\n",
    "    'max_features': [2, 3, 4, 5],\n",
    "    'min_samples_leaf': [3, 4, 5], #, 10, 50, 90\n",
    "    'min_samples_split': [8, 10, 12],\n",
    "    'n_estimators': [100] #, 200, 300, 1000, 2000\n",
    "}\n",
    "Accuracy score: -0.00237\n",
    "\n",
    "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,\n",
    "           max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=5,\n",
    "           min_samples_split=12, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=None, oob_score=False,\n",
    "           random_state=None, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "param_grid = {\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth': [80, 90, 100], # , 110\n",
    "    'max_features': [2, 3, 4, 5],\n",
    "    'min_samples_leaf': [3, 4, 5, 10], #, 50, 90\n",
    "    'min_samples_split': [8, 10, 12, 15],\n",
    "    'n_estimators': [100, 200] #, 300, 1000, 2000\n",
    "}\n",
    "Accuracy score: -0.00337"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
       "           max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=10,\n",
       "           min_samples_split=8, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=100, n_jobs=None, oob_score=False,\n",
       "           random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 579,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: -0.0752174669968022\n"
     ]
    }
   ],
   "source": [
    "best_rfr= RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=9,\n",
    "           max_features='auto', max_leaf_nodes=None,\n",
    "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "           min_samples_leaf=5, min_samples_split=2,\n",
    "           min_weight_fraction_leaf=0.0, n_estimators=61, n_jobs=None,\n",
    "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
    "\n",
    "best_rfr.fit(X_train, y_train)\n",
    "print(\"score:\", best_rfr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: -0.002040411695668931\n"
     ]
    }
   ],
   "source": [
    "better_rfr= RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
    "           max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=10,\n",
    "           min_samples_split=8, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=None, oob_score=False,\n",
    "           random_state=None, verbose=0, warm_start=False)\n",
    "better_rfr.fit(X_train, y_train)\n",
    "print(\"score:\", better_rfr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.03257\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test \n",
    "history_X = X_train\n",
    "history_y = y_train\n",
    "predictions = list()\n",
    "for t in range(len(y_test)):\n",
    "    rfr = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
    "           max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=10,\n",
    "           min_samples_split=8, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=None, oob_score=False,\n",
    "           random_state=None, verbose=0, warm_start=False)\n",
    "    rfr_fit = rfr.fit(history_X, history_y)\n",
    "    output = rfr_fit.predict(X_test[t].reshape((1,6)))\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = y_test[t]\n",
    "    history_X = np.append(history_X, X_test[t].reshape((1,6)), axis=0)\n",
    "    history_y = np.append(history_y,obs)\n",
    "    #print('{} of {} predicted={}, expected={}'.format(t, len(y_test), yhat, obs))\n",
    "pred = np.array(predictions)\n",
    "error = mean_squared_error(y_test, predictions)\n",
    "print('Test MSE: %.5f' % error)\n",
    "\n",
    "# plt.plot(test)\n",
    "# plt.plot(predictions, color='red')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(25, 5))\n",
    "axs.plot(predictions, color='black')\n",
    "axs.plot(y_test, color='red', alpha = 0.8)\n",
    "axs.set_xlim([00, 300])\n",
    "axs.set_ylim([-0.5, 0.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(25, 5))\n",
    "axs.plot(predictions, color='black')\n",
    "axs.plot(y_test, color='red', alpha = 0.8)\n",
    "axs.set_xlim([300, 600])\n",
    "#axs.set_ylim([-0.5, 0.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 1.36728\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test \n",
    "history_X = X_train\n",
    "history_y = y_train\n",
    "predictions = list()\n",
    "rfr = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
    "       max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "       min_impurity_split=None, min_samples_leaf=3,\n",
    "       min_samples_split=12, min_weight_fraction_leaf=0.0,\n",
    "       n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
    "       verbose=0, warm_start=False)\n",
    "rfr_fit = rfr.fit(X_train, y_train)\n",
    "for t in range(len(y_test)):\n",
    "    output = rfr_fit.predict(X_test[t].reshape((1,6)))\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "#     obs = y_test[t]\n",
    "#     history_X = np.append(history_X, X_test[t].reshape((1,6)), axis=0)\n",
    "#     history_y = np.append(history_y,obs)\n",
    "    #print('{} of {} predicted={}, expected={}'.format(t, len(y_test), yhat, obs))\n",
    "pred = np.array(predictions)\n",
    "error = mean_squared_error(y_test, predictions)\n",
    "print('Test MSE: %.5f' % error)\n",
    "\n",
    "# plt.plot(test)\n",
    "# plt.plot(predictions, color='red')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nTp1CaWmpT0EyJ5KJiIiIqDVeV1s88sgjMBgMwVwLEREREZH/3DqSXYGyvwGwcn27vXEHc4C5dySXlpYCaF+QHBsbi7y8PADweSJZuR4RERERkTtWWxARERFReHHfWE/5289N8hptsif7NdvsUdMgWRRFxMXF+Xwc9wEQX6oxoqOjYTQafb49IiIiIgp/Xk8kr1ixAoBjd+fs7OygLYiIiIiIyB+Ce5DsnEgW7PbAVFsoPzs38ws09832ysrKkJSUBFU7NgmMiYlx/dzaZnutXY8dyURERETUEq+C5McffxwJCQmorq7GX//6V/Tq1QuDBw9udJmcnBzk5OQAAFauXImkpKTAr7YLUKvV5+x9J6KuiY9bRNQVeXrsEjUaICoK2qQkCPHxENVqJMTGAn481qmioqBy9i0nxcUBkZHtPlZrJElCTU0NtFotLBYLioqK0KNHj3Y9RrvXWQwbNgyRXq43OTkZdXV1SExMhBCk+g6icxVfcxFRV8PHLWrKqyA5ISEBgKNr7fzzz8eJEyeaBcnZ2dmNJpWVzUHONUlJSefsfSeiromPW0TUFXl67IpvaIDNbEZtWRki6usRY7OhorQUkkbT7tvTV1YiyjmVXF5SAjk6ut3Hak1NTQ0kSUJaWhpOnjyJI0eOYNCgQe16jBadk9jx8fGor69HfX29V9fTaDSwWq3Iy8tDVFSUz7dLRK3jay4i6mr4uHXu8HZPjTa/J2cymdDQ0OD6+ccff0R6erp/qyMiIiIiChb3zfDcqi384n59f4/VCqUfOSMjAwBQXFzc7ikgpdrCl432ACAxMREAUF5e3q7bJSIiIqLw1eZEcnV1Nf73f/8XAGC323HxxRdjxIgRQV8YEREREVG7uHUky0q/sJ/hr/tme373LbdCCZLdhza6devWrmPFxsYC8D1IVoLr0tJSDo8QERERUSNtBsndu3fH008/3RFrITqnlJeXY/r06Vi7dm2zqhgiIiJqv0ab7Tl7jf2eInbfbC9IE8nKRntpaWmu09o7kWwwGAD4ttGe++1xIpmIiIiImvJ9C2giCogzZ87g9OnTrk0qiYiIKEAkCbKz0iJg1RaS9OvP7qFyANXU1AAIzESyEiT7M5FMREREROSOQTJRiFgsFgDAgQMHQrwSIiKiMOPWkRyUagv3UDmAAllt0bNnT/zpT3/C7373O5+up3Qkc2MdIiIiImqqzWoLIgoOJUjev39/iFdCREQUZuz2wFdbuF8/SBPJSpAciGoLQRCwdOlSn6+n0+lgMBhYbUFEREREzXAimShElCC5qKgIRUVFIV4NERFRGJHlZkGyv9UW7hPJwQqSq6qqIIoiDAaDq5qivRPJ/khMTGS1BRERERE1wyCZKESUIBlgvQW1TpIklJeXo6amBg0NDbBarbDZbLDZbJBlOdTLIyLqlAS3ieRAVVugg6otDAYDBEFAbGwsVCoVEhISgnJbniQlJbHagoiIiIiaYZBMFCLuQTLrLag1f/rTnzB8+HAMGjQI/fr1Q2ZmJjIyMpCRkYHZs2eHenlERJ2TJP0aICub7gVysz1/j9WK6upqxMbGAgBiY2ORkJAAUVl/B+rWrRurLYiIiIioGXYkE4WI2WwGAMTFxWHfvn0hXg11RkVFRXjrrbcwefJkjBs3DhaLBTbnRFxOTg6OHj0a4hUSEXVS7tUWziC2K1RbVFRUIC4uDoDj9YE9SIF1WxITE/HNN9+E5LaJiIiIqPNikEwUIspE8uTJk/HWW2/hxIkT6NevX4hXRZ3Jpk2bYLfbsXz5cmRmZjY6r7q6GseOHQvNwoiIOjtJAgQBgFu1hb/hr1uoG4xqi/r6euzduxfXXnstAGDZsmUwmUwBvx1vJCUlobKyEjabDWo13y4QERERkQNfGRKFiNVqBQDceeedeO+99/D888/jhRdeCPGqqCPYbDZs3rwZgiAgMjISsizDaDTCaDSitrYWdXV1sFgs2LFjB7Kzs5uFyAAQERHRqB6FiIh+Jdjtv1ZaKJvt+Rn+NppIDsKk8McffwyTyYRp06YBAC644IKA34a3kpKSIMsyKioqkJycHLJ1hKPc3Fx8//33qKysRGVlJSwWC9RqNQYPHoz09HRERUWhT58+DPCJiIioU+IrFKIQUULAnj174qabbsJLL72EpUuXok+fPiFeGQXbvn37cP/997d4niAIiI6OhlarhV6vx5133tni5SIiImCz2WC320PSn0lE1KnJ8q+TyEogF8DN9oJRbfH+++8jMTERY8aMCfixfZWUlAQAKCsrY5AcQF999RXmz5+PhoYGAIBKpUJERIRrI11FVFQU0tPToVKpoFKpIIoidDod5s2bhxkzZkBwTtsTERERdTQGyUQhonQka7VazJ8/H2vWrMFXX33FIPkcUFFRAcBRXZGRkQFBEBAVFYXo6GhERkZ69QZRq9UCcHwgodfrg7peIqIuJ1jVFioVIEl+9y031dDQgJycHMycObNTfDjYrVs3AI4gmQLj6NGjmDdvHtLT07F69Wr07NkTBoMBKpUKVqsVx44dQ1FREaqrq7Fv3z4UFRVBkiTXn/z8fNx555144IEHWv03olKpkJKSgmnTprX6QTQRERGRPxgkE4WIMpGs0WiQmpoKnU6HkydPhnhV1BGqqqoAAP3790dqamq7jqEEyWazmUEyEVFTLW2252+1hd0OOSICQkNDQCeS9+3bh6VLl6K+vh5XX311wI7rj8TERAAMkgPp9ddfhyzL+Pe//+0K6hUajQZDhw7F0KFDAQCzZs1qdn1JkvD222/jhx9+aPU2bDYbvv32WzzzzDNYsGABdDpdYO8EEVEbVBUVkCIjAQ+PP9GrVkFz6FCL5zVMnQrT9Ok+365QVwfY7ZBjY32+LhH5hkEyUYhYLBZERERAEAQIgoDevXsjNzc31MuiDqAEyXFxce0+RkREBACwJ5mIqClZbhwkB6rawm4HtFqgoSGgm+099thjqKmpwYYNG3DhhRcG7Lj+cK+2IP/Z7Xa89957mDRpUrMQ2VsqlQqzZ8/G7NmzPV5u586duPnmm3HgwIFOUZNCROeWuD/+EaZLL0X9TTe1eL5m/37oPvwQ1qFDITV5L6T98UdEfP11u4Lk6H/8A6qSElQ/80x7lk1EPmCQTBQiFovFNVUKAH369MGRI0dCuCLqKJWVlVCr1YiKimr3MRgkExG1Qgl5nUFyoKotBJsNslYLAQjoZnuFhYUYP348Jk+eHLBj+is2NhZarRalpaWhXkpY2LNnD4qLi/Hb3/426Lc1evRo120ySCaijqYqL4dYXNzymbKMqA0bICUloXrFCseHs24MDz0EVXV1u25XLCho/XaJKKAYJBOFiMVigUajcf3ep08f/Oc//4HVam10OoWfqqoqxMXF+bVZjvIhhMlkCtSyiIjCgzNIdgXIzufU6JdfRtT69a6L2Xv3RtWzz3p/XJsNsvNDvEBVW8iyjNLSUnTv3j0gxwsUQRDQq1cv/PLLL6FeSpdnt9vx5ptvQq/Xd8iHBQkJCRgwYAD27NkT9NsiImrEbgdsNkfNRBOGxx6DZt8+CGYz6u6+u1mIDAByVBSEwsJ23bSqqgpCTQ1gsbR4bCIKHAbJRCHS0kSyzWZDfn4+N9wLc0qQ7A9OJBMRtUKZSFY224uKQt0ddzSaVNIcOgT1sWO+Hdduh+x83g5UtUV1dTUsFku76w6CKTU1lUGym2+++QZHjx5FZWUl9u3bh/r6ekRGRiIrKwuzZs3CkCFDXJc1m814+OGHcfDgQRQXF6OoqAhz5sxBZGRkh6z1ggsuwDvvvAO73d4pNm8konOD4BxwUdXWNjtP8+OPsKelwXzRRTBlZ7d4fTk6GqoWQug2yTJUzupAVUUFpB49fD8GEXmNQTJRiCgdyQolPM7NzWWQHOYCESQrH0IwSCYiakxoUm0BoFnfYuSrrzqCZFl2Bc5tHtdmg6xUEgWo2qKkpAQAOt1EMgCkpaVh165doV5Gp/Ddd99h9uzZkGUZADBgwAAkJCSgqKgIX375JdavX48HH3wQc+bMgdFoxL333otdu3bhkksuQVpaGn77299iypQpHbbeCy+8EJs2bcKhQ4cwfPjwDrtdIjq3CQ0Njr+bhsF2OwSjEZYxY9AwZ06r15eioyEYjS0+Nwt1dYhau9YVVss6HYy33go5JgZCfT1gtQIAVGVlDJKJgoxBMlGImM3mZhPJAHDy5Elkt/IpLYWHqqoq9PDzBY7yb8dsNgdiSURE4cMZ9sHTJKZyniR5vpw7uz3g1RZKkNxZJ5JLSkrQ0NAAvV4f6uWEjMlkwrJly9CrVy+88847iIuLa7THQUVFBf7whz9g+fLlePzxx2F3fsjw1FNP4YYbbgjJmseOHYuIiAgsXLgQK1eudP1ORBRMgvN9SdOpYqGmBgAgGQwery9HRTk+qDWZgCbPO9qvv4Zu507Ye/WCYLdDVVwMy6hRsFxyCVSVla7LieXlCMwzNBG1hkEyUYg07UJOSEhAXFwccnNzQ7gq6ghVVVUYOHCgX8dQ3hAySCYiakKZFvYwaezqT7bbvQ6Slc32AEAI8ERycnJyQI4XSGlpaQCAgoIC9OvXL8SrCQ1JknDffffhxIkTeP3115GSktLsMgkJCdi4cSO+/fZb7N69GzExMcjOzkb//v1DsGKH7t2749///jcWL16M66+/HjqdDtdffz2WLVuG2NjYkK2LiMJbo4lkt6lipepC9iZIBqAyGiE1CZI1P/0EOToalWvXQqivR+I110B0Poe6B8mq8vLA3BkiahWDZKIQaVptATimkhkkhz92JBMRBVHTzfZa4gyPBbsdsrfHdZ9IPgeC5NTUVADAL7/80iWD5E8//RSvv/46NBoNbr/9dgwdOtSn61dXV+Pxxx/H1q1bsWzZMkyYMKHVywqCgDFjxmDMmDH+LjtgRo8ejV27duHTTz/Ff//7X6xbtw5bt27F9ddfj+uuuw69e/dudh2z2YyTJ0/ixIkTrk2hNRoNhg8f7vr3QETUKmUTcLsdgskE2RkGuyaSY2I8Xl2OjnZc3mgEkpIanac5dAjWwYMBlQpydDTkqChXkCy4B8llZQG5K0TUOgbJRCHStNoCAHr37o2vvvoqRCuijmC1WlFXV8eOZCKiYGmhI7kZ92oLb9lsv+4EH6AgubS0FDqdDjFtvLkOBfcguas5duwYFixYgKioKNhsNnzyySd47bXXMGDAgEY1HbIsw2w2Q6fTNbr+jh07cM8996C2thaLFy/G0qVLO/ouBERUVBSuuuoqXHXVVbj11luxatUqvPjii1i9ejX69OmDbt264a677sLQoUNx2223Yd++fa5qjqbGjRuHTZs2NftvRUSkUCaSAUCorXUFyd5OJEvOieSmHctCZSXEggKYLr/cdZq9e3eoioocx3dutCdHRbU4kSw0NCDy9ddh69sXllGjIGs0AB/LiNqNQTJRiFgslma7d2dmZuLtt9+GyWTiC/UwVV1dDQCIj4/36zjsSCYiapmgdCR7CJIbVVt4e1y3ieRAVlskJydD8HLDv47Uo0cPqNVq5Ofnh3opLZIkCTt37kRJSQl69OiBiRMnQqPR4OjRo7jtttsQHR2Njz76CGazGTNnzsTUqVMhCAJuueUWPPzww6ivr8eiRYvw/fff48UXX8SECRMgyzI+/vhj3HXXXRg+fDieeOIJnyeZO6shQ4bgpZdeQmFhIbZt24Z9+/bh8OHDuOWWW5CSkoKioiIsWrQIgwYNQlZWFvR6PWw2GxoaGvDf//4Xzz77LDZu3IiFCxeG+q4QUSelbIQHOIJkOL9to/K2I9k5kawyGhudrjl0CABgdXs8lrp3h1hQ4Lh8ZSWgUsGWmQlVRUXjg9rtiHnySWj37nVbqICaRx+F5fzzfbh3RKRgkEwUIhaLpdlUanp6OmRZ7rJfI6W2VTk/MWe1BRFRkPgykexLIGy3O6aYBCGg1RadcaM9ABBFESkpKR06kVxWVoaamhpYrVZIkoSkpCTo9XpUV1fj2LFjsNlsyMrKgt1ux6OPPoqPP/7Ydd2kpCR0794dR48eRUxMDNatW+fa2Hb79u34z3/+g59++gn//Oc/8cknn8BoNKK8vBypqamYN28eUlI5Y2ZwAAAgAElEQVRSUF5eDqPRiMGDB+PVV1+FoY3Qoyvq1asX7rjjDgCO1yTXXXcdDh8+jH/961+t1ncMHz4ce/fuxapVqzB37txOOUFPRKHnHiSrjEYoz5RKtUWbHclKtYUykSxJ0H7/PXSffAJotbC5vT+2d+8OzQ8/ALIMVWUlpNhYSElJUB87hsjNm6HbscNxLJsNQm0t6u68E/aUFKhzcxH1yitQ//wzg2SidmKQTBQiVqu1WbVFRkYGACAvL49BcpiqdHZ4+TuRzM32iIha4UOQ7EtHsmCzAWq147gBDJL79u0bkGMFQ2pqaodNJB85cgTZ2dleX16tVmPFihW44oor8OOPP2Lbtm0wGo0YN24clixZgoSEBNdlU1JScMsttwAAxowZg3feeQcxMTG4+eabMXz4cDz99NOoqKhAfHw8+vTpgxkzZoRliNxUXFwc3nrrLZSVlbk2V2zN/fffj6lTp+LVV191BdFERO4aVVs4w2PAOZGsVkNu4xu3kvPbukqQrDlwAIaHHwYAWEaPBtw2qpe6d4dgMkGoqYGqqgpSfDykpCSIX30F/Vtvwd69O2wDBgAArAMHwjxliuPnkSOh37oVquLiANxjonMTg2SiEGmpIzk9PR2AI0im8BSoiWR2JBMRtULZbM9DXYTc3olktRoQxYBWW4wdOzYgxwqGtLQ0fPLJJx1yW8rk8z333IM+ffpAEASUlZXBbDYjKioK/fv3hyiKOHnyJERRxLBhwzDAGRL06NEDU5whQVuuvfZaXHvttY1Oe/TRRwN7Z7oQvV7fZogMACNGjEB6ejp++umnDlgVEXVFgtuAi3s9hVBT46i1aKPGSVY6kuvrAcBVXVH91FOwZmU1uqy9e3fHZYqLoaqshBwXBykxEbBaIVitMN56K6wjR7Z4O1JyMsTSUh/vHREpGCQThYjFYmkWJCcnJ0On0+H06dOhWRQFXaCrLTiRTETUhDKRrITFLfG1I1mWHZdVqRxhss3m3xrhePyuqqrqtNUWgGMiubi4GGaz2fW8Eyz1zuBg6tSpyGoSGLgbPXp0UNdBrcvIyMCZM2dCvQwi6qSabranUNXWtllrAcCxoa1WC5VzIllVWgqIoqMbucm3jOxK/3JxMVRVVbCmpsKemAgAkJKSYP3Nb1q9GXv37lDn5np9v4ioMQ/f+SOiYGopSBYEARkZGWEzkSzLMsrLy5GXl4eysjLU19dDUt7gOzX9vaKiAqVB+IRYdm6+JMsyrFZrwI/vLU4kExEFl2uzPU+TT0q1hexlsYUSODurLXydSC4oKMBnn33W6IPisrIyAEB351RVZ6SsTVlrMBmd02tRzok06nwyMjI47EBErRJMJsemtCrVrz3HcE4ke9mtLkVHu64rFhdDSkpqsapKUiaSi4ocHcnOagsAMGVne6y3krp1g1hS4viQmIh8xolkohCxWCwtTvekp6d3qSBZlmXk5+fDbDZjx44d2LFjB2JjY2GxWHDq1CnUuPVjKfR6vWs38Lq6OkyaNAljx45Fbm4u3nrrLUiShD/+8Y9YtGgRRE8TZa2QJAlPPvkkNm7cCIvF4gqOIyMjYTKZYLfbkZycjJSUFMTExCAvLw82mw06nQ56vR6LFi3C9OnT/f5v05KqqiqoVCq/N6oRRRFqtZoTyURETSkhr6fnD+U8byeLnceURdExkexDkLxnzx7MnDkTAKBSqTBlyhTU1NSgzvlGuTNPJCtrKykpQUpKSlBvi0Fy55eRkYHKykrU1NScEx3SROQbwWSCrNcDERFQuU8k19TA7qxwbIscFQXB+XygKi11TR43u1x0NOSoKKhPnwasVkjx8bD174/6uXPRMGOGx9uwJyc7KjCqqiD7uW8N0bmIQTJRiFgsFmjcNgxQZGRk4Msvv4QsyxDa6JHqKPn5+di7dy/Onj2Ls2fPQpIk6HQ61NXV4csvv2w0nTJu3DjIsgy9Xo+rr74avXv3hsFgQH19vetPQ0MD6uvroVaroVKpsH37duTk5ECr1WLmzJmoq6vDypUrUVpaiscee8yntTY0NOBPf/oT3n33XVx11VXo3bs31GrHQ53RaIRer4dGo0FhYSEKCgpQU1ODYcOGISIiAiaTCT/88AOeeuopTJs2DSrnJ9lGoxH79+9HVlYWklt5MeOtqqoqxMbGuo7tD61WyyCZiKgp5RsonjqSfay2ENwnkn2stigqKgIA/N///R8OHTqE7du3o1evXhBFEZmZmRgyZIjXx+poynNeML4p1JQSrEc6N1uizsd9U+ihQ4eGeDVE1NkIJhNknQ6yKDaeSK6tdXQke0GOjnZVW4glJbCOGNHqZe3du0P7/fcAACkuDlCrUT9/fpu3ITmf28TSUtgYJBP5jEEyUYi0VG0BOF6k19fXo6ysrFNMKZ05cwaXX345ap2fKhsMBoiiCJPJhMjISAwePBgLFy6EwWDA4MGDXRvf+OLhhx9GXV0dDAaDK2B9+OGH8c9//hP9+vXD9ddfj9LSUhw+fBjFxcWwWq2IiIhAcnIyioqK8PPPP+PUqVNISUnBd999h4MHD+L+++/H4sWLfQ7jt23bhkWLFuHTTz/FxIkT8frrr+PBBx+E1WpFQkICXnnlFYwZM6bF6yr1Ge63KcsyqqurUVZWhtraWhQUFPhda6HQarWstiAiakoJfT19YOf8gFFoUq/UKmdwLCvVFt5eD7922Z9//vm45pprsHz5cq+vG2pJzq8Jd0SQXF9fj4iIiBY/ZKfOQQmSz5w5wyCZiJoRTCZAp4McEeEKgyHLUPkQJEtRUVBVVwM2G1QVFbB7eD9sGTMG+vfeg5SUBJuHbv2m3PuV0b+/19fzhslkwooVKzB58mRccsklAT02UWfBIJkoBOx2O+x2e6vVFoDjRXqog+Tq6mosWrQIKpUK77//PrKysoLylVO1Wt0sXH3ooYdw+PBhPPDAA1ixYoVrUqklOp0OaWlp+Pzzz6HVarFhwwZMnjy5XWu58sorkZSUhJdffhn5+fn485//jIsuugg33HADnnrqKcyePRtTpkzBmDFjEBkZibS0NJw9exafffYZvvjiC5SXl0OtVrvefJeXlzfrZD7vvPPatbamIiIiGCQTETWlhLyegmSl2sLbigplAlnZbM+HagvlcbqlD487O+W5rKSkJOi3ZTQaWWvRybkHyURETQkNDZB1OkjR0Y4wGIBQXw/Y7d5ttgdntUVhIVRlZYAkubqQW1I/f75XE8hNuU8kt4ckSTh79izMZjOsViusVqurTvGFF17AJ598AkmSGCRT2GKQTBQCnt5U9nd+Kvq3v/0Na9asQaJz99lgq6+vx8KFCxETE4MJEyZg06ZN2L9/PwBg7dq1GOHha0XBoNFo8NprryEnJwe7d+9G7969cf7556Nnz57QarWor69HaWkpkpOTkZqaClEUYbPZIEmSX2/WtVotrr/+ejz//PP47LPPMGrUKKxfvx56vR7jx4/H6tWr8frrr+Ojjz5qdL3k5GRMmDABmZmZsFgsKC0thSzL6NatGxITE9GtWzdER0fjzJkzAftvGRERwWoLIqImXBvoeQiSXbUX7am2UKnaFSS39OFxZxcREYG4uLgOmUhmkNz5xcTEICEhgUEyEbVIMJsd1RZRURAKChynOQNlbzfbU6otlJDX00Rye8nR0ZD1eqi8/JDUarVi165d+Oabb1BZWYnPP/8cxcXFLV5WEASIogi7j5vyEnUlDJKJQkAJ/1oKPNPT0/HMM8/gwQcfxM0334zt27cHfT2SJGHJkiX49NNPodfrsX37dvTu3Rv33HMPxo8fj1GjRgV9DS2JiIjA1KlTMXXq1BbPz8zMbPS70oXsr7vuugvDhw9HQkICRowY4frfKTY2Fg888ADuvfdeGI1G1NbWIi8vD3FxcRg4cGCHd1qzI5mIqAW+TCR7W1HhttkeRPHXYNkLXXkiGXBsuMcgmRQZGRkMkomoZSYT5NhYyAaDa7M95W9vJ5Kl6GgIRqOjdgLwOJHcboKAEkHAz++/j+0tbAzv7pdffsEnn3yCiooK6PV6xMbG4rzzzsP48eMRHR0NjUYDrVYLjUYDjUaDXr164ZprroHkQwUWUVfDIJkoBJSqg9Z6AOfMmYP8/HysWrUKDQ0N0Ov1QV3P1q1b8eGHH2L58uW45pprcOzYMYwePRqipx3vw5her8cVV1zR6vmiKMJgMMBgMAR9F3tP2JFMROe6hoYGzJs3D3q9HklJSTCZTFg+axbi4HmzPVdHcjsmkmUfN9tjkOwdo9HIjfa6gIyMDPzwww/tuq7VakVxcTHOnj3r6sQeOXJkl5zWJ6LmhIYGyHo9pKgoR6WFJEFwBrVeb7YXFQXY7RDz8wEAdmfFUqBUVVXhrrvuwoxvvkEigBec38BtTWxsLC699FLMmDEDEydO9GpwSaVScSKZwhqDZKIQ8OZrrkOHDoUkSTh69ChGjhwZ1PV88cUX6NatG2699VYIgtDqZnLUubAjmYjOdWfPnsXXX3+N7s6JpeLiYszIzMQQwHO1hXKerx3JflRbdNUgOTk52VV1FUxGoxHR0dFBvx3yT0ZGBnbs2IEVK1Yg2dkzKkkSjh8/jmPHjsFut6OiogJlZWUAHIGKSqWCIAioq6tzbUysMBgMeOyxx3DNNdd0+H0hosASTCZHtUVMDCBJiL/9dggNDQC8n0iWnd9MUefmQoqLAwLwQVNJSQnWrl2LlJQUbN68GcePH8cDEyfiIrMZE7ds8fv4TbHagsIdg2SiEPBUbaEYPHgwAODw4cNBD5L37duHUaNGdXg1A/mHHclEdK5TvuHz6KOP4uKLL8bQoUNhVz5gC8Jme7JKBajVPldbaLXaLvscm5SU1CETyfX19a5gkjqvqVOn4osvvsDatWsbbSYcFxeHYcOGQaPRoF+/fkhOToYgCJAkyfXHYDCgZ8+e6NWrF6Kjo1FRUYEnnngCa9euZZBMFAYEkwlyRATMY8ZAfeyY67lSuvBC2Hv29OoYkvMDRc2hQ7Cnpvq9pj179mDhwoUoLy+HLMuIiIjAunXrMDovD+Kbbzoqrjy9XmgHURRZbUFhjUEyUQh4M52UlpaGmJgYHDp0KKhrqaiowKlTpzB37tyg3g4FnlarRV1dXaiXQUQUMu7Pp8q3fGxKuOWpnsn5prEjqi3MZnOrVVZdQXJyMoxGY9A7jOvq6lht0QUMGTIE27dvh9lshslkcp0eExMDVTvCmL1792LdunWw2WwB2+uCiEJAkhyb7en1kFJSUPvAA+06jK1/f9j694dgNsM8caJfS6qoqHBtJv/vf/8boihCr9cjJSUF0rZtgCxDqKmBHBfn1+00pVKpGCRTWOOzNVEIeFNtoVKpMGjQIBw+fDioa9m3bx8A4Lzzzgvq7VDgsSOZiM517kGy8uGsTfmmhocJYLm9E8mi6AihfexI7qq1FoCjIxkASktLgxokc7O9riUiIiIg3cYDBw6E2WzGqVOnkJWVFYCVEVFImM2ALEPW6fw6jNSjB6qef97n68myjAZnjQYAVFZW4tFHH0V1dTXeeOMNDBgwoPHlY2MBAKrqatiDECSz2qJzEOrqEHvvvai97z7YMzJCvZywwSCZKASUN75tTSgNGTIEW7ZsQXl5OV566SW88cYbyMzMxMUXX4yMjAxotVrIsgybzYazZ88iNjYWN910k09r2bdvH0RRxG9+85v23h0KEQbJRORu9+7dyM3NxenTp/HTTz+hX79+GDVqFHQ6HS677LKw7J91D5JFUYQgCLA7J5Jlb6otvJwYcp9IhihC8KFWyGKxdOnNxNyD5MzMzKDdTn19fVj+GyXPBg4cCAA4cuQIg2SiLkxwfkPB3yC5Paqrq7FgwQJ8/fXXzc679957MWjQoGanS0qQXFODQEe+7EjuPFTFxVCfOgV1bi6D5ABikEwUAt5uvDN48GDU1dUhOzsbZWVlyM7OxtmzZ7Fq1apmm5Uoevbsicsvv9yrddTX12PPnj0YNGgQv07aBel0OnYkExEAID8/H9dffz0Ax2PDoEGD8O677+K1114DANx3331YsmSJx2PU1NRg9+7dMBgM6NevH1JSUjp9r6/786kgCIiIiIDNh45kwduvnrpttieLIoRzdCI5WGw2G0wmE1+LnIOysrIgiiKOHDmC6dOnh3o5RNROygesHRkky7KML7/8EsuXL8eJEyewZMkSGJyb+kVHR2PgwIEYPXp0i9eVnJcTqqoCvi52JHcertdrbp3+5D8GyUQh4EuQDDg2E9q2bZurfsJiseDs2bOwWq2u3bATEhIwc+ZMPPjgg+jRowfsdjt+/vlnFBYWwmw2IzU1FQUFBThx4gQqKipw5swZnD17FgCwYMGCIN5bChatVssgmYgAwNWnv3nzZlx00UUQRREWiwUFBQWYP3++q8bIkzVr1mDVqlWu37OysvCf//ynU0/TNn0+1Wg0rolkT0Gya1rZ20DYvdpCFL2eZFbW2JWDZGUDvJKSkqDdhtFoBABWW5yDIiIi0LdvXxw9ejTUSyEiPwjOWolABMmHDx/GO++8g/z8fKhUKsTGxkKv10MQBFeFRUlJCQ4cOICzZ88iMTERGzduxCWXXOL1bSi9yKrqar/X2xSrLToR52tCXwYAqG0MkolCwJuOZAAYPnw4HnroIWRnZ6Nfv36u07VaLTJa+GrG008/jenTp+Oqq65qdLry9RpRFNG7d28kJiZi3Lhx6Nu3L/r27YtLL73U/ztFHY7VFkTnBqPRiNraWnTv3r3VCeEjR45AEASMGjUKonPaVqvVonfv3hg1ahR2794NWZY9Thjv2bMHgwcPxooVK7Bz506sWbMG33//PcaNGxeU+xUITauitFqtV0EyfOxIbrrZ3rk0kZyYmAiVShXUiWQGyee2gQMHYv/+/aFeBhH5wVVtodf7dZzPPvsMc+fOhVqtRkZGBiRJQlVVVaPNPXU6HZKSkjBmzBhccsklmDFjBnQ+BtjKRHIwgmROJHcenEgODgbJRCGgTJG29cZSpVLh9ttv9/q4I0eOxK5du3Dy5EmIooisrCykpKRAFEXXp7V6P5/cqfNgkEwU3goLC3H33Xfjyy+/BACsX78eU6ZMafGyR44cQUZGRotB3IgRI7BlyxYUFhYiJSWlxeubzWYcOHAA8+bNwwUXXIBBgwbh5Zdfxueff96pg2Sr841BSxPJ3nQk+1pt4dpsz4dJo67ekSyKIvr27YvvvvsuaLdRX18PAOxIPkcNHDgQ27dvx08//YS4uDjY7XZ89dVX2L9/PwoKClwfiI0fPx6JiYmtHqe6uhr79u3Drl27EB8fj2XLlnXgvSA6tylBMvycSN60aRMSExOxe/duJCQkBGBlrdBoIEdFBW0imUFyJ8GJ5KBgkEwUAsob37Y222uP/v37o3///s1OT01NDfhtUWixI5kofB09ehSzZ8+G2WzG0qVLsWHDBmzbtq3VIPno0aMtbiYDOD5kBIAffvih1SD54MGDMJlMuOCCCwAAMTExGDlyJD7//HPcd999AbhHwdH0g9lGHcme+p193WxPuZxa7fjjQ5BsNpuD8nzfka644gqsXr0aFRUVQXljX1dXBwDsSD5HDRs2DACa7fERGxuL1NRU7NmzB+vWrQPgmFqPjIyEJEmQJAmyLLt+Vv4dKSHO2LFjO/UHYUThJBATyZWVlcjJycG8efOCGyI7SbGxEFhtEdY4kRwcDJIp4CRJwqFDhxq9uRsyZIjrq7bkfbUFkSdarRZWqxWSJEHlafKOiLqUiooK3HzzzdBoNHj33XfRr18/nD17Fu+//36LNQkNDQ04depUqxtVDRo0CFqtFvv378e0adNavMzevXsBAOeff77rtPHjx+P5559HdXU1Yp27m3c2LU0kS8qbBQ+vO9rbkeyqtvBxIrmrfxto6tSpeOGFF7Bz507MmTMn4MdntcW57dJLL8XmzZtRWVmJhoYG2Gw2jB49GgMGDIAgCLDb7Th48CC+/vprFBUVoaGhwbVHiPuf+Ph4jBw5EsOGDcPll1+ORx99FG+//Xaj10hqtbrLf7BD1Bm5gmQ/JpK3b98Oi8WC3//+94FalkdybGzQqi0YJHcSnEgOCgbJFFC7d+/GI488ghMnTjQ6vUePHvjLX/6Cq6++OkQr61y8rbYg8kT592OxWHzuBSOijtFWL7Hi448/xoEDBxAfH4+1a9eiuLgYW7dudfXjX3755XjjjTfw1VdfNeu1//nnnyFJUqsTycoHup46SPfu3YvMzEx069bNddr48ePx3HPP4euvv8YVV1zhxb3teE0329NqtbArbxY6UbVFZw3ivTV06FCkpaXh/fffZ5BMAadSqTxukiWKIkaMGIERI0Z4fcwHHngAixcvbvFberGxsdBqtdBoNEhKSsLy5csxZsyYdq2diADx5ElofvoJgH9B8o4dOzBgwAAMGTIkUEvzSDIYIBYXB/y4DJI7D0EZLmCQHFAMkilg7HY7li5disjISDz77LPo3r07AMdk1erVq/Hwww9j+vTpnExG8ze+RO3BIJmoc/v2228xd+5cfPzxxy1ukKo4ceIEbrvtNtdGMoMGDcK//vUvnHfeea7LjB8/HpGRkfjwww9x6aWXoqSkBCUlJRg6dCiOHDkCwNEz2poLLrgAr7zyCl544QWMHz/e9RVwADh+/Di++OILTJ06tdF1Ro4cCb1ejy+//LLFIFmSJJw8eRJZWVne/QcJgqbPpxEREb9OJHsKktXOl8C+brYnioAo+jSRbLVau/w3kARBwJVXXol169bh73//O2677baA3ielI5lBMgXK7373O6hUKhQUFDQ63WQyoby8HDabDWazGTk5OVi1ahVee+21EK2UqIuz2xF3zz0QGhoAjQaSH4/jp06dwoQJE7z6AD4QpNhYqI8fD/hx2ZHciThfrwmstggoBskUMN988w1KSkqwZs2aZl+vFUURixYtwv79+zFq1KgQrdA/RqMxYG9wWG1BgaD8+2FPMlHn9N5778FkMuGzzz7DvHnzWryMzWbD3XffDZ1Oh//+97+w2Wzo169fs7oavV6P7OxsvPPOO1iwYAEWLlyIn3/+GZMmTcIvv/wCnU6HzMzMVtdy9913o6CgACtXrsTKlSubnT9ixAjccccdjU7TarUYPnw4Dhw40OIxN2zYgIceegjTp0/H9OnTYbFYMH369A57Awj8+nyqfFVdo9HAZjI5NtHxsA5XtUVrgbAkNT7PeTuyKEJuR0dyOHxwvHjxYpw+fRpPPvkkcnNz8eyzzwIASktL8fbbb8PmnPZJSEjAnDlzfPp3oHywwSCZAkUQBMyYMaPNyz377LN49tlnkZeXh/T09A5YGVF4EX/5BUJDA4w33wxzdjbQzionu92O0tJSJCcnB3iFrZPi4qCqqQFk2fO+Cj4SRdH1nEihpQTIDJIDi0EyBcz27dtdb3SbmjBhAkRRRE5OTqtBsiRJqKqqgt1ub/TVWl8cPHgQH330EUpLS3HZZZfhxIkTKC0txaJFi5o9KcmyjO3bt2P//v2orq6GWq3GrFmzWvxq2/r16/HII4/gH//4B37729+6TrfZbDh06BCGDRvmetP/wQcfQKvVtvjfQdH0jS9ReyhBsvLviYg6l88//xyAozaitSB5165d+OGHH/DCCy+gT58+Ho/34IMP4uOPP8bUqVNRX1+PG264AR999BG6deuGe+65x+M3fgwGA1588UV89dVXMBqNiImJcQV98fHxGDBgQIvXGz58ODZt2gSbzQa1+teXjbIs49VXX0VycjI+/PBDbN++HYDjQ9frrrvO4/0IpKYdyVqtFpLNBmg0nieSlfNamRiKX7QI4pkzzc/QattVbREOQXJiYqLr9dC6detwxx13wGq14sYbb0RhYWGjy/bt29e1caM3WG1BoXLttdfiueeew+bNm3HPPfegoKAARUVFsNlssNlssNvtrj9JSUmNvilCRIA6NxcAYDn/fEh+bJBXUVEBu93u+lZzR5BjYwG7HUJdHeSYmIAdl5vtdR6stggOr4NkSZJw//33IyEhAffff38w10RdkNVqxQcffIApU6a0uON2XFwcLrjgAuTk5DTa/b2goABvvfUWvvvuO3z//feoqqoCAFx33XV44oknvApaZVlGYWEh/v73v2Pjxo1QqVSIjIx0fUVNrVZjy5YtWLx4MebMmYP4+HiYTCYsX74cmzZtgk6nQ1xcHIxGI9566y28+uqrGDt2rOv4p0+fxl//+leoVCosWbIESUlJGDt2LHJzc7FkyRL88MMPGDVqFH7/+99j79692Lp1KwDgpptuwuOPP97iJmgWiwUqlarRm3IiXynBBCeSiTqfwsJCHD9+HKIo4rvvvmv1crt27UJUVFSrm+C5S0tLw4oVK/CHP/wBCxYswGOPPYannnrK6zUJgoCLLrrI68sDwG9+8xusXbsWx48fx+DBg12nHzhwAMeOHcPKlSsxceJEVFZW4tFHH8Vjjz2GgQMHQpZlSJIEWZYBOJ6rlT+A44NUrVbrCiCzsrLQt29fn9YGOJ5PNRqNKxTXarWQfelIbumNnixDzMuDdfhwWEaOdJ0sdesGOTraUW3hwxuScAmSFUuWLMHmzZuxcOFCnDlzBvHx8Xj//fcxYMAAGI1GjB49Gjt37vQpSFaqLVp6DUkUTCkpKZg0aRJWrVqFVatWtXn5BQsW4NZbb3Vt2peUlNSh38Ig6mzUubmAWg17Wppfxyl2dhV3ZJAsOfcvUNXUwB7AIFkURVZbdBYMkoPC6xTrgw8+QEpKChoaGoK5HuqiXn75ZVRUVLS6YzwAZGdn4/HHH0d+fj7S0tJgNpsxZ84c5ObmIisrC1deeSUGDBiAM2fOYP369fj6668xaNAgnHfeeZg8ebJrwyF3J06cwLx585CXlwdBELBw4UIsWbIEUVFR+Pbbb5Geng6LxYI///nP+Aii85IAACAASURBVOtf/4onnngC6enpKCoqgslkwuLFi3HfffdBpVKhvLwcs2bNwvz587FmzRpMnDgRn332GVasWAGNRoP3338fN954I1atWoWxY8fif/7nf1BYWIi77roLmzdvxn333QdBEHDXXXfBaDRi3bp1mDRpEi677LJm6w63N5UUGgySiTovZRp55syZ2LJlC4qLi5u9OZJlGZ988gnGjx/v9XPCrFmzMHDgQI99yIE0fPhwAMCPP/7oCpKNRiPWr18PnU6HGTNmwGAwIDU1FU8//TSys7MbfXPHF+vXr8eUKVN8uk7T51OtVgub8i2NtiaSBaHlyWK7HZBlWEaOREMLG8vJonhOTiQrEhMTsXDhQjz33HOYNm0aHnvsMde/bb1ej7Fjx2Lnzp34y1/+4vUxjUYjdDodP2CnkHjkkUdcj3U9evRAr169oNFooFarIYoi1Go11Go1tm7dildeeQX//Oc/XdcdPHgw7rzzTuh0Ouh0OhgMBqhUKgwYMAD6dn7Fn6grUZ88CVtm5q97D7STEiR3aLWFwQAAEKqqgJSUgB2XE8mdiNKRzG/wBpRX/28vLy/Hvn37MHPmTLz33nvBXhN1EdXV1Xj++edRVFSEbdu2Ydq0aZg8eXKrl7/yyivxt7/9DbfffjvefPNNbNiwAbm5udi4cWOzsPX888/Hli1bcPjwYXzwwQd44oknMHfuXGRmZiI/Px8nT55E7969kZOTA5vNhhUrVuDCCy9s9Mb64osvdv385ptv4tChQ/jwww9x7NgxXHbZZbj88ssbTWYlJibizTffxI033oibb74ZBoMBVVVV6NGjB1atWoWBAwdi4sSJePfdd1FRUYHDhw/jvvvuw5IlS3D33XejoqICer0e8fHxaGhowKZNm/DNN9+0GiSzH5n8xWoLaklZWRmOHz+O6upq12mRkZHo06cP4uPjodfruelpB9i9eze6deuG+fPnY8uWLdi7d2+zqePjx4+jsLAQd999t9fHFQQBQ4cODfRyW9W7d28YDAZ8++232LFjB7777jsYjUbIsoy5c+fC4HwTplz2jTfewJkzZ5CQkABRFBtN6gmC4PpjsVhgNpvRs2dPAI4p1xUrVmDSpEk+hYnKRLJCo9E4qi0Az0Gycn4LE0Our0G2tg612nE9LzsVw6Uj2d3SpUvxu9/9rsUP+adMmYK//OUvOHHiRIvnt6Suro61FhQyffr0wbJly9q83LBhw3DVVVfh9OnTsNlsqK2txYYNG3DnnXc2u+y0adPw0ksvBWO5RJ2HLEOdmwtzC9WQviopKQHQsRPJclwcAEDl9po5EERRZJDcSSgBsi/fJKO2efVKfcOGDbjhhhs8TiPn5OQgJycHALBy5f+zd+bhUVR5276rqtd09gSQhB1kR/ZFREAFXHlxQRDGBbdBxGVcGF9HHcdxHJxxQ2TcRmdkGGRxVBD4RAGVRR1ZJCi8IsgqISGE7Emnl6r6/ujuspN0kk6ns5FzX1cu6OqqU6c7neo6z3nO83uW1NTU6PSwhWEymVrNa//yyy954403SEpKYtasWSxatKjGKIrU1FTeffddpk2bxqhRoygpKWHKlClMnz69yr633347t99+O+BbHvzCCy/w2muvoaoqCQkJ9OjRg1WrVhETE8P69esZMGBArf0dN24c48aNq3Gf1NRUNm/ezBNPPEFpaSkTJkxgypQpxgBw7NixLFmyhHXr1gFwySWXGL/vDh06VGhr6NChfPvttyE/D5IkYbPZWs1nRdAwBLLE7XZ7vT5Lrem6dbbz888/M2bMGIqLi6vdJy0tjf/85z988cUXbNu2jWXLlmGz2Rqxl03HmTNnOHPmDHFxcYaAGQ10XefQoUMcO3YMs9nMtm3b+Oijj5gzZw4XXXQRdrudDRs2cM0116CqqnE/9dlnnwE+l3Fz/hscMmQIK1asAOCOO+4gPT2dQYMGMWHChCoC6eWXXx7ROZ555hmmT5/O+vXrmTVrVljHmEwmZFmucA2Mj49H0jRMJhPJqalQQ16jyWYj1mYjpvJ7X1CAyWQiLjUVR4jfi5yYiGwykZqcbERk1ITb7SYxMbFZ/44jobrB/vTp03n88cfZtm0bo0aNCqstVVWJi4s7694jwdnHlVdeWeHxvHnz2LNnDxaLBafTSUFBAR999BH/+Mc/yMnJqRAJBOKeS3CWcfo0ptJSlEGDsNXzcx0outq3b9/GM1wF7hfmz/c9lmXU+fPRx46tV7N2ux1Zls+av/WWfN2SrVZkkwnFbMbSQl9Dc6RWIXnXrl0kJCTQrVs39u3bV+1+EyZMqFBcLDc3Nzo9bGGkpqa2mtceeJ0ffvgh5557bgX3W3WMGjWKxYsX8/HHHyPLMg888ECt75fFYuHRRx/l7rvvxmQyGY4Vj8eDJEmYTKaov+e/+93vjP8XFRUZ/+/ZsycAr7zyCoqi0LVr12rPPWTIEP7+97/z888/V1naVlxcjKIoreazImgYysvLAd8Mfn0+S63punW2s2DBAiN2IC0tzXCDFhcXc+jQIYqKinjnnXcYM2aMkd323HPPMWfOnKbsdqNQVlbG4MGDjYFKWloaF110EePHjyclJYX4+HgcDofh1pZlucJPwElbeZvT6eSxxx5jzZo1Fc537bXX8sgjj1BYWMjVV1/NsmXLjAz9YPr06YPNZmvWf4N9+/bliy++4LbbbuOpp54ytgd/P9aXCy64gL59+/Lmm2+GlRcNvmtXUVFRhe9TTdPwuN14vV7OFBSg15BRmKJplBcWUlrpvZdzc0n2eilxOikP8Xuxl5Xh8HrJzc6GWga7qqqiaRqqqjbr33E0iYmJYfjw4Tz33HNMnDiR9DCWCwdWdbWW90hwdlG5UGr37t1Zvnw5v//97ytcMwGSk5MpLy8nLop5rAJBU2HZuZN4r5eCNm3w1vP6feTIERITEykuLq7REBFVZBnbnDnIeXkAxKxcifPLLymtNAFUV7xeL263+6z5TmvJY0VHQQF2rxd3cTFFLfQ1NCZpaWlh7VerkPzjjz+yc+dOdu/ejdvtxul0snDhQu677756d1LQsgnkstZ1ueZFF13ERRddVOfzJfjD8AOEU4gv2nTr1o2EhAQyMzM577zzaiwKM2LECF599VV2797N6NGjKzx3tuUlCpoGkZEsCMbpdLJ06VIuu+yykDmzAWfgNddcw7x587jwwgvZunUrCxcuZPr06STXo9J2SyAjI4OSkhLuu+8+UlJS2L59Ox9++KFRmLU+KIrCAw88wAUXXIDH48Hr9TJ+/Hij2Opzzz3HjTfeyOeff47D4aiwhH/YsGH1Pn9DM3XqVEpLSytMskYbSZIYMGAAmzdvrtNxoTKSjWJ7tcVOVBNtgX8ZpF7d93SgUJ+moYfRv0C/WhMvvvgil19+ObNnz+Yf//hHhcxLTdM4efIkJSUlyLKMx+Ph2LFjItpCcNaQnJzMLbfcwmuvvVZlkhF83xmPPfYYv/71r0WhPkGLRjlyBAC1a9d6txWqlkRjUH7FFcb/rVu3omRl1btNkZHcfAhEWoiM5OhSq5A8c+ZMZs6cCcC+fftYs2aNEJEFQOscHMmyzKBBg9i8eTNDhw6tcd/hw4cjSRJbtmypUhSppKREZCQL6o3ISBYE8+GHH5Kfn8+tt95a437nnHMOS5YsAXyRPxMmTOCZZ57hhRdeaIxuNhk7d+4E4Ne//jVJSUnccccdOJ1OfvzxR4qKiigqKqKsrAxd19E0DU3Tav1/4N+xY8fWGLEkSRKDBg1i0KBBjfVyo0qvXr3485//3ODnad++PadOncLj8YQ9WezxeCp8n1osll8ykmuLnVAUpJoykqvpgx7ITg4jby/SSfeWTrdu3XjxxReZM2cOo0ePZuDAgbjdbtxuN8eOHQvpNrvrrruaoKcCQcPwwAMP0KNHDzyB64mf2NhY1q5dyx//+Ef27NnD888/X6MxRSBozig5OeixsehRmAhsKiE5GLV9e5STJ+vdjshIbkb4r8EiIzm6iNLIgogJiFetTRAdMmQImzdvrtVFlpiYSJ8+fXjllVd45ZVXqjw/ePDghuqioJUQDSE5MzOT999/n8LCQnRdD/kDhNw2ceJEevXqBfjcsB999BH5+fkV8r+Dl3UH/u9yuXC73Xg8Hux2OyNHjqRfv371eStaPU6nk5deeokBAwZw/vnnh31cr169uPvuu1m0aBG9evVi//79ZGdnG4KppmmUlZVx9OhRnE4nsiyjKIrxr6IozJ49O2ShoWiRl5fHG2+8wQ033EDXEI6XY8eOkZOTw/Dhw2tsZ+fOnZx77rkkJSUZ2+x2e4sVd89G2rdvj67r5OTkhBWHAD6hNlh0tlgsOP2DN70Wp5+uKEY172ACQnK1jmS/kJwycyalt9+O8+qrqz1Ha5x0D3DllVeyefNmFi5cyPHjx4mNjcVisTBkyBD69+9PfHy88f0wcuTIsH/nAkFLwOFwcMMNN1TZnpqaytVXX83f/vY3nn32WQ4cOMBbb71Fly5dGr+TAkE9kXNyUKMk/ubk5NC9e/eotBUpavv2WHbvDruYbnXIsmxEyAmaFkNAFkJyVKmTkNyvXz8x2BcYtNbB0aWXXsratWu54IILat13wYIFfPPNNyGfawnLmQXNm8rRFi6XC6fTicfjMYRaVVUNwS+wzH7nzp388MMPFBcXs3z5ciNrua689tprrF69ms6dOzN79mw2bdoUUTs2m40VK1a02L8JTdN46qmn2LZtG2VlZTidTkwmE/Hx8ZSUlFBUVFRt1pvVauWWW25h7ty5ERex8Hg8vPHGG5w8eZKFCxfWeZnsQw89xObNm3nqqaeIjY2lZ8+eFXKAExMTmTJlCnFxcRUmBjRN44cffuDZZ5+t1pF75swZtm/fzpkzZ0hOTmbSpEmYTOHfepw4cYIbb7yRgwcPsnLlShYuXFghguPAgQP87//+L06nkyVLllRbUFXTNHbt2hVxIThB4xAogJiVlRW2qOjxeKpEW5QEBgv+a161VCMkG9EW1XxWXaNHI+flYV+9GtNPP9V4itY66R6ga9euvPTSS03dDYGgWSFJEvfccw8DBgzg7rvv5oorruCWW24hMTGRX/3qV8TGxjZ1FwWCsFBOnUINM1O1JgKTyM3BkYzbjZyXh5aSEnE7wpHcjAg4kiutDhHUD+FIFkRMa12uOWDAAL744ouw9hWTL4KGJPC3t3DhQl599VUOHToU9ux3QFyeOHEi8+fPNwqJBUTI4MehnsvMzOTaa6/l+uuvJzU1lf/7v//jmWee4brrrsPpdJKbm4skSVXcq4qiYLFYsFqtmM1mTp8+zYwZM7jpppsYOHBgw7xRlZAkiWHDhjFlyhQ6depU72vYyy+/zFtvvcWFF15ImzZtsNvteDweSkpKcDgcxMfHExcXZwj5wRw7doy///3vvPnmm6SkpNCjRw/S0tJITk5mzpw5hrAWCo/Hw29+8xtWrVoFwBVXXFEnN3IAi8XC22+/zYYNG7j66qtJTEwM+9jCwkLGjRvHb37zGyZNmkS7du1IT09ny5YtbN26lYMHD1bY/5JLLuG1116rNQu1rKyMZ555hmXLlmGxWHj++ed57rnnQrq7zjvvPDweD7Nnz+b1119HVVV+97vfceedd3LHHXcAcPjwYQoKClrsZEVrIVhIDhe3213BkWw2m5HxDUrDEZKlGhzJ1RXS05OSKLvpJqxbthiic3UE7pWaoq6DQCBo3owbN46PP/6Yu+66i4ULFwKwfPlyXn75ZQYMGCDykwXNG11HPn0adxRW2ebn5+PxeJqHkAzIWVn1FpKFI7l5IBzJDYMQkgURE3DZiMGRQNA0tG3blmuvvZasrCzi4uK44oorSE5Oxmw2Gz+BGfHgqIJevXoxePBgFH9+aCSVeHv06MGSJUv485//THl5OU8//TSzZs0CIC4urkJhpZro1KkT7777Lo8//jhFRUV16kOkuFwuXnrpJV588UUkScJisVQYrNUkoId6nJeXx7XXXhuRGxhgzpw5bN68mUOHDnHw4EF2795NVlYWn332GcuWLSPFfyN79OhRtm/fTllZGaqqsmPHDjZt2sRNN91EmzZt+NWvfhXxe5Kenm78/upCQkIC8+fP55577uGVV14xYk9sNhvnn38+U6dOZdSoUaSnp/PJJ5/wxBNPMG7cOGbNmsWll15K9+7dq2QPl5WV8etf/5pvvvmGGTNmMHfuXDp37syECROMnOMAZrOZMWPGcObMGa6//nrjPYiNjeXJJ58kLy+P+Ph49u3bB4iVIM2dSIXk4GK8Vqs1bCFZl+WIHMnG8TYbUi0rOgL5qK1t0l0gEIRHp06dWLduHQDbtm1jzpw5XH755aSmplZxJsfGxtKhQwdiYmIwmUzYbDYGDRrExIkTz/qCuYLmh1RSguR0orVpU++2Tp06BRD2+KGhCLirlZMn8fbvH3E7QkhuRghHcoMghGRBxLjdbqxWq5gtFwiaCEVRQuZvNxYDBw5kxYoV9W6nc+fORvG3xuLEiRNs3bqVzMxMXC5XtVnQoXKiK++XkpLC3LlzI74W9unThz59+lTYtmPHDmbOnMmoUaOqPc5kMvHEE080eYGqyy+/nIMHDyJJEidOnODYsWMMGTKkSvGgWbNm0bNnT1566SXmz5/P/Pnzq21TURQWLVrElClTjG1t2rSpNpoiPT2dzz//nGXLlpGVlcXcuXO56667ePnll419unbtSrdu3er5agUNSWJiIjabLTqOZE0LL9qihmJ71WYk+9EtllqrgLf2aAuBQFA7gfuHCy+8kM2bN/PJJ5+wY8cOvJUcdAUFBRw5csSoNVFcXMy//vUv+vTpw4YNG8SYTNCoyDk5AKhREH8zMzMBmtyRrLVrB7Jc74J7kiSJaItmgiEgi99HVBFCsiBiXC6XcNgIBIIWSYcOHZgxY0ZTd6Nahg8fzurVq9m8ebOxLSUlhfPPP5/k5GQURcFkMtUpb7ghCcR2dOzYkY4dO1a73+jRoxk9ejTHjx9n69atZGdnV8hjlmUZSZIYOnQoI0eOrFMfrFZrBVf1v//9bzIzM0lISMBsNmOxWELGiwiaD5Ik0b59+zoJyaEykmVA8zVY88GyXHO0RW0rriwWJH90RXW01hgwgUAQGSkpKcycOZOZM2fWuq+mabzzzjs88cQT7Ny5s9aiswJBNFFOnwZAq6eQ7PV6eemll0hKSqJ3797R6FrkmExobdqg1OE+JBQiI7kZ4f891DbxL6gbzWMEKqidcJw1jYzb7RYDI4FAIGgg+vbtS9++fZu6Gw1Cp06d6hXFEQ6yLNcoaguaJ3UVkivfiwSEZD2MY3WTqeZoi1qEZN1qRaolkqe1FiYWCAQNjyzLTJ8+nWeffZbly5cLIVnQqETDkazrOgsWLCAjI4PXX3+d+Pj4aHUvYtS0NJTMTKTSUvRaanpUh4i2aD4Y5gCRkRxVhJDcApCzs0m65x6Kf/Mb3GPGNHV3DISQLBAIBAKBIJqkpaXx9ddfh71/KCFZAbRwlnhXk5FsDDpqi7awWmt1JAshWSAQNCQOh4PJkyezZs0apk6dWmGlUlFRESdOnMDtdleolRGI6aoJu91Ot27djJxmWZbp37+/iOkRGCg5OWA2o9ehSHMw5eXlzJs3jw8++IBrr72WyZMnR7mHkaF26IBtzRpSpk6l+KGHcE2YUOc2ZFkWjuTmgshIbhCEkNwCcPz730ilpVh27mx2QrK4mRAIBAKBQBAt2rdvz6lTp1BV1SgIWhOVM5ItFgsS/miL2pDlkBnJgUFHWI7kMIVkcb8kEAgaihkzZrB8+XKmTp3aoOd56KGHePDBBxv0HIKWg3z6tM+NHGE294oVK/jggw946KGHuP/++6Pcu8gpmzYNb3o6jiVLMO/bF5GQLBzJzQeRkdwwCCG5maMcO4b1s89AkjDv3w+AfOoUcnEx4Ks4rnbp0iSxFyIjWSAQCAQCQTRp3749Xq+X3NzcsIrueDyeCiKt2Wz2OZLDOZmihM5IDjPagjCE5EBGsrm2tgQCgSBChg0bxrp16yiqFLVjt9vp0KEDdrsdRVEq1CKojeLiYg4fPkx5eTkATz75JNu3b2+Q/gtaJsqpU2ht2kR8/MaNG+natWuzm5zQUlMpnzIF65dfohw/HlEbQkhuRgTu8zTN9/8wTAqC2hFCcjPHvm4dusWCa9IkbGvXohw7RtLcuRVmVErmzKH8f/6n0fsmoi0EAoFAIBBEk/bt2wOQlZUVlpDscrkqiLRWq/WXYnu1UF1GctjRFhaLkadcHR5/W+J+SSAQNCSDBg2Kans2m402QSLhiBEjWLt2LbquhyVEC6pny5YtbN26lZ9//hmHw8G5557L0KFDyc7Opm/fvnTv3r3C/kVFRaxbt47Nmzcb3ykmk4lx48Zx5ZVXkpCQ0Gh9t69ciXLiBADK8eO4LrwwonbKysr48ssvuemmm6LZvaiiduyIdcsW0PU6u65FtEXzoUKRPY9HCMlRQgjJzRw5Jwc1LQ3XiBHY1qwh9rXXQFUpfvhhdIeD2JdfxnzgAOVN0DchJAsEAoFAIIgmAeHitL8afG14PJ4K9yJmszlsIRlZDl18xe32PVfLYEO3Wn0DlBoGmSLaQiAQnA0MHDiQpUuXcvToUbp27drU3WmRlJSU8OCDD7Ju3TrMZjPp6ek4nU6WL19u7BMTE8Pbb7/NhRdeiCRJ7Nixg9mzZ3Pq1CnS0tIM0bioqIi1a9fyzDPP8MQTTzBt2jTkBl6hLDmdOP75T/TYWHS7HT0+HneEBR63bduGy+ViQgSxEY2Ft1MnbCUlSPn56MnJdTpWCMnNiKD7PMnrDasYs6B2hJDczJGLitATEvD27g2Aec8ePAMG4LrkEgC8a9eiHD3aYOdXfv4Z5dgx5DNnsH3+OXJurvHcs3v2gCSRfOONOK+6CucNNzRYPwQCgUAgEJz9BAo7lZWV1bqvqqqoqlql2F7YQrKihIymkDye2mMt8AnJ6LrP4VLNxHog2kJMvAsEgpbMwIEDAdizZ48QkiNkyZIlrFu3jkceeYTZs2cbE4yZmZns27ePpKQkHn30UWbMmFHhuC5durBq1SqGDRtmuMF1XWf37t08/fTTPPTQQ7z11lvcdtttnHfeedjtdgAkSSIhIYGkpKSoiMxyVhYAxffei3vs2Hq1tXHjRmJjYxk5cmS9+9VQqJ06AWA6dgz99GnkGia4tTZt8PbqZTwORFsIB3/TI3k8v9TEEAX3ooYQkps5UkEBas+e6LGxqB07ovz8M+VBM3dqly5YPvqoYfJedJ2ERx9FPnMGAG+3briHDjVcN9/u34/NamWwJGHZsUMIyQKBQCAQCOpFTEwMEJ6Q7HK5MAFXffklCf4B7uC8PCyACtQ2dNOry0j2eCCcTGO/OCy5XL6YixAEHMlCSBYIBC2ZXr16YbPZ2LNnD1dffXVTd6dFsmnTJvr06cN9991XYXt6ejrp6ekAvP/++yxbtgyn04mmadjtdm688Ubi4+MrHCNJEkOGDOH9999n9erVvPDCC8ybNy/keVNSUrjmmmu49957SU1Njbj/SnY2AJo/gqouaJpGZmYmBw8eZP369SxbtozJkyc36+9GtXNnACzbt2P/6KPQxXkDKAq5770HfhE/UCxY07SwCgcLGhCvF91uRyotFY7kKCKE5GaOXFCAlpgIgKdfP+TcXNxBWUTezp3B40E5eRK1Y8eonjvgRC6dNQvXhReipaVVeP61//f/SEtLY1qvXpgOHYrquQUCgUAgELQ+HA4HAKWlpbXu63a7aQ+ce/QoclISmt91dRyIHTCAbrU1IMuhq3i73dUKw8HofjeZ5HKhx8VV20cQQrJAIGjZmM1m+vbty549e5q6Ky2SwsJCtm/fzpw5c2rcLyEhgbvuuivsdmVZ5pprrmHKlCkcPnyY/fv3GznKmqZRUFDAf//7XxYvXszatWt58803GTp0aESvQTl1CgD1nHMA8Hq9ZGdnk5ubi6IoKIqC2WzGZDLRrl07LBYLGRkZLF26lLVr1xoTxIqicOutt/LQQw9F1I/GQktKQnc4sK9ZA5JEwXPPofsnu4OxbN+OY/Fi5Lw8NP+EQMABLoTkpkfyeNBiY5FKS4UjOYoIIbk543YjOZ2GkFx62204r70W3T/TBaD6lxYpR46gZGXh6dkT3b9/fbFkZADguugitLZtQ3TPl5GsJSUh5+dH5ZwCgUAgEAhaLwFHcklJSa37ulwubPicWaW33IJ7zBiOHD7M7HXreOWCC2oXkuvpSA4IyYSIxwgghGSBQHC2MHDgQP71r38xZswYJk2axIMPPmjEEQlqZvPmzaiq2mCZwLIs06NHD3r06FHludtvv529e/dy5513Mn36dFauXMmQIUPqfA4lKwuvzca3Bw+ycdMm3nnnHQoKCqrd32azUV5eTkxMDFdffTUDBw7k3HPPpWfPniQlJdX5/I2OJKF26oTphx9wXXQR3v79Q+7m9esgcn6+ISQHxGNVVSsUBBY0MpoGqopuswG+jGRBdBBCcjNGLiwEMIRhPS4OtZLjxduxI8gy9vXrMe/ejfOaayj99a+jcn7z7t2oaWkhRWTwDY6sVitaYiJSWZmvOI0YKAkEAoFAIIgQi8WCxWIJK9rC7XZjBSRZNkTdgGDrCcN1oitK6KWq4WYkB6ItgiuCV8LlchlOLYFAIGjJ3HjjjRQVFVFYWMgbb7zBBx98wHXXXceAAQOIjY01ftq2bUvbasaPrZUNGzaQmJgYkYAbDfr3789HH33E1Vdfzc0338zvf/97Jk2aRGKYBrTDhw9TtGIFRYcPM2PyZAAuu+wyJkyYQGpqKrqu4/F48Hq9uN1uMjMzyc/PZ8iQIYwfP94oEtjS8PqFZGcNcS6aXxQPNtYFO5IFTYhfODaMmMKRHDWEkNyMkf0zfFpNF16rFTUtDfPu6TsoXwAAIABJREFU3QCY/S7ieuP1Yv7+e1zjx1e7i8vlwmKxGEK3XFiI5q+2LhAIBAKBQBAJMTExYUVbBDuS8btNAkKyqwaXsEGg+EolpDCjLQLnDFWwL4DH4xFuZIFAcFbQu3dvFi5cCMDOnTtZtGgRb731Ft4QLr8JEybQu3dv8vLyOHPmDGfOnKGoqAhd14mNjSU5ORlN0xg1ahSzZ89uca7NnTt3csgf7ajrOrquG8XVdF2ntLSUw4cPU1JSwrFjx8jIyOCGG25o0knFNm3asHTpUm688UYeeOABJEmid+/e3HnnnUybNi1kUbjy8nKWLl3K/Pnz+bfbTVzv3vzj4Yfp168fHTp0aIJX0bg4J09G7dwZb8+e1e5Tk5CshorPEjQagVVngUgS4UiOHkJIbsZI4QjJgLdLF5QTJ1A7dMB05AhSfj56HZeLWD/9tELOsVxSguR04h48uNpjjGiLgJCcny+EZIFAIBAIBPXC4XCEJSR7PB6fI1mSDOE3IEa4a3AJGyhKyEFF2NEWYTiSA6u3QiHn5mJfvRrcbtwjRuCJMLdSIBAIGpthw4bxzjvvUFhYSE5ODiUlJcbPvn37WLJkCV988QUpKSkkJyeTnJxMjx49kCSJ4uJiTp06haqqzJ8/nw8//JAuXboAvkiALl26kJycjNfrxev1YjKZiIuLIz4+HqvVislkokOHDrRt2zak+Ak+J2hxcTFFRUWGi9rj8eBwOLDb7TgcDmJiYlAUBUmSjHYkSUKWZRwOB/Hx8dj8E4bBrF69mrlz56LrNZftSk5OJjExkdjYWJ5++mlmzJhRvzc9CnTp0oWtW7eSkZHBF198wYYNG3jwwQd55ZVXSE1NJSYmBl3XKSgoQJZljh07Rn5+PheNG8ek/HzU666j9NJLm/plNBpq9+44u3evcR89IQFkGTkvz9gWHG0haEL892eBaAvhSI4eQkhuxgSiLbRalpy4zz8fubCQ0ptvJnHePCzffYdr3LiwzyM5ncQtXAiKUsGBo6an4xk0qPrzVhaS/f0VCAQCgUAgiJRwhWSXy4UVkCUJ1S/WBkTbsKItTKbQxfY8nvCK7QX2qSUjOeBIVg4dwrxvH+ATq2Peew/JnwVtPnCAAiEkCwSCFkZCQkKV2ILLL7+cBx98sIJAWx1r1qzh9ddf59ixY4Dvuv7pp5+GdQ1vDGw2G/Hx8SQlJdG+fXtsNhufffYZI0aM4MUXX8RkMhmvUZZl4zVbrdawYyMaG0mSGDx4MIMHD+b+++9n+fLlfPLJJzidToqLi9F1ndTUVMAnPM+cOZMxvXphuekmSvyF9gRByDJaYmIFR3JASG60aAtNw7p1K1JhId4+ffCee27jnLeZEzALGI7kZnJdORsQQnIzJhBtUVvxPNfFF+O6+GLQNHSHA/Pu3Xg7dUJNTw8rs9i0dy+oKoV//COeOuQ2BWckA0ii4J5AIBAIBIJ64nA4ws5ItuHPSK7kSA4r2kKSqo+2cDhqPTyQy1xTtEUgBkzOySFx3jwkp9N4Tu3cmaLnniNm+XLM339fe38FAoGghRBY2l8bkydPZrI/czeA1+s18uVNJhMej4eioiKKi4txuVx4PB7DKVsTARdzQkIC8fHxmEwmysvLKS0tpaysjNLSUsMxGoikCERUlJaWUlhYaDiac3Nzyc7Oxu12M27cOBYsWNBsheK6IMsyM2fOZObMmTXup/gnQdV27RqjWy0OLTm5STOS7e+9h+OddwBQO3Yk/803G+W8zR6/cGxkJItoi6ghhORmjFxYCGbzLx/8Wg+Q8QwYgO2TT7B98gnll11Gyf33//K8rhOzeDHu4cPx9utnbLZkZIDJhKdv37D7puv6L45k/yy0XEPVVoFAIBAIBIJwqKsjWZIk8Iu6AXdYWG42RTHy8yoQZrE9whCStfJy0mSZ2IULQdfJf/VVI09Rj4/3OZlSUnxLYnXdJ24LBAJBK8ZkMmEymSo8ttvttAsSMQfVsGpWEF2k4mJMP/0EgJaW1sS9aZ5oSUlNlpGsHD+OY+lS3BdcgNq+PfYPPvCtlKomVqs1IVUqticykqOHEJKbMVJhoU+krcOgwnnVVb5lmrqO7dNPcU6d6nMmA7aPPyZmxQqUM2coDhKSzRkZePr0MYrGhEMge9BisYDdjm6zVbh4CgQCgUAgEESCw+EgLyhrsDoMR7Ik/eIOliQsFkvYGcmhoi3CLbYXjiP5jowMOubmYtm1i5I5c1C7dq2yj5qSAqqKVFTky1oUCAQCgaAJsa1Zg3LqFMrx41h27fKt3lEUVFEPKSRaUhKmI0eMx42Zkez417/QrVaK587FvHcvdk3D9PPPeHv0aPBzN3sqO5JFtEXUEEJyM0YuKKg1H7kynqFD8QwdipSfT/JttxG7aBGusWNB03C8/TYAij+DCnwF/UyHD1N6yy11Ok8FIRlfyLzISBYIBAKBQFBfwo22cLlcvwjJQcJvuEKyriihoy3CLbYXEJKrO5eu06G4mO8cDs75wx9wDx8ecjctORkA+cwZVCEkCwQCgaAJkYqLiX31VVAUtJQUyvzGNO2cc8KKzWyNGI5kTQNZbtSMZOXIEdxDhqAnJRmT1cqRI0JIJoQjWQjJUUMIyc2YSITkAHpSEs7rriNm6VLMGRm+bbGxuEeOxLxnD2gasS+/jPm77wDwDBxYp/YDA7RAUZvKyzkEAoFAIBAIIiEmJiasaAuPx+OLtpDlCsJvfR3J4UZb1FZsT87Px6yqfJ+SwviRI6ttR0tJ8e1/5gxqt26191sgEAgEggZCPnMGgOLf/tZnSBPUipaU5FtZVFzsM9g1VrSF14uSnY3rwgt952vfHsxmTEePEkaliLOfyo7kRnCItxaEkNyMkQsLUTt1ivj4sl/9ivIrrjDcNprDge3zz7F88w2mAwewffop3h49KJ88GW+vXnVqO1DEJuBI1hISkHNyIu6rQCAQCAQCAdQtI9kGvhzAoBgws9kcnpAsyyEzkiW3u4rrqqysjPfff5+TJ0/idrtJTk7mtltv9e1fnZB88iS6ppHnrxZeHYYjWUzICwQCgaCJkf3RUoE8f0HtBN4rOT8fNSGh0RzJyqlToGlGlCmKgrdTJ0xHjzboeVsKhiPZfx8mHMnRQwjJzRVd9zmS67PEUZKMwUkAr1+Ytn38MQAlc+fi7d27zk1XjrbQkpIwHTgQeV8FAoFAIBAI+CXaQtM0w9UTikCxPb1SQRmr1RpetIUsh4y2COVIfvzxx1mxYgWyLGO1WnE6naxatYqvFaXaaAslKwtd1ymMi6uxH4F7NcXvAhMIBAKBoKkwhORKOoKgeoInhNUuXRotI1nJzPSdJyAkA2qXLsaK9FZPwJHsrwUmhOToIYTkZoJ8+rQvcsKPpKrgdkccbVEdAYezdfNmdKs14uycKkJyICPZnwvUlEglJSjZ2SIXSCAQCASCFojD4QB8LuDY2Nhq93O73VihSmXysB3JJpPvvkXXKziapUpC8oEDB3jvvfe4/fbb+cMf/oAsy2zatIn77ruPjOJipB07+LlHD6xWK7Iss2PHDgAuOXCAZI+H4hpeAwAWC3pcnLGcWCAQCARnN+bvvyd20SIKnn8evZbJxsZGCMl1Rw84kv3vneS/p2hwR3IIIdnbpQvWTZt8MRvN7LPV2ASE44AjWRTbix5CSG4mxL76Kpb//rfK9uCLQjTQ4+PREhORCwrwDBrkG0RFQKiMZDQN5fhxJKcTPS4OtUOHqPW7Ltj/8x9iPviA3JUrwT/7JBAIBAKBoGUQ47/hLy0trVFINortVfqut1gseMIZLPgdQ4Fq9IAvP0/TKkRb/PWvf8XhcPCb3/zGcEhfcsklrF+/HvfEiWxYu5an166t0rwDGKwoXDB+fK1d0VJShJAsEAgErQFNw/HmmyjHj6McP463X7+oNa0cP45UWop6zjmGuFlX5Lw8dLv9l1xZQa1UjqhqTEeyHhuLHh9vbPN26QJA4sMPV1mxpZvNlNx/f73iU1sUlYrtiYzk6CGE5OaArmP+4QdcY8dSOmvWL9vNZrTU1KifTu3UySck1+NLq3JGsu53TifNmWPsU/zww7guuaQePY0M09Gj4PFgOnwYb9++jX5+gUAgEAgEkRNwJNeWkxxwJIcSksOOtgDfwCIgJPuPCziSy8rK+OSTT5g9ezbJldxZHTt2JOH880mNiWHorFm4XC7Ky8sZPHgwZrOZpPvvJ6ZtWwYF39tVg5acLIRkgUAgaAVYvvwS008/AdHNxleOHydp9mwAvN26UfC3v0XUjpyfL9zIdUS32dCtVsOR3JhCspqWVmFVladfP1zjxyNVuoeSvF7Mu3dj/u67ViMkGxnJfkFdRFtEDyEkNwPk7GykwkI8AweitW/f4OdTO3fG/N13ePr3j7iNytEWnr59fT8DBuDp1w/HsmXEvv46nsGDG/2LSDl5EgDTwYNCSBYIBAKBoIURHG1RE263m1hAquSaCldIDojHkqqi+zcZyyD99zfff/89mqYxatSokE1IMTF0bNuWhOHDqzyXWFKCa/Dg2vsBqCkpWI4fD2tfgUAgEDRfpJISLDt34qpmNUrMypXGKhS5oCBq5zXt3w+AZ8gQX0ZuiMKx4SCE5AiQJPRA1Cc0XrG9zMyqmo7dTvEjj1TdWVVJveqqqH7mmj3B5gCTSQjJUaRpw2wFAJgDF/1evRrlfO7Bg1E7dMATQZE9o43K0RZt2lD4wguUzZqFZ/hwih98EMnlIumOO0iZPp2U6dNxvP563U7icvm+BOvyB69pKFlZAJgPHqzb+QQCgUAgEDQ5wdEWNeF2u33RFpWE5LAzkoPjLPwYgwy/I3n37t0ADBo0KHQbFguSf5VWMFJxMVJJic8pFAZacrLPydTAg06BQCAQNCy2deuI+8tfjPzaYKTSUkyHDlF+6aUgSdEVkg8fRrdaKZ80yYicjAQ5L88XWymoE1pCAlJREYARg9WgQrLLhZybG34UqqL46jG0IiE54EjGbPaJyUJIjhrCkdwMMO3fj26zofrzbBoa9/nn4z7//Hq1UTnaojJqhw4UPvEE1u3bATBnZGDZsYPSu+6qsJ/100+xffJJleMlXTcynkrmzqX8qqvC6peck+PLwpEkTEJIFggEAoGgxRFutIXL5cIuSVWK7VmtVkpKSmo9T4VoiwCVoi0yMjLo0KEDqdVEjekWi3FMMIFJbTXMlWZacjJoGlJhYcS5lgKBQCBoegJmJtOhQ1VEPtOBA6DrePr394l6UYy2MB0+jNq1K96uXX2Pjx5Fran4fHk5cQsXUnbddajduxub5bw8tBCrbAQ1oyUkGCJtY0RbKFlZoOt1qqmlJSW1KiHZyEg2mXyOZJGRHDWEI7kZYP7hB7znnvuLM6YFUDnaIhSe4cMpmTuXkrlzcY8YgXL6tK8yegBdJ2bZMpRTp3zLboJ+dKsV9/nnoyUmYv7++7D7FZj59QwYgHLiBJLTGdkLFAgEAoFA0CQECuyF5UiWZfQIM5JrdCT7728yMjKqdyPjy90L5UhWsrN9TYcrJKekAIicZIFAIGjhBMxMyuHDVZ4z798PkoS3Z0+0xMToiXq67qsP1K2bT1g0m311g2rAvmYN1s8/xx5k6pKcTiSnU0RbREBwtEVjOJIDkxBqHWpqaQkJSK1ISA5eZaabzSEn/gWRIRzJTYhy9CiW7dsxHT5M2XXXNXV36kQ4QnIwWmoqeDxIxcVGVVHlxAmU7GxK7r6b8smTQx4X95e/YP7uO58AHRQiXx0BIdk1fjzm775DOXQIbz2yoAUCgUAgEDQu4WYku1wuYiSpSgZkXaMtJE0zMpKDHcm5ubn8/PPPzKqhWF51QrJ86hQA2jnn1N4Pfqn4ruTlIfwyAoFA0DKRCgp8K2TxOZIrY/rhB9SOHdEdDp87NEqOZPnUKaTSUrzdu4Oi4O3YEeXYser7WVpKzMqVAJj9EU6AUSxOCMl1RwshJDekI1kqLgZA90++h4OWlBTyc3nWEuxINpt/iboQ1BshJDchjnfewfLNNyDLeIYNa+ru1InKGcm1EZgpk0+fRvULyZYdO3xtjRhR7XGe/v2xfvEFclYWWhg5g8rJk+h2O+6RIwFIeOopw6lUeueduMaODau/AoFAIBAImobqMpJ1XWfx4sVs2rQJq9VKQUEB0yXJKIwXwGKx4AkjB0+vwZGsm81kZGQANeQj+06GFCra4vRpdIcD3f9aaiMwaG9NTiGBQCA42wi4kbV27TBVdiTrOub9+3H5Iya1xERMP/5Ya5vGxGS7dtWf138ub7duAKhduvjMWNVgX7kSqaQE1/jxvrF2bi5aaqoQkuuBlpDgm4x2Ohsl2kL23yPVSUiOpgu+BWA4kk0mn5gsMpKjhoi2aELk/Hw8gwaRu2oVngEDmro7daK2jOTKaH4hWQlasmnZvh21U6cavxQDVUjNe/eGdR4lMxM1LQ0tOZnSW2/FNXo07iFDQNOw/b//F1YbAoFAIBAImo6AIzk451hVVe655x4ee+wxjh8/zs6dO/n666+xShJ6pUlti8XCyZMnueyyy1i8eHH1S0trEpJNJhYvXozdbmdADfdoutUK1TiS1bZtw3q9gFHYKDCIFwgEAkHLw/zTTwCUT5yInJeHFOQ4lk+eRCouxtunDxBeXq2SmUnSvfeSNHs21k2bqt3PdPgwyDJef80lb5cuyLm5hmu1Qps//UTMf/6D65JLKJs61dfvPXt8fRRCcsRoiYkAyIWFjSIkS/57JM1/zxQOemIiUmlp64l48Hp993qyLDKSo4xwJDchckEBng4djMrgLYk6R1u0aQOAnJsLgFRWhnnvXpzXXFPjcWqnTuhxcZj37cM1aVKt51FOnsTrLyrgnDbN2O54+23sH36IVFYWtjtIIIgamoZ5zx4s33yD5PHgHj4c96hRTd0rgUAgaJZYLBZMJlMFR/LOnTtZtWoV9957L4888giFhYU8+r//S9LmzVUcyddeey0FBQVkZWXxu9/9jjfffBO73Y6maVgsFjp27Mhtt93GeP/SUylYaPbf36zbuJHPPvuMP/3pT4awHYpqoy1On0arg5CMxYLucES18JJAIBAIGhfTgQOoHToYJjHToUPGymP72rUAeIKEZKm8HJxOsNsrNqRpmHfvJnbRIl+mco8exD3/PFrbtiENaKYff0RNSwP/StyAoBy7cCF6fDy6yYRz2jS0xETiXnoJLT6ektmzfStn4uOxfPstrvHjjZx+ISTXnUB8p1xUZERb6MH1oaKMVFrqE0kr1YmoiWCxO6DPnM1IHo9RPFlkJEcXISQ3FbqOXFBgOFBaGnWNttCSkkCWkU+f9h332WegqrhGj675QEnyxVts3YoeE0PZtGnVVzN3u1Gys3FdeGHVp0aMwP6f/2D+9lvcY8aE1WeBIBrImZnEP/sspp9+8gkOflFZCMkCgUAQGkmScDgcFTKS9/pXJs2aNQtJkkhMTOS9Zctg9GhKKw2iRo8ezejRo9F1neXLl/Ppp58iyzKyLFNeXs6uXbtYv349CydP5sayMrTgpY5uN2fOnGHB3/7GmDFjuOWWW2rsq261ho62yMmpc42GaOZlCgQCgaDxMf30E57+/fF27QqA/eOPMf38M/KpU9hXr6b8qqtQO3cGgkS9ggK0SkJy/DPPYPnqK/T4eAr/8Ae83bqRcsMNWD//vIqQLJWWYtm9G+eUKcY2b+/eqB06GKt65cJCMJnwDBqE6fBhin/7W/S4OADcgwZh/ewz3/gcfIXJ6hCXIPChJSQAvvc65aefmAF03LoVe4iVRlp8PK6LLw6rBlR1yMXF6A5Hndqo8JlrBUIyXi+Y/JKnyEiOKkJIbiKk8nJwu40LTkujro5kFAUtOdk3y6lp2FevxnvuuXh796710JLbbyf2n//0VZbdvJnS2283ZvzUdu1QO3UCwPx//weaZiwXCsbTpw+6w4Flxw4hJDcT5MxMTCdPVtimm0x4Bg70LT9pgcinTmFftQrLjh3GF5Wcn49usVD80EO4xo4lZuVKYt59N6T7QCouJv7Pf6Z80iRcF13UFC9BIBAImgUxMTEVHMl79+4lNTWVdsFxWOXlvn+rmdSWJIkZM2YwY8aMCttLSkqYN28e769ezUhgwYMP8rt//hOTycTSF19kcmYm/UeN4tHXXjNcRdVitfqiMYIGK1JpKVJpKWodB2lCSBYIBIKWi5ybi5ybi6d3b/S4OLw9emD56issX30FgPuCCyi56y5jfyPSKD8frX37X9rJysLy1Vc4p0yh9LbbjIKy7hEjsH71FSX33FNhrGT57399Bq2gMa4eF0f+3/9uPI5/+mmsn32GkpWFHh9fYd+ym2/G27Urkt896+3cuV4CZ2sloOtI+fn0/cc/eBDo8vHHOL78MuT+nn79wi7IGwqppATNPxkQdh+DhOTWQAVHsslkFN8T1B8hJDcRgbykwB9zS8PlcqEoipH/Ew5aSgrK6dOYd+1COXGC4nnzwvqS0tLTKXr8cZQjR4h/5hninn/eeE6328lbuhTdbseckeErXBgqy9Bkwj10qK/An66LL8cmRHI6if/jH32/rxAUP/ggrokTG7lX0SH+L3/BdOAA7iFDjFl+3WajbNo0Iwvc27076Dqmo0erTHqY9+zBnJGBOSMD27p16HFxlMyeXa+bDIFAIGiJOByOKkJy//79kYK/v/2REpWjLWojNjaW1157jeKrriLuySfZ8+23jPQX6R1fWsqd55zD8y+/jB7G0t7AuSW32zdIAeScHKDmwkihaHXV1AUCgeAswrR/P4BhlCpYsMBnHvOjV4pJ0oOE5GBsmzaBJOG89lpDRAZwjRmDdcsWzHv34jnvPGO7dds2tNRUvL16Vdu38okTif/qKyxff+1zLgdFa6rp6ThvuKGuL1dQCd2v65j370f3eJgPXP3b3zJhwoQK+1m2byfur3/1RVPUg0giOwPak9RaJq2DHckmE5LT2bT9OYsQQnITEZgFaqlCstvtDt+N7EdLTUU5dgz7mjVoyckhIyhqQu3alfy//Q3TsWOg6ygnThD3/PNYtm7FNWkSlowMvL16VXtB9fTti3XLFqSCgurjMQQNTkAodV5/va9qcdCMevz8+Vi3bm00IVnOysL+0Ue+yYUgXBMmGFnbYaPrKEeP4rzqKkqD3AaVCVRTNh0+XEVINv30EygKZVOnYtm9G/N//4u1T58Ked8CgUDQGgiOtnC5XBw4cICLL7644k4RCskBUtq2Jb5NGxY+9hj//OYbdF1nbloabdevJ89mI5xkw0ChP8ff/45utfpcaP7rfJ0dycnJyDt21PVlCAQCgaAZYP7hBzCbje8AFKWKeBxMSHeopmHbuBHPwIFVcvbdw4aBxYLjn//EEyQaW3btwnnFFTWu6HQPG4aekIBUWEh5CzXsNHd0ux1MJsx79uAB/g+43Gyu8hkwxNx6ippycbFhXAq7j63ZkWw2hyw+KYgMISQ3EYE/3pYqaLrd7rDzkQOoqalYvvkG5eRJyq6/PrIig1Yr3p49AfD27EnMsmXYNm7EPXo0poMHKZs+vdpDA84gJScHbwt9388GlOPHASibMcP3hRuEa8wY7KtWIZWUNEo2l+Pdd7F+9lmFfkjl5ZgOH6bwr3+tU1tSYSGS01mre1hr2xbd4QjpOjMfPIi3c2fKZs2ibNYsku64A/P+/Yi5U0FLRjl0CMxmI4ZIUDvKsWO+5bGDBvkKqbRCgqMtDh48iNfrpV+/fhV38ju99DrejwTQ/e7mfr168ay/+K9t9Wrfc6bwbpG93bqhJSZi3boVNA3J6cQ9dCgQmSNZcjpDF14SCAQCQbPGtH+/z4gS5hg3ICjaNmzA8dZbvsKtfnNL6c03Vz3Absd5xRXYNmxA+fnnX9qJja1dHDaZKJs6FfMPP6B27x7eCxLUDUlCS0xEOXkSjyxzGNCCi/n6CYw75XoKyVJpKWod7zN0ux3daj2rhWTH229jf/993wNdR/UXnsRsRs7Lw/bJJ5SPHSvus+qJEJKbiNbqSA7k0kTFcSpJlE+YgGPxYmJWrgRNwzN4cLW7B5xBck4O1LD0R9CwmI4fR0tNrSIig19Ifv99LN98g+uSSxq0H5LTiWXbNsonTaLk/vuN7fbly3EsXoyclVUhr6w2lFOnAFBri6GQJLzdumE6fLjidl3HdOAArgsuMDZ5e/XCvHu3iGMRtFxUlYTf/x7MZvL+/vfIJhBbGfZVq3C89RaoKnp8PJq/JoBus1Fy3314zz0X8OfwOp2+79azkDZt2rBr1y7gl0J7/SsXrwsUuYtQSA6I9JKq+h6eOOFb9VSHNr19+5K3bJn/gZfkm27CsmsXmEx1vscz8jJDFF4SCAQCQTPG48F88CDOK68M/xiTCT0uDtOPP+Lt2dOYhNTt9mpX7pbOnk3p7NkRddE5daowpzQwWnw8cm4urrZtcf/4I6r//iKYwBhYCiooHAlSSUmNjvfq0BMTz2oh2bxvH2q7dka9IY//3lFt1w7Ll18Su2ABusnU4FrD2U7LrGh1FmAIyS202J7L5aq7kOwXcj39+qGmp0enH5dcAoqC/b330B2OCst8qpw/yJGsZGbieOMNpLP4ItpcUY4f9xVxCIG3Vy+01FSsmzc3eD8sW7cilZdXmcF3XXIJSJIvn6y8HELMJIdCyc4GwhCS8eUkm44cqdC2fOoUUkmJ4bgH8PTujZyfb+RtCgQtDfO33yLn5SGfOoVt/fqm7k6zx7R3L4433sA9bBhFjz2Ge8QIvN264e3WDTk3l7jnnjPE09hFi0h84AFfobezkMGDB5OZmUl2djZ79+7F4XDQJeAq8SPV05Fs5OapKqgqiffei+3jj30RFZHEZZhMvirs+Cev61g4VqsmL1MgEAgEzRvTkSPg8eAJUfS9JtRzzkHt0IHCP/2Jsptvpuzmm3FGunJX0OTofm2nvEMHoBrK85qqAAAgAElEQVRHsj+GMzg/u+4n0pEjXMGrneVCslRYiLdXL+PvyTNkCACld9xB3uLFgK8wpqB+CEdyEyEXFPhmkFrol0QkjuTA0oto5t9qbdqQ/8YbSPn5PldWDX3SHQ50ux05Jwfb+vXYV63Cum0brnHjKrg9taQknNdcIxyg9cCyYwfm776rsE23WnFedx2m48d9OV6hkCTKL72UmKVLMe/ciWfYsDqd13TgAEp2Nq6xY2vd17ZxI2p6epWcYq1NGzyDBmH/4ANiVq7EPXAgRU8++YvgUA1yVhZAWEuMvN26gduNcuSIsbzMdPCg77mgbOZA38z79+Oq49IlgaA5YNu40ZcZ27EjMcuW4Ro3Dt3vsBVUxfr112AyUfzII+h2O+6gqurmnTtJeOIJYt59l7Kbb8aycydSSQmWnTtx+wvFnU0M9Tuzdu3axVdffcXAgQORKwuzASE5woxkPdCepiHn5SGVl+O8/nqcl18ecaRI+cSJ2D/4oEq2ZTgYQnJeXkTnFggEAkHjY92wgZh33/WtOqyjkFz41FNgs4VcqSloeQRWIrn8QnJIR7LNBtTTkex2g9eLFoEjWUtMxPTjj8S88w7O//mfsAoLtyTk/PzQK8IkyRcxabeLCfsoIITkJkIqKDAGDC2RSDKSvb16UfinP9UYPxEJano6hONw9l88lJwc34W3TRv0mBjsa9YENeZzJblGjkQLbtPl8hXqq/Rl4B44sE7xB60BqaCAuPnzkdzuXwbiug4ejy/70e1GrcaRDFA2bRrWLVuIe/ll8t94o9ZqtHJODpbdu5Gzs4l57z1QVTy9etWYTSlnZWH+/ntKb7kl5IRB2TXXELdoEZ5zz/UtgVm4kNK77qqxL0p2tu9LK4wbQc9554HFQsITT/hyva1WLP/9LygK3iDHnbdLF7BYMO3f75vwaGAsX31F3PPPI6kqJXfcQfnkyQ1+zsbC/O23vtUIJ05g3baN0pkzcU2a1NTdOquRiouxfvUVziuvxDV+PIkPP0zSPfdQ9Pvf172YZSvBsn07ngEDQg4oPcOG4Ro/Hvvq1XiGDkUqKQF82Ypno5Dcv39/rFYr//nPf/jxxx956qmnqu4UKLZXz2gLVBX59GkA3AMG1Ot7Xe3SBfcFF+AJWl0SLpp/MCcGOAKBQNDwmHfvxtu7d/UirsdD4rx5eHr3pvS220IblpxO4hYswNulC0WPP26swA2XllovSRCawGpzt78uSEghOeBIrkdGsuy/B6xrsT3wRT1Ydu8mZsUK5LIySu6+O+J+NDtcLl/sWw3RYlpysrjPigJCSG4i5IKCFpuPDJE5kpEkPH6HUVOhtm2LnJODXFSE57zzKJ43r8Lz5u+/J+G3v0XJyakgJMe8/z4xS5ZUac89ciRFf/hDQ3e7SZDy8nw3VnWcIY9ZsQLJ5SL/9ddRO3Y0tifOnWuI9t6aim5ZLBTfdx+J8+Zh2batRrHP8vXXxL3wApK/IJNnyBDM336LbdMmymbOrPY428aNIEnVZiN5hg83lr7E/OtfvqKOmzejpqUZxZnKr7qK8iBntZKdXWuhvQBau3bkL1hA/J//TOzrr/9y3gEDKt6kmkx4evbE9sknmPfsCd2YolAyezbeytmhEWDbtAksFjRFwbJr11kjJMtZWSQ89pjvgb+CtmPpUiMaR9AwWLZvB68X18UX4+3Zk4IXXyT+ySdxvPlmnYtZtgbkrCyUEycoryFf0Tl5MtYvvsDx2msAuMaOxfrll0hFRWed09tqtTJgwAA+/fRTAC677LKqO/mFZPzunjoTlJEcEJLrKgKEoujxxyM6Tk9IAFkWAxyBQCBoYOSsLBJ+9ztcY8dS/OijIfexZGRg+vFHTD/+iPnAAQr++tcqKxTNP/0EmkbZrFm4hw9vjK4LmjEBIdnjN02FirZAUcBsrpeQHDATRJKR7Jw6FefUqcS++CLWTz+l9KabIhKkmyOByI6aJmi0pCRxnxUFREZyEyEXFBgZOi2RSDKSmwNa27aYTpxAzs2t4PwMoPqXogYKpwHgdmNbuxbP4MHkLVli/LhHjqxQMfdsI/Ghh4hbtKhOx8inT2Nft47yCRMqiMgArgkTwOMBqPJcZbx9+6Lb7b6bsxrOFf+nP6G2b0/+okWcWbrU53g/7zysGzYYVY+roGnYNm7EM2hQWIJB2c03U/DSS5Rfeilq+/Y+sViSiH31VZRDh4z9lOzssPKRA6hdu5L/+usVPlOFf/pTlf2cU6f6+nrOOSF/5NxcnxgdZpZztbjdmHfvxjV6NJ6+fVFOnKhfezWhqvXvbx2wbdoEkkTBK69wZuVKiu+7z+dk//rrRutDa8S8dy+6w2G4j709e+IeMeKsvm7WB8uOHQC4ahiIevv0QU1Px3TkCGrHjr4VDaqK7eOPG6ubjcowf7zRgAED6OBfJlqBaEVbqKqRRR9JJEXUkGW0hAQxwBEIooGq4vjHP4xJIoEgGPP33wNg3bIFy5dfhtzHsm0busNByb33YvrhB2wbNlTZx/TDDwA11ukRtB7cF15I2fTpeP1jwpBCMr6Ce9EQkiOJtgjgnDIFyeXCvmYNUn4+Un7+L0WMWyhyYSFQcx0yISRHB+FIbiLk/Hw8Awc2dTciJiJHcjNAbdvWuECGEpK11FSfG8hfOA3AunUrcn4+xQ8+6Hvej7dLF9/A3+utNT+3pSEVF6NkZ6OcOUPJnDlhB/nHLF0Kuh7SDVw+fjyOt99GS0iofdZTlvF264Zy+HC1u1i2bwdNo3jePNQgh3P5xInEvfACsX/7G1pQv9W0NFyTJmH+/nvknBxKb701rNcE4O3dm5LevY3HUnExSbNnE/fiixQsWACShHz6NOr48WG3CYCiVPhMhcI9cmSNy9atmzYR9/zzWLZtwx1GNrRlyxa8vXtXEUvM+/YhOZ24R4zAtH8/1q++8gn/0c5x1zQSHn0U3W6nKNRS9Wijadg2bMAzaJAhaLrPPx+tXTscS5Zg8k9WaG3bVnCYC+qPee9ePH37Vig4pqalIRcUIJWV1Rpb09qwfvklanp6xVilykgS5RMm4Fi8GPfgwajduuEePpyY996j/MorIyq6Eox8+rSvKKKuUz5hAlpaWr3aqy+BnOSQbmRAcrvRiVxIrhBtkZuLHhPT5J9LMcARCKKDaf9+7O+9hxYTg/OGG5q6O4JmhnnfPvTYWNS2bYl99VXyhgypuArT68X61Ve4R46k/PLLsW3cSMy771J+8cUQFKdk3r8fNT39rFsVJIgMNT2dslmzfFGahI62gPoLybJ/NW59nMRq9+54zjuPmCVLjJXXenw8RQ8/jKeFuusD9081RchqSUnI337bWF06a6lV/XK73Tz55JN4vV5UVWXUqFFMmzatMfp29uL1IpWU1DhT0txxu93EtcAlEMHimdq1a9UdFAWtTRvj4i9nZhKzdClqx45VYjnU9HTQNJRTp3z/b+aYfvoJNTUVPYxIFeX4cd9/PB6sW7aEJbApmZnYNmzAOXlyyHxiPSmJ8ksvrd4pXAm1WzesGzf6nKuVCyzhE5K1du2qOp8vuADHv/9d0aGn66DrePv2xbZhA3pMDK7zzw+rH6HQ4+Ioufde4v/4R+IWLKD0V78CTUNtAtHFddFFxKxcSfxf/4q+aBFF8+ZV++UvlZQQP38+rosvrhLrYtm+Hcxm3AMH+qJCNM3nsq7FPV5X7KtX+1wgioJUWhrRkqy6YEwczJr1y0ZZpmzGDGJfecWXqw2gaXh69TKKHwrqh1RQ4ItpqFRcNXCtVE6eFDnJQShHj2L+7ruKn9NqcE2ciH39eqOoaOktt5B0zz3EvvYabn9lavBVA7du22YU8gyJJFF83324L7wQANu6dcSsWOF7qrSU0jlzIn9RUWD8+PHcfPPNzKwuqihKGcmSpqGcPl3rxF5joLVpgxI0mS0QCCLDvH+/798DB4hcrhGEg+R0ohw6FJWYtcbCvHcvnn79KLv+ehIffhj7Rx/hnD79l+czMpBKSvj/7J1nmBRV2obvCp0nz5CGzEhWBAUUQQElmFZUVlFXRTEHXFn1kzUA5oARdVddQUR3VwwEBRURUFFckoBEAcl58kzPdKqu+n5UdzM9maEnwbmvay6lq6b61HR39annvO/z+AYMAEmi6KabSHz4YRxz5hzdzzBQt2yJef6PoPGjhOYXFVUk607ncYXtSYWFAMddQFA4bhzWVavMfxgGjq++InHiRAqeeKJRWrVI1axIloqKzDlkTeePgqqFZIvFwsSJE7Hb7WiaxoQJE+jZsyedahAiIjCJlNw3UnN9v9/PwYMHaVOZz20DJWxdYbhc6KmpFe4jHz6MsmMHSQ8+CIpCwYQJZULZIoLI/v0NXkiWDx8m6a9/RU9IwH3PPehNm0aC3MpD3b0bMFNd7d9+Wy0h2fnhhxhWq9lqXQHusWOrPWYtIwP7l18iHzpUtirO78e6dq0pTJcOy3M4yJk+PeohOSeHlBtuwPHFF6bvcqlqgprg79ePotGjcX3wAZaNGwGq7ZEcU2SZwocewrZoEY4vv8T6228VCsnqzp0AWJcvB5+P+NdfR9m3D8Vux759O/4ePcDhiIjHyt69MRWS5f37cU2fTrBlS5T9+7H8+mtEwKotbD/+iOFw4DvnnKjHvcOHm+8fzMlY6nXXYV+4kKJ6FJLlI0fqt62+BsjZ2di//hp12zbcY8dGhLjwZyJQ6qay5HVTCMlHccyZAxYL3osuqnJfPTU16hoXzMjAd/752BYvxrZ4cfS+zZrhGzQIo4KuGfu332JdtSryOYx8n1ks0RZP9YTT6eS5556reIeQtUVF32VVEWVtkZkZmSPUJ1rbtuZN3QnY7SQQVIaclXVMiznK/v1HxRSr1SwQKTEnVENCcqWLaQLAXPw1EhPLDaAmEDDnjyFBzIiPj77v8ftJmDgRy/r1uO+6C++FF5qvZYsW5R+vASDl5KAcOID34ovRunePdPYEuncHVUXOzCTuH//AiI+PLNAGevTA368frn//G3///gRbtTJzd3JzCZToWhQIAKTQe7+iimTsdqTwHKYmxw9VJB+PtQWY967eSy+N/Ns7dCipN95odro2QiE5UpFcRdheeN96uXc/QahyhipJEvZQiEkwGCQYDEY+GIKaEa72rEjIbOh8+umnHDlyhFGVCIYNlbBIo7VrV+HkJti8OdY1a7B//z2S30/O1KnlVtiGq0+V/ftrbbyxwvHFF4ApoCc88wwAxdddR/ENN5S7v7JnD4bdjufKK01/uf37K223loqLsS1bhueii2KWPqx16ACAumMH/rQ0pBKeTZZ168Dvr9RLtCR6Sgr+M8/EHgr78w4ZEpMxhisSLBs3YnTpQqBjx5gc91jRTjkF7ZRTsK5cGWXLUho15OksFRUR//rr2JYsMYW+xEQCp56K58orAQiGvEjDPsnyoUM4//MfJE0DQE9IoOjWW49N5NB14l9/HcNiIf/ZZ0m+5x6sK1cev5Ds8+H85BO8Q4eWOxmwrF1rhhhWsnBgxMfj69cP+5IlFN1yS+ztPKqB5bffSHz4YQr+/veqLUoMw5xAKkrFSeN1RMLjj6Pu2gWqSsKECRQ89RSGzYZ13TqwWNBKfSaCLVoAjeO6WVdIBQXYFy/GO2RIjVtjC8eNMzsjSiLL5ndeOR0dYdTt26NeC+XAAfO7LWTX0+Dxes3PayXnWCklrC2UzMwGsbgRbNfOHM++feb/CwQnAeHvwLwpU8p8b2AYZebs9vnziSuV4+G54gqKbr/96DG3bDHt6rKykHJzy5+fBgI4//Mf/Gee2aiqaWOJdeVKEiZNouiGG8q1AHF+8gnOjz6KekzLyIjYyikHD6Ju2YLWoQNx77yD68MPkdxugm3aUPSXv1TLdq1aVNChWBMsGzYARxe7i0aPJnnsWJJKdOsFW7cm/9FHo+aP7nvvJfn220l44gm0jh2Rc3IAM79AIChJuCK5IiFZdzqRQwthNSFibRHrzk67nUD37pHPSCw/d3WBnJdn3htVEsIc/i4QQvLxUS0VQNd1Hn74YQ4dOsTw4cPpWE9iyYmCY948c4WzEbbBBAIB3njjDXr27MmgY/WDbQDoKSlgsRAMiZTl7tO0KXJ2Npa1a9E6dSpXRAYzXd1wuRq8ICJ5PNgXLMA3YACF99+PZdMm4t58E7US/2F1zx6CrVubNhHTpmFdtQpvJUKydcUKCATwxbC6VGvbFmQZx1dfEf/KK2V9pKxWAj16VPt43qFDsa5cSbBly9hN+CQJzzXXNJiWyWCLFpW2RCs7dphCVTCIbckStA4dyH/hBSxNm1KQlRXZz3A60VNTI0Ky87PPsC9eTLBpUyS/Hzk7G9/AgdX/O3q9OD/7DMv69RSOG4fetCn+M84wPcaPZ4Ki6yRMnoz1559Rt26l4KmnojbLR46YFSclVtorwjd0KLalS4l76y0C3brhGzq0TitpHJ99BoBz1qwqb7pc772HY9YsAArHj8c3cGCtj688JLcbdedOikaPRuvYkcSJE0m5/vrI9sBpp5UV5e129LS0er1uyllZ2L77DilUXRVs0QLf4MH1Nh7199/N6+fxjEFVa+RnHGzVCls4dNIwUPbvJ9CjB5KmYdm0qebjqSu83prbWsBRawuPByk/v0F0BITzG9Rdu4SQLDhpCAefWX79NUpIVnbtIumBB/CMGEHx9deDpmFftIi4f/wDf58+eP70JwBsy5bhmD2bYLNmeEeMQM7KQs7Kwt+/P9aff8ayfXvZ6jrDIP6117AtXozzk08oGjMGz8iRdXbODQFl+3bin30WdB3H3LlmQUGpDg/rypVoHTpErJeUAwewf/99pOIbWTYrkYcNI+GFFzCsVrTOnbEtWmTaqS1fHqlgNux2fOecc8zijWXNGhInTSLvxRfRYhBqZ121CsNuRwt1oQUzMsibMsUMGwudU6B79zJilJ6SQuEDD+CaNi1y/oHTTy83d0dwclOVtYXhcCCFrDRrglRYaM5/aqH4JXDqqVj/9z+U3btJHD+eojvugD//OebPUxvIeXmVViPDUVcAkUdxfFRLSJZlmcmTJ1NUVMRLL73Enj17ytgafPfdd3z33XcAPP/886Q1AJ+5+kBV1crPff9+1JUr0W+8kbQGbodQEr/fz+HDh7n//vvZu3cvU6ZMoUmTJvU9rBohvfUW9jZtsFdQES517IiiKKg7d6L/5S+Vvp5Khw6o2dnYG9j7XVq4EOXVV80qjmAQfD7km2/G1ro1tG6Nsngx7N2LtYJxq4cOYfTuTUqPHqgdOpD422/E3XQT0rJlR1uJk5IwevcGSUJZvRqpWTOSzjsvpquWyimnoK5fD23bol9+edQ2IyPj2D5Dl1yC+sknSFdfTVojfe9WhdyhA/J331X4nlX27YPTToPkZKRvvkG67z7SmjYt97qlZGRgyczEnpCAumwZxkUXwcSJcPAg6pVXkpyVhVGd9/2ePai33QYFBRiDBhF/7bXESxLSkCEoy5bR/IYbKnzPGP36EZwwoeLzff995OXLMXr1wrl2LbZ9+zB69oxsl375BUVVcQ0ejKuqsQ4bhjpjBnGLFsGiRWi9ekFdVZjs2YO6Zg20b4/6xx80OXgQ47TTKtxdXbkS47TTkNxukubOJXjllbBlC2Rk1LjFvyTS//6HPG0awddeg0qCx6S9e1FUlbgzz8To1w+pRQukEuKjpW/fct+LSkYGluxsbOW9JgUFKJMng9tN8NFHIdbXVk1DeeABpK1box8eOhSq4R9fEmnJEuRPPiH4wgtwHCE7UiCAoqokdu4c+/OtArlbN+RFi0izWMDnQw0Gkbt2RSouRv7mG9KczkrfA/WNrGnIcXHHNedUVZUEtxtZVXF16ICzvr/PExNRbTYSG4hns0BQFyj79iGpKgnbt0d9X8uzZiH7/Vg+/ZSEb74xQ4C9XowePbC+/DLWcFfO8OEoXi9J06ejDRyIdPAgiqoi/eUvKCtWkHTgQPTnye9HfuUV5B9/RL/lFqQ9e0icPp341FT0WhZMqrxXrCv8ftRXX4XkZIJ33YX1ySdp8uuvGCUX30MLxvqNN6KWtF665ZaoQ8WFfnjjDQBsAGPGIP/zn7g+/jhiiwHA++9DYiLExxMcNw7jnHMgKwvlmWeQwuJ0GIeD4M03o0ydCrpOyvffo/fvX+lpye+9h7RnD8FJk8qfX+7di/rjj+iXX05aSUG7uq/Jn/5k/oRQgAbwagoaGM7Q3Mlut5f7eZfT0pB37KjxtUA2DOSUlFq5lkjnnosyfTppb71lFo3s2oXcUK5bVaB4vdC8eeVjNQxUVSUxGKzevaygXI7JfM3lctGtWzfWrl1bRkgeMmQIQ0q0i2eVqG47mUhLS6v03F0zZuDQdXIGDUJvJH+j5cuXc/XVV6NpGhaLhUmTJtGnT5/G+xq3bm0KrBWM3+J0khhq4S9o3x5/JecZn5qKZdMmckL7VNg6V8fEf/MNVo8nUqkYbN4cT/PmkXN2paTgWLqUrCNHykyypKIiUg8epCgtDU9WFq7TT8fx5Zd4J0zA/u23UfsW33ADniuuIPWHH/BeeCHuUItXrIjLyMCamUneY4+VX71wrO/B0AT3mH+vkeCIj8eVk0P2rl1lwxcCAdK2bsVz5ZV4hw/H2rIlnk6dICur3OtWXJMm2H74AfeXXxKfm0v+OecQyMoCVSXVZsO3Zg3ualSgO2fNwpmbS/4zzxA4/XTIzjY3nHYarssvrzCxWNm1C8v8+WTfcEP5icReL6kzZuA980wKxo8n5ZZb0F94gfxnn43sH790KZa4OHISE6v3mk+ZguR2k/qXv+D99FPcd96JdflybIsXR61aey+7LBJ0VlMcc+Zg/eknwPQZBsh57DGS77sPbcoU8idOLLctSz54kJS9e3FfeilYrcS9/jq+v/8d26JFeIcNwz1uXPT+WVmR4AkUxWxFrWKxJ/7zz7GtWYP744/xXnZZxeewejUuTSM7NRUjKwvS082fkpTzd49LScG2dCnZJbY5PvvMrH44eBDy883g02uvpXD8ePN9EyMcH3+Ma9MmCh59FP8552DZsMG0FFm2DH/fvlUfQNdR9u5F2buXhBdeIKhpuD/7rNK/U1U4d+/GqWlkQZ1fm6yJiSRoGnnr1iEFAiRqGvnx8ciGQbymkbtpU4Ouim1WWIhfUcit4d9NyssjVdPw79iBVdPIt9nM61w9k9y8OcENG6I6RQSCE5mU335D1jSMX38lu8TcNGnJEoxOnfBedhmW9esxVBV/374EevaEoiLzJ4R0552krFlD8PHHMeLisEgSWa1akdysGfovv+AO2RioO3bgmD0bdedO3FdfTfHIkWAYJBQUYJ08mcCXX5odSbJM8XXXmc8VQ6q6V6wrXNOm4dixw5yf9epFcno68quvooU6pIJt2uA/4wwS/H7yO3as2bXx2muhhBWinJWFbckSlMxMLBs3ovz1r2hdupi5CX6/2ZkTthwC1K1bUZ98Ek2S0Dp1QlmwgOwbb4QKbL0s69aR+M47ALjbtSv3uzl+yhQMIOeyy8y5i0BQC/hCYcCFhYXlft5duo49Pz9qLlwZ8v79UZ7Kcbt2IVssNZ7/VEpKCqmKghTqFAls3YpF0xrEdasqkg8dIti8eeXzp2CQtGCQ4t27KW4E51TXpFezw7FKIbmgoABFUXC5XPj9ftavX8+IESOOe4AnK5bNmwl069aoqkw2bdqEpmmMHz+eoUOH0uUEDxSIhO1IEoFu3Srft2VLbD/8AH4/lo0bSXz0UfJeegmtit+rbdTduwl064b73nvL3R5MT4dAwAz3KiXQhj28w95n/r59ccyebYbuXXQRnlBlsHPmTJwffoj9q68gEMAbKw+0Erjvvhvpttvq3Qe2sRAMvZbyoUMES3l9Knv2QDBo+tq1bImnimpurUMH7PPnE//KK+hpaUfFPElC69ChUmuUklhXrEDr0qVsorXdbvoRV4C6eTNJf/sb1tWr8ZVjo2NfvBjJ7ab4z38Gux33XXeR8PzzJN9zT8Q2yLpiBf4zz6y+RYUkRfySbUuWgNeLfeFC9KQkgm3bAqDs3o3j00+PT0j2eHDOmIGemIjerBl6s2Z4//Qn9PR0im68kbi33yZ53Djyn3qqzHeFde1aAAI9exJs1gzXtGnYFi2KBGP6+/c3QxMNA8cXX+CaMSOqEsh33nkUPvxwxWKyYWAJPYfjiy9MW5AK9lV27EBPSsIIhVZUl2CrVkhutxnGExeHfcECXFOnomVkoGVkUHzttRhOJwlPP03iI4/gGzAAo6SobrFQfN11kbCMCJpm/oA55lLV2XJODq5//xvfuefiHzAAwPQ1l2XUzZurJSTbFi8m/uWXI+eBLGNfuLDcm1Xr0qUY8fFVihBydra5+BGDavJjRSvhhx72QA+mp0fsIpTMzAYtJHP4MPrxdJiEBAs5FCx4XMeKIVrbtqa/q0DQyLF9+y16aiqBM8+scB85Oxs5JwetY0fUbdtQdu4kmJGBnJODun07RaNH4xs4sEobJyMxkcK77ybhuefM74lRo8BiIdC1K/aFC0kuMScOpqdTMGEC/n79zAckiYLx44n75z8jQaPqzp24pk0j7/XXG2xoXE2RcnNxfP453mHDCITC5Ny33Ybz88/NYhtNw/7111j/9z/TSu547v1KzCH0pk0j+SL4fLhmzED94w8Cp55K0ejRkblWBE3D+fHH6AkJaB06kPTQQ9iWLcN3wQVln8fjIe611wimp6M3a4br/ffxn3lmVMaLumkTth9+oPjqq4957iIQHAtVeSQbDodZTFOOB3xpHDNn4ioVJA/EfJErgqqide2KZe1a0+rwwIHaeZ5aQMrLQ6+qo1RVMRISIh7ngppRpZCcm5vLW2+9ha7rGIZBv379OLOSyYCgcuSDB6tX9dSAyMzMRJZl7r777shF8URGT0sDWSbYpk35lYHLliIAACAASURBVJAlCLZqZfpK7tmDdflyMAzsCxbgrk8hORBA2bcP39lnV7hLJEztwIEyQrK6cycAWuvW5uG6d8dwOjHi4ykqIeoW3n8/aBpScTHeu++unZASVcUQqfXVJiwkK6WEZNuPP5o3AxwNMawK7/DhEAhg/+EHM5ywxI2AlpGB46uvqvQ3lnJyULdto2j06GM+F61zZ4yEBKwrVpQRkuXsbByzZ5shg927A+AfMIC8V14h7o03sP76K2AGUNQkWNE7bBi2H3/EvnAhxVdfbYZSht6H4cmcnJNTVsisJrZly5A8HgqffLLM58Z72WUEW7Qg4fnnSXzsMfJefjkqSCM8qQu2bg2SRNGYMVhXraLwr38l6W9/I2HixKjj+c47L3Lzrf7+O85PPkFv0sQMSywHZfdu5Lw8Ar16YVmzBuuqVRV+Z6l//FGjcLLw9SelRNin/+yzKXj88aj3U96UKbjefjvyegJgGMjZ2Wjt2kV5X0tuNyk334zkdkceK3zgAXwlXn/7vHkQDEa/Hx0OtPbtqy3aWX7/HcPhoPChhwiceir2RYtwvfOOKXy0bx+1b9y776InJZEX7oSoADk7u97Cd/XmzUFRUPfuNW9mLBZTTA2938MCa0NFOnCAYEgEqRGh81T27wdJIthAFvm19u2x/fgjUnExRgO2FhEIKkXTiPvHP5B0nbxnn61wnqhu2waYYXnxL76IZcMGghkZWFatAijrbVwJ/vPOI69pU4KtWkU6s4puvRV/iTmxnpyM1qVLWfHGbo/q6rHPm0fcW2+hbt5c7wUisca6di3oOt5LLok8Fujdm/zevSP/Tnj6aaw//2wWAtTGQqfNRtFtt1W+j6qa3tgAhkGwRQsc8+fjO//8Mq+f64MPUA4dIv/FFwk2a0byPfeYvsovv2zmg/j9xL/6Knpa2lExWyCoJeTQfLYyj2R03bTrqeTzZVu8GNf06fjOPbfM/VCZYNIY4h02DMPhQGvXDufHH0OJ0PsGi64jFxSgJyZWvWtysvBIPk6qVGjatm3Liy++WBdjOfHxepHz8iKp9Y2FrKwsUlJSTgoRGQBFIXDqqdUKcgucdhpIErYVKyKVgrYff6TozjvrrYpW2bcPgsFKq8iCoZYFZd++SCVCGOv//ofetOnR4CaLhYKJE83Kw5LnZLFQ+Pe/x3r4guNAD11blIMHI48p+/YR/9xz5vamTSOBJ1WiKHhHjMBbTgeKlpEBfj/2BQtwzJqF5PXiGzSIojFjoib21hrcBEaQZfy9e2NdsYK4KVPMYL7wppwc0HUKHn006vm0Tp2qFO2qQ6BXL1Ok7tiR4ptuinoOf58+uKZPx7JqFb5hw2p0fPvChQSbN4+I4GWev08fCh57jMTHHyfpb3/Dc8klkcpk69q1+EPe5ADeCy/Ee+GFAOQ//TS2pUtNQRAzRM7fv39kX3+/fsjFxTg+/xx/z54EStwwhglfxwrvvZek8eNxTZ2Kv2fPspPcQAB1926KyzlGVfh79cJ9111IobY/w+Eos1gRfry0VQe6Ttpll6GUCihRf/8dye3G+6c/EWzSBPvChThnzjRvNmUZ/H4c8+fj79s3qjoJQOvSBdvixdUKflRCAWjhKjbv4MG43nsP++LFURX2UlGRGfaUnY1UUGDexFaAnJ1NsJ6EZBSFYMuWKHv3AuZ7Blk2g0gUBTkz8+i+hkH8s88SOP30agVY1jpeL+TmRhbQaoIRer0lr9escDue4L4YEv7+Tr7lFnNB1WKh4NFHCYaCoQSCxoD6xx/mdd5qJemRRyI394bTifv22yNVyuq2bSDL+M4+G1daGq7p03F+9hlSURF6WlqlAdnloZWqnjUSEvCfc84xj997wQW4pk/H9dFHeELXPOXAAWzff48ctowKP4fTie/cc9E6dMBwOMz7gwZcCGFZuxYjLq7SxeDCe+4h+fffYxqkfVxIEp7LLyfun//EsmoVgRJzS3XjRrOL6k9/Mv/2QP7EiSQ98ggpY8aY1Z+BAFJ+PvlPPy06HQW1TlhIrqwiGTAXjCsQki3r1hH/yisETjuNwgcfrNPONd/gwfgGDzbnx4YB+/YdVx5IXSDl54OuR8L0KkNPTkbZtw/rsmWRxwyLxdRFThbN6zhpuN9wJyDhVim9kQnJ2dnZjcJcPZbkv/BCtfbTU1MJdOuGfcEC5CNH8Pfpg3XlSqw//xxVCVeXqLt2AVSaIKynpGDY7WVaVSS3G+uvv+IZMSJKPKuOqC6ofwyXCyMuLnKtAbB99x3IMjlTp6I3bRqTMMRwVXPcm2+ip6aitWuH47PPIBBA8ngiz6/s31+jm8Aw/r59sS1ejP2bb0x7g9CkS09NxXfBBdUXxY8VWSZvypRyW82C7dujp6VhW7GiRkKyfPgwlnXrzCrnSlrZAr16UfDoo7imTyfun/+M2lZRhbDerBmeykKCJAn3bbeZE9PXXyd/0qQyN7rWFSvMttD0dNxjx5IwYQKu6dPNCvUSKAcPmgtWNXltLZaaewrLMsFmzcpUyoYr2opuvBEjLg49JYX4l17CvnAhlrVrTeuGggI8V1xR5pCBLl2wz5+PsnMnetOmFXeiGAbqrl1RN9VGYiKBbt2wbNgQtWvYIgjDwLJuHf5KbsTl3Fy0UtXMdUmwdWuUP/4wReVwW7EsE2zSJOpaom7bhu2nn0y7mXPPxahGxUdtEl5M0Js1q/lBwhVDSUm477gjFsOKCf6ePfGMGBHxkLctXYrz008pHD++nkcmqBKPx1yQiGHwcEPCGWqv9p9zTsQCpyR6QkIkKyR8Xcx7/nnsS5ZAaPHQsnkziY8/TuDUU0GWUXbvNrtsHA7cd9wRtXDs79On/mwlHA48l16Kc+ZMLGvWRB7WunTBX6oIQzl8GOd//hNZyA1bYulJSbjvvrvSxcQ6xzCwrlljWpZV8j41kpPJmTGjQdl6eC+6COesWbg++IDCkBWhumOHOR9t2pSim2+O7Kudeir5TzyB7fvvjz7WrVulNisCQSxRFKVqIdnjwSgn7Fk+coSEp54i2LKl2bFXD/ZncLSLUNq3DxpwZ4bt229xfP01QLl/z9IEW7bEsmYNCU89FfW496KLcN93X62M8URDCMl1SLhKMHg8Nz31QGZm5kknJB8L/gEDcIWCHYqvvx5l3z5sP/xQb0KysmtXpMqsQiSJYHp6GSHZunw5BIP4Qv6hgsZHsHlz5EOHzH/oOvZFi0yPuOOo2ivzHK1bmwJkMEjB//0fWrduxE+ejGPuXLMNqn178z3WrFm57YfVxd+3L74hQ/AOGlT3E/+KxixJ+Pv0MW9MAgGwWI7psLYffwTAe/75Ve7r79cP/9lnm5/TcMCGqkb8y2uE1UrhuHEkPfBAlF9kScLVpv4+ffBdcAGO2bNxzJ5d7r41sbY4XvSmTaMETgDLtm0EW7aMtDL7zjsP19SpxL32GobdjpaRgffCC8tdFNNCXmrJ48ZhqCo5779frkgqZ2cjud1lFum0jAwc8+dDMBipYghX+CLLZhV5RUKyrps2KfUY0qq1bYv1558Boq79epMmUZXf9oULwWJB8npxfvKJWYFdhVhm+/57LBs2oLtcZnvyMX5eKiO8mHBccypVxXvJJeZCVRVWVnWK3U7RnXdG/mk4nTi+/JKirKxGlbFx0hEIkHzvvQTbtKFgwoQGJcDFAqmgAOfMmQCR/5bBYjGzQjp1Mi0q0tPRunbFXdKz0uvFNX066vbtoOsEW7aMzJn9AwZEPOwbAsU33GBmIoQEYiMursLFKzk7GykvD+XIEWxLliDn5GBbtgw5M5P8p5+uv46HUq318sGDyJmZ+K+6qurfbWjvYYuFouuvJ/7ll0kucY3UOnWi4JFHylQaB3r1KpvRIRDUEYqiYISuHaUpWZFcHtZVq5CKiih4+eV6nZ+Eu5ilPXsatJDsnDXLtOY744wqM67A9IT3hDo6wzi++Qb7vHn4Bg6MadD3iYoQkuuQyE1PDAWduiA7O5uetWXmfgLgCwnJhsuFdsopBHr0wPbzz9Uyz68N1J07zdXDKm7Ygy1bYtm61Zzs5uUB5k2/npaG1rlzXQxVUAsEW7TAsmEDjtmzzdb6rCy8t98e2yexWPAOHozetGnE87DwgQfM0JauXaFkMNpxYDgcFD7wQEyOFUv8/fqZITSrVh0N6qkmtp9+QuvYsfrCviTFvPJa69qV3ClTUMsJzzAg6qar8K9/xde/P1IgUGZfPTGx9qrCKyHYrJm56FUCdds2870XxmKhePRobIsW4R471lz8qOh46elmK5thYFmzBtsPP5RbMa2E/ONLeyFrnTrBnDlmVV2oQlvdvRssFvw9e2JZt67C55by8sw2vPqytgA8I0eaiz+GEWV1pDdrFglexO/H9v33+Pr3B0XBMWsWjlmz8Pz5zxWGZkp5ecS/8or5j0CAQO/ekZbjWBDp8joeIVmSKgylbUh4RozA8cUX2OfNM+12BA0S208/oRw4gHLgAPZvvkFPSCDYrl29XCePBfnQIeSsrCqzLtTt2wEofOghDIsFqbRAYhi43nuP+JdfJveNN7Bs3IivvO/IUgslDRpFqXbnjZ6aCqmpBDMyInMD69KlJDz3HGlXXhnZTx49Gq65plaGWxopN5eU22+Pyg8I34eWtrZrLPguuAAjIQEptMBuWK1msHIMFyoFglggy3LVFcnhQpFSqLt2YTgcx1c8EgOMuDizo2LfvnodR6WE8qGKR46kuERXQqVYrWXswty33ILl11+Je+01cv/xDxAWOJUihOQ6RDl4EMNur/d20GMlKyuL1Hq8yW3o6Glp+Hv3NoO3ZBmtUyfT6uLw4ZhWgVYXddeuaq3EBdPTsS1dSnwpD/TiUaNO2JbMkwGtSxdsS5fievddIPT+POusmD+P+29/i35AVU+aqg//GWegJyVhX7jwmIRk+fBh1K1bo1ov64vgKadEBTJWiMVyzGJ5bRNs3txc/PL5wGZDys1FzsxEK+Xn7R0+vIwlR7lIEvnPPANA0r33Yl+4EO+wYSiHD0clyFdkG6R16mRu3749Ijgoe/agtW5NoFcvrCtXImdmmiF2pVBCidE1DW6MBYbTWW4FYLBpU2xZWSTffDOSppke1MOGobVrh9a6NdZ163DMnm16eJfzXWf/5hsIBCh44gkSJk5E2b8/9kKyxVKv1dx1hd68Of4+fbAvWkTx6NENr0pQAIaBY/ZsM+QtPp64KVMACLZpQ+4//9ng5lW2H37APn8+gW7dcHzxBZLfT/bMmVHhrqUJWwj5+/aNdH+UxnA4SJg4kaTx45EKCwlUkAVwsuA/91zy7XYsob+d9ZdfzO6OUaPq5HNsXbkSye3Gc8UVGHFxSB4P1p9+Mhc4wlkojQ1JanTB9YKTk2pZW1RQkRzO5GgI3/fBli1Rw5ZtDYiwnZCvf/8q86Gqhd1O4bhxqDt3NpjMjIaMEJLrEOXQIfNmqwFcEKqLx+PB7XbTpJwbYMFRCp58MvK6hlu91W3b8Ne1kOzxmIJKNS6k3qFDkYJBfGeddfTCK0kiIb6R47nySlM8C7dh2myiSiPWKIpp+TBnDlJeXtVeXIaBnJVl3jyCOeER1Bg95IuoHDlCsHXrSJVcWNA9HrxDhxL39tsk33UXyqFDFI4fj2/gQMAUkvXU1DIthsEWLTCcTixbt0Z8s9Xduwl0704gFPqk/vEH/nK+R+XsbPOcGuBirW/wYOTMTKTQTZCekhLx1PSMGoXvggtIGTMG10cfmSEwJQkEcHz5JYEzzjDDIVUVZf/+mI5PPnIEmjdvcAJdbeHv0wfr8uUoBw40+ArXhoZUUBBVkQmmv3lJ0VQqLEQqLCz7y7JsVr2XM3dXduzANWMGBINImoa6bRvue+7B37s3jjlzMJxOnP/9L7YlS8wwx0qQs7ORvF4sGzZg/d//QNPMDTYbhXffjRHjxSbH3LmoW7diWb8evVkzpMOHsaxfj//ssyv8Hcu2bQTT0ysUkcEUmYvGjMH5+edmeLXI2CDQp08kGE5PSMD+zjvm5zgtzXxfVdP71Pbtt9iWLsVwOim69dZyFyety5ahbttG8bXXgtWKbcUK9LQ0im67LfIeLhozxty5Ed2PCgSNkUorkkP32+EshOiNBurOnQ0m6DLYsiVSqSyQekfXTds9wzCDoqk8H6q6aKeeWmV3jsBECMl1iHLoUOSN3ljIDt3kCo/kKigxGdPatQNFwbJtW6UBS7WBEhYlQkJLZegtW1bYkixo3FRWUSSIDd4hQ3B8/rmZpty9O54rryxXsLf+9BOuf//b9C7H9NPVhQh0XIQ9cZWDB7H99FMkBEkr1aJWE3yDBhH33nvIHg/aKacQ/9JLWDZuxJBlLOvWRVUoR5BltIyMSLWeFF7Qa9s20pKo7NkD5Qg0DVlIDrZqVbbzoAR6WpppufD553gHDowIJWD6p8o5ORTef78Z3NeiRcyFZOXQIYxGZhV2PPhDFmOWNWsajZAs5efjDAWx+gYMqJebM6mwkNTrrzc97UugJyWR8/77YLcj5eeTcvPN5d/QY3quFz78cJlFC9eHH5rXhZB1jr9PH7wXXAAOh2ndoOtYV6zA9eGH+M86q3wB1u8n7u23sYdCgsC0a9FD4Wzq9u042ren+C9/OZ4/QxRSfj7qli0UX3cd3gsvRI+LI23UKCxr11YqJKtbt0ZbCFWA56qr8Fx+OXJeXrli58lMuHPLsno1CV99hZyVhe/883HfemvlgrJh4PrwQ9A05OJi5IIC8p99NloM1nXi3n4bOTMT64oVFDz+OJZffzUXQ0vuJwRkgaBOUBQFvZRHeRgjZAOoHD6Ma+rUyHeU4XKZxV5uN1p5c856QGvbFr7/Hik3NxKmWt+of/wRWSC2f/edaUMUCgYU1A1CSK4rDAP50CH8jaz1OzMzE0BYWxwLVita+/YRUaEukUOvl5i4CwS1S7BdO/xnn41l/XqzddTvp/iGG6L2UXbvJuH55wm2akXRHXegx8dHKlQFNSfsiWv/6iusy5djOBymSBODbgojMZHc114zrSZUlcSJE7EtXhzZ7qtAZNE6dcIxdy72L79EDtlVBFu3xnC50NPSKmwJDO/bWO0Ziq+7zky9fvZZim+8EcNiQc7KwjlzJr7zzyfQuzdgWinVRkWyEUOrjIaOnp6O3qQJ1rVrI4GYDR37woU4PvvMXFzftIm8kN1DGGX3brOaKAZVRBWh7N4NgQDFV18dWdiRs7Nxvf8+9sWL8V58Mfavv0byeHDfdVeZhVh1xw4cs2aBxUKgWzd8/fphJCcjHziAdflyikeNMu1GykOWKbr1VhIfe4yksWPxXHFFlBgt5+ZiX7QI+fBhPCNGoHXsSLBlSzOnIiT2Jf7979gXLjQrTGtQfS8fOWL6H5ewPLOuXg2Ggb9Pn0h4Y6B7d6xr1lBUwXGkvLxyLYQqxGIRc9FyCKanQ9OmuD78EMntJtCzJ/YvvyTYtClap04kPPlkmQUN38CBFI8ciZyVReG4cUiBAHFvvonrnXciwonWuTNSURFyZibeiy/GtmQJyfffj+Tx4C+xyCcQCOoOWZYrFpJDc1bH7NnIubnmd49hIBUXo4T8iEtnctQX4XsXy++/V7rYWJdEMjwILbC3bSs6cOsYISTHGsNA3boVqSh6KiYVFyP5fI0uaC8rKwtAWFscI1rHjth+/LHOA/fk0OsVFMK/QFDrFEycCED85Mk4Z84k2KZNpIoMwPX++xguF3nPP1+1/YWg2oRFXuvy5RiJiWR/9BGosZvOlAzfyAuHxVWB//TTcXz+OXH/+If5gKqidexoHq91a7MiuRzknBz0pKSYjr8uMRwOCp58ksQHH4z4sgP4zzzTrEYOff8FW7XC+uuvoOuxsaLwek2f7EY2pzouJAl/r17Yli2L3d+xlglX6/oGD8Y5Y0Z0NZOuk/jYY8i5uRTdcgueyy+vlfmSGroh91500VEvb8PAtnQpjtmzze6SefMInHFGuSGbvvPPN1to58zBtmgR9m++Ie/VV3F88QXIcpWifqBnT/JefJGE554j7p//LLu9Rw8K7703suhSGu+QIZHOiECnTsRNnYpUVIT7zjvL2OyEcXz6Keq2bXiuvpqEJ55Azsmh4PHHIwKAdcUKjMTEyDUKzIp31/vvI+XkRNloyIcOEfevf0Xua2JhIXRSI0noffsizZmD1rkz+c8+S8Ljj+MM+VPrCQn4/vSnyO7qjh3YliyJfN79vXtjJCVhXb4cx9y5R4+rqmgdOmC4XLhvvx1f//4kTphwUuVXCAQNjep4JMu5uQS6dyf/pZfAMEi++25TQyA2Vg2xQDvlFFAU1C1baiwky0eOYMTHR877eLGuXWsuDksSyu7dDaZ6+2Sicd65NGDkd98l6b33Ktxe38mbx4qwtqgZWqdO2L/+moTQJC6Mr1+/iIdmbRBpkxavl0BQZ7jvvBPLmjXEP/98mW2F48cLETnWyDLBZs1Q9u/HO3hwgxBhA336kP3JJ0d9Te32yGRZa9MG+4IFEfHP/vXXWFesAMxW8foM2osFekoKue+8E+VBayQlRYmCwfR0CATM0MFQRXlJpNxcnDNn4hk5slpVjOEKb6ORWDzEisDpp2P/9lscc+bg698fw2ptMG2mZQgEsKxfj2/YMHx9+uCcMQPrypWROZDl11+Rs7IItmmD69130dq0IXDmmdU7dihoszooe/ea1bElLb8kCc/llxP/0kskPfggcnY2hX/9a/kHkCSK7riD4muvxbp8OfGvvELCxIlY16zBN2hQtWxptG7dyHn//bIezBZLpX7DAL5zziHO4TDD+wzDrOxXFCy//RbJ5AijN2mCnpiI86OPAEw/XYeDYJs2xD///FFbhbVr8ffvH7UYEQhZpyQ+/TR6iVBwy/r1ZlGEYZgddzGwEDrZMfr1gzlzKLrpJpAkikePJum++5CKisibPBmtREChlJtL6o03Ylu0CK1jx4jIX/DEE0h5eeY+Xi+JEyagbt2K9+KLwWYjcMYZFEyciJyTEzPhRiAQHBuVWVtgsZjzV007GngsSRRfcQXxr76KnpKCUaI4pV6x2zE6dsSyeXPNfl/XSfrrX9E6d6Zg0qQaD0PKzTVzOzQNy4YNeC66CADH7t1oDaR6+2Si/u++TiDsX3+NPH06xUOH4i1PLLTZykz6GjphawshJB8b/t690bp2jbQtgxkMpezbV6tCspKZaX7pVDO4QyAQHD9GfDy5b79dpurUSEhodIuHjQW9aVNTSB4ypL6HEqGi6sBgmzZIXi9y6PrsevddDIcDPTkZPSkJ36BBdTvQ2sBiqVTQDHv6KgcOlBWSDYP4N9/EumwZ1jVryHv55SrFNeuyZSDLGH37lvG+PZHx9+2LlpGB61//wvWvfwFQMGEC/n796nlkZbFs2YLk8+Hv2ZNgRgZ6amqUkGxfuBAjLo68114jZfRo7N9+Wy0h2frTTyQ8/zz5Tz8dET8rQ9m3D61VqzIV3L7zzsP2ww/I2dn4zjuvyuc2EhLwDRmC7X//w7psGf7+/XHffXeVzx9BVWsm+jscFF93nVmVarGQ/+STGHFxuKZNMwMnIwM0zKp/vx9/794Ujx6N84MP8Iwcida2LfGvvBLZP9iqVeQGPIx2yin4+/VDPnw46riBbt1w3303RmIiUmGhCGSOAcbgweRMm4YeDojq2JHiv/wFw+WKEpEBjORkvIMHY1+4EH/fvkc3SFLk/WQA+U89Rdy//mXmNYQQlhYCQf1SWdgemD7JktsdFcLtGzSIuGnT0Dp0qIshVhuje3fUuXNr1BGlbt+OnJeHdfly1E2boqyWqoP1l19w/uc/kXDtMIGePUGWccydG9VNKKgbhJAcK3Qd2/ffY5x9Nu777msQFVKxICsrC5fLhUOsZh8TelpamZZo19SpOObMqdWWVDkry0yBFggEdYqRkCBSfusQ/5lnYlitjWLiqJUI3JPz85G8XvKfeuqker9EhOT9+8u0WVuXLsW6bBm+gQOx/fQTcVOmUPjIIxUfLGRLEDj9dCyJiRCydDoZMOLiyHvjDdQtW1B378b5wQfYFi9umELy2rUgywR69DBtOXr3xrZ0aaQ63/bLL3gvvBDD4cA7eDCOr7/GXVhY4YIMgLp5MwkvvgjBIJa1a6svJJd3nbBYKHjyyWM7KUmi8MEHUTdtInDGGXVmXeb585/x/PnPUY/lT55cdnhFRWa18RlngMNBwVNPRbaV/P9ykWUKJkyodBdR2RojJCkiIocpvv76Cnf3jBxpVvcPHFjhPnrz5hQ8/njMhigQCI6fKoVkl4tgq1bRnVhWq2mJFwrjaygYp56KNHMmyq5dBI9R5A77GRtxcbimTyf/hReq/f2p7NlDwrPPEmzenKIxYyJzBMNuNxfXJIn8p54yv5MFdcqJoXY2BGSZ/KeewpKUBMXF9T2amJGdnS2qkWNEsEULM205Kyu6xTKGyFlZwtZCIBCc8HhGjsQzcmR9D6NaBEO+beqePVhXrCDYokWZqrMTHT01FcNmKzdwzzlzJlr79hQ+9BB6aiqOuXMrFRSVHTtQDhwwxZXaHnhDRJLQunZF69oV9Y8/sC1cCB4PNBCRz7J+Pa6pU00B95RTItXlvgEDsC9YQNxrr5k7yjKeUHWyb+hQHF98QfzLLyP5/RQ+8EC5lhGu995DT0rCsNmq12Lr96McOlSpAHesGA5H9S046hjD5TItKwQnFMG2bcl9//36HoZAIDhGKrW2AArvuafcsOXaDKCtKUao+CHxySfRXS7T9mnMmGoJuNa1awm2bYt3+HBc776LsmdPZG5cEVJuLsr+/cRNnYrhcJD34osVdvVUlDEgqF0aflJHY8JqhWNo+Xr11Ve55557qr1/cXExa9euZceOHTUZXY3IzMwkVQS3xYRgqPpAOXgwZsdUN20i/rnniH/+eZTdu1GEkCwQCAQNCiM+Hj05mc1R5gAAH8NJREFUGftXX2H57Td8Q4fWaQhrg0CSCLZqhbprV/TDbjfqzp34zzkHFAXfeedBMIh1+fIKD2VbuhRkGV8DrMKta3wDBiD5fFhXr677JzcMpPx807Mw9KNu2WKGu+XmEjjtNIqvvTaye6B3b7L/8x9yZswgZ8YMsv/7X4IhuzctIwOtQwesy5djWbMG23fflXk6qaAAy5YteIcMIdCrF+rWrWaHVyUoBw+CrhNs1Sq25y4QCAQCQRXIslypkBzo0yfyPdjgadkSz+WXo7Vvb9rLHTiAfcmSqn/P78eycSP+nj0j87ZwhXJlJD76KEkPPYS6ZQvuu+5quHkQJzGiIrkemTdvHlu2bOGxxx6jRakWp9J8++23/P3vf+fQoUMA3HfffTz88MPl7ltUVMQll1xC586dGTx4MJMnT478HsCkSZO47bbbqhxfMBhk//79dCyR6iyoOcH0dMD0iAycfnpMjumYP99McDcMsFiQCgoIViOoSCAQCAR1h3fYMGw//4zWsWP5GQonAYHTT8fxxRdIHk+kRd6yeTMYBoFQpYvWqRN6Whq2pUvxleN/LRUX45g/37Q2ETcVBE49FSMxEecnn6D+8QcQ8rqt5apU2/ff4wy1uJZGT0khb/LkcjuvjORkjPIOKEnkP/00UiBAwrPPYvvpJzyjRkXtYl29GnQdf9++5g3sl19W2WKr7N0LQLB162M5PYFAIBAIjhtFUSq1tmhUhIJnwySEAj7L3TUnB8c335ghy3l54PcT6NULvXlzgs2bY12zBu+IERU+lZyVhbpzJ57LLsM7bFijsLE7GRFCcj3h9XrZtm0bAN988w0333xzhftu2LCBMWPG0KVLFyZMmMCCBQt44403OPfccznnnHPK7D9jxgy2bdvG7t27mTdvHj169OC6664DTPH63//+d4VCstvtZvPmzbjdbv773/+yc+dO7ihx0RDUHL1JE1CUmFYkK/v2mTeSTie2H380n0dUkAsEAkGDovimmyi+6ab6Hka94u/bF8esWaaHa6gqRd2wARSFQJcu5k6ShG/AABzz5iHn5KCnpEQdw/H550huN8U33FDXw2+YKAqeCy/E+emnppCs66CqZH/wAUapv11V2BcswDVtGug67jvuKFfIB/MGMX7yZIKtW5t+haV8HP19+tTIvissMvvOPRfXe+8hHzwY5SNrXbECIzHRXGwIJdlbtmypXEgOWamIimSBQCAQ1DUnlJBcCq1TJ6yrV0cVBwCg6yQ89xyWDRsimVB6WpqZlwAEevXC9sMPEAyCopR7bMu6dQBCRG7gCCG5nti6dSvBYBBJkpg/f36lQvJbb72Fy+Xi888/JzExkaFDh7J+/Xr+9re/8fPPP6OU+BB6PB7efvttBg4cyDPPPMNvv/3GpZdeGtknNTWVRx99lN9//53OnTuzZ88eHn30UVatWkVBQUGZ5544cSI3iBu22CDLBFu0QA4JyVJhIQnPPYdUVBS1m/+MMygePbpah1QOHMB33nnmxXzZMgBhbSEQCASCBkegWzcMhwPrihURIdmyYQNax45QQoz0nX8+ji++IGX0aLT27aNsQNSdO/ENGGD+jgCIXqRQ9u8n+dZbcXz1VaXhXaVR9u8n7q230Nq1QyosxPn55/guuKBcCxb7kiWg6xQ8+mitVPr6+vfH9d57OObNM61OAAwD66pV+M86C2QZvUULjIQE1C1b4OKLo37f9v33qFu2UDRmDLYffyTYooUIiRMIBAJBnVOVtUVjRuvYEXQd5Y8/osKj7fPmYdmwgcJx4/CV04EX6NkT+9dfo27dita1a7nHtq5Zg5GQQLB9+1obv+D4EUJyPbFhwwYArrjiCmbPns2sWbNwOp0oioKqqthsNhISEiguLmbevHncddddJCYmAuB0Ohk/fjy33347S5cuZdCgQZHjTp06laysLO6//37at29P+1IfwIsvvpjHHnuMefPm0blzZ9544w2WLVvGyJEjadasGTabjU6dOpGamkpycjIdjjGVU1A5wRYtUA4cAMD6669Y1qwhcNppGDYbAOrevTjmz6+WkCwVFCC53QRbtcLfp0/kcV1YWwgEAoGgoWGxEDjjDKwrVyK53RAIYNm6Fc/ll0ftpnXsSO5bb2FfsABl376obf4zzqBozJi6HHWjItiyJf6+fbHPn0/x1Veb2R1VYRjEvf46htVKwcSJWJctI+4f/0D5449o70bDNKWwL1yI1qVLrdlF6M2bo3XujGPWLByzZkVt8511lvk/kkSga9fIeykc6CcVFhL35ptIRUVYV69G2bePgsceq5VxCgQCgUBQGbIsn7gVyaEFfcu2bREhWT50CNf77+Pv3dvMAykHf6gyOe6ddwh06ULxNddgJCUd3cEwsKxbh79nz0hFs6BhIoTkemLjxo3ExcVx7733MmfOHMaOHVvhvjabjVtvvTXqsSFDhpCUlMSnn34aEZI3b97Myy+/zIUXXkjfvn3LPVbTpk0566yzmDdvHmPHjuWrr77i4osv5sUXX4zZuQkqJtiihdnqYRhY1q/HcDjIf+65SGuH49NPcU2bFnVjBKDs2YOcmRmVFB6+wQ6mp6OnpKCdcgrq9u3C2kIgEAgEDRJf375Yf/6Z1KuuijwWKFHJEibYrl2UF5+g+nguv5zERx4h7s03cd9zD4QWqitCzszEsn49RWPGoKem4hs0iLh338X+3XcUhYRky+rVxL/2GigK8uHDuCuZs8aCgscfRy0VLG1YLJHWWIDi664jadw4XO+9h/v++wFwfPYZUnExvoEDsf3wA77zzqt1v2iBQCAQCMrjRLa20FNS0NPSULdvNx8wDOJffx0kyZwjVBAqbSQl4RsyBHXDBjPr6eef8V54YUQ0ljwe5KysmOVJCWoPISTXExs3bqRbt2507tyZ5cuXU1BQgKZpBINBNE3D4/FQWFhIQUEBbdq0oWkpvzmbzcaIESOYOXMmBQUF+Hw+7r33XhISEqoUha+44goefvhhHnroIfLy8rjssstq81QFJQimpyN5PEh5eWZLb9euUf5AwZAfoHLw4NHWXV0n4ZlnkA8fJvuTTyIVRuHK5mDLlgB4hw7FLkmihVMgEAgEDRLfoEFIgQCS3w+AYbfj7927nkd1YhHo2ZPiUaPMMLz9+8mfPLnSqh7Lli3m7/XqBYARH4+vXz8c8+aZPoaAnJ9PsHVr05tY149aTtQSemoq/ioWxbVOnfCMHInj00+xLl9ujrOgAN+gQRQ++CC+886LnJNAIBAIBHWNoigYRrkRsycEWkZGJHDPvmABlrVrcY8dW2VOQuEDDwCg/PEHCc8/j/Ojj6K2Gw5HVLe1oGEihOR6QNd1Nm3axNVXXw1Aeno66enpx3ycq666ig8++IBrrrmGI0eOkJuby7Rp00itYvJ9zTXXMHPmTD777DMSExMZOHBgjc5DcOyEg2Msmzej7N6Nt4QtCZhCM0QLybYlS1D27DF/b906AqELq7Jvn+m73KwZAN7LLsMrFgUEAoFA0FCxWvFeckl9j+LERpIovukm9GbNiJsyBduPP+IrNdcoibp5M1itaO3aRR4rvuEGjMREM7wPs/KoeOTIKC/rhkDR9ddjWK3IubkAGKqK56qrQJbxlxNGLRAIBAJBXXEiW1sABLp3x7p8OY7Zs3F+9BGBHj3M6uJqEszIIPfddyNzjQiSJGwtGgFCSI4he/bsITMzk9zQhDaMYRjk5uayceNGZs+ezbZt2ygqKqJ79+7H9Xw9e/ZkwoQJzJo1i6SkJKZPn86p5bSIlkZVVV5//XWGDRvGpZdeirU6HnqCmBDo0gXD5TJbPwCt1HsgLDTLBw5gWb8e23ffYV29Gq19e9T9+7GuWnVUSD540BSRLZa6PQmBQCAQCAQNGu/w4Ti++ALnhx/iGzAA1PKn/JbffyfQqVPU9mDr1qYtRkPHaj2mUEGBQCAQCOqKE9naAsAzYgTWFStwvfsuhs1G4f33H7sALElR3dmCxoMQkmPIfffdx8qVKyvdp1u3bowaNQqHw8HFpZKmjxVJkrjjjju4owY+gh06dGDJkiWkpKQc1xgEx4aRkID79tuJf/VVUFUCnTtHb3c40JOTUQ4exLp6NZbff0dPSaHozjtxfPYZ1hUrKLrzTpAklP37I7YWAoFAIBAIBBFkmaLRo0l44gns8+fjHTG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      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(25, 5))\n",
    "axs.plot(predictions, color='black')\n",
    "axs.plot(y_test, color='red', alpha = 0.8)\n",
    "# axs.set_xlim([400, 600])\n",
    "# axs.set_ylim([-0.5, 0.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3357, int)"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = [0]+[(100*(b - a) / a) for a, b in zip(y[::1], y[1::1])]\n",
    "len(test), type(test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((49,), array([-37, -28, -25, -24, -23, -19, -18, -17, -16, -15, -14, -13, -12,\n",
       "        -11, -10,  -9,  -8,  -7,  -6,  -5,  -4,  -3,  -2,  -1,   0,   1,\n",
       "          2,   3,   4,   5,   6,   7,   8,   9,  10,  12,  13,  14,  16,\n",
       "         19,  24,  28,  29,  33,  34,  37,  40,  47,  69]))"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lab_enc = preprocessing.LabelEncoder()\n",
    "encoded = lab_enc.fit_transform(test)\n",
    "lab_enc.classes_.shape, lab_enc.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tt = [-1.5, 0.2, 6, 4, -3]\n",
    "# lab_enc = preprocessing.LabelEncoder()\n",
    "# encoded = lab_enc.fit_transform(tt)\n",
    "#list(encoded.classes_)\n",
    "# le.transform([\"tokyo\", \"tokyo\", \"paris\"])\n",
    "# encoded.inverse_transform([4])\n",
    "#lab_enc.inverse_transform(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize= (12,6))\n",
    "ax.hist(test, bins = 30, alpha =0.5)\n",
    "ax.set_title(\"Fights per Event\", fontsize = 15)\n",
    "ax.set_ylabel(\"Event Count\", fontsize = 15)\n",
    "ax.set_xlabel(\"Fights\", fontsize = 15)\n",
    "#plt.xticks(n.index, rotation='vertical')\n",
    "ax.legend()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "DateTime = df.pop('DateTime').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.603170e+01, 2.197157e+06, 3.117000e+03, 2.550350e+05,\n",
       "       1.666520e+06, 1.000000e+00])"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.values\n",
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "X_train, X_test, y_train, y_test = X[0:size], X[size:len(X)], encoded[0:size], encoded[size:len(y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2517, 6)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: 0.5297619047619048\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "print(\"score:\", rf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: -0.6174756117807165\n"
     ]
    }
   ],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "X_train, X_test, y_train, y_test = X[0:size], X[size:len(X)], test[0:size], test[size:len(y)]\n",
    "rfr = RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=90,\n",
    "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=3,\n",
    "           min_samples_split=12, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
    "           verbose=0, warm_start=False)\n",
    "\n",
    "rfr.fit(X_train, y_train)\n",
    "print(\"score:\", rfr.score(X_test, y_test, sample_weight=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.00562\n"
     ]
    }
   ],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "X_train, X_test, y_train, y_test = X[0:size], X[size:len(X)], test[0:size], test[size:len(y)]\n",
    "model = RandomForestRegressor()\n",
    "depth_param = np.arange(80, 90, 1)\n",
    "num_samples_param = np.arange(5,95,10)\n",
    "n_trees = np.arange(1,70, 10)\n",
    "parameters = {'max_depth' : depth_param,\n",
    "             'min_samples_leaf' : num_samples_param,\n",
    "             'n_estimators': n_trees}\n",
    "clf = GridSearchCV(model, parameters, cv=10, n_jobs=-1)\n",
    "clf.fit(X_train,y_train)\n",
    "print('Accuracy score: {:.3}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "max_features must be in (0, n_features]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-428-7db02c9ae5d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m            verbose=0, warm_start=False)\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mbest_rf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    331\u001b[0m                     \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m                     verbose=self.verbose, class_weight=self.class_weight)\n\u001b[0;32m--> 333\u001b[0;31m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    915\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight)\u001b[0m\n\u001b[1;32m    117\u001b[0m             \u001b[0mcurr_sample_weight\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mcompute_sample_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'balanced'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1140\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1142\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1143\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    240\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"max_depth must be greater than zero. \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mmax_features\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_features_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"max_features must be in (0, n_features]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    243\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_leaf_nodes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIntegral\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m             raise ValueError(\"max_leaf_nodes must be integral number but was \"\n",
      "\u001b[0;31mValueError\u001b[0m: max_features must be in (0, n_features]"
     ]
    }
   ],
   "source": [
    "best_rf = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
    "           max_features=7, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=3,\n",
    "           min_samples_split=12, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
    "           verbose=0, warm_start=False)\n",
    "\n",
    "best_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.583\n"
     ]
    }
   ],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "X_train, X_test, y_train, y_test = X[0:size], X[size:len(X)], encoded[0:size], encoded[size:len(y)]\n",
    "model = RandomForestClassifier()\n",
    "depth_param = np.arange(1, 12, 1)\n",
    "num_samples_param = np.arange(5,95,10)\n",
    "n_trees = np.arange(1,70, 10)\n",
    "parameters = {'max_depth' : depth_param,\n",
    "             'min_samples_leaf' : num_samples_param,\n",
    "             'n_estimators': n_trees}\n",
    "clf = GridSearchCV(model, parameters, cv=10, n_jobs=-1)\n",
    "clf.fit(X_train,y_train)\n",
    "print('Accuracy score: {:.3}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=1, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=5, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=11, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.00368\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test \n",
    "history_X = X_train\n",
    "history_y = y_train\n",
    "predictions = list()\n",
    "for t in range(len(y_test)):\n",
    "    rf = RandomForestClassifier()\n",
    "    rf_fit = rf.fit(history_X, history_y)\n",
    "    output = rf_fit.predict(X_test[t].reshape((1,6)))\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = y_test[t]\n",
    "    history_X = np.append(history_X, X_test[t].reshape((1,6)), axis=0)\n",
    "    history_y = np.append(history_y, obs)\n",
    "    #print('{} of {} predicted={}, expected={}'.format(t, len(y_test), yhat, obs))\n",
    "predictions= lab_enc.inverse_transform(np.array(predictions))\n",
    "label = lab_enc.inverse_transform(y_test)\n",
    "error = mean_squared_error(label/100, predictions/100)\n",
    "print('Test MSE: %.5f' % error)\n",
    "\n",
    "# plt.plot(test)\n",
    "# plt.plot(predictions, color='red')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "y_pass, y_future =y[0:size],y[size:len(y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(840,)"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_future.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2,  1,  0,  0,  0,  0,  7,  2,  9,  0])"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.73"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_future[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7573"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_future[0] +((label[1]/100)*y_future[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = [y_future[0]]+[a +(a * (b/100)) for a, b in zip(y_future[::1], label[1::1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.73, 2.7573, 2.76, 2.76, 2.76, 2.76, 2.9532, 3.0192, 3.2918, 3.32]"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.73, 2.76, 2.76, 2.76, 2.76, 2.76, 2.96, 3.02, 3.32, 3.32])"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_future[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0001489223214285713"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_future, test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(25, 5))\n",
    "axs.plot(y_future, color='black')\n",
    "axs.plot(test, color='red', alpha = 0.8)\n",
    "#axs.set_xlim([400, 500])\n",
    "#axs.set_ylim([5.0, 6.0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the feature importance\n",
    "feat_scores = pd.Series(rf.feature_importances_,\n",
    "                           index=df.columns)\n",
    "feat_scores = feat_scores.sort_values()\n",
    "ax = feat_scores.plot(kind='barh', \n",
    "                      figsize=(10,8),\n",
    "                      color='b')\n",
    "ax.set_title('Average Gini Importance')\n",
    "ax.set_xlabel('Average contribution to information gain');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.00198\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test \n",
    "history_X = X_train\n",
    "history_y = y_train\n",
    "predictions = list()\n",
    "for t in range(len(y_test)):\n",
    "    rf = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=1, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=5, min_samples_split=2,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=11, n_jobs=None,\n",
    "            oob_score=False, random_state=None, verbose=0,\n",
    "            warm_start=False)\n",
    "    rf_fit = rf.fit(history_X, history_y)\n",
    "    output = rf_fit.predict(X_test[t].reshape((1,6)))\n",
    "    yhat = output[0]\n",
    "    predictions.append(yhat)\n",
    "    obs = y_test[t]\n",
    "    history_X = np.append(history_X, X_test[t].reshape((1,6)), axis=0)\n",
    "    history_y = np.append(history_y, obs)\n",
    "    #print('{} of {} predicted={}, expected={}'.format(t, len(y_test), yhat, obs))\n",
    "predictions= lab_enc.inverse_transform(np.array(predictions))\n",
    "label2 = lab_enc.inverse_transform(y_test)\n",
    "error = mean_squared_error(label2/100, predictions/100)\n",
    "print('Test MSE: %.5f' % error)\n",
    "\n",
    "# plt.plot(test)\n",
    "# plt.plot(predictions, color='red')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0001489223214285713"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "y_pass, y_future =y[0:size],y[size:len(y)]\n",
    "test2 = [y_future[0]]+[a +(a * (b/100)) for a, b in zip(y_future[::1], label2[1::1])]\n",
    "mean_squared_error(y_future, test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the feature importance\n",
    "feat_scores = pd.Series(rf.feature_importances_,\n",
    "                           index=df.columns)\n",
    "feat_scores = feat_scores.sort_values()\n",
    "ax = feat_scores.plot(kind='barh', \n",
    "                      figsize=(10,8),\n",
    "                      color='b')\n",
    "ax.set_title('Average Gini Importance')\n",
    "ax.set_xlabel('Average contribution to information gain');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(label2, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34027777777777785"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(label2, predictions, average= 'weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5833333333333334"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "recall_score(label2, predictions, average= 'weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taken from \n",
    "# http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cm,['F', 'T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "X_train, X_test, y_train, y_test = X[0:size], X[size:len(X)], encoded[0:size], encoded[size:len(y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss='deviance', learning_rate=.0007, n_estimators=2500, \\\n",
    "                                 min_samples_leaf=10, subsample=.4, max_depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(loss='deviance', learning_rate=.07, n_estimators=50, \\\n",
    "                                 min_samples_leaf=10, subsample=.5, max_depth=2)\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss='deviance', learning_rate=0.01, n_estimators=1000, \\\n",
    "                                 subsample=0.5, min_samples_leaf=12, max_depth=2, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('GBLR Prediction accuracy: ', 0.5833333333333334)"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_train, X_test, y_train, y_test = train_test_split(df.drop(['Survived'], axis=1), df['Survived'], test_size=0.2, random_state=42)\n",
    "gbl = GradientBoostingClassifier(loss='deviance', learning_rate=0.0007, n_estimators=2500, \\\n",
    "                                 subsample=0.5, min_samples_leaf=12, max_depth=2, random_state=13)\\\n",
    "                                .fit(X_train, y_train)\n",
    "    \n",
    "(\"GBLR Prediction accuracy: \", accuracy_score(y_test, gbl.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-414-c3ed3ecca257>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m              'subsample': sub}\n\u001b[1;32m     12\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy score: {:.3}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    720\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1189\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1190\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    709\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 711\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    713\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 930\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    931\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    831\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    832\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 833\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    834\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    519\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    520\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    425\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = GradientBoostingClassifier()\n",
    "depth_param = np.arange(1, 5, 1)\n",
    "num_samples_param = np.arange(5,15,1)\n",
    "n_trees = np.arange(1000,3000, 500)\n",
    "learning = np.arange(0.0001,0.0007, 0.0002)\n",
    "sub = np.arange(0.1,0.5, 0.1)\n",
    "parameters = {'max_depth' : depth_param,\n",
    "             'min_samples_leaf' : num_samples_param,\n",
    "             'n_estimators': 2,\n",
    "             'learning_rate': 2500,\n",
    "             'subsample': 0.5}\n",
    "clf = GridSearchCV(model, parameters, cv=10, n_jobs=-1)\n",
    "clf.fit(X_train,y_train)\n",
    "print('Accuracy score: {:.3}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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