twosigma.ipynb

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   "source": [
    "# Two Sigma Financial Modelling #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Some thoughts\n",
    "\n",
    "- This competition is constructed more like a reinforcement learning problem than a supervised learning problem. However, reinforcement learning in a noisy non-controlled environment can be very difficult. One cannot tell a good reward should be regarded as an improvement or just plain noise. What is more, the meric R value used by Kaggle is not a good reward function for reinforcement learning or a good evaluation metric for this problem at all. These individual rewards are very hard to make sense since it is highly influenced by the mean target value of the given timestamp. Now I think about it, perhaps using the cumulative R value till the given timestamp is better? In any case, I did not employ a reinforcement learning algorithm for this task. Most reinforcement learning algorithms I am aware of are either slow or prone to be unstable in noisy environment and Kaggle's time constrain on its own Kernel does not help either. However, treating it as a supervised learning problem means we are discarding the reward for the test set. Oh well.\n",
    "\n",
    "- Financial data can be too noisy for exotic algorithms. Most importantly, learning historical data might not be the most effective way for financial prediction. Looking for similarity to the oil prices and stock indices might yield better results. However, we have no idea about the time frame of this dataset. Therefore, a simple Linear Regression algorithm such as Ridge, Lasso or Elastic Net is used here. Perhaps stack or blend with something else later on. In fact I did see other participants using stacking xtree x 0.65 + linear_regression x 0.35 which gained slight improvement.\n",
    "\n",
    "- This dataset has a huge amount of missing values. I used median of the whole column. However, doing some clustering and taking the median of each cluster might be a better choice. One could also look into the fact why there are so many missing values. Since the whole dataset is a portfolio of assets during a period of time, are the values missing because they belong to assets that are no longer available, i.e., got sold?\n",
    "\n",
    "- After reading some research paper on financial data prediction, factor transformation such as X' = 1 / (1 + exp(-X)) is used often and fundamental factor model (two cross linear regression) might also show some improvements.\n"
   ]
  },
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import make_scorer\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#import kagglegym\n",
    "from plot_learning_curve import plot_learning_curve\n",
    "import seaborn as sns\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "#from subprocess import check_output\n",
    "#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_36</th>\n",
       "      <th>technical_37</th>\n",
       "      <th>technical_38</th>\n",
       "      <th>technical_39</th>\n",
       "      <th>technical_40</th>\n",
       "      <th>technical_41</th>\n",
       "      <th>technical_42</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>technical_44</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370326</td>\n",
       "      <td>-0.006316</td>\n",
       "      <td>0.222831</td>\n",
       "      <td>-0.213030</td>\n",
       "      <td>0.729277</td>\n",
       "      <td>-0.335633</td>\n",
       "      <td>0.113292</td>\n",
       "      <td>1.621238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.775208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.414776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>-0.038064</td>\n",
       "      <td>-0.017425</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>-0.034134</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.114285</td>\n",
       "      <td>-0.210185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.273607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.010622</td>\n",
       "      <td>-0.050577</td>\n",
       "      <td>3.379575</td>\n",
       "      <td>-0.157525</td>\n",
       "      <td>-0.068550</td>\n",
       "      <td>-0.155937</td>\n",
       "      <td>1.219439</td>\n",
       "      <td>-0.764516</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.175710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.020940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007262</td>\n",
       "      <td>...</td>\n",
       "      <td>1.035936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.211506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.015959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.176693</td>\n",
       "      <td>-0.025284</td>\n",
       "      <td>-0.057680</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.180894</td>\n",
       "      <td>0.139445</td>\n",
       "      <td>-0.125687</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.630232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   id  timestamp  derived_0  derived_1  derived_2  derived_3  derived_4  \\\n",
       "0  10          0   0.370326  -0.006316   0.222831  -0.213030   0.729277   \n",
       "1  11          0   0.014765  -0.038064  -0.017425   0.320652  -0.034134   \n",
       "2  12          0  -0.010622  -0.050577   3.379575  -0.157525  -0.068550   \n",
       "3  25          0        NaN        NaN        NaN        NaN        NaN   \n",
       "4  26          0   0.176693  -0.025284  -0.057680   0.015100   0.180894   \n",
       "\n",
       "   fundamental_0  fundamental_1  fundamental_2    ...     technical_36  \\\n",
       "0      -0.335633       0.113292       1.621238    ...         0.775208   \n",
       "1       0.004413       0.114285      -0.210185    ...         0.025590   \n",
       "2      -0.155937       1.219439      -0.764516    ...         0.151881   \n",
       "3       0.178495            NaN      -0.007262    ...         1.035936   \n",
       "4       0.139445      -0.125687      -0.018707    ...         0.630232   \n",
       "\n",
       "   technical_37  technical_38  technical_39  technical_40  technical_41  \\\n",
       "0           NaN           NaN           NaN     -0.414776           NaN   \n",
       "1           NaN           NaN           NaN     -0.273607           NaN   \n",
       "2           NaN           NaN           NaN     -0.175710           NaN   \n",
       "3           NaN           NaN           NaN     -0.211506           NaN   \n",
       "4           NaN           NaN           NaN     -0.001957           NaN   \n",
       "\n",
       "   technical_42  technical_43  technical_44         y  \n",
       "0           NaN          -2.0           NaN -0.011753  \n",
       "1           NaN          -2.0           NaN -0.001240  \n",
       "2           NaN          -2.0           NaN -0.020940  \n",
       "3           NaN          -2.0           NaN -0.015959  \n",
       "4           NaN           0.0           NaN -0.007338  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_hdf(\"../input/train.h5\")\n",
    "df_train.head()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "A lot of features (or factors, in the financial world) in this datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_36</th>\n",
       "      <th>technical_37</th>\n",
       "      <th>technical_38</th>\n",
       "      <th>technical_39</th>\n",
       "      <th>technical_40</th>\n",
       "      <th>technical_41</th>\n",
       "      <th>technical_42</th>\n",
       "      <th>technical_43</th>\n",
       "      <th>technical_44</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.710756e+06</td>\n",
       "      <td>1.710756e+06</td>\n",
       "      <td>1.637797e+06</td>\n",
       "      <td>1.629727e+06</td>\n",
       "      <td>1.312105e+06</td>\n",
       "      <td>1.561285e+06</td>\n",
       "      <td>1.304298e+06</td>\n",
       "      <td>1.686809e+06</td>\n",
       "      <td>1.031686e+06</td>\n",
       "      <td>1.341916e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.708204e+06</td>\n",
       "      <td>1.691591e+06</td>\n",
       "      <td>1.691591e+06</td>\n",
       "      <td>1.690740e+06</td>\n",
       "      <td>1.708520e+06</td>\n",
       "      <td>1.666567e+06</td>\n",
       "      <td>1.690755e+06</td>\n",
       "      <td>1.706070e+06</td>\n",
       "      <td>1.473977e+06</td>\n",
       "      <td>1.710756e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.093858e+03</td>\n",
       "      <td>9.456257e+02</td>\n",
       "      <td>-4.537569e+00</td>\n",
       "      <td>7.729437e+11</td>\n",
       "      <td>-3.321289e-01</td>\n",
       "      <td>-5.047151e-01</td>\n",
       "      <td>1.803233e+01</td>\n",
       "      <td>-2.041142e-02</td>\n",
       "      <td>-5.703752e+08</td>\n",
       "      <td>-1.623607e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.584284e-02</td>\n",
       "      <td>-9.108464e-02</td>\n",
       "      <td>-8.161018e-02</td>\n",
       "      <td>-7.290059e-02</td>\n",
       "      <td>4.907529e-02</td>\n",
       "      <td>5.236320e-03</td>\n",
       "      <td>-1.700147e-02</td>\n",
       "      <td>-9.735643e-01</td>\n",
       "      <td>3.881556e-04</td>\n",
       "      <td>2.217474e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.308563e+02</td>\n",
       "      <td>5.195685e+02</td>\n",
       "      <td>2.497790e+02</td>\n",
       "      <td>7.620848e+13</td>\n",
       "      <td>6.521051e+01</td>\n",
       "      <td>1.020845e+02</td>\n",
       "      <td>9.260062e+02</td>\n",
       "      <td>2.496619e-01</td>\n",
       "      <td>7.502489e+10</td>\n",
       "      <td>3.675968e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.129349e-01</td>\n",
       "      <td>2.476959e-01</td>\n",
       "      <td>2.333819e-01</td>\n",
       "      <td>2.230485e-01</td>\n",
       "      <td>3.105295e-01</td>\n",
       "      <td>1.134394e-01</td>\n",
       "      <td>2.121332e-01</td>\n",
       "      <td>9.626583e-01</td>\n",
       "      <td>3.013458e-02</td>\n",
       "      <td>2.242171e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.017497e+04</td>\n",
       "      <td>-7.375435e-02</td>\n",
       "      <td>-9.848880e+03</td>\n",
       "      <td>-3.434176e+04</td>\n",
       "      <td>-8.551914e+03</td>\n",
       "      <td>-2.344957e+00</td>\n",
       "      <td>-1.043737e+13</td>\n",
       "      <td>-1.077101e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.687572e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-5.250904e-01</td>\n",
       "      <td>-4.449529e-01</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-1.265686e-01</td>\n",
       "      <td>-8.609413e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.500000e+02</td>\n",
       "      <td>5.040000e+02</td>\n",
       "      <td>-1.449710e-01</td>\n",
       "      <td>-2.956479e-02</td>\n",
       "      <td>-5.967524e-02</td>\n",
       "      <td>-1.655826e-01</td>\n",
       "      <td>-1.057050e-01</td>\n",
       "      <td>-1.996543e-01</td>\n",
       "      <td>-1.960470e-01</td>\n",
       "      <td>-2.280967e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.050297e-01</td>\n",
       "      <td>-4.651562e-04</td>\n",
       "      <td>-1.992532e-04</td>\n",
       "      <td>-2.203252e-05</td>\n",
       "      <td>-1.521701e-01</td>\n",
       "      <td>-7.377038e-02</td>\n",
       "      <td>-3.887695e-15</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-1.998819e-02</td>\n",
       "      <td>-9.561389e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.098000e+03</td>\n",
       "      <td>9.560000e+02</td>\n",
       "      <td>-8.368272e-04</td>\n",
       "      <td>5.523058e-03</td>\n",
       "      <td>2.109505e-02</td>\n",
       "      <td>2.475614e-03</td>\n",
       "      <td>1.175234e-02</td>\n",
       "      <td>-4.064488e-02</td>\n",
       "      <td>-7.395084e-03</td>\n",
       "      <td>-3.029069e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.502064e-02</td>\n",
       "      <td>-3.951567e-12</td>\n",
       "      <td>-1.418487e-13</td>\n",
       "      <td>-1.591224e-16</td>\n",
       "      <td>-1.476793e-02</td>\n",
       "      <td>9.782702e-05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-6.597540e-01</td>\n",
       "      <td>1.117279e-05</td>\n",
       "      <td>-1.570681e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.657000e+03</td>\n",
       "      <td>1.401000e+03</td>\n",
       "      <td>1.199108e-01</td>\n",
       "      <td>1.078554e-01</td>\n",
       "      <td>1.952209e-01</td>\n",
       "      <td>3.037236e-01</td>\n",
       "      <td>1.556464e-01</td>\n",
       "      <td>1.303819e-01</td>\n",
       "      <td>1.832071e-01</td>\n",
       "      <td>1.764751e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.909600e-01</td>\n",
       "      <td>-5.219879e-40</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.772415e-01</td>\n",
       "      <td>7.855728e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-5.188884e-08</td>\n",
       "      <td>2.047074e-02</td>\n",
       "      <td>9.520990e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.158000e+03</td>\n",
       "      <td>1.812000e+03</td>\n",
       "      <td>3.252527e+03</td>\n",
       "      <td>1.068448e+16</td>\n",
       "      <td>3.823001e+03</td>\n",
       "      <td>1.239737e+03</td>\n",
       "      <td>6.785965e+04</td>\n",
       "      <td>1.378195e+00</td>\n",
       "      <td>5.203165e+02</td>\n",
       "      <td>7.677125e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.957758e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.569265e+00</td>\n",
       "      <td>6.844833e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.435858e-01</td>\n",
       "      <td>9.349781e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id     timestamp     derived_0     derived_1     derived_2  \\\n",
       "count  1.710756e+06  1.710756e+06  1.637797e+06  1.629727e+06  1.312105e+06   \n",
       "mean   1.093858e+03  9.456257e+02 -4.537569e+00  7.729437e+11 -3.321289e-01   \n",
       "std    6.308563e+02  5.195685e+02  2.497790e+02  7.620848e+13  6.521051e+01   \n",
       "min    0.000000e+00  0.000000e+00 -2.017497e+04 -7.375435e-02 -9.848880e+03   \n",
       "25%    5.500000e+02  5.040000e+02 -1.449710e-01 -2.956479e-02 -5.967524e-02   \n",
       "50%    1.098000e+03  9.560000e+02 -8.368272e-04  5.523058e-03  2.109505e-02   \n",
       "75%    1.657000e+03  1.401000e+03  1.199108e-01  1.078554e-01  1.952209e-01   \n",
       "max    2.158000e+03  1.812000e+03  3.252527e+03  1.068448e+16  3.823001e+03   \n",
       "\n",
       "          derived_3     derived_4  fundamental_0  fundamental_1  \\\n",
       "count  1.561285e+06  1.304298e+06   1.686809e+06   1.031686e+06   \n",
       "mean  -5.047151e-01  1.803233e+01  -2.041142e-02  -5.703752e+08   \n",
       "std    1.020845e+02  9.260062e+02   2.496619e-01   7.502489e+10   \n",
       "min   -3.434176e+04 -8.551914e+03  -2.344957e+00  -1.043737e+13   \n",
       "25%   -1.655826e-01 -1.057050e-01  -1.996543e-01  -1.960470e-01   \n",
       "50%    2.475614e-03  1.175234e-02  -4.064488e-02  -7.395084e-03   \n",
       "75%    3.037236e-01  1.556464e-01   1.303819e-01   1.832071e-01   \n",
       "max    1.239737e+03  6.785965e+04   1.378195e+00   5.203165e+02   \n",
       "\n",
       "       fundamental_2      ...       technical_36  technical_37  technical_38  \\\n",
       "count   1.341916e+06      ...       1.708204e+06  1.691591e+06  1.691591e+06   \n",
       "mean   -1.623607e-01      ...      -8.584284e-02 -9.108464e-02 -8.161018e-02   \n",
       "std     3.675968e+00      ...       6.129349e-01  2.476959e-01  2.333819e-01   \n",
       "min    -1.077101e+03      ...      -1.687572e+00 -1.000000e+00 -1.000000e+00   \n",
       "25%    -2.280967e-01      ...      -4.050297e-01 -4.651562e-04 -1.992532e-04   \n",
       "50%    -3.029069e-02      ...      -8.502064e-02 -3.951567e-12 -1.418487e-13   \n",
       "75%     1.764751e-01      ...       1.909600e-01 -5.219879e-40  0.000000e+00   \n",
       "max     7.677125e+01      ...       4.957758e+01  0.000000e+00  0.000000e+00   \n",
       "\n",
       "       technical_39  technical_40  technical_41  technical_42  technical_43  \\\n",
       "count  1.690740e+06  1.708520e+06  1.666567e+06  1.690755e+06  1.706070e+06   \n",
       "mean  -7.290059e-02  4.907529e-02  5.236320e-03 -1.700147e-02 -9.735643e-01   \n",
       "std    2.230485e-01  3.105295e-01  1.134394e-01  2.121332e-01  9.626583e-01   \n",
       "min   -1.000000e+00 -5.250904e-01 -4.449529e-01 -1.000000e+00 -2.000000e+00   \n",
       "25%   -2.203252e-05 -1.521701e-01 -7.377038e-02 -3.887695e-15 -2.000000e+00   \n",
       "50%   -1.591224e-16 -1.476793e-02  9.782702e-05  0.000000e+00 -6.597540e-01   \n",
       "75%    0.000000e+00  1.772415e-01  7.855728e-02  0.000000e+00 -5.188884e-08   \n",
       "max    0.000000e+00  1.569265e+00  6.844833e-01  1.000000e+00  0.000000e+00   \n",
       "\n",
       "       technical_44             y  \n",
       "count  1.473977e+06  1.710756e+06  \n",
       "mean   3.881556e-04  2.217474e-04  \n",
       "std    3.013458e-02  2.242171e-02  \n",
       "min   -1.265686e-01 -8.609413e-02  \n",
       "25%   -1.998819e-02 -9.561389e-03  \n",
       "50%    1.117279e-05 -1.570681e-04  \n",
       "75%    2.047074e-02  9.520990e-03  \n",
       "max    1.435858e-01  9.349781e-02  \n",
       "\n",
       "[8 rows x 111 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1710756, 217)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Exploaratory Data Analysis ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Each id is a financial asset. Let's see what y looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe40f7049b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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9D6JsCQAaTfY9DNtvXjqHp353Pu+XQSVh2Q4Upf0tRJbyL1vSO2p+ZyZGu+vh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TAAAg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\nFh2KwobpkrltmLYb1Qq4yTxUf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FapryuZhvJaLqW5GItaSNwwK/bEsVhAE01933jTQlQd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\nRERERETkyv8Hcv5wO4pAQlwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe43d9bccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train[df_train.id==10].y.plot(figsize=(13,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe40a5c0f28>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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WCbQbkTVFPoOn7EYreoBHNoOvv3gLAnEDGV2wSFBgEXMS5Mn7qvoeZWjZNIIFlXYjQtYH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3AAAg\nAElEQVSBKlkkNGohBYV3nft+ANvxpDQSRIh4Q0KX0LddqIpCUzfTMA0NzpgdzAWEov2GpQuzAWQQ\n3r+qVB4BD+TwlsaxuTo2d4ZCx4zn39iCpip03UqChH2JnJdYeggeiCaBfKainBiRBarH0yRoAncj\ngaCPcJtlCr6ywuW9gQM/CKT1CICccJlbJET3RFnxcpHiJon5toXdXhyoJuLT13MJlyeF2oC4SCDG\nBjJTbSA2p+BhbzhGcokQZVmI3LwIiP4tq0hwPT9Mv+bsOSJrZRIQxqI8zbRMaKpSQJOQbVAh2rO2\nqGg5O205iZPHmhi7PtYZVqh9gTkFgShQLS1cJsGbWTaovVHoTigqBkV43yOhvvbLz93I/bdhsB3/\nu7AivQdZSxwqXC6qSSjvblSJJuEDH/gAPvCBD0z82y/8wi/Q/29ZFn7rt36L+bef/OQn8clPfrKK\nlzGB2ZYFTVWwuTuC6/l49c1drC406CJKXF2yEpdZXTQg7oq5Ln+x6A6c3FMEoLpI7YHtYrUlXmzb\nTdLRkDsEHUROAi0SMsNvMixQo5uRbMB5rAGpyG7PpgUnkNzs2Te6rOe0qEigyYtMdyN54bKh86dd\n5HvMSnKkQmzbZQrwSZCazCQhT9e0PwwLXF5X1DLUKHAuoO8zCdnAuSwoioJOwyxsgTpyImctxuFt\nabaON653sb1n0w5hEnsDB5dv7uHeU7PMz5emLguEoLs9G5qqyIn0NBVBEB6WdE2hYYWs+yu2QJ1+\nHJ7fvCFofow47kZAaBfbsHQpAwjSccty0OGBHGA7OdYK0f1KwFs3lueqsUHdG4ap4LLZDmnMdWoI\nsItu38F8pyYsLmWEyz2GhXGWtTQgb39KYJmaMKAtfd+K9hSfMAcEjQVSQLgZh69u30EAfmOq0+AX\nCd2+g06TPRFSFQULnZr0JIHcV2LhcrbV9XY3n7MRwalIe3R1vTdV+MV5BfzXlrzW0mtYl+FuFD5u\ntnBZZj3k4cK5ecy0THz9hTX8xBP35GoC2w57LyCIU5fD90uaqfktUKtxyASOaOIyEKWaRjfTpRt7\nsMce7j8dd+MMkvwrTFz2udWmqCsGhBZyvcE4t7MRkJhSlBgVuZ4PZ+xnHiZJEZOnSFAVhVs8ZcGk\nXbfsnATZSQLPsi4Zc66pCi2IZEBTSFMbWpzky5kkSHpOsywRCeQmCTncjQSTBF6BRUC7TJyih9js\nyjhe5PGFH4xc4eZhZhSb/dGY+x3lRehp7kg7gCWRTltOIsvh6G9e2UAA4JF7Fpg/JwdC0ZQjDwUl\nTdURWUKSgxRrCsJ1NxLkdoiSSFVVwa/85Nvws08/kPs95AUxFMg1SYjuV0fQ0SYi1vT93qjp6DTN\n0kWCiB4kA7LekY4xfTyRBaowJ8GFrikTHdCZlgkl8dgs3LjVR6dhSB/iamZIR+G5Q6WpeKI9hZXt\nkYasuxG1C+V8HzwaYxAE2O07wnPDwkwN3b6TSccFkkWCgOJiatBURThJIFS0PHQjILZBfXNjmoPP\nsslNQ1SQdgcONFWh+wS5ZkSThCAI0C9ZJGiqivdcWMHAdvE3r2zm+lt77Aknoum93y5qgVqhu9GR\nLRKA0Hax23fw3BvhF/lAYmRvSmwmHiM1lEDUFQMQ+fUGlPOfB1VEasty19v1/JqEolMEIB6bCS1Q\no8Uq5OaJNSMA/6CbvBlZoUAikA7aNN0ofG2Zk4QSdCNR4nJcpMjnJDAPZW4oKss6PMYe2uwDwdBh\nH35YaNUNtBtG5iQhCILwkC/YPETaFlogVzBJAMJANdcLpNORkxB5pS/NiLMSvvHSOgDgnfcfY/68\n3TCggF8k+EGAnZ4jfXCkuRrR9UITl5lhavFzTD0vmSQo7EkCq7tFuvC8DfT0SjvTNz/5HorSjchh\n6KDoRkBYPN/aHXGnH1fW9vDr//abuHyTz4Emh+G8QWoE6UC1nZ4NQ1eZ97WuqTB1VVgkDKL7N7m+\naKqKTtPkJtk7Yw+bOyPpKQIgbqgAcbeZHCSFmgTBfk+gC3Q1SWRlCrQ5IY1DO6R2iq6/hRw2qCTD\npiZYnxVFQaOmCzvweTMSCGiRsD6tQ6PTprrcJCGN9MSF7L2iImHkePD8QEhxksH7Hg4pR1957rr0\n37ieD9cLhKnx6Ywkp6AFqpYjzyMLR7tIiDbhv/h26KB0312z9GdSdCORJkFwAAPijIQidKNYFF28\nCiQLeFaYVF5h5tgrWSSQpGvOYu16/gT/UNj5yQhTqye623k7bFSTkOp6DUfRoZirSZAVLmfTjViH\njlzCZY1fCIcpjtnfY9ZkhL6PmlynY3W+gY3dofC+c9xwMRVtHqLU5TzTFhnEAuH8lCMapMbRJADA\nOqNI2OnZePnyNu45MUPXsTQ0VUW7YXBfl8jrngVDyzFJUPmTBF5yLaVqML57Xk5CXsg0f0QgtIo8\ngWSydCNelsjKfAMBwORte76P333mJVy8sYcvfYt/IInpQcU1CUA8ScmaQNVreoZwmT0JnG1b2N5j\nT+Vubg0QQF6PAGRnJZB7g1BGRUWCSINIoEkYlgDZh2pdCwuwdHOOFHtpy9QkFjs5igSJSQIQHrBF\neThFNQmdhomZpomrjEC1mG4kFi4D01OrIAhokUDQkhBg58lIEGF1oYl7TszgxUvb0sF2omkpAdFk\nxZOEYutiVYG8wBEvEsg4f3vPxsml5kRXX6bj5HkB/bDT0AVdMSDpbFRkklB+VETDbDIoF628wmXX\nK1Uk6JoCRQFszueePoyKunOuF3Z+eBOCZFcyj7MRkBQup+hGGQF1spMEMd0oEi8J6EYyGgBDMBqX\nLfYaNByO/X7yvB4g1CUEAbC+zZ8mkEW+Jdg8RMVmnmmLDEQiwyzsCOlG/EnCN19eRwDg8QfFltCd\npoUux90oLwUl3fggvGumuxGhG4kmCRxNAovPnZVEKousJkQWyOEuzyTBMFQoENONSLo669AtouF9\n/ptv4kp0wPr2G7e4lDeyThXWJESHv609G57vY7fvCNfMhqVzp4u+H04CWd3auZYF1/OZTQeiRyDp\n7DLISl0m9+xidK/x9h0gLG6zps1EuJx1+CLTEp5wGQjXlXRzjgaECb7HPKnL5HOpZxUJ9dDFjnd9\nbe/ZsEyNO0EX4eSxFm51R1PXC0vcngZ5vnRBOnI8OO7kxEVGuFxVkQCEAuYAwF88LydgJtQhEd0o\n7ZRGhcu5NQl36EZSSIoB0+4glIohHD/yNQlZFqgkobWQcFmyWyEC4YrXM7qphq6ibunSmgTXCwqn\nLQPhaNMyNO4mnr7BRZoR1/OFnZ9k9Z23wzZD6UZsTQLvUFwzNaiKmN8JiAXQSS1FGoORC8vQMrUE\ngJif7UoWe1mTBDLOrkumcK7MRzaoAg52X2JaQhdTQZFQxOKOBdLx/6OvX8ntFiGiG822LOiawrRB\n/caLa1AU4B0cqhHBTMvE0PaYh6S4QJE7OE5pEiidL58mIcvdiGX2YAuEy3mQNeHNQhG6kaooMA1N\nqLMa2C63aOUVCZs7Q/zfX3kDrbqBB8/MYXN3RN1/0qDfdbPgJKETaxK6/dARSHTArVvhJIF1qAz/\nHWgy3i/VejF0Cdej91aEbpQ9SQjPAmILVBm6kZxweSdDkwCENMbeYDyxphAGgkiTsJjDBpXo8bL2\ni2bNgOcH3M9xe8/GfJtt55oFIl5O6xL6DHF7Gjx3o7T9KRAejOuWLtx76fSigiLhnfcfg2mo+Mpz\nN6QS08kabQmoQ1N0IzpJKEg3qsDd6EgXCUuJMf0DpyeLBFNikuD7fHejLAHT3rBYRgJQjcetLN0I\nCF/jQWkSgNi+koX0qFBEN3K9gHZ2WEiOWGUPSgS6pqLTMLDdS3NGXSgAapyOiqIoaNb5XTaCkSAn\nwYpSslgHv4GdbWtLILpGHcnvMWsyIqJNsUAcjniHHSCeWog2j9hKd/q9kb/PKpBl8e6HVvDQ2Xl8\n6/Vb+PeffzWXgFmUU6CqChZn6lOThI2dIV6/3sUDp+cyD6vk56xDV56MBEBANxK6G8kLl4WThKrp\nRhmmBzyQz1FE92DBMtRMuhHvHlmOAhOTRUIQBPg/Pv8KnLGPn/j+e/CuB1cAAM+9fov5GLsSnWsR\nOo3QWnN7byR13TSixHNWA4I0nFgHMZqVwNAl3KCThAJFAof6RCcJM4RulGV5Ll4T82gSFIgnAp1m\nGLfZS1BTd3vZdLc8moShE1puZmrPanxbW2fsoTcc59YjEJw8Fn6fU0XCMNsClUc3StufErTq+oFN\nEuqWjnfedwybuyN858pO5u+Ta88y+PtSmm5UOifhDt1IDDJiVADcm9AjAKC2VUU1CZqqQAGbXwsk\nXE0KcEQVRYGmKpmik2df28QVTqBHPwflot0w0Iss9LIwdn3arSsKUxCERW5i0rERC818ZpeTIFkk\nFFngZlsWdvbsiUPhINrsRWPpZs3ItEAV0XQ0VYXB+YxIQJ4M4q4qO0xNjm4k9kTPSzdalXA4Ipum\nqMCNhcsCTUJFdCNdU/HzP3QBJ5da+LO/vYY/+sYV6b/d6dlQFH5nemm2jt5wPPH5fuOlNQDA9z6Q\nnT4vKhJ28xYJU+5GfPewWJMw/Ti8fAWR29Zo7EFRilsrE1C6UQnhcqtuSE3qkrBM/nSUCOl5VI2l\n2TpURcHaVlwsfvPldTz3+i08cHoO735oBQ+fC8P0vv0Gu0jY6UXe+gULY1VVMNsysb1nSxUJdc7h\nDYjXcFYzQ5SVcP1WH3VLzyW+julG/ElCw9Jj4bLQzTCbbqSqIV02U5PQs9FumsLriNqPJ2iMhH7U\nETQX59oWVEWRnCS4Us5zombQd66GB+BFhk2zDE6SSUJKl9C381mgJkFpWVNFgoHekE+bqrJIAOLM\nhK9IZCbQRogpMUlI041yThLIGTJr4iWDI10kdBoGOk0Td5+cmepKmhIWqK4fTDl0ECiKAk1TmQcw\nIB5bF62+tYRPOQu+H+Bfffbb+Defe5H5c+qnLzNJqJvhqFHCvaWKSYIM3Yh8bqIiIaQbiW644sJl\nINzQ7LE3sQENBB1BApJSLOo4D20Xpq7ynZmM6SRRPwhy+f8bgkOZ7PcY0414kwQyEZHrdCzO1qCp\nirBIGDDSWtMwJcTdRQ9MLNQtHb/48Ucw17bw+3/2Oj3IZ2GnZ2OmaXJpi4QGsbkbHxC/8dI6NFXB\n2+9bynx8skkm03Lj587mNycx7W5UrQWqSMdlS3Y8sxBrzYppEra6di6qEYFl8EO9yH3MWzd0TcXS\nbI3eE4PRGP/hT16Foav46Q/fB0VRMNOycHq5jVeu7jAFw+F1VowOQjDXrmFnz8FWN9spSZS63IsO\nuixNEc9a2vXCwK3jC41c74FMdHlFAhG3mgI3NALP9zPpRkD4fYk0CUEQYHvPztz7CVVmN0H1Jc1F\n0SRLU1XMtS05TYLtcafeSfBSl8euj3//J69CUYAPve1k5uOwsLrQhKYquMqbJBSwQKXFVOpzatYM\nWpSzQIuEivaGe0/N4thcHX/9nfVs9oCTPS2tpfa1ojkJAKJG851JghCKouC/+6m34x997OGpn2lR\nRyBLuCzivBs6v9u/VdK3WldV4STBHntwvQDXNvrMrkzsgpNdMcdOMeKq0/N9+EE5TQJ5Pt7nThap\n+UhIJxKauV4gniQY5ScJwOSGNpQ4pDfrYn4neRxRsVEztakDsO14CAL5DjkJGUvzs30/gOcHUgtP\nLFzmWaDmmyRoqopjc3XcuDXgFlEymoS4yJ++Pvo5CuQ8mO/U8IsffxQ1U8OnP/cSXrkqHjGHhwWx\nBemxlHj5+mYfV9d7ePjcgpSmgoR+sdaA3HSj1CSBNCnyWqDyJwl8rZU99nKP1FmghU6BScLY9dAf\njguJfy1j+n4lkJlsrcw30BuO0RuO8QdffB27fQd/9z1naNgaADx89wI8P8DLl7cn/taPnF6K2p8S\nzLUt+EGAy9F0WrRmykwSeMJlYHrytb49hOcHWM2hRwASdCPGZ+/5PnqDMWaaJixKTxS7G8mk8Oqa\neMrfH7kYu36mWUaHNUmgHXLx3y7M1LCzZwsnGkEQ7kEyerEGR3v2x9+4grWtAZ5420mcXmlnPg4L\nhq5iZaGBNzf6E+tFfzSeytJII2uSME03EtugylCc8kBRFLz34VU4ro9vvCxuHMm4G1kp0xK74CQB\nCGmid4TLEliarTM7Q4qiwNQ1IXdVpEkAwo4CTyC3vWejbunSh6c0NE0RfsHJA+iLl7amfp6HciGi\nbiRRNm2ZwNQ1jF2fecAgNzcR0omFZr5wnGtGriNAwUlCa5LK4fsBhraX+ZmK+J0EUkVCqsjIY38K\nJMKrUgemPN8jea/cMLUC1J7VhSaGtktFemnICNqsFHczidgCtZqNIIlTx1r4+Y9dgO8H+F/+03O4\ncWva/5ugP3Lher7w2iMOR8QGNaYaiQXLBOQwwdMk6JoqPVFJW+aSgxBL9yM1SVB4kwS2u1FZZyNA\nTmvGwy6HwiADy9S4uS7DDLMDAFiOaHhfee4GvvjsdZxYbOLDj9818TuPnAtD9Z5LUY5C8au81S0P\npCi4eKMLQI5uNGSscUTfloduRO6j4zn0CABQM/juRnuDMQJgYpJgZ9CNsixQgbDRIZry70hmCnQY\nWQm7fQe6pmZOZhdnaggQNyNZcKI9Nsv+FIinPsmJ8ebOEJ/76iV0miY+9v6zmY8hwqmlFmzHm6BI\n9UfuVJZGGnk1Cc2MIoFoRauiGwHAey+sQAHwFxmUI1GqPAE/J6HYJKGM+Q3BkS8SRDB0lTuWDoIA\nfiB2OwjHjuwvYUdi3CiC6LGBycPzC6wiIceBUkTdSIIeLktOEsiNwCrQ4iKBuFGIJwmiIk5RFFim\nFlm35S/W6IYWdWRlu+YyNqgD2xM+jmVO043iwk9ugVMVBbo2vVCQwlbme1RVBQ1LR59DRZM5AKVB\n3Vw4B2zSzZKhG7Hu32EOPU4RXDi7gP/yw/ehP3LxL37/W1xnMJqRIFgHYhvUEYIgwNdfXIOpq3js\n/KLUa4npRqwiwZFOWwamw/c84SQhEsaxJglBhiaBRTcae6VFywBgGMU1CbuSXVwWRNkmMuJ+Iuj/\ngy++DgD46Q/fN9UAOXe8g2ZNn7JCLWt/SkCyEogVqejxRKnLhG7EKvIblg5TV6eEy+Q5jy/K258C\nSeHy9OdOqDszTVMYvkjgB3x6cRKsNTUJGftTILZHTzZLugMHM83se3ahk22DKpuRACTyfRJF33/4\nwqtwXB8/9qG7Szdc4lC1mHLUH44zGximoUJVlFyaBCAOakujX7EmAQjPKg+dm8fr17u4tslvGsmY\nM8TC5fD9UrFzEbpRRqNZFm/pIsE0VK5wmWfjl0S4WLD5tf2RW6pI0NQ8k4TtKepG7BCTZ5Ig3lir\nmyRE9CbGAY8WCUSTINSMiIXLQNhxOZlzhE0Q043CBWkoWXjR0S2nmzF2fbiej4agW1QzpjuTRVx7\ndE2dol6QAlP2e2zU+G5NxO0pj3Ul8UG/wdElxCE72cJlFkVOhq5UFt/36HE8/e7T2NgZ4c+fZYdc\nxTkF/MNWMivhyloPa9tDPHrPopTYEIgPctsppxPfD7Dbc3K53Uy5G1HaEH+SEOTRJOhsulEQBLAd\nr7T9KZB0N8qvSehK8MF5EE22ZIoEIuj3gwAffOw4zp+cnfodVVVw4dwCtrr2xGEk1p6UnCREB88g\nCDVGomtQXCTw719FUTDbtqYmX8TtLI+zESDWJCR565SemGGBKkc3ElOBtyXsT4H4gEteJysgjIeF\nmWktUxpkuiKzlpAigKy933ptE3/76ibuPTWLdz+0kvn3WUiLl4MgwGDkZtJ+FEVB3dKYFqiaqkyt\n8VmBar1hqAWsgtqYBElg/qogM0GmSJgWLkd7dQG6kX5Hk1AeRkR7YUHURSPgdfu3JTqIWdAyxFHJ\njlW37+DNjckKdmC70DWVujiJQDUJGWI/0mEs7W5EihLGwt4fjqEgPqCLE5eDTBeSX/qxx/CPf+SR\nQq8zzZ+VpXBREVhWSrFwkjDt2lFEkMuixNFJguT32KwZfOGyEwrjslxBkognCewiQabAFW36eex/\ny+Cp770LqqLg2dc2mT/fltAEWKaGTtPExvYQX8/hakTQsHTomjJ16NobOPCDfBSUKU2CwN2I/JtY\nkzB5fcVBVKmi1fURoHxGAjA9DcmDHYkgKx6EuR0SCeDknphpmvjRD97N/T3qcpSwQo1drMprEgiy\nrpu6QLgsskAFwnW1OxhPXAfXN/swdXUi20gGojC15CTBlJgwSdONMvZmSjfqSE4SoutuYLtwvUCK\n7rYoEahGpitSwuWEJsEZe/h3n38FqqLgH/zAvaXNBICQpgmAipdHjgc/CJhZGmmQTI4kdqNiKr3v\n0IkIt0gYo1XAlj4Lj9wdUgEv32S7TQKJwEjBOldLrSNOlGeUZ38lCDUJd+hGpWBofAEtqcBEPvzc\nIkGykyCCnmGBSg6PJ6Iu+QsXJylHg5G8C44lOLQnUdkkgfJDGXSj6HWnb5Y0giAU32ZNEubaViGO\nMRCPi8k4PytIjYDF70yCLHg1UZHA6EwWsfY09OlpWd7vsVHT4Yx9ZkGdpa1ggVArWA5H3YGDqxt9\n1ExNWOBmJS7LFshl0KobuPfUDN643qUHtSRIhzerWbA0W8Ot7ghff3ENdUvDI3fPS78GRVHQaZpT\n7kY0XCvHwZEXpsY6OCmCLBf+JIFNN6oqIwGQM6TggXrUl5kkFKQbzbQs/NzTD+AXPv6IkNpx4ewC\nFExaoZLipqwmYT5HkdDgCEqBhHCZs/+Q+4Ec4n0/wI2tAVYWGrkPQ6Iwtd1+nHkhk8TtC9wMk8ii\nG21J7v91KwzFJEWVjLMRgUzqcp5JQtzYGuMP//IyNndHePKdJ+kEoCxmWyaaNZ1OEvKEmjUsfWJi\nFQQBugOHGTiXJVzujcZM162yqJmhdW/SxjgNsjaIphi6pkJTlYnEZbPgeSvUJNyZJJRCSDdiLxqk\nAhPTjdgdBWp/mtFJECHLApVccG+7N+Qup3UJ/ZEr3UmVEXUB1WkSTIHTRG84RqtuJCzreN8PP+ip\nKrQbBlRFiTUJEh3B5M95kwSZw368+cWPkVe4DITf1ZQmIWeRQBbyAaPoGdqudNoyfbyagU7DmJok\neL6P3/7PL6Dbd/CRlGgzDdGhbDDK5rpWhcfuCe+/bzFCrnYkHc6WZuvw/NA28XvOL+Uubmaaob89\ni6deZJJALVA9trYAAMg/sRpVsXB58t91lU03oh22CooEGUMKHijPucBhW45uJH5/7314FWdWOsLf\n6TRNnFnt4NU3d+njFvmuWZhpmSBnZNkigUVD7AmEy8nHJuvqZneEsevnSlomEBUJ3X74OmZa2XQj\n3w8QQLzfE+hZwmVJJkFY4Bv0dfJ49izMt2tQIA5UGxLrXYkJHfk+r6738Id/eQWzLRN/773lxMpJ\nKIqCU8daWN8eUjo2IDanIKhbOmzHo+vKyPHgjH1mMUUeLxlQR+B6PmzHq8zZKI3luQa2uiPumZLs\nVVkakZqpUeFyGdc3Tb3jblQaph4e8lkOHSKPcAJDU+C6/pQegEwS5ktpEsS8R3J4PDbbwMmlJl65\nukMvziAIQqtOyQ6vRRfQA9IkcAqAIAjQnyoS2K/Jpc4r5UehPKiKgpmWiZ29eBwM5KAbZTgCZQmX\ngUlrv+onCXKLT5NT9ITXmViAzcPKfAMbu8OJ1/af/vwNvHR5G4/ds4in33NG+Pem4JrNkyVRFkRg\n/Oyr05SjHUkBI7FBBYDHH5SnGhHMNC2MXX+iq7tTgIIyrUmI7jFGIU6oRCwbW64Fqs6mG9kSrh95\nYOh81zkRyrobAZxJQkZOQl48fG4enh9QV7vdnHkYPGiqSg/wWQn1PGtKAOgNHdQtjWsqQSe00T5Z\nJGmZwDI0KODQjcgkoWHGHdosenEFFqjbe7a0WUa7YaI7cBAEAdexhwVDVzHTMoWBavEkIfu+0jUV\nNVPD+vYQrufjJ77/fGXXK8HJpRYCANc2+wkHOzm6ERAbh8Rak+nDvmiSUHWQWhrL8w0ECO18WZCd\nmFoJZ0PH9QsXCbqm3KEblYUoddmX0CRomooA0yN3SjdqF0soBMhCFHC95MnhqGZqePDMPMauj1fe\n3KU/8/xAWuBKRrGZ7kY5uew8WByP+5HjwfMDNOtGpk6CTHDyJqPmxVzbwk4v7NLKpgtnB5BlP046\nnj35eHmcJljTriJ0I2C6a+iMQ4u9QkXCQhNBAKxvh9OEb768jj/6+hUsz9Xxib/zYCbtwOJMv4gg\nbr+cjdI4NtfA6kIDL17amrp/ZC1IiXi5VTdw/+m53K+BHCp2E1aKOwUS33Weu5FoksBYn/KGqVVJ\nNwIi17oCwuXQfrJYarFISF/EJliER+4OC9PnounVTs+GpiqVHH5I9ztTkyCYJOwNxkIHtrmUa9x1\nan+az9kIiB3s2JOEyUO3afCvCz/HZJpoEnh78/aeLU01nmmaGLs+Ro6Xa5IAhOvGVtfmvifqbiR5\n3ZEu/ENn5vDO++UsmPOAOhxt9KQc7AjSdruiLIlWnU/13f8iIVzHb3IoR7LNkJqpTwiXraJ0I+2O\ncLk0Yk/t6ZvMlZkkRH+f/iK2JX2SRdBUBQHYmzAQdwksU8OFsyGH+cVIlyDjM58EuWgPLCeBM0lI\n3sRZ2Q2kkyMjNCuD2ZYFzw/QG46l3Y2yhMsDCfoB1WQkNj/Z509C16f5s+R6l6WN8SxdZd4HD0So\neePWAJdvdvG7z7wEy9Dwj3/4Yan3R5N1U4cDUiDvR0YCD4+dX4Tj+njp0mTI1faeLWVBSmgW73zg\nWKGilxwqSEcZKEg3Sk0SyGGedXASaRJ4znC8MDUyLatqkmAypmcyCN2gaoWEmmS9YnW0ZZoCeXBm\ntY1W3aBWqLs9GzOtaRFnEZA9K+uQW7PCDj4vcZkISJnP0UpPEsJGQRG6EcDOlDL9PbkAACAASURB\nVAHCoq9VN+g9ZeoadzJNr1lJTULyb5IYux56w7H03t8mWQkDJ9ckAQDuWm7DD4KpJGMCKlyWvK/m\n2hY0VcHf/4H7KhErp0HFy+s9KQc7grSTFi9IDQjfq6YqzCl+Hh1EEaxEwYek8ZWG9CQhCmYMgiDU\nJJSkG/GKWVm8pYsEYivF2lA8gbMHAQ0fSm16W3s2DF0+xEj02LxKMHnBnT81C11TqHg5Ly2FWpJm\n0I3cijQJPMvVZJEQC814dKODmSTMJhJtZVxKAJkAMm/i91gQ0o1yahI8f5JSR6532RRH3iShzOGH\n2KBevNHF//iZb8Aee/i5px/ACUmhnKKESZ1p+kCRz6gs3nbPEgDg2dc26L/5fkgfkLEgPbvawS/9\n2KP4uMDVRgRCM5mYJBRIfJ8SLsu4G7GEy5k5Cami1al6kqDlFi6HdI/i2TZxo4VNfwOqKxJURcHD\n5+ax03Nwdb2HnZ5TKNuBhaWZsBua5TKkKgpqlo5ByprS9cKuuKhBRdfUqJC9casPTVXoRC0vws7r\ndLGSthM1DX4uEqFlyNGN+HvztqRZAQER3+71x7knCWeiBOQrHEedPMJlAPjZpx/Af/tTb6cNnKpx\nfLEJBaENap6peL0W3lvDqSJh+m8VRUGzbhwa3QhgG3IAYcNPU5XMM0vNDF03SRhekbRlIL6Wy+oS\n3tJFgiidk44fhe5G7M7YdhSkVqYaFwUPAZNBKZah4fzJWVxZ76Hbd3ILXKUnCRXRjeKiZPL5kpU+\n5Zzz6EY5FvUymKMOR470Zk/4neUsUBnuRsRdKYdQOE0hAfIL0IkbRDqgRjZcjgXicPRHX7+C65t9\nfOTxu/COnCNuy9Cmrlm6+RwQ3QgIQ67aDQPPvnaLHpBDnrG8w9nD5xakN/M0Zjh0I9PITm5Ngutu\nxKQbRTkJjOWJRzfSqHB58o8qnyQI8m94IPaTJMQxLwiFkudupCpKYZcSFh6OLBe/9sLNKG25nB6B\n4KnH78I//LsP0gOoCA1Lx9CeXBP6EjbNSU1CEAS4fquP5flG4YZPjUE3cj0f/ZE7ceA2Df4kQYZe\nTECvYwbfWzZtmaCTyEpI5jrI4PRy+B1d4hYJ+SYJK/MNnF0VC+fLwDI0HJtvhHSjIaEb5dAkRAUp\n1Q4x3I2AsAgQFwn7szcszdahKMCaQJMg0wghv0Ncr8yCLn20mL1TJBSHSJMgI2RidcZcz8de3yll\nf5p8Xp7wJM1ve4hQji5t5S4SZOzhgIOlGxGh2eFPEmIb1DxJvsJsASlNwnROQn/kom5pUg4cBAbj\nGq1Kk1BmkrA4U6OUukfPL+KHP3Au92OE9IFUKvUBBKmloaoKHr17Ed2+g4s3ugBiymFZxxkZkC4y\nEWoC4fU628rXqCDroZvSJLDuMVIkiOhG6QaLwkkAr9LdCIgdvXhUTRYIVStP+FwSMd2IVSR4qFta\npRSOC2cXoCjAV5+/CaC662ymaeLdD61IvdY6Y5JAM04E3VpdU9FuGNjuOdjpORjaHp0sFkHNDCdH\nyb2SRUlhrRcEMuGpydcPsBt4ee/7TiIrYbfnwNRV6UP96mIDpq5yvfnzCJcPCqeWmuiPXLwZUaRk\n7EjTInkR3Sh8zDD8Mz3l3O9JgqGrWOjUsMaZJIwkAyPJ97UXuYSVniQUMHFI4i1dJIg0CbJhasDk\nYrHTsxGgnP1p8nl5k4Q0v+2hM2GR8MKlLQzsfN1UkeguibyuODzEomQ+3Yj83u2gSQDCDlEe2kCz\nrpeyQGW5pQztce4Oebo7nPz/8u5GbE2CDG2KB01VcfeJGSzN1vAr/+AdwokdD5apTV2zh0E3AqZd\njmJno2o6vCJ0oi4ySQz2fB/dgYPZnC49RnQvTdGNGPcYOUwJhcuMgyYrW6Zy4bKARsoD6U4WNZsQ\nTWOLZIlkoVU3cO54hx4kyjobFUHD0jCy3YlrgHSIs+6/uVaYuhyLlovpEYCYTpOcurKcqixDpRSO\nNOh+n0eTwDh8kSTsRclQuHYz1iR0ByE9SraY1FQVp461cG2zz7TdJOtz1ddeGRDx8qvXQpMVmXU6\nrUnYzbAqbtYNBInfJ4inF/unV1ueb2C37zC1Oo7sJCFaS0gxVFiTQM6QdyYJxUEPUIzDMeEbijoL\ntEub2IyqEC0DcRcuc5IQXUCnllto1Q28eGmbHk5lxZsykfVAlTkJGZOEaOEwdY3ruHTQmoSdXlgk\nmIYq9ZzNmgHb8ZihO1KTBBbdyHZzC3J1xjWalzaWNUko2qn65Z94DL/+c48X8qUHwiJ/epKQT7Rf\nFR46Mw9dU2n6Mg1SO4hJQmOSbtTtjxEE+bvi6bRi1w+gKOzDPqnp8tpHs9y2qrZANQUTYh7IFKZo\nc0eY25HDjjoPHj63QP//QUys0mjUwsPYKDFNIJTErA7xbNuCPfbwRnRYXF0sN0kAJqc4zEmCke1m\nKDNJ0DjaGgB44eItaKqCe0/NSr32TuLe7fad3Pa7p1fa8PwAb270p36Wl250EDgVac7IPS9TJExN\nEgYONFXh/m2TY4O635MEIClenqYc2WM/F92I0M+ssnSjkg5Hd4oEsDUJsZCJ/xHFlRqjSCi5aMfd\nCrlJgqooePDMHLb3bLyeo0pPPkZmmFplFqhskV+60jcNfiI2zUnY50lCUpMwzGGtycsWACQ1CSm3\nFN8PMwnyHjZY4npysJYOUyOThGF6klDO2lHX1MJdEiDiGKc6g4MclLAqYZkaHjwzh2sbfWzsDAsJ\nh8s8d93SaZFQNFyLXit0khBw1z9SOIiEy6wDF4tuVLUmgTU9y0I3p+A0DV6Ymu8HsJ1iWSJZeOTu\nZJFw8JMEondJdk0HVJAqfr/k2nwxcgQrN0mIXkfGJEEU4pknnJNHN+oOHFy6sYfzJ2ekv29SxNy8\nNYDnB9J6BILTK3xdwshxoaC6CV0VIJMEINz/ZCbILLoRCTplgZeVcBBFwrHIBnUt5XDkB0GoSbhD\nN/ruQtxx4i8aorAumlDqxotFFRkJQKJbwRkVjRwPpqFObMREl/Ct10L/bNmDkqGrUDBtJ5lGdZoE\nNs2LdqFokSDgkNIwtf29hOuWDlNXsR1NEmQXf9rNGDhTPxvaLnRNFX6OtZS7EREJ56XRMOlG0Wcn\nK6SsWRoUBejb1WkSqgDZ/JKTQCruPmC6EZCgHL22SZ1binLc82KubZUuEkLNgIpkmBqPzqcoChSF\nTTfKniTsM91IQCPlYaciulF6klBG3J+Fu5bb9FB5KJOEKAshSeugjZ6MSQIpxl67tgsFKOWoQ+hG\nSYcj0SSBNe3x89CNOMnhL17cQgDgQmLCkwVigXp1PeTo554kROLlyze7Uz8jHPj9sDMtioWZGt3b\nZN0f00XCbsq1Kg2alcAoEhRlf/erFY7DEdmjDpRudMfdqDwMTqgXICdkYnVpadpySU2CnlEF2mNv\nSuhHdAlkEZQ9UIZ2kho3jZLArahI4I3mpzQJ+jTnnL6WHG4UZaAoCmYj/uwwR5LvyWis+rUX1qZ+\nNrA9NDJcZ9LuRv2CHXKD0fXKW+ypioKGpU/Rjaq2dswLVuAeeU1l7IeL4tG7Y11CkcTjMpjr1LA3\ncOD7QSJILf9zJxO6PT8QNklURckVpgZERUJqnalauGwKaKQ8EOHyXGF3I06RMNq/e0RVFDz+wDLq\nll7YPrQM0taUQCKjJ6NbS4oEzw+wNFsvNVFk0Y2YkwTOBJu8DiCfcDl9+Pr2G6EFOcktkoGmqhNu\nPHknCccXm9A1FZdvTmcl7IcWpixURaF7oywlNKlJGDkunLEvVSSwJgmtOn8CUQWW56JJQipQLc+0\nlKyD1N2o6CQho9Esi7d0kWAKxtKel30IZVmgVqZJEAS2AGyl/HynNuESkYeXLRIJE5DPqawOgLdY\n94ZjmHpMQbGMyKWE8RnEdKP9v4RnWyZ2+04Y0iVIEk3i+x5ZxWzLxJ/81VXs9uyJn8ks3mm3lGHB\nDrmuEzFq/N0WmQix3JpGVBh3OONsVuDeYdGNgPCeP7vaxitXd3Bjc4C6pRW2Nc2L2baFIAg3ljJU\nJ0OPO/2eFwjpF6qqFNAkKNzEZbNiTUKerIRuv9zkh7ixpelGeTNr8uLjH7obv/Hz7zmUwyArdVnG\nAhWYLGDLOBsBiSIhoY1guxuVMyoh0Bj7vh8EeOHiLcw0TRoaJgsyTQDyTxJ0LRQvv7nRmzrHjBzv\nttIjEBDKkYz9KZCktSVSqTn2pwCfHkuKhP3EQuTal6YbxdPS7D2XThII3aioJuEO3ag8RNafPBu/\nJFgWqNt7NlRFoYKkouAFDxHwlPJkmgDkO7yxPOfTGHv5uOw88Lih/eF4ogNFiwnWon5AwmVg8uAg\n+5mahoa/996zcFwfn/vq5YmfyRQJuhYKpMniUlSQW8UkAQinUlVaoFaBmD4Q3yN5Qnr2A4/dswjP\nD3CrOzpQCghpSuz2nYSzUoEiQVMm6UZZkwTG8pQ5SdhnC1SaDSJBNwqCAJs7Q2zsjFC39FKUJ5KU\nmgS9R2r7c1jTNfXQ7r9GigYCJCYJWcLlxL1RNGmZoBa9Dnscv47dvgMFkwdw0STBFxS2abA0CVfX\neugOxrhwbj43vSdZGHQKhOKdicTL1zYnpwm3a5Fwain8vmXXaMvQoCoKhraLbj+auAimpCQHIZnr\n4wcB+qPxvjobAeF5cWm2PmWDmjaaEcGK7M/3+mUnCXfoRqUhErjJJDCyFovtvRFm22YuL3sWYtGJ\n/CQBiHUJlqnlspU0DT61h6AqdyODUySkK33Rou4ekAUqMLmh5Tl8vu+RVSzN1vDFZ69hczccP7qe\nj7HrS23sNVOji0vRjqQwTC1Hh6JZN8IUSEYC9KEVCYzrKC5cDmdzfOz8Ev3/B1skhDSZ3b7DpFrI\nQte12N3IC8RFgiqmG8m6G43GHhSlfPOBQDQh9oMAV9d7+NO/eRP/+j8/j1/+V1/Ff/Ovv4b1nSGW\nZsvpyEJL3nSRcPvZUFaFeoIGQiCbU5KctK+WEC0DfHejVsOY2AMtgYsf2e9lqCgsC9Tn3gh1gA/n\n0CMQtBMNxSL3LEu87Ho+XM8/sElmHpBJQlvywK4oCuqWhqHtZgapAUl3o/i6HNougkAul6EsVuYb\n6I/cCbqTnYduRCcJkbtRYU1CNe5Gt98VdIAQ0o2kwtSisWP0934Q8oHPrGanVWYh5pOxD8ieHzA7\nb/fdNQtNVXJzsq0cdKOym3mogVAnNBCu52PkeBNFgiVwo6AWqPssXAZSRUKOzV7XVPzQ+87h33zu\nRfw/X7mEn336gVyHfcuIk0T7BUPCWDa9RYq9pFsTKd6GtgtDl7OE3Q/w6Ea1nAVylTi51MRCp3Z4\nk4ReSDcijkd5YWgquglNgogrripiC1R2TkJYWPh+QBspjhNORasSWMa0ksm1MwgC/LPfexYvXd6m\n/9ZpGHj7vUs4f2oW33PvYqnntQyNTvwIDnvatp+g1sgTwuUxNFXJ7GCHgZkh9ayM/SnA1yQspHSB\nhDnAaobJ7PcEZG1JFrvPv3ELigI8eEZej0CQZB10mvkPsSQdOxmqdjvanxLcc2IGP/ahe6jRgwzC\n4D6X0gLzahIOwtmI4FikS7i5NcA9J2YA5JwkpN2NClugigN5pR+n1F9/l8OgdJZi48c0JWgv4q1X\n4Y2uCyYJI+orPv311UwdP/7EPbmFYKYe20nyuilVFQnANL2J8AdZdCOWoJoUT/ttgQpMBmLl7VA/\n/uAy/vAvL+Mvnr+Bj7zrLnowkp0kkM5JUa49e5KQnzZGJiiD0ZgeSIf7ZO0oCypcnqAbuYciWiZQ\nFAWPnV/EF/76zQMJUiMggtvdvk3TlotgQrjs+cL7q+gkAQjXTFONHbyqsj8Fkuv6ZHPhO1d28NLl\nbZxdbeODj53A+VOzWJ6rV1acWIZGNWkE+61JOEykXWeA8P5rN7IDwYghxObuqJT9KTBtFz12PQxt\nFzPNyWYdz1UPSFyzEvtJWos4GI3x+rUuzq12Ch1Ck4VBXuEyQMTLysQkYUQzbG6/605RFHz48bty\n/U3d0rGxM6T7YV53o4MsEojD0VqySBjLUyrJ75DCtawFKi+QVxZvbbqRVk7IRC1Qo8ViqyL70/B5\n+dHvWVXpf/GOU3j/o8dzPR/ZpEWOIFXlJABRUZIoElg3sSjk7SA1CXMF6UZAeJD62PvPIQiAz375\nYq7OomXGk4SiScK8SYKCfAUWK/fhsN0zTNYkwXZRlxSX7xfe/dAKNFXBudXOgT0n0R9s7dnoDsaY\nK+iqRITLQRDA8yXoRqxJQlQ4KBlFAoHtTDu1lQG55tMT4i8+ew0A8ONPnMf3PXocK/ONSu0hyTQ2\nWTgd6UkCU7gsz/t+/MFlPP7gcunPhlqgRtQu3kGStV4QFHE3Io2qFy9tww+CXNanSbSj12kZxYwO\ndE3FyaUWrm306H1FJwmHRLusGnVLx8jxpIoEXVNhmdpEkRA3Iff/PqQORwnxch5zhnTDpHjisjiQ\nVxZHb+XKAXIIZbsbSYSpqaRLGy4wVTkbhY/NHxURO60qR4lkRC8K/CCHS5mRbObzGSodpwHJIiG+\nJEXC8gPVJLSL0Y0I3nZ+EWdX2/irl9fpwVFmImEZGuWWDnOmaBOkU3TJ/zd0NdcBiZW6PLTdqZH+\nQSJtO+kHAUY5bGr3C+eOd/Avf+n9lXHsZUDWnKtroXixzCQBiCmNovVPVRSmKM4X6LniLmz8d/bY\nK8TF5oG1ru/2Hfz1dzZwYqmJ8ydnKnuuJCxTR4Cw0ULW0LdCkUDeYxAE6A9dnFiSc/f5kQ/cXcnr\nIAdhcjAm4taZJptuJLJAlclJ0FJBp89fDPUIF87lpxoBMb++zD1wZqWNSzf3cG2jj9Mr7duablQE\n5FojScZZn1WrZkwIlw9ykrBMJwmxDWoec4apIqHgPpKla5XFW3uSQDnvLDpLNt3I0CcFTFUWCaLE\nZWcsz2+TBYvfncbYzX+45CFMy01OEsKNJiksYtFJCA7UArWZdDfKv9krioIfjjbE//erlwDIFRtk\ngbfHHgb2WPrvktAZkwQn+h7zgNrKRQsvEWAf5jjbTOWcDG0XAQ4nIyENs0KOvQxmWhYUAFfWQ8pB\n4SKBduEDuB4/TA0Ii4RAEKbGdDdKTV+DIEwkrpRuxFjXv/LcdXh+gA8+dmLfvhfLiBstBEe5SEjT\njUZOOEVplXT2y4t0mNouh7duSdGN8iQuhxO3b7+xhWZNx9mVYpNDMkkoQjUiIOLly2vh/U8+i/pt\nSDcqAtJUu7k1gKoomdOqZPYEkDhfHECRMNu2YBrqhMNRnsDIdCFR9JzHy/PIi7d0kSBKXM6jSRjv\nQ5EQi6MYkwSqSahwkiBIoyRwvfyHSx4sXYUz9ukhgxXCIxwPU+Hy/h/EkiLQol3qB0/P4f67ZnPT\njYCwC1FUuMwK/BsXKhIm6UbD24BrTScJhJJ1iBkJhw1dU9FqGPRgXDTETdfjg1QQiO8vTVXA2n/I\n8JPVldVT65rj+ghQbcMjva77foA/f/Y6TEPFux9aqex50mAFqh1lTUJoWqDQ90jW8FbjYOl+aeEy\nz92LZZlMUIhu5AW4vtnH9p6Nh87OF3Y0JK9zpkTw4pmoQCG6hOERmySQ/XJ7z0a7mR2I1qrrcMax\nG99BThJURcGx2QbWtof0fGPnOLOFjdj4v8smLvNs9GXxli4SDI4LBiCnSUjza7f3RgCA+SonCYxd\nOI9SXhZ0kiAIIBq7Pj1ElAW58AkloM+4iS2OABGI+aAynZ8qQAq/opu9oij44ffH43Up4XIiUG1g\nu1CU/IUhy+a3SJGQFC4Dt0eHNJ2jMSgYOHdUkDwUFQ0FI5MEcuASNUkUlUM3CrInCYSimccaUBZp\nit3zF7ewuTvCux5c3lcqWjolHUhaoB6Nw1oSoTWlTtcCcv+1D3iSYEaHqphuxNMkZFugSuUkJEKq\nSMpyEetTgsWZGj7+obvx0XedLvwYxxeb0FQFl292AdzewuUiSO4zIvtTAtJsJKnFLGOU/cTKfB32\n2MNOlOSeZ5KgKJPuYGWFy3cmCSUgo0kQdQfSOQlkklB0g05CYwj8CEZjsgBUOUmIRuWOgG7k+aUz\nEgjSRQlTuCyigx2gcBmIO7NlDsX3nJzBo3eHm4lMR4Ms8PbYw3DkomHpuSPleWFqeTISANYk4fCF\ncVaKY1w0cO6oIFkkFOU3kwM2oSsIE5czLFBFmgSyvlYdpAZM042++LehYPmDbztR2XOwwJokDG0X\nqqJU2tC5nUCsKQF2o+cgEB6q9ATdiDNJECRxi2x704jtyYNYj3C2mB4BCF//Rx4/jbMljA4MPRQv\nX13vUztx4OhMEpLNORlaFikGSMF4kJMEINYlrEfi5VFOinjy94pboN6hG5WGpqrQVEXsbiRMXJ7M\nSdjes9FpGJUcXHVBFZhndCULUboxQZEONP/5Jrs6rJtYpJOINQkHw/t+/6PH8b5HVkuNhAHgZz5y\nP37y+8/j3lOzmb9rmZOThCJdUF2fHjkWoxtNThJuBxqFmeKA3w6v6TCRTGstPEkgRYKdPUnQVLYm\nIStxGYi7/HlcP2SRzL/Z6o7wrdc3cXa1TSkZ+wXLZBcJdetg9SkHiYalU1MF0kA4aLoREB6Gy0wS\nilig9odjvHJ1B3cttzBzgJkoPJxeacP1fFzf7MeahCOyFk5MEiSKBKJtJJOEAy8S5sIi4WakS3By\nntmIvb2iFD/jaNodulElSPqCJyETrpJ0AgmCANt7diX2p4D4C96P7psl4GsSVFskTG6ovZw5CUST\ncFB0o+99YBk/+9EHcnfy05hpWXjynaek+KtJzn1/NEajgLUnyw6yGN1ocpIwug3oRukisqhu46gg\nWcDONksWCYRuJJwkFM9JIPfvfk4Sxq6PL33rOoIA+OBj+ztFAKY1MgCx5D2612Pd0uG4oQMb1STU\nD5ZuBEwWCbt9B6qiTB0IRRo3GQ0iAbmGX7i4BdcLSlGNqkQyVO2oahIAuUlCi9KNwmuyNxzDMrUD\nYx4szxMb1NDhKO8kgayHZQww7rgbVQQzq0gQVHHJLIP+yIXj+pWIlpOPzZwk7GP3TcbdqJrnm6SK\n9IdjKMrkAU/U+TnoScJhgCzw/dEYztgvOEmY7Ny6XhiYl5c2VjM1qIpCDwKD26BISBeag7d6kdCM\nKXFFp4yxJiH8LEXCZZ4mQehuFN2v5Hoc7YsmIQ7X+vNvXUfd0vG9DyxX9vg88OhGR7lISKYu96km\n4TAmCfrEJKHdMKauP0tAN3Jz0Y3C37myHtoNl6EaVQnicHRpbe/I0Y0KFwkJulH7AGlwy4lANSC/\n9or8nlXivCU6Q+bBW75IMCKXnTRop1o0SUjoBnYqdDYC4s2ZFaZGcxKqnCQwRuVJ+H4YrlSVJiFN\nFemNXDRrk64FIl9rqkkQ0MG+20G+E6J1KUKjSYepkYI4r/eyoiho1HR6EL8dhMvU0pBoEt7idCNS\nJBR1NgLiolJGuKypClg5PaKcBINOEiJ3o30wYSBry7cvbmG35+A9F1YqLUJ4SK+hvh9gdMip5PuN\npA0qaSActHAZCA/DJFNmt+8wKSmGDN1ISrgcr501U8PdJ/YndyMvTi61IvHyHi3yj4pwuagmISlc\nPijRMgC06wbqlk4nCY7jQYH8vkuKu6LORkDyDHmHblQKhq5lWKBmaxLGnp9IW656ksCnG1WqSRAc\nyIG481e1uxHRQPQYN3G6kEiCfC5HepJgTBYJRVx70pOEMqnZzZoeC5edw3dtmXY3uiNcBopnJAAM\nd6MMulHenASN0t/C39mPSQINhozewwcfy5c+XxRpuhE5qB3lojUZqNaPvOgP8jBGQA5V3b4D2/GY\nB0lVUWDoKpNSm0eTkPydB8/MHxiFJQuGruLEYhNX13ux09sRcdVKvo88k4Ru34Ez9uC4/oEK6hVF\nwcp8Hevbg7BZMPZgmvLUIcsoXyTccTeqCKauctwOsi3RkvxaYn9a2SRBEKa2P5oE/oEciDvQlbkb\nJRxIwqTOMVqpQ7DQAvWANQmHAXJw2uqG11aRkLCpScK4RJFQN9AfjhEEwW0xSdC10HhgSrj8FqUb\nEbFyGQvmaXcjUZgaewPy/QAK2NQNg1o7T7obVTlJSB7a7j01K50AXBZputFRtj8lIEXCYOTSIv2w\nJglAdiJvuN+zLLXz5yQAxVOW9wunV9oYuz7euB5aoR7EBO0gkFe43KyHv783cA5ctEywPNeA6wXY\n6o5gj/1caxz53oqmLQPiRnMeHN0TliQMg61JcCU6C0mnjiqD1IB4gsEqEval+5aRuEyLhKomCWb8\nfCPHg+cHjEkCf7rhvQU0CbRIKEE3SrvJxJOE/NdOo6bD8wM4Y/+2CFMDwmlTbIH61i4SVhea+Kkf\nuBcffXdxv/Up4bLg0EQOVGnxshcE3MOWlhLS2/tAnVSijjEAfPBtBzNFAJJ0ozgBHDg6DjMsTNKN\nDtPdKHwda5HlZIdDuTMNTUw3kuj0JnU6t4segYCIl3vDMUxdFTIhvptQXJMwjouEA54wE13Cze0B\nbMeljVgZ1KqYJAgazXlwdFcvSZi6Bs8P4Pn+xA1FNQmCRSNpgVp1kUAfW0Q32hd3I06RUIKmwnw+\nPX4+XqUvElOTIu52GfXuB9J0o0aBRS5t01um2CM0nv5oTA9Ah815NQ1tQrh8lD3pZfCh7zlZ6u/J\ndUG+X5HmhxYJfgA1Uaz7fsAtLoyUmG4/qJNAVNB6Ad5+77FKH1eEqQTwt0CRQIXLIxf9A3aQSWJq\nksCZZpiGRp3ZkoiNSrJfO/md1YUGFmfqhV7vfuF0wub3qIiWgXB9UBA2AGQmAnVLh6KEk4Q4SO1g\n78PlucjhaGsIe+zncv2ik4SCQWpAMt3+TpFQCsngnboVfyGkOyZaNBRFgaYqcPdlkiCgG409KEp1\nB3Yg6STE0SS4xTvQWc/HKxIMQZFAJgkyQrPvVpCFgnw+Rbr2iqJA11Sa8q2MFwAAIABJREFUcEtG\n7UWuneSBgFApDnuSYOlxZ5BkSRxVT/qDADnEk8JLTDeKiwQklgXPF00SIh1XapJQdWH3yR+8AENX\nK10js5CmbN4u07b9RHqSUIQSWQXIgZgIRXmTBEtX0e3zJwkydKOZlol33H8Mj91ze1ifJnHqWJNa\nEx92A6dKqIqCmqXD1FUpBypVUdCsGSHdiEy4DppulHA4sh0PlpljkkDdjarQJJSjGx2dq6ggksE7\n9cT5XvYQqusqXC/Ads9Gw9IruzFpmjNnklDLIYKRQdYkwa1Yk5AUnfIi0xVFgWmozJwE1wu7lUf5\nQJi+looIl4HJLBC3oLsRMD1JUKPv5zBhGiotovqj8VuWalQV8rgb8ehGUpOEfbRABSAVVlg1rERC\nOhAXCbW3QJEwiNyNlmYPp7NO1kqScCuaJDDpqxIaRAJVUfDzP3Sh6EvdVxi6huOLTby50UPtiGlh\n3v/oaq4mZatuHLomAQCubfbDoi2PJsEoP0mgdKOSwuWju3pJglx0aV2CyKFj4u81lU4SqpoiAOJJ\nwmjsVd55O3BNAnEgEUwSgPBm4eUkHGWqETA9Li7apTM0hdqglaMbxYFqt0uSrJWgGw1HLuZug+TT\n72bEictEuCx2NwIwZYPq+QH3utBSGpn9MGE4LPAmCW8F4XJvOMbI8Q59krBOJwnsdcA0VHh+MLV/\n0P3+CDSdzqy0wyLhCE0SAODHnzif6/dbdQPrO0P0IhvUgy4SGjUdnYaBK2t7APLpC6wKLFDjHK87\nwuVSoLSXlOOBTOIyEFZrvdEYA9uttkgQfMHh6KraBUBkNwqA2sRWnbjsCDQJQKgZ4eUkHGXRMhC7\n9xAUpS2E067Il77ERChOXR7fNkmyphFqimwntLm7M0koh7QFqihMrdgkYbL5sR/BkIcFk6NJONJ0\no+h+29wlDmyHYz9MpjVkfeO7G7HNMPJYoN7uIKFqR0mTUATNmg7fD7BBrs1DsOY9Nt+ggv483wcp\n8MwSdCP9jgVqNTBSbhsEHhXGZk8SdnthpbovkwTGFzwae5V33jRVha4pTDtYAJTTXplwmdKNEpME\nxgHPNNiWdZ7vH2n7U4LkwlJEuAyE1+g4LVwucP2QA8Bg5GLk3B5FArmOdnrFxd13EIMKl4kFqqhI\niH7kp9YosSbh6E4SCP1u2gL18O+T/QIpgGiRcMDiUILkOqmpCrdZULYp+N2AM3eKBABx05GkHh/0\nJAEAViLKEZBPd1UJ3UjARkmCWKzzcPRPWRmgKYzpIoFqEsQfUXJkWWWRoKe4uwRBEMBxvFx2WrJI\nUjfSqDongU4uHE8YwmNyXpPr+UdiQc9CkqtdxSShzPdIqAS94Rgj+/ZIkiXXUZlU6juIMWWBKqIb\n8SYJgWCSoE9aO4/GYRLpQQqM9xMT9Le3gLsRoVJt7oQ0n8Mq0pMH4k7T5NKGeLRaWXrxdwPOrLbx\n/d9zEh947MRhv5RDBTlP3DzEImF5Ptbo5NFdnVxqYnGmhntKpHlrKSc5Hn73D18S/vzorl6SIOOc\nMWfRyKIoJicN1dKN2JMEx/URAJXTjQC+hzQAjL19ohu5Hj1kMDUJukoD15I857cC3QiIOwqaWlwk\nHLobkSKh+PdIJglb3RECAPXboFNF7l8ySTgsTvRRAbkubFokSNCN/Gm6kc65VknxQK5Hx/FCe8Mj\nwAUH2EXCUS5cNVWdeM+Hdf8lu7QiH30ri250BIoETVXx93/g3sN+GYcOmpUwGENTlUOZrCwXnCTM\nd2r4nz/5nlLPTa7lLE0CYcLwcDTaNyVAvfjd6UVDxj1ncpJQ+//bu/tYOer73uOfmdkHPz9yfByI\noRdC0oQ4TzeBGLvV5Rgf09gGE0zV9F5aCFyipDfEoU2vEm6kVjep1IhWaRulgkopSE0fCC1GCn+Q\nYuqCAyhIgTqU/IGUGwERHBNjHz+dh92d3/1j9zc7s09nn2d29v2SouDjY5/x7O7MfH/fh1/fjst1\nHLmOUzfjdlBzxaXmkx+k/jcuhy/WLXsSsk0ay8egcVmqrpD1MtozPN2ol9dxRU39cbfTlvrJXnhP\nBuVG8R/TKLMZJns9bF1u1DxIWDqTUJ1ulJZdYaXydXmc9kmQop+5OOq+pegEqVY78gYZ7Jpyo2qQ\nkP57yrgIP0+sWp6NZSFi84bugoR+yLQ53Wh+sX7fkLCx/0RkQyNQw4q+aauJKZMZTLmRVF7Fq51x\na0cGDqKGN59xlx6B2qcgISjzqjQu5zJuw07+XKh3IXI8JTMWF3R7YellNTLruTKm3MdhV3B7GYF6\nolLDmISHH8qN+itT875oazO12h2XO+lJGMCktjjVZhIcDWZBJ0nC14HYGpdz7WUSquVG9fd7KR3l\nRiirDRLiMLG+u3KjfrDPR7Ul67Vs71Qz6X/KWkK47CWs1OZDaHj6R7+DhIw35ExCzgtKe2r1uyfB\ndRxlM26wT0KzFahcKJgIK/r+WJQb2SkHvayQB3tuFE1PmYRctjxtyT6QL0/AiL2gcbmHXalRVfv5\nbmcztdqVKt+YpjvVV/dJqO64nIamZSuf9VQsGZV8X3MLRS3LZ1IxVrOVFZEgIZ5rQrj0sZ1MQu39\nJE3lRigLP1PEleHKZz1tWJMP/nuYXNeR41QD4EaMMUH/WdO/p98HNmqaZRJKfnuNsXblLZdx+36B\n9Fy3rp4smAYygCAhn3HlG9NwG2+78tfXXZ4r/QZn5wpNI31bc16b4SiVzHiVG/WSSchUV29tRqZ2\nxbgdjuNo5fKsbAyZhPnvOcqN+qr2893eZmrRr7e143KpvBixsJiycqNgDGo5SFiRgM/IoCUhkxAe\nF90yk5BpnJkmSEif8PNYXJkEqdqXEEfG1HPdltONCkW/LhNcK/1PWUtYqidhKXZlbP3qfN9r3srl\nRtEXcL5Qrh/rZZONZpplVaRqENXPB/N8ztP5haLmF0vNg4Rg5af6+vjGqOSPSeNy5QFqeQ83X3ue\nCkU/eB27nb8cvvAmobTHvj9OnSk3XyXhmEaZV1l9qv66+efdZgtMJz0JoUxCwQ5hSFMmIVdd1Dif\nkAlggxZeLIgrk+A41cbUbjIJxUpZL+VG6REtN4rvczhZ6UuIYzGkUcl62FJZBIkgoeWOy231JISC\nhH7LuE5dPdnCYvnXA+lJqNkMKKzfjctS+UE1mErTJEjINwhcbGQ8Dvsk2H9/LzffbKgOvNfXMbxS\nn4QHoPp9EuI/plHmVMoArVaBuFP5tk56EjKhTILtr0pXJsGOkC1qPiEbDg5auMQvrrIOSW0FCcH9\nhHKj1FuVgHIjSfroeyZ00cRKXTK5eug/O+PWLzSHLdW0LBEkBDfERnOT26kltTe9QQQJnufW1ZMt\nVDIJg5puJNVnVaQBBQlZNyhdaZ5JqG80syVYrXaDTYt+lhsVi351BGqXAVa4nGBZAh6A7PvDXgjp\nSehd+L3RznSj8E3IN0bGNP9zXmj/l6C/Kk2ZhGz5M3H63GJ5THACPiODZjMJrhPPmEnL9m+107i8\n0GQEKpmE9MhlveD1jrPc6H2/skH/9/arWr4vB8Xz3Ibl4xaZhDbkmvUklNrb0beaSejf+FPLc526\nerJB9iSENzirNZCehNDDQbN0YL5BEFfdDTv9b998aARqt4LG5XAmocs9F1YkrNyodlPBJBzTqAv3\nq7S6BnoNphst9bDlOo4811Gh5Ad9RqnKJOQq07bOjs+0Lftv7GVMcz+0lUkIyovrFwUdR6lvMh83\na1aUg4NVY7p45DWoRglrJ0hI/xVsCdkmPQkl37S1Um1vqAMpN/LcpiNQB7H61qi0x+r3dCOpesGW\nmn+Iqys/1WOymYR2ysFGnS0r60vjcqgnoR+ZhCSskoZ7K3IZNzU798ap7UyCa3sSql9rZ0U2U1nd\nChY8UpVJsNO2yj0ySfiMDJq9NsW9keHWSzdqWT7T8pw3G4FaarMHEaNl9cqcfjk7H2smIU6NJmSG\ntVNulP4r2BKqm3XVryy0Nd3IHVyQ4LnNR6AOJpPQeJKQNKhyo1DD21KNy8UG5UZjkEl4z8XrdeEF\nK/Wrl6zv+u8IZxIWe3wdV0Z6EuJ/uAu/h5KwuVsaZCOZhBY9CUG5UfWzabN8zUagSvbGFcokpDFI\nqGQSxiFIsP/GOOu+Jen6Hf9lye/JNelJaNVHg9G1ekU5qxT3ezMunutqYbHQ9PfJJLSh2XSjdi8a\nm9Yvl+c6umhiZd+PLePVj6+yL+ogphvlm6yySIMJEvKRcqP2R6DaczIO040uvGClvnbHVT39HZnQ\nCNRCyZfrOF0HWCsSlkkIlxvFNX4xbSKNy62mGzUYgWpLjzrJJKSq3KguSEjPv60ZW4I4Cp+/oKS2\nQePyOGzOOW5skDCumYRGEzLDCBLaEJRiNGhkaqec5b99+EJ95N0TAyo3cuQbI99Um6jtCshAexIa\nZRIG0pMQKjdasnG5UbkRF/V2BNONKuVGvbyGkUxCAjZTCwfL41D/PQztZhJsHBDuSSi1MSUm4zkq\nFlM63ShXs7nfGLwnlyek3KgdzfZJoNwona698mIZ39fkhuVLf3MKea7TcjO1+QXKjZZkLxqFUHOH\nqczhb2dlwXPdgQQI5b+7ks4vGbmZ8n8PpSehQZBQHMA+CeF68mZBQr7BPgm2BKvVKieqgulGldn0\nvQQJdtUwn/MSkZ4Pfw4Yf9ofnfYk+H77jctS+RoyXyilutzo5BiVG21cs0ye62jT+uQ/iOWb7JNA\nuVE6ffS9k7rkghVxH0ZsGlWjhM2RSVhaoxGo7ayGDYMXqiW3xznQnoSgtKdBuVHJV8Zz+zq9or2e\nhPpmarvxzTiUG/WDPU92BGpvmYTy67Q8Iau/4WwUQUJ/RKYbtQjEewkSinOFdDcunx2fxuUNa5bp\na3dcNbDFsn5q1pPg+37s93ug35aebkQmYUmuWxnJV2zQfBd3kGAzCaGb8EB7EnKNV1kk9bwC3Yh9\nwHOc5g94Qc9IKHAZp83U+iEb7kko+j29d2xJQVIefsLZqHEo7RiGSCahZblR/QjU9hqXy/u/2CAh\nl5CAsx9suZG9nyTlczJodlfZpPNcR67jaKHYoLyYIAEp47mOjMrv70YLN+yT0KZc1o3UKCZl98VM\naOMha6FQUsZzBzLZp1GTsDWQICFjdxPONp1P3agEqjrdiIt6OzJ97EmwjctJeSB33eoOwWyk1h/R\nHZdbZBKcbjMJ5Z4Ee51JYybBSsrnBGWO45Tv9zX3uCLlRkih4BnSb5xNIEhoUzbjRYKEYDUs5pVq\nu4oXziQsLJbqNpDql6WmG/Vzj4Tyzyv/fa3GkwVjWYsNehLIJLQlGypbKxT9IDvTjVXLs8rnPG1Y\n0//NA7tl/z08kPVHpHG5nZ6EBtONWjcuuyr5JrhBpapxuSZIGJdMwijJZb26exyZBKSRfU832yuB\nxuU25TJuZJ8Eu3If98qCbcwt1mQSBtGPIC093ajfD2HVLdNbbX7ToGfEZhK4qLeldjO1XoK9bMbV\n/7nlv2rViuFvMd9MPufp3HyRnoQ+yYZKuDptXC61mUmQpHPz5fndqWpczhEkJF0u49ZtGEqQgDTy\ngkxCkyCBTEJ7shk3WvPu2+k5cTcuN+5JyA9o9ORSOy73u9zI/rxWW6YHI+vC5UYJyfSMCptxmVss\nyqj3MbYXTazS2pXJCRKqZWs8kPVD+z0J5f/vpnFZks7Nl1ex0ppJcCQtG4N9EkZNvkEmgXIjpFF1\nQmbzcqOlFml4ylL5ISOJjcvVTEKo3Kiw9IvaraC0p0F0WSj6fS/vsVmCVhuduG5546+FyAhUehI6\nYafVzFUeysIrxWlg30eUG/VHJlP9XDXrFQr/Xuf7JFSChLlyJiFNPQkZzwnOy7K81/L8IR6NehLI\nJCCNMg0WmsPmF4tLVqYQJEjKZsvpR1O52VV7EpKSSfCD/y8U/YGVGwUzpGsmPxhjImNY+yVYAV5i\nN8R8NpoeLtGT0BG7Mnx+wQYJ6TpvNmimcbk/7Psl4zktRx53PQI1YzMJBTlK1/vRcZwgM7IsAZsN\nol6u0oMYDm7ZcRlp5DUoWQ+bX1y6fJ1Phco1isZUg4NqT0IyGpdtJmFhsXxcg8oklPdBqO9JKA5g\nt2VJuuyiNdr+/s26+v2bW35fudGsfp+EuIO4UWEfytIaJOSCIIGHsn6w74+lHppsIFDqNJNQ+b2z\nc0Xlc15f915JgjyZrUSz1wtbPWA3T6XcCGnTqGQ9rBwktL5OcRVTaNflSklNUsqN7E3aBi3BDqUD\nyiSUx8N5dalYezHt93SjbMbT7Xvft+T35TJupMGmxI7LHclWLhTVcqN0nbd1K3PKeI5WryCT0A+2\nHG2p658tpTGNMgmt9kmw5W8LxUT1tvSLXcShaTmZwsMw8lmvrYlcwCjKBM+Q9UGC75u2BuFwFVP1\nprVY9LU8n6CeBJtJqBxPECQMsIY3n/XqdlwOgoSYHi5zWU+nzy8Gv672JKTrYXdQ7ENfWjMJv3Xt\n5Zq+8mLKO/okyCQskanzGoxALbUzAjUU3Kepadmy/yaChGSqDsMo30faKZEDRlFQjdJgnwS78EqQ\n0AY7Z71QeQhPSpBQ25m+0OaL2otcpr6pK/4gITp9isblztjzNJfSIGHlsqxW0o/QNzZjuNT1z5YJ\nlTruSaj+Xpqalq1qJiF9/7Y0qPbeJet+D/Rb9RmyPpMwv1h+Hli2xGJGup4WupQLZRKk6kN53DXv\n1R2Xyy+wfVEHmknINSg3GlBPQrvyWU8l3wTBgT0fjEBtT7a2J4HzhhbsQ/zSPQnl/2+0T0KrPxvO\nAObSmEmwPTJkEhIpV7NpKEEC0qpVT0K7mQSeFlQtx7Ar5n4bN7phCHbLqyk3GmwmoUW5UUwPl7Xp\nYZs6I5PQHvtQZs9fLoWrt+ifrFd+fyz1+bLXJ2M6zCSEriOpzCRQbpRotZuGtrMBIDCKMjV9rWEE\nCR3IJTT9WM0klF9g+6IOso43n3VVLPmR1cG4Mwm1F/UiI1A74rmOwu9kMglopdqTsEQmocE+CX47\n041CwUcqexJoXE60YNGpcr9v5z0LjCKvpq81zFamLF+il4+nBVVvirbcqJiQi0aQSSgNr3E52FAt\nPHI09p6E6EXdlh3F/fqMCsdxIq9d2noS0F/VEahL9CS49ansdlZlw8H9IK9lcSFISLbaciOCBKSV\n12K6EZmEDgQjUBN20QgyCX60cXnQ040kRfoS4m5czteUG9GT0LnwgxlBAlppN0jwbCah4QjU5n8u\nEiSkMZOQoychycIjUKXqoiDlRkib2g15w2hc7oC9KdqymuqOywnbTG0YPQm2tCe063LsPQm1F3Wm\nG3UsQyYBbQqmGy3x+XIbjUBtK5MQKjcik4AhCxad6noQuZ8gXTI11ShhjEDtQDDdyDYyJaScJUgV\n+Xa60TB6EioX0NDmZfH3JESzG0U2U+tYlkwC2mQDyqU+X26jTEI7+ySkvHH5w5dfoNeOn9W73rk2\n7kNBA80al+MeVAL0m1dTjRJGkNCBIJNQjGYS4k4/2hW32n0ShtKTUKwvN8rE3bgcvD5kEjoVfu1s\neR3QSLubqVUzCT30JKSw3OjiydX6X5/cGvdhoInaRSd7f437fg/0W1v7JNC4vLRsTfoxKdONajvT\n5wsxZRLiblzONM4kxF0ONkqyoQc+MglopbqZWuf7JLQzPjrt5UZItmrlQKXcqI3sFzCKWu2TMLdA\n43Lb7Ep1oXYEatybqdXMuLUPyYNM0Se7J6EyfYqehI4x3Qjtsg/u9nPXTMNyo6BxeXwzCUi22ml5\nSVkUBPrNPkMWG+6TYDMJlBstqXZlodqTkIzG5Vh6EgrJ6UnI117UgyCBh912Md0I7VqxLKP/ufd9\n2rJpVcvva1Vu1G5PApkEDFuzEaiUGyFtvAZjqq1qT0LrMIAgQfU7LidlZcHeTIs1PQmD3DHXlvYs\nNBqBGlsmIXpMQbkRF/W2ESSgE9vev3nJ76luplb9mg0Y2p5uRCYBQ5avG1TC/QTpVG1cZp+EngSZ\nhITtwFjbdDJfKCmf9Vqm8ntlb9p2lUUK9yTEc0PP12R6ir5f3kV4gOchbSLlRmRg0AdBJiE0OaPT\nTEIapxsh2arDOSqLgm0EtsAoqo7Rb1xu5NZstNoITwuSsrbmvXbH5Zhr3m0UaI9nYbE08JW32nGw\nUgIalxuMQKXUqDPhwGCQmSiMD/tMFZ6u107pBuVGiFPtvjtJWRQE+i3TcrpRScvz3pKLrT2VG506\ndUpf/OIX9Ytf/EIXXXSRvvnNb2rt2vrZ0I888oj++q//WpL02c9+VjfeeKMk6ZZbbtHx48e1bNky\nSdJ3vvMdbdy4sZdD6ootsSkWa3oSYl6prr7AlXKjQkn5JZoJe5XPNig3sj0AsQcJ1deHpuXOkElA\nv3Xfk0C5EeJTOy2vWm7EdRHpUrvXVtj8QqmtjXl7+lTcf//92rZtm37wgx9o27Ztuv/+++u+59Sp\nU/rWt76lhx56SN/73vf0rW99S7Ozs8Hv33vvvXr00Uf16KOPxhIgSNUHqGAHRjsSLWk7Li+WlM8O\nto2k9oFcqk59ii+TYCcuVTMJcb82o4aeBPRby+lGrYKE0PuPIAHD5rqOMp5bN/KcciOkTXX4TeNy\no6WalqUeg4TDhw9r//79kqT9+/friSeeqPueo0ePavv27Vq3bp3Wrl2r7du36+mnn+7lx/ZdtqYn\nISmNTNUo0JcxRguF9iK/XuRrHsilapASW+Ny3T4JZBI6ZR/MPNfhZoi+aJRJaKd0I7yTM+VGiEM+\n61bLjdgnASll39PFJuVG7TxP9rQsfeLECW3atEmSNDExoRMnTtR9z8zMjDZvrk7KmJyc1MzMTPDr\nr3zlK3JdV9PT0/rc5z4XSzNqsONyoWa6Udz7JHjVerJiyajkm8H3JCRwM7V8zT4JJd9QMtMhe77I\nIqBfqo3LDXZcbrVPQuU96KjaAwUMUy7rhe4nduQ5QQLSpdl0o0LRV8k3/QkSbr31Vv3yl7+s+/rB\ngwcjv3aczqfN3HvvvZqcnNTZs2d111136dFHHw0yE0uZmFjd0c9aSjbjyjjlvzdXScFcsHFV339O\nJ1bMFSRJXsbTqjXLJUlrVuX7dkyN/p7sslz5Pzw3+H2ncvF8x+Y1baWn+s0GKXIcTUyslm+M8nkv\n1tcmLCnH0cqa1XlJ5fKOJB5vEo9p1Az7HC47vyhJymSr76l8vnLtvKD5tXP5yvKfW5b3tGnTmiEc\naWd4L/Yu6edweT6j+cWiJiZWa+XKk5KktWuXJ+64k3Y8o2icz2Gh8kyezUbv+7NnFyRJa1YvW/L8\nLPnE98ADDzT9vY0bN+r48ePatGmTjh8/rg0bNtR9z+TkpH70ox8Fv56ZmdGVV14Z/J4krVq1Snv3\n7tWxY8faDhLeeutMW9/Xrqzn6vxcQW+9dUZnKze/2dk5vTXgRuFWbDr0/NyifvHGKUmSY0xf/u0T\nE6sb/j12F74zZxeC3z9nz8fJ8zoTw2qLMUau4+js+UW99daZ8gqQ6f97oBvNzmPSFCqvq+c6iTve\nUTmHSRbHOTw/X35Pzc8Xg58dXDtPnddbmcbXCjsUIZfxEve6817s3SicQ891NLdQ0ltvndGpU3OS\npPPnFhJ13KNwHpNu3M/h7Gz5vX323GLkPByvvOfdyvNkq0ChpyfgqakpHTp0SJJ06NAh7dy5s+57\nduzYoaNHj2p2dlazs7M6evSoduzYoWKxqLfffluSVCgUdOTIEV1++eW9HE5PstlQI5Od5hN3T0Jo\nxq3dSG3QNby140al8nSjOGvZHcdRPueGplHQk9ApW2YU114XSB+vYU9C+drZzmZqNC0jLrlQT0KJ\nngSkVLivNWx+obzA005lSE+1I3feeacOHjyohx9+WBdeeKG++c1vSpJ+8pOf6B//8R/19a9/XevW\nrdPnPvc5HThwQJL0e7/3e1q3bp3Onz+vO+64Q4VCQb7va9u2bfrN3/zNXg6nJ7mMW7fjctwNnq7j\nyKkcz0KlfnLQN1a7uUbtjstx17LnMl5kx2WmG3XGTjeilwP9YvuPG/YktLh2eq4rx6FpGfHJZTyV\nfKNiyQ8GlcR9vwf6rTrdKNqTEOy2nB9w4/L69ev14IMP1n1969at2rp1a/DrAwcOBEGCtWLFCv3L\nv/xLLz++r3IZT+fny3Va7cz6HgbHceR5joolo4VKucgwdijNZdy6HZdjDxIqmR7fN/KNiT3LM2oy\nNC6jz2wPWqnBCNSl9pi57MK12jK5anAHB7RgA9RC5Z4ixX+/B/rNTpKr3UwtCBIGPd0oTbIZN9g0\nrDrdKP4HKs9zVfJ9zVdW0YeRos/nvORlErLlIM6mzdhxuTP29WOaDPrFrryaBpupLbUq++X/8ZFY\nJtkBUmjvnUIptCjItRHpEpSs15YbLbZfbsSnoiKXcVUolPcjCHZcTsDKQsZ1VCqZak/CEIKEXMar\n60mIu0ylXG7kB/N+CRI6wwhU9Jsth2y0mdpS104CBMQpvPeO7amh3AhpY6/DvWQSeGKoyGZcGSnY\nj0BKRpDgea6KvgkyCcMoN8pnvaAHQpKKRT+yS2oc8llXxZIf9I3EvYfFqMlkCBLQf67rBI2fkmTj\nBR64kGS50N47SVoUBPopCBKa9SSQSWifnfpSKJYSVaPouY5KQ5xuJFUnP9gygkIxAZmEyr97rtKV\nTyahM2QSMAiu6yicyU7StRNoxt5PFoqlxAwqAfrNcZzgGTIsKDdqo3GZJ4aKYGWhshOdlIzV6ozn\nVKYbDbEnIevJqBwcGGOS0ZNQ+fnnbZDABb0jmcrM+rhfR6SL6ziREag8cGEU2PvJYsEP3r8Etkgj\nz3NUbJpJIEhom314WixW049uAupmM54b3SdhGD0Jdq+ESsBkFP/DpT0mu4FTEprKR0k1k8DYSfSP\n69b2JFC6geTLh/YDsvXavGeRRp7rtuhJGPA+CWliG5kKlWkHnus/L4j/AAAc1UlEQVQkornOqzQu\nD7cnoTL5YbEUXDiTUm4UZBISkOUZJTY4YLoR+olMAkZR7UKYxHsW6VSuRmlcbrScTEL7IpmESpCQ\nBOXG5bgyCaWgUTjuTIINXM7NFyTRk9CpiydXafpjW7Rj6zviPhSkSLknITrdyFEysrBAM9XG5WT1\nIAL9Zheaw+YX2CehY/aiUbBBQkJWqmtHoLaTHuqVTcUuFErBf8cdJNhMz1xQbpSM12dUZDxXv7Xz\n8rgPAynjOtEgoWQMK7JIvEi5ET0JSDHPdZtmEtpZdGY5tqI63agcJCRlJay8mVq13MiuqA9SeDxc\nUjIJ9piqjcu8dYG4uW603MhPUBYWaMYuOi0U/GCVleAWaZRp0Lg8t1hSLuO2tYEgT1oVwbSDYkml\nkp+Yxlh7wz0/X5TrOEMpswlnEoIgwYu34bW2cZmeBCB+5UxC9dcln0wCkq86zZByI6Sb5zVuXG6n\n1EgiSAjYlfJCwnoSbFBwbr6gfM4bSjN1eDfKQikZmYR8XeMyb10gbq4rMgkYOUHfXSE08pzsNFLI\ncxs3Lrdbus6noiIbmpucpCAhnEloN/Lrla1TC2cS4t5xOdgnwfYkJOT1AcZZXU8CmQSMgOo+CaXg\nAYr3LdKoYeMymYTO5UI7LicpSLBlNefmC0PZbVmKbjSTnJ4Em0koTzdKSjkYMM4a9STwsIWky4cm\n+FFuhDTLVPpaLd+UB+Esy5NJ6EguNALV901iHkLtcRgznPGnUrOehLiDhGgmgZ4EIH61I1CTNPQB\naKZhuRH3FKRQudzIyFQWcxY62G1ZIkgIZLPVIKHk+4lZVciEjmNomYRs8noSbKaHngQgOWo3U/NN\ncrKwQDN20WkhtE8CwS3SyAa/NhieJ0joTqTcqJScG114dWNoPQnZ6ni4QrH8hoo7SMjXTDdi1QeI\nXzmTUP01PQkYBcFwjmK4cZn3LdLHLqjavgS7RwKNyx1K6nSjcNnT8DIJ1aaupJUbBY3UTKIAYuc6\nTrTeNUHXTqCZjOfIcWzjMvskIL3s9bhYWc3pNJPAjssV4Z6ERAUJ4XKjOHsSEpJJsCg3AuLnugpq\nXSUalzEaHMdRLutpseAHZUZJuecD/WTf10EmYcFmEig36kjWPhhXoqykNC6HH4aXDbsnoegHPQmx\nj0CtCxK4oANx82pGoPrGyKO2GyMgn3HLm6dWmu2HsQcRMGz2Wba+J4Fyo47Ycpq5SpSVlNWweDIJ\nlaauxeRkEnI1Pz8pQRwwzlzXkVF1QzV6EjAqypmEEu9ZpFomyCTUlBvlySR0xNa82xOYlNRjJpae\nhOoM6aT0JNQGKWQSgPjZ1VebTaAnAaMil/W0ULAjz3nPIp3se7vo1zYuEyR0xK5U20xCUm50cWQS\nMp4rz3US1ZNQriGtHgM9CUD87PXJkEnAiMmFyo0okUNaea6dblTbuEy5UUcynitHoUxCQh5C4+hJ\nkMqZlcWCr2JC9kmQqmPrpOQEccA4swGB75dLjozhs4nRYBuXS75PYIvUqt0nYa7yjLucTEJnHMdR\nNuNqbpFMgmRTscnJJEjVXgmJTAKQBHYyTMk31U2pEnLtBFoJlxgn5X4P9JsdF19kn4TeZTOu5heS\n1pMw/M3UJCmf8aI7LifgoTw84YieBCB+QSbBECRgtOQrmen5xSI9CUitaiaBxuWe2dVzKTlBQhyb\nqUnVpq4kZRLC5UZkEoD42cukb0x151rquzECgkzCQinIiAFpU7dPAj0J3Qs/CCcmSIip3CifdaM7\nLicgSAiXGyWlZwQYZ9WeBBOMQSWTgFFgM9NGybnfA/1Wv08C0426Fg0SknFq4hiBKpUvoCXfBFFn\nEoIEyo2AZAkHCUEmgQcujIDIIAwWnZBSdp8EO4RmfqEkx6nfe6oZPhkh4ZOWlBrF8A233fRQP9iA\n5Nx8Qa7jJCJoigQJCTgeYNzZMg16EjBqwiO1KTdCWgXlRqFMwrJcpu0dxof31DkCsgkcsRlfJqH8\nc8/NFRKRRZCiF/WkBHHAOHNDm6n5DkECRkd40Skp93ug3+rLjUodDcFJxtNfQoQzCUm50YUfhvO5\n4b1c9gJ6dq6YnCAhgUEcMM5sQs83onEZIyWfwPs90G/BjsuhzdQIErqUxMZlW09W3gV5eC+XHQ9X\nLPmJqf+3mYSM57SdKgMwOG7lmuSzTwJGTCSTkJB7HNBv9dONih2VrlNuFBK9aCQjfrLHMcw9EiQp\nF8paJCWTYMutkvLaAOMuGIFaCRCk5CywAK1EyldZdEJK2f7Nku+rWPJVLJmOnicJEkLCD8OZhNzo\n7ArHMPsRpGomQYr2asTJloMl5bUBxl24cVl+5Wt8PjECwuWrvGeRVtXN1ExojwSChK4ksSfBRoFD\nzySEgpIk7LYsVY+JjdSAZAjvuFz7NSDJKDfCOLBl6sWS0fxCeY+E5XnKjbqSxMbYIJMw5CAhvHFZ\nUsqNqkFCMl4bYNy5ofF6pvKxTMq1E2glT7kRxkA1k+CTSehVJrJPQjIejO0Nd9jlRpFMQkKCBHtR\nT8prA4w7W25kfDECFSMlfI/jPYu0souqpVK43IhMQldySZxuVHkgHnpPQgKDBJvpodwISIZIuVGl\n4igp106glSTe74F+q5Yb+ZpfLJcbkUnoUhIvGsvzGTmOtHZVbqg/N9E9CQl5bYBx12i6EbvXYhQk\ncZoh0G/hHZfnKDfqTTaBOzCuWp7VPbd8VJvWLx/qz01iTwLlRkCyBD0JhhGoGC2RciMCW6RUZLrR\ngs0kUG7UlVwCexIk6dIL1wz9Z4YvoJmEBAk0LgPJYgMC4xtbbUR9N0ZCEisHgH4L9kkoMQK1Z+EV\n83FfWUhi43KwT0KCAjhgnIX3SfCDngQ+n0i+yGZqLDwhpex7u+iHehLy7QcJXM1DIiNQx/yiESk3\nSshDuQ1cWPUBksFxqqls25dAJgGjwHPdICvNexZpFfQkdDndKBlPfwmRDT0Yj3tzbCIzCWymBiSK\nvQH5frV5mSAeo8IuDLJPAtLKPi91u08CT1sh1ChWJXEE6uoVWX348gv0kXdPxH0oAFRdgTXGqEQm\nASPGlhzxnkVaRTMJjEDtSaQnYcwvGuFzkZQgwXUcff6mD8R9GAAq7GUyXG407gssGB1BCeuYlxcj\nvewQnqJvVCr5ksqj9duVjKe/hMhmmJtsuY4TrLJQ3gOgkfBmajaTQOUGRkVQbkRgi5SqZhIoN+oZ\n5UZR9gKalEwCgGQJphv5przrsrh2YnTYAR3jPs0Q6ZUJ75OwWFQ243Y0gY6nvxCChCjbl5CU6UYA\nkiXIJPj0JGD0MDEPaecF+yT4mlsodZRFkAgSIiLlRlw0gnIjMgkAGgmXG1V7ErheYDTYhcFxLy9G\nenk1mQSChB5EN1fh1ASZBIIEAA1UN1NTNZNA6QZGhM0k8J5FWrmOI9dxVPTL+yR0skeCRJAQ4blO\n0HRHJqF6ASVIANBIuNyI6UYYNXZhkPcs0szzHBWLvhYWKTfqieM4TDsIoScBQCv2MhluXKYnAaOC\nEagYB57raG6hKKPOdluWCBLq2FVzgoRwT0JnkSeA8dBoBCrXTowKuxBGYIs081xHZ+cKkjobfyoR\nJNTJ0sgUoCcBQCuREahMN8KICRqX6UlAimU8V+fnO99tWSJIqJMjkxDYtG65Mp6jtatycR8KgAQi\nk4BRRiYB48DzHJnKf3dabtTZd48BW1rDRUPat/1XtPOj79TKZdm4DwVAAjVqXObaiVFBTwLGQXjh\nptNMAkFCDaYdVDmOQ4AAoClbblRiuhFG0Acv26j/98Zmvffi9XEfCjAwmVD5/PI8mYSebFid1/Hl\nWW50ALCEYLoR+yRgBF2wbrnu2Pu+uA8DGCgyCX306T3v1fxiSQ43OgBoye6ubAyZBABIInudlggS\nerYsl+m4sQMAxpFTuff4vlGJfRIAIHHCPTfskwAAGIpoT4IviUwCACRJpodyI4IEAEBXvAYjUMkk\nAEByhPf9WpYnSAAADIHNJBhf1RGo9HMBQGJEG5cpNwIADIFTufmUyCQAQCKFR6BSbgQAGAqvwWZq\n9CQAQHL0MgKVIAEA0JXqPglkEgAgiex0I0dSPkuQAAAYAtt/QCYBAJLJXpPzOa/jPcAIEgAAXXFD\n040qMQKZBABIEDvdaHm+8z3ACBIAAF1xIz0J7JMAAElj90notB9BIkgAAHQpXG5ETwIAJI/nlh/1\nCRIAAENTLTcqBwqO2CcBAJLENi53ukeCRJAAAOhSJJNgDFkEAEiYapBAJgEAMCSVLHa5cdk39CMA\nQMJQbgQAGLrangQyCQCQLNXGZcqNAABDEhmBSiYBABKHciMAwNCFR6CSSQCA5KHcCAAwdLU7LhMk\nAECyMN0IABALz3XkG6lEuREAJE7GI5MAAIiB4zgq+Ua+MeyRAAAJ86sXr9N7L1mv91yyvuM/23nu\nAQCACtctNy6XfKNMlnUnAEiSd2xcqS996sNd/Vmu6ACArnmuI+Mz3QgA0oYgAQDQNddxghGoNC4D\nQHoQJAAAumZ7Ekq+kUdPAgCkBkECAKBrdroRmQQASBeCBABA11zbk2DoSQCANCFIAAB0zXUUlBuR\nSQCA9CBIAAB0zXXLjcvGiEwCAKQIQQIAoGuu46hQ9Mv/TZAAAKnRU5Bw6tQp3XbbbZqentZtt92m\n2dnZht93++2366Mf/ag+85nPRL7+2muv6eabb9auXbt08OBBLS4u9nI4AIAhc11HhRJBAgCkTU9B\nwv33369t27bpBz/4gbZt26b777+/4ffdcccd+sY3vlH39XvvvVe33nqr/vVf/1Vr1qzRww8/3Mvh\nAACGzHUdFSuZBEagAkB69BQkHD58WPv375ck7d+/X0888UTD79u2bZtWrlwZ+ZoxRs8995x2794t\nSbrxxht1+PDhXg4HADBkbmWfBIlMAgCkSU9BwokTJ7Rp0yZJ0sTEhE6cONH2nz158qTWrFmjTCYj\nSdq8ebNmZmZ6ORwAwJC5oewBjcsAkB6Zpb7h1ltv1S9/+cu6rx88eDDya8dx5Awx1TwxsXpoPyut\nOIf9wXnsHeewd3Gdw1zOC/57xfLcyL+Wo378ScA57A/OY+84h71ZMkh44IEHmv7exo0bdfz4cW3a\ntEnHjx/Xhg0b2v7B69ev1+nTp1UsFpXJZPTmm29qcnKy7T//1ltn2v5e1JuYWM057APOY+84h72L\n8xz6vh/8d6FQHOnXkvdi7ziH/cF57B3nsD2tAqmeyo2mpqZ06NAhSdKhQ4e0c+fOtv+s4zi66qqr\n9Pjjj0uSHnnkEU1NTfVyOACAIQuXG9GTAADp0VOQcOedd+qHP/yhpqen9cwzz+jOO++UJP3kJz/R\nPffcE3zfb//2b+sLX/iCnn32Wf36r/+6nn76aUnSl770Jf3t3/6tdu3apVOnTunmm2/u5XAAAEMW\nCRKYbgQAqbFkuVEr69ev14MPPlj39a1bt2rr1q3Br//+7/++4Z/fsmULY08BYISFswc0LgNAerDj\nMgCga+EggXIjAEgPggQAQNfoSQCAdCJIAAB0zaPcCABSiSABANC1cK8ymQQASA+CBABA12hcBoB0\nIkgAAHQtHBgwAhUA0oMgAQDQtXBgQCYBANKDIAEA0DWH6UYAkEoECQCArkWnG3FLAYC04IoOAOha\nOC4gkwAA6UGQAADoGj0JAJBOBAkAgK6FswdkEgAgPQgSAABdI5MAAOlEkAAA6JrLPgkAkEoECQCA\nrrmREagxHggAoK+4pAMAukZPAgCkE0ECAKBr4ewB+yQAQHpwRQcAdC1SbkRPAgCkBkECAKBrrst0\nIwBII4IEAEDXoo3LBAkAkBYECQCArpFJAIB0IkgAAHSNTAIApBNBAgCga2QSACCdCBIAAF0LxwVk\nEgAgPQgSAABd88gkAEAqESQAALrmuOyTAABpRJAAAOhaODAgkwAA6UGQAADoWjgwoCcBANKDIAEA\n0DUyCQCQTgQJAICuOaG7CJkEAEgPggQAQNc81w39N0ECAKQFQQIAoGvhuMBhuhEApAZBAgCga/Qk\nAEA6ESQAALrmMt0IAFKJIAEA0DWXHZcBIJUIEgAAXQuXG5FJAID0IEgAAHQtHBeQSQCA9CBIAAB0\njZ4EAEgnggQAQNdsYOAoWnoEABhtBAkAgK7ZwIAsAgCkC0ECAKBrNjigHwEA0oUgAQDQNTIJAJBO\nBAkAgK6RSQCAdCJIAAB0zQYJZBIAIF0IEgAAXbOxAUECAKQLQQIAoGuUGwFAOhEkAAC65tnGZfZI\nAIBUIUgAAHSNTAIApBNBAgCgaw4jUAEglQgSAABd85huBACpRJAAAOhaUG5ETwIApApBAgCga4xA\nBYB0IkgAAHSNxmUASCeCBABA11walwEglQgSAABdI5MAAOlEkAAA6JrnOnrPlnV6z8Xr4z4UAEAf\nZeI+AADA6HIcR//7v38k7sMAAPQZmQQAAAAAEQQJAAAAACIIEgAAAABEECQAAAAAiCBIAAAAABBB\nkAAAAAAggiABAAAAQARBAgAAAIAIggQAAAAAEQQJAAAAACIIEgAAAABEECQAAAAAiCBIAAAAABBB\nkAAAAAAggiABAAAAQARBAgAAAIAIggQAAAAAEQQJAAAAACIIEgAAAABEECQAAAAAiCBIAAAAABBB\nkAAAAAAggiABAAAAQIRjjDFxHwQAAACA5CCTAAAAACCCIAEAAABABEECAAAAgAiCBAAAAAARBAkA\nAAAAIggSAAAAAESMVJDw1FNPaffu3dq1a5fuv//+uA8nEb785S9r27Zt2rt3b/C1U6dO6bbbbtP0\n9LRuu+02zc7OSpKMMfra176mXbt2ad++ffrP//zP4M888sgjmp6e1vT0tB555JHg6y+99JL27dun\nXbt26Wtf+5rSODH3jTfe0C233KJPfOIT2rNnjx588EFJnMdOLCws6MCBA7r++uu1Z88e/eVf/qUk\n6bXXXtPNN9+sXbt26eDBg1pcXJQkLS4u6uDBg9q1a5duvvlmvf7668Hfdd9992nXrl3avXu3nn76\n6eDr4/L5L5VK2r9/vz7zmc9I4hx2Y2pqSvv27dMNN9ygT37yk5L4PHfq9OnTuuuuu3TdddfpN37j\nN/TCCy9wDjv0s5/9TDfccEPwv4985CN64IEHOI8deuCBB7Rnzx7t3btXd999txYWFrguDosZEcVi\n0ezcudO8+uqrZmFhwezbt8+88sorcR9W7H70ox+Zl156yezZsyf42p/+6Z+a++67zxhjzH333We+\n8Y1vGGOMOXLkiLn99tuN7/vmhRdeMAcOHDDGGHPy5EkzNTVlTp48aU6dOmWmpqbMqVOnjDHG3HTT\nTeaFF14wvu+b22+/3Rw5cmTI/8LBm5mZMS+99JIxxpgzZ86Y6elp88orr3AeO+D7vjl79qwxxpjF\nxUVz4MAB88ILL5i77rrLfP/73zfGGPPVr37VfPe73zXGGPN3f/d35qtf/aoxxpjvf//75gtf+IIx\nxphXXnnF7Nu3zywsLJhXX33V7Ny50xSLxbH6/H/nO98xd999t7nzzjuNMYZz2IVrrrnGnDhxIvI1\nPs+d+cM//EPz0EMPGWOMWVhYMLOzs5zDHhSLRXP11Veb119/nfPYgTfffNNcc801Zm5uzhhTvh7+\n8z//M9fFIRmZTMKxY8d0ySWXaMuWLcrlctqzZ48OHz4c92HF7mMf+5jWrl0b+drhw4e1f/9+SdL+\n/fv1xBNPRL7uOI4+9KEP6fTp0zp+/LiOHj2q7du3a926dVq7dq22b9+up59+WsePH9fZs2f1oQ99\nSI7jaP/+/ak855s2bdIVV1whSVq1apUuvfRSzczMcB474DiOVq5cKUkqFosqFotyHEfPPfecdu/e\nLUm68cYbg3/3k08+qRtvvFGStHv3bj377LMyxujw4cPas2ePcrmctmzZoksuuUTHjh0bm8//m2++\nqSNHjujAgQOSyiuLnMP+4PPcvjNnzuj5558P3oe5XE5r1qzhHPbg2Wef1ZYtW3TRRRdxHjtUKpU0\nPz+vYrGo+fl5TUxMcF0ckpEJEmZmZrR58+bg15OTk5qZmYnxiJLrxIkT2rRpkyRpYmJCJ06ckFR/\nDjdv3qyZmZmm57bZ96fZ66+/rp/+9Kf64Ac/yHnsUKlU0g033KCrr75aV199tbZs2aI1a9Yok8lI\niv67Z2Zm9I53vEOSlMlktHr1ap08ebLtc5jWz/+f/Mmf6Etf+pJct3xpPnnyJOewS7fffrs++clP\n6p/+6Z8kcV3sxOuvv64NGzboy1/+svbv36977rlH58+f5xz24LHHHgvKgjmP7ZucnNSnP/1pXXPN\nNdqxY4dWrVqlK664guvikIxMkIDuOI4jx3HiPoyRcO7cOd111136yle+olWrVkV+j/O4NM/z9Oij\nj+rf//3fdezYMf3sZz+L+5BGyr/9279pw4YNev/73x/3oYy8f/iHf9Ajjzyiv/mbv9F3v/tdPf/8\n85Hf5/PcWrFY1Msvv6xPfepTOnTokJYvX15Xq805bN/i4qKefPJJXXfddXW/x3lsbXZ2VocPH9bh\nw4f19NNPa25uLtJPgMEamSBhcnJSb775ZvDrmZkZTU5OxnhEybVx40YdP35cknT8+HFt2LBBUv05\nfPPNNzU5Odn03Db7/jQqFAq66667tG/fPk1PT0viPHZrzZo1uuqqq/Tiiy/q9OnTKhaLkqL/7snJ\nSb3xxhuSyg8kZ86c0fr169s+h2n8/P/4xz/Wk08+qampKd1999167rnn9PWvf51z2AX779q4caN2\n7dqlY8eO8XnuwObNm7V582Z98IMflCRdd911evnllzmHXXrqqad0xRVX6IILLpDEvaUTzzzzjN75\nzndqw4YNymazmp6e1o9//GOui0MyMkHC1q1b9fOf/1yvvfaaFhcX9dhjj2lqairuw0qkqakpHTp0\nSJJ06NAh7dy5M/J1Y4xefPFFrV69Wps2bdKOHTt09OhRzc7OanZ2VkePHtWOHTu0adMmrVq1Si++\n+KKMMZG/K02MMbrnnnt06aWX6rbbbgu+znls39tvv63Tp09Lkubn5/XMM8/osssu01VXXaXHH39c\nUnk6h/3MTk1NBRM6Hn/8cX384x+X4ziamprSY489psXFRb322mv6+c9/rg984ANj8fn//d//fT31\n1FN68skn9ed//uf6+Mc/rj/7sz/jHHbo/PnzOnv2bPDfP/zhD3X55Zfzee7AxMSENm/eHGQDn332\nWV122WWcwy499thj2rNnT/BrzmP7LrzwQv3Hf/yH5ubmZIzRs88+q3e9611cF4dl+L3S3Tty5IiZ\nnp42O3fuNN/+9rfjPpxE+OIXv2i2b99u3ve+95lf+7VfMw899JB5++23ze/8zu+YXbt2md/93d81\nJ0+eNMaUJ9D80R/9kdm5c6fZu3evOXbsWPD3fO973zPXXnutufbaa83DDz8cfP3YsWNmz549ZufO\nneaP//iPje/7Q/83Dtrzzz9v3v3ud5u9e/ea66+/3lx//fXmyJEjnMcO/PSnPzU33HCD2bt3r9mz\nZ4/5q7/6K2OMMa+++qq56aabzLXXXms+//nPm4WFBWOMMfPz8+bzn/+8ufbaa81NN91kXn311eDv\n+va3v2127txppqenI5M6xunz/9xzzwXTjTiHnXn11VfNvn37zL59+8wnPvGJ4N/J57kzL7/8srnx\nxhvN3r17zWc/+1lz6tQpzmEXzp07Z6688kpz+vTp4Gucx878xV/8hdm9e7fZs2eP+YM/+INgQhHX\nxcFzjEnhUF0AAAAAXRuZciMAAAAAw0GQAAAAACCCIAEAAABABEECAAAAgAiCBAAAAAARBAkAAAAA\nIggSAAAAAEQQJAAAAACI+P+mjgvwG7kuMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe475344cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train[df_train.id==10].y.diff().plot(figsize=(13,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe40be8a5c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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SiIiIAAQ+C3sGnagusajSNBytnWtrUJJvxIp5haq9vkGvgQB+zhNFS0r1AaRaaE8C7zAQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe40be403c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Still way too unstable, let's look at the cumsum\n",
    "df_train[df_train.id==10].y.cumsum().plot(figsize=(13,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Looking like moving average.\n",
    "\n",
    "Since I am going to use linear regression for this problem. Outliers need to be removed first. Checking to see if there is any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb59e6e5550>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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ishYIZwLYprAh6IbhpUjcE5qoNEbtAavVajz44IN44YUX8MQTT+A///M/ceLECWzbtg3L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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb59e7f3320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_train.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb59e73b0b8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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LLqqee+65qqqq6le/+tWRuk1j2rE8I1VVVb/85S+rSy65pHrb295Wbd++/Qjd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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb59e6cc5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zooming in\n",
    "df_train[df_train.y<-0.086092].y.hist(figsize=(13,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb59e3b0048>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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4x60YtemKwgQMDg5m+fLlSZLly5fnoYceesvznnjiiezatSvz58+fzPGYZIey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\nCAUAAKAQCgAAQPH/AGDbqlmLkY/+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb59e30c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zooming in\n",
    "df_train[df_train.y>0.093496].y.hist(figsize=(13,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#clipping\n",
    "low_y_cut = -0.086093\n",
    "high_y_cut = 0.093497\n",
    "\n",
    "y_within_the_cut = (df_train.y > low_y_cut) & (df_train.y < high_y_cut)\n",
    "new_df_train = df_train[y_within_the_cut].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Next, how many missing values are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "timestamp              0\n",
       "derived_0          72959\n",
       "derived_1          81029\n",
       "derived_2         398651\n",
       "derived_3         149471\n",
       "derived_4         406458\n",
       "fundamental_0      23947\n",
       "fundamental_1     679070\n",
       "fundamental_2     368840\n",
       "fundamental_3     454380\n",
       "fundamental_5     962020\n",
       "fundamental_6     701625\n",
       "fundamental_7      26340\n",
       "fundamental_8     373166\n",
       "fundamental_9     565567\n",
       "fundamental_10    112977\n",
       "fundamental_11    368840\n",
       "fundamental_12    110871\n",
       "fundamental_13    355138\n",
       "fundamental_14    356084\n",
       "fundamental_15    354897\n",
       "fundamental_16    355138\n",
       "fundamental_17     97222\n",
       "fundamental_18     15833\n",
       "fundamental_19     54588\n",
       "fundamental_20    110871\n",
       "fundamental_21     54333\n",
       "fundamental_22    558488\n",
       "fundamental_23    356723\n",
       "                   ...  \n",
       "technical_13        4764\n",
       "technical_14       14184\n",
       "technical_16       19981\n",
       "technical_17        4279\n",
       "technical_18       20016\n",
       "technical_19        2320\n",
       "technical_20        4764\n",
       "technical_21        2236\n",
       "technical_22           0\n",
       "technical_24       71146\n",
       "technical_25      208056\n",
       "technical_27        2420\n",
       "technical_28      262916\n",
       "technical_29       61615\n",
       "technical_30        4764\n",
       "technical_31      182678\n",
       "technical_32       19165\n",
       "technical_33       14535\n",
       "technical_34           0\n",
       "technical_35        3155\n",
       "technical_36        2552\n",
       "technical_37       19165\n",
       "technical_38       19165\n",
       "technical_39       20016\n",
       "technical_40        2236\n",
       "technical_41       44189\n",
       "technical_42       20001\n",
       "technical_43        4686\n",
       "technical_44      236779\n",
       "y                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "That is a lot of missing values. We could fillna with median for a quick and ditry try but it is best to use clustering for later experiment.\n",
    "\n",
    "However, before we do, it is best to keep these nan vales because they might follow a certain pattern. Why are they missing in the first place? Are they missing because some assets are being sold so they are no long available or perhaps they do not have much information in the first place?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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       "      <td>-0.038064</td>\n",
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       "      <td>-0.068550</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.007262</td>\n",
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       "      <th>4</th>\n",
       "      <td>26</td>\n",
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       "      <td>-0.025284</td>\n",
       "      <td>-0.057680</td>\n",
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       "      <td>0.180894</td>\n",
       "      <td>0.139445</td>\n",
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       "      <td>-0.018707</td>\n",
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       "      <th>5</th>\n",
       "      <td>27</td>\n",
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       "      <td>0.166239</td>\n",
       "      <td>-6.080701</td>\n",
       "      <td>-0.992249</td>\n",
       "      <td>-0.125916</td>\n",
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       "      <th>6</th>\n",
       "      <td>31</td>\n",
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       "      <td>0.014931</td>\n",
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       "      <td>0.014063</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-0.065292</td>\n",
       "      <td>-0.011286</td>\n",
       "      <td>0.026365</td>\n",
       "      <td>0.210249</td>\n",
       "      <td>0.167494</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003511</td>\n",
       "      <td>-0.034270</td>\n",
       "      <td>0.082372</td>\n",
       "      <td>-0.023937</td>\n",
       "      <td>-0.025750</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>0.263451</td>\n",
       "      <td>-0.241212</td>\n",
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       "      <td>1</td>\n",
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       "      <th>9</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.083330</td>\n",
       "      <td>0.081935</td>\n",
       "      <td>-2.048438</td>\n",
       "      <td>-0.206856</td>\n",
       "      <td>-0.839563</td>\n",
       "      <td>-0.234100</td>\n",
       "      <td>-0.291853</td>\n",
       "      <td>-5.722336</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.435826</td>\n",
       "      <td>1.797289</td>\n",
       "      <td>0.363570</td>\n",
       "      <td>-0.005651</td>\n",
       "      <td>0.442866</td>\n",
       "      <td>0.125375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292311</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.285388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.193590</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.034991</td>\n",
       "      <td>-0.019258</td>\n",
       "      <td>0.055769</td>\n",
       "      <td>-0.084496</td>\n",
       "      <td>0.259828</td>\n",
       "      <td>0.198800</td>\n",
       "      <td>0.265104</td>\n",
       "      <td>-0.160462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.212615</td>\n",
       "      <td>-0.979520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.395701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.780873</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0.071704</td>\n",
       "      <td>-0.044019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.038046</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.331778</td>\n",
       "      <td>-0.021114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.116360</td>\n",
       "      <td>0.164506</td>\n",
       "      <td>0.156510</td>\n",
       "      <td>-0.129252</td>\n",
       "      <td>1.310059</td>\n",
       "      <td>-0.264846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.370725</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.026824</td>\n",
       "      <td>-0.024105</td>\n",
       "      <td>-0.028991</td>\n",
       "      <td>0.433277</td>\n",
       "      <td>-0.006366</td>\n",
       "      <td>0.295138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.285946</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.367122</td>\n",
       "      <td>0.675543</td>\n",
       "      <td>-0.008483</td>\n",
       "      <td>-0.367778</td>\n",
       "      <td>-0.015529</td>\n",
       "      <td>0.129848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.251624</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0.453271</td>\n",
       "      <td>-0.036301</td>\n",
       "      <td>0.094657</td>\n",
       "      <td>-0.521106</td>\n",
       "      <td>0.049715</td>\n",
       "      <td>-0.352013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.182694</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.122734</td>\n",
       "      <td>0.038939</td>\n",
       "      <td>0.148015</td>\n",
       "      <td>2.493512</td>\n",
       "      <td>0.009746</td>\n",
       "      <td>-0.063111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.162878</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.062361</td>\n",
       "      <td>-0.063724</td>\n",
       "      <td>0.021274</td>\n",
       "      <td>0.032845</td>\n",
       "      <td>0.174025</td>\n",
       "      <td>0.122335</td>\n",
       "      <td>-0.133188</td>\n",
       "      <td>-0.108286</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175489</td>\n",
       "      <td>-0.024445</td>\n",
       "      <td>-0.034079</td>\n",
       "      <td>-0.020533</td>\n",
       "      <td>0.282478</td>\n",
       "      <td>-0.069417</td>\n",
       "      <td>0.247840</td>\n",
       "      <td>0.303953</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.190705</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>-0.056835</td>\n",
       "      <td>0.063464</td>\n",
       "      <td>-0.149507</td>\n",
       "      <td>0.267959</td>\n",
       "      <td>-0.392849</td>\n",
       "      <td>0.080084</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008503</td>\n",
       "      <td>0.048732</td>\n",
       "      <td>0.123793</td>\n",
       "      <td>0.266366</td>\n",
       "      <td>-0.009772</td>\n",
       "      <td>-0.248937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.157415</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043156</td>\n",
       "      <td>0.540945</td>\n",
       "      <td>0.278394</td>\n",
       "      <td>0.563343</td>\n",
       "      <td>-0.144004</td>\n",
       "      <td>-0.024376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.337090</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0.086832</td>\n",
       "      <td>-0.022962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071911</td>\n",
       "      <td>0.112818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.082307</td>\n",
       "      <td>-0.039761</td>\n",
       "      <td>0.102000</td>\n",
       "      <td>-0.798021</td>\n",
       "      <td>0.149622</td>\n",
       "      <td>0.028448</td>\n",
       "      <td>0.062647</td>\n",
       "      <td>0.044142</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005351</td>\n",
       "      <td>-0.031086</td>\n",
       "      <td>0.078334</td>\n",
       "      <td>0.417328</td>\n",
       "      <td>-0.020146</td>\n",
       "      <td>0.390538</td>\n",
       "      <td>0.256594</td>\n",
       "      <td>-0.167223</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.054391</td>\n",
       "      <td>0.330190</td>\n",
       "      <td>-0.816961</td>\n",
       "      <td>0.012602</td>\n",
       "      <td>-0.036462</td>\n",
       "      <td>-0.115197</td>\n",
       "      <td>-0.237428</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003925</td>\n",
       "      <td>-0.037522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.028244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049041</td>\n",
       "      <td>0.084827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710726</th>\n",
       "      <td>2100</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.079269</td>\n",
       "      <td>-0.021573</td>\n",
       "      <td>-0.033426</td>\n",
       "      <td>-0.333376</td>\n",
       "      <td>0.061463</td>\n",
       "      <td>0.217718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.127397</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710727</th>\n",
       "      <td>2101</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.204339</td>\n",
       "      <td>0.134338</td>\n",
       "      <td>0.143729</td>\n",
       "      <td>0.702932</td>\n",
       "      <td>-0.290513</td>\n",
       "      <td>0.031102</td>\n",
       "      <td>-0.243759</td>\n",
       "      <td>-0.205067</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710728</th>\n",
       "      <td>2102</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.095661</td>\n",
       "      <td>-0.051734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.004488</td>\n",
       "      <td>-0.249359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710729</th>\n",
       "      <td>2104</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.134436</td>\n",
       "      <td>-0.030198</td>\n",
       "      <td>-0.057608</td>\n",
       "      <td>0.016190</td>\n",
       "      <td>0.146911</td>\n",
       "      <td>-0.340176</td>\n",
       "      <td>-0.104457</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710730</th>\n",
       "      <td>2107</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.180348</td>\n",
       "      <td>0.061339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.418095</td>\n",
       "      <td>-0.620948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710731</th>\n",
       "      <td>2108</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.247698</td>\n",
       "      <td>0.026739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.280510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.198069</td>\n",
       "      <td>-0.620398</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710732</th>\n",
       "      <td>2109</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.425230</td>\n",
       "      <td>0.266186</td>\n",
       "      <td>0.075126</td>\n",
       "      <td>0.039726</td>\n",
       "      <td>-0.060703</td>\n",
       "      <td>-0.059133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286111</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710733</th>\n",
       "      <td>2114</td>\n",
       "      <td>1812</td>\n",
       "      <td>-2.337000</td>\n",
       "      <td>1.459039</td>\n",
       "      <td>3.495335</td>\n",
       "      <td>-0.347759</td>\n",
       "      <td>0.257406</td>\n",
       "      <td>0.172330</td>\n",
       "      <td>-0.098562</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710734</th>\n",
       "      <td>2117</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.136062</td>\n",
       "      <td>-0.006129</td>\n",
       "      <td>-2.083220</td>\n",
       "      <td>-0.316265</td>\n",
       "      <td>-0.480613</td>\n",
       "      <td>-0.270387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.297367</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710735</th>\n",
       "      <td>2118</td>\n",
       "      <td>1812</td>\n",
       "      <td>-2.458985</td>\n",
       "      <td>1.501635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.204721</td>\n",
       "      <td>-0.136476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710736</th>\n",
       "      <td>2120</td>\n",
       "      <td>1812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.398379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.222814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.554627</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710737</th>\n",
       "      <td>2121</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.126793</td>\n",
       "      <td>0.040152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.054294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.423501</td>\n",
       "      <td>-0.416516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710738</th>\n",
       "      <td>2126</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.181550</td>\n",
       "      <td>-0.039677</td>\n",
       "      <td>-0.082102</td>\n",
       "      <td>-0.140690</td>\n",
       "      <td>-0.231005</td>\n",
       "      <td>-0.145413</td>\n",
       "      <td>-0.150222</td>\n",
       "      <td>0.158479</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710739</th>\n",
       "      <td>2129</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.064731</td>\n",
       "      <td>-0.044927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.615191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040299</td>\n",
       "      <td>-0.058827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710740</th>\n",
       "      <td>2130</td>\n",
       "      <td>1812</td>\n",
       "      <td>-8.833108</td>\n",
       "      <td>3.455564</td>\n",
       "      <td>1.174958</td>\n",
       "      <td>-0.439014</td>\n",
       "      <td>-1.401483</td>\n",
       "      <td>-0.219093</td>\n",
       "      <td>0.087497</td>\n",
       "      <td>-1.461111</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710741</th>\n",
       "      <td>2131</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.149930</td>\n",
       "      <td>0.070203</td>\n",
       "      <td>0.363126</td>\n",
       "      <td>-0.010869</td>\n",
       "      <td>0.164914</td>\n",
       "      <td>-0.262682</td>\n",
       "      <td>-0.315310</td>\n",
       "      <td>0.019663</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710742</th>\n",
       "      <td>2137</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.059745</td>\n",
       "      <td>-0.052186</td>\n",
       "      <td>-0.062670</td>\n",
       "      <td>-1.842369</td>\n",
       "      <td>0.073483</td>\n",
       "      <td>0.287247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710743</th>\n",
       "      <td>2138</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.219608</td>\n",
       "      <td>-0.034336</td>\n",
       "      <td>-0.031817</td>\n",
       "      <td>0.270888</td>\n",
       "      <td>0.045701</td>\n",
       "      <td>0.134292</td>\n",
       "      <td>0.364995</td>\n",
       "      <td>0.064963</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710744</th>\n",
       "      <td>2139</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.062132</td>\n",
       "      <td>0.070332</td>\n",
       "      <td>0.351273</td>\n",
       "      <td>0.478199</td>\n",
       "      <td>0.092099</td>\n",
       "      <td>0.494140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.330590</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710745</th>\n",
       "      <td>2140</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.213264</td>\n",
       "      <td>0.029603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.273137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.464013</td>\n",
       "      <td>-0.404886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710746</th>\n",
       "      <td>2142</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.223395</td>\n",
       "      <td>-0.042492</td>\n",
       "      <td>-0.060381</td>\n",
       "      <td>0.016019</td>\n",
       "      <td>0.218667</td>\n",
       "      <td>-0.160979</td>\n",
       "      <td>-0.138038</td>\n",
       "      <td>0.348972</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710747</th>\n",
       "      <td>2145</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.154051</td>\n",
       "      <td>-0.029331</td>\n",
       "      <td>-0.010545</td>\n",
       "      <td>0.019339</td>\n",
       "      <td>-0.260369</td>\n",
       "      <td>-0.227888</td>\n",
       "      <td>0.006721</td>\n",
       "      <td>0.008255</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710748</th>\n",
       "      <td>2146</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.238458</td>\n",
       "      <td>0.316407</td>\n",
       "      <td>0.632261</td>\n",
       "      <td>0.531651</td>\n",
       "      <td>-0.154740</td>\n",
       "      <td>0.069316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.543269</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710749</th>\n",
       "      <td>2148</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.089476</td>\n",
       "      <td>-0.038628</td>\n",
       "      <td>0.776538</td>\n",
       "      <td>8.809832</td>\n",
       "      <td>-0.177850</td>\n",
       "      <td>0.388419</td>\n",
       "      <td>0.193054</td>\n",
       "      <td>0.175415</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710750</th>\n",
       "      <td>2149</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.254593</td>\n",
       "      <td>0.064668</td>\n",
       "      <td>-0.034742</td>\n",
       "      <td>-1.336611</td>\n",
       "      <td>-0.037401</td>\n",
       "      <td>0.244451</td>\n",
       "      <td>0.049125</td>\n",
       "      <td>-0.130919</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710751</th>\n",
       "      <td>2150</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.123364</td>\n",
       "      <td>-0.055977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.255730</td>\n",
       "      <td>-0.108285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710752</th>\n",
       "      <td>2151</td>\n",
       "      <td>1812</td>\n",
       "      <td>-10.437184</td>\n",
       "      <td>0.080905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.369380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.293557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710753</th>\n",
       "      <td>2154</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.077930</td>\n",
       "      <td>-0.038748</td>\n",
       "      <td>-0.031859</td>\n",
       "      <td>0.646608</td>\n",
       "      <td>-0.145526</td>\n",
       "      <td>-0.119539</td>\n",
       "      <td>-0.151587</td>\n",
       "      <td>-0.130524</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710754</th>\n",
       "      <td>2156</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.269845</td>\n",
       "      <td>-0.005322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.117539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.214088</td>\n",
       "      <td>-0.307293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710755</th>\n",
       "      <td>2158</td>\n",
       "      <td>1812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.456266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1688296 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  timestamp  derived_0  derived_1  derived_2  derived_3  \\\n",
       "0          10          0   0.370326  -0.006316   0.222831  -0.213030   \n",
       "1          11          0   0.014765  -0.038064  -0.017425   0.320652   \n",
       "2          12          0  -0.010622  -0.050577   3.379575  -0.157525   \n",
       "3          25          0        NaN        NaN        NaN        NaN   \n",
       "4          26          0   0.176693  -0.025284  -0.057680   0.015100   \n",
       "5          27          0   0.346856   0.166239  -6.080701  -0.992249   \n",
       "6          31          0   0.072036   0.014931        NaN   0.014063   \n",
       "7          38          0   0.300062   0.071251  -0.074451  -0.065292   \n",
       "8          39          0  -0.003511  -0.034270   0.082372  -0.023937   \n",
       "9          40          0  -0.083330   0.081935  -2.048438  -0.206856   \n",
       "10         41          0   0.435826   1.797289   0.363570  -0.005651   \n",
       "11         43          0        NaN        NaN        NaN        NaN   \n",
       "12         44          0   0.034991  -0.019258   0.055769  -0.084496   \n",
       "13         49          0        NaN   0.212615  -0.979520        NaN   \n",
       "14         54          0   0.071704  -0.044019        NaN   0.038046   \n",
       "15         59          0   0.116360   0.164506   0.156510  -0.129252   \n",
       "16         60          0   0.026824  -0.024105  -0.028991   0.433277   \n",
       "17         62          0   0.367122   0.675543  -0.008483  -0.367778   \n",
       "18         63          0   0.453271  -0.036301   0.094657  -0.521106   \n",
       "19         68          0  -0.122734   0.038939   0.148015   2.493512   \n",
       "20         69          0  -0.062361  -0.063724   0.021274   0.032845   \n",
       "21         70          0   0.175489  -0.024445  -0.034079  -0.020533   \n",
       "22         76          0  -0.190705   0.006221  -0.056835   0.063464   \n",
       "23         79          0   0.008503   0.048732   0.123793   0.266366   \n",
       "24         80          0   0.043156   0.540945   0.278394   0.563343   \n",
       "25         82          0   0.086832  -0.022962        NaN   0.006783   \n",
       "26         83          0  -0.082307  -0.039761   0.102000  -0.798021   \n",
       "27         85          0   0.005351  -0.031086   0.078334   0.417328   \n",
       "28         87          0   0.045536   0.054391   0.330190  -0.816961   \n",
       "29         90          0  -0.003925  -0.037522        NaN  -0.028244   \n",
       "...       ...        ...        ...        ...        ...        ...   \n",
       "1710726  2100       1812   0.079269  -0.021573  -0.033426  -0.333376   \n",
       "1710727  2101       1812  -0.204339   0.134338   0.143729   0.702932   \n",
       "1710728  2102       1812   0.095661  -0.051734        NaN   0.035816   \n",
       "1710729  2104       1812   0.134436  -0.030198  -0.057608   0.016190   \n",
       "1710730  2107       1812  -0.180348   0.061339        NaN   0.049680   \n",
       "1710731  2108       1812  -0.247698   0.026739        NaN  -0.280510   \n",
       "1710732  2109       1812  -0.425230   0.266186   0.075126   0.039726   \n",
       "1710733  2114       1812  -2.337000   1.459039   3.495335  -0.347759   \n",
       "1710734  2117       1812  -0.136062  -0.006129  -2.083220  -0.316265   \n",
       "1710735  2118       1812  -2.458985   1.501635        NaN        NaN   \n",
       "1710736  2120       1812        NaN        NaN  -0.398379        NaN   \n",
       "1710737  2121       1812   0.126793   0.040152        NaN  -0.054294   \n",
       "1710738  2126       1812  -0.181550  -0.039677  -0.082102  -0.140690   \n",
       "1710739  2129       1812  -0.064731  -0.044927        NaN   0.615191   \n",
       "1710740  2130       1812  -8.833108   3.455564   1.174958  -0.439014   \n",
       "1710741  2131       1812  -0.149930   0.070203   0.363126  -0.010869   \n",
       "1710742  2137       1812  -0.059745  -0.052186  -0.062670  -1.842369   \n",
       "1710743  2138       1812   0.219608  -0.034336  -0.031817   0.270888   \n",
       "1710744  2139       1812   0.062132   0.070332   0.351273   0.478199   \n",
       "1710745  2140       1812   0.213264   0.029603        NaN  -0.273137   \n",
       "1710746  2142       1812  -0.223395  -0.042492  -0.060381   0.016019   \n",
       "1710747  2145       1812  -0.154051  -0.029331  -0.010545   0.019339   \n",
       "1710748  2146       1812  -0.238458   0.316407   0.632261   0.531651   \n",
       "1710749  2148       1812   0.089476  -0.038628   0.776538   8.809832   \n",
       "1710750  2149       1812   0.254593   0.064668  -0.034742  -1.336611   \n",
       "1710751  2150       1812  -0.123364  -0.055977        NaN   0.010906   \n",
       "1710752  2151       1812 -10.437184   0.080905        NaN   3.369380   \n",
       "1710753  2154       1812  -0.077930  -0.038748  -0.031859   0.646608   \n",
       "1710754  2156       1812  -0.269845  -0.005322        NaN  -0.117539   \n",
       "1710755  2158       1812        NaN        NaN        NaN        NaN   \n",
       "\n",
       "         derived_4  fundamental_0  fundamental_1  fundamental_2  \\\n",
       "0         0.729277      -0.335633       0.113292       1.621238   \n",
       "1        -0.034134       0.004413       0.114285      -0.210185   \n",
       "2        -0.068550      -0.155937       1.219439      -0.764516   \n",
       "3              NaN       0.178495            NaN      -0.007262   \n",
       "4         0.180894       0.139445      -0.125687      -0.018707   \n",
       "5        -0.125916       0.345812            NaN      -0.584239   \n",
       "6              NaN      -0.193205            NaN            NaN   \n",
       "7        -0.011286       0.026365       0.210249       0.167494   \n",
       "8        -0.025750       0.007815       0.263451      -0.241212   \n",
       "9        -0.839563      -0.234100      -0.291853      -5.722336   \n",
       "10        0.442866       0.125375            NaN       0.292311   \n",
       "11             NaN      -0.285388            NaN      -0.193590   \n",
       "12        0.259828       0.198800       0.265104      -0.160462   \n",
       "13             NaN       0.395701            NaN      -0.780873   \n",
       "14             NaN       0.331778      -0.021114            NaN   \n",
       "15        1.310059      -0.264846            NaN       0.370725   \n",
       "16       -0.006366       0.295138            NaN      -0.285946   \n",
       "17       -0.015529       0.129848            NaN      -0.251624   \n",
       "18        0.049715      -0.352013            NaN      -0.182694   \n",
       "19        0.009746      -0.063111            NaN      -0.162878   \n",
       "20        0.174025       0.122335      -0.133188      -0.108286   \n",
       "21        0.282478      -0.069417       0.247840       0.303953   \n",
       "22       -0.149507       0.267959      -0.392849       0.080084   \n",
       "23       -0.009772      -0.248937            NaN      -0.157415   \n",
       "24       -0.144004      -0.024376            NaN      -0.337090   \n",
       "25             NaN       0.071911       0.112818            NaN   \n",
       "26        0.149622       0.028448       0.062647       0.044142   \n",
       "27       -0.020146       0.390538       0.256594      -0.167223   \n",
       "28        0.012602      -0.036462      -0.115197      -0.237428   \n",
       "29             NaN      -0.049041       0.084827            NaN   \n",
       "...            ...            ...            ...            ...   \n",
       "1710726   0.061463       0.217718            NaN       0.127397   \n",
       "1710727  -0.290513       0.031102      -0.243759      -0.205067   \n",
       "1710728        NaN      -0.004488      -0.249359            NaN   \n",
       "1710729   0.146911      -0.340176      -0.104457       0.002421   \n",
       "1710730        NaN      -0.418095      -0.620948            NaN   \n",
       "1710731        NaN       0.198069      -0.620398            NaN   \n",
       "1710732  -0.060703      -0.059133            NaN       0.286111   \n",
       "1710733   0.257406       0.172330      -0.098562      -0.000398   \n",
       "1710734  -0.480613      -0.270387            NaN      -0.297367   \n",
       "1710735        NaN       0.204721      -0.136476            NaN   \n",
       "1710736        NaN      -0.222814            NaN      -0.554627   \n",
       "1710737        NaN      -0.423501      -0.416516            NaN   \n",
       "1710738  -0.231005      -0.145413      -0.150222       0.158479   \n",
       "1710739        NaN       0.040299      -0.058827            NaN   \n",
       "1710740  -1.401483      -0.219093       0.087497      -1.461111   \n",
       "1710741   0.164914      -0.262682      -0.315310       0.019663   \n",
       "1710742   0.073483       0.287247            NaN       0.001499   \n",
       "1710743   0.045701       0.134292       0.364995       0.064963   \n",
       "1710744   0.092099       0.494140            NaN      -0.330590   \n",
       "1710745        NaN      -0.464013      -0.404886            NaN   \n",
       "1710746   0.218667      -0.160979      -0.138038       0.348972   \n",
       "1710747  -0.260369      -0.227888       0.006721       0.008255   \n",
       "1710748  -0.154740       0.069316            NaN      -0.543269   \n",
       "1710749  -0.177850       0.388419       0.193054       0.175415   \n",
       "1710750  -0.037401       0.244451       0.049125      -0.130919   \n",
       "1710751        NaN      -0.255730      -0.108285            NaN   \n",
       "1710752        NaN      -0.293557            NaN            NaN   \n",
       "1710753  -0.145526      -0.119539      -0.151587      -0.130524   \n",
       "1710754        NaN       0.214088      -0.307293            NaN   \n",
       "1710755        NaN            NaN      -6.456266            NaN   \n",
       "\n",
       "               ...         technical_35_nan  technical_36_nan  \\\n",
       "0              ...                        0                 0   \n",
       "1              ...                        0                 0   \n",
       "2              ...                        0                 0   \n",
       "3              ...                        0                 0   \n",
       "4              ...                        0                 0   \n",
       "5              ...                        0                 0   \n",
       "6              ...                        0                 0   \n",
       "7              ...                        0                 0   \n",
       "8              ...                        0                 0   \n",
       "9              ...                        0                 0   \n",
       "10             ...                        0                 0   \n",
       "11             ...                        0                 0   \n",
       "12             ...                        0                 0   \n",
       "13             ...                        0                 0   \n",
       "14             ...                        0                 0   \n",
       "15             ...                        0                 0   \n",
       "16             ...                        0                 0   \n",
       "17             ...                        0                 0   \n",
       "18             ...                        0                 0   \n",
       "19             ...                        0                 0   \n",
       "20             ...                        0                 0   \n",
       "21             ...                        0                 0   \n",
       "22             ...                        0                 0   \n",
       "23             ...                        0                 0   \n",
       "24             ...                        0                 0   \n",
       "25             ...                        0                 0   \n",
       "26             ...                        0                 0   \n",
       "27             ...                        0                 0   \n",
       "28             ...                        0                 0   \n",
       "29             ...                        0                 0   \n",
       "...            ...                      ...               ...   \n",
       "1710726        ...                        0                 0   \n",
       "1710727        ...                        0                 0   \n",
       "1710728        ...                        0                 0   \n",
       "1710729        ...                        0                 0   \n",
       "1710730        ...                        0                 0   \n",
       "1710731        ...                        0                 0   \n",
       "1710732        ...                        0                 0   \n",
       "1710733        ...                        0                 0   \n",
       "1710734        ...                        0                 0   \n",
       "1710735        ...                        0                 0   \n",
       "1710736        ...                        0                 0   \n",
       "1710737        ...                        0                 0   \n",
       "1710738        ...                        0                 0   \n",
       "1710739        ...                        0                 0   \n",
       "1710740        ...                        0                 0   \n",
       "1710741        ...                        0                 0   \n",
       "1710742        ...                        0                 0   \n",
       "1710743        ...                        0                 0   \n",
       "1710744        ...                        0                 0   \n",
       "1710745        ...                        0                 0   \n",
       "1710746        ...                        0                 0   \n",
       "1710747        ...                        0                 0   \n",
       "1710748        ...                        0                 0   \n",
       "1710749        ...                        0                 0   \n",
       "1710750        ...                        0                 0   \n",
       "1710751        ...                        0                 0   \n",
       "1710752        ...                        0                 0   \n",
       "1710753        ...                        0                 0   \n",
       "1710754        ...                        0                 0   \n",
       "1710755        ...                        0                 0   \n",
       "\n",
       "         technical_37_nan  technical_38_nan  technical_39_nan  \\\n",
       "0                       1                 1                 1   \n",
       "1                       1                 1                 1   \n",
       "2                       1                 1                 1   \n",
       "3                       1                 1                 1   \n",
       "4                       1                 1                 1   \n",
       "5                       1                 1                 1   \n",
       "6                       1                 1                 1   \n",
       "7                       1                 1                 1   \n",
       "8                       1                 1                 1   \n",
       "9                       1                 1                 1   \n",
       "10                      1                 1                 1   \n",
       "11                      1                 1                 1   \n",
       "12                      1                 1                 1   \n",
       "13                      1                 1                 1   \n",
       "14                      1                 1                 1   \n",
       "15                      1                 1                 1   \n",
       "16                      1                 1                 1   \n",
       "17                      1                 1                 1   \n",
       "18                      1                 1                 1   \n",
       "19                      1                 1                 1   \n",
       "20                      1                 1                 1   \n",
       "21                      1                 1                 1   \n",
       "22                      1                 1                 1   \n",
       "23                      1                 1                 1   \n",
       "24                      1                 1                 1   \n",
       "25                      1                 1                 1   \n",
       "26                      1                 1                 1   \n",
       "27                      1                 1                 1   \n",
       "28                      1                 1                 1   \n",
       "29                      1                 1                 1   \n",
       "...                   ...               ...               ...   \n",
       "1710726                 0                 0                 0   \n",
       "1710727                 0                 0                 0   \n",
       "1710728                 0                 0                 0   \n",
       "1710729                 0                 0                 0   \n",
       "1710730                 0                 0                 0   \n",
       "1710731                 0                 0                 0   \n",
       "1710732                 0                 0                 0   \n",
       "1710733                 0                 0                 0   \n",
       "1710734                 0                 0                 0   \n",
       "1710735                 0                 0                 0   \n",
       "1710736                 0                 0                 0   \n",
       "1710737                 0                 0                 0   \n",
       "1710738                 0                 0                 0   \n",
       "1710739                 0                 0                 0   \n",
       "1710740                 0                 0                 0   \n",
       "1710741                 0                 0                 0   \n",
       "1710742                 0                 0                 0   \n",
       "1710743                 0                 0                 0   \n",
       "1710744                 0                 0                 0   \n",
       "1710745                 0                 0                 0   \n",
       "1710746                 0                 0                 0   \n",
       "1710747                 0                 0                 0   \n",
       "1710748                 0                 0                 0   \n",
       "1710749                 0                 0                 0   \n",
       "1710750                 0                 0                 0   \n",
       "1710751                 0                 0                 0   \n",
       "1710752                 0                 0                 0   \n",
       "1710753                 0                 0                 0   \n",
       "1710754                 0                 0                 0   \n",
       "1710755                 0                 0                 0   \n",
       "\n",
       "         technical_40_nan  technical_41_nan  technical_42_nan  \\\n",
       "0                       0                 1                 1   \n",
       "1                       0                 1                 1   \n",
       "2                       0                 1                 1   \n",
       "3                       0                 1                 1   \n",
       "4                       0                 1                 1   \n",
       "5                       0                 1                 1   \n",
       "6                       0                 1                 1   \n",
       "7                       0                 1                 1   \n",
       "8                       0                 1                 1   \n",
       "9                       0                 1                 1   \n",
       "10                      0                 1                 1   \n",
       "11                      0                 1                 1   \n",
       "12                      0                 1                 1   \n",
       "13                      0                 1                 1   \n",
       "14                      0                 1                 1   \n",
       "15                      0                 1                 1   \n",
       "16                      0                 1                 1   \n",
       "17                      0                 1                 1   \n",
       "18                      0                 1                 1   \n",
       "19                      0                 1                 1   \n",
       "20                      0                 1                 1   \n",
       "21                      0                 1                 1   \n",
       "22                      0                 1                 1   \n",
       "23                      0                 1                 1   \n",
       "24                      0                 1                 1   \n",
       "25                      0                 1                 1   \n",
       "26                      0                 1                 1   \n",
       "27                      0                 1                 1   \n",
       "28                      0                 1                 1   \n",
       "29                      0                 1                 1   \n",
       "...                   ...               ...               ...   \n",
       "1710726                 0                 0                 0   \n",
       "1710727                 0                 0                 0   \n",
       "1710728                 0                 0                 0   \n",
       "1710729                 0                 0                 0   \n",
       "1710730                 0                 0                 0   \n",
       "1710731                 0                 0                 0   \n",
       "1710732                 0                 0                 0   \n",
       "1710733                 0                 0                 0   \n",
       "1710734                 0                 0                 0   \n",
       "1710735                 0                 0                 0   \n",
       "1710736                 0                 0                 0   \n",
       "1710737                 0                 0                 0   \n",
       "1710738                 0                 0                 0   \n",
       "1710739                 0                 0                 0   \n",
       "1710740                 0                 0                 0   \n",
       "1710741                 0                 0                 0   \n",
       "1710742                 0                 0                 0   \n",
       "1710743                 0                 0                 0   \n",
       "1710744                 0                 0                 0   \n",
       "1710745                 0                 0                 0   \n",
       "1710746                 0                 0                 0   \n",
       "1710747                 0                 0                 0   \n",
       "1710748                 0                 0                 0   \n",
       "1710749                 0                 0                 0   \n",
       "1710750                 0                 0                 0   \n",
       "1710751                 0                 0                 0   \n",
       "1710752                 0                 0                 0   \n",
       "1710753                 0                 0                 0   \n",
       "1710754                 0                 0                 0   \n",
       "1710755                 0                 0                 0   \n",
       "\n",
       "         technical_43_nan  technical_44_nan  \n",
       "0                       0                 1  \n",
       "1                       0                 1  \n",
       "2                       0                 1  \n",
       "3                       0                 1  \n",
       "4                       0                 1  \n",
       "5                       0                 1  \n",
       "6                       0                 1  \n",
       "7                       0                 1  \n",
       "8                       0                 1  \n",
       "9                       0                 1  \n",
       "10                      0                 1  \n",
       "11                      0                 1  \n",
       "12                      0                 1  \n",
       "13                      0                 1  \n",
       "14                      0                 1  \n",
       "15                      0                 1  \n",
       "16                      0                 1  \n",
       "17                      0                 1  \n",
       "18                      0                 1  \n",
       "19                      0                 1  \n",
       "20                      0                 1  \n",
       "21                      0                 1  \n",
       "22                      0                 1  \n",
       "23                      0                 1  \n",
       "24                      0                 1  \n",
       "25                      0                 1  \n",
       "26                      0                 1  \n",
       "27                      0                 1  \n",
       "28                      0                 1  \n",
       "29                      0                 1  \n",
       "...                   ...               ...  \n",
       "1710726                 0                 0  \n",
       "1710727                 0                 0  \n",
       "1710728                 0                 0  \n",
       "1710729                 0                 0  \n",
       "1710730                 0                 0  \n",
       "1710731                 0                 0  \n",
       "1710732                 0                 0  \n",
       "1710733                 0                 0  \n",
       "1710734                 0                 0  \n",
       "1710735                 0                 0  \n",
       "1710736                 0                 0  \n",
       "1710737                 0                 0  \n",
       "1710738                 0                 0  \n",
       "1710739                 0                 0  \n",
       "1710740                 0                 0  \n",
       "1710741                 0                 0  \n",
       "1710742                 0                 0  \n",
       "1710743                 0                 0  \n",
       "1710744                 0                 0  \n",
       "1710745                 0                 0  \n",
       "1710746                 0                 0  \n",
       "1710747                 0                 0  \n",
       "1710748                 0                 0  \n",
       "1710749                 0                 0  \n",
       "1710750                 0                 0  \n",
       "1710751                 0                 0  \n",
       "1710752                 0                 0  \n",
       "1710753                 0                 0  \n",
       "1710754                 0                 0  \n",
       "1710755                 0                 1  \n",
       "\n",
       "[1688296 rows x 217 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping the original columns that contain nan values\n",
    "\n",
    "cols = [column for column in new_df_train.columns if column not in ['id', 'timestamp', 'y']]\n",
    "\n",
    "for col in cols:\n",
    "    temp = new_df_train[col].isnull()\n",
    "    if np.sum(temp) > 0:\n",
    "        new_df_train[col + '_nan'] = temp * 1\n",
    "        \n",
    "new_df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_35_nan</th>\n",
       "      <th>technical_36_nan</th>\n",
       "      <th>technical_37_nan</th>\n",
       "      <th>technical_38_nan</th>\n",
       "      <th>technical_39_nan</th>\n",
       "      <th>technical_40_nan</th>\n",
       "      <th>technical_41_nan</th>\n",
       "      <th>technical_42_nan</th>\n",
       "      <th>technical_43_nan</th>\n",
       "      <th>technical_44_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370326</td>\n",
       "      <td>-0.006316</td>\n",
       "      <td>0.222831</td>\n",
       "      <td>-0.213030</td>\n",
       "      <td>0.729277</td>\n",
       "      <td>-0.335633</td>\n",
       "      <td>0.113292</td>\n",
       "      <td>1.621238</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014765</td>\n",
       "      <td>-0.038064</td>\n",
       "      <td>-0.017425</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>-0.034134</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.114285</td>\n",
       "      <td>-0.210185</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.010622</td>\n",
       "      <td>-0.050577</td>\n",
       "      <td>3.379575</td>\n",
       "      <td>-0.157525</td>\n",
       "      <td>-0.068550</td>\n",
       "      <td>-0.155937</td>\n",
       "      <td>1.219439</td>\n",
       "      <td>-0.764516</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000413</td>\n",
       "      <td>0.005194</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.178495</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.007262</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.176693</td>\n",
       "      <td>-0.025284</td>\n",
       "      <td>-0.057680</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.180894</td>\n",
       "      <td>0.139445</td>\n",
       "      <td>-0.125687</td>\n",
       "      <td>-0.018707</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.346856</td>\n",
       "      <td>0.166239</td>\n",
       "      <td>-6.080701</td>\n",
       "      <td>-0.992249</td>\n",
       "      <td>-0.125916</td>\n",
       "      <td>0.345812</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.584239</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.072036</td>\n",
       "      <td>0.014931</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.014063</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.193205</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.300062</td>\n",
       "      <td>0.071251</td>\n",
       "      <td>-0.074451</td>\n",
       "      <td>-0.065292</td>\n",
       "      <td>-0.011286</td>\n",
       "      <td>0.026365</td>\n",
       "      <td>0.210249</td>\n",
       "      <td>0.167494</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003511</td>\n",
       "      <td>-0.034270</td>\n",
       "      <td>0.082372</td>\n",
       "      <td>-0.023937</td>\n",
       "      <td>-0.025750</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>0.263451</td>\n",
       "      <td>-0.241212</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.083330</td>\n",
       "      <td>0.081935</td>\n",
       "      <td>-2.048438</td>\n",
       "      <td>-0.206856</td>\n",
       "      <td>-0.839563</td>\n",
       "      <td>-0.234100</td>\n",
       "      <td>-0.291853</td>\n",
       "      <td>-5.722336</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0.435826</td>\n",
       "      <td>1.797289</td>\n",
       "      <td>0.363570</td>\n",
       "      <td>-0.005651</td>\n",
       "      <td>0.442866</td>\n",
       "      <td>0.125375</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>0.292311</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000413</td>\n",
       "      <td>0.005194</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.285388</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.193590</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.034991</td>\n",
       "      <td>-0.019258</td>\n",
       "      <td>0.055769</td>\n",
       "      <td>-0.084496</td>\n",
       "      <td>0.259828</td>\n",
       "      <td>0.198800</td>\n",
       "      <td>0.265104</td>\n",
       "      <td>-0.160462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000413</td>\n",
       "      <td>0.212615</td>\n",
       "      <td>-0.979520</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.395701</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.780873</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0.071704</td>\n",
       "      <td>-0.044019</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.038046</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.331778</td>\n",
       "      <td>-0.021114</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0.116360</td>\n",
       "      <td>0.164506</td>\n",
       "      <td>0.156510</td>\n",
       "      <td>-0.129252</td>\n",
       "      <td>1.310059</td>\n",
       "      <td>-0.264846</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>0.370725</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.026824</td>\n",
       "      <td>-0.024105</td>\n",
       "      <td>-0.028991</td>\n",
       "      <td>0.433277</td>\n",
       "      <td>-0.006366</td>\n",
       "      <td>0.295138</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.285946</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0.367122</td>\n",
       "      <td>0.675543</td>\n",
       "      <td>-0.008483</td>\n",
       "      <td>-0.367778</td>\n",
       "      <td>-0.015529</td>\n",
       "      <td>0.129848</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.251624</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0.453271</td>\n",
       "      <td>-0.036301</td>\n",
       "      <td>0.094657</td>\n",
       "      <td>-0.521106</td>\n",
       "      <td>0.049715</td>\n",
       "      <td>-0.352013</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.182694</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.122734</td>\n",
       "      <td>0.038939</td>\n",
       "      <td>0.148015</td>\n",
       "      <td>2.493512</td>\n",
       "      <td>0.009746</td>\n",
       "      <td>-0.063111</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.162878</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.062361</td>\n",
       "      <td>-0.063724</td>\n",
       "      <td>0.021274</td>\n",
       "      <td>0.032845</td>\n",
       "      <td>0.174025</td>\n",
       "      <td>0.122335</td>\n",
       "      <td>-0.133188</td>\n",
       "      <td>-0.108286</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175489</td>\n",
       "      <td>-0.024445</td>\n",
       "      <td>-0.034079</td>\n",
       "      <td>-0.020533</td>\n",
       "      <td>0.282478</td>\n",
       "      <td>-0.069417</td>\n",
       "      <td>0.247840</td>\n",
       "      <td>0.303953</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.190705</td>\n",
       "      <td>0.006221</td>\n",
       "      <td>-0.056835</td>\n",
       "      <td>0.063464</td>\n",
       "      <td>-0.149507</td>\n",
       "      <td>0.267959</td>\n",
       "      <td>-0.392849</td>\n",
       "      <td>0.080084</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008503</td>\n",
       "      <td>0.048732</td>\n",
       "      <td>0.123793</td>\n",
       "      <td>0.266366</td>\n",
       "      <td>-0.009772</td>\n",
       "      <td>-0.248937</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.157415</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043156</td>\n",
       "      <td>0.540945</td>\n",
       "      <td>0.278394</td>\n",
       "      <td>0.563343</td>\n",
       "      <td>-0.144004</td>\n",
       "      <td>-0.024376</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.337090</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0.086832</td>\n",
       "      <td>-0.022962</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.006783</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.071911</td>\n",
       "      <td>0.112818</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.082307</td>\n",
       "      <td>-0.039761</td>\n",
       "      <td>0.102000</td>\n",
       "      <td>-0.798021</td>\n",
       "      <td>0.149622</td>\n",
       "      <td>0.028448</td>\n",
       "      <td>0.062647</td>\n",
       "      <td>0.044142</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005351</td>\n",
       "      <td>-0.031086</td>\n",
       "      <td>0.078334</td>\n",
       "      <td>0.417328</td>\n",
       "      <td>-0.020146</td>\n",
       "      <td>0.390538</td>\n",
       "      <td>0.256594</td>\n",
       "      <td>-0.167223</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.054391</td>\n",
       "      <td>0.330190</td>\n",
       "      <td>-0.816961</td>\n",
       "      <td>0.012602</td>\n",
       "      <td>-0.036462</td>\n",
       "      <td>-0.115197</td>\n",
       "      <td>-0.237428</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003925</td>\n",
       "      <td>-0.037522</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.028244</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.049041</td>\n",
       "      <td>0.084827</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710726</th>\n",
       "      <td>2100</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.079269</td>\n",
       "      <td>-0.021573</td>\n",
       "      <td>-0.033426</td>\n",
       "      <td>-0.333376</td>\n",
       "      <td>0.061463</td>\n",
       "      <td>0.217718</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>0.127397</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710727</th>\n",
       "      <td>2101</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.204339</td>\n",
       "      <td>0.134338</td>\n",
       "      <td>0.143729</td>\n",
       "      <td>0.702932</td>\n",
       "      <td>-0.290513</td>\n",
       "      <td>0.031102</td>\n",
       "      <td>-0.243759</td>\n",
       "      <td>-0.205067</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710728</th>\n",
       "      <td>2102</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.095661</td>\n",
       "      <td>-0.051734</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.035816</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.004488</td>\n",
       "      <td>-0.249359</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710729</th>\n",
       "      <td>2104</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.134436</td>\n",
       "      <td>-0.030198</td>\n",
       "      <td>-0.057608</td>\n",
       "      <td>0.016190</td>\n",
       "      <td>0.146911</td>\n",
       "      <td>-0.340176</td>\n",
       "      <td>-0.104457</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710730</th>\n",
       "      <td>2107</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.180348</td>\n",
       "      <td>0.061339</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.049680</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.418095</td>\n",
       "      <td>-0.620948</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710731</th>\n",
       "      <td>2108</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.247698</td>\n",
       "      <td>0.026739</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.280510</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.198069</td>\n",
       "      <td>-0.620398</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710732</th>\n",
       "      <td>2109</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.425230</td>\n",
       "      <td>0.266186</td>\n",
       "      <td>0.075126</td>\n",
       "      <td>0.039726</td>\n",
       "      <td>-0.060703</td>\n",
       "      <td>-0.059133</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>0.286111</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710733</th>\n",
       "      <td>2114</td>\n",
       "      <td>1812</td>\n",
       "      <td>-2.337000</td>\n",
       "      <td>1.459039</td>\n",
       "      <td>3.495335</td>\n",
       "      <td>-0.347759</td>\n",
       "      <td>0.257406</td>\n",
       "      <td>0.172330</td>\n",
       "      <td>-0.098562</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710734</th>\n",
       "      <td>2117</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.136062</td>\n",
       "      <td>-0.006129</td>\n",
       "      <td>-2.083220</td>\n",
       "      <td>-0.316265</td>\n",
       "      <td>-0.480613</td>\n",
       "      <td>-0.270387</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.297367</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710735</th>\n",
       "      <td>2118</td>\n",
       "      <td>1812</td>\n",
       "      <td>-2.458985</td>\n",
       "      <td>1.501635</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.204721</td>\n",
       "      <td>-0.136476</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710736</th>\n",
       "      <td>2120</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.000413</td>\n",
       "      <td>0.005194</td>\n",
       "      <td>-0.398379</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.222814</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.554627</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710737</th>\n",
       "      <td>2121</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.126793</td>\n",
       "      <td>0.040152</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.054294</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.423501</td>\n",
       "      <td>-0.416516</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710738</th>\n",
       "      <td>2126</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.181550</td>\n",
       "      <td>-0.039677</td>\n",
       "      <td>-0.082102</td>\n",
       "      <td>-0.140690</td>\n",
       "      <td>-0.231005</td>\n",
       "      <td>-0.145413</td>\n",
       "      <td>-0.150222</td>\n",
       "      <td>0.158479</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710739</th>\n",
       "      <td>2129</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.064731</td>\n",
       "      <td>-0.044927</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.615191</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.040299</td>\n",
       "      <td>-0.058827</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710740</th>\n",
       "      <td>2130</td>\n",
       "      <td>1812</td>\n",
       "      <td>-8.833108</td>\n",
       "      <td>3.455564</td>\n",
       "      <td>1.174958</td>\n",
       "      <td>-0.439014</td>\n",
       "      <td>-1.401483</td>\n",
       "      <td>-0.219093</td>\n",
       "      <td>0.087497</td>\n",
       "      <td>-1.461111</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710741</th>\n",
       "      <td>2131</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.149930</td>\n",
       "      <td>0.070203</td>\n",
       "      <td>0.363126</td>\n",
       "      <td>-0.010869</td>\n",
       "      <td>0.164914</td>\n",
       "      <td>-0.262682</td>\n",
       "      <td>-0.315310</td>\n",
       "      <td>0.019663</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710742</th>\n",
       "      <td>2137</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.059745</td>\n",
       "      <td>-0.052186</td>\n",
       "      <td>-0.062670</td>\n",
       "      <td>-1.842369</td>\n",
       "      <td>0.073483</td>\n",
       "      <td>0.287247</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710743</th>\n",
       "      <td>2138</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.219608</td>\n",
       "      <td>-0.034336</td>\n",
       "      <td>-0.031817</td>\n",
       "      <td>0.270888</td>\n",
       "      <td>0.045701</td>\n",
       "      <td>0.134292</td>\n",
       "      <td>0.364995</td>\n",
       "      <td>0.064963</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710744</th>\n",
       "      <td>2139</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.062132</td>\n",
       "      <td>0.070332</td>\n",
       "      <td>0.351273</td>\n",
       "      <td>0.478199</td>\n",
       "      <td>0.092099</td>\n",
       "      <td>0.494140</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.330590</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710745</th>\n",
       "      <td>2140</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.213264</td>\n",
       "      <td>0.029603</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.273137</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.464013</td>\n",
       "      <td>-0.404886</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710746</th>\n",
       "      <td>2142</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.223395</td>\n",
       "      <td>-0.042492</td>\n",
       "      <td>-0.060381</td>\n",
       "      <td>0.016019</td>\n",
       "      <td>0.218667</td>\n",
       "      <td>-0.160979</td>\n",
       "      <td>-0.138038</td>\n",
       "      <td>0.348972</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710747</th>\n",
       "      <td>2145</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.154051</td>\n",
       "      <td>-0.029331</td>\n",
       "      <td>-0.010545</td>\n",
       "      <td>0.019339</td>\n",
       "      <td>-0.260369</td>\n",
       "      <td>-0.227888</td>\n",
       "      <td>0.006721</td>\n",
       "      <td>0.008255</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710748</th>\n",
       "      <td>2146</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.238458</td>\n",
       "      <td>0.316407</td>\n",
       "      <td>0.632261</td>\n",
       "      <td>0.531651</td>\n",
       "      <td>-0.154740</td>\n",
       "      <td>0.069316</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.543269</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710749</th>\n",
       "      <td>2148</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.089476</td>\n",
       "      <td>-0.038628</td>\n",
       "      <td>0.776538</td>\n",
       "      <td>8.809832</td>\n",
       "      <td>-0.177850</td>\n",
       "      <td>0.388419</td>\n",
       "      <td>0.193054</td>\n",
       "      <td>0.175415</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710750</th>\n",
       "      <td>2149</td>\n",
       "      <td>1812</td>\n",
       "      <td>0.254593</td>\n",
       "      <td>0.064668</td>\n",
       "      <td>-0.034742</td>\n",
       "      <td>-1.336611</td>\n",
       "      <td>-0.037401</td>\n",
       "      <td>0.244451</td>\n",
       "      <td>0.049125</td>\n",
       "      <td>-0.130919</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710751</th>\n",
       "      <td>2150</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.123364</td>\n",
       "      <td>-0.055977</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.010906</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.255730</td>\n",
       "      <td>-0.108285</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710752</th>\n",
       "      <td>2151</td>\n",
       "      <td>1812</td>\n",
       "      <td>-10.437184</td>\n",
       "      <td>0.080905</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>3.369380</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.293557</td>\n",
       "      <td>-0.006489</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710753</th>\n",
       "      <td>2154</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.077930</td>\n",
       "      <td>-0.038748</td>\n",
       "      <td>-0.031859</td>\n",
       "      <td>0.646608</td>\n",
       "      <td>-0.145526</td>\n",
       "      <td>-0.119539</td>\n",
       "      <td>-0.151587</td>\n",
       "      <td>-0.130524</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710754</th>\n",
       "      <td>2156</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.269845</td>\n",
       "      <td>-0.005322</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.117539</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.214088</td>\n",
       "      <td>-0.307293</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710755</th>\n",
       "      <td>2158</td>\n",
       "      <td>1812</td>\n",
       "      <td>-0.000413</td>\n",
       "      <td>0.005194</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.040856</td>\n",
       "      <td>-6.456266</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1688296 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  timestamp  derived_0  derived_1  derived_2  derived_3  \\\n",
       "0          10          0   0.370326  -0.006316   0.222831  -0.213030   \n",
       "1          11          0   0.014765  -0.038064  -0.017425   0.320652   \n",
       "2          12          0  -0.010622  -0.050577   3.379575  -0.157525   \n",
       "3          25          0  -0.000413   0.005194   0.020909   0.002887   \n",
       "4          26          0   0.176693  -0.025284  -0.057680   0.015100   \n",
       "5          27          0   0.346856   0.166239  -6.080701  -0.992249   \n",
       "6          31          0   0.072036   0.014931   0.020909   0.014063   \n",
       "7          38          0   0.300062   0.071251  -0.074451  -0.065292   \n",
       "8          39          0  -0.003511  -0.034270   0.082372  -0.023937   \n",
       "9          40          0  -0.083330   0.081935  -2.048438  -0.206856   \n",
       "10         41          0   0.435826   1.797289   0.363570  -0.005651   \n",
       "11         43          0  -0.000413   0.005194   0.020909   0.002887   \n",
       "12         44          0   0.034991  -0.019258   0.055769  -0.084496   \n",
       "13         49          0  -0.000413   0.212615  -0.979520   0.002887   \n",
       "14         54          0   0.071704  -0.044019   0.020909   0.038046   \n",
       "15         59          0   0.116360   0.164506   0.156510  -0.129252   \n",
       "16         60          0   0.026824  -0.024105  -0.028991   0.433277   \n",
       "17         62          0   0.367122   0.675543  -0.008483  -0.367778   \n",
       "18         63          0   0.453271  -0.036301   0.094657  -0.521106   \n",
       "19         68          0  -0.122734   0.038939   0.148015   2.493512   \n",
       "20         69          0  -0.062361  -0.063724   0.021274   0.032845   \n",
       "21         70          0   0.175489  -0.024445  -0.034079  -0.020533   \n",
       "22         76          0  -0.190705   0.006221  -0.056835   0.063464   \n",
       "23         79          0   0.008503   0.048732   0.123793   0.266366   \n",
       "24         80          0   0.043156   0.540945   0.278394   0.563343   \n",
       "25         82          0   0.086832  -0.022962   0.020909   0.006783   \n",
       "26         83          0  -0.082307  -0.039761   0.102000  -0.798021   \n",
       "27         85          0   0.005351  -0.031086   0.078334   0.417328   \n",
       "28         87          0   0.045536   0.054391   0.330190  -0.816961   \n",
       "29         90          0  -0.003925  -0.037522   0.020909  -0.028244   \n",
       "...       ...        ...        ...        ...        ...        ...   \n",
       "1710726  2100       1812   0.079269  -0.021573  -0.033426  -0.333376   \n",
       "1710727  2101       1812  -0.204339   0.134338   0.143729   0.702932   \n",
       "1710728  2102       1812   0.095661  -0.051734   0.020909   0.035816   \n",
       "1710729  2104       1812   0.134436  -0.030198  -0.057608   0.016190   \n",
       "1710730  2107       1812  -0.180348   0.061339   0.020909   0.049680   \n",
       "1710731  2108       1812  -0.247698   0.026739   0.020909  -0.280510   \n",
       "1710732  2109       1812  -0.425230   0.266186   0.075126   0.039726   \n",
       "1710733  2114       1812  -2.337000   1.459039   3.495335  -0.347759   \n",
       "1710734  2117       1812  -0.136062  -0.006129  -2.083220  -0.316265   \n",
       "1710735  2118       1812  -2.458985   1.501635   0.020909   0.002887   \n",
       "1710736  2120       1812  -0.000413   0.005194  -0.398379   0.002887   \n",
       "1710737  2121       1812   0.126793   0.040152   0.020909  -0.054294   \n",
       "1710738  2126       1812  -0.181550  -0.039677  -0.082102  -0.140690   \n",
       "1710739  2129       1812  -0.064731  -0.044927   0.020909   0.615191   \n",
       "1710740  2130       1812  -8.833108   3.455564   1.174958  -0.439014   \n",
       "1710741  2131       1812  -0.149930   0.070203   0.363126  -0.010869   \n",
       "1710742  2137       1812  -0.059745  -0.052186  -0.062670  -1.842369   \n",
       "1710743  2138       1812   0.219608  -0.034336  -0.031817   0.270888   \n",
       "1710744  2139       1812   0.062132   0.070332   0.351273   0.478199   \n",
       "1710745  2140       1812   0.213264   0.029603   0.020909  -0.273137   \n",
       "1710746  2142       1812  -0.223395  -0.042492  -0.060381   0.016019   \n",
       "1710747  2145       1812  -0.154051  -0.029331  -0.010545   0.019339   \n",
       "1710748  2146       1812  -0.238458   0.316407   0.632261   0.531651   \n",
       "1710749  2148       1812   0.089476  -0.038628   0.776538   8.809832   \n",
       "1710750  2149       1812   0.254593   0.064668  -0.034742  -1.336611   \n",
       "1710751  2150       1812  -0.123364  -0.055977   0.020909   0.010906   \n",
       "1710752  2151       1812 -10.437184   0.080905   0.020909   3.369380   \n",
       "1710753  2154       1812  -0.077930  -0.038748  -0.031859   0.646608   \n",
       "1710754  2156       1812  -0.269845  -0.005322   0.020909  -0.117539   \n",
       "1710755  2158       1812  -0.000413   0.005194   0.020909   0.002887   \n",
       "\n",
       "         derived_4  fundamental_0  fundamental_1  fundamental_2  \\\n",
       "0         0.729277      -0.335633       0.113292       1.621238   \n",
       "1        -0.034134       0.004413       0.114285      -0.210185   \n",
       "2        -0.068550      -0.155937       1.219439      -0.764516   \n",
       "3         0.011996       0.178495      -0.006489      -0.007262   \n",
       "4         0.180894       0.139445      -0.125687      -0.018707   \n",
       "5        -0.125916       0.345812      -0.006489      -0.584239   \n",
       "6         0.011996      -0.193205      -0.006489      -0.029062   \n",
       "7        -0.011286       0.026365       0.210249       0.167494   \n",
       "8        -0.025750       0.007815       0.263451      -0.241212   \n",
       "9        -0.839563      -0.234100      -0.291853      -5.722336   \n",
       "10        0.442866       0.125375      -0.006489       0.292311   \n",
       "11        0.011996      -0.285388      -0.006489      -0.193590   \n",
       "12        0.259828       0.198800       0.265104      -0.160462   \n",
       "13        0.011996       0.395701      -0.006489      -0.780873   \n",
       "14        0.011996       0.331778      -0.021114      -0.029062   \n",
       "15        1.310059      -0.264846      -0.006489       0.370725   \n",
       "16       -0.006366       0.295138      -0.006489      -0.285946   \n",
       "17       -0.015529       0.129848      -0.006489      -0.251624   \n",
       "18        0.049715      -0.352013      -0.006489      -0.182694   \n",
       "19        0.009746      -0.063111      -0.006489      -0.162878   \n",
       "20        0.174025       0.122335      -0.133188      -0.108286   \n",
       "21        0.282478      -0.069417       0.247840       0.303953   \n",
       "22       -0.149507       0.267959      -0.392849       0.080084   \n",
       "23       -0.009772      -0.248937      -0.006489      -0.157415   \n",
       "24       -0.144004      -0.024376      -0.006489      -0.337090   \n",
       "25        0.011996       0.071911       0.112818      -0.029062   \n",
       "26        0.149622       0.028448       0.062647       0.044142   \n",
       "27       -0.020146       0.390538       0.256594      -0.167223   \n",
       "28        0.012602      -0.036462      -0.115197      -0.237428   \n",
       "29        0.011996      -0.049041       0.084827      -0.029062   \n",
       "...            ...            ...            ...            ...   \n",
       "1710726   0.061463       0.217718      -0.006489       0.127397   \n",
       "1710727  -0.290513       0.031102      -0.243759      -0.205067   \n",
       "1710728   0.011996      -0.004488      -0.249359      -0.029062   \n",
       "1710729   0.146911      -0.340176      -0.104457       0.002421   \n",
       "1710730   0.011996      -0.418095      -0.620948      -0.029062   \n",
       "1710731   0.011996       0.198069      -0.620398      -0.029062   \n",
       "1710732  -0.060703      -0.059133      -0.006489       0.286111   \n",
       "1710733   0.257406       0.172330      -0.098562      -0.000398   \n",
       "1710734  -0.480613      -0.270387      -0.006489      -0.297367   \n",
       "1710735   0.011996       0.204721      -0.136476      -0.029062   \n",
       "1710736   0.011996      -0.222814      -0.006489      -0.554627   \n",
       "1710737   0.011996      -0.423501      -0.416516      -0.029062   \n",
       "1710738  -0.231005      -0.145413      -0.150222       0.158479   \n",
       "1710739   0.011996       0.040299      -0.058827      -0.029062   \n",
       "1710740  -1.401483      -0.219093       0.087497      -1.461111   \n",
       "1710741   0.164914      -0.262682      -0.315310       0.019663   \n",
       "1710742   0.073483       0.287247      -0.006489       0.001499   \n",
       "1710743   0.045701       0.134292       0.364995       0.064963   \n",
       "1710744   0.092099       0.494140      -0.006489      -0.330590   \n",
       "1710745   0.011996      -0.464013      -0.404886      -0.029062   \n",
       "1710746   0.218667      -0.160979      -0.138038       0.348972   \n",
       "1710747  -0.260369      -0.227888       0.006721       0.008255   \n",
       "1710748  -0.154740       0.069316      -0.006489      -0.543269   \n",
       "1710749  -0.177850       0.388419       0.193054       0.175415   \n",
       "1710750  -0.037401       0.244451       0.049125      -0.130919   \n",
       "1710751   0.011996      -0.255730      -0.108285      -0.029062   \n",
       "1710752   0.011996      -0.293557      -0.006489      -0.029062   \n",
       "1710753  -0.145526      -0.119539      -0.151587      -0.130524   \n",
       "1710754   0.011996       0.214088      -0.307293      -0.029062   \n",
       "1710755   0.011996      -0.040856      -6.456266      -0.029062   \n",
       "\n",
       "               ...         technical_35_nan  technical_36_nan  \\\n",
       "0              ...                        0                 0   \n",
       "1              ...                        0                 0   \n",
       "2              ...                        0                 0   \n",
       "3              ...                        0                 0   \n",
       "4              ...                        0                 0   \n",
       "5              ...                        0                 0   \n",
       "6              ...                        0                 0   \n",
       "7              ...                        0                 0   \n",
       "8              ...                        0                 0   \n",
       "9              ...                        0                 0   \n",
       "10             ...                        0                 0   \n",
       "11             ...                        0                 0   \n",
       "12             ...                        0                 0   \n",
       "13             ...                        0                 0   \n",
       "14             ...                        0                 0   \n",
       "15             ...                        0                 0   \n",
       "16             ...                        0                 0   \n",
       "17             ...                        0                 0   \n",
       "18             ...                        0                 0   \n",
       "19             ...                        0                 0   \n",
       "20             ...                        0                 0   \n",
       "21             ...                        0                 0   \n",
       "22             ...                        0                 0   \n",
       "23             ...                        0                 0   \n",
       "24             ...                        0                 0   \n",
       "25             ...                        0                 0   \n",
       "26             ...                        0                 0   \n",
       "27             ...                        0                 0   \n",
       "28             ...                        0                 0   \n",
       "29             ...                        0                 0   \n",
       "...            ...                      ...               ...   \n",
       "1710726        ...                        0                 0   \n",
       "1710727        ...                        0                 0   \n",
       "1710728        ...                        0                 0   \n",
       "1710729        ...                        0                 0   \n",
       "1710730        ...                        0                 0   \n",
       "1710731        ...                        0                 0   \n",
       "1710732        ...                        0                 0   \n",
       "1710733        ...                        0                 0   \n",
       "1710734        ...                        0                 0   \n",
       "1710735        ...                        0                 0   \n",
       "1710736        ...                        0                 0   \n",
       "1710737        ...                        0                 0   \n",
       "1710738        ...                        0                 0   \n",
       "1710739        ...                        0                 0   \n",
       "1710740        ...                        0                 0   \n",
       "1710741        ...                        0                 0   \n",
       "1710742        ...                        0                 0   \n",
       "1710743        ...                        0                 0   \n",
       "1710744        ...                        0                 0   \n",
       "1710745        ...                        0                 0   \n",
       "1710746        ...                        0                 0   \n",
       "1710747        ...                        0                 0   \n",
       "1710748        ...                        0                 0   \n",
       "1710749        ...                        0                 0   \n",
       "1710750        ...                        0                 0   \n",
       "1710751        ...                        0                 0   \n",
       "1710752        ...                        0                 0   \n",
       "1710753        ...                        0                 0   \n",
       "1710754        ...                        0                 0   \n",
       "1710755        ...                        0                 0   \n",
       "\n",
       "         technical_37_nan  technical_38_nan  technical_39_nan  \\\n",
       "0                       1                 1                 1   \n",
       "1                       1                 1                 1   \n",
       "2                       1                 1                 1   \n",
       "3                       1                 1                 1   \n",
       "4                       1                 1                 1   \n",
       "5                       1                 1                 1   \n",
       "6                       1                 1                 1   \n",
       "7                       1                 1                 1   \n",
       "8                       1                 1                 1   \n",
       "9                       1                 1                 1   \n",
       "10                      1                 1                 1   \n",
       "11                      1                 1                 1   \n",
       "12                      1                 1                 1   \n",
       "13                      1                 1                 1   \n",
       "14                      1                 1                 1   \n",
       "15                      1                 1                 1   \n",
       "16                      1                 1                 1   \n",
       "17                      1                 1                 1   \n",
       "18                      1                 1                 1   \n",
       "19                      1                 1                 1   \n",
       "20                      1                 1                 1   \n",
       "21                      1                 1                 1   \n",
       "22                      1                 1                 1   \n",
       "23                      1                 1                 1   \n",
       "24                      1                 1                 1   \n",
       "25                      1                 1                 1   \n",
       "26                      1                 1                 1   \n",
       "27                      1                 1                 1   \n",
       "28                      1                 1                 1   \n",
       "29                      1                 1                 1   \n",
       "...                   ...               ...               ...   \n",
       "1710726                 0                 0                 0   \n",
       "1710727                 0                 0                 0   \n",
       "1710728                 0                 0                 0   \n",
       "1710729                 0                 0                 0   \n",
       "1710730                 0                 0                 0   \n",
       "1710731                 0                 0                 0   \n",
       "1710732                 0                 0                 0   \n",
       "1710733                 0                 0                 0   \n",
       "1710734                 0                 0                 0   \n",
       "1710735                 0                 0                 0   \n",
       "1710736                 0                 0                 0   \n",
       "1710737                 0                 0                 0   \n",
       "1710738                 0                 0                 0   \n",
       "1710739                 0                 0                 0   \n",
       "1710740                 0                 0                 0   \n",
       "1710741                 0                 0                 0   \n",
       "1710742                 0                 0                 0   \n",
       "1710743                 0                 0                 0   \n",
       "1710744                 0                 0                 0   \n",
       "1710745                 0                 0                 0   \n",
       "1710746                 0                 0                 0   \n",
       "1710747                 0                 0                 0   \n",
       "1710748                 0                 0                 0   \n",
       "1710749                 0                 0                 0   \n",
       "1710750                 0                 0                 0   \n",
       "1710751                 0                 0                 0   \n",
       "1710752                 0                 0                 0   \n",
       "1710753                 0                 0                 0   \n",
       "1710754                 0                 0                 0   \n",
       "1710755                 0                 0                 0   \n",
       "\n",
       "         technical_40_nan  technical_41_nan  technical_42_nan  \\\n",
       "0                       0                 1                 1   \n",
       "1                       0                 1                 1   \n",
       "2                       0                 1                 1   \n",
       "3                       0                 1                 1   \n",
       "4                       0                 1                 1   \n",
       "5                       0                 1                 1   \n",
       "6                       0                 1                 1   \n",
       "7                       0                 1                 1   \n",
       "8                       0                 1                 1   \n",
       "9                       0                 1                 1   \n",
       "10                      0                 1                 1   \n",
       "11                      0                 1                 1   \n",
       "12                      0                 1                 1   \n",
       "13                      0                 1                 1   \n",
       "14                      0                 1                 1   \n",
       "15                      0                 1                 1   \n",
       "16                      0                 1                 1   \n",
       "17                      0                 1                 1   \n",
       "18                      0                 1                 1   \n",
       "19                      0                 1                 1   \n",
       "20                      0                 1                 1   \n",
       "21                      0                 1                 1   \n",
       "22                      0                 1                 1   \n",
       "23                      0                 1                 1   \n",
       "24                      0                 1                 1   \n",
       "25                      0                 1                 1   \n",
       "26                      0                 1                 1   \n",
       "27                      0                 1                 1   \n",
       "28                      0                 1                 1   \n",
       "29                      0                 1                 1   \n",
       "...                   ...               ...               ...   \n",
       "1710726                 0                 0                 0   \n",
       "1710727                 0                 0                 0   \n",
       "1710728                 0                 0                 0   \n",
       "1710729                 0                 0                 0   \n",
       "1710730                 0                 0                 0   \n",
       "1710731                 0                 0                 0   \n",
       "1710732                 0                 0                 0   \n",
       "1710733                 0                 0                 0   \n",
       "1710734                 0                 0                 0   \n",
       "1710735                 0                 0                 0   \n",
       "1710736                 0                 0                 0   \n",
       "1710737                 0                 0                 0   \n",
       "1710738                 0                 0                 0   \n",
       "1710739                 0                 0                 0   \n",
       "1710740                 0                 0                 0   \n",
       "1710741                 0                 0                 0   \n",
       "1710742                 0                 0                 0   \n",
       "1710743                 0                 0                 0   \n",
       "1710744                 0                 0                 0   \n",
       "1710745                 0                 0                 0   \n",
       "1710746                 0                 0                 0   \n",
       "1710747                 0                 0                 0   \n",
       "1710748                 0                 0                 0   \n",
       "1710749                 0                 0                 0   \n",
       "1710750                 0                 0                 0   \n",
       "1710751                 0                 0                 0   \n",
       "1710752                 0                 0                 0   \n",
       "1710753                 0                 0                 0   \n",
       "1710754                 0                 0                 0   \n",
       "1710755                 0                 0                 0   \n",
       "\n",
       "         technical_43_nan  technical_44_nan  \n",
       "0                       0                 1  \n",
       "1                       0                 1  \n",
       "2                       0                 1  \n",
       "3                       0                 1  \n",
       "4                       0                 1  \n",
       "5                       0                 1  \n",
       "6                       0                 1  \n",
       "7                       0                 1  \n",
       "8                       0                 1  \n",
       "9                       0                 1  \n",
       "10                      0                 1  \n",
       "11                      0                 1  \n",
       "12                      0                 1  \n",
       "13                      0                 1  \n",
       "14                      0                 1  \n",
       "15                      0                 1  \n",
       "16                      0                 1  \n",
       "17                      0                 1  \n",
       "18                      0                 1  \n",
       "19                      0                 1  \n",
       "20                      0                 1  \n",
       "21                      0                 1  \n",
       "22                      0                 1  \n",
       "23                      0                 1  \n",
       "24                      0                 1  \n",
       "25                      0                 1  \n",
       "26                      0                 1  \n",
       "27                      0                 1  \n",
       "28                      0                 1  \n",
       "29                      0                 1  \n",
       "...                   ...               ...  \n",
       "1710726                 0                 0  \n",
       "1710727                 0                 0  \n",
       "1710728                 0                 0  \n",
       "1710729                 0                 0  \n",
       "1710730                 0                 0  \n",
       "1710731                 0                 0  \n",
       "1710732                 0                 0  \n",
       "1710733                 0                 0  \n",
       "1710734                 0                 0  \n",
       "1710735                 0                 0  \n",
       "1710736                 0                 0  \n",
       "1710737                 0                 0  \n",
       "1710738                 0                 0  \n",
       "1710739                 0                 0  \n",
       "1710740                 0                 0  \n",
       "1710741                 0                 0  \n",
       "1710742                 0                 0  \n",
       "1710743                 0                 0  \n",
       "1710744                 0                 0  \n",
       "1710745                 0                 0  \n",
       "1710746                 0                 0  \n",
       "1710747                 0                 0  \n",
       "1710748                 0                 0  \n",
       "1710749                 0                 0  \n",
       "1710750                 0                 0  \n",
       "1710751                 0                 0  \n",
       "1710752                 0                 0  \n",
       "1710753                 0                 0  \n",
       "1710754                 0                 0  \n",
       "1710755                 0                 1  \n",
       "\n",
       "[1688296 rows x 217 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill missing values (quick and dirty)\n",
    "new_df_train.fillna(new_df_train.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Time to select the best features to learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# correlation coeficient\n",
    "x_cols = [col for col in new_df_train.columns if col not in ['id', 'timestamp', 'y']]\n",
    "\n",
    "coefs = []\n",
    "\n",
    "for col in x_cols:\n",
    "    coefs.append(np.corrcoef(new_df_train[col], new_df_train.y)[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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64oknxFoOAAAA7R3hPxvWrl1bb2k8Pj6+3vDfrl27tGvXLr399tuSpClTpujT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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb59e266240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pos = np.arange(len(x_cols))\n",
    "width = 0.9\n",
    "fig, ax = plt.subplots(figsize=(12,40))\n",
    "rects = ax.barh(y_pos, np.array(coefs), color='y')\n",
    "ax.set_yticks(y_pos+((width)/2.))\n",
    "ax.set_yticklabels(x_cols, rotation='horizontal')\n",
    "ax.set_xlabel(\"Correlation coefficient\")\n",
    "ax.set_title(\"Correlation coefficient\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[94, 177, 64, 69, 23, 164, 96, 30, 40, 26, 11, 89, 0, 45, 38, 24, 49, 5, 10, 170, 52, 50, 102, 104, 37, 14, 35, 54, 51, 152, 17, 78, 101, 4, 31, 83, 6, 65, 27, 43, 1, 124, 132, 48, 107, 105, 16, 18, 81, 2, 67, 36, 33, 39, 99, 46, 19, 144, 80, 98, 13, 97, 188, 180, 82, 21, 42, 201, 56, 117, 20, 28, 204, 196, 9, 12, 53, 72, 76, 62, 212, 95, 8, 55, 66, 178, 181, 185, 190, 29, 88, 205, 92, 91, 3, 34, 63, 197, 75, 71, 150, 192, 44, 60, 195, 203, 86, 182, 194, 209, 138, 169, 114, 118, 79, 173, 41, 32, 61, 122, 174, 7, 131, 198, 111, 213, 179, 58, 199, 100, 73, 47, 106, 25, 184, 175, 68, 187, 193, 200, 15, 59, 119, 133, 90, 210, 70, 77, 134, 161, 139, 163, 84, 157, 171, 108, 115, 123, 167, 168, 109, 146, 172, 155, 154, 120, 151, 141, 126, 149, 158, 110, 125, 128, 135, 142, 127, 156, 162, 74, 113, 165, 148, 160, 130, 87, 153, 140, 166, 189, 211, 191, 208, 145, 112, 176, 183, 186, 202, 206, 207, 147, 22, 136, 129, 121, 137, 116, 143, 57, 159, 103, 93, 85]\n"
     ]
    }
   ],
   "source": [
    "# sorting coefs in ascending order and return its index\n",
    "index = sorted(range(len(coefs)), key=lambda i: abs(coefs[i]))\n",
    "print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('fundamental_31_nan', -0.007567235510688752)\n",
      "('fundamental_53', 0.00801017747612311)\n",
      "('fundamental_47_nan', -0.0082980366481522384)\n",
      "('technical_40', -0.0095880282514506313)\n",
      "('technical_30', 0.0096581342886955125)\n",
      "('technical_20', -0.018752338839215128)\n"
     ]
    }
   ],
   "source": [
    "# Let's selected the top 6 features\n",
    "feature_cols = index[-6:]\n",
    "for i in feature_cols:\n",
    "    print((x_cols[i], coefs[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fundamental_31_nan\n",
      "(False, True)\n",
      "fundamental_53\n",
      "(-629.18549, 61.721714)\n",
      "fundamental_47_nan\n",
      "(False, True)\n",
      "technical_40\n",
      "(-0.5250904, 1.5692652)\n",
      "technical_30\n",
      "(0.0, 0.064815201)\n",
      "technical_20\n",
      "(0.0, 0.067848153)\n"
     ]
    }
   ],
   "source": [
    "# Time to check feature colinearity\n",
    "selected_cols = [x_cols[i] for i in feature_cols]\n",
    "\n",
    "for feature in [x_cols[i] for i in feature_cols]:\n",
    "    print(feature)\n",
    "    print((min(new_df_train[feature]), max(new_df_train[feature])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb59e154ba8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V5ePjY57uAYDffvvtkfs7OztbnFVjMBjg7u6OGjVq4MiRI8jNza3U5xYVFaGsrAwAUFhY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\ngM5hQAEAAJ3zAgRf1UXaTzfJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb59de3ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(new_df_train[selected_cols].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "fundamental_31_nan is strongly correlated with fundamental_47_nan. Discard one of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb59de01160>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VlpamX3/9VeHh4QoICJAkVa9e3fWedu3ayW63q379+m7/EE2aNHEltVGjRsrM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb59de63828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(new_df_train[['fundamental_53','fundamental_47_nan','technical_40', 'technical_30', 'technical_20']].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Machine Learning ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# metric used by Kaggle\n",
    "\n",
    "def R_metric(y, y_pred):\n",
    "    R2 = 1 - np.sum((y - y_pred)**2) / np.sum((y - np.mean(y))**2)\n",
    "    R = np.sign(R2) * np.sqrt(abs(R2))\n",
    "    return R\n",
    "\n",
    "R_scorer = make_scorer(R_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "Looking the learning curve to check for variance/bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[learning_curve] Training set sizes: [ 151946  493826  835706 1177586 1519466]\n",
      "[CV]  ................................................................\n",
      "[CV]  ................................................................\n",
      "[CV]  ................................................................\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.005696, total=   1.3s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.023957, total=   3.2s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.022142, total=   4.2s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   3 out of  50 | elapsed:    5.0s remaining:  1.3min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................. , score=0.008488, total=   1.3s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.020180, total=   6.0s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.005711, total=   2.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.020405, total=   7.2s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.016743, total=   3.8s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.006466, total=   1.3s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   9 out of  50 | elapsed:   11.0s remaining:   50.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................ , score=-0.027937, total=   2.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.017364, total=   5.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.027692, total=   3.9s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.013973, total=   6.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.026326, total=   1.2s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.028022, total=   2.7s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  15 out of  50 | elapsed:   18.5s remaining:   43.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................ , score=-0.027801, total=   5.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.022888, total=   6.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.028819, total=   3.8s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.012687, total=   1.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.006788, total=   2.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.027131, total=   5.3s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  21 out of  50 | elapsed:   24.4s remaining:   33.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................. , score=0.025025, total=   6.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.013483, total=   4.0s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.017977, total=   1.3s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.008316, total=   2.5s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.015178, total=   5.3s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.012340, total=   6.6s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  27 out of  50 | elapsed:   31.8s remaining:   27.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................. , score=0.015143, total=   4.1s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.029280, total=   1.5s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.022337, total=   2.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.015713, total=   5.4s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.011959, total=   6.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.018361, total=   3.7s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 out of  50 | elapsed:   37.6s remaining:   19.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................. , score=0.009627, total=   1.2s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.011548, total=   2.5s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.013891, total=   5.3s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.014266, total=   3.7s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.013431, total=   6.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.043181, total=   1.2s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  39 out of  50 | elapsed:   44.3s remaining:   12.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................ , score=-0.012661, total=   2.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.015204, total=   5.1s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.015387, total=   3.9s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.014216, total=   6.6s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................ , score=-0.051292, total=   1.2s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.022605, total=   2.5s\n",
      "[CV]  ................................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  45 out of  50 | elapsed:   51.6s remaining:    5.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................ , score=-0.007443, total=   5.3s\n",
      "[CV]  ................................................................\n",
      "[CV] ................................. , score=0.007576, total=   6.5s\n",
      "[CV] ................................. , score=0.023907, total=   3.8s\n",
      "[CV] ................................. , score=0.028829, total=   3.8s\n",
      "[CV] ................................. , score=0.026485, total=   4.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:   56.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.5/dist-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pq7nSpw2a4r+tJ8jbNDtOXcOpOXDqTtcPOirujmFlxMdFkG0prP2JnRqgAe6O\nm4YASYLJ1Gie7miK/7a11dTrquuuWxMACu4+Du4WKUVRUVAxqAZUVNokx2PJKnb3hQj+31ldk9kA\nRfCFh6OFh1e/lcHhQMnLK5cY+C/noGZnY9j/U8BDqBYLYa+9UqfV8NCNZZIGd1JBaAgxqrFMklGa\nTFScZFT8WU0TF4zlZ3p06k53xy47Ds11T97VXO9EqyDIK4p61l8QMZ9tIvGNfxBy+CjWDu3JuPUW\ncq9yNcFiUPEbi6zsrJbgThJ013YG3ENPG0BxT4PtaUUwGqUTpfBSFAWj4v/XqwMO3QnuxMD9gAaZ\nRRoZhXmu/QIkC6picCUHqJgMJoyqEYNiqNu+JkEiCYBonIzG0r4MVYgZfw3Gg+U7RjnPbU/B4qdc\nLQR2O9jtKDab/6vd9YqtzDaOirdV7DawlXm121GKCiEvF4PV6t7OXh+/mWrRTCZ0swndaEI3GdG8\nyYJrnebzmXc731eTCd1oRDObXdu4ExHvj9lEaEwk0TYnmslc/jgmE5E7viVlRWkHqNBff6PdY/MB\nSpOAqhjKXJF5Hv/E/bv1dKbUdXdSQOAkwTMEdTP40hZ1S1XUck9J+CcLpf+PNXvp45yeZMHgbjnw\nTRYMigGjamz0yYIkAKLJK550l18fAI+iaffi6HP2g2XUREJCJNmeplNdB0eA5KLSJMO9ve9+Pvvr\nthI0mxXNWgJ2m2v4ZJ8kRnU4UO12FLsDxW5zvdpct0iUwkKUHNdnqs2GUgfDVFednpXX5i/LCN/7\nP+ytE7G3bo09yf2T2MoVrGvCPXmVn4qSBHB1ajQoPklCgFsNjfTLWgRfoNsEGjpaoGRBb/zJgiQA\nosmzjR5LPhD26sulTwFUd2yG+qQoYDK7muvdq6rT4cahObBpdnfHOwe6ruHQHTg010RLBkWtmy8J\nTUPxJAsOO4rNXvpqd6B6Egjvq730x2Yn0qxSnFNQwf524te9H7Afh6GwkIR33iu3XlcUHAnxrmTA\nkxi0TixNEFq3xpEQf/ZJQtlJrMB1+0HXcD8Y79MfQS1NEiKMrrk9pNOiqIbaJguR5ihiQwKPd1DX\nJAE4C3pYBLpq9e9BresoPgOLlH0cy/Ne18p8XtFjXr49umWwmArZRo8NfsCvJk8HJavThkN34tAd\npffkNVdQUgME+Wo/UlddqooeEoJey2eL1So6AUbs3kPor+UnVynpcB4n5j+KKf0MpjMZ7tczrtf0\nM4T+cpAiI6SHAAAgAElEQVTwfT8HPKZuMJQmCZ4EwbcVIak1jvj4mncaVBVc9w/cvP0RfGRrKJ5B\nnppAp0XRdPglCw3YLV8SgLMRHu76KaPKf7/EKHRzmScSfBIE3TdZ0LTS5kvP+wAJhf/7miQjPgWW\nZKTO6LqOQ3Ng1VxB3jMYjkNzoLkHtmmQIB9EGbfe4r3n77f+9lsp6daVkm5dA++oaRgsOaVJgSdJ\n8PxkZBD2037C9wYewEo3GPwTgzKtCPbWiTjjajF/hifIe8tZy06LnkcgJUkQQSYJQGPhG1jrUK2T\nSU9LRXWSEZ/tAyYjISFoJmvtkhFd92YhjS0Z0XW9tGe9+0reUVxMRlEuTl1DRy9/T09RUFtI4uTp\n6NfqjTcJPXyEkg7nkXnrzVV3AFRVnAnxOBPiKTm/e+BtnE6M2Rb/1oMzZzClZ3iXw/f+D2W3FnB3\nzWTC4UkMyrYiuNc7Y2Jq/ndVrU6Lnj/0SpIE6bQoGoAkACKwugyqCVGgmRs2GfHdt+yxqr2se4O8\nVbPh1Jx+z8hrutN1e1kxuLITXcepKmAyuhqTKzt2leUo+zuoYrnqDWqozL+9XxLjOYUnQav47yT3\nqhHV7/FfEwYDjsRWOBJbUUyPwNs4HJiysjBW0IpgSj9D+K7dFY43oYWEuJMDV0JgbH8OenSsXx8F\nLSqyZv9PXH8w/uuk06IIEkkARONXz1f4mq7hcDpcQ4167sdrDhya5jOkrdm/SJTeMfYLHwmR6Eoj\nmAPhrBIfSgNQoM89gT82Et1p9G/JqezYTg10J4rmdHWmc+LqDFVfMy4ajdiTkrAnJVFcwSaK3Y4x\nI7N8XwSfhCHk+Anv9mWHVHGGhWFPSsTRunXAVgR769ZokRE1K3e1Oy26WxKqShKk06KogCQAokXQ\ndA2b04bdaXcFefeVvH+Q9/+iVFWFsx8KJ0jKBtP6SKDCwiCs5o8S+qUHug4OR+lPAycJusmEvU0K\n9jYVz86pWK0YM7NIKMqj+NdjpYmC55ZD+hlCjxyrcH9nRETAfgi+HRd1n4lfqkX1jLHsqQg1eLKh\nzHDMgSaTEi1CE/12EyIwh9NBkaPI1USvOXBoDpzuyWkCPZbTpIN8c6Ao3pEQfTWqJCEkBHvbNtji\nupDbJXCfBKWkxNVq4PM0Q9lWhdDDRyo8hzMq0v/xxzKtCPbWrdFDa/i0RoVPNvh0WvTrk+BZB1gj\nUCzF/nM5lJssSnV96DuhVNlZKD39GKTTcKMk33yiWXA4HeTacihxlPhfySu4h/aUP/UmqykkCaGh\n2M5th+3cdhVuoxYVYfTti1A2YTh1OuBjkx6OmBh3cpDoTQr8Hn9MbOUaDroKlQ7P7C2swZ1AuJVJ\nEiqfXMrTobdMYuHJJhRcI0CXnYHSM/Ok33ZlWiYMhqBNcd0cybeiaNLKBv7m9BidqIHqJAma5koG\nKksSPAPE10OSoIWHYzuvPbbz2le4jVpQWGkrQsix44QdOFjh/o64uMAtCO7l8D17aff4Iu/2tRqe\nuSqeqaarmsq5TJKgVDVLiDexKPtkh1J6Tk9rhE+S4J3aGtW/tcLTQlFidI2o6dmnBXWslARANEkS\n+EWNeZqma5skADh1XEmC+756XScJkRFYIztg7dgh8Aa6jppf4JMgnCmXMIQcPkzY/l8C717BeVOW\nLSfs55/RzCHoZjOhsZHgBN3smgFTCzGjh4S45obwLHvem83oIWa0ENe+9dbp0JtYVHF8Hfd4DO7d\nKt1WB9WOkl1Iha0VCqX/1mVbLPy2U/yTCE+LRSNurZAEQDQpDqeDHJuFEqcVoyKBX9SxypKEhEh0\nU0ENWhLqIUlQFLToKKzRUVi7dAq8ja5jyM0N2IoQ88nGgLsYc/No9c93/NbF1LKIusFQaYJQmkCU\nJhSuz33Whfjs65OA+B3Pd1+z+3NT+ZkwK+W9tVBFx8cAtzTO6jaIN3coTQxiNm6i1at/J/TwEZxd\nu1M0836s10+ofl1qQRIA0STYnXbXFb878BsVCfwiSM62JaEBkgRnbCzO2Nhyoy2G/nooYD8D67nt\nOL5gnmuSKJuNaJNCYXYeis2GarWh2KwoVpv3c9c6z3v/z3w/V2021JIS19Tenn0qGHehrmiBkgdz\niF/S4F1nNmOODsesqwESFP/3ns8DJTeez72tHzW8DRLz2X/9Rs00/ryP6Cm3kwf1mgRIAiAaNQn8\noklqpElCRcMzn7nzdkrO7+ZdDomLIK+SeR5qTdddM1NafZKHsgmFzYZSZr13O0+SYS3dTnUvV/S5\nMTev9HOHI2CxajhSQ8XVq6j1w52AVNT6EfPZfwMeL3zls5IAiJZHAr9o9qqbJDgcpYnCWSYJtR6e\nua4oCrrJ5GqqJ6Kqbn91z+lEsdtdiYU7eYgNNZCXkVsmefBt1Qjc+uFJSips/bBaz7r1w3Bgfz39\nIlwkARCNigR+IXyoKgR4tO9skoR6G565KTAY0A0GnKGlo3U64iIoaVUPrR1l+bZ+eFou3AlDu4cf\nI+TEyXK7OLtWMBdGHQlqArBlyxYWL16MpmlMnDiRyZMn+31us9l48MEH2bdvH7GxsSxfvpxzzjmH\nbdu28cwzz2C32zGZTMyePZshQ4YEqRaiLtiddnKtFko0mwR+IWribJKEshN0eV5VtfT2td8wzxW8\n+p2tqqtcn1YK74ReZZabI9/Wj0j/1o8zUyYFvDVTdO+sei1S0BIAp9PJggULWLNmDUlJSUyYMIHh\nw4fTuXNn7zZr164lOjqajRs3kpqayrJly1ixYgVxcXG8+OKLJCUlceDAAe644w6++uqrYFVFnAWb\n00aeNUcCvxD1qTpJgq+ESHRD+anOqyVQQlH2M89cE2WXfeegqOg4gRKSshmJ7hlbIMC5y756O+z5\nfhb4sDVLdFwl8J7DZ7FsouN3a+bIUddTAPfOar5PAezZs4f27dvTrp1r5KyxY8eyadMmvwRg8+bN\nTJ8+HYBRo0axYMECdF2nR4/S2b+6dOmC1WrFZrNhrsYoWKJx8AR+q2bHoKgS+IVoLuppavPaqNYd\n94RIdGMtugFWluBUlegE+Cxn3DXkjLuG0PBYYqNb17w8tRC0BCA9PZ3k5GTvclJSEnv27Cm3TUqK\na5IOo9FIVFQUFouF+Ph47zaffvopPXr0qFbwj4sLx2hsHIEmMbHsvGLNm6e+VoeV3JJcdM1GTEQY\nUMNJUJqAhITIYBehwbSkukLLqq/UNTjCTeHEhjZMfGjSnQAPHjzIsmXLeO2116q1vcVSVM8lqp7E\nxCgyMvKDXYwGk5gYxYm0TPJtuVidtmY9eE9CQiRZWQXBLkaDaEl1hZZVX6lr8BQanNhD6+47srKL\nzaDN+5iUlMTp06e9y+np6SQlJZXbJi0tDQCHw0F+fj5xcXEAnD59munTp7N06VLOPffchiu4qBGr\nw0p6QTqZxRk4dGezDv5CCNGUBC0B6NWrF0eOHOH48ePYbDZSU1MZPny43zbDhw9n/fr1gKupf/Dg\nwSiKQl5eHpMnT+b++++nf//+wSi+qILVYSWjyBX4nRL4hRCi0QlaAmA0Gpk3bx6TJk1izJgxjB49\nmi5durBy5Uo2bdoEwIQJE8jJyWHkyJGsWbOGBx54AIA333yTY8eO8cILLzBu3DjGjRtHVlZWsKoi\nfPgHfk0CvxBCNFKKrtfzwMyNSGO5794c+wBYHVbybDnYnPZyQb+x3WOrT1LX5qsl1VfqGjyhhjBi\nQ2Pr7HiV9QFo0p0ARfCVDfxyxS+EEE2DJACiVortxeTbcnFoTlRVlcAvhBBNjCQAokbKBn5VDVo3\nEiGEEGdBEgBRLRL4hRCieZEEQFRKAr8QQjRPkgCIgCTwCyFE8yYJgPBTbC8mz5aLUwK/EEI0a5IA\nCEACvxBCtDSSALRwRfYi8m15OHUnqiKBXwghWgpJAFqoQnshBbb80sCvSOAXQoiWRBKAFkYCvxBC\nCJAEoMUotBeSb81DQ5PAL4QQQhKA5q5c4A/eBJBCCCEaEUkAmikJ/EIIISojCUAzI4FfCCFEdUgC\n0EwU2AoosOVL4BdCCFEtkgA0cQW2AvJteejoEviFEEJUmyQATVTZwK+gBLtIQgghmhBJAJqYfGs+\nhfZ8dEBRFAn8QgghakUSgCaifOAXQgghak8SgEZM13XybfkU2gsACfxCCCHqjiQAjVCgwC+EEELU\nJUkAGhEJ/EIIIRqKJACNgAR+IYQQDU0SgCCSwC+EECJYJAEIAl3XybXmUmgrRFEk8AshhGh4kgA0\nIF3XybPlYc3PpcheiKpK4BdCCBEckgA0AE/g91zxhytRctUvhBAiqCQBqEdlA79c8QshhGgsJAGo\nBxL4hRBCNHaSANQhT+e+InuRBH4hhBCNmiQAdcDbq99eiKooEviFEEI0ekGdPH7Lli2MGjWKkSNH\nsmrVqnKf22w2Zs6cyciRI5k4cSInTpwAwGKxcMstt9CvXz8WLFjQ0MX20nWdnBILpwpOUeIsxqCq\n0rlPCCFEkxC0BMDpdLJgwQJWr15NamoqH330Eb/++qvfNmvXriU6OpqNGzdy2223sWzZMgBCQkK4\n9957efDBB4NRdK+skkxKnCUY1KDmUUIIIUSNBS1y7dmzh/bt29OuXTvMZjNjx45l06ZNftts3ryZ\n66+/HoBRo0axfft2dF0nPDycAQMGEBISEoyieyl6UE8vhBBC1FrQEoD09HSSk5O9y0lJSaSnp5fb\nJiUlBQCj0UhUVBQWi6VByymEEELUt48PpzL+g2s4f00HLnt7COsPvlvv52xRnQDj4sIxGg11d8DC\nEuyavVa7JiRE1l05moCWVF+pa/PVkuorda1bTs2JzWnDoTmwa3bsTjsOzYHNaePTQ5+ycMtC77Y/\nZ+9jysbbiY4O46aeN9VbmYKWACQlJXH69Gnvcnp6OklJSeW2SUtLIzk5GYfDQX5+PnFxcbU+p8VS\nVOt9A8kqyseuO2q8X0JCJFlZBXValsasJdVX6tp8NZf6fnw4ldV7X+K3nEN0jO3EpF53MbrDWL9t\nGmtdnZoTh+7A4QmgusMbSO2a3bVe8132XR9onZ2QUAM5BQV+68vvH+D47vMHWlf6WhrwdWp+z3jh\nF4sZkTS26g0rkZgYVeFnQUsAevXqxZEjRzh+/DhJSUmkpqbyzDPP+G0zfPhw1q9fT79+/fj0008Z\nPHiw9LIXQoha+vhwKnO2zPIuH7QcYM6WWeRYLVzc5lIcuiuwRjjMZFryAgYy/1f/9dULxPYA+1R0\nfLtfsNd0LYi/vfKMqgmTasKoGv1eQ8whFX7mWW9Sje73Rjb8ui5ggnDAsr9+y1+vR6/sxEYj8+bN\nY9KkSTidTsaPH0+XLl1YuXIlPXv2ZMSIEUyYMIHZs2czcuRIYmJiWL58uXf/4cOHU1BQgN1u57//\n/S+vvfYanTt3DlZ1hBCiwem6ToG9gFxrDnm2XHJKcsi15ZJrzXH/5Pos5/JT1v8CHufJHQsDrm8I\nRk9wVIwYDSbva4Qpwhsg/V9NPsuBg2tl68uui4uNpKTA6VpvMGFUTO5X97JqLF3nc1yjYqyzC9L/\nZe3loOVAufVd47rXyfEroui63mL6smdk5Nfp8bKKMuQWQDW0pPpKXZuv+qxv2UCea80lxx3E8zzv\n3eu9wd2WS541F6furNY5DIqhwm0VFMZ1vt4bFCPDw3Da8AmY5gBBteL1gQOx2SfIlr4Gu1W3Mfwd\nl22Z8Xh55Gtc32XCWR27Ud4CEEKI5kbXdQrtheRYLd5A7grmFvLcV+M57qvxPGuON7DXNJBHh8QQ\nY46hfVR7okNiiQmJITYklhhzDNEhMcSGxBETEuP+iSXGHEuEKYIJH14b8EqzS1xXFgx70rvcGIJi\nS+Lpg/Hq3pf5LecQXeO7c++Fs846+FdFEgAhhCjDE8hzbaVN6c7MEk5mpnuvvj1X6Xnu5Rz31frZ\nBvIYsyuYR7uDd6x7fbTZFdgjTBG1vmqe1OuugFead/SaUqvjibozusNYRncYS6ghjNjQ2AY5pyQA\nQohmyz+Ql94L921GD7ScZ83FUc3be76B/Nyo9q4r7gCBPMYcQ2xoLNFm13KkKbLBm7/LXml2jO3E\nHb2mlHsKQLQMkgAIIepNdR45qw5d1ylyFPo0n5feI/ft5ObtDOezXW0Cebuoc32az11N6W3ikzA6\nwhpFID8bnitNISQBEELUi4oeObM6SrgoZbC3yTzXluPtve4b2PP8mtmrH8hVRXUH7VhvII92X417\ng7pPYPcsR5giUJWKB0eV++KiuZEEQAhRJ4odxZwuTCOt4BRphWk8t+vZgNvN+3putY7nCeTRITEV\nBvJAgb2qQC6EcJEEQAhRJV3XybFaSCs8RVpBGqcKT2JxZnA48xhphSdJK0zDUpJdrWMpKIzteG1p\nz/UAgTw6JIZIU6QEciHqkSQAQgjsmp0zRemkFaS5Arr7Kj6t8JT7J40SR3HAfUMMISRHpNAtrjsp\nkW1IiUghJaItL+1+npMFJ8pt3yWuK0sueaq+qySEqIIkAEK0AIX2AndwP8WpgpOcLkzjVOEpThee\n4lTBKTKKz1Q4zGpsSCznRXegTUQbkiNTaBPRluSIFM5v25lwRyzxoQkBO8GZDWZ55EyIRkwSACGa\nOE3XyC7J4lTBKffVu8+Vu/tKPs+WG3Bfg2KgdXgSfRMvJCXSdeWeEpHivpJ3Xc2HmyIC7ltVpzh5\n5EyIxk0SACEaOZvT5upc5w7ovlfunvUVTUsdZgynTUQbeiX2dl3BR7ShTaT7NaINieGtMar19zUg\nj5wJ0XhJAiBEEOm6Tr4tj7TCNE4VnCx35Z5WeIrM4owK908IbeVz772Nz5W7K9BHm2Oa1DPqQoiG\nIwmAEPXIqTnJKM7wa5ovew++0F4YcF+jaiI5PJmByYN8grvrHnxKZBuSwpMJNYY2cI2EEM2FJABC\nnAXXs++eR+NOkaNl8FvGUe+V/JnC9AoHsIkyRdE28hxvc7yng50n0LcKS5TH4IQQ9UYSACEqoOs6\nFqvFe7/d1Sx/yq+Z3mK1BNxXQSExvDUXtOrlvnpPcTfLu3rQp0S0Icpc8TSdQghR3yQBEM1Cbcac\nL3323d2xztvBLs3bTF/iLAm4r+fZ9+7xPdxX7m1IiWxLt5SORDjjSApPwmQw10dVhRCiTkgCIJq8\nysacv6BVL59Ode6OdQWukeuqeva9Q0wn/0fiItt4e9LHh8YH7Fwn48ULIZoKSQBEk7d670sB11c0\n5rxBMZAUnkzfxAtpE6D3fHJECuGm8PosshBCBJ0kAKLJ+y3nUMD1CgoTut7oN6hNm8i2tApLrNdn\n34UQoimQb0HRpP2UtQ+DYsCpO8t91iWuK48NmR+EUgkhROMnCYBokuyanVf3vsyq3S9W+JidjDkv\nhBAVkwRANDm/5Rzika0Psi/rfySFJ7Ng2BJyrDky5rwQQtSAJACiydB0jTd/eoO//vAsNs3GNZ2u\nY85FjxBtjgaQgC+EEDUgCYBoEk7kH+exbQ/zffq3xIXG89SQhQw/98pgF0sIIZosSQBEo6brOu8d\nXMuyb5+kyFHEiHNH8ujg+SSEJQS7aEII0aRJAiAarTNF6Tzx9aNsPbmFKFMUSy5+irEdr5XZ7YQQ\nog5IAiAaHV3X+fhwKkt2LCDPlsvQNhfzxNDFJEckB7toQgjRbEgCIBoVS0k2i7+Zz2dHPyHUGMaj\ng59gYteb5KpfCCHqmCQAotH44vhm5n/9GFklmfRrfSGLhi2lXfS5wS6WEEI0S5IAiKArsBXw1LeL\n2fDrOkyqifv6z+b/evwZg2oIdtGEEKLZkgRABNWOtO3M2zaXtMJTnB9/AYsvXkrnuC7BLpYQQjR7\nkgCIoCh2FLPy+2f45/5/YFAMTOkzjcm9p2JSTcEumhBCtAiSAIgGtzvjRx7dOoejeUfoENORxRcv\npWer3sEulhBCtChqME++ZcsWRo0axciRI1m1alW5z202GzNnzmTkyJFMnDiREydOeD97+eWXGTly\nJKNGjeKrr75qyGKLWrI5baz84Vlu/fgPHMs7yv/1+DP/vnq9BH8hhAiCaiUAxcXFLF++nPvvvx+A\nQ4cO8d///vesTux0OlmwYAGrV68mNTWVjz76iF9//dVvm7Vr1xIdHc3GjRu57bbbWLZsGQC//vor\nqamppKamsnr1aubPn4/TWX46WNF4/JK9nz+mTuDVvS+THJHCq6P+zgMDHyLUGBrsogkhRItUrQTg\niSeewOl0sn//fgCSk5N5/vnnz+rEe/bsoX379rRr1w6z2czYsWPZtGmT3zabN2/m+uuvB2DUqFFs\n374dXdfZtGkTY8eOxWw2065dO9q3b8+ePXvOqjyifjg0B3/d8Vf+kDqBA5ZfmND1Rt679gMGJF8U\n7KIJIUSLVq0E4JdffuGBBx7AZHJ10IqIiEDTtLM6cXp6OsnJpSO7JSUlkZ6eXm6blJQUAIxGI1FR\nUVgslmrtK4LvSO5hbvvkTyzdtpS4kFheGLGKeUMWEGGKDHbRhBCixatWJ0Cz2ey3bLVa0XW9XgpU\nn+LiwjEa6/DZ8sIS7Jq9VrsmJDTfIKjpGq//+DqLv1pMiaOE67pdx6Lhi4gLiwt20RpEc/63Lasl\n1RVaVn2lrsERbgonNjSqQc5VrQRgwIABvPTSS9hsNnbs2MGaNWsYPnz4WZ04KSmJ06dPe5fT09NJ\nSkoqt01aWhrJyck4HA7y8/OJi4ur1r6BWCxFZ1XmsrKK8rHrjhrvl5AQSVZWQZ2WpbE4VXCSedvm\nsvP0N8SGxLJo2F/4Q/+JZGUVkFXUPOvsqzn/25bVkuoKLau+UtfgKTQ4sYfW3YVqYmLFyUS1bgHc\nd9996LpOREQETz/9NL179+aee+45q0L16tWLI0eOcPz4cWw2G6mpqeWSiuHDh7N+/XoAPv30UwYP\nHoyiKAwfPpzU1FRsNhvHjx/nyJEj9O4tPcmDSdd1Nhx8j/EfXMPO099w2TlXsG7cR1x13uhgF00I\nIUQAVbYAOJ1O1q1bx9SpU5k6dWrdndhoZN68eUyaNAmn08n48ePp0qULK1eupGfPnowYMYIJEyYw\ne/ZsRo4cSUxMDMuXLwegS5cujB49mjFjxmAwGJg3bx4GgwwbGyyZxRnM//oxvjzxORGmCBYMXcK4\nzr+XCXyEEKIRU/Rq3Mz//e9/z7p16xqiPPUqIyO/To+XVZTR4m8BfHbkYxZ98wQ51hwGJg9i4bAn\naRPZ1m+b5lTfqkhdm6+WVF+pa/CEGsKIDY2ts+Od9S2AQYMG8cknn9RZgUTTl2vNYc6W+3ngy5mU\nOEp46KJHeeWq18sFfyGEEI1TtToBrl+/njVr1hAaGkpYWBi6rqMoCtu3b6/v8olG6KsTX/LE14+Q\nUZxBr1Z9WHzxUs6L6RDsYgkhhKiBaiUA7733Xn2XQzQBhfYCln27lPcOvoNRNTHjwlncdsEdGFWZ\nUkIIIZqaan1zt23bFofDweHDhwHo0KEDRqN86bck353+lse2PcTJghN0jevG4oufolt892AXSwgh\nRC1VK4rv3buXGTNmYDab0XUdh8PBc889xwUXXFDf5RNBVuIo4bldy3nzpzdQFIU7ek1hap/pmA3m\nqncWQgjRaFUrAVi8eDFLlixhyJAhAGzfvp2FCxfy9ttv12vhRHDty9zLI1vn8FvuIdpHn8eii5fS\nJ7FvsIslhBCiDlR7NkBP8AcYMmQIxcXF9VYoEVx2zc4LP/6Vm/9zI7/lHuKP3W/hnWs2SPAXQohm\npFotAGFhYezYsYNBgwYBsHPnTsLCwuq1YCI4DloO8MjWOezP/onkiBQWDHuSwSlDqt5RCCFEk1Kt\nBGDu3Lnce++93kmB7HY7f/3rX+u1YKJhOTUnf/9pDc/vWoFds3Nd598ze+BcoswNMymFEEKIhlWt\nBKB379589tlnfk8BeKYGFk3f8bxjPLrtIXad+Z6E0FbMG7KAK84dEexiCSGEqEfV6gPw9ddfU1JS\nQteuXenatSvFxcUyCFAzoOs6/97/T8Z/eC27znzPyPajWDfuQwn+QgjRAlQrAXjqqaeIjCydLzky\nMpKnnnqq3gol6t/pwtNM/e8kFu+Yj1k185dLnmHZZSuJC40PdtGEEEI0gGrdAvAM/euhqipOp7Pe\nCiXqj67rpP72AU/uWEi+PZ9hbS9h/tDFtA5PCnbRhBBCNKBqJQARERHs3r2bPn36ALB7927Cw8Pr\ntWCi7mWXZLNw++NsOvYZYcZw5g1ZwPguN8i0vUII0QJVKwGYPXs206ZNo3Pnzui6zqFDh3j++efr\nu2yiDm0+9l/mb38MS0k2/ZMGsnDYk5wT1S7YxRJCCBEk1UoA+vXrR2pqKlu2bEFRFDp27EiPHj3q\nu2yiDuTZ8li6czEfHtqAWTXzwICHuLnHrahKtbp/CCGEaKYqTQAeeOABJk2aRPfu3dF1naeffpqo\nqCgsFgv33XcfEydObKhyilrYfmob87bNJb3oND0SLmDxxU/RKbZzsIslhBCiEag0Afjpp5/o3t01\n49v7779P586dee211zh9+jRTpkyRBKCRKrIXsfz7p/n3L//EqBi5u+893NFrCiZVxm4QQgjhUmkC\nEBIS4n3//fffc+WVVwKQnJwsHccaqR/P/MCjWx/iWP5ROsV0ZvElS+mR0DPYxRJCCOGm6RqarqGg\nYlRUDKoBVTFiUA1EGCMarBxV9gFIT08nJiaGnTt3MmPGDO96q9VarwUTNWNz2njhx5W8se81dF3n\ntgvuYFq/ewkxhFS9sxBCiDqj6zpO3fWovEExYFBUDKoRVTFgVIyYDCbMBnPQ+2JVmgBMnjyZ6667\nDpPJRP/+/enc2XX/+Mcff6RNmzYNUkBRtZ+zfuKRrXP4NecA50S2Y9HFf+HCpAHBLpYQQjRbvlfx\nBkXB6A7wBtWISXUFeINiaNSt5ZUmAKNHj2bAgAFkZmZ6+wIApKSksHDhwnovnKicQ3Pw2t5VvLT7\nBa4PZ2wAACAASURBVBy6g4ldb+L+AQ8Sbmq4JiQhhGiOmspV/Nmo8hZAYmIiiYmJfuuSkmTUuGA7\nnHuIR76aw/+y9tI6PIn5QxczrO0lwS6WEEI0GZqu4XA6cGpauat4o2rErJoxqsZGfRV/Nqo1DoBo\nPDRd462f/85ff3gWq9PK1R2v5aGLHiU6JCbYRRNCiEbHqTnR0VExYFRVDIoBVTViUAyYDWbaRMcT\nZi8KdjGDQhKAJuRkwQke2/ow36XvJC4kjicvWcaV7a8KdrGEECJovE31uuIN8N6metVIiCGk0qt4\ng2po4BI3HpIANAG6rrPu4Ls8/e0SihxFXNFuBPOGLCAhrFWwiyaEEPWuqqt4k2pq0YG8tiQBaOQy\nis4wf/tjbDnxBZGmSBZdvJRrOo5rtvekhBAtT1VX8WbVjMlgku+9OiYJQCP28eFUFn8znzxbLoNT\nhrJg2BKSI1KCXSwhhKgx36t4T4c7xR3gPU31chXfsCQBaIRySiws3jGfT498TKgxjLmD5nFDtz80\n6cdNhBDNm67raLqGroNBVTEqBlTVgEFxNdWHGEIwGozyPdaISALQyGw58QVPfP0omcUZ9E3sx8KL\n/0L76POCXSwhhKjwKt6gGjCpJkIMIaiKKk31TYQkAI1Ega2Ap79dwvpf38Okmph54f3cesEd0iQm\nhGgwnqt4p+ZE13W5im/mJAFoBHamfcO8bXM5VXiS7vHns+jipXSN6xbsYgkhmhlvMz16wIloPEPY\nJkfHEmorCHZxRT2TBCCIShwlrPzhGd76+e8YFAOTe09lSu+7MRnMwS6aEKIJ8oxPj65gUFXX8LWK\nwdtMb1SNmBRTlVfx0oTfMgQlAcjJyeG+++7j5MmTtG3blhUrVhATU34ku/Xr1/Piiy8CMHXqVK6/\n/noAli9fzoYNG8jLy2PXrl0NWva6sidjN49uncORvMOcF92BxRc/Ra/E3sEulhCikfIdm95zD951\n9e76MSgGTAaTPBMvqi0oN3JWrVrFkCFD+OyzzxgyZAirVq0qt01OTg7PP/8877zzDmvXruX5558n\nNzcXgCuuuIK1a9c2dLHrhN1pY+m2pfzfxzdxJO8wN59/K+9cs0GCvxAtnKZrODQHDqfr/rsB1d2x\nLpRwYwQxIbEkhSfTNvIc2kS1ISkyhVbhrYkPSyA2NJaokChCjaES/EW1BaUFYNOmTfzjH/8A4Lrr\nruOWW25h9uzZftts3bqVYcOGERsbC8CwYcP46quvuPrqq+nbt2+Dl7kuHLD8wqNb57A/+2faRLRl\n4cVPMjB5ULCLJYSoZ3L1LhqjoCQAWVlZtG7dGnDNNpiVlVVum/T0dJKTk73LSUlJpKenn9V54+LC\nMRrr8D9XYQl2zV7lZk7NyYvfvciyr5dh1+z8oecfePyyx4kKiaq7sjRyCQmRwS5Cg5G6Nl8V1dfz\neJyCK7Ab3PfcPa9GxYjZ2Pjnh/eVmNhyvp9aUl191VsCcNttt5GZmVlu/cyZM/2WFUVpsP8QFkvd\nzviUVZSPXXdUus3RvCM8uvUhdmfsolVYIk8MXcT1fa4mK6uArIKW0cs2ISGSrCypa3PTUurq6Tkf\nHx+BJbu43PPvBsWAWQ0v17FOBxzuHytOCikOVhVqLDExioyM/GAXo0E097pWltzUWwLw+uuvV/hZ\nQkICZ86coXXr1pw5c4b4+Phy2yQlJbFz507vcnp6OhdddFF9FLVeaLrGO7/8i2e/f5oSRzG/O28s\ncwc9RmxoXLCLJoTwUXr1rrqa5t3Pvnua5o2q0fVoXEwsYfbmn/CIliMotwCGDx/Ohg0bmDx5Mhs2\nbGDEiBHltrn44ot59tlnvR3/tm7dyqxZsxq6qLVyujCNedvm8k3a18SExLJw2BJGnTcm2MUSosWp\n6rE419W7uVqD2zSVpnshqisoCcDkyZOZOXMm7777Lm3atGHFihUA7N27l7fffpvFixcTGxvL3Xff\nzYQJEwCYNm2at0PgU089xUcffURxcTGXXnopEydO5J577glGVfzous4HhzawdOciCuwFXHrO5Tw+\nZCGJ4a2DXTQhmp2yg9p4muYDdayT4WmFKE/RdV0PdiEaSl3f58kqyvD2AcgqzmT+9sf44vhmIkwR\nPDhwLtd1Hh/wS6el3Dv1aEn1lbrWnboa1KauNPd7xb6krs1HUPoANGfrD77Liu+f4YBlPx1jOzEo\neQipv32AxWphYPJFLBj2JG0jzwl2MYVotOSxOCGCTxKAGlp/8F2mbLzdu3zQcoCDlgMYFSNzLnqE\nP3S/WSbK+P/27j0oqvPuA/h3d9kLK5cF3IVmQm2kVaON2mm8UIgJt8WIKAZstZ20ojOm6sSAGg06\n2jiN0di00Y4TI2lTO2NsB6PQVjvREUUhaHReTYjVzmu0Bmx0UYTFC9c9v/cPZF8JFwFZDux+P/8k\n+3CW83t8djjfPec55yGf5v7mDnQ5sU6v1d+fTc9T80RqYADooS3/89sO2x8PjMTPnvx5P1dD1H8e\nPLBr0fKtXActtFpdy4Feq4MWWuh1LQf2wXTPO5EvYgDoof+t/neH7VdvV/RzJUSP7sGJdK3X2rX3\nH2aj0Wjvn5LXtvnW3jqhzhoYCH299147JfJ2DAA9NCJkFC7c+le79uGWKBWqIerYN2fIazUa6DRa\naO8f1DX3D+o6rQ56Tct1dl5rJ/ItDAA9lPXD5W3mALRa8NRLKlRDvqaj6+ta9+z4///G3vptvWVi\nHeekEFF7DAA9NOt7Lc8l2Hrmd/jfWy13ASx46iU8/0SKypXRYPbN6+taDVomzmm00Dx4Gl53/8DO\n6+tE9IgYAHph1vcyMOt7GW2eA0DUkdbHzEJav6m3/FfrvuWt4+vrRESexgBA1ENdTZwz6oww6prd\n394NWoP7NDwP7EQ0kDAAEN334MNpHpw419H1dT+NX4dPoAszB0K5a1CjfCKiHmEAIK+niAIRgUC6\nvL6u0+pg0Bnuz5TnxDki8m4MAORVXIoCk5+RD6YhInoIBgDyGooisJqtMOh4Cp6I6GEYAMg7CGAz\n2+Cn40eaiKg7+NeSBjURgZ/WD0PNVp7eJyLqAQYAGrQUUWDSmRDqH6Z2KUREgw4DAA1KLsWFAEMg\ngo3BapdCRDQoMQDQoOMSBcFGCwIMAWqXQkQ0aDEA0KCiiIIwYxhMepPapRARDWoMADRoiAis/jbo\ndXq1SyEiGvQYAGjQCB8SwSf0ERH1EQYAGtBEBH46Pww18TY/IqK+xABAA5aiKPDX+yPEFKp2KURE\nXocBgAYklygINAQiyBikdilERF6JAYAGHJfiQogpFGa9We1SiIi8FgMADSgigqH+Vhj9jGqXQkTk\n1RgAaMBovc2PC/oQEXke/9KS6kQEOq0OQ81W3uZHRNRPGABIVYooMOgMCDMN5W1+RET9iAGAVKMo\nCsx6MyymELVLISLyOQwApIpmccHCBX2IiFTDAED9zqUoCDOFwV/vr3YpREQ+S5UZVzU1NcjMzITd\nbkdmZiacTmeH2+Xn58Nut8NutyM/Px8AUFdXh4ULF2Lq1KlISUnB22+/3Z+l0yMSEVjNVh78iYhU\npkoAyM3NRXR0NA4dOoTo6Gjk5ua226ampgbbtm1DXl4e9uzZg23btrmDwvz58/Hxxx8jPz8fZ86c\nwbFjx/q7C9QbAlj9bTDoDGpXQkTk81QJAIWFhUhLSwMApKWl4fDhw+22KSkpQUxMDCwWC4KDgxET\nE4Pi4mL4+/tj8uTJAACDwYDRo0fD4XD0a/3UMyICnUaH8CERvMefiGiAUCUAVFVVwWazAQCsViuq\nqqrabeNwOBAREeF+HR4e3u5AX1tbi6NHjyI6OtqzBVOviQhMfiZYzTbe5kdENIB47OvYvHnzcPPm\nzXbtWVlZbV5rNJpeHRiam5uxbNkyvPjii4iMjOzWe0JCzPDz0/V4X526W48mpalXbw0L8/7Z7y7F\nhSBjEIJNwYAPPdbfag1Uu4R+40t9BXyrv+yr9/NYANi5c2enPwsLC0NlZSVsNhsqKysRGtp+udfw\n8HCcOnXK/drhcGDixInu12vXrsV3vvMdzJs3r9s1VVff6/a23VF17zaapLnH7wsLC0BV1Z0+rWWg\ncYkCi9GCxkYtYAJu3Litdkn9wmoNZF+9lC/1l331Hl2FG1UuAcTHx6OgoAAAUFBQgISEhHbbxMbG\noqSkBE6nE06nEyUlJYiNjQUAvPPOO7hz5w5Wr17dr3VT9yiiIMwYhiH6IWqXQkREnVAlACxcuBCf\nfPIJ7HY7SktLsXDhQgDAF198gTVr1gAALBYLFi9ejIyMDGRkZGDJkiWwWCy4fv063nvvPXz55ZeY\nNWsWZs6ciT179qjRDepA64I+Jr1J7VKIiKgLGhERtYvoL319mqfq3g1eArhPRKDVaGE129ot6OPt\np9gexL56L1/qL/vqPbq6BMB7suiRiQj8dH4YarJypj8R0SDBAECPRFEU+Ov9EWJqP5GTiIgGLgYA\n6jWXKAg0BCLIGKR2KURE1EMMANQrLkVBiCkEZr0P3eBPRORFGACoxxRFMNR/KIx+RrVLISKiXmIA\noB4REdjMNj7Tn4hokONfceoWEYFOq8NQs7XdbX5ERDT4MADQQymiwKAzIMw0lLf5ERF5CQYA6pKi\nKDDrzbCYQtQuhYiI+hADAHWqWVywGC0IMHj/yoVERL6GAYA65FIUhJnC4K/3V7sUIiLyAAYAakdE\nYDVbYdAZ1C6FiIg8hAGA2tBAA6vZBp1Wp3YpRETkQQwABOD+gj5aPwz154I+RES+gAGAICIw6owI\n9Q9TuxQiIuonDAA+ziUKAvQBCDYGq10KERH1IwYAH+ZSXLCYQjBEP0TtUoiIqJ8xAPgoRRSEmYbC\npDepXQoREamAAcAHiQhs/uFc0IeIyIfxCOBDRARajRa2IeFc0IeIyMcxAPgIEYGfzg9DTbzNj4iI\nGAB8gqIo8Nf7I8QUqnYpREQ0QDAAeDmXKAg0BCLIGKR2KURENIAwAHgxl6IgxBQCs96sdilERDTA\nMAB4KUXhgj5ERNQ5BgBvJIDNbONtfkRE1CkeIbyIiECn1cFqtnGmPxERdYkBwEsoosCgMyDMNJQH\nfyIieigGAC+gKArMejMsphC1SyEiokGCAWCQc4mCYGMwAgwBapdCRESDCAPAIOZSXFzQh4iIeoUB\nYJASEVjNNt7mR0REvcIAMAhpoIHVbINOq1O7FCIiGqRUWRKupqYGmZmZsNvtyMzMhNPp7HC7/Px8\n2O122O125Ofnu9sXLFiAGTNmICUlBevWrYPL5eqv0lUlItBpdLCZw3nwJyKiR6JKAMjNzUV0dDQO\nHTqE6Oho5ObmttumpqYG27ZtQ15eHvbs2YNt27a5g8LWrVvx97//Hfv370d1dTU+/vjj/u5CvxMR\nmPxMvMefiIj6hCoBoLCwEGlpaQCAtLQ0HD58uN02JSUliImJgcViQXBwMGJiYlBcXAwACAhomfHe\n3NyMpqYmrz8gukSBWT+Eq/kREVGfUSUAVFVVwWazAQCsViuqqqrabeNwOBAREeF+HR4eDofD4X69\nYMEC/OhHP8KQIUOQnJzs+aJV4lJcCDGGINgYrHYpRETkRTw2CXDevHm4efNmu/asrKw2rzUaTa++\nwf/xj39EQ0MDVqxYgZMnTyImJuah7wkJMcPPrw+vnd+tR5PS1Ku3hoU9/L59RVFgHWKF0c/Yq30M\nJFZroNol9Bv21Xv5Un/ZV+/nsQCwc+fOTn8WFhaGyspK2Gw2VFZWIjS0/ant8PBwnDp1yv3a4XBg\n4sSJbbYxGo1ISEhAYWFhtwJAdfW97negG6ru3UaTNPf4fWFhAaiqutPlNiICq78NtY2NABp7WeHA\nYLUG4saN22qX0S/YV+/lS/1lX71HV+FGlUsA8fHxKCgoAAAUFBQgISGh3TaxsbEoKSmB0+mE0+lE\nSUkJYmNjcffuXVRWVgJomQNQVFSE4cOH92v9niQi0ECD8CERXM2PiIg8RpUjzMKFC5GVlYWPPvoI\njz32GLZs2QIA+OKLL/DXv/4VGzZsgMViweLFi5GRkQEAWLJkCSwWC27evIlFixahsbERIoJJkyZh\nzpw5anSjzymiQK/TY6jJ6vUTG4mISF0aERG1i+gvfX2ap+rejT67BODNC/p4+ym2B7Gv3suX+su+\neo+uLgHwHPMA0CwuBBuDEWjwzYkoRETU/xgAVOZSFISaQmHWm9UuhYiIfAgDgIoURWA1W7mgDxER\n9TsGALUIYDPbONOfiIhUwaNPPxMRaDVahA+J4Ex/IiJSjSrPAfBVrbf5hQ8J58GfiIhUxTMA/cSl\nuBBgCESwMZgHfyIiUh0DQD9wiYJgowUBhoc//5+IiKg/MAB4mCIKwoxhMOlNapdCRETkxgDgQa0L\n+uh1erVLISIiaoMBwEM00MA2JBxaDedZEhHRwMMA0MdEBH5aPwz154I+REQ0cDEA9CFFUeCv90eI\nKVTtUoiIiLrEANBHXKIg0BCIIGOQ2qUQERE9FANAH3ApLoRwQR8iIhpEGAAekSIKhvpbYfQzql0K\nERFRtzEAPAI/rR7BxhAu6ENERIMOj1yPINhkUbsEIiKiXuFN6kRERD6IAYCIiMgHMQAQERH5IAYA\nIiIiH8QAQERE5IMYAIiIiHwQAwAREZEPYgAgIiLyQQwAREREPogBgIiIyAcxABAREfkgBgAiIiIf\npBERUbsIIiIi6l88A0BEROSDGACIiIh8EAMAERGRD2IAICIi8kEMAERERD6IAYCIiMgHMQD0wLVr\n1/Diiy9i2rRpSElJwZ///GcAQE1NDTIzM2G325GZmQmn0wkAEBG88cYbSEpKQmpqKv71r3+5f1d+\nfj7sdjvsdjvy8/Pd7efOnUNqaiqSkpLwxhtvoPUuzc724WkulwtpaWl46aWXAAAVFRWYPXs2kpKS\nkJWVhcbGRgBAY2MjsrKykJSUhNmzZ+Pq1avu37Fjxw4kJSUhOTkZxcXF7vbjx48jOTkZSUlJyM3N\ndbd3tg9Pq62txdKlSzF16lQ8//zzOHv2rNeO7c6dO5GSkoLp06dj2bJlaGho8KqxzcnJQXR0NKZP\nn+5uU3Msu9qHJ/r61ltvYerUqUhNTcWSJUtQW1vr/llfjVlvPhee6m+rDz74ACNHjsStW7cADP6x\n9TihbnM4HHLu3DkREbl9+7bY7Xa5ePGivPXWW7Jjxw4REdmxY4ds3rxZRESKiopkwYIFoiiKnD17\nVjIyMkREpLq6WuLj46W6ulpqamokPj5eampqREQkPT1dzp49K4qiyIIFC6SoqEhEpNN9eNoHH3wg\ny5Ytk4ULF4qIyNKlS2X//v0iIrJ27Vr58MMPRURk165dsnbtWhER2b9/v7zyyisiInLx4kVJTU2V\nhoYGKS8vl4SEBGlubpbm5mZJSEiQ8vJyaWhokNTUVLl48WKX+/C0lStXSl5enoiINDQ0iNPp9Mqx\nvX79usTFxUldXZ2ItPx7792716vG9tSpU3Lu3DlJSUlxt6k5lp3tw1N9LS4ulqamJhER2bx5s7uO\nvhyznn4uPNlfEZGvv/5a5s+fL88995xUVVWJyOAfW0/jGYAesNlsGDNmDAAgICAAw4cPh8PhQGFh\nIdLS0gAAaWlpOHz4MAC42zUaDcaPH4/a2lpUVlaipKQEMTExsFgsCA4ORkxMDIqLi1FZWYk7d+5g\n/Pjx0Gg0SEtLQ2FhYZvf9c19eNL169dRVFSEjIwMAC1J9+TJk0hOTgYAzJo1y13fkSNHMGvWLABA\ncnIyTpw4ARFBYWEhUlJSYDAYEBkZiWHDhqGsrAxlZWUYNmwYIiMjYTAYkJKSgsLCwi734Um3b9/G\n6dOn3X01GAwICgry2rF1uVyor69Hc3Mz6uvrYbVavWpsJ0yYgODg4DZtao5lZ/vwVF9jY2Ph5+cH\nABg/fjyuX7/urqOvxqynn4u+0lF/AWDjxo149dVXodFo3G2DfWw9jQGgl65evYoLFy5g3LhxqKqq\ngs1mAwBYrVZUVVUBABwOByIiItzviYiIgMPhaNceHh7eYXvr9gA63Ycnvfnmm3j11Veh1bZ8TKqr\nqxEUFOT+w/JgfQ6HA9/61rcAAH5+fggMDER1dXW3+9ra3tU+POnq1asIDQ1FTk4O0tLSsGbNGty7\nd88rxzY8PBzz589HXFwcYmNjERAQgDFjxnjt2LZScyy7eo+n7d27F1OmTOmwjkcZs55+Ljzp8OHD\nsNlsGDVqVJt2bx/bR8UA0At3797F0qVLsXr1agQEBLT5mUajaZNAPaE/9nH06FGEhobi+9//vkf3\nM1A0Nzfj/PnzmDt3LgoKCuDv79/mOijgPWPrdDpRWFiIwsJCFBcXo66urs+v0w503jKWD7N9+3bo\ndDrMmDFD1To8qa6uDjt27MArr7zSb/scCGPbFxgAeqipqQlLly5Famoq7HY7ACAsLMx9yqeyshKh\noaEAWlJl66k3oOWUenh4eLt2h8PRYXvr9l3tw1POnDmDI0eOID4+HsuWLcPJkyexYcMG1NbWorm5\nuV194eHhuHbtGoCWg+nt27cREhLS7b62toeEhHS6D0+KiIhAREQExo0bBwCYOnUqzp8/75VjW1pa\niscffxyhoaHQ6/Ww2+04c+aM145tKzXHsqv3eMq+fftQVFSEt99+232w6ssx6+nnwlPKy8tx9epV\nzJw5E/Hx8bh+/TpeeOEF3Lhxw2vHtq8wAPSAiGDNmjUYPnw4MjMz3e3x8fEoKCgAABQUFCAhIaFN\nu4jgs88+Q2BgIGw2G2JjY1FSUgKn0wmn04mSkhLExsbCZrMhICAAn332GUSkw9/1zX14yvLly3H8\n+HEcOXIEv/vd7zB58mT89re/xaRJk3Dw4EEALbNo4+Pj3fW1zqQ9ePAgJk+eDI1Gg/j4eBw4cACN\njY2oqKjAlStXMHbsWDz11FO4cuUKKioq0NjYiAMHDiA+Ph4ajabTfXiS1WpFREQELl++DAA4ceIE\noqKivHJsH3vsMXz++eeoq6uDiODEiRP47ne/67Vj20rNsexsH55y/Phx/OEPf8D27dvh7+/f5t+g\nr8asp58LTxk5ciROnDiBI0eO4MiRI4iIiMC+fftgtVq9cmz7VD9POhzUTp8+LSNGjJDp06fLjBkz\nZMaMGVJUVCS3bt2Sn//855KUlCS/+MUvpLq6WkREFEWR119/XRISEmT69OlSVlbm/l179uyRxMRE\nSUxMlI8++sjdXlZWJikpKZKQkCDr168XRVFERDrdR384efKk+y6A8vJySU9Pl8TERHn55ZeloaFB\nRETq6+vl5ZdflsTERElPT5fy8nL3+999911JSEgQu93unlEr0jJ71m63S0JCgrz77rvu9s724Wnn\nz5+XWbNmyfTp02XRokVSU1PjtWO7detWSU5OlpSUFFmxYoV7xra3jG12drbExMTI6NGj5ZlnnpG8\nvDxVx7KrfXiir4mJiTJlyhT336nW2foifTdmvflceKq/D4qLi3PfBTDYx9bTuBwwERGRD+IlACIi\nIh/EAEBEROSDGACIiIh8EAMAERGRD2IAICIi8kEMAEQD1OzZszFz5kxMmzYNo0ePxsyZMzFz5kzk\n5OT0+HctWLCgzWptncnJycGZM2d6U+6gUlpaih//+Mdql0GkKt4GSDTAXb16Fenp6fj000873cbl\nckGn0/VjVYNbaWkptmzZgry8PLVLIVKNn9oFEFHPlZaWYvPmzRgxYgT+/e9/Y/ny5aiursauXbvQ\n3NwMjUaD1157DZMmTQIATJkyBX/6058QFRWFuXPn4gc/+AHOnj0Lh8OB1NRUZGdnAwDmzp2LRYsW\nYcqUKVixYgUCAgJw6dIlXL9+HU8//TTefPNNaDQaXLt2DStXrsStW7fw7W9/Gy6XC3FxcZg7d267\nWo8ePYodO3agsbERBoMBq1evxtixY7Fv3z7k5eVh165d0Gg0mDdvHmbMmIHZs2fj/fffx8GDB9HU\n1ASTyYT169dj1KhRaG5uxpgxY5CdnY1Dhw7B6XRiw4YNOH78OEpLS+FyubB161YMHz4cpaWl+M1v\nfoOoqChcuHABZrMZmzZtQlRUVLdrvHTpEnJyclBfXw9FUZCRkYF58+Z5dGyJ+o26zyEiooepqKiQ\niRMntmn75JNPZNSoUfL555+7227duuX+/4sXL8qzzz7rfv3MM8/Il19+KSIic+bMkWXLlonL5RKn\n0ykTJkxwP8Vtzpw5cuzYMRERWb58ufzsZz+ThoYGaWhokOTkZDlx4oSIiPzyl790r41eXl4u48eP\nl927d7er/fLly/KTn/xE7ty5IyIiFy5ckLi4OPfPV65cKZs3b5YtW7bIsmXL3O2tT3ITETl27JjM\nmTNHRESamppkxIgR8pe//EVERP7xj3/I+PHj3TVv375dVq1a5f43GjlypJw+fVpERPLy8mT27Nnu\nn7X+f1c1vv766/L++++7a2ldM57IG/AMANEgFRUV1eYZ61999RWWL1+OyspK6HQ6OBwO3Lp1q8PF\nhZ5//nlotVoEBQXhiSeeQEVFBSIjI9ttl5iYCIPBAAAYPXo0KioqMHnyZHz66af49a9/DQCIjIx0\nn2n4puLiYnz11Vf46U9/6m5rbGxEdXU1QkJC8Ktf/QovvPACRAR79+51b1NWVobc3FzU1tZCo9G0\nm78wbdo0d006nc693O2YMWNw7Ngx93ZPPPEEnn76aQAta9mvX78edXV13a5xwoQJeOedd3Dnzh1M\nnjy5034SDUYMAESDlNlsbvM6Ozsb69atQ1xcHFwuF8aNG4fGxsYO39t6UAcArVbrXu3tm4xGY7e2\n64yI4LnnnsPGjRs7/PmNGzdQX18PjUaDu3fvIiAgAPX19cjOzsbu3bvx5JNP4uuvv0ZiYmKHdel0\nujY16nQ6uFyuDvfV2fKtXdU4bdo0/PCHP0RJSQnee+89FBQUYNOmTd3qO9FAx7sAiLzE7du367KJ\nXwAAAY1JREFU8fjjjwMA8vLy0NTU5LF9TZw40b0S3H//+99OJyjGxsbi2LFjuHTpEoCWg21ZWRmA\nlm/Z2dnZeO2117Bo0SIsX74cLpcL9fX1cLlciIiIAADs3r2713X+5z//wdmzZwEAf/vb3zB69Og2\nq+M9rMYrV67AarUiPT0dixcvdrcTeQOeASDyEqtXr8ZLL72E4OBgPPvsswgMDPTYvtatW4dVq1ah\noKAAkZGRGDt2bIf7i4qKwsaNG7Fq1So0NjaiqakJEyZMwNixY7Fp0yaMHTsWU6dOBQCcPHkSv//9\n75GdnY0lS5YgPT0dFosFdru913U++eST2L17N9auXQt/f/8Ov713VeOBAwfwz3/+E3q9HhqNBqtX\nr+51LUQDDW8DJKIeq6+vh16vd881SE9Px4cffohhw4apXZobb/Uj6hrPABBRj12+fBk5OTkQEbhc\nLmRlZQ2ogz8RPRzPABAREfkgTgIkIiLyQQwAREREPogBgIiIyAcxABAREfkgBgAiIiIfxABARETk\ng/4P4+nyOQ4G4XEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb59dbe46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = new_df_train[['fundamental_53','technical_40', 'fundamental_47_nan','technical_30', 'technical_20']]\n",
    "target =  new_df_train.y\n",
    "\n",
    "plot_learning_curve(Ridge(random_state=1, normalize = True), 'Learning Curve', \n",
    "                    features, target, cv=10, scoring=R_scorer, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "The curve seems surprisingly nice to start with. Ridge is a good choice in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Mimicking the Kaggle train/test console\n",
    "\n",
    "features = new_df_train[new_df_train.timestamp<906][['fundamental_53','fundamental_47_nan','technical_40', 'technical_30', 'technical_20']]\n",
    "target = new_df_train[new_df_train.timestamp<906].y\n",
    "\n",
    "rdg = Ridge(random_state=1, normalize = True)\n",
    "model = rdg.fit(features, target)\n",
    "y_pred = model.predict(new_df_train[new_df_train.timestamp>=906][['fundamental_53','fundamental_47_nan','technical_40', 'technical_30', 'technical_20']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Further Investigation ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# How bad is the performance?\n",
    "\n",
    "test = new_df_train[new_df_train.timestamp>=906]\n",
    "y = test.y\n",
    "\n",
    "y_compare = np.row_stack((y_pred, y))\n",
    "\n",
    "# Standard deviation of y_true and y_pred\n",
    "y_std = np.std(y_compare, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fda487edeb8>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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aWrRz5051dXVJkl555RU9+uij+sxnPqNHH31Ux48fz2z1AAAAsIwfvnxeE5GY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\nzMeEeUUxB1sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda49d4a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,10))\n",
    "plt.plot(sorted(y_std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It looks bad. About a third are having high standard deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>derived_0</th>\n",
       "      <th>derived_1</th>\n",
       "      <th>derived_2</th>\n",
       "      <th>derived_3</th>\n",
       "      <th>derived_4</th>\n",
       "      <th>fundamental_0</th>\n",
       "      <th>fundamental_1</th>\n",
       "      <th>fundamental_2</th>\n",
       "      <th>...</th>\n",
       "      <th>technical_35_nan</th>\n",
       "      <th>technical_36_nan</th>\n",
       "      <th>technical_37_nan</th>\n",
       "      <th>technical_38_nan</th>\n",
       "      <th>technical_39_nan</th>\n",
       "      <th>technical_40_nan</th>\n",
       "      <th>technical_41_nan</th>\n",
       "      <th>technical_42_nan</th>\n",
       "      <th>technical_43_nan</th>\n",
       "      <th>technical_44_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1560718</th>\n",
       "      <td>1583</td>\n",
       "      <td>1673</td>\n",
       "      <td>-1.389630</td>\n",
       "      <td>1.236950</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.928441</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.038455</td>\n",
       "      <td>-0.237333</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543449</th>\n",
       "      <td>1583</td>\n",
       "      <td>1657</td>\n",
       "      <td>-1.389630</td>\n",
       "      <td>1.236950</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.928441</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.038455</td>\n",
       "      <td>-0.237333</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537320</th>\n",
       "      <td>82</td>\n",
       "      <td>1652</td>\n",
       "      <td>-2.950205</td>\n",
       "      <td>0.352620</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.883966</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.442402</td>\n",
       "      <td>-0.236858</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504996</th>\n",
       "      <td>1539</td>\n",
       "      <td>1621</td>\n",
       "      <td>-3.669785</td>\n",
       "      <td>4.514560</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.870691</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.400616</td>\n",
       "      <td>-0.096888</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432462</th>\n",
       "      <td>1583</td>\n",
       "      <td>1551</td>\n",
       "      <td>-1.389014</td>\n",
       "      <td>1.236127</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.774125</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.009864</td>\n",
       "      <td>-0.237333</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502166</th>\n",
       "      <td>82</td>\n",
       "      <td>1619</td>\n",
       "      <td>-2.155774</td>\n",
       "      <td>0.475541</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.881352</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.390700</td>\n",
       "      <td>-0.236858</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525540</th>\n",
       "      <td>82</td>\n",
       "      <td>1641</td>\n",
       "      <td>-2.675037</td>\n",
       "      <td>0.429784</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.897755</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.438636</td>\n",
       "      <td>-0.236858</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541273</th>\n",
       "      <td>1539</td>\n",
       "      <td>1655</td>\n",
       "      <td>-3.896938</td>\n",
       "      <td>1.004463</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.857795</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.447692</td>\n",
       "      <td>-0.096888</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445741</th>\n",
       "      <td>1583</td>\n",
       "      <td>1564</td>\n",
       "      <td>-1.389572</td>\n",
       "      <td>1.236872</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.913822</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>0.033877</td>\n",
       "      <td>-0.237333</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540554</th>\n",
       "      <td>82</td>\n",
       "      <td>1655</td>\n",
       "      <td>-3.004200</td>\n",
       "      <td>0.337761</td>\n",
       "      <td>0.020909</td>\n",
       "      <td>-0.880981</td>\n",
       "      <td>0.011996</td>\n",
       "      <td>-0.442402</td>\n",
       "      <td>-0.236858</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  timestamp  derived_0  derived_1  derived_2  derived_3  \\\n",
       "1560718  1583       1673  -1.389630   1.236950   0.020909  -0.928441   \n",
       "1543449  1583       1657  -1.389630   1.236950   0.020909  -0.928441   \n",
       "1537320    82       1652  -2.950205   0.352620   0.020909  -0.883966   \n",
       "1504996  1539       1621  -3.669785   4.514560   0.020909  -0.870691   \n",
       "1432462  1583       1551  -1.389014   1.236127   0.020909  -0.774125   \n",
       "1502166    82       1619  -2.155774   0.475541   0.020909  -0.881352   \n",
       "1525540    82       1641  -2.675037   0.429784   0.020909  -0.897755   \n",
       "1541273  1539       1655  -3.896938   1.004463   0.020909  -0.857795   \n",
       "1445741  1583       1564  -1.389572   1.236872   0.020909  -0.913822   \n",
       "1540554    82       1655  -3.004200   0.337761   0.020909  -0.880981   \n",
       "\n",
       "         derived_4  fundamental_0  fundamental_1  fundamental_2  \\\n",
       "1560718   0.011996       0.038455      -0.237333      -0.029062   \n",
       "1543449   0.011996       0.038455      -0.237333      -0.029062   \n",
       "1537320   0.011996      -0.442402      -0.236858      -0.029062   \n",
       "1504996   0.011996      -0.400616      -0.096888      -0.029062   \n",
       "1432462   0.011996      -0.009864      -0.237333      -0.029062   \n",
       "1502166   0.011996      -0.390700      -0.236858      -0.029062   \n",
       "1525540   0.011996      -0.438636      -0.236858      -0.029062   \n",
       "1541273   0.011996      -0.447692      -0.096888      -0.029062   \n",
       "1445741   0.011996       0.033877      -0.237333      -0.029062   \n",
       "1540554   0.011996      -0.442402      -0.236858      -0.029062   \n",
       "\n",
       "               ...         technical_35_nan  technical_36_nan  \\\n",
       "1560718        ...                        0                 0   \n",
       "1543449        ...                        0                 0   \n",
       "1537320        ...                        0                 0   \n",
       "1504996        ...                        0                 0   \n",
       "1432462        ...                        0                 0   \n",
       "1502166        ...                        0                 0   \n",
       "1525540        ...                        0                 0   \n",
       "1541273        ...                        0                 0   \n",
       "1445741        ...                        0                 0   \n",
       "1540554        ...                        0                 0   \n",
       "\n",
       "         technical_37_nan  technical_38_nan  technical_39_nan  \\\n",
       "1560718                 0                 0                 0   \n",
       "1543449                 0                 0                 0   \n",
       "1537320                 0                 0                 0   \n",
       "1504996                 0                 0                 0   \n",
       "1432462                 0                 0                 0   \n",
       "1502166                 0                 0                 0   \n",
       "1525540                 0                 0                 0   \n",
       "1541273                 0                 0                 0   \n",
       "1445741                 0                 0                 0   \n",
       "1540554                 0                 0                 0   \n",
       "\n",
       "         technical_40_nan  technical_41_nan  technical_42_nan  \\\n",
       "1560718                 0                 0                 0   \n",
       "1543449                 0                 0                 0   \n",
       "1537320                 0                 0                 0   \n",
       "1504996                 0                 0                 0   \n",
       "1432462                 0                 0                 0   \n",
       "1502166                 0                 0                 0   \n",
       "1525540                 0                 0                 0   \n",
       "1541273                 0                 0                 0   \n",
       "1445741                 0                 0                 0   \n",
       "1540554                 0                 0                 0   \n",
       "\n",
       "         technical_43_nan  technical_44_nan  \n",
       "1560718                 0                 0  \n",
       "1543449                 0                 0  \n",
       "1537320                 0                 0  \n",
       "1504996                 0                 0  \n",
       "1432462                 0                 0  \n",
       "1502166                 0                 0  \n",
       "1525540                 0                 0  \n",
       "1541273                 0                 0  \n",
       "1445741                 0                 0  \n",
       "1540554                 0                 0  \n",
       "\n",
       "[10 rows x 217 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Again, ranking by index here\n",
    "index = sorted(range(len(y_std)), key=lambda i: y_std[i])\n",
    "\n",
    "# Find out the rows with the worst standard deviations\n",
    "y_trouble = index[-10:]\n",
    "test.iloc[y_trouble]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.04871322,  0.04900475,  0.05074556,  0.05171403,  0.05212434,\n",
       "        0.052984  ,  0.05328798,  0.05572273,  0.05632951,  0.06448812])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_std[y_trouble]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fda516b8518>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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Cac+xz0MhnbBdKOtw8vly1jx0+9WWgrSl2Fj6g0aPvHZLHQUqIPKAmZnq9HkI\nqbk5DzZpS/Vvfws7Tn8E5t/yJvu5FYqh2pJjn4dMgumNDiMPhBBCJh7X9Aphm9m/uBKNL90afz1W\nqnXw0Gjwu6YtDYsqGUFi5EGF4px5vS7QqPcdiNjz9k3iVJqHPF2K/cBA92dni6+2lPPz8muO5pSF\nYLp5040AgLm3/3nWaRWL2CQua6nWtFTFPJGprFTNSK/SvUMDBdOEEEKKx/EHcOHyPwAAHHzoePSc\nrkmcKfXBWTA9qXnVhVRbqicjDybBq9j/oN1W58Xnyd+v9w30VM1D7shD2uuqyIOb82CleVAZtSVe\nrwlDv4QmcXkE9fmp2L2gYj6NCCMPhBBCisH1hz9tmzIE08OiSquZqZoHhUEXlGrFlLTGaGs0tlrq\n1JZCBNOB5kG3fxbBtItTo9Q8uEYe+mlLxpX4UV9DibkJfw+r2tIomsSZHOtRUJV5GKDzQAghY0bt\ne/dXx0geAqFIV3yPsRQGk4HhXG3JbXP7cXVlGMuPPMRSQWwiD74Pr9eD32gk0pZsS7V6nTa8YWoe\nAL3uIcspdpiLMp3Gyxh5cGWUfR5yNImzjzyMqLLUkGl85cvYct7TUPv+9wZPpgmmR+08GqDzQAgh\nY0T929/Ctsc9Fpt+/VdGPZUkzj/8mm3C3gJitSVLo9PrdID1dcy95Y2o7b/bYg5qmp/4GKav++fM\n+1cK17Sl8G+VYNrWaNR0mI6tPLv2eQgcmdQu03k1D1n2d9U8iNe2jlEbj6ZSra7naIM3idv0a7+E\nqS98DnN732K/06g/fwN0HgghZIxo3HEbAGD6Ex8b8UwUiCvPOZb0w67G0GoeevBWljHzvvcCYf+B\nkE4HM+97L+b/7E+w5aLzneYsMv/GyzD/ut9znLkF46B5CCMG9drgswgwCqbFsTvt9LQl5w7ToWC6\nH3nQ6h5ynuPUa1cxvu9YqtVbFyIPmvn6CuOx9OpgIuL32zHyYF1taZRpS8PE5ZocA6eJzgMhhJDi\nsfgB1OZ7B3n22iZxvR7m/+A1WPyt38T8H/9hfN9OG7UjhwEA9Xv2u8w4PrfVVfeKMlUlpVRros9D\n4Dz4jQbQUBjFNp9tqwV0BuNGn3UeQaygeQAArKojD5kMbCfjTvFcVsG06dijXnmWp1VA2lJqta5R\npi2VcTyX9zfqz98AnQdCCCHF4Prjq1txjSIPHfW2vo/G124HANS//c3Yvso8+wxz8NbXh2NMjCry\nAPvIgxc+UPYcAAAgAElEQVQ22mso0pYA/eqw1CRO5XzlSn0JDfSZlMjDKATTzqVaNT1M0hip5sFS\nd6Qi0jzYl2odhxV4a5wcgeq/bzoPhBAyTlR4NSpGnh/+ZhOAPvLgmSq2dAqKFrQtctLHBd8flH10\n1DzIaUsArJwHT9dhWuwV4GAk+bVadO0PXfOQhTyCae2xRx158PV/OzaJi66ptGpLk+QwiKjeV8q9\nXJW2VhXoPBBCCCkG15QD3TZhtaWWZnXWWKq1mD4PfbH2hEQeer7ZUJH7MQQOmF+vDz4LEZtV51ZL\nndqSNfIgaAr8tMhD7j4PWSIPjoaernu6yIj7PCSPLTh+w+rzMArBdNWM9DFwoOg8EEIIKZ6MP4Bb\nznsa6mGVJF3aksEAcU5b0rGesZRmSJUMgBTHS9Y8DNKWHJ0HqVSrsmyu7NTYIjoPs4HmYVTVlgr4\nbD2btKVRG7VFlmoNHQ+XcztJmodMn2XFnBoBOg+EEEIKwVmoqvixnvrC5wbj2TSJk8dot/PnkHe7\n/Xz9cdY8IH6OBikQLtWWGoDnDfpuhFhFHiTNQ95SrTVBuB1GHlYLjDzkvWZcHCEAyNrnoUxMaUuq\n7uEGvCyRhw2KsXFgRaDzQAgh48SoVyNtKeIH0FCqVXceCqmQZFODf5xIK9UqRwiCcxg5DZLzoNWc\nxDQPLbUhmDEtxY+lLaVoHoYtmFYP4LR15sjDCAXTsVSlSUpbqioVvtcX4jzcfPPN2LNnD3bv3o13\nvOMdiddbrRYuueQS7N69GxdddBHuv/9+AMD999+PH//xH8f555+P888/H5dddlm0zx133IGnP/3p\n2L17N970pjfB3+gXESGEVB0bA6zTQe2B7wfbmIeLRR6klXTt+O38modIzDqU353yNQ+er3e2AEPa\nUlgaVRZN2zhobU2TOJNxaDrf9YG5UknNg+Mxx6JUq0zMeXAt1dp/j+mlWsXHZWkeyjmMNWNg7+Z2\nHrrdLi6//HK8613vwr59+/DRj34U3/nOd2LbXHvttdi0aRNuvPFGPO95z8PevXuj104//XRcf/31\nuP7663H55ZdHz7/+9a/HG9/4Rvzrv/4r9u/fj5tvvjnvVAkhhIyYTRf/Grb9xI+idvdd6RsLjkAs\nlG9KEel28htdLUcHpOqkRR4SaUtCh2kg6rsRYVttSaF5MK5eG52HEjUPWbZ1PWZW52GUpVrFzzdj\nn4fUaksjjDxULl2oas6jQG7n4bbbbsOjHvUonHbaaWg2mzj33HPxyU9+MrbNTTfdhGc+85kAgD17\n9uCWW24xRhIeeughrKys4Cd/8ifheR4uuOCCxJiEEEIqhkXkYfpjHwEATH3xC6nGgbFUqy5tqdPJ\nbQgONfJQkoHiyZ9F5DwoNk5UWxIE08DAiQixye9vt+MRjUjzYLhGTOfGRfOQt0ncMCIPcgrQOAim\n5fNYQtrSSDpol3me81ShqxC5nYcDBw7glFNOif7euXMnDhw4kNjm1FNPBQA0Gg0sLi7i6NGjAPqp\nSxdccAF+5Vd+BV/84heVY55yyimJMQkhZEMycoPCgIMBVjt4MP1HUqd5MO3XLqDaUt5KS1UjbTVX\nbhLXi2sefLlRnMYAjDksbV2p1mzOg4vmIfcKchbnwdWYHsvIQwHVltIcT2oeBu+7wvd6RQ228njE\nIx6BT33qU1haWsIdd9yBl770pdi3b1/m8ZaW5tBo1NM3LJAdOxZLPR6ZTHgdEWs2zUYP5etm5NfR\n+kL0cNvWeUA1n2YTaLWwcPIYsHk2+brAlCe8J+F9b9k0E+XATzcbsfc9O+UB89PR32nnZOvSXHKe\nB/tN6mqel/mcbtk8q37/izPWc8uFcA62bZ2PoggzzTpm5OMuNGN/Li32951bmMXcjkVgOv769q2K\ncwbEeh3M1REzBBcXprG4YxFoDLZJnHuDXqXenBqcr1O39d9irYd5xTw2L86o52di8LFgyxbN+wvZ\nOp94qtkYrMWqPtcd2xfiXaj9bvy1hYXEPuK1ErJ9+yKwtaTvuXytNgcm4/xMQ3nuRWLnYaF/TdX8\nnvm6F+4J2ntI0QRG+uzMFGaHdbzguzE724yaYGrvL8F3d8tSSe8/A7mdh507d+LBBx+M/j5w4AB2\n7tyZ2OaBBx7AKaecgk6ng+XlZSwtLcHzPDSDk3jmmWfi9NNPx913350Y88EHH0yMqeLo0ZN5344T\nO3Ys4uDB5VKPSSYPXkfEhenjq9gUPBavmypcR7VDy9gWPD58eAW9xeR8tm7fgfr3v4e1e7+H9cee\nxGbDeO1ODw8H76n58GDbh4+ewHyniykA660Ojh9cxo7gtbXlk+ieWEdo3unOSbj9kSMn0JW2aTx4\nBEsAer0eDjue03Dchx8+ibZi3+ljJ5WfX9HMrqwhNEcPH1rG5p6PBoD1tTaOS8edProSzQkAjh48\nhiUAJ1s9nDi4jCWvFjMWDh88jl49aUBv7fYQLt+tLp/ETLsTaVGXl9ewdnAZm9fbCF2Ro0dW0BHn\n0mpF50+mixqOBNs21nr9+R05hhPidyD4/9jDJ9ByPLfe8nFsDx4/fPSE8rMLqR9ewVbpuXargzA+\nc1Axp4MHl2POw+aVk9F5OHTwOPzV5Cr7zMo6ZNPx0MHj8LuKrt9DQL7XLK62Ih/r5Mpa7NzLyPej\n8Brrdc3fqakjK9gSPD5yZCXx3RwG29BPw1ldbWFlSMfb2vNRD46BLjALoOf7ynMxt7KGeejvIWVh\ncvJypy2dddZZ2L9/P+677z60Wi3s27cPu3btim2za9cuXHfddQCAG264AWeffTY8z8ORI0fQDUJf\n9913H/bv34/TTjsNj3jEI7CwsICvfOUr8H0fH/rQh/DUpz4171QJIYSUhSbloLe9b055hw5mH89Q\nqlXZmMx23JBJKNWq0zyokNOWolKtgStgmbYkpqt7rVa8Io+q2pKDYNqPVVsK0pa0fR60w+hxSJFR\n5uWnVhGSNA/jUG3JVKo1o2BaW+ZX2k51/KFTtSZxo/78DeSOPDQaDVx22WW4+OKL0e128exnPxtn\nnHEGrrzySpx55pl46lOfigsvvBCXXnopdu/ejc2bN+OKK64AANx666246qqr0Gg0UKvV8IY3vAFb\ntvR9zte97nV4zWteg7W1NTzlKU/BU57ylLxTJYSQscev8A+KjS7B39aPTdQOH3LLLbfVPKQZJxZ4\nofMwlEqtboPOXPNO1O++Cyfe+EfZjyk6WzbVlrpStSW5VKvBUPanpoJKS221gZlVMB3TPISlWgus\ntuSyf9GCad0uSs1D6m7FYWwSl7Xakst33u0QE8MYaD0K0Tycc845OOecc2LPveIVr4geT09P46qr\nrkrst2fPHuzZs0c55llnnYWPfvSjRUyPEEJIyeiqpvSW+gkftYMWkQedoWla5XX94VVtXyHB9OLv\nvgoA3J2HxLkzOA/y+ezIfR5sO0z7wFSzX2mpJZVq9RV1/uW5mD5XpWB6SB2mS2gSh7ZFtaVRY/p8\nymgSt9Gp8EIRO0wTQggpBhsjKDACvaNHc0QeDAaGTRnRFLzQsMtj1On2zTqmazqWgCc32JNfT0Qe\n+seKnAa5z4Nh1dlvBlGK1rq66lHWJnFiqdbZIPN+nPo8JCIPFmlLtscuC/F759okTpW2pt4wuc+w\nqZqRXlVnUoDOAyGEkOJJMZ5tymkmehWonpf3cVy5VEZILFJKSke3yq5DfFs93yltyYsiD+q0JW3e\nuh9EHiAZx+Jx/WzOg7pUq+acZFm9jh17CKVa5X1a6ZGHkTctM3w+rk3iPMvIgzEyNWwyHm/uDy/H\nQhAhLJSqOTUCdB4IIYQUg02+ctZmXJq0pYSBVUDaQyFN4nQ//BnH1BrKNsQE04rjy1GNsMN0Q5e2\nZDDygwqK3qomKmBIfTE2CBOcBzQa8BsN/TFyGp2pRrsy8uB4DBvBdJYIR5GMIm1pFA5TTiN9/m17\nMXvNO+029v30a2XUTqMFdB4IIWScqPBqlJVjEDzfF4O6GGmWToerUTOsaksFGwDeumvkIX6+TEJ7\nT05B6cabxMFW8+D7QK3WjxLIzk4UeTA4mEbBdNxc8WdmDdGYIactZdlfft2mw/So8/9NzoPclTyN\nqNqSn/L9HUHaUlWp8L1+pE3iCCGEbEzsUjJ0kYfB44RRXGi1pSEYL5kjD5pVdttjuqQtBZqHUGdg\n22E6dB7QbOrnm7HDdEzzAAAzM4VVW5r67H+gt2XJfv8CIgLxyINmI9V5HqFBHfvOumoe5HQkm8jc\npDoPaT7BGLxvOg+EEEKKweqHP1vakk7/ED3lef1ter38AtRWdaotReh6GuiQ07yMzoOm2pJr5AEA\n4MFvTCU1D+HnnlUwXY87D/7srDaVy0Ur4B04gC3PPNd+HrrXHVPwPAvNw8iNyGGkLYWPa5rEl1FG\nW4Z5uieszwPTlgghhBSPVdpSxvFUmodwvEI0D+3kMYsia+TBNW1Jd0yLyEPSeXCIPABAc0qrR/Bi\nn51LkzjJeTBFHhyugZprs0JAOc9Uob64j5wW55K2NFLNQ3bBdLxSk6la2igE01Uz0qsfeaDzQAgh\nY0XVfugEbH7sLasmJcZLM4CDlUzP1H067RjhvIqIPBSteXCutiSdL0PkQdY8hIZwaLD7UqlWY5dg\nD/CnmpEewQ9XmG2qLVn2eQACzYO2w7RD5EH1WWf57PIeUzlmxTQPGgfehpizYdunZdSRl1EhL4hU\nEDoPhBBCikcbecg4RuzxwPiIIhgFRh7CleGhlMrMOmaeyEOvN/A5XSIPocFedxBMA8DU1CAqIKc8\nmYxD06mR01yK0jyEUSbbeWjHd3CE5VLAmvmqohnGilTDJk+fB1sh9KgdpgrhV3ihiM4DIYSQYnBd\nNcwYefCMaUsp1Vws5uAVUW2p4FVDL4fmwYNBoAqkpi0lBNOmlBXPgz81NegVEYwRfU4ZNQ9yuVh/\nZra/mt1WGf9DiALExlc8Jxn6M+/+a9S/+Q27Y1ZV8yBTQLWlxGOJNG3TUKnK+a7INExQME0IIaR4\nijCIdGkSqmotUVpOASuXCbFvBgo2CIuqtqTu+qyutuRLTeL8qSl47bZV5CF6qlaPr58WJpjud5n2\n1laTzo3LOW4rHMWcgun67bdh8TWX6veRnFMPvtperJrmQePAWyFpXbTvYhSaB1MxgaIP5fv2h2Ha\nEiGEkEKo8A+KTUqF08qibltVRaWsBoAq8hAZlNVZAkxWL0rBodqSJ68ih5GFSDAdaB+mZ4LxDBEC\nz+trHkKCfaPjmlagTZ9dTaF5ANS6BwfD1lOlLWVBmHtt+bjx9URkSyuYHu31JzuaMYchr2C63cbm\nC/43pv/pH6Ttyn/PzsUbhszIO4tbQOeBEEJI8disvBcpmBY1D3mNgSLSlnRkNQx03ZRdj2mleQj+\nDhqzRSv7M6HzkF5tKcJJ82AfeQjnoozIjCLy4LISLzuCLlGqkVZbyq558CSnsXbgQTT/8zNofvom\n/THGwIh2xfc8+5oXFXNqROg8EEIIKQYbx8Clkogp8pBoDtd/3Sugz0O0yl8h48W52lKin4Z9kziE\naUtSqVY/NNh1qWFhepRQ2jVKfRI/n9i8BpgiV8lSrf3Ig/t5iaPUt6R87sp5ivsorm1xH2vNw6g7\nTMvkqLaUiDhF1bf0DspIxeFDwiqqUKH7jg46D4QQQopB/M2zWb1NM9KyrMgWYXCpVqNdKdgAyN3n\nIUpbUrwedpQOjxVVW4o3ifPTIg9AXzDdFNKW5IiBY+QhSimpx80VUfOQ3Mnh3GeJMqVFBFSOcZa0\npVFXHkp8PjnSlmLOgy/8bbgGSjeih3i8TE3iip9GUVAwTQgh40SFQ9lWZI0K2K56ujoPyshDS/va\nyMjT5yEllSvR8EvSPEQRiGmz8+D5gfBXFDDLzkNsX0NaTPjUjkcA9To6P3ZW/AWD5sGpw3SGyINy\nHGHuqSU2LdOWlKLkiqQtOTeJs4g81B74/sBxLZOy76lpH2GV7jsa6DwQQggpBrk8aMo26dGJwUPr\nhnJFrNYOs0lc5mpLeSIPMAvKNWlLkeE/FU9bSo08qJwHRWQoYRyr5jYziyNf+Eqiz4Nv0jw4CaYz\nNIlTRh5SDhSLPNg2iRuxEamIDA0qbjnOTe4sHv4dprMdOICtj3sseo/8Qf3xNxoVXihi2hIhhJDi\nKULzoBnPuCJbROQhrMCTx3gpOm3JsVRroqpVdL5VzkP8nHmBYDrUGXQffQb8ZhPd//bDwfaaVefw\nmEK1Jbk/g7Ng2kOyQRxSNA9OaUsZqi0px3c4pmWTuJGXapXp+QMBvHOTuHjkIVpcCP6rHTkMLxBS\nR0yq85B26xuD903ngRBCSDFYRRVccpqzpC3l/+HN1DhMRpeK4mgYhDn/eSIPMYGzslKQuUnc+vnP\nwqHvfg+d//6Y5HiJg3nwpwSHITQ2w8O69nnQOJnGyINT2lJBkQfxfaVqHsZEMK1IW/LDkrmOaUty\ntaXE9zR83TUdqkiqZrQz8kAIIaQQqvYDlwcXIy3tcebIg+K5doU0D2GqkKtgWjpHvrHPg2Sw9aQ+\nDwAwPT2IAGjPcTLyIPdniFdbMsw5fEpnQM2GmodhCKazpC057GPd56FamgfP94F6HX6tlqtJXKxY\ngvS9jV2LZb3XMo10VlsihBBSOlX+YbGJPAxLMB3mTruWalXgyWklWfB9zL/xddj0vF9OPO80TFDx\nKFczs1i1pXTNQyha9SXDf+A8GN6DVG0pSltSaVIKiTzkS1tSCqazkFZtSTxmIJiONCQF62MKQyWY\n9ry+jiWvYDp0FsLvrSqaNer374rtfG31MRWOPFAwTQghY8WY/KDaGERFRB4S/R7yax4iwXQu48XH\n3J9fYXc8E0V0zo5pHhTomsTJeoWa0IjPdMyG2CTOUG3JRjCtdR5CzUM+wbQy8pBJMG2/T5i25E/P\n9J0f7XdlNAa1d/wYZt7zLoWgvde/Bmq13JqH3BHDKpL2PQPcHAI6D4QQQgqhyqtxo4g8yEaIzQ94\nCkWsRrtqG1LJM55YqtVG8xBWW5IN/7S0peDc+2KH6boceXAVTA858pClp0ea5iGNIIrkT0/rxwPg\nFaDfycL8Gy7D7N+9J/mC7/evgXo9Q3qgOvJgdB7KvtflPZ5tShIF02RotNvYsud/Yvadbx/1TAgh\nFaJwo7Rsioo8aDpMF1FtKVqNHsa5zjpmUZEHpeZBOmfdsNpSfH3RT9U8QNFhWo48FOM8RJoHZRWq\nfJoH67LAuudUU1YJpkMHyKassem5gqnfd4/6hcAJ9Wv1RIWuVORSrTaRh7I1D3mPl/a9CLE9TIUj\nD3QeKkr97rsw9V9fxsLv/c6op0IIqRJVdh5cHIM844nGh0wRmodh9nnIPmCOXQXD1UHzkEhb8gKT\nQZcaFo4tdpiWNQ+GakvKvPe0yIOiSZxT5EFu2Oa4fzROqsMhPI7SlsyRh8ql8vR6/WugVnNuEhev\ntuQn39uIoiwAijPSi/rOV/keH0DnoaL4c3OjngIhpIpU+YelpLQlVaUXT2Gcaiv1iPupjPI84uTo\n4AWLYPNEHrKmLclRg+BvY6UdXZM4cS7RHOWcevV4KkyaB6foXBB5SERITCgjAimlWgUiQX5z2nyc\nKqTySMf2azWgXnPXFmn7PFQg8lAURc+XkQfijKIpDiGEjN0PqoSTYRfb1k4A7MXSI7KdK68QwXTB\n5JmL7yPKpVGNE0Yaor/VaUvR71JqkzghbUmKPIjRhcTn46J5CFftVZEDlw7TYQlc0eEZsmA6ijzM\nhJEHzT6jijyYvlteIJh2rrYkObNyh2mlONztECPH94ETJ4oZp+LQQq0qVQtXEkKqQZV/WFwiD56X\n+l4SXZJDTCkOPUfBtMKAVaayuFJU5CF8L7m0HCmaB2nsKCXFVTDd3xu+2OdBXtEvSDCNwHnIk3ZU\nu+u7mP7Ex/q7NBycB5VVG9M8pDSJCyIPfjMlbWlEmgctPb9/DdQyCKbF6FYstTCjVmkY5Dy383/6\nVuz4oVPR+NKt5mPY3p4YeSDOVNlAIISMjnG5N+immZZ/b7Otyeg06SFsEFfhLaunzLzrr1C7Z7/0\nvGZ+WckxhpdmsMhjdzWah9RqS8H/YodpudqSa4dpDaHhrayMZTnMlmeeO/hjyr34ZOt/7hr8kdZh\nWiCKbKVpHlxTg4aJ7wPoOw9+gX0eKpG2VJCRPnflnwIAmjfeYN4w9W1V/x5P56GqVMELJ4RUjyo7\nD0ULpnVjm+6PjpqHxDwdow7NGz+Bxde+GlvOe5p+XJPBbIlzCpaL5kF+TtMkLqq2ZJqLJ0UeZAfE\n2XnQRR6CY6jE7Zbnqv7A94XDiNVw7CJirZ99Co6975+CfVIOpkxbSmkSVzUdQK8XRB5qyQpdNvsG\nePCT76Nq7zUH9Xv2oyZeWyIT0ueBzkNVKeDHhhAygYz7/cDJwdBsazI6cy68xOr+W5zr2n33AgDq\nBx7Ub1TEYlBezUNkiKgE0/Hn9NWW+mNoBdOKakt+lPqkWGHO0yQucFDypC11f+CR6rlYR8S8ZAdt\nwFow7U+bnQejMH2Y6NKuej0AQYdpx6iIlyXyMKbM/MsHsO0nflT9omU0s+rQeagq4sVThACHEDIZ\nVPmHxUrzUMB4iSo9aufBarVe2sa1QZx34mT6uEUsBuVyHlLGSaQtBfN1TlvqOym+uF/NVG0pv+Yh\nU4fogO4P/TdhXg7nV9TtmIToujlZpy1VTPPg+4MO03nTliLJQ/BA4Yxo+18MiaH20FFex8nn6nfc\njvq9/T4bvrU4onzYYbqiiJUHaidW0FtYGOFsCCGVocrOg4BV4ysXg8tSQ5B7tVZYybYxJrxVR+ch\nK8NMW5IjPLpSrRaCad/zjH0ejNoVxbja1LNaDX6joXb2LM+3J+pbsjh4YeUhi2N68KOzbBt5GGnv\nA5kg8uDXaoDvO/d5SNM8KL+346B5yDXH5L5bdz05x3jlwchDVRFuGt7K8ggnQgghdni6NKMYGVZ4\npccJQ0OzXRbNQyxtyQLvZN95SPTm0UZNHI2NqNpSvrQl31bz4PvwOp2+kSifv9CZSElbEjUPsShE\nWqTBJfIA9PskKCNFludqbQ3+3Bz82Vm3z0WRouQpntMSXmPNqeR4Iqp+JiMU00alWut1dyfdjzsP\nVn0eyiaLIzDMeVPzQJwRfyhXVkY4EUJIpRiTyIMVTpEH4bG86mlKaXJlvSDnQSSPgRHZDzkiDzHN\ng8W23U4yZQkYCKZTmsTFeiYE1Za8KGdec1xoDGPDvP3pprobuOV3xFtf6/eL8GrwxFKiqdelMDeT\nlkQzJ6+1Dn962sK5rdB33ff7361are9Eyo0F09B0mB40d6xYipYtpac6VQM6D1VEDOkB8JYZeSCE\n9BlqXm5erDQPLiu8mv1aBlFzrn4IUBujBryTfU2aPzevHTfWAGskmgfBeVBmLUlPdrpK5wFe6DyY\nm8T5TdF5EMyMNIG0Y+TBb04P0sxi59sHOh14x49p9wUAb20N/sxs34jXpC3V7vouln7m8Wh84fPJ\n10XnwSXysN4a9HiQ9xWpWgUi34+qLeVpEuepyilX7b3aMg5zHAJ0HipG7b57seOULZi74o+j5xh5\nIIREVPnHSuU8+L7e2M8YefBWV/Xb5c0Tb7X1YyuwSlsaSbUlKRpjSluSDeduN9ldGojy+40pK54H\nNDQdpuVmdFbOg/5QmJ4eaB6ka2Xp55+C7Y8+TZPWFLC21hcty86DwPyf/BEa37kTm176AsXcBM2D\nw7Xstdb7pWaNOhRUI5UnJPz8PC9oEud2PVa62lKeFf4sixXR551yXEYeiC3Nm/4NADBz3b9Ez1Hz\nQAiJqLLzIBLMc+G3L8HWJ/903JkAYNNhWpeO5K2tDZ6Xxyk78hAIpv3ZWXlgzZwy6h/yRh4M48gi\nZq/biUcMQlLSlqJxRMF0fXiaB7/ZVJdq7fXQ+Pod/d11gnYEaUszM8lrSHzc7juT/pSiA7UnzC/t\nuhOdh/X1fuQh1Eto0pPUImLzYYZKzwe8Wj99zTXy0NV0mI7+r0DkIcvxChZMjwt0HsYBsSIEIWRj\nU2XnQTG3+nfvRP2e/QPjQTYabMeLRR5OqrcB8q9gys5D2jyjyMO8fptchn/GMeQ0HgfBNDpqzcPA\neTDMxfPihrYh8lCoYFpn/Bv299bX+xWPDJEHL3AeIDa+U43vkrbUavV1IWmRhxF915VajHDFvFYD\n6pJGxGrQCa225Hq/sVk0EbetKHQeqkaFLxZCSAWosvMgIjsJ8iJILHyfMgak1fG1NcRC/gl9QfYV\nfU9OW0rBLm1JY9g6OFC5StAKpVqVmhmF86BOW0pbZQ/GiQmmhXKvhWsepgaRIt15rWnMHN/vX0dB\n5EF7fjumyING86A7XkAomE7tEVGltCWgX6o1qLbkPDc5NS66PyheF7erOjmc+txjjRA6D+PAGF1Q\nhJAhU+X7gcKAi4yy0HnIOn/R+FpbhTb9RzRCsizGFCSY9nRzcsUlUhPbTx7DstoSAK/bS4k8mJrE\nIV6qNXQefD8uHFeNo3yPKZGH9fWkIyo81jbaarfh9XpB5EHxPsKjR5EHtfPge3Z9HmLzCwXTWTQP\no/r++37/u1yr9YXzuZvEVUjzEOJwbhtf/iJqd9/lsI94kVlGmiq8mMwmcWPAyFrUE0KqR5WdBxWh\nE9HtwIdgVFtEHnQNxbxVSfMg4trsKxF5aBlfT8wxiDxgZka/jd8bVPfMGHnIrXlwSVvqdpIN4oCB\noWwqhyuXanXp86D6rTOWap3un89u1zmi4633ryF/ZjpxjNhnpNI8iHMLIzoO1521YLpyOfF+P/pU\nrzs7D/Hz00tGwEbqKLkb6Uu/sAsAcOi791vuobo+DfeuisPIwzgwbsYCIWR4VPl+oDLgorSluObB\nruSsxnlYW40/n1jlz75ip+xYbNo+jDzI6TE2kYchOg+JyIfReZAM305nEDEQCZ7TLmgFY2fXPCjG\nTDMQk5cAACAASURBVBFMAxhEH6IXLIywtX6EyZ+ZNesiTJEHwP5SEz5Hr1XxyINO8xBcR3693r++\nXOYilp+PlaM3RB7KvtdlOV6uOaY40xWOPNB5GAeqbCwQQsplXO4HcjqCKm3JIU889tigeegfz+Ec\nyXOQq/ekrSIHZWONZUddoyHi+EVFHgZ/mF/3g2pLWdKWgL7B0xTTloRxsmgeTAS9EryW7DwIjqau\nklHogE4nIw+xsQLNQ/x8BE5SrEmcZn+Z0DltNtONw7xlh4smTFuyuQ4S+0rfB/m6Hpf7GpDx+6y5\nX+nGrTh0HqqG642IELKxqPL9QGnABZGGRHUWx5Qik+ZBILYqrjPODOfQaztGHhRNyhJ/2zzW4kv/\nWyIfxyltSdMkLjQadSkrYeShoRNMy+fIQvOQ0mEaCKJFsfeg3WUw7HoYeZgxOw+tFMG0TpCdmGzw\nPQg0NX3BtOJ44hwdVuOb/3YDGrd/1W4uWQgjDV6t3+cBcEtdilVb8gfXQnheqhB5sKUtFFUoss/D\nGEUeqHkYB8bIGyWEDJuK/qDqiNKWpMiDTdqD1nlYg79pk3o71d+2xwCAdTfNg9V2RaQt5RVdG50H\nadtOd2AgilhHHsS0pYFgOtzP9zxl2osySpBWqhVIpC1Z6UqCXiGxqkcKvI6iVKswY+sFv/C5sJqX\nTdqS7bXXbmPzL10EADj40HG7fbIQpr/VHSIP4bUnC6bl9z7SUq2O24uV44pMWxojW4+Rh3Ggqt43\nIaR8qnw/UBlturQl30eqI6RzHlZXYa4epNhfN66Ea5M47ZgFpS1lTuvQRR7StkUgbldEHlIF04rI\nQ0w7ER4n1E7kTFsKV+9dRe7AQDCNmVmzXiUSTA/OR2zejtkCUeShmbHDtGLb2oEHtcfLhEnzUKsN\nzldK5KF2373YsXMzZv7m3UnnoUodpkMsrz+vKzoPjscw9XmQz0GFIw90HqoG05YIIQbshMYjQpVb\nH2Z4RIZGxqhAQvMwQF6xTq1QZ9IABGlLUVpJ1siDznkw7WPaJsfHnroSL7/e6QwiBiKB0egZc/E9\ndYdpXyjVGjgmzRs+gZn3vdc8NxNhKpEsmLZw1jwx8mAUTAeGYiOlz0Ma4T7rQtpS5Dxo5moj/AZQ\ne+D7dnPIief7fcchcv7M85v+yPUAgMVXv1L4/qP/vuT3qvyOVPRe1xach5wd7UUSkTc6DyQXVTYW\nCCHlUsH7wcw//B02/fJFsZz2hNA3Z5M40ZCIVVtKbIfBD7qF5kF2xrwgbclvTsMJg/MQM7LkFCHb\ncfNGHkzHVGkesjSJC8cRoxYNhWA6SIlq3vwpzL/u98xzM+BbCKa1Y0alWlM0D2GTuKaiw7RO82A4\nx1GUZCoeeVj4rd/EtjNOj6/m20YeHnwguV3RiN+pLJoHudqSRZ+Hyi6UZElbEi4x7fuqQvTFEmoe\nqoYy8jA+FxQhZMhU8Ad18ZKXAgDWz39W8kVj2lIKCiPQbzb7BpjsjKiOaTOuTJi2NN0Eli3nmTZm\nrrTojM6DiKq6jYDco8DrdpVpS3ZN4vqr8f7UFLx2ezCOqHmo1SJbKpYC4mo8hdGhVju+amuTthSU\nakWK8xAZ+w11nwdtEzodUeSh2a/WFBxvNojAeKsn4S8sJuZhol5C5CFygMVqS1kF08LCwWCRYYR2\njuMKv3jNpkVflESfa5pg2n3osmDkoWqobuwVNBYIISOiyvcD8UdYMg6iH1zDqn+CmPMQ/Dc7199X\n7vUg4uI8yJGHsLrOtL7pm2ZQ/TF0aTRVjDyElWRUfR5sBdPAQGAsrsyH+8UqMGXXg4TRgCyRh/D6\n6XeYNlhpkWBa0+zOUTAdaWqa05FxKDo+fk1zbgxj1x4oM/IwSFtyKiUraR4ihzU8L1WotmR7PLHa\nUq45Ot63KgSdh3GgysYCIaRcxuV2IBu93a76eZsxMHA0/NnZ/t+h7kGV/pRnFVMUtNrOU7VdUZqH\ngFQdhwnxHCmPKabpBE6eoUmczfuJSpuKkYfw9brCodDOTc9AMG1wHnRYlmqNnEkXzYMpr70VamrS\nBdO2n3mYttTbssVq+0wIaUu2gun4/tL3Qb4eR+g8+DkiD5nmqNsnEXmobuiBzkPVoGCaEGJiXO4H\ncjpC1GFaeN3lvYTOw1w/8iCLpmPkiTyEfRumc2oedPPJGkHIG3kwplUlnYdM1ZYAIfIwlRwnijxo\nGse5vseow7Tc58FCMB1+zlNTZs1DaCjm7PMQRRfWhchDWl5KWpQoIKy21NvxCKu5ZCI0/muDUq3J\n3i2m/dOqLY3JfQ0Y3MuAYqMFY3QO6DyMA2N0QRFChsyQnYepm/4Njc9/rrgB5bQlB5RVgqK0pbXk\na+F+OTQPXlias+labclwjCKqLeUJOaUZ6Kq0JYPmwUtpEgcIkZvIURhUW1KWb9XNzYCVYFq7c2gM\n18wrvNH1oNY8qBf89McblGrVNImLXTd258NbPdl/oPrMiiISuwtN4tK+Z8K58XTOgzy+SOkLJZbH\ny9PnweTIS88762lKhM5D1WDkgRBiYsj3g8VLXoqF1/x2/oHECAOQrUlcbDwpbSk0mFR103NEHiLB\ndFPVFMxyTAnPYiU8ddyCIg9KnYnKecjTJA4YCIxVHaZVOgh5HjZMq5vEWTkSptQjMW0pnJ8qbSnc\n34WwCaEubUkXQVG9LjPEnPkwyuAL0Zapz9+C+te/ZjeA/DlbpS1lna0jrmlLnZyah2gf6bhjpHlg\ntaVxgM4DISRkyPcDb31tYJhraHz+c/A6bbSf/D/0G8lGQVbnwfdjDkKUSy9rKETSVmytctLd0paM\nDc9sc5xN4+b63BXGmohotBjSlgbOQ/r7iVbqFR2mRYci6jTteXC1FsNrwdgkTjek6DykVb2R5pxJ\n8xDuIkQevCxN4lSYDPAsqN6TEHkII0ebXvR8AJZdrQVHyOv1BJ2Bn3h9ZNh+xzo5KoTFzq0UMZXP\nQYU1D3QexgE6D4SQkGHfD3q+Pi0lYOnpTwOQYjRIxmqi2pJ1OlDceYgMWJNx7vKDLk8jNETzCqZ1\n8xFXteHbm8s5Ig/xUqwpY0dpS9kiD5FRGFRb8uuKKkWyGDv4jJ0rC0alWuORB7tx/MF8bWw01fXm\n0uchJEyDijWJ01wTlpqHwp0HFWLakkpMb7t/+LguOJWoSLUlW0TNQ56IoMtrFYNpS+PAGIWyCCHD\nJv4D0/z4Piy+4HlulU+Mw/vZxzKtwnb797G4jsFyPuL/SuchsZPdmAq89XX4jcagoowtsu7CRvPg\nMmbeakuDPDLzcaJqS4YmcZpVYvE9J6stYdDnQTY+ZfGsbm7yS5HmoR1/WzapUIIDkPysLedh63gI\n+0dC7WZ6taWRRR5Mc/G8frnWrPsHj+VSrdXQPNiRKW1JUcY6NW2JkQeSj2p+gQghI0D6sdr8a7/Y\nf/CFLwCPPjP/+L1edudBtTorpy0FWK8yy85DYLgY98+jeWi37Qw705jy31n7PLhsm7av7Yqnoc9D\nVG3Jqs+DSvOg6PMgPu9o/PrTQXQog2DaEw04VyMtRfMwc+0/aveJ0pam0jUPqtV4T+nYBK91y3Ae\navFSu7bInbNtNA9l4fr5dzI0ibNN0RwTGHmoGMp6w2N0QRFChovWaBYbF+XB983dm13GgTBfRZM4\npx/UKPKQfo9MS7tSGmDha631QaUgF4zOQ8rqt82YeX4HUsaJXVMWmgdjNavgN8yfm4c/NRU1PfNE\nzYO80h+tQhveg4ow8pAQTAvbWEQejKVaDc/5mg7Ts3/3N/o5OwmmXR3scjQPWdKW5C7mlay2lEXz\nkGmOlpEmRh5IHthhmhASobsfFGHwI1hJc6nfHttZb9h7smDalmh7KW1JuU2AmGJhHFOx7/p6Px0m\nb+RB+5rD+8/lPEjOi+yE6Y4TRR4U5oGpSZw07onL3oDavffGPq9olVau5JQ7bUnu8+DglCqcB+vO\n5w59HhKRB/Ea02FrUJeweh/1dFCmeVkQ+3zEyEPwXBWcB0tiaUu2pfSVn7VhwUG7TzVg5KFqmDx+\nQgjR/aAWqXkoMPIQ/R+NmS/yYGW4mIzRFLwwbckZvSGgTW1wiDzkWkQS05aUxqfw2EYwbbrWgt+w\nzk88Dq2nn6+cQyKqYZqbiTBtqehSrabtZWwNvHD/ltCEMEupVtPYQ9U8CN8/VRlfmbvuQuNrtwv7\nx6stJb6jpvdaNScij2DatM8Y2XqFOA8333wz9uzZg927d+Md73hH4vVWq4VLLrkEu3fvxkUXXYT7\n778fAPDZz34Wz3rWs/D0pz8dz3rWs3DLLbdE+zz3uc/Fnj17cP755+P888/H4cOHi5jqeFK1Lw4h\nZHQUEHnwVpax+KJfR+O2ryRf7PUicXOuuckGoaq0qs2PpTyOKmVCF3lwmWfIepC2FBh2phQn4xzG\nSvOgKNWqOs+RWN1hlVi0rSPNQ9z0iJwrx/coNomLf042zkN4cPe0pciRs3U8BLwgbSmWGqf7nC1X\n46P5lFJtybNLW/rhH8bMBwTth1xtSfpee6oVfN/H7F/9BZae9FPmjvJ5cV3h7+ZMW4r2kSNeGyht\nqdvt4vLLL8d73vMe7Ny5ExdeeCF27dqFRz/60dE21157LTZt2oQbb7wR+/btw969e/G2t70NS0tL\nePvb346dO3fi29/+Np7//OfjP/7jP6L99u7di7POOivvFMcfOg+EkJACIg/T//QPmPngP2P6ox/G\nofsPJcbP0g06OTfJOFDNz+XeFhlsFmlL4Y+wTdqShNdahz+9zW1OKWMWUm0pz++AS4dpQ7UlJ8G0\n6hiRAeqQtmQiEky30o1uGTGH38Z50L3umLYEIW0p0esgsY/d0CGpndWtBzJkQLikaqn2B/pRjODv\nqdu/ii1P34POf39Mch/fR+PLX0Tjru+idvQIeqf+gPtxXbDNQBL1ZUU6bGNk6+WOPNx222141KMe\nhdNOOw3NZhPnnnsuPvnJT8a2uemmm/DMZz4TALBnzx7ccsst8H0fj33sY7Fz504AwBlnnIH19XW0\n5GYvZKwuKELIkNH9WLmkLYUiVtX9tqhqS+J4QLJJHGCXlhGtTGrEtqrbo0uTOGnOXqutr4STOI7B\nKNcYs56LQ1BU5CGtSZxK8+DaJM4mrSdVMO0YeQh6SSQF00KUQHGBzP/+72D2r68ON3BPWyog8iCm\nLWmvCddSrcNstBartlRAnwfhc5n6/C2Y+ce/U+zkw+sYmkEWRY5qS9kiD5rn5e9VhSMPuZ2HAwcO\n4JRTTon+3rlzJw4cOJDY5tRTTwUANBoNLC4u4ujRo7FtbrjhBjz2sY9FUwjlvfa1r8X555+Pq6++\nGv5GNqDHKA+OEDJkCkhb8hcWzONn1TwY05aSzoNVHr9F2pJsIKauwJqO224Nyozazk01ZhYj0GV8\nx3GMnaoVzoNv0DwYG3oZIw9B3rxLnwcDYdoSTB2mZXo9zL7rr1F/8IHBfLNGHlych9AJjiIPTURp\nKzrHp0KC6ViH6QxGrSc5D4n3lnZfq5IN2M3ZJE6bSleh95hCJaot3Xnnndi7dy+uueaa6Lm9e/di\n586dWFlZwctf/nJcf/31uOCCC4zjLC3NoaG64Q2RHTsWix1w02ziqfm5JuaLPg6pFIVfR2RymRss\nsMSum27X/jr6wZ3qMYAoH3nH9oVUw0jed9PCdPR48+IMsGMR4TLb4kwDizsWY6VW5+fShck7ti8A\n8/PATN+gn56N79OcbmDbtrgzND0VGLmq9wcA9UHaweZN4TwDOh1Mzc0A0/3jbd+2AGzWnFfBaJ2e\nqseOtSiciy2bZwfHEN7z9m0LwDbDZ3Zy8HtW9xzvEzMDB2hhdgpoNvTjiB9z4DzML84lf3eC8zY9\nVUuOERh5Tek8YGv/s5mba2JuS//3rTk7Hdt1+9b5/vlZjD8PAFPyeCKN/jFnvB5mts5HT89OD0yb\nbeHYIYcOxYzszVvmE+LwhflpLOxYjBmJsd/hxRkAwKbNc8B2u89k69Jcfx5e/9jbfmBbNM5m4Xd/\n+/bFwTWhWN5dWpLeD9D/UAHUfL+Y35Jm0jTcurk/15nZJjAf/5xsjlkTHPyFuSlAugZUjtLmxRkg\n+Gi2bVtIvu+iCDQ4M806ZmyOMTs4P1u3JG22kNh5Ca6xmZkpILj91GpefJuH4mNt2744vPeck9zO\nw86dO/Hggw9Gfx84cCBKRRK3eeCBB3DKKaeg0+lgeXkZS0tLAIAHH3wQL3vZy/DWt74Vp59+emwf\nAFhYWMB5552H2267LdV5OHr0ZN6348SOHYs4eHC50DGnj69ik/TciRNrOFnwcUh1GMZ1RNypf+dO\n9LZvh79ladRTMTJ3Yg2hmXTw4DJ2hC90OtbX0dRqF1uEMUR2BKtfBw8c06YnhMcM9w3/Xj6+ivCn\n7vixk1g/uIyt3R7qAFYePoHVg8tY6nSjH54TK4P3ouPgQ8eBhR4WTrYwC2Ct3cWM8Hqr1cHyoWWI\nKoX1tTam0XdbDinOiXfkOLYHj48dW0Ur3KbXww7fR6sH+K0OpgEcOrQMv6UJ0q+vR++9td7GMeHz\nWF5ei87Fw0dX0A6OMSe850MHl+H3DA7UiRPReN1uD0cc7hPzqy3MhcOsrGFqvY2mZpzwMwIQOQEn\n1ruJ3x3v2AlsB7C+2sJxeS7tNnYAaLV7OCa81jh6AksATp5YR+vICrYAaHUB8V0feug4fMygeXwV\nmyEP28XDuvd9stU/5vKJ2DWwttqKrpHDh1fQmxrsX//2fmwVhjh2fBXzXT9mDK2srGP14DKwuhqd\nf/F3eCa4zo8fX0X76EnYKGSOHDmB7sFlbDp+on9dLbcxc6KFBQDHjqxE71u8JsTvSsjRIyvoSOcj\n3K7X7eFwAb8lm4JrP3bcw8tYArDW6qK72op9b1X3nR3S371uN/KFTiyvwm/7iLn8Cufh+LGTmDm5\nhiaAw4eW0ZsZzu/kUq//+a+vtZPXtYKZI8vRd/vokRXofjHE87LU7aEBYG2tDa/dvz/1en7s86of\nXoldm4cPrwztPdtgcgpzpy2dddZZ2L9/P+677z60Wi3s27cPu3btim2za9cuXHfddQD66Ulnn302\nPM/D8ePH8cIXvhCvetWr8PjHPz7avtPp4MiRIwCAdruNT3/60zjjjDPyTnV8GaNQFiFjyeoqtv7M\n47H1cT826pmkU4BgWi/QFJ7PonswVYJRibAt7m2eJLxOCKZ9xTipaUuaOcTEwsVpHjILWXOkLSXy\n6G3fh6lJXOhMmnLr5WiVSvOgqbbkXI52Wkhb0qWJSWPWDscLBPiGtCWvrdFgCmlLqiZxxn3CMZtT\ng+OKAtw8mocS0pb6pZIz5OJ3Jc2D7UfdzZbSlgnLY3h5qy3p2EhN4hqNBi677DJcfPHF6Ha7ePaz\nn40zzjgDV155Jc4880w89alPxYUXXohLL70Uu3fvxubNm3HFFVcAAN73vvfh3nvvxdVXX42rr+6L\nl6655hrMzs7i4osvRrvdRq/Xw5Oe9CQ85znPyTvV8UBxsShLmJGJZO4PL4e3toYTl//hqKeyofDW\nVgEAtRMrI56JBbrbgYOxrzVmZUPatd+BymgLDUNV7nKWUq1WfR5SzkWaA2ab/mrUPMgCUYt9TOQx\nDFN0JrHnDE3ijNWWbHQCMQNUIOt7q9fh1+tGwXRCEH9Iqi4GQ9OzlsaozyWYTjaJ01Y3c9Q8FFZt\nyTSXDO85tj8QuxaM+L66M/2oEfs8ZDnn1Dz0Oeecc3DOOefEnnvFK14RPZ6ensZVV12V2O8lL3kJ\nXvKSlyjH/OAHP1jE1CaDMbqgSD7m37YXAOg8lE2FV3hktJVZXETOuh+8WCOnrmulyBTBtKJqiotg\nOpyNnErlKcZJc6TkSkThUF2hx4HNNWFY4Y4LRLMZC55rQz3d2LG6+ikGaeQ8GATTxiZx8t/CEzrR\nu0nMncb0dDLyYBinduhgcn46wXTadZRBMB3pZIReItrIg+35KKXakuC8FyCYtv3ue0U0rLTF9nyL\nn5dzhFYfBRynPg/sMF01VBcLnQdCSIjOuHD5ERONWbH5UlZnxDQ3Q7UlJ+ch+D+xSqxIW8pcbSk8\nh+Kqu2GO3upqfB4iRUQbUiIGTuMYd1c4D6YO01kiD+J+clTDVG0pZVy/2exXMLKMPMhpSybnIe68\naeZk2fMgHMtrxZsQAtCX/hxZtSVFBkTUOyWb85CIxNnOtYxqSzZlmcXNxUhRJ2NZaxVjVFmTzkPV\nyHDzJITkZIxu2lrDyMXYF43SY8cGz4vnIUuXadV57IVpS/0fWac+B+I2LmlLLnOPncPQeUhPW5r6\n909h+4/9sHoc+W+bNLG01/NEHtLGUVxHviJtydjnIcSmSZyuVGsWppqAnLZk8JTUzoNm47T0EtO+\nOtZbgxKzwb6xpmPi1KqkeQgd60LSluJ9HrSUnbZkewzhXuulpUgm8OzTlhh5ILkYJ8OGkHFknHRF\nOqPUKfIw2K92XHAehhJ5MIzn0CTO6DzIP7pZNQ+iWDhlNXLm2vfLg0pz0HxOLg6BpUGcit9Lnkfd\nccLPydQkLi31SUQwgMLV66L6PACAPz3db3ao+17IUSmHyENss7yahzCa0VofdMZOFUw7LiaW0ueh\nAOeh59s5Rr5f7Mq+DsfIAzo505Z0jJFgms5D1WDkgZDyGScHvZC0JUHbsLKsft55RU0zN2PaksOY\nkcFm4TyknAsxHUU0CqP3nKWDbsKBkVdaM1BY5CH6J/03xqrDdNYmcYIBKpLHeWg2E03iPEPkqXb4\ncPwJk/OQcv6VlZq0Ew2ch/V1IfIQCKYFYzTe8NDyfJQReRC7g2d0HnyxyaBrilCF7CBPdGhsF1lU\n2h+ZCr3HNOg8jAOmC2ptLb5qQQgZYHljz2Qol0Gvh4Xf+k1M3fzpwXNFOA++JmKRN/KgMnaitKVO\nYhOXPP5oW9nwVG0rplioSIk8QBRM287RkLbkaVbC5c7YxjFzGIapqWK9pPOgrEAUnhOXuSgF03HH\nJCEUdWF6Oql5MHQArsnVlkwOgPazF5631DxEtFqDKmbhcXWr667Vlnx/eAaoodrS9D//U7LLt2r/\n0CG11Tz4fskdpt3TllKjnNHQFgsB0jmxLgM8Aug8VA3HyMOO0x+BrU/6qSFOiJDxpPGlW7HjB7Zi\n5u//Nn3jikYeGl+8FbPvey+2XPiMwZMFaB50xmTMiMvgPCiN5CjyEP7IOhrE4eZOaUspK9kpDpiy\nx0HqPGXnQVOq1bSP6XXnyIM0jpxGpjuOKW0JfadCmXJiFXkInQc5bckyKqIattlPW4o5Yj2986BM\nW0oYaSnzyJO21G71oyUIIhdAPA0mg8PorCNKHVBVuEUosyu9vuklL8DiK19mHtL3486DzTxF56EM\nMqQtGSuPOVC/6zuY3/uWQsYqAzoPFUO5EqdblQluLPV77xnijAgZT2be/w8AgPk3vyF944o6D8qI\nSAHOg5x/rBrPKhpjWG2PrYYC+Uu1Bv8n8uVV42SMJEUpErHIg+XOspFaSLUlzWNX0ow127QloH9u\nDN8XXzY8FU3itJqHLDQVgmmdQef7qB3JmLYk7hI9nyH/v9sdnNswbcmlVGua8zeke5knpC0lPmMA\n0x/5UOocIhG+Q7WlMAUtNUqXB9dqS50M1ZZi5yx5nC3n7kbzU5807FMt6DxUDYeVF2/5+JAnQ0ql\nogbs2OJipBW0elQ0qYayojOwFaK2QbdCrvtRNBk3Ss1DTz+/DM6DXZM4h1KtMQdMUarVFpPmYVKq\nLQH985+pVKuvr7aUYqwbR52e7q/+Cterpyl96h17ONk3wDZtSTW3LB2mez4GkY4w8uDQJE45tvB4\nWPeyaC7q9+uJJZ91c2gIHcptv/sV1DzE7o1ZzrfivSS0OBWHzkPVcHEeHn64kEN6Rw6XGxokanS1\nvsnwqarjphTuxg2aaBXQRftksypuWL1VPlb9LTyn7DCdxXmwEkwL+dmmMWXEDtOuq5G2zoNT5KGg\ndJRY2lLKb4ypSRwQOA+mJnGGyIPYK0Akh2A61A94rfXBcxrBdELvEM5PmrM6A0DzWdhqHsTzH+4T\nCaY1zo61kzb8yINTkzidPSFGHmwrrSm0UkPD9voT+jxou4MXASMPxBrFxasraVZ7+Gjuw3kry9j+\noz+ELef+fO6xSE5E46+qxuw44XDjzSXYHCYpkQcPQh5x5rQl9WPtj6LJEDaVllStINqKJsX/LTQP\nqXnIGkMw1mHaFZPzoDNKyoo8xDpMK35jlJoHzTnwau4lRMPXw23ksXPc76LKReLKt0bz4CmcB9+U\nepT2uWXpedDrJdN+tGlLjn0egvFzo3pPYrUlXbQlFE1rvn+Rlki8FtKmMkzjfHCU/n+2cxI/r7wl\nrccUOg9VwyEcXETkIbyZTv3Xl3OPRfIRu0nSeSiXivZ58GspaUu+P3AwXCIPOmPDJv3BlI6jMnbD\nbTrZNA+eXGI00WFaZcg6CKZFhA7TvmPkweQ8xBeAHN5/2iq0Lamr2cJzYbUlg2DaeH+SDE8xrSc8\ntuyceWmfl4FQfOyti5EHtfOQaBAXzDfZtVzhaKkeZxBMqyIPMcG0ah+H54a2EBKeU0PkIdRfag3+\n4Jryej14NvfcWOShhHt0hsjDUFNeGXkg1jikLRURechUz3wcWV0d9QzSaZd0Q9oouPzYVPV822ge\nagU6DzYaCtOKesyIiRtgyrQl2y6z4rY21ZacytYqVt2LiDz4FpGHFDLV/Fdu7yfPo3hPVKYt6TUP\nKgfEquxstHpdXJO4KG1pfRB50EWedM6DTZM4JRkiD57fG+yTJpjO4jAOa+EpSjnTv+f6d7/Tf6D7\n/oXfK4c+D8pFh1GTR/PgEHWpMnQeqobSeVBvWojmwfVHstfD7DvfjtoYVXiaf9PrseNRO1G/89uj\nnoqZDp2HoWDz4z5GkR65LGO0QuzgPFhVAtI12jJFDkyRU1WefIa0pcQqseclDdfMfR6EUq25XIYv\nzgAAIABJREFU+zwIj4voMJ1T8xBdN70emh/5ELb/8CNR/8bXk2Onah48tyZximpLyVKtOdKWpsO0\nJTHyoNYQqJ0H2F0nscfCNpk0D3HnQat3U6zOK520otOWFERRI1Pk4a7v9h9oii1E96qebz3PctKW\ngmNlSFuyLtVq0yTOtE/FoPNQNVQXleZL5hUReXD8QWr+2w1Y+L3fwdIv/Fz+Y5fE3FV/BgCYuvlT\nI56JmViX0ao2LRtHbK7xqjoPaWmMvg/Ug9t4VsG0pvJSJs2DKiUnSlsqSDAtN4lT5fBn1Ty4dJg2\nvXdIDtqoqy1Jn1n97rvgdTqo339vctuUPg+o1ZCpY3Ys8qARTGdBKZhWf/6JHg+AOfIwjLSlXi8p\nmNZ9d62rLQ3feYh1B09xHrS/X5HmIUOfhwJX6+t33I4t5z1tsAjqulCQJW1pAqINInQeRkT9299S\n/9grLzBd2lIBkQfHFW7vaN9hUVatqDi5cobLgJGHYnERTFfUWVPmLyecB/fIgzZHOhZ50BhgpuZn\nCgMrXM1TpS1ZrfRFhlzwd5oORDWvtO1DYh2m06dmPQedrsRlzBzGR6zzsJgyomrO1kkRjaeVajVW\nWwqiR7JjUoRgel1dbUlcqQ9/t/ypqfj8XNOWsjgPIb0eootLVSkt7TNPe25Y+i2xVKvWeQjSlmyq\nLVl85t6QnIdNL/p1TH3hc5h/4+viL9hGHmJ9HoYomK5u4IHOwyiY+o9/x9affQIWL3mp4lXLmwUK\nijy4GqlZOq9WhYp7/mLkQZsykoOZv3l332ndKLh83lV1LC0MhcjIK7jaknY8g3GT6HSrckayGsRR\n5GGIfR6iUq0N9eu2Y8p/jzryIH4WYgqTaoU9pUmcXzM3idMZljEHJiFQVsw58aKGIG1JFEynaR56\n27ZL83VNWwqdB9g7D+J7TAim1d816wWvUiMPNe3pitKW0qot2Woeer3ktVoE8liukQdBn2i98JQl\nBYlpS0Rk6tbPAwBmrn1/8kXblQYAnkEE7D30EJr/dkPqXJxXXMfZeaiqgRgi5okWPNf67bdh8dWv\nxNaffUKh444F46x5UK7wio/9bNWWLHLwtfeGnnpVNzGuPHZgIHmuho4UwVALpqW/XdKWBKJSraro\nRhqmyEMQrZn6z89g/m17U+ehJM81KjuIckqZ+LpVnwf7qoDi9y8yhjWaB9uc89hhFYJpXanW2qFD\n6C0sAjMz8fk5dpg27qudqHDOZcG0TvOQFnlUPDe0aksx50H9nmsPfL8/B221pf7n7tn2eahq/6k8\n1ZZE532MofMwCgw3G+VKg3bVSv/lW3raOdj8Sxehfvtt5rk4hji1XUfHgYqW44xoD0/zUDt6pNDx\nJo6qponZaB4yCKZj946e2nnQdpi2rLYkRx4SnX0BuxSehOZhmNWWhMhDaNjpJpl479LrMS1J/8Xm\nDR+3n5d8jFyRB8TPo+w8KNOWDNWWjOfXsIqvcR7yGLxqwbToPAjHOXwI/rZt8T4LnpeccloERHzd\ndXXY9weif1Wp1iyfedGRB8V7imwTg8PkhY6pLnJed4w86M5LXnRj2WYTideX7j6Z5fiJAzHyQERM\nF47iNa2XajCG69//Xv//hx40z4VpS1Ysvuw3MHvlnxY4mSTeMGtHV3VlvQyswuMlhP2zYOM81NwF\n01oxr/hY2+fBcK5MaUu9jGlLkvOQqLakIC3dQ+sQiB2mXUk4E4rzKi8KhFGVlWUs/Y//H9P/8gH9\nmHmMJ+GziKUPyVEdwFIwbR8hV1Zb0gmms7zHKUXkQemo+qgdPoTe9u3xOZmMYZ3DEKUt9fdLNH1T\nEZx3z/cTx8srmHaO5mXBIvIQbaeJGMTSlmw0D21NRKYorJxGBbF7puNvdYUdAhfoPFQNl7Ql+cun\nuAH5XspH7GqkTm1M52HmA/+IhTe/ocDJKBimYLqiguDKYGM0j4KUFVDP72Uq1Zqayw3EQ/M2+6r+\njmkowvOaz3mA6p6WOK5DqVbFe/brdfc8aNN7Dx0uzbXVvOHjaHzrm9j04ovt5uk6H3llPzLWFQZc\npHlwTFsKMZ3z0AGUoxo5nAd/OtkkzhNXvkMHafk4vHa7r3dIcx5S05bizoNz6lLgPEVOh7ZUq3va\n0rCdB79WMztL3a7+Hip0mLZK3SkpbWnwftydB+tSrdHBfPvrvMKOBp2HqqG0FVJWydAXT+945DYs\nvOrl8W1SSg66pseMd9pShVaUVVg2iavfcTtmr7rC6Ye28pWmhonFDTj2PahSnq2wyt+49fOo3bM/\naVAOSTCt/VE0pS3Jr6nSltJyumWypC2lGaO6RXKhw7Qz0rFU0Z3EOQ0N2+PHU8fMlSft+4i96WAe\nUZqJSNhhWqf78FL6PKi2DxlCnwdE1ZY0modwGocEsbSt85DyXGR02kTDxEZ9ctqSQ5O4TS943qA/\nh4phRx7SdB7drt6uCD933zLyMKy0pbyI9y3b+26WPg8Vhs5D1VBdVLovWbgS4HlofPMbAIDZv/ub\n+DZpNzXbG023i+aNn4it7owdWfNqS/qix9KWDJ/L1l1PxsKbXofGl79oP/gQqjdNFBWNPIi54Evn\n7sa2J/y4fjX6nnvsf8h6GgNelb5imFOq5iF2XvP2eQgNT/09Lao8lXa96+YQS9lxizwkjHuVUybP\nS1gVT52nw1wS80mkkAlCaXnMlLQl37VUq/h6T/MZmpy9lPc8EEzrNA/9/cNKS77kPPjw9Klw2sCD\n9IJt2pJY7lTc3aayWUDt0EFs/pXnyBsOHg5J8zBwHmrQ6lrC7VLTlixX33VOlQZv+Tjqd9yePq52\nUSFD2tIw+zww8kCscbl52hjDac6D5YU/895rsPmXn4OFSy+x2r6KZF65K8uYjAmmLVZlTpywH7tC\nBnElia24VyjykHY/EI3Cu+7C3F+8bfDayZOYe+ubUQv0TzHE9xsT9tpoHjRi6/6E1HMDtHnoqbhE\nHoLXnFMJQoL9tD0OTCScB4WDolmRrQWRh1j/AdWYWe9huqiIKoUipVRratqSTGzFNVjwks9vngpB\n06o+D4rIw8mT/UMtLFhoHhRpS8ooUNa0JbnDtHDvF46t+83yVpaD/TqY+szNQEvYf2jVloJzmqJ5\n8HrpaUter2dXwMTxXrxlz89h664nD5q/peHa3yNEmHume834Bx7oPFQOF+dBuIH72sLLKT+C4oVv\n+BI0bvtKf7gHHzCPV2WyrsiUZHg7C6ZdViU2YtqSi6GlWUma+szN2HbG6XarWcMgLT1EEh5O/9M/\nRI9n3/VXmP/Tt2LTr/6ieYzY48FD7Y+iKXIgGwQpaUtWTnI4qTDSqkopCscM73cumgfxWFHaUgGa\nB1V6lnROw/fmHTvW32zTJvsxHeaTSE8K56ESroZpS6ZqS6YSwtpzLnzeNbnaksJYtyRsEhfXPCg0\nBKJjaCmYVs5RHNNF8yBGHuRSrSphsMW5mP3LP8eWZ52H2vFjgyeHVFUwrdpSFFXodvX3DsdqS55Q\nyUhb5ECg8Z07+4dRLZgYD+T4Xc8SeWCfBzJUlBdvyg9dli6XIUIYffHlL56IXDwtlY88ODoPNs2y\nQiiYNhPTPAweL/zOb6F27GHMibX5y0RhpJnSUTpPeGL0OOymG/6gxtB1ibaqtqRPW5LnpuxGnTFt\nKbrfmSq+hUZpxj4PsQ7TzsiGfjK6o9U8LAfOw2LceUgYTHkiD85pS5pzUK+bIwUWHaa1mocs708h\nmFalLUV5+DUL58H34R17OH0+DpqH2GdgKtUqzdvE1C2fST4ZfKYLr/ltND/yofR52RI5fhp7I3Ie\nepZN4iwWtBzTlqzJmbbkZXEeMhynytB5GAWmC0f1hUrRPADQOw8pOdCisGnm2vejdv99xu3Hmoyr\n78Y0lnYbWx/3WMz90eUZJyXgWm3JwWHMnMYxzricn1gaj3Cu8q782NYzN+0vY8plF6+h6Ie6i9r+\nuzH/2kujdAdtqVYLzYN1nwdo5ubrttcQbmJyHoJxotz1VMG0xoERj1FotSV15CEkFEz3Nm02j5kx\n8pCcm1A2V+c86Bwoz7FJnPh6uCqecB6yf0d8pWBaMV64UFavI1axS5GGM/uud2D7Gadj+mP/j703\nj7OjKNfHn17OPvuSfV8IS0IIIWxRiIGwKiKLCIisiiigV8QvXK7IRX5cFxQBlQsu6JVFBUSQgKBh\nXyIxBAKBhCRkT2Yy+8yZs/by+6NP96mqruquc2YgQef9fCBnuqurqrurq9633ud93r8QDQU8T9l5\ngmAsoq6j2JbospWKtv49JO64FYlf3Y36i79QVR38mAfC8OF5HrSy50E4d1QYMA2eB0lChEgMVqqd\n3yl4Z4UwV9v7X7iMeB5GRFq4sCVBWfdjDqNNCxL2Ay4Fnw2bGAbiv/sNlI6O4a23GvkAPA/qju3Q\ndmxH6tah70yTzBJSLFgjsKXhE4pS1L8YVBsv0zKxFXXnnlltr8JhjLYNxbJglzLmUlnnCRam+vPP\nRvKXdyHxs9udYzJ5HkRjkMJ+8wOAvd9c46E6z0MZtsQqtUQ7pXOhxrIQthSSIK2SOrk7/aznwfnH\ni3nwwZZC2pAVy6Lrkoh5CIItBQakB3keRO9wKFStETnPg3dMY5RfRQEbAKyWAthjjzwkaJRde+Vg\nS973IgNbqnLOrr/wXNTcdENV1wZKGNLBjWewxZ4H6BXCliqhnx6KVAqbGwpV67+IjBgPe0KCgo0q\niXkgF25RlWFWscCNLt2HEIk9/EfUXnUl6s9j2SGA1LevRfxXd1dVb1VSdcxDwHXDqZSTSqtMvZUY\nD/+OE1wlY5Z8xybH81DNe7ZtKMUiYn9/uvJrXZGJebBt2KkUAEDJZsrFtDKER921EwCg9vb46xUo\n80IGGGnPA5hFtsqAaVfIeASRsCw+lWLZDaKNIXoeuN6dELYlFrY0rJ4H8t1KULVWzbYUJB70ZRiT\nxLmwpVzZ88DdfBlizAP3O3GTxMlCSFnYkis82BLBpiiSIdH3VipusLsoYDpCxjyEJ4mTog8X5b8I\nkyp37KWfJ7XZVGmSOEjfi7QHZQ/IiPGwJyQQtuQ/J84wLRFsGKY0sueHWclUSx6HyOsrfe0k7/oZ\naq/95rC2x5PyxF5+jskf3IzGIw6Wo7YMKDOszBYVwpYqmlj+jT0PUtlfRUruUNzGw7Cwc8cXqQSW\ndt3teMLpq+t5KBahlIIoeRlthbv/FJaXP2ZkqVoVRmH1FtkhxDzYisIxHhSxUhZSp0+GM8M0x8gS\nzcdengcfC1FAnZX2jfeeAzwPQWxLQYqf8Hsj2xrGPA9l2BLheeAonWTMgz1U44G8lvw37HphwDQH\n2x80pvcAZr4cMC0BWxJtuGkE25KM56Fa4yFMWEN/CJ6HqmBLsu2MwJZGRFp4g4qZWFP/9f/Q+ImF\n5YUuaCCG8J37dmiG2Xiwa2v57XZ3D2s7geLtHpefU+qW70HfuAFqx+7wy4MgRMPIbEFNlDJ5GarE\n9P+7idRukmgnqdLdZ1KGY7EL8UTGHn3E+VtRgGTSo6NsPG4Rkr+8S3ytgKpVLuYhQPln6wqFLUmM\nSxJ2wu4aOwW8MsLEZqI62d9khulKJfBZWP5jAGovvcjxUA2mueeHzfPA4p8sYhywbYbFPKgK/72J\n+sahavUbSUPxPJTYlgrBVK2U56oa44GUamIeyO9BYWIeigV/3ZbgWe0pCWFbgi4R8+Aa5aQhFSTV\nwpYqeJ/6W29C6e6qrH7ZgGlynOzFhkA1MmI87G0iAVtK3n0n9DVvebsVgYph2M66aOFgpcqBb9fU\ncI+rnR9iDITL/R60cxQkQc9wOJXyYmUxDxV5Pf4dYUsVMExRz5t6VkOY8IdjbITAQ1L/813nb1V1\njIcSbElfw1DLskaQgG1JoRiCJKha2f6xxgPFhz7UmAeR8UDIED0PngGvDUPANPUsiXsgJPL2aqht\nuyAMkh2K8UAKu9NLGDM+RifDEENTAHGeB18cgK+At1axxhmVd0JUr6jWKA+2xDGIhcZDUJ+p7vvr\nrMTzAAg9DxTTntstkaG1p4SEnPHul6RhFcwddoUxD1XDliRFyWTRcOIx0EqwTtk2KIINWeOh0nkP\n2KsNjhHjYW+TSibPArFbIdoBD1OgmB1uoSHC64MMtCYW5x53s31+KBKEW5f5OIPuUzajr4wE5HmI\nPv0klE7mmVViEPwbGg8VBbKFBExXtXANi/HAa5c5ZpoASp4HMmCaFHaci7wHUp6HANhSELWoi7Ov\n1ngwLaHx4CnA1RgPnD4G0sHK1AnIsy2ZZtlGDQpA5/0t2x+bee5BVK2mGXz/IrYlYXnityDPw5C+\nFV6SOFIY46HqmAdenR5Vq6znofSbYVtSAjwP0t60D1oI44EHTbMjZUiSMF7KMzBsKa8jF7aUzToG\nd+CFIaddKFs2A6XAefZhIjNPylz7EZYR42FPSNDgqSAQTSGo6UQBSh9ozAP50YlElFF1D3geZCBh\nPKF2YJnyCi/QrUpRBHke9Nf+gfrPn4WGU46nLxC9q3QaShfjhv2Iw5b0lSsQ+/19lV1UmtQrjnng\nUbXuKdiSxHygFIuO8kJ4HsL6JKRqlXHHywZMi2BLVF2VGA8mbBavDoDSFIa6S2sSkJ0hU7X6lXXu\nfGwY4g0O9l0Tu+sVCVsPCXnl3V9gLg2+8eAZcELHAwFVEcZ2VP7NeJ6HgsB4cPtH7ZwT10MJjtPg\n/fYqrc7z4Mbhue1yFVhRfAjZ3oepiJKJF8NgS2FsS7ak54Gj1zR9bAGaD5wFBHwLkVUrobS3h9fv\n2/uQfJ4yrHRsfe67pr65kPZGPA8jIi1cz4MgcFEUIEZKKGypeuMhbLIGxMaL8gF4HqJL/4LEnT/l\nNEYszJaFuovOK58LeD7JW3+IhhOPCU7eNpxUcgK2JW37VgCcZF+CZ9syewZa9ptKH/yIJ4lrPPEY\n1F15WeCC4ZNK7lnkefAm7z3keZDxRBpFRwlJJqFkQjwP3polgB7xduGD2g9Sdm0GBuUpjzZ9LExc\ng0ci5oHi7w8QCqpD9EEx+bAaqTpZBZ0LneEo3ZYpbTy07D/NY2YKFfK+WA+DBxPiK3FBu922pgWP\nbakkcQK2pSrEC5jOyXkeuDEPMp6Dkqg7dyD1o+/TB4cYMM0Vz9DaS9Q0Ms8DT/SI82+Q8eBlgLfk\nNg44geTaNmc9VNJp4WU1/3UNar7zn8LznrHo060qNx6UILYl0fzN++b2YkOBJ3vJqPw3k6BBUgls\niTIeBEpsiDHggymJyvP6XJBQnAXKuSqR90F/cxVSN14vvbDUX3huecIoFhH704POQusqFJYFbd1a\nxB5/tHxRwPNJ/c93EVm5AmoPEdzN3I/QPVuFVJrnQVTGDZr9l+SirkTJKClqUgoq+XwIBc8eyg7f\nBxbzwPxdNLyYB2Qzgr4y9yEyGGTGTLWeB8D5foYU86CCu7VdKmPLKlqido1h9DxwYUI82JJV3pWU\nGDPapvfl+sP2hdMfIetNENuUqvqZtNw2wvog8jwMiaq1FDCdD9lYII0HUgGukG2p9rJL6GvJf0Ou\nL8Pr3PLisSxMqMcrO8zCU2S9eBVRkjidpGoVxDxoJcjTcLAthek2MkZ2teuibMB0mHA8E5TsxQbF\niPGwJyTwo5E3HijYkmDXOnQxYga+tnULYg/cK7UwUDhNmfoJPLbKwmo40rjkaCR/+hNEXnguvB1G\nEr++G3Vfvhg1V11JKYCstyQwe7QrZCAea6R9UJ4HXq4BViqZPGXYmz4CIpU8zy1byaRO7T4PD1Xr\nsND4SrDaKEYRHtuSbfOx376YBw4TEFu3wDCmvAnMfOUz1IbTeDBNBysf9E5kPQbCmIfSPVcV88D8\nzXvGvDFZLJbvSZAHghIRtj+oQ6J4jGphS2QdrDDvh6KU9nIF8GFLVeUt0Bwom2gjxxujZMwD2acg\n44HqT2nne8cO+lpA2uMl5XlgYUuydX/QQr5vLmyplKDRtgKIV1SP6rfyPA9MVWHzKzHGEz+/A9qa\nt4lrOZsopbI80da/h9jDf/TXDcjDlohjVZO37EWyl4zKEfGkAs+DkieUd9J1Riq0YTvjzGJWf+G5\nqPvaVxD5x6thPZVaxEgFTu3rLR93k1VJiJLN0rtWYWLb0N57DwAQfelFIubB8sdpSCR4CYSHBRkP\n2SzUkosVmQxapoxB8nvfFZcnDRMZZT+Mhrevj1/3R1mI569tWI/I8ldCy8q4g6mFbLgCpodhV5C3\nwPoWHhczn0w654PiHjiLJh3/QO5Oh3seIs8uE8dMcL5ZxWSMB5mYI0L5s9ldY7ItYNjyPNiaXoXX\niTWc/M+FZ9A6mzACGAVv00Ym1szXNZvun/tuLZsLHwnMsK0IjAcJz4Mi2E0PVCTD6lWUctB0UDEi\nz0NYhmn6HCMExMk1jKQgJ6ShJmMQCJipfHV+WCLLthQEW1LVcsyMjOchaH0NmTvcMaW/vRo1N1yH\npk8c6S8j6T1rWngI6i67BOqO7VTdAIJ1rDB9TmZs76UyYjzsbcIZS7G/P43Ynx70nyDdtCTkhfQI\niD7ifB51F34e0eef5Z5Wenu5x6kyMrvuxIdF1qlI754BUBQ0fOp4NM+eIS5D9sWyYJeyjpK7eopp\n+vssAzsivRWMsREUMN144jFonj8bSlcX9LXvQMlkkPrxD4XlKeykzK55mOeBMB6GE161R4UwmJqO\nnI+GU04Ql5XxUhQKqDv/HESfeoJoY5gCpj+kmAfFNMuwJYDLuFRWbmzmX/gVfrde0ZghytReezW9\n0RAGW5LNaM8powSwLQUGlwbUyf5WSNhSJXXw/uYZaLxvtlAo3xNrbPEWBNm5k7pHNuaBMCJ57yDo\n/lV+X8sSEPNAKqCkDAW2BJTjHrgnac9D1XkeXGgcC3ki/w2uoCLPQyBV64dpNLgSYjzYESLmQTT3\nusaDbcnNj2YAbCkUVVE6H2RsVzgfeXMr6X2V9XCTawnrXSLPf0SkCt/siHyQInLb1n35YnScdiYD\nrygPWmqhpyhc+QM7+vwziC19TNgPl8Gi3AAv5kFiB4w0HnKEUiMDeSIk8trywPPULrtpApEyA4ft\n7koZpi/gViq2gFisFdOgl3MOP7f6/kao6QHo7zhuUrVjt9xESRlAErClkL6r/X3wSnxEjAd95QqY\nU6fBbmrmFxBRXfIWWIlJPfr8M4g9+Th1jAtbGqrxYNvVLQ6y7Gthngf2Pqi+EcXInW+ZmAcw3x7b\nNZ+XxORCQaREKs/DUNmWSvemD0eeB46BwvlmFWbjI7BODMXzQNTxQcCWZGIeREbeEI2HQM+DZzwQ\nAciyxoMsvKTSPA8MVSu3TVF8yIchvH6RbEs88TwPAVStruEh6Xmg1tdKjYfSXMYNrPaU98o2eGze\n2A9CAIi8DMM9rvaAjHge9oQEfTRhH5Rot588Tnxwoo9YlH/Bu07KfS4BWyIXS9KQCJoUfJUwH5Bt\nQ1v7LqXcqCQMyjTLBgO1q2eWg4k5fRI2HwBb4nkemg+fh8ZjjyofiEbkmCWogGkJOEeY25aCLe39\nxoO6fRsaTzwGjcd8XFiGG6Mi2IkN3BHKZFDz/74B/c03/Ocoz0PpX9Jo7+pC7A/3hy9e5Duv9vlL\nGw9lzwN4jEuMIiykaqXw/3LGA48Rxfs9DLAlOuZBAJng7QrL1Mn+djNMy9YjqhPgw7l4ikahwH8v\nvDoB2hMq2x/WSPACuPlKXBBUhqtAke1JsC35Yh6G6KWzJWBLw+V58AVbs8cEQgWZBxkPbHtDVSCH\niywjDLZExjwIFGoPdmjbUor7UGBLHlSwn1gHWa+sbwyHdMgb+/45gyuVwJb2YkOBJyPGw56QoI85\n4INQ39/o7WazQnkhKNiS4CNmMj8b+8yiC2QlKDGl2JYE3hGyjxUGHccfuBdNRx2G5A9vLtdNMiKZ\nJlByoXqQDgAwDCiZQaZ/MgHTxIRTScxDSWw1hNqwJMog0bfhCJju/2jBllxjRyvhSrnCw4yLWFYC\nnk/it79G4p5fIvWDm/0nQ+Jg6i48F3VXfBmxB38fWI5aGKoNrJeNgaI8D+HGgzBOQYZFhN3FphJc\nMYYILz6jEswvWcaNeeB9Dt6O9jBlmB4GzwMvlkQc88B5L4K2q/I8+Kha3TwPIralcM9DpYQAZape\n+N8TD8ZRgQQaD55hVopnYXOFBBkPPJpZynjglAsSN44hALakMGOhGtpgSqoZLxxRQowHm8rzIA6Y\nthXV+R5k3jVRj4+cITQBrnNe7S8Th/jWFsmYB9/5apgMSejoiOdhRKqSoEk3YPDWX3Qe6s8+nX+S\nVE4KEjEPbDsRGqYUmmwKcp4HStkmPQ9kHytRbE0TkeeWAQBij/3ZO0x6HhTboheTUnCaYhg+z4PM\nh69kBaxWgJznwjJ9C23skYfQdPABVDI3dWCgXGAYYh7IAPVhZYX6oKSS3UNChPEzvIzG7jXkblRQ\nG2Rin5JEVvwDAKBt3BDcV4oLvMrnXwlsKe54E7nGlI9ticHDc+oWGpysF00Iuxle40GRgi3tRRmm\n2ZgDgA81LBT5Rh2vTlRgPFDoML5XRLEsflxFYMwDwdVPtSfveRDGPFQrIZ50ANV5HsKUvAqpWqU8\nD5XAliQ+n2FLZEoaPlzPQznmQbiukgHTFbMtMTcbRhjieR7KxoO6dUtwHWExDx7RgMQmi6g+KuaB\nrHzEeBiREFFkMXKMqB27xRSnBh+qJKQiZfrgppb3rpNIxsUqKYk7f4rkT26hy5h844Hub8jkRn6o\nAoiE0kPDlsBS8QGA6TceKOjT5k3Q3vcrhEqA54F0qyZu/zHiv/uNv/+mf6Ksu/QiaNu3UTknKGpV\nmYm1EtiSC8cIWIiUvl7oq1aGt/tBiYSBwx3PgrFazqIrqYDz2uDtPnu88iEZbcl2Zbx0POH2kwMz\nUdVyv4KMet4OrwhGJErwyDzPIC52X1n2/VXoeQgNmGZZbCpRCgFuhmmucs2rI0i5CQiZeoT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dSf+elhY10i29JIA5gU1xNAwZbyZe9QoqxECAPNiwUEKSkKmWTNHdfE92XzAqaZ9xJ9cim09zeW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\nqyBO7F/30M8YeWhEbxFrUmLBdDV+IHtR5GHsO9eg7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DH6/Gj31X3hor7nXmGEleg7pozl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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda516b8e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's look at id=82, the worst of all\n",
    "\n",
    "df_y_pred = pd.DataFrame(index = test.index)\n",
    "df_y_pred['y'] = y_pred\n",
    "\n",
    "plt.figure(figsize=(13,10))\n",
    "index = test[test.id==82].index\n",
    "plt.plot(test[test.id==82].y, color='r')\n",
    "plt.plot(df_y_pred[test.id==82].y)\n",
    "plt.axvline(1540554, color='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Ouch. Don't think the prediction caught the trend. Something is going on between timeframe 1500000 and 1600000. Further investigation required."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}